Lightning arrester on-line monitoring system and method based on Internet of Things

Abstract

The invention discloses a lightning arrester on-line monitoring system and method based on the Internet of Things, and relates to the technical field of data analysis. The method comprises the following steps: collecting electrical data when a lightning arrester works in history and electrical data when a fault occurs in the working process, analyzing the electrical data, and extracting feature data influencing the work of the lightning arrester; collecting characteristic data, and training a working state model of the lightning arrester; drawing a real-time working state curve graph of the lightning arrester; meanwhile, drawing a historical working state curve chart of the lightning arrester, analyzing the state curve, and calculating a fault threshold value of a fault of the lightning arrester; the lightning arrester is monitored in real time, and real-time early warning is carried out when a fault threshold value is exceeded; and inputting the collected real-time data as historical data into the lightning arrester working state model, and optimizing the model. According to the invention, early warning can be automatically carried out on lightning arrester equipment exceeding a fault threshold range, and fault accidents caused by negligence of workers are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an online monitoring system and method for a lightning arrester based on the Internet of Things. Background Art

[0002] The Internet of Things (IoT) refers to connecting various devices, sensors and objects in the physical world through the Internet, enabling them to communicate and exchange data with each other. The IoT digitizes devices in the real world and remotely controls, monitors and manages them through the Internet, thereby realizing intelligent systems and services.

[0003] With the rapid advancement of industrialization and intelligent manufacturing, the intelligent and digital management of industrial products has become a core element to improve production efficiency and ensure the safe operation of equipment. Through the Internet of Things (IoT) technology, the operating status of various industrial products can be monitored in real time and automatic control can be achieved. Although industrial product status monitoring and fault warning technology have made significant progress, there are still some challenges in practical applications. First, the operating status data is easily affected by environmental conditions and equipment parameters. Due to differences in products and production processes, the dimensions and scales of the data vary, which increases the complexity of data processing and analysis. Secondly, many products currently lack intelligent early warning mechanisms and cannot respond in a timely manner in terms of product monitoring. Summary of the invention

[0004] The object of the present invention is to provide an online monitoring system and method for a lightning arrester based on the Internet of Things to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: an online monitoring method for a lightning arrester based on the Internet of Things, the online monitoring method for a lightning arrester comprising the following steps: Step S1, collecting the electrical data of the arrester when it is working and the electrical data when a fault occurs during the working process, analyzing the electrical data, and extracting the characteristic data that affects the operation of the arrester according to the data analysis results; Step S2, collecting several groups of characteristic data when the arrester fails in history, and training the arrester working state model; Step S3, collecting real-time characteristic data of the arrester when it is working, and drawing a real-time working state curve of the arrester; at the same time, drawing a historical working state curve of the arrester, analyzing the state curve, and calculating the fault threshold of the arrester failure; Step S4: monitor the arrester in real time, issue a real-time warning when the fault threshold is exceeded, and perform inspection and maintenance; Step S5: back up the electrical data of the arrester collected in real time when it is working, input the collected real-time data into the arrester working state model as historical data, and optimize the model.

