CN112734977B - Equipment risk early warning system and algorithm based on Internet of things - Google Patents
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- G—PHYSICS
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Abstract
The invention discloses an equipment risk early warning system and algorithm based on the Internet of things, which belong to the technical field of equipment risk early warning, and comprise a collection end, a network information transmission end, a historical data packet, a calculation center and an early warning output end, wherein the collection end and the historical data packet are both connected with the calculation center through the network information transmission end, the collection end collects a GNSS time sequence of monitoring data samples and transmits the GNSS time sequence to the Internet, and the Internet transmits the GNSS time sequence to the calculation center. The risk detection system has the advantages that early warning can be timely made on risks, abnormal data can be screened by autonomous operation, the abnormal data can be rapidly provided, risk processing personnel can master risk sources, the change trend of the monitored data can be accurately analyzed, and the abnormal information detection system has better abnormal information detection capability and lower false alarm rate.
Description
Technical Field
The invention relates to the technical field of equipment risk early warning, in particular to an equipment risk early warning system and an algorithm based on the Internet of things.
Background
Equipment risk is determined by both the probability of failure and the consequences of the failure. The equipment data, the operation data, the routing inspection information and the like are evaluated based on the equipment state evaluation rule, and the fault risk probability of the equipment is calculated. By utilizing various online monitoring means and integrating factors such as the application environment and the climate of the equipment, longitudinal (history and current situation) and transverse (operation conditions of the same equipment) comparative analysis is carried out on the equipment state, the early signs of the fault are identified, the fault position, the fault severity and the development trend are judged and early warned, and the optimal maintenance opportunity is determined, so that the whole life cycle management of the power distribution equipment is realized. With the increase of various state monitoring data, operation data and environmental data, how to utilize the data to analyze the risk of future problems of equipment and provide guidance for planning maintenance, resource allocation and the like becomes an important problem.
Patent number CN201610507928.8 discloses a method for early warning of equipment risk, which comprises the following steps: 1) analyzing the correlation of the influencing factors by utilizing fault historical data; 2) establishing a distributed event filtering system; 3) defining a trigger event type; 4) defining an event filtering system; 5) the filter system is self-learning. The method and the device perform early warning on equipment risks by adopting a mode of event generation and filtering, parallel computation and parameter optimization. The method can be rapidly and flexibly carried out, and the quantitative early warning is accurate. By the event generation method, concerned time points and concerned equipment can be flexibly defined; by using a distributed event filtering system, time filtering is performed in parallel, and the analysis efficiency is improved; and warning the event set with the highest risk to the user in an event filtering mode. The technology is simple and practical to realize, and rapid deployment and multi-scene application can be realized. However, the risk early warning cannot rapidly and accurately provide abnormal data, so that risk monitoring personnel cannot rapidly and accurately locate the risk source.
Disclosure of Invention
The invention aims to provide an equipment risk early warning system and an algorithm based on the Internet of things, which can not only timely early warn risks, but also automatically run to realize screening of abnormal data, quickly provide the abnormal data, accelerate the risk processing personnel to grasp risk sources, apply the LWR and Pettitt algorithms to the identification and early warning of GNSS abnormal information, accurately analyze and monitor the change trend of the data, have the advantages of better abnormal information inspection capability and lower false alarm rate, and solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: an equipment risk early warning system based on the Internet of things comprises a collecting end, a network information transmission end, a historical data packet, a computing center and an early warning output end, wherein the collecting end and the historical data packet are connected with the computing center through the network information transmission end, the collecting end collects a GNSS time sequence of a monitoring data sample and uploads the GNSS time sequence to the Internet, the Internet transmits the GNSS time sequence to the computing center, all data detected when equipment normally operates are recorded into the data packet to form the historical data packet, and the historical data packet provides comparable data for the data sample;
the computing center comprises a copying unit, a primary computing unit, a secondary computing unit and an analyzing unit, wherein the copying unit is used for copying, extracting and complementing the GNSS time sequence of the data sample to generate abnormal data and legacy data, the historical data packet is connected with the copying unit and is used for complementing the legacy data after the abnormal data is extracted, the complemented legacy data is abnormal-free data, the primary computing unit is connected with the copying unit and the collecting end,
the system comprises a primary calculation unit, a secondary calculation unit, an analysis unit, a primary calculation unit, a secondary calculation unit, an acquisition end, a secondary calculation unit, an early warning output end and a data acquisition unit, wherein the primary calculation unit receives abnormal data transmitted by a copying unit and sample data transmitted by the acquisition end, the two groups of data are respectively fitted to obtain fitted function values, the secondary calculation unit is connected with the primary calculation unit and the acquisition end, the secondary calculation unit receives the two groups of function values transmitted by the primary calculation unit and monitoring data samples transmitted by the acquisition end, the two groups of function values are subjected to subtraction based on the monitoring data samples to obtain two groups of residual error sequences, the analysis unit is connected with the secondary calculation unit, the analysis unit receives the residual error sequences of the secondary calculation unit, analyzes the operation trends of the two groups of residual error sequences, transmits abnormal data and early warning instructions according to the operation trends, and the early warning output end receives early warning instructions and then gives early warning prompts and outputs corresponding abnormal data.
