CN118397771B - Cable anti-theft system - Google Patents
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- G—PHYSICS
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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Abstract
The invention discloses a cable anti-theft system, which relates to the technical field of cables, and an intelligent cable anti-theft system is constructed by integrating a data monitoring module, an operation state judging module and a maintenance management module, wherein the data monitoring module is used for collecting power data in each monitoring area and power consumption of anti-theft system equipment in real time; the running state judging module calculates the weight coefficient of the power data of each area through weighted average to generate the running state evaluation coefficient of the power system; the maintenance management module is combined with the machine learning model to analyze the received data, predict risk events of the cable system in advance and generate early warning signals, so that potential faults can be effectively identified in advance, the reliability and safety of the system are improved, and even if a power system breaks down, the system can also rely on perfect data analysis and early warning functions, so that the risk of system shutdown is reduced.
Description
Technical Field
The invention relates to the technical field of cables, in particular to a cable anti-theft system.
Background
A cable anti-theft system is a security solution specifically designed to protect cables from theft. The system generally includes a variety of technologies and devices such as monitoring cameras, vibration sensors, fiber optic sensors, alarm systems, and the like. When someone tries to cut, move or steal the cable, the system immediately triggers an alarm to inform relevant personnel to take action so as to ensure the safety and timely recovery of the cable.
In addition, the cable anti-theft system can be integrated into a larger safety management platform, and remote monitoring and real-time data analysis are realized through networking monitoring. The comprehensive management mode not only improves the effectiveness of theft prevention, but also can provide detailed event records and analysis reports, thereby helping management personnel to better understand the safety condition and optimizing the protective measures.
The defects in the prior art are that:
In the prior art, the cable anti-theft system can be integrated into a larger safety management platform, and remote monitoring and real-time data analysis are realized through networking monitoring. But if the power system fails, the whole anti-theft system can lose power supply, and the monitoring equipment, the sensors, the server and the network equipment are directly stopped. While some anti-theft systems may design a backup power source or Uninterruptible Power Supply (UPS) to cope with power failures, these redundant systems also rely on power to remain operational. If the fault is too long, the backup power source is exhausted and the entire system still fails. In addition, maintenance and reliability of the backup power system itself can also affect its effectiveness.
Disclosure of Invention
The invention aims to provide a cable anti-theft system which aims to solve the defects in the background technology.
In order to achieve the above object, the present invention provides the following technical solutions: a cable anti-theft system comprises a data monitoring module, an operation state judging module and a maintenance management module;
The data monitoring module is used for dividing the power system into a plurality of monitoring areas, collecting real-time power data in each monitoring area and sending the collected real-time power data to the running state judging module; the system is also used for monitoring the real-time power consumption of monitoring equipment, sensors, servers and network equipment in the cable anti-theft system and sending the real-time power consumption to the maintenance management module;
The running state judging module is used for receiving the real-time power data sent by the data monitoring module, determining the weight coefficient of the real-time power data of each monitoring area, carrying out weighted average calculation on the running state evaluation coefficient of the power system according to the weight coefficient of the real-time power data of each monitoring area, and sending the running state evaluation coefficient of the power system to the maintenance management module;
And the maintenance management module is used for analyzing the running state evaluation coefficient of the received power system and the real-time power consumption of monitoring equipment, sensors, servers and network equipment in the cable anti-theft system, and predicting and early warning the risk event of the cable system in advance through the machine learning model.
In a preferred embodiment, real-time voltage data and real-time current data in the real-time power data are respectively acquired, the real-time voltage data and the real-time current data are analyzed, a power load abnormality index and a power grid frequency fluctuation index of the real-time power data are respectively acquired according to analysis results, and a weight coefficient of the real-time power data of each monitoring area is determined.
