WO2023168950A1

WO2023168950A1 - Data collection method and system for smart meter-reading terminal

Info

Publication number: WO2023168950A1
Application number: PCT/CN2022/127382
Authority: WO
Inventors: 余转丽; 成胜荣; 李荣荣
Original assignee: 浙江万胜智能科技股份有限公司
Priority date: 2022-03-11
Filing date: 2022-10-25
Publication date: 2023-09-14
Also published as: CN114298863A; CN114298863B

Abstract

Provided in the present invention are a data collection method and system for a smart meter-reading terminal. The method comprises: periodically collecting multi-dimensional electricity consumption parameters of a first user within a first time period by means of an electric-energy collection module, so as to obtain a first electricity consumption parameter set; performing electricity consumption analysis on the first electricity consumption parameter set by means of an electricity consumption analysis module, so as to obtain a first analysis result, wherein the first analysis result comprises an analysis result regarding an electricity consumption level and an analysis result regarding abnormal electricity consumption; evaluating and analyzing an analysis result regarding transmission loss of data transmitted by a first data transmission channel, wherein the first data transmission channel is used for transmitting electricity consumption data to a master station; overhauling and updating the first data transmission channel according to the analysis result regarding transmission loss; and transmitting the first electricity consumption parameter set and the first analysis result to the master station by using the updated first data transmission channel.

Description

A data collection method and system for intelligent meter reading terminals

Technical field

The invention relates to the technical field of power system terminals, and in particular to a data collection method and system for intelligent meter reading terminals.

Background technique

Electricity is a necessary industry to maintain production and life. The collection of terminal power consumption data in the power system involves the calculation and collection of electricity bills, whether terminal power consumption is normal, and the reasonable and efficient development of the electric power industry.

At present, power consumption data is generally collected, transmitted and saved through the electricity meter at the power user terminal in the power system, in conjunction with the concentrator and main station in the power system, and analyzed by the power supply network. There are also some smart meters that can analyze some power data at the terminal for reference by users and the power grid.

technical problem

In the existing technology, after the terminal collects the power consumption data, due to the noise of data transmission during the power grid data transmission process, the data will be lost or damaged after transmission, which will cause the main station to be unable to analyze based on accurate power consumption data. There are problems. There are technical problems in the collection and transmission of terminal power consumption data, such as loss and low accuracy of data collection and analysis.

Technical solutions

This application provides a data collection method and system for an intelligent meter reading terminal, which is used to solve the technical problems in the existing technology that there is loss in the collection and transmission of power consumption data at terminals of the power system, and the accuracy of data collection and analysis is low.

In view of the above problems, this application provides a data collection method and system for an intelligent meter reading terminal.

A first aspect of the present application provides a data collection method for an intelligent meter reading terminal. The method includes: periodically collecting and obtaining the multi-dimensional usage of the first user within a first time period through the power collection module. Electrical parameters are used to obtain a first set of electricity parameters; the power usage analysis module is used to analyze the power usage of the first set of electricity parameters to obtain a first analysis result, wherein the first analysis result includes Power usage level analysis results and abnormal power usage analysis results; evaluate and analyze the transmission loss analysis results of the data transmitted by the first data transmission channel, wherein the first data transmission channel is used to transmit power usage data to the main station; according to the Transmission loss analysis results, maintenance and update of the first data transmission channel; using the updated first data transmission channel to transmit the first power parameter set and the first analysis result to the main station .

A second aspect of this application provides a data collection system for an intelligent meter reading terminal. The system includes: a first acquisition unit configured to periodically collect and obtain the data of the first user in a first time period through an electric energy collection module. The multi-dimensional power consumption parameters in the power consumption parameter are used to obtain a first power consumption parameter set; the first processing unit is used to analyze the power consumption situation of the first power consumption parameter set through the power consumption analysis module, and obtain a first analysis result, Wherein, the first analysis result includes the power consumption level analysis result and the abnormal power consumption analysis result; the second processing unit is used to evaluate and analyze the transmission loss analysis result of the transmission data of the first data transmission channel, wherein the first The data transmission channel is used to transmit power consumption data to the main station; the third processing unit is used to perform maintenance and update on the first data transmission channel according to the transmission loss analysis results; the fourth processing unit is used to use the update The first data transmission channel transmits the first power parameter set and the first analysis result to the main station.

The third aspect of this application provides a data collection system for an intelligent meter reading terminal, including: a processor, the processor is coupled to a memory, and the memory is used to store a program. When the program is processed by the When the processor is executed, the system is enabled to perform the steps of the method described in the first aspect.

A fourth aspect of the present application provides a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

beneficial effects

One or more technical solutions provided in this application have at least the following technical effects or advantages:

The method provided by the embodiment of the present application collects multi-dimensional power consumption parameters of power users within a preset time period through a smart meter terminal to obtain a first set of power usage parameters, and then directly performs the first set of power usage parameters on the smart meter terminal. Power consumption parameter analysis, analyze the user's power consumption level and abnormal power usage, obtain the analysis results, and then evaluate the data transmission loss of the data transmission channel currently used to transmit power consumption data, obtain the data transmission loss analysis results, according to the data transmission As a result of the loss, the configuration information of the data transmission channel is overhauled and updated, and finally the updated data transmission channel is used to transmit the power consumption data and corresponding analysis results to the main station. By collecting and analyzing the user's power consumption data at the smart meter terminal, the embodiment of this application can accurately analyze the user's power consumption data at the terminal, reduce the pressure on the main station's power consumption data processing, and improve the accuracy of power consumption data analysis. stability and efficiency. During the analysis process, it mainly analyzes the user's power consumption level and abnormal power usage conditions. It can analyze power usage data in multiple dimensions, handle load increases or abnormal power usage in a timely manner, and improve the comprehensiveness of power usage data analysis. When transmitting electricity consumption data and corresponding analysis results, by analyzing the data transmission loss of the data transmission channel, the data transmission channel is inspected and updated to reduce the degree of data transmission loss and improve the integrity and integrity of electricity consumption data collection, analysis and transmission. Technical effects of accuracy.

The above description is only an overview of the technical solutions of the present application. In order to have a clearer understanding of the technical means of the present application, they can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and understandable. , the specific implementation methods of the present application are specifically listed below.

Description of the drawings

Figure 1 is a schematic flow chart of the data collection method of an intelligent meter reading terminal provided by this application;

Figure 2 is a schematic flow chart of constructing and obtaining abnormal power consumption analysis results in a data collection method of an intelligent meter reading terminal provided by this application;

Figure 3 is a schematic flow chart of the maintenance and update of the first data transmission channel in a data collection method of an intelligent meter reading terminal provided by this application;

Figure 4 is a schematic structural diagram of a data collection system of an intelligent meter reading terminal provided by this application;

Figure 5 is a schematic structural diagram of an exemplary electronic device of the present application.