[0006] In step S1, the electrical data of the arrester when it is working and the electrical data when a fault occurs during the working process are collected in history, the electrical data are analyzed, and the specific steps of extracting characteristic data affecting the operation of the arrester according to the data analysis results include: Step S1-1, collect the electrical data of the arrester when it works normally in history, and set it as follows: And the electrical data when a fault occurs during operation, the collection is: , where P T P represents the electrical data set of the arrester when it is working normally in history; T 1. P T 2. ..., P T n Indicates the 1st, 2nd, ..., nth electrical data values ​​when the arrester is working normally in the history; P F P represents the electrical data set when a fault occurs during the operation of the arrester in history; F 1. P F 2. ..., P F n Indicates the 1st, 2nd, ..., nth electrical data values ​​when a lightning arrester fails during operation in history, where n is a positive integer; the electrical data when the lightning arrester is operating normally and the electrical data P when a lightning arrester fails during operation are collected N times, , , the n types of electrical data values ​​collected N times are used as sample data for analysis, N is a positive integer, and n is a positive integer; Step S1-2: extract the data value of each electrical data within the range according to the collected sample data, and calculate the difference average value of each electrical data in normal operation and failure. The calculation formula is: ; Obtain the difference average values ​​of n kinds of electrical data when they are working normally and when they fail in sequence ;in, It represents the average difference between different electrical data in normal operation and failure. T P represents the electrical data set of the arrester when it is working normally in history; F represents the electrical data set when a fault occurs during the operation of the arrester in history; N represents the number of collected data values; They represent the difference averages of the 1st, 2nd, ..., nth types of electrical data respectively; Step S1-3, calculate the difference threshold of electrical data, the formula is: ; Wherein, P_ch is the electrical data difference threshold; They represent the difference averages of the 1st, 2nd, ..., nth types of electrical data respectively; n is the number of types of electrical data; Step S1-4, respectively judge the average values ​​of the n types of electrical data differences, and set the electrical data greater than the electrical data difference threshold P_ch as the characteristic data that affects the operation of the arrester. The characteristic data set is ; Where p1, p2, ..., pm represent the 1st, 2nd, ..., mth characteristic data, m is a positive integer, .

[0007] In step S1-2, the process of extracting the data value of each electrical data within the range according to the collected sample data is specifically as follows: Step S1-2-1, calculate the average value of each type of electrical data collected, and record the average value of each type of electrical data as , , where N represents the number of collected data values; P_i represents the data value; Step S1-2-2: Calculate the deviation degree of different electrical data , ,in, Indicates the degree of deviation of different electrical data; Step S1-2-3, is a positional parameter, As the scale parameter, construct a normal distribution function. According to the normal distribution law, the range of extracted data is .

[0008] In step S2, the process of collecting several groups of characteristic data when the arrester fails in history and training the arrester working state model is specifically as follows: Step S2-1: Collect the characteristic data of the arrester when it is working normally in history according to the set characteristic data type ,in Indicates the first type of feature data list; Indicates the second type of feature data list; Represents the mth feature data list, where the data list stores M collected data values, where M is a positive integer; Step 2-2: Fit the arrester working state curve through the collected data. The working state function is: ; Among them, S work (t) represents the working difference value when the arrester works for t; w represents the arrester working time; t represents the arrester working time, ; p1(t) represents the characteristic data value of p1 corresponding to the characteristic data type of the arrester working time t; M represents the number of characteristic data values, M is a positive integer; p1(t) and p m (t) respectively represent the characteristic data types p1 and p corresponding to the arrester working time t m The characteristic data value of .

[0009] In step S3, the real-time characteristic data of the arrester when working is collected, and a real-time working state curve of the arrester is drawn; at the same time, a historical working state curve of the arrester is drawn, and the state curve is analyzed to calculate the fault threshold of the arrester fault. Specifically, the process is as follows: Step S3-1, collecting characteristic data values ​​of the arrester in real time when it is working; Step S3-2: According to the arrester working state model, the arrester historical working state curve S is fitted respectively. work (t) 1_1 , S work (t) 1_2 ,...,S work (t) 1_c And the real-time working state curve S of the arrester work (t) 2 , where S work (t) 1_1 , S work (t) 1_2 ,...,S work (t) 1_c Respectively represent the historical working state curves of the 1st, 2nd, ..., cth lightning arresters; S work (t) 2 Represent the real-time working state curve of the arrester. Place the curves in the same coordinate system, analyze them at the same time scale t, and calculate the local change rate of the curve corresponding to the same time length t. The calculation formula is: ; Wherein, k(t) represents the local change rate of the working state curve corresponding to the working time of the arrester being t; Indicates the change in the working time of the lightning arrester; Indicates the arrester working time is The working difference value at time Indicates the arrester working time is The working difference value at time Step S3-3, calculate the local change rate k(t) of the curve corresponding to the same time length t, extract the local maximum change rate k_max(t) and the local minimum change rate k_min(t), and calculate the fault threshold of the arrester failure. The calculation formula is: ; ; Among them, x(t)_max represents the upper limit of the fault threshold; x(t)_min represents the lower limit of the fault threshold; k_max(t) represents the local maximum change rate; k_min(t) represents the local minimum change rate.