In a preferred embodiment, the method for obtaining the power load abnormality index is as follows:
collecting electrical load data, including voltage and current, representing the collected time series data as: ； The total number of N time series data representing the power load value at time t is denoised by using a moving average filter, and the specific calculation expression is: = ; wherein, Representing the denoised power load value, k being the window size, modeling the denoised load data, capturing its trend and seasonal pattern, and the SARIMA model being represented as:= ; in the method, in the process of the invention, 、、、And、、、Autoregressive and moving average coefficients, respectively, p is a positive integer greater than 0,Representing p observations before a time t,P error items before the time point t represent the errors between the observed value and the predicted value, load prediction is performed by using the SARIMA model, and the prediction errors are calculated, namely；For predicting errors, features are extracted from the time sequence, statistical features in a time window are extracted, wherein the statistical features comprise the mean and the variance of the statistical features, and a specific calculation expression is as follows:= And =; Wherein, As the mean value of the time series,For the variance of the time sequence, the mean value and the variance of the time sequence are analyzed, and the power load abnormality index is calculated, wherein the specific calculation expression is as follows: ; in the method, in the process of the invention, Is an index of power load abnormality.
In a preferred embodiment, the method for obtaining the power grid frequency fluctuation index is as follows:
Collecting power grid frequency data, carrying out smoothing treatment on the data, and carrying out wavelet transformation on the preprocessed frequency data by using a wavelet function to obtain a wavelet coefficient of a frequency signal, wherein the specific calculation expression is as follows: = ; wherein, Is a frequency signal which is a function of the frequency,Is a function of the wavelet,Is a scale parameter of the sample,Is a parameter of the translation and,And (3) for wavelet coefficients, carrying out threshold processing on the wavelet coefficients, and removing coefficients smaller than a threshold value to reduce noise influence, wherein a threshold processing formula is as follows:= ; wherein, T is a threshold value, For the processed wavelet coefficients, the frequency signal is reconstructed using the retained wavelet coefficients, the reconstructed signal being expressed as:； Reconstructing the frequency signal;
And performing empirical mode decomposition on the reconstructed signals to obtain a series of eigenmode functions, performing extreme point fitting on the original signals to obtain a first IMF, then subtracting the first IMF from the original signals, performing fitting on the obtained new signals until the new signals cannot be decomposed, performing frequency analysis on each IMF, extracting frequency variation values, and summing the frequency variation values of the IMFs to obtain the power grid frequency fluctuation index through calculation.
In a preferred embodiment, the power load abnormality index and the grid frequency fluctuation index are normalized, and the score of the real-time power data of each monitoring area is calculated through the normalized power load abnormality index and the grid frequency fluctuation index.
In a preferred embodiment, the scoring of the real-time power data of each monitoring area is respectively assigned with weights according to a time weighting method, and the weight value of the real-time power data in each monitoring area is determined specifically as follows:
Collecting real-time power data from each monitoring area, dividing monitoring time periods according to a certain time interval, performing time weighted calculation on the real-time power data of each time period, determining an attenuation coefficient alpha according to the length of the time interval, and then calculating weights according to the distance between the time and the current time, wherein an exponential weighting formula is as follows: = ; in the method, in the process of the invention, For the weight value of s time periods in each monitored area,Carrying out normalization processing on the weight value of each time period in each monitoring area for the time of the current time from the starting time of the time period, and assigning the score of each time period according to the calculated weight to obtain the weight value of the real-time power data in each monitoring area; and carrying out weighted average calculation on the weight coefficient of the real-time power data of the monitoring area to obtain the running state evaluation coefficient of the power system.
In a preferred embodiment, the early warning of the risk event of the cable system is predicted in advance by a linear regression model, which specifically comprises:
Taking the running state evaluation coefficient of the power system and the real-time power consumption of monitoring equipment, sensors, servers and network equipment in the cable anti-theft system as the input of a model;
preprocessing model input data, and modeling according to historical data characteristics of the power system and the cable anti-theft system, wherein the historical data characteristics comprise historical running state evaluation coefficients of the power system and historical power consumption of monitoring equipment, sensors, servers and network equipment;
Training using a linear regression model using the collected data, the formula of the linear regression model is as follows: ; wherein, As a risk factor for a cable system,,,...,As a feature of the historical data,,,,...,Is a coefficient of the model, c is an error term;
Predicting new real-time data by using a trained linear regression model, comparing the risk coefficient of the cable system with a preset risk coefficient reference threshold value in historical data, and generating an early warning signal at the moment if the risk coefficient of the cable anti-theft system is greater than or equal to the risk coefficient reference threshold value; if the risk coefficient of the cable anti-theft system is smaller than the risk coefficient reference threshold value, no early warning signal is generated at the moment.