Explanation of reference numerals: first obtaining unit 11, first processing unit 12, second processing unit 13, third processing unit 14, fourth processing unit 15, electronic device 300, memory 301, processor 302, communication interface 303, Bus Architecture 304.

Embodiments of the invention

This application provides a data collection method and system for an intelligent meter reading terminal, which is used to solve the technical problems in the prior art that there is loss in the collection and transmission of power consumption data at terminals of the power system, and the accuracy of data collection and analysis is low.

Application Overview

With the popularization of automated production and intelligent life, the importance of electricity in the national economy has increased, and electricity has become a necessary industry to maintain production and life. The collection of terminal electricity consumption data of electricity users in the power system involves the calculation and collection of electricity bills, the adjustment of electricity prices, whether the terminal electricity consumption is normal, and the reasonable and efficient development of the electric power industry.

At present, power consumption data is generally collected, transmitted and saved through the electricity meter at the power user terminal in the power system, in conjunction with the concentrator and main station in the power system, and analyzed by the power supply network system. At present, there are also some smart meters that can analyze some power data at the terminal for reference by users and the power grid, but the analysis content only includes more basic parts such as power intensity and electricity bill calculation.

In response to the above technical problems, the general idea of the technical solution provided by this application is as follows:

The method provided by the embodiment of the present application collects multi-dimensional power consumption parameters of power users within a preset time period through a smart meter terminal to obtain a first set of power usage parameters, and then directly performs the first set of power usage parameters on the smart meter terminal. Power consumption parameter analysis, analyze the user's power consumption level and abnormal power usage, obtain the analysis results, and then evaluate the data transmission loss of the data transmission channel currently used to transmit power consumption data, obtain the data transmission loss analysis results, according to the data transmission As a result of the loss, the configuration information of the data transmission channel is overhauled and updated, and finally the updated data transmission channel is used to transmit the power consumption data and corresponding analysis results to the main station.

After introducing the basic principles of the present application, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than the entire application. Embodiments, it should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application. It should also be noted that, for convenience of description, only some but not all parts relevant to the present application are shown in the drawings.

Embodiment 1

As shown in Figure 1, this application provides a data collection method for a smart meter reading terminal. The method is applied to a data collection system of a smart meter reading terminal. The system includes an electric energy collection module and a power consumption analysis module. The methods include:

S100: Use the power collection module to periodically collect and obtain the multi-dimensional power consumption parameters of the first user in the first time period, and obtain the first power consumption parameter set;

In the embodiment of this application, the first user is any user in the power grid system who uses electricity for production and life. For example, the first user can be a home user, a factory user, a shopping mall user, etc. The data collection system of the above-mentioned smart meter reading terminal is installed in the smart meter of the end user in the power grid system. The power collection module and the power consumption analysis module are used to collect and analyze power consumption data respectively. The smart meter reading system The data collection system of the meter terminal is connected to the concentrator and the main station in the power grid system. For example, it can be connected through optical fiber or wireless private network, and can transmit the collected power consumption data and the analysis results of the power consumption data to the main station. stand.

Optionally, the data collection system of the smart meter reading terminal includes a processor and a memory, which are used to execute the method provided by the embodiment of the present application, provide support for the realization of the functions of the power collection module and the power consumption analysis module, and temporarily store the corresponding data. The power consumption data and analysis result data can also be included in the system to connect to the power grid system, including communication interfaces.

The above-mentioned power collection module periodically collects and obtains the multi-dimensional power consumption parameters of the first user in the first time period according to a preset period. Specifically, the preset period can be a period with a time span of any size. For example Specifically, it can be a week, a month, a quarter, etc.

Multi-dimensional power consumption parameters include but are not limited to: parameter information such as voltage, current, power, and effective power. The process of collecting power data by the power collection module is similar to the process of collecting power data by smart meters in the existing technology. The first user All power consumption parameter information in the first period is collected and summarized to obtain the above-mentioned first power consumption parameter set.

S200: Use the power usage analysis module to analyze the power usage situation of the first power usage parameter set to obtain a first analysis result, where the first analysis result includes a power usage level analysis result and abnormal power usage. analysis results;

Specifically, the first power consumption parameter set includes multi-dimensional power consumption parameters of the first user within a first time period, which can reflect changes in the total power consumption and power consumption intensity of the first user during the first time period. , whether abnormal power consumption occurs, etc. Among them, abnormal power consumption refers to the power load of the first user at a certain time in the first time period far exceeding the historical level of the first user. In the case of abnormal power consumption, It is necessary to further investigate and analyze whether the first user has problems such as electricity theft or power system failure, and then take relevant measures.

The power consumption analysis module is used to analyze the power consumption situation of the first power consumption parameter set, specifically analyze the overall power consumption level of the first user in the first time period and whether abnormal power consumption occurs, and obtain the power consumption level analysis results and The abnormal power consumption analysis result is used as the first analysis result. A program is stored in the power consumption analysis module. When the program is run by the processor in the data collection system of the smart meter reading terminal, the method in step S200 is executed.

Step S200 in the method provided by the embodiment of this application includes:

S210: Based on the first time period, arrange the power parameters in the first power parameter set in time order to obtain a first power parameter sequence;

S220: Construct a first power parameter change curve according to the first power parameter sequence;

S230: Fit the first power parameter change curve to obtain the first power parameter change function;

S240: Obtain the section in the first power parameter change function in which the power parameter fluctuation exceeds the preset fluctuation threshold, and obtain the first fluctuation section set;

S250: Analyze the power consumption situation of the first power parameter set according to the first fluctuation section set and the first power parameter change function, and obtain the power consumption level analysis results and abnormal power consumption analysis results respectively.

Specifically, based on the positive time sequence within the aforementioned first time period, the power consumption parameters in the first power consumption parameter set are arranged in time sequence to obtain the first power consumption parameter sequence.

In the process of obtaining the first power consumption parameter sequence, since the first power consumption parameter set includes the multi-dimensional power consumption parameters of the first user, when arranging the multi-dimensional power consumption parameters according to the time series, the multi-dimensional power consumption parameters can be arranged separately. The power consumption parameters in each dimension are arranged, for example, the power consumption parameters in each dimension such as voltage, current, power, and effective power consumption are respectively sorted to obtain multiple sequences, which are aggregated as the first power consumption parameter sequence.

Then, based on the multiple sequences in the aforementioned first power parameter sequence, using the time in the first time period as the abscissa and the multi-dimensional power parameters as the ordinate, multiple coordinate points are constructed in the two-dimensional coordinate system, And connected by curves, multiple change curves of power consumption parameters in multiple dimensions are constructed respectively, and are aggregated as the first power consumption parameter change curve.