Furthermore, each item of data stored in the historical data packet is consistent with each item of sample data type obtained by monitoring of the acquisition end.
Further, the operation trend of the data sample gradually changes to low risk, and then the early warning output end does not make risk early warning.
And further, the operation trend of the data sample and the operation trend of the abnormal-free data are both converted to high risk, and then the early warning output end gives a risk early warning and synchronously outputs the abnormal data.
Furthermore, the operation trend of the data sample is changed to high risk and the operation trend of the abnormal-free data is changed to low risk or no change, the early warning output end firstly gives a risk early warning, the analysis unit feeds back information to the copying unit, and the abnormal data and the left-over data are reselected.
Further, after the operation trend of the data sample obtained through analysis is gradually changed to be low risk, the collected sample data is recorded into a historical data packet.
According to another aspect of the invention, an algorithm of an equipment risk early warning system based on the internet of things is provided, which comprises the following steps:
s101: collecting, namely monitoring the running equipment and collecting a GNSS time sequence of monitoring data samples;
s102: screening, namely comparing the collected sample data with historical data of the equipment, screening the data according to comparison information, and selecting abnormal data;
s103: fitting, namely fitting the unscreened sample data and the screened sample data respectively by using a Local Weighted Regression (LWR) algorithm to obtain a fitted function value;
s104: performing difference, namely performing difference on the two groups of function values and the data sample respectively to obtain two groups of residual error sequences based on LWR;
s105: calculating, namely taking the two groups of LWR residual sequences as test data, and obtaining an operation trend through the slope of a fitting trend term by using a Pettitt algorithm;
s106: and early warning, namely analyzing the two sets of operation trends, judging whether the equipment is operated towards an unfavorable direction due to the trend change, and timely finding abnormal data and early warning.
Compared with the prior art, the invention has the beneficial effects that: the equipment risk early warning system and algorithm based on the Internet of things, provided by the invention, can not only timely early warn risks, but also automatically run to realize screening of abnormal data, quickly provide the abnormal data, accelerate the risk processing personnel to grasp risk sources, apply the LWR and Pettitt algorithms to the identification and early warning of GNSS abnormal information, accurately analyze the change trend of monitored data, and have the advantages of better abnormal information inspection capability and lower false alarm rate.
Drawings
Fig. 1 is an overall structure diagram of an equipment risk early warning system based on the internet of things;
FIG. 2 is a structure diagram of a computing center of the risk early warning system of the device based on the Internet of things;
FIG. 3 is a connection diagram of a computing center of the Internet of things-based equipment risk early warning system of the present invention;
FIG. 4 is a schematic diagram of an Internet of things-based equipment risk early warning system of the present invention;
fig. 5 is a flow chart of the risk early warning algorithm of the device based on the internet of things.
In the figure: 1. a collection end; 2. a network information transmission terminal; 3. a historical data packet; 4. a calculation center; 41. a copy unit; 42. a preliminary calculation unit; 43. a secondary computing unit; 44. an analysis unit; 5. and an early warning output end.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 4, an equipment risk early warning system based on the internet of things comprises a collection end 1, a network information transmission end 2, a historical data packet 3, a calculation center 4 and an early warning output end 5, wherein the collection end 1 and the historical data packet 3 are both connected with the calculation center 4 through the network information transmission end 2, the collection end 1 collects a GNSS time sequence of a monitoring data sample and transmits the GNSS time sequence to the internet, the internet transmits the GNSS time sequence to the calculation center 4, various data detected when the equipment normally operates are recorded into a data packet to form the historical data packet 3, and the historical data packet 3 provides comparable data for the data sample; the data stored in the historical data packet 3 is consistent with the sample data types obtained by monitoring of the acquisition terminal 1.
The computing center 4 comprises a copying unit 41, a primary computing unit 42, a secondary computing unit 43 and an analyzing unit 44, wherein the copying unit 41 copies, extracts and complements a GNSS time sequence of data samples to generate abnormal data and legacy data, the historical data packet 3 is connected with the copying unit 41 and complements the legacy data after extracting the abnormal data, the complemented legacy data is abnormal-free data, the primary computing unit 42 is connected with the copying unit 41 and the acquisition end 1, the acquisition end 1 is a temperature sensor connected with equipment, a motor power sensor, a cylinder pressure sensor and parameters of the equipment, the network information transmission end 2 is a wireless signal transmitter connected with the Internet, the copying unit 41 is a copier, the primary computing unit 42, the secondary computing unit 43 and the analyzing unit 44 are central processing units, the early warning output end 5 is a display, a secondary computing unit 43 and the analyzing unit 44, One or more of a voice announcer, an indicator light and an audio player.