[0010] In step S4, the process of real-time monitoring of the arrester, real-time warning when exceeding the fault threshold, and inspection and maintenance is as follows: the real-time working state curve S work (t) 2 Calculate the local change rate and judge when When the system issues a warning in real time, maintenance personnel will conduct inspection and maintenance; otherwise, no warning will be issued and the lightning arrester will continue to operate.

[0011] In step S5, the electrical data of the arrester collected in real time during operation is backed up, and the collected real-time data is input into the arrester working state model as historical data. The specific steps for optimizing the model are as follows: Step S5-1, backing up the electrical data of the arrester collected in real time when it is working; Step S5-2: input the collected real-time data as historical data into the arrester working state model to optimize the model.

[0012] An online monitoring system for lightning arresters based on the Internet of Things, the online monitoring system for lightning arresters comprising a data acquisition module, a data analysis module, a state model training module, a state curve drawing module, a fault threshold calculation module, a real-time monitoring and early warning module, a data backup and model optimization module, a communication module and a user interface module; The data acquisition module is used to collect the working data and historical data of the lightning arrester in real time, and collect the data when the lightning arrester fails for subsequent analysis; The data analysis module is used to process and analyze the collected electrical data and extract characteristic data that affects the working state of the arrester; The state model training module is used to train the working state model of the arrester according to historical data, simulate the working state of the arrester under normal and fault conditions, and is used for online monitoring and prediction; The state curve drawing module is used to draw the working state curve of the lightning arrester according to the real-time and historical data for visual analysis. The curve graph includes the real-time state and historical state curve of the lightning arrester, which is monitored by technicians and dynamically displays the real-time and historical working state curves of the lightning arrester to help technicians understand the working condition of the lightning arrester; The fault threshold calculation module is used to calculate the fault threshold of the arrester by comparing and analyzing the real-time curve and the historical curve, determine whether the current working state of the arrester is normal, analyze the local change rate of the curve, extract the maximum and minimum change rates, and obtain the threshold range of the fault; The real-time monitoring and early warning module is used to monitor the working state of the arrester in real time. When the working state of the arrester exceeds the fault threshold, the system issues an early warning to notify the maintenance personnel to carry out maintenance; The data backup and model optimization module is used to back up the arrester data collected in real time, input it into the state model as new historical data, and optimize the arrester state model; The communication module is used to upload the collected data to the central control system to ensure real-time transmission of the data; The user interface module provides an operation interface for users, allowing technicians to view real-time data, curve graphs and alarm information, and provides a visual operation interface for maintenance personnel to help monitor the arrester status, view historical data and receive alarm notifications; The data analysis module includes a data processing unit and a feature extraction unit; The data processing unit is used to perform range extraction on the data according to the normal distribution law; The feature extraction unit extracts key feature data based on comparative analysis of historical normal data and fault data, and calculates the difference average values ​​of different types of electrical data, providing a basis for fault prediction and analysis.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention collects real-time data through Internet of Things sensors, analyzes historical lightning arrester working data, calculates characteristic data that affects the work of the lightning arrester, and adopts the law of normal distribution when selecting data, extracts data with the greatest impact on the distribution, screens out a part of unimportant data, reduces the amount of data, and prevents interference from other data; the present invention uses a state curve fitting method, fits multiple curves for comparative analysis, and calculates the range of fault warning more accurately; the present invention can automatically warn of lightning arrester equipment that exceeds the fault threshold range, and can avoid fault accidents caused by negligence of staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of the steps of an online monitoring method for a lightning arrester based on the Internet of Things of the present invention; Figure 2 A schematic diagram of a state curve of an online monitoring method for a lightning arrester based on the Internet of Things of the present invention; Figure 3 The present invention is a schematic structural diagram of an online monitoring system for lightning arresters based on the Internet of Things. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Example: Figure 1-Figure 3 As shown, the present invention provides a technical solution, an online monitoring method for a lightning arrester based on the Internet of Things, and the online monitoring method for a lightning arrester comprises the following steps: Step S1, collecting the electrical data of the arrester when it is working and the electrical data when a fault occurs during the working process, analyzing the electrical data, and extracting the characteristic data that affects the operation of the arrester according to the data analysis results; In step S1, the electrical data of the arrester when it is working and the electrical data when a fault occurs during the working process are collected in history, the electrical data are analyzed, and the specific steps of extracting characteristic data affecting the operation of the arrester according to the data analysis results include: Step S1-1, collect the electrical data of the arrester when it works normally in history, and set it as follows: And the electrical data when a fault occurs during operation, the collection is: , where P T P represents the electrical data set of the arrester when it is working normally in history; T 1. P T 2. ..., P T n Indicates the 1st, 2nd, ..., nth electrical data values ​​when the arrester is working normally in the history; P F P represents the electrical data set when a fault occurs during the operation of the arrester in history; F 1. P F 2. ..., P F n Indicates the 1st, 2nd, ..., nth electrical data values ​​when a lightning arrester fails during operation in history, where n is a positive integer; the electrical data when the lightning arrester is operating normally and the electrical data P when a lightning arrester fails during operation are collected N times, , , the n types of electrical data values ​​collected N times are used as sample data for analysis, N is a positive integer, and n is a positive integer; Step S1-2: extract the data value of each electrical data within the range according to the collected sample data, and calculate the difference average value of each electrical data in normal operation and failure. The calculation formula is: ; Obtain the difference average values ​​of n kinds of electrical data when they are working normally and when they fail in sequence ;in, It represents the average difference between different electrical data in normal operation and failure. T P represents the electrical data set of the arrester when it is working normally in history; F represents the electrical data set when a fault occurs during the operation of the arrester in history; N represents the number of collected data values; They represent the difference averages of the 1st, 2nd, ..., nth types of electrical data respectively; In step S1-2, the process of extracting the data value of each electrical data within the range according to the collected sample data is specifically as follows: Step S1-2-1, calculate the average value of each type of electrical data collected, and record the average value of each type of electrical data as , , where N represents the number of collected data values; P_i represents the data value; Step S1-2-2: Calculate the deviation degree of different electrical data , ,in, Indicates the degree of deviation of different electrical data; Step S1-2-3, is a positional parameter, As the scale parameter, construct a normal distribution function. According to the normal distribution law, the range of extracted data is .