Further, fitting is performed based on multiple change curves of multi-dimensional power parameters in the first power parameter change curve. First, the multiple change curves are fitted into one curve, and then the function is further performed on a curve obtained by fitting. Fitting can better express the change function of all coordinate points in the curve. For example, the above fitting can be fitted by the least squares method, but it is not limited to this. In this way, a change function that can better characterize the multi-dimensional power consumption parameters is obtained, which is the above-mentioned first power consumption parameter change function. This function can represent the overall power consumption parameter level of the first user in the first time period.

In another possible embodiment of the present application, fitting is performed respectively according to multiple change curves of multi-dimensional power parameters in the first power parameter change curve to obtain multiple power parameter change functions. For example, this In the embodiment, the least squares method is used for fitting. Then, the electricity parameter change functions obtained by multiple fittings are used as the first electricity parameter change function, that is, the first electricity parameter change function is a set, in which the number of electricity parameter change functions is related to the number of multi-dimensional electricity parameter changes. The number of dimensions is the same.

Based on the obtained first power parameter change function, collect and obtain the section in which the power parameter fluctuation exceeds the preset fluctuation threshold. The preset fluctuation threshold is specifically the threshold of the power parameter fluctuation within the preset time threshold. This time The threshold and fluctuation size threshold can be set according to actual business. For example, the preset fluctuation threshold is the threshold of the fluctuation size between the minimum value and the maximum value of the first user's power consumption parameter within a day when the preset time threshold is. It may also be that the preset time threshold is the threshold of the minimum and maximum quality inspection fluctuations of the first user's power consumption within six hours.

If the fluctuation of the power consumption parameter in a certain time period within the first power consumption parameter change function exceeds the preset fluctuation threshold, the time and power consumption parameters in the section are extracted, and all the bands are extracted to obtain the first fluctuation section set. .

It should be noted that if the first power consumption parameter change function includes multiple change functions of multi-dimensional power consumption parameters, then preset fluctuation thresholds in the multiple change functions are respectively set, such as the power consumption change threshold, and multiple power consumption change thresholds are extracted respectively. The set of fluctuation sections within the change function is used to obtain the first set of fluctuation sections.

According to the above-mentioned first fluctuation section set and the first power consumption parameter change function, the power consumption situation of the first power consumption parameter set is analyzed. Among them, if the user has abnormal power consumption, it will be reflected in the fluctuation of the power consumption parameters. , and the user's overall power consumption and power consumption level will be reflected in the first power consumption parameter change function. Therefore, the first user's power consumption is performed based on the first power consumption parameter change function and the first fluctuation section set respectively. Analysis, the power consumption level analysis results and the abnormal power consumption analysis results are obtained respectively.

The embodiment of this application constructs a curve through the user's power consumption parameters and obtains the power consumption parameter change function through fitting, and extracts and obtains the fluctuation section set, which can visually analyze the user's power consumption level and abnormal power usage, and improve the power consumption parameter analysis. Efficiency and effectiveness.

As shown in Figure 2, step S250 in the method provided by this application includes step S251, which includes:

S251-1: Obtain the first fluctuation frequency information and the first fluctuation amplitude information according to the first fluctuation section set;

S251-2: Construct and obtain an abnormal fluctuation analysis model based on the first user's historical power consumption parameter set;

S251-3: Input the first fluctuation frequency information and the first fluctuation amplitude information into the abnormal fluctuation analysis model to obtain an output result;

S251-4: Obtain abnormal fluctuation analysis results according to the output results;

S251-5: Determine whether the abnormal fluctuation analysis result is greater than a preset threshold, and obtain the first judgment result;

S251-6: Use the abnormal fluctuation analysis result and the first judgment result as the abnormal power consumption analysis result.

Step S251 analyzes the abnormal power consumption situation of the first user based on the first fluctuation section set, which will be described in detail below.

According to the aforementioned first set of fluctuation sections, which includes multiple fluctuation sections, according to the number of fluctuation sections and the time span of the first time period, it can be obtained that the number of occurrences of the first user exceeding the preset value in the first time period can be obtained The frequency of the fluctuation section of the fluctuation threshold is used to obtain the first fluctuation frequency information.

And, according to the aforementioned first set of fluctuation sections, although the fluctuation amplitude in each fluctuation section exceeds the fluctuation amplitude of the preset fluctuation threshold, the amplitude of the fluctuation still needs to be analyzed and each fluctuation section is collected. The fluctuation amplitude of the internal power consumption parameter is calculated to obtain the first fluctuation amplitude information.

Then, based on the first user's historical power consumption parameter set, an abnormal fluctuation analysis model is constructed. The historical power consumption parameter set is specifically collected by the first user in multiple historical time periods that have the same time span as the first time period. A collection of power consumption parameters.

Step S251-2 in the method provided by this application includes the following steps:

A100: Obtain the first user's historical fluctuation frequency information set and historical fluctuation amplitude information set according to the first user's historical power consumption parameter set;

A200: Construct and obtain a first abnormal fluctuation analysis tree model and a second abnormal fluctuation analysis tree model based on the historical fluctuation frequency information set and the historical fluctuation amplitude information set respectively;

A300: Obtain the abnormal output nodes of the first abnormal fluctuation analysis tree model and the second abnormal fluctuation analysis tree model;

A400: Construct an electricity user portrait of the first user based on the electricity consumption level in the electricity consumption level analysis result;

A500: According to the electricity user portrait, respectively adjust the height of the abnormal output node in the first abnormal fluctuation analysis tree model and the second abnormal fluctuation analysis tree model;

A600: Merge the adjusted first abnormal fluctuation analysis tree model and the second abnormal fluctuation analysis tree model to obtain the abnormal fluctuation analysis model.

Specifically, according to the aforementioned set of historical electricity consumption parameters of the first user, a historical electricity consumption parameter change curve is constructed according to the aforementioned steps and fitted to obtain a historical electricity consumption parameter change function, which respectively includes multiple data in multiple time periods in the history. Historical power parameter change function. Further extract the segments in the historical power parameter change function that are greater than the preset fluctuation threshold to obtain the first user's historical fluctuation frequency information set and historical fluctuation amplitude information set.

It should be noted that the power consumption parameters in the historical power consumption parameter set are the power consumption parameters in the recent history when the first user's power demand and related power grid construction have not changed significantly. Therefore, the current preset fluctuation threshold is directly used. Extract historical fluctuation information to improve the accuracy and effectiveness of the construction and use of abnormal fluctuation analysis models.

Optionally, the first abnormal fluctuation analysis tree model and the second abnormal fluctuation analysis tree model are constructed according to the historical fluctuation frequency information set and the historical fluctuation amplitude information set respectively.