In the technical scheme, the invention has the technical effects and advantages that:
1. According to the invention, the power data in each monitoring area and the power consumption of the anti-theft system equipment are collected, the power data in each area are calculated by using weighted average, the running state evaluation coefficient of the power system is generated, the received data are analyzed by combining with a machine learning model, the risk event of the cable system is predicted in advance, and an early warning signal is generated, so that repair or adjustment measures are timely taken, and potential faults or accidents are prevented. By the method, the anti-theft system failure caused by the power system fault can be effectively prevented, the safety and the operation efficiency of the system can be improved through the intelligent analysis and early warning mechanism, the loss is reduced, and the response capability of maintenance management is improved.
2. The invention effectively realizes the real-time monitoring of the running state of the electric power system and the cable anti-theft system by collecting and monitoring the real-time data of the monitoring areas and the equipment of the electric power system, simultaneously uses the running state judging module to carry out weight analysis and evaluation on the collected data and synthesizes the real-time electric power data of each monitoring area, thereby accurately calculating the running state evaluation coefficient of the electric power system and providing reliable data support for the maintenance and management of the system. The system and the method can not only timely find out the problems in the operation of the power system, help operation and maintenance personnel to quickly respond, improve the stability and reliability of the power system, help to reduce potential fault risks and improve the operation efficiency and safety of the system.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a block diagram of a system according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1 and 2, the cable anti-theft system according to the present embodiment includes a data monitoring module, an operation state judging module, and a maintenance management module;
The data monitoring module is used for dividing the power system into a plurality of monitoring areas, collecting real-time power data in each monitoring area and sending the collected real-time power data to the running state judging module; the system is also used for monitoring the real-time power consumption of monitoring equipment, sensors, servers and network equipment in the cable anti-theft system and sending the real-time power consumption to the maintenance management module;
The running state judging module is used for receiving the real-time power data sent by the data monitoring module, determining the weight coefficient of the real-time power data of each monitoring area, carrying out weighted average calculation on the running state evaluation coefficient of the power system according to the weight coefficient of the real-time power data of each monitoring area, and sending the running state evaluation coefficient of the power system to the maintenance management module;
And the maintenance management module is used for analyzing the running state evaluation coefficient of the received power system and the real-time power consumption of monitoring equipment, sensors, servers and network equipment in the cable anti-theft system, and predicting and early warning the risk event of the cable system in advance through the machine learning model.
The system comprises a data monitoring module, a running state judging module, a power system monitoring module, a real-time power data acquisition module and a data storage module, wherein the data monitoring module is used for dividing the power system into a plurality of monitoring areas, acquiring real-time power data in each monitoring area and sending the acquired real-time power data to the running state judging module; the system is also used for monitoring the real-time power consumption of monitoring equipment, sensors, servers and network equipment in the cable anti-theft system and sending the real-time power consumption to the maintenance management module.
The range of power systems that specifically need to be monitored, including all critical equipment and cables. The type of power data that needs to be collected, such as voltage, current, power factor, frequency, etc., is determined. How to divide the monitoring area is determined according to factors such as geographic position, equipment distribution, power load and the like.
The entire power system is divided into several independent monitoring areas, each of which should cover one or more critical equipment or cable segments. The boundary of each monitoring area is defined according to the topological structure and the equipment position of the power system. Each monitored area is assigned a unique identifier or number to facilitate subsequent data management and analysis.
According to the monitoring index, a proper sensor and monitoring equipment such as a current sensor, a voltage sensor, a power analyzer and the like are selected. And determining the specific installation position of each monitoring device, and ensuring the key point of each monitoring area to be covered. Communication (e.g., wireless sensor network, fiber optic communication, etc.) is selected and configured to ensure that the data of the monitoring device can be transmitted to the central monitoring system in real time.
A data acquisition unit (DCU) is installed in each monitoring area for collecting sensor data and performing preliminary processing. The data transmission network is designed and deployed to ensure that each DCU can transmit data to the central server in real time. Either wired (e.g., ethernet, fiber optic) or wireless (e.g., wi-Fi, loRa) modes may be selected. The data of each monitoring area is stored and processed in a centralized way through a central monitoring system.