[0017] Step S1-3, calculate the difference threshold of electrical data, the formula is: ; Wherein, P_ch is the electrical data difference threshold; They represent the difference averages of the 1st, 2nd, ..., nth types of electrical data respectively; n is the number of types of electrical data; Step S1-4, respectively judge the average values ​​of the n types of electrical data differences, and set the electrical data greater than the electrical data difference threshold P_ch as the characteristic data that affects the operation of the arrester. The characteristic data set is ; Where p1, p2, ..., pm represent the 1st, 2nd, ..., mth characteristic data, m is a positive integer, .

[0018] Electrical data under normal working conditions (P T gather): Voltage (U): 10kV; Current (I): 20mA; Leakage current (IL): 5µA; Temperature (T): 30°C; Electrical data in case of fault (PF gather): Voltage (U): 7kV; Current (I): 50mA; Leakage current (IL): 20µA; Temperature (T): 80°C; The data is summarized as: The normal data are as follows: (from left to right the data are voltage (U) (kv), current (I) (mA), leakage current (IL) ( ) and temperature (T) (degrees Celsius); [10, 20, 5, 30], [11, 18, 4, 28] and [9, 22, 6, 32]; The fault data is as follows: [7, 50, 20, 80], [6, 55, 25, 85], and [8, 45, 18, 75]; Step S2, collecting several groups of characteristic data when the arrester fails in history, and training the arrester working state model; In step S2, the process of collecting several groups of characteristic data when the arrester fails in history and training the arrester working state model is specifically as follows: Step S2-1: Collect the characteristic data of the arrester when it is working normally in history according to the set characteristic data type ,in Indicates the first type of feature data list; Indicates the second type of feature data list; Represents the mth feature data list, where the data list stores M collected data values, where M is a positive integer; Step 2-2: Fit the arrester working state curve through the collected data. The working state function is: ; Among them, S work (t) represents the working difference value when the arrester works for t; w represents the arrester working time; t represents the arrester working time, ; p1(t) represents the characteristic data value of p1 corresponding to the characteristic data type of the arrester working time t; M represents the number of characteristic data values, M is a positive integer; p1(t) and p m (t) respectively represent the characteristic data types p1 and p corresponding to the arrester working time t m The characteristic data value of .