The following takes the construction of the first abnormal fluctuation analysis tree model based on the historical fluctuation frequency information collection as an example to illustrate the construction process of the abnormal fluctuation analysis tree model. Specifically, based on the historical fluctuation frequency information set, a piece of fluctuation frequency information is randomly selected to construct a first-level classification node, and the fluctuation frequency information is used as the classification threshold of the first-level classification node. The first-level classification node can All the input fluctuation frequency information is classified into two categories, and the input fluctuation frequency information is classified into two categories: greater than or equal to the classification threshold and less than the classification threshold, which are used as the classification threshold of the first-level classification node.

Then, the fluctuation frequency information that is different from the classification threshold of the first-level classification node is randomly selected again from the historical fluctuation frequency information set as the classification threshold of the second-level classification node. The second-level classification node can be based on the second-level classification node of the first-level classification node. The classification results are again classified into two categories and four-category results are obtained.

Repeat the above steps and continue to build higher-level classification nodes of the first abnormal fluctuation analysis tree model until the first abnormal fluctuation analysis tree model can classify all the input historical fluctuation frequency information sets into a single data, or the first abnormal fluctuation analysis tree model The level height of the classification node reaches the preset height.

Among them, within the set of fluctuation frequency information, the numerical sizes of most of the relatively normal fluctuation frequency information are similar, that is, the frequency of fluctuations of the first user in multiple time periods is generally similar. For example, a home user in There will be fluctuations on weekends or in the evening, and factory users will have fluctuations at the end of the month or on weekends. Therefore, most of the fluctuation frequency information is similar and forms dense data clusters. If the first user fluctuates multiple times in a time period, the fluctuation frequency information in that period is much larger than most other fluctuation frequency information. In the classification of multiple classification node classification thresholds, the larger fluctuation frequency information is more easily classified as a single fluctuation frequency information, that is, it is easier to be classified as a single data by a low-level classification node, which can be considered as abnormal fluctuation frequency information. , and most approximate fluctuation frequency information requires multiple classifications by high-level classification nodes to be classified into a single data. Even the top-level classification nodes cannot be classified into a single data. It can be considered that such data is similar to most other data. , which is the normal power fluctuation frequency information.

According to the actual power consumption of the first user, a classification node is set up that outputs abnormal fluctuation frequency information. As an abnormal output node, the single fluctuation frequency information obtained by the classification of the classification node and the nodes below is output as abnormal fluctuation frequency information. The single data classified by nodes above the classification node and the fluctuation frequency information that is not classified as single data are output as normal fluctuation frequency information.

In this way, all classification nodes for obtaining the first abnormal fluctuation analysis tree model are constructed, and the first abnormal fluctuation analysis tree model is obtained. Based on the same principle, continue to build a second abnormal fluctuation analysis tree model based on the historical fluctuation amplitude information collection, in which the abnormal fluctuation amplitude of the first user can be analyzed, and the analysis shows that the first user has a larger amplitude and a larger amplitude. Information on small abnormal fluctuation amplitudes.

Since the power consumption levels of different power users are different, different power users will have different impacts on the power system when abnormal fluctuations in power consumption occur. For example, the power consumption levels of users such as enterprises and factories are greater than the power consumption levels of household users. , so when users such as enterprises and factories experience abnormal fluctuations in power consumption, it is more likely to affect the normal operation of the power system. Therefore, the detection stringency of abnormal fluctuation frequency and abnormal fluctuation amplitude for different power users is different, and it is necessary to make personalized adjustments according to the power users.

Based on the construction of the first abnormal fluctuation analysis tree model and the second abnormal fluctuation analysis tree model of all classification nodes, obtain the abnormal output nodes in the two models, the single fluctuation frequency information and the single fluctuation amplitude obtained by classifying the nodes below the two abnormal output nodes. The information output is abnormal fluctuation frequency information and abnormal fluctuation amplitude information.

Based on the power consumption level of the first user reflected in the power consumption level analysis result in the aforementioned first analysis result, a power consumption user portrait of the first user is constructed. During the specific construction process, optionally, the electricity user profile is constructed based on the electricity consumption of the first user. According to the amount of electricity consumption, multiple user portraits are constructed, such as small household users, medium-sized enterprise users, large-scale enterprise users, etc. Enterprise users, etc., and obtain the electricity user portrait of the first user through matching.

Further, the abnormal output nodes in the above two models are adjusted according to the power user portrait of the first user. Specifically, the classification node level height of the abnormal output node is adjusted according to the power user portrait.

Optionally, for user portraits with higher power consumption, adjust the level height of the two abnormal output nodes in a larger direction. The higher-level abnormal output nodes can output more abnormal fluctuation frequency information and abnormal fluctuation amplitude information. In other words, after the first user inputs the current fluctuation frequency information and fluctuation amplitude information into the two abnormal fluctuation analysis tree models, it is easier for the higher abnormal output node to output the current fluctuation frequency information and fluctuation amplitude information as abnormal fluctuation information, which improves the The strictness of abnormal fluctuation detection for large power users. Correspondingly, for user portraits with low power consumption, adjust the level height of the two abnormal output nodes in a smaller direction. After adjustment, input the current fluctuation frequency information and fluctuation amplitude information into the two abnormal fluctuation analysis tree models, the lower It is more difficult for abnormal output nodes at a higher level to output the current fluctuation frequency information and fluctuation amplitude information as abnormal fluctuation information. This is because abnormal output nodes at a lower level classify the input fluctuation frequency information and fluctuation amplitude information less times. , which reduces the stringency of abnormal fluctuation detection for users with small power consumption.

Optionally, according to the electricity user profile, the height of the abnormal output node in the first abnormal fluctuation analysis tree model and the second abnormal fluctuation analysis tree model can be adjusted according to the actual needs of the power grid business to achieve personalization. Detection of abnormal power fluctuations.

In this way, the adjusted first abnormal fluctuation analysis tree model and the second abnormal fluctuation analysis tree model are obtained, and the two models are merged to obtain the above abnormal fluctuation analysis model.

After constructing and obtaining the abnormal fluctuation analysis model, the first fluctuation frequency information and the first fluctuation amplitude information of the current first user are combined with the historical fluctuation frequency information set and the historical fluctuation amplitude information set in the above-mentioned historical power consumption parameter set to input the abnormality Fluctuation analysis model, the model classifies the input data multiple times, and finally outputs abnormal fluctuation frequency information and abnormal fluctuation amplitude information to obtain the output result. Then it is determined whether the current first fluctuation frequency information and the first fluctuation amplitude information belong to the output abnormal fluctuation data, and the abnormal fluctuation analysis result is obtained. After the current detection is completed, the current first fluctuation frequency information and first fluctuation amplitude information can also be used as historical data to update the model or rebuild a new model, and update the abnormal fluctuation analysis model in real time or regularly to maintain the model effect.