And installing monitoring equipment, DCU and communication equipment according to the design plan, and performing system debugging to ensure the normal operation of each equipment. And testing the stability and the speed of the data transmission network, and ensuring that the data of each monitoring area can be timely transmitted to a central monitoring system. And carrying out system integration test, verifying the functions and performances of the whole data acquisition system, ensuring that the data of each monitoring area can be accurately acquired and processed, and sending the acquired real-time power data to the running state judging module.
Monitoring equipment, a sensor, a server and real-time power consumption of network equipment in the cable anti-theft system are monitored, and the monitoring equipment mainly comprises:
The types of devices that specifically require monitoring include monitoring devices (cameras, etc.), sensors, servers, and network devices. The type of power data to be collected, such as current, voltage, power, energy consumption, etc., is determined. Suitable power monitoring devices, such as smart meters, power meters and current sensors, are selected for measuring the real-time power consumption of each device. The specific installation position of the power monitoring equipment is determined, so that each equipment needing to be monitored can be independently measured.
A data acquisition unit (DCU) is installed at each monitoring point for collecting data of the power monitoring device. And designing and deploying a data transmission network to ensure that the power monitoring data can be transmitted to the central monitoring system in real time. And installing power monitoring equipment at the power input ends of each monitoring equipment, each sensor, each server and each network equipment. The power monitoring devices are connected to the corresponding DCUs, ensuring that data can be collected and transmitted. And system debugging is performed, so that the normal operation of the power monitoring equipment and the DCU is ensured, and data can be accurately measured and transmitted.
And collecting power consumption data of each device in real time through the power monitoring device and transmitting the power consumption data to the DCU. And transmitting the real-time power consumption data collected by the DCU to a central monitoring system through a wired or wireless network. The central monitoring system receives and stores real-time power consumption data transmitted from each DCU. And cleaning and processing the received data, and sending the processed real-time power consumption to a maintenance management module after removing noise and abnormal values.
The running state judging module is used for receiving the real-time power data sent by the data monitoring module, determining the weight coefficient of the real-time power data of each monitoring area, carrying out weighted average calculation on the running state evaluation coefficient of the power system according to the weight coefficient of the real-time power data of each monitoring area, and sending the running state evaluation coefficient of the power system to the maintenance management module.
And processing the real-time power data sent by the data monitoring module, respectively acquiring real-time voltage data and real-time current data in the real-time power data, analyzing the real-time voltage data and the real-time current data, respectively acquiring a power load abnormality index and a power grid frequency fluctuation index of the real-time power data according to analysis results, and determining the weight coefficient of the real-time power data of each monitoring area.
The invention processes the real-time power data sent by the data monitoring module, respectively acquires the real-time voltage data and the real-time current data, and determines the weight coefficient of the real-time power data of each monitoring area by analyzing and acquiring the power load abnormality index and the power grid frequency fluctuation index, thereby improving the response capability of the cable anti-theft system to the abnormal condition of the power system, ensuring that the monitoring system can accurately detect and early warn the abnormality of the power load and the frequency fluctuation in real time, optimizing the resource configuration and improving the reliability and the stability of the system.
The method for acquiring the power load abnormality index comprises the following steps:
collecting electrical load data, including voltage and current, representing the collected time series data as: ； The total number of N time series data representing the power load value at time t is denoised by using a moving average filter, and the specific calculation expression is: = ; wherein, Representing the denoised power load value, k being the window size, modeling the denoised load data, capturing its trend and seasonal pattern, and the SARIMA model being represented as:= ; in the method, in the process of the invention, 、、、And、、、Autoregressive and moving average coefficients, respectively, p is a positive integer greater than 0,Representing p observations before a time t,P error items before the time point t represent the errors between the observed value and the predicted value, load prediction is performed by using the SARIMA model, and the prediction errors are calculated, namely；For predicting errors, features are extracted from the time sequence, statistical features in a time window are extracted, wherein the statistical features comprise the mean and the variance of the statistical features, and a specific calculation expression is as follows:= And =; Wherein, As the mean value of the time series,For the variance of the time sequence, the mean value and the variance of the time sequence are analyzed, and the power load abnormality index is calculated, wherein the specific calculation expression is as follows: ; in the method, in the process of the invention, Is an index of power load abnormality.