[0019] like Figure 2 As shown, step S3, collect real-time characteristic data of the arrester when it is working, and draw a real-time working state curve of the arrester; at the same time, draw a historical working state curve of the arrester, analyze the state curve, and calculate the fault threshold of the arrester failure; In step S3, the real-time characteristic data of the arrester when working is collected, and a real-time working state curve of the arrester is drawn; at the same time, a historical working state curve of the arrester is drawn, and the state curve is analyzed to calculate the fault threshold of the arrester fault. Specifically, the process is as follows: Step S3-1, collecting characteristic data values ​​of the arrester in real time when it is working; Step S3-2: According to the arrester working state model, the arrester historical working state curve S is fitted respectively. work (t) 1_1 , S work (t) 1_2 ,...,S work (t) 1_c And the real-time working state curve S of the arrester work (t) 2 , where S work (t) 1_1 , S work (t) 1_2 ,...,S work (t) 1_c Respectively represent the historical working state curves of the 1st, 2nd, ..., cth lightning arresters; S work (t) 2 Represent the real-time working state curve of the arrester. Place the curves in the same coordinate system, analyze them at the same time scale t, and calculate the local change rate of the curve corresponding to the same time length t. The calculation formula is: ; Wherein, k(t) represents the local change rate of the working state curve corresponding to the working time of the arrester being t; Indicates the change in the working time of the lightning arrester; Indicates the arrester working time is The working difference value at time Indicates the arrester working time is The working difference value at time Step S3-3, calculate the local change rate k(t) of the curve corresponding to the same time length t, extract the local maximum change rate k_max(t) and the local minimum change rate k_min(t), and calculate the fault threshold of the arrester failure. The calculation formula is: ; ; Among them, x(t)_max represents the upper limit of the fault threshold; x(t)_min represents the lower limit of the fault threshold; k_max(t) represents the local maximum change rate; k_min(t) represents the local minimum change rate.

[0020] Step S4: monitor the arrester in real time, issue a real-time warning when the fault threshold is exceeded, and perform inspection and maintenance; In step S4, the process of real-time monitoring of the arrester, real-time warning when exceeding the fault threshold, and inspection and maintenance is as follows: the real-time working state curve S work (t) 2 Calculate the local change rate and judge when When the system issues a warning in real time, maintenance personnel will conduct inspection and maintenance; otherwise, no warning will be issued and the arrester will continue to operate.

[0021] Step S5: back up the electrical data of the arrester collected in real time when it is working, input the collected real-time data into the arrester working state model as historical data, and optimize the model.

[0022] In step S5, the electrical data of the arrester collected in real time during operation is backed up, and the collected real-time data is input into the arrester working state model as historical data. The specific steps for optimizing the model are as follows: Step S5-1, backing up the electrical data of the arrester collected in real time when it is working; Step S5-2: input the collected real-time data as historical data into the arrester working state model to optimize the model.