In actual power grid business, if a power user occasionally experiences abnormal power fluctuations, this is a normal phenomenon and does not require further monitoring and analysis. If a power user frequently experiences abnormal power fluctuations, further processing is required.

Therefore, based on the aforementioned abnormal fluctuation analysis results, it is determined whether it is greater than a preset threshold. Optionally, the preset threshold includes a fluctuation frequency information threshold and a fluctuation amplitude information threshold. The abnormal fluctuation frequency information threshold includes a threshold for the number of times the first user's fluctuation frequency information is output as abnormal fluctuation frequency information within the most recent first time periods. , if the number exceeds this number, the first user has experienced abnormal power fluctuation frequency many times, and further actual processing is required. If the number of times the first user's fluctuation frequency information is output as abnormal fluctuation frequency information does not exceed the threshold, for example, only the current first fluctuation frequency information is output as abnormal fluctuation frequency information for the first time, no further processing is required.

Optionally, the fluctuation amplitude information threshold includes a numerical threshold for outputting all fluctuation amplitude information within the first user's first fluctuation section set as an abnormal fluctuation amplitude within a first time period. If the first user's first fluctuation amplitude If the number of fluctuation amplitude information outputted as abnormal fluctuations in the information exceeds the threshold, further actual inspection and analysis of the electricity consumption of the first user is required, and if the fluctuation amplitude information output within the first fluctuation amplitude information of the first user is abnormal fluctuation If the number of amplitudes does not exceed this threshold, no further processing is required.

After judging whether the abnormal fluctuation analysis result is greater than a preset threshold, the first judgment result is obtained, and the abnormal fluctuation analysis result and the first judgment result are used as the above-mentioned abnormal power consumption analysis results.

By constructing an abnormal fluctuation analysis model based on a tree model, the embodiment of the present application can combine the current power consumption parameters with the historical power consumption parameters to analyze whether the current power consumption parameters are abnormal, with high accuracy and without the need for identification data in supervised learning. process, the model construction efficiency is higher. The strictness of the detection of abnormal power consumption parameters of the model is adjusted according to the power consumption level of power users, and the abnormal power consumption detection of power users is personalized and the detection effect is better.

Moreover, after detecting and obtaining the user's abnormal fluctuation frequency and abnormal fluctuation amplitude, the embodiment of the present application also sets a preset threshold to further determine whether further practical measures are needed, which is more in line with the actual business of the power grid and the analysis of abnormal power consumption. It is more accurate and user-friendly, and can be used as reference data for power consumption analysis, which can effectively improve the efficiency of power grid business.

Step S250 in the method provided by this application also includes step S252, which includes:

S252-1: Obtain the slope change information of the first power parameter change function according to the first power parameter change function;

S252-2: Obtain the total electricity consumption information of the first electricity parameter change function according to the first electricity parameter change function;

S252-3: Obtain the power consumption level analysis result based on the slope change information and the total power consumption information.

Specifically, according to the aforementioned first power consumption parameter change function, the degree and direction of change of the slope in the power consumption parameter change function in the first time period are obtained, and the change in the power consumption level of the first user in the first time period is obtained. information.

According to the aforementioned first power consumption parameter change function, the overall power consumption information of the first user in the first time period is calculated and obtained, and the total power consumption information is obtained. For example, the first power consumption parameter change function can be integrated multiple times to calculate the total power consumption information. If the first power consumption parameter change function includes multiple change functions of multi-dimensional power consumption parameters, one of them can be calculated. The electric power change function is integrated to obtain the total electricity consumption information. Among them, the total electricity consumption information is not the actual total electricity consumption in kilowatt-hours of the first user, but data information reflecting the total electricity consumption level of the first user, which is used as data for analyzing the electricity consumption level.

The above-mentioned slope change information and total power consumption information are used as the power consumption level analysis results of the first power consumption parameter set, and combined with the aforementioned abnormal power consumption analysis results, a first analysis result is obtained.

The embodiment of the present application obtains the power consumption level information of the first user in the first time period through the first power consumption parameter change function analysis, and combines the abnormal power consumption analysis to obtain the overall power consumption analysis results, realizing multi-dimensional power consumption. Parameter analysis serves as reference data for power price adjustment, power distribution and power grid system adjustment, and the analysis results are more accurate and comprehensive.

S300: Evaluate and analyze the transmission loss analysis results of the data transmitted by the first data transmission channel, where the first data transmission channel is used to transmit power consumption data to the main station;

User power consumption data collected by terminals in the power grid system need to be transmitted to the main station through concentrators and communication channels for other services. During the transmission process, due to problems such as communication reliability and stability, data transmission is lost, resulting in the main station being unable to obtain accurate and complete power consumption data.

In the embodiment of this application, the collection and preliminary analysis of power consumption data are carried out in the terminal system, and it is also necessary to ensure that the power consumption data and analysis results are completely and accurately transmitted to the main station. Therefore, there is a greater need to ensure the integrity of data transmission.

Specifically, the integrity of the transmission data of the first data transmission channel used to transmit power consumption data to the main station is evaluated, and as a data basis, the first data transmission channel is adjusted.

Step S300 in the method provided by the embodiment of this application includes:

S310: Collect and obtain the configuration information of the first data transmission channel, and obtain the first configuration information set;

S320: Construct a virtual data transmission model based on the first configuration information set;

S330: Use the virtual data transmission model to perform data transmission and obtain the data transmission result;

S340: Perform data transmission loss analysis on the data transmission result to obtain the transmission loss analysis result.

Specifically, the configuration information of the first data transmission channel is collected according to the current communication connection mode between the first user terminal and the main station. For example, the communication connection mode between the first user terminal and the main station can be Power carrier communication, wireless communication, optical fiber communication, etc. collect communication interface, communication protocol, communication address, communication signal, communication speed, communication bandwidth, communication distance, communication base station and other information according to different communication methods to obtain the first configuration information set. Specifically, it can be obtained through information collection during the construction process of the communication system in the power grid system.

Then, based on the first configuration information set, a virtual data transmission model is constructed in a suitable development environment to simulate the first data transmission channel for data transmission. In the process of using the virtual data transmission model for data transmission, the first data transmission model can be used. The user's historical power consumption parameters are transmitted, and multiple simulated data transmissions can be performed to improve the simulation accuracy and obtain the data transmission results.

Then, perform data transmission loss analysis on the data transmission result and the data before transmission to obtain the transmission loss analysis result. For example, during the specific analysis process, the proportion of loss data in all transmission data and the importance of loss data in all transmission data are analyzed to obtain the transmission loss analysis result.