High load abnormality indexes are often accompanied by severe load fluctuations, which means that the supply-demand balance of the power system is broken, possibly due to a sudden increase or decrease in the electrical load. Severe load fluctuations may lead to voltage and frequency fluctuations that affect the stability of the power system and the proper operation of the equipment.
A high anomaly index may indicate that certain critical devices in the system (e.g., transformers, lines, switching devices) are malfunctioning or severely aging. Equipment failure can lead to reduced power transmission and distribution efficiency and even large area blackouts and equipment damage.
An increase in the abnormality index is often accompanied by a decrease in the quality of the electric power, such as an increase in the Total Harmonic Distortion (THD), a voltage dip, a frequency deviation, and the like. The power quality problem can affect the performance and service life of the electric equipment, and can cause malfunction of industrial production equipment, failure of a computer system and the like.
A high abnormality index may mean that the system load approaches or exceeds the design capacity, resulting in overload of the system. Overload operation increases heat loss from the lines and equipment, shortens equipment life, and increases risk of failure.
The acquisition of the power grid frequency fluctuation index is beneficial to evaluating the stability of the running state of the power system, because the frequency fluctuation reflects the instant change of supply and demand balance and the response capability of the system, the higher the frequency fluctuation index is, the more likely the power system is to have unbalance of supply and demand, insufficient regulation capability or potential faults, the abnormality of the power system can be timely found and dealt with by monitoring the index, and the reliable running of the system is ensured.
The method for acquiring the power grid frequency fluctuation index comprises the following steps:
Collecting power grid frequency data, carrying out smoothing treatment on the data, and carrying out wavelet transformation on the preprocessed frequency data by using a wavelet function to obtain a wavelet coefficient of a frequency signal, wherein the specific calculation expression is as follows: = ; wherein, Is a frequency signal which is a function of the frequency,Is a function of the wavelet,Is a scale parameter of the sample,Is a parameter of the translation and,And (3) for wavelet coefficients, carrying out threshold processing on the wavelet coefficients, and removing coefficients smaller than a threshold value to reduce noise influence, wherein a threshold processing formula is as follows:= ; wherein, T is a threshold value, For the processed wavelet coefficients, the frequency signal is reconstructed using the retained wavelet coefficients, the reconstructed signal being expressed as:； Reconstructing the frequency signal;
And performing empirical mode decomposition on the reconstructed signals to obtain a series of eigenmode functions, performing extreme point fitting on the original signals to obtain a first IMF, then subtracting the first IMF from the original signals, performing fitting on the obtained new signals until the new signals cannot be decomposed, performing frequency analysis on each IMF, extracting frequency variation values, and summing the frequency variation values of the IMFs to obtain the power grid frequency fluctuation index through calculation.
The larger the frequency fluctuation index is, the more unstable or abnormal the operation state of the electric power operation system is. An increase in frequency ripple generally indicates that a problem arises in the balance between supply and demand in the power system. If the power supply capacity of the power system fails to meet the needs of the user, or the needs of the user suddenly increase without a corresponding increase in the power supply capacity, frequency fluctuations may result.
The increase in frequency ripple may be related to rapid changes in load. If the load fluctuation is large, the power system needs to be quickly adjusted to keep the frequency stable, but if the system is insufficient in adjustment capability or the adjustment response speed is slow, the frequency fluctuation is increased.
The increase in frequency ripple may also be due to malfunction or aging of critical devices (e.g., generators, transformers, etc.) in the power system, resulting in a decrease in power supply capacity, which in turn affects the frequency stability of the system.
And carrying out normalization processing on the power load abnormality index and the power grid frequency fluctuation index, and calculating the scores of the real-time power data of each monitoring area through the power load abnormality index and the power grid frequency fluctuation index after normalization processing.
For example, the present invention may calculate the real-time power data score of each monitoring area using the following formula:=+ ; in the method, in the process of the invention, For scoring of real-time power data for each monitored area,As an index of the abnormality of the electrical load,Is the power grid frequency fluctuation index.