The primary calculation unit 42 receives the abnormal data transmitted by the copying unit 41 and the sample data transmitted by the acquisition terminal 1, and fits the two groups of data respectively to obtain fitted function values, the secondary calculation unit 43 is connected with the primary calculation unit 42 and the acquisition terminal 1, the secondary calculation unit 43 receives the two groups of function values sent by the primary calculation unit 42 and the monitoring data sample sent by the acquisition terminal 1, and performs subtraction on the two groups of function values based on the monitoring data sample to obtain two groups of residual error sequences, the analysis unit 44 is connected with the secondary calculation unit 43, the analysis unit 44 receives the residual error sequences of the secondary calculation unit 43 and analyzes the operation trends of the two groups of residual error sequences, transmits abnormal data and an early warning instruction according to the operation trends, and the early warning output terminal 5 makes an early warning prompt and outputs corresponding abnormal data after receiving the early warning instruction; if the operation trend of the data sample gradually changes to low risk, the early warning output end 5 does not make risk early warning; the operation trend of the data sample and the operation trend of the abnormal-free data are both converted to high risk, and then the early warning output end 5 gives a risk early warning and synchronously outputs the abnormal data; if the operation trend of the data sample is changed to high risk and the operation trend of the abnormal-free data is changed to low risk or no change, the early warning output end 5 firstly gives a risk early warning, the analysis unit 44 feeds back information to the copying unit 41, and the abnormal data and the left-over data are reselected; and after the operation trend of the data sample obtained by analysis is gradually changed to low risk, the collected sample data is recorded into the historical data packet 3.
Referring to fig. 5, in order to better show the principle of the equipment risk early warning system based on the internet of things, the embodiment provides an equipment risk early warning algorithm based on the internet of things, which includes the following steps:
s101: collecting, namely monitoring the running equipment and collecting a GNSS time sequence of monitoring data samples; the time sequence is preliminarily analyzed after the acquisition, and the mean value mu and the standard deviation sigma are calculated;
s102: screening, namely comparing the collected sample data with historical data of the equipment, screening the data according to the comparison information, and selecting abnormal data;
s103: fitting, namely fitting the unscreened sample data and the screened sample data respectively by using a Local Weighted Regression (LWR) algorithm to obtain a fitted function value; describing a fitting method LWR, the LWR can be calculated only by a weight function and neighborhood parameters, the neighborhood parameters come from the historical data packet 3, and the calculation of key parameters is shown as a formula:
wherein, the numerical difference between the neighborhood parameters is set by adopting the d value of the Euclidean distance, and the following formula is shown:
the weight function is set by a cubic weight function method, and the weight function W (di) in the weighted least squares regression is as follows:
W(di)＝(1-di3)3，0≤d≤1
local fitting is carried out by a weighted least square method at each point of the data interval, and the local fitting is fitted into a polynomial function which is used as the estimation of a regression function in numerical values;
s104: performing difference, performing difference between the two groups of function values and the data sample respectively to obtain two groups of residual error sequences based on LWR, and establishing a residual error map according to the residual error sequences;
s105: calculating, namely taking the two groups of LWR residual sequences as test data, and obtaining an operation trend through the slope of a fitting trend term by using a Pettitt algorithm; an outlier of the time series X (t) is given, set as the median, and a null hypothesis H is set in order to check that the mean value does not vary0With the alternative hypothesis H1 that the mean value changes, the data before and after the generation of the data point, τ, are compared based on rank, and the Pettitt statistic is expressed as k (τ), and the calculation formula is shown in formula (3):
determining the time at which the absolute value of k(s) is maximum defines two statistics:
wherein: k refers to the final Pettitt statistic, T refers to the position of the corresponding abnormal point, and the significance probability pair H related to the position0Is approximately P ≈ 2exp [ -6K2(i3+i2)]If P is<0.5, the change is considered to be obvious, abnormal points in the trend item are obtained by fitting the slope of the trend item, and the position where the deformation information changes is found in time and early warning is carried out;
s106: and early warning, namely analyzing the two sets of operation trends, judging whether the equipment is operated towards an unfavorable direction due to the trend change, and timely finding abnormal data and early warning.
In summary, the following steps: the equipment risk early warning system and algorithm based on the Internet of things, provided by the invention, can not only timely early warn risks, but also automatically run to realize screening of abnormal data, quickly provide the abnormal data, accelerate the risk processing personnel to grasp risk sources, apply the LWR and Pettitt algorithms to the identification and early warning of GNSS abnormal information, accurately analyze the change trend of monitored data, and have the advantages of better abnormal information inspection capability and lower false alarm rate.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical solutions and the inventive concepts of the present invention within the technical scope of the present invention.