The embodiment of this application collects the configuration information of the data transmission channels of the terminal and the main station in the power grid system, constructs a virtual data transmission model, analyzes the loss degree of the power parameter data transmitted by the data transmission channel, and obtains the transmission loss analysis results as maintenance update data. The reference data of the transmission channel can improve the accuracy and effect of data transmission channel updates, and improve the integrity and accuracy of data collection and transmission of terminal meters in the power grid system.

S400: According to the transmission loss analysis result, perform maintenance and update on the first data transmission channel;

Based on the above transmission loss analysis results, targeted maintenance and updates are performed on the first data transmission channel to improve the integrity and accuracy of data transmitted by the first data transmission channel.

As shown in Figure 3, step S400 in the method provided by the embodiment of the present application includes:

S410: Obtain the first adjustment parameter according to the degree of data transmission loss in the transmission loss analysis result;

S420: Obtain the second adjustment parameter according to the power consumption level of the first user in the power consumption level analysis result;

S430: Adjust the first configuration information set according to the first adjustment parameter and the second adjustment parameter to obtain a second configuration information set;

S440: Use the second configuration information set to perform maintenance and update on the first data transmission channel.

Specifically, the first adjustment parameter is obtained based on the degree of data transmission loss in the aforementioned transmission loss analysis result, specifically the degree of data transmission loss in the transmission loss analysis result and the proportion of the transmission loss data in all transmission data. . The first adjustment parameter is used to adjust the relevant configuration information of the first data transmission channel. If the degree of data transmission loss in the transmission loss analysis result is large, the first adjustment parameter will be larger, and then the related configuration of the first data transmission channel needs to be The degree of adjustment is also large. Even when the first adjustment parameter is large, the communication connection transmission mode of the first data transmission channel needs to be changed to reduce data loss during the transmission process.

Optionally, the second adjustment parameter is further obtained based on the power consumption level in the power consumption level analysis result of the first user, where the power consumption levels of different users are different, and the importance of the power consumption parameters of different users is also different. For example, for household users with low electricity consumption levels, their electricity consumption parameters are of low importance. Generally, they only need to be able to accurately calculate electricity consumption costs. Therefore, these users are not very concerned about the integrity of electricity consumption parameter data transmission. , the accuracy and real-time requirements are low, the corresponding second adjustment parameter is small, and the degree of adjustment to the relevant configuration of the first data transmission channel is also small.

For another example, for enterprises or factory users with high power consumption levels, their power consumption parameters are of higher importance, and the consequences of abnormal power consumption will be greater. It is necessary to ensure complete and accurate transmission and analysis of power consumption parameters. This group of users When transmitting power parameter data, the requirements for completeness, accuracy and real-time are relatively high, and the corresponding second adjustment parameter is relatively large, which in turn requires a greater degree of adjustment to the relevant configuration of the first data transmission channel.

The specific sizes of the first adjustment parameter and the second adjustment parameter can be set multiple times according to the actual degree of data transmission loss and the actual user power consumption level, and can be matched and selected based on the current first user's transmission loss analysis result and power consumption level. .

The first configuration information set is adjusted sequentially according to the above-mentioned first adjustment parameter and the second adjustment parameter to improve the integrity and accuracy of the data transmitted by the first data transmission channel, and obtain the second configuration information set. During the specific adjustment process, the first configuration information set can be adjusted when the first adjustment parameter and the second adjustment parameter are greater than a certain threshold. If both the first adjustment parameter and the second adjustment parameter are less than a certain threshold, it is considered that The current first configuration information set can meet the current data transmission needs of the first user without adjustment and update, thereby reducing costs. The threshold can be set according to actual data transmission requirements.

Finally, based on the adjusted second configuration information set, the relevant configuration of the first data transmission channel is inspected and updated to improve the stability and reliability of data transmission of the first data transmission channel.

By obtaining adjustment parameters based on the degree of data transmission loss and the user's power consumption level, the embodiment of this application can adjust the specific configuration of the data transmission channel in multiple dimensions, improve the data transmission quality based on actual needs, and ensure a certain data transmission quality. Reduce data transmission construction costs to a certain extent and achieve the technical effect of improving the integrity and accuracy of power grid terminal data collection and transmission.

S500: Use the updated first data transmission channel to transmit the first power parameter set and the first analysis result to the main station.

The updated first data transmission channel is used to transmit the first power parameter set and the first analysis result to the main station in the power grid system, and the power supply network system performs calculation and analysis.

To sum up, by collecting and analyzing the user's power consumption data at the smart meter terminal, the embodiment of the present application can accurately analyze the user's power consumption data at the terminal, reduce the pressure on the main station's power consumption data analysis and processing, and improve the power consumption of the main station. The accuracy and efficiency of power consumption data analysis. During the analysis process, the user's power consumption level and abnormal power usage conditions are mainly analyzed. When analyzing abnormal power usage conditions, specific methods and specific models are used to analyze the user's power usage more accurately. Abnormal power consumption fluctuations can analyze power consumption data in multiple dimensions, handle load increases or abnormal power consumption in a timely manner, and improve the comprehensiveness of power consumption data analysis. When transmitting power consumption data and corresponding analysis results, through analysis Data transmission loss in the data transmission channel, the data transmission channel should be overhauled and updated to reduce the degree of data transmission loss and achieve the technical effect of improving the integrity and accuracy of electricity data collection, analysis and transmission.

Embodiment 2

Based on the same inventive concept as the data collection method of an intelligent meter reading terminal in the previous embodiment, as shown in Figure 4, this application provides a data collection system for an intelligent meter reading terminal, wherein the system includes:

The first acquisition unit 11 is configured to periodically collect and obtain the multi-dimensional power consumption parameters of the first user in the first time period through the power collection module, and obtain the first power consumption parameter set;

The first processing unit 12 is configured to analyze the power consumption situation of the first power consumption parameter set through the power consumption analysis module to obtain a first analysis result, wherein the first analysis result includes a power consumption level analysis result. and abnormal power usage analysis results;

The second processing unit 13 is used to evaluate and analyze the transmission loss analysis results of the data transmitted through the first data transmission channel, where the first data transmission channel is used to transmit power consumption data to the main station;

The third processing unit 14 is configured to perform maintenance and update on the first data transmission channel according to the transmission loss analysis results;

The fourth processing unit 15 is configured to transmit the first power parameter set and the first analysis result to the main station using the updated first data transmission channel.

Further, the system also includes:

A fifth processing unit, configured to arrange the power parameters in the first power parameter set in time order based on the first time period to obtain a first power parameter sequence;

A first construction unit configured to construct a first power parameter change curve according to the first power parameter sequence;

The sixth processing unit is used to fit the first power parameter change curve to obtain the first power parameter change function;

The second obtaining unit is used to obtain the section in the first power parameter change function in which the power parameter fluctuation exceeds the preset fluctuation threshold, and obtain the first fluctuation section set;

A seventh processing unit, configured to analyze the power consumption of the first power parameter set according to the first fluctuation section set and the first power parameter change function, and obtain the power consumption level analysis results and abnormality respectively. Electrical analysis results.