The scoring of the real-time power data of each monitoring area is respectively subjected to weight assignment according to a time weighting method, and the weight value of the real-time power data in each monitoring area is determined, specifically:
Collecting real-time power data from each monitoring area, dividing monitoring time periods according to a certain time interval, such as time periods of an hour, a half hour or less, and performing time weighted calculation on the real-time power data of each time period to consider the influence degree of different time periods on the system operation; the weight is usually calculated by an exponential weighting method or a linear weighting method, a damping coefficient alpha is determined according to the length of a time interval, and then the weight is calculated according to the distance between the time and the current time, wherein a common exponential weighting formula is as follows: = ; in the method, in the process of the invention, For the weight value of s time periods in each monitored area,For the time of the current time from the starting time of the time period, carrying out normalization processing on the weight value of each time period in each monitoring area, ensuring the sum of the weights of the time periods to be 1,
And assigning the score of each time period according to the calculated weight to obtain the weight value of the real-time power data in each monitoring area.
And carrying out weighted average calculation on the weight coefficient of the real-time power data of the monitoring area to obtain the running state evaluation coefficient of the power system, and sending the running state evaluation coefficient of the power system to the maintenance management module.
The real-time power data is subjected to weight assignment according to the time weighting method, so that the influence degree of different time periods on the running state of the power system can be reflected more accurately, the accuracy and the credibility of the evaluation result are improved, the actual running condition of the power system can be known in real time, and therefore more comprehensive and effective data support is provided for monitoring and adjusting the running of the system.
In the embodiment, the data monitoring module is used for collecting and monitoring the real-time data of the monitoring area and equipment of the power system, so that the real-time monitoring of the running state of the power system and the cable anti-theft system is effectively realized, meanwhile, the running state judging module is used for carrying out weight analysis and evaluation on the collected data, and the real-time power data of each monitoring area are synthesized, so that the running state evaluation coefficient of the power system is accurately calculated, and reliable data support is provided for the maintenance and management of the system. The scheme can timely find out the problems in the operation of the power system, helps operation and maintenance personnel to quickly respond, improves the stability and reliability of the power system, is beneficial to reducing potential fault risks, and improves the operation efficiency and safety of the system.
Example 2
And the maintenance management module is used for analyzing the running state evaluation coefficient of the received power system and the real-time power consumption of monitoring equipment, sensors, servers and network equipment in the cable anti-theft system, and predicting and early warning the risk event of the cable system in advance through the machine learning model.
Wherein the machine learning model comprises: linear regression model: for predicting the linear relationship of the continuous output variable. Logistic regression model: for binary classification problems, the probability of an event occurring is predicted. Decision tree model: a tree model of the decision is made based on the conditions of the features. Random forest model: an integrated learning model consisting of a plurality of decision trees is used for classification and regression problems. Support vector machine model: the supervised learning algorithm for classification and regression problems enables data classification and the like in a high-dimensional space.
In the invention, the risk event of the cable system is predicted and early-warned in advance through a linear regression model, and the method specifically comprises the following steps:
Taking the running state evaluation coefficient of the power system and the real-time power consumption of monitoring equipment, sensors, servers and network equipment in the cable anti-theft system as the input of a model;
The method comprises the steps of preprocessing model input data, including data cleaning, missing value processing, abnormal value detection and processing, characteristic engineering and the like, so as to ensure data quality and accuracy.
Modeling is performed according to historical data characteristics of the power system and the cable anti-theft system. The historical data features comprise historical running state evaluation coefficients of the power system and historical power consumption of monitoring equipment, sensors, servers and network equipment;
Training using a linear regression model using the collected data, the formula of the linear regression model is as follows: ; wherein, As a risk factor for a cable system,,,...,As a feature of the historical data,,,,...,Is a coefficient of the model, c is an error term;
And evaluating the trained model by using the evaluation data set, and evaluating the performance and accuracy of the model. Some metrics, such as mean square error (Mean Squared Error, MSE) or decision coefficients (Coefficient of Determination, R), etc., may be used to evaluate the fit of the model.