Claims (7)
1. The equipment risk early warning system based on the Internet of things is characterized by comprising a collecting end (1), a network information transmission end (2), a historical data packet (3), a computing center (4) and an early warning output end (5), wherein the collecting end (1) and the historical data packet (3) are connected with the computing center (4) through the network information transmission end (2), the collecting end (1) collects a GNSS time sequence of a monitored data sample and uploads the GNSS time sequence to the Internet, the Internet transmits the GNSS time sequence to the computing center (4), all data detected when equipment normally operates are recorded into the data packet to form the historical data packet (3), and the historical data packet (3) provides comparable data for the data sample;
the computing center (4) comprises a copying unit (41), a primary computing unit (42), a secondary computing unit (43) and an analyzing unit (44), wherein the copying unit (41) copies, extracts and complements a GNSS time sequence of data samples to generate abnormal data and legacy data, the historical data packet (3) is connected with the copying unit (41), the historical data packet (3) complements the legacy data after the abnormal data is extracted, the complemented legacy data is the abnormal-free data, and the primary computing unit (42) is connected with the copying unit (41) and the collecting end (1);
the system comprises a primary calculation unit (42), a secondary calculation unit (43), an analysis unit (44), an early warning output end (5), an early warning prompt and corresponding abnormal data output end, wherein the primary calculation unit (42) receives abnormal data transmitted by a copying unit (41) and sample data transmitted by a collection end (1), the two groups of data are respectively fitted to obtain fitted function values, the secondary calculation unit (43) is connected with the primary calculation unit (42) and a monitoring data sample transmitted by the collection end (1), the two groups of function values are differenced on the basis of the monitoring data sample to obtain two groups of residual sequences, the analysis unit (44) is connected with the secondary calculation unit (43), the analysis unit (44) receives the residual sequences of the secondary calculation unit (43), analyzes the operation trends of the two groups of residual sequences, abnormal data and early warning instructions are transmitted according to the operation trends, and the early warning output end (5) receives the early warning instructions and then gives early warning prompts and outputs corresponding abnormal data .
2. The Internet of things-based equipment risk early warning system according to claim 1, wherein data stored in the historical data packet (3) is consistent with sample data types obtained by monitoring of the acquisition end (1).
3. The IOT-based equipment risk early warning system of claim 1, wherein the operation trend of the data samples gradually changes to low risk, and the early warning output end (5) does not make risk early warning.
4. The equipment risk early warning system based on the internet of things as claimed in claim 1, wherein the operation trend of the data sample and the operation trend of the abnormal-free data are both changed to high risk, and then the early warning output end (5) makes risk early warning and synchronously outputs the abnormal data.
5. The equipment risk early warning system based on the internet of things as claimed in claim 1, wherein the operation trend of the data sample is changed to a high risk and the operation trend of the abnormal-free data is changed to a low risk or no change, the early warning output end (5) firstly makes a risk early warning, the analysis unit (44) feeds back information to the copying unit (41), and the abnormal data and the left-over data are reselected.
6. The equipment risk early warning system based on the internet of things as claimed in claim 1, wherein the collected sample data is recorded into a historical data packet (3) after the operation trend of the data sample obtained by analysis gradually changes to low risk.
7. An algorithm of the internet of things based equipment risk early warning system according to any one of claims 1 to 6, characterized by comprising the following steps:
s101: collecting, namely monitoring the running equipment and collecting a GNSS time sequence of monitoring data samples;
s102: screening, namely comparing the collected sample data with historical data of the equipment, screening the data according to the comparison information, and selecting abnormal data;
s103: fitting, namely fitting the unscreened sample data and the screened sample data respectively by using a Local Weighted Regression (LWR) algorithm to obtain a fitted function value;
s104: performing difference, namely performing difference on the two groups of function values and the data sample respectively to obtain two groups of residual error sequences based on LWR;
s105: calculating, namely taking the two groups of LWR residual sequences as test data, and obtaining an operation trend through the slope of a fitting trend term by using a Pettitt algorithm;
s106: and early warning, namely analyzing the two sets of operation trends, judging whether the equipment is operated towards an unfavorable direction due to the trend change, and timely finding abnormal data and early warning.
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| CN116866216B (en) * | 2023-07-10 | 2024-07-09 | 上海朗晖慧科技术有限公司 | Equipment data screening and monitoring method and system based on Internet of things |
| CN117474345B (en) * | 2023-12-28 | 2024-04-02 | 广州恩伟博科技有限公司 | Intelligent environment-friendly remote real-time monitoring and early warning method and system |
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