Further, the system also includes:

An eighth processing unit, configured to obtain first fluctuation frequency information and first fluctuation amplitude information according to the first fluctuation section set;

A second construction unit, configured to construct and obtain an abnormal fluctuation analysis model based on the historical power consumption parameter set of the first user;

A third obtaining unit, configured to input the first fluctuation frequency information and the first fluctuation amplitude information into the abnormal fluctuation analysis model and obtain an output result;

The fourth obtaining unit is used to obtain abnormal fluctuation analysis results according to the output results;

The first judgment unit is used to judge whether the abnormal fluctuation analysis result is greater than a preset threshold and obtain the first judgment result;

A ninth processing unit, configured to use the abnormal fluctuation analysis result and the first judgment result as the abnormal power consumption analysis result.

Further, the system also includes:

A fifth obtaining unit, configured to obtain the slope change information of the first power parameter change function according to the first power parameter change function;

A sixth obtaining unit, configured to obtain the total electricity consumption information of the first electricity parameter change function according to the first electricity parameter change function;

A tenth processing unit, configured to obtain the power consumption level analysis result based on the slope change information and the total power consumption information.

Further, the system also includes:

A seventh obtaining unit, configured to obtain the first user's historical fluctuation frequency information set and historical fluctuation amplitude information set according to the first user's historical power consumption parameter set;

A third construction unit configured to construct and obtain a first abnormal fluctuation analysis tree model and a second abnormal fluctuation analysis tree model based on the historical fluctuation frequency information set and the historical fluctuation amplitude information set respectively;

An eighth obtaining unit is used to obtain the abnormal output nodes of the first abnormal fluctuation analysis tree model and the second abnormal fluctuation analysis tree model;

A fourth construction unit, configured to construct an electricity user portrait of the first user based on the electricity consumption level within the electricity consumption level analysis result;

An eleventh processing unit, configured to respectively adjust the height of the abnormal output node in the first abnormal fluctuation analysis tree model and the second abnormal fluctuation analysis tree model according to the electricity user portrait;

The twelfth processing unit is configured to merge the adjusted first abnormal fluctuation analysis tree model and the second abnormal fluctuation analysis tree model to obtain the abnormal fluctuation analysis model.

Further, the system also includes:

The ninth acquisition unit is used to collect and acquire the configuration information of the first data transmission channel and obtain the first configuration information set;

A fourth building unit configured to build a virtual data transmission model based on the first configuration information set;

The tenth obtaining unit is used to perform data transmission using the virtual data transmission model and obtain data transmission results;

A thirteenth processing unit, configured to perform data transmission loss analysis on the data transmission result to obtain the transmission loss analysis result.

Further, the system also includes:

An eleventh obtaining unit, configured to obtain the first adjustment parameter according to the degree of data transmission loss in the transmission loss analysis result;

A twelfth obtaining unit, configured to obtain a second adjustment parameter according to the power consumption level of the first user in the power consumption level analysis result;

A fourteenth processing unit, configured to adjust the first configuration information set according to the first adjustment parameter and the second adjustment parameter to obtain a second configuration information set;

The fifteenth processing unit is configured to use the second configuration information set to perform maintenance and update on the first data transmission channel.

Embodiment 3

Based on the same inventive concept as the data collection method of a smart meter reading terminal in the previous embodiment, this application also provides a computer-readable storage medium, a computer program is stored on the storage medium, and the computer program is processed When the processor is executed, the method in Embodiment 1 is implemented.

Example electronic device

The electronic device of the present application is described below with reference to Figure 5,

Based on the same inventive concept as the data collection method of an intelligent meter reading terminal in the previous embodiment, this application also provides a data collection system of an intelligent meter reading terminal, including: a processor, the processor is coupled to a memory, The memory is used to store a program. When the program is executed by the processor, the system can perform the steps of the method described in Embodiment 1.

The electronic device 300 includes: a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Among them, the communication interface 303, the processor 302 and the memory 301 can be connected to each other through a bus architecture 304; the bus architecture 304 can be a peripheral component interconnection module (peripheral component interconnection module). component interconnect (PCI for short) bus or extended industry standard architecture (EISA for short) bus, etc. The bus architecture 304 can be divided into an address bus, a data bus, a control bus, etc. For ease of presentation, only one thick line is used in Figure 5, but it does not mean that there is only one bus or one type of bus.

The processor 302 may be a CPU, a microprocessor, an ASIC, or one or more integrated circuits used to control the execution of the program of the present application.

Communication interface 303 uses any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, wireless access network (radio access network (RAN), wireless local area networks (WLAN), wired access network, etc.

Memory 301 may be ROM or other types of static storage devices that can store static information and instructions, RAM or other types of dynamic storage devices that can store information and instructions, or it may be electrically erasable programmable read-only memory (electrically erasable Programmable read only memory (EEPROM), compact discread only memory (CD ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), disk storage media or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures that can be accessed by a computer, without limitation. The memory may exist independently and be connected to the processor through the bus architecture 304. Memory can also be integrated with the processor.

Among them, the memory 301 is used to store computer execution instructions for executing the solution of the present application, and the processor 302 controls the execution. The processor 302 is used to execute the computer execution instructions stored in the memory 301, thereby implementing the data collection method of the smart meter reading terminal provided in the above embodiments of the present application.

Those of ordinary skill in the art can understand that the first, second, and other numerical numbers involved in this application are only for convenience of description, and are not used to limit the scope of this application, nor do they indicate a sequence. "And/or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and/or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the related objects are in an "or" relationship. "At least one" means one or more. At least two means two or more. "At least one", "any one" or similar expressions refer to any combination of these items, including any combination of single items (items) or plural items (items). For example, at least one item (number, species) in a, b, or c can mean: a ,b,c,a b,a c,b c, or a b c, where a, b, c can be single or multiple.

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in this application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, e.g., the computer instructions may be transferred from a website, computer, server, or data center Transmission to another website site, computer, server or data center by wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center integrated with one or more available media. The available media may be magnetic media (eg, floppy disk, hard disk, magnetic tape), optical media (eg, DVD), or semiconductor media (eg, solid state disk (Solid State Disk, SSD)), etc.

The various illustrative logic units and circuits described in this application may be implemented by a general purpose processor, a digital signal processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of the foregoing designed to implement or operate the functions described. The general-purpose processor may be a microprocessor. Alternatively, the general-purpose processor may also be any conventional processor, controller, microcontroller or state machine. A processor may also be implemented as a combination of computing devices, such as a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration. accomplish.