Predicting new real-time data by using a trained linear regression model, comparing the risk coefficient of the cable system of the model output item with a preset risk coefficient reference threshold value in historical data, and if the risk coefficient of the cable anti-theft system is greater than or equal to the risk coefficient reference threshold value, indicating that the cable anti-theft system is likely to be abnormal, and generating an early warning signal at the moment; so as to take measures in time to repair or adjust so as to prevent potential faults or accidents.
If the risk coefficient of the cable anti-theft system is smaller than the risk coefficient reference threshold value, the cable anti-theft system is possibly in a normal state, and an early warning signal is not generated at the moment. The running state of the cable anti-theft system is continuously monitored, and the model and the reference threshold value are updated periodically so as to adapt to system changes and improve the prediction accuracy.
According to the invention, the risk event of the cable system is predicted and early-warned in advance through the linear regression model, the running state evaluation coefficient of the power system and the real-time power consumption of each device in the cable anti-theft system can be effectively used as input data, the data quality is ensured through data preprocessing and characteristic engineering, and the model training and evaluation are performed by utilizing the historical data characteristics, so that the accuracy and reliability of the model are ensured. In practical application, the model can predict the risk coefficient of the cable system in real time, compare the risk coefficient with a reference threshold value and timely generate an early warning signal, thereby effectively preventing potential faults or accidents. The model and the reference threshold value are continuously monitored and updated, the change of the system can be adapted, the prediction accuracy is improved, the safety and the operation efficiency of the cable anti-theft system are finally improved, the loss is reduced, and the response capability of maintenance management is improved.
In the embodiment, the risk event of the cable system is predicted and early-warned in advance through the linear regression model, so that the running state evaluation coefficient of the power system and the real-time power consumption of each device in the cable anti-theft system can be effectively used as input data, the data quality is ensured through data preprocessing and feature engineering, model training and evaluation are performed by utilizing the historical data features, and the accuracy and reliability of the model are ensured. In practical application, the model can predict the risk coefficient of the cable system in real time, compare the risk coefficient with a reference threshold value and timely generate an early warning signal, thereby effectively preventing potential faults or accidents. The model and the reference threshold value are continuously monitored and updated, the change of the system can be adapted, the prediction accuracy is improved, the safety and the operation efficiency of the cable anti-theft system are finally improved, the loss is reduced, and the response capability of maintenance management is improved.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). 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, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.
Claims (4)
1. A cable anti-theft system, characterized by: the system comprises a data monitoring module, an operation state judging module and a maintenance management module;
The data monitoring module is used for dividing the power system into a plurality of monitoring areas, collecting real-time power data in each monitoring area and sending the collected real-time power data to the running state judging module; the system is also used for monitoring the real-time power consumption of monitoring equipment, sensors, servers and network equipment in the cable anti-theft system and sending the real-time power consumption to the maintenance management module;
The running state judging module is used for receiving the real-time power data sent by the data monitoring module, determining the weight coefficient of the real-time power data of each monitoring area, carrying out weighted average calculation on the running state evaluation coefficient of the power system according to the weight coefficient of the real-time power data of each monitoring area, and sending the running state evaluation coefficient of the power system to the maintenance management module, wherein the running state evaluation coefficient of the power system is specifically as follows:
Respectively acquiring real-time voltage data and real-time current data in the real-time power data, analyzing the real-time voltage data and the real-time current data, respectively acquiring a power load abnormality index and a power grid frequency fluctuation index of the real-time power data according to analysis results, and determining a weight coefficient of the real-time power data of each monitoring area;
the method for acquiring the power load abnormality index comprises the following steps:
collecting electrical load data, including voltage and current, representing the collected time series data as: ； The total number of N time series data representing the power load value at time t is denoised by using a moving average filter, and the specific calculation expression is: ; wherein, Representing the denoised power load value, k being the window size, modeling the denoised load data, capturing its trend and seasonal pattern, and the SARIMA model being represented as: ; in the method, in the process of the invention, AndAutoregressive and moving average coefficients, respectively, p is a positive integer greater than 0,Representing p observations before a time t,P error items before the time point t represent the errors between the observed value and the predicted value, load prediction is performed by using the SARIMA model, and the prediction errors are calculated, namely；For predicting errors, features are extracted from the time sequence, statistical features in a time window are extracted, wherein the statistical features comprise the mean and the variance of the statistical features, and a specific calculation expression is as follows: And ; Wherein, As the mean value of the time series,For the variance of the time sequence, the mean value and the variance of the time sequence are analyzed, and the power load abnormality index is calculated, wherein the specific calculation expression is as follows: ; in the method, in the process of the invention, Is an electrical load abnormality index;