[0023] like Figure 3 As shown, an online monitoring system for lightning arresters based on the Internet of Things, the online monitoring system for lightning arresters includes a data acquisition module, a data analysis module, a state model training module, a state curve drawing module, a fault threshold calculation module, a real-time monitoring and early warning module, a data backup and model optimization module, a communication module and a user interface module; The data acquisition module is used to collect the working data and historical data of the lightning arrester in real time, and collect the data when the lightning arrester fails for subsequent analysis; The data analysis module is used to process and analyze the collected electrical data and extract characteristic data that affects the working state of the arrester; The state model training module is used to train the working state model of the arrester according to historical data, simulate the working state of the arrester under normal and fault conditions, and is used for online monitoring and prediction; The state curve drawing module is used to draw the working state curve of the lightning arrester according to the real-time and historical data for visual analysis. The curve graph includes the real-time state and historical state curve of the lightning arrester, which is monitored by technicians and dynamically displays the real-time and historical working state curves of the lightning arrester to help technicians understand the working condition of the lightning arrester; The fault threshold calculation module is used to calculate the fault threshold of the arrester by comparing and analyzing the real-time curve and the historical curve, determine whether the current working state of the arrester is normal, analyze the local change rate of the curve, extract the maximum and minimum change rates, and obtain the threshold range of the fault; The real-time monitoring and early warning module is used to monitor the working state of the arrester in real time. When the working state of the arrester exceeds the fault threshold, the system issues an early warning to notify the maintenance personnel to carry out maintenance; The data backup and model optimization module is used to back up the arrester data collected in real time, input it into the state model as new historical data, and optimize the arrester state model; The communication module is used to upload the collected data to the central control system to ensure real-time transmission of the data; The user interface module provides an operation interface for users, allowing technicians to view real-time data, curve graphs and alarm information, and provides a visual operation interface for maintenance personnel to help monitor the arrester status, view historical data and receive alarm notifications; The data analysis module includes a data processing unit and a feature extraction unit; The data processing unit is used to perform range extraction on the data according to the normal distribution law; The feature extraction unit extracts key feature data based on comparative analysis of historical normal data and fault data, and calculates the difference average values ​​of different types of electrical data, providing a basis for fault prediction and analysis.

[0024] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A lightning arrester online monitoring method based on the Internet of Things, characterized in that: The arrester online monitoring method comprises the following steps: Step S1, collecting the electrical data of the arrester when it is working and the electrical data when a fault occurs during the working process, analyzing the electrical data, and extracting the characteristic data that affects the operation of the arrester according to the data analysis results; Step S2, collecting several groups of characteristic data when the arrester fails in history, and training the arrester working state model; Step S3, collecting real-time characteristic data of the arrester when it is working, and drawing a real-time working state curve of the arrester; at the same time, drawing a historical working state curve of the arrester, analyzing the state curve, and calculating the fault threshold of the arrester failure; Step S4: monitor the arrester in real time, issue a real-time warning when the fault threshold is exceeded, and perform inspection and maintenance; Step S5: back up the electrical data of the arrester collected in real time when it is working, input the collected real-time data into the arrester working state model as historical data, and optimize the model.

2. The method for online monitoring of lightning arresters based on the Internet of Things according to claim 1, characterized in that: In step S1, the electrical data of the arrester when it is working and the electrical data when a fault occurs during the working process are collected in history, the electrical data are analyzed, and the specific steps of extracting characteristic data affecting the operation of the arrester according to the data analysis results include: Step S1-1, collect the electrical data of the arrester when it works normally in history, and set it as follows: And the electrical data when a fault occurs during operation, the collection is: , where P T P represents the electrical data set of the arrester when it is working normally in history; T 1. P T 2. ..., P T n Indicates the 1st, 2nd, ..., nth electrical data values ​​when the arrester is working normally in the history; P F P represents the electrical data set when a fault occurs during the operation of the arrester in history; F 1. P F 2. ..., P F n Indicates the 1st, 2nd, ..., nth electrical data values ​​when a lightning arrester fails during operation in history, where n is a positive integer; the electrical data when the lightning arrester is operating normally and the electrical data P when a lightning arrester fails during operation are collected N times, , , the n types of electrical data values ​​collected N times are used as sample data for analysis, N is a positive integer, and n is a positive integer; Step S1-2: extract the data value of each electrical data within the range according to the collected sample data, and calculate the difference average value of each electrical data in normal operation and failure. The calculation formula is: ; Obtain the difference average values ​​of n kinds of electrical data when they are working normally and when they fail in sequence ;in, It represents the average difference between different electrical data in normal operation and failure. T P represents the electrical data set of the arrester when it is working normally in history; F represents