The steps of the method or algorithm described in this application may be directly embedded in hardware, a software unit executed by a processor, or a combination of the two. Software units can be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CDs ROM or any other form of storage media in this field. For example, the storage medium can be connected to the processor, so that the processor can read information from the storage medium and can store and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be installed in the ASIC, and the ASIC can be installed in the terminal. Optionally, the processor and the storage medium may also be provided in different components in the terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing device, causing a series of operating steps to be performed on the computer or other programmable device to produce computer-implemented processing, thereby executing on the computer or other programmable device. Instructions provide steps for implementing the functions specified in a process or processes of a flowchart diagram and/or a block or blocks of a block diagram.

Although the present application has been described in conjunction with specific features and embodiments thereof, it will be apparent that various modifications and combinations may be made without departing from the spirit and scope of the application. Accordingly, the specification and drawings are merely illustrative of the present application and are deemed to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. In this way, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technology, the present application is intended to include these modifications and variations.

Claims

A data collection method for an intelligent meter reading terminal, characterized in that the method is applied to a data collection system of an intelligent meter reading terminal. The system includes an electric energy collection module and a power consumption analysis module. The method includes:

The power collection module periodically collects and obtains the multi-dimensional power consumption parameters of the first user within the first time period to obtain the first power consumption parameter set;

The power usage analysis module analyzes the power usage situation of the first power usage parameter set to obtain a first analysis result, where the first analysis result includes a power usage level analysis result and an abnormal power usage analysis result. ;

Evaluate and analyze the transmission loss analysis results of the data transmitted by the first data transmission channel, wherein the first data transmission channel is used to transmit power consumption data to the main station;

According to the transmission loss analysis results, perform maintenance and update on the first data transmission channel;

The updated first data transmission channel is used to transmit the first power parameter set and the first analysis result to the main station.
The method according to claim 1, characterized in that the analysis of the power consumption situation of the first power consumption parameter set through the power consumption analysis module includes:

Based on the first time period, arrange the power parameters in the first power parameter set in chronological order to obtain a first power parameter sequence;

Construct a first power parameter change curve according to the first power parameter sequence;

Fit the first power parameter change curve to obtain the first power parameter change function;

Obtain the section in the first power parameter change function in which the power parameter fluctuation exceeds the preset fluctuation threshold, and obtain the first fluctuation section set;

The power consumption situation of the first power consumption parameter set is analyzed according to the first fluctuation section set and the first power consumption parameter change function, and the power consumption level analysis results and abnormal power consumption analysis results are obtained respectively.
The method according to claim 2, wherein the analysis of the power usage of the first power parameter set based on the first fluctuation section set and the first power parameter change function includes:

Obtain first fluctuation frequency information and first fluctuation amplitude information according to the first fluctuation section set;

Construct and obtain an abnormal fluctuation analysis model based on the first user's historical power consumption parameter set;

Input the first fluctuation frequency information and the first fluctuation amplitude information into the abnormal fluctuation analysis model to obtain an output result;

According to the output results, obtain abnormal fluctuation analysis results;

Determine whether the abnormal fluctuation analysis result is greater than a preset threshold, and obtain the first judgment result;

The abnormal fluctuation analysis result and the first judgment result are used as the abnormal power consumption analysis result.
The method according to claim 2, wherein the analysis of the power usage of the first power parameter set based on the first fluctuation section set and the first power parameter change function further includes:

Obtain the slope change information of the first power parameter change function according to the first power parameter change function;

According to the first power consumption parameter change function, obtain the total power consumption information of the first power consumption parameter change function;

The power consumption level analysis result is obtained according to the slope change information and the total power consumption information.
The method according to claim 3, wherein said constructing and obtaining an abnormal fluctuation analysis model based on the first user's historical power consumption parameter set includes:

According to the first user's historical power consumption parameter set, obtain the first user's historical fluctuation frequency information set and historical fluctuation amplitude information set;

Construct and obtain a first abnormal fluctuation analysis tree model and a second abnormal fluctuation analysis tree model based on the historical fluctuation frequency information set and the historical fluctuation amplitude information set respectively;

Obtain the abnormal output nodes of the first abnormal fluctuation analysis tree model and the second abnormal fluctuation analysis tree model;

Construct an electricity user portrait of the first user based on the electricity consumption level within the electricity consumption level analysis result;

According to the electricity user portrait, adjust the height of the abnormal output node in the first abnormal fluctuation analysis tree model and the second abnormal fluctuation analysis tree model respectively;

The adjusted first abnormal fluctuation analysis tree model and the second abnormal fluctuation analysis tree model are combined to obtain the abnormal fluctuation analysis model.
The method according to claim 1, characterized in that the evaluation and analysis of the transmission loss analysis results of the transmission data of the first data transmission channel includes:

Collect and obtain the configuration information of the first data transmission channel to obtain a first configuration information set;

Construct a virtual data transmission model based on the first configuration information set;

Use the virtual data transmission model to perform data transmission and obtain data transmission results;

Perform data transmission loss analysis on the data transmission result to obtain the transmission loss analysis result.
The method according to claim 6, characterized in that, according to the transmission loss analysis result, performing maintenance and update on the first data transmission channel includes:

Obtain the first adjustment parameter according to the degree of data transmission loss in the transmission loss analysis result;

Obtain a second adjustment parameter according to the power consumption level of the first user in the power consumption level analysis result;

Adjust the first configuration information set according to the first adjustment parameter and the second adjustment parameter to obtain a second configuration information set;

The second configuration information set is used to perform maintenance and update on the first data transmission channel.
A data collection system for intelligent meter reading terminals, characterized in that the system includes:

The first acquisition unit is configured to periodically collect and obtain the multi-dimensional power consumption parameters of the first user in the first time period through the power collection module, and obtain the first power consumption parameter set;

The first processing unit is configured to analyze the power consumption situation of the first power consumption parameter set through the power consumption analysis module to obtain a first analysis result, wherein the first analysis result includes the power consumption level analysis result and Abnormal power consumption analysis results;

The second processing unit is used to evaluate and analyze the transmission loss analysis results of the data transmitted through the first data transmission channel, where the first data transmission channel is used to transmit power consumption data to the main station;

A third processing unit configured to perform maintenance and update on the first data transmission channel according to the transmission loss analysis results;

A fourth processing unit is configured to transmit the first power parameter set and the first analysis result to the main station using the updated first data transmission channel.
A data collection system for an intelligent meter reading terminal, characterized in that it includes: a processor, the processor is coupled to a memory, the memory is used to store a program, and when the program is executed by the processor, the system To perform the steps of the method according to any one of claims 1 to 7.
A computer-readable storage medium, characterized in that a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.