The method for acquiring the power grid frequency fluctuation index comprises the following steps:
Collecting power grid frequency data, carrying out smoothing treatment on the data, and carrying out wavelet transformation on the preprocessed frequency data by using a wavelet function to obtain a wavelet coefficient of a frequency signal, wherein the specific calculation expression is as follows: ; wherein, Is a frequency signal which is a function of the frequency,Is a function of the wavelet,Is a scale parameter of the sample,Is a parameter of the translation and,And (3) for wavelet coefficients, carrying out threshold processing on the wavelet coefficients, and removing coefficients smaller than a threshold value to reduce noise influence, wherein a threshold processing formula is as follows: ; wherein, T is a threshold value, For the processed wavelet coefficients, the frequency signal is reconstructed using the retained wavelet coefficients, the reconstructed signal being expressed as:； Reconstructing the frequency signal;
performing empirical mode decomposition on the reconstructed signals to obtain a series of eigenmode functions, performing extreme point fitting on the original signals to obtain a first IMF, then subtracting the first IMF from the original signals, performing fitting on the obtained new signals until the new signals cannot be decomposed, performing frequency analysis on each IMF, extracting frequency variation values, and summing the frequency variation values of the IMFs to obtain a power grid frequency fluctuation index through calculation;
And the maintenance management module is used for analyzing the running state evaluation coefficient of the received power system and the real-time power consumption of monitoring equipment, sensors, servers and network equipment in the cable anti-theft system, and predicting and early warning the risk event of the cable system in advance through the machine learning model.
2. A cable theft protection system according to claim 1, characterized in that: and carrying out normalization processing on the power load abnormality index and the power grid frequency fluctuation index, and calculating the scores of the real-time power data of each monitoring area through the power load abnormality index and the power grid frequency fluctuation index after normalization processing.
3. A cable theft protection system according to claim 2, characterized in that: the scoring of the real-time power data of each monitoring area is respectively subjected to weight assignment according to a time weighting method, and the weight value of the real-time power data in each monitoring area is determined, specifically:
Collecting real-time power data from each monitoring area, dividing monitoring time periods according to a certain time interval, performing time weighted calculation on the real-time power data of each time period, determining an attenuation coefficient alpha according to the length of the time interval, and then calculating weights according to the distance between the time and the current time, wherein an exponential weighting formula is as follows: ; in the method, in the process of the invention, For the weight value of s time periods in each monitored area,Carrying out normalization processing on the weight value of each time period in each monitoring area for the time of the current time from the starting time of the time period, and assigning the score of each time period according to the calculated weight to obtain the weight value of the real-time power data in each monitoring area; and carrying out weighted average calculation on the weight coefficient of the real-time power data of the monitoring area to obtain the running state evaluation coefficient of the power system.
4. A cable theft protection system according to claim 3, characterized in that: the risk event of the cable system is predicted and early-warned in advance through a linear regression model, and the method specifically comprises the following steps:
Taking the running state evaluation coefficient of the power system and the real-time power consumption of monitoring equipment, sensors, servers and network equipment in the cable anti-theft system as the input of a model;
preprocessing model input data, and modeling according to historical data characteristics of the power system and the cable anti-theft system, wherein the historical data characteristics comprise historical running state evaluation coefficients of the power system and historical power consumption of monitoring equipment, sensors, servers and network equipment;
Training using a linear regression model using the collected data, the formula of the linear regression model is as follows: ; wherein, As a risk factor for a cable system,As a feature of the historical data,Is a coefficient of the model, c is an error term;
Predicting new real-time data by using a trained linear regression model, comparing the risk coefficient of the cable system with a preset risk coefficient reference threshold value in historical data, and generating an early warning signal at the moment if the risk coefficient of the cable anti-theft system is greater than or equal to the risk coefficient reference threshold value; if the risk coefficient of the cable anti-theft system is smaller than the risk coefficient reference threshold value, no early warning signal is generated at the moment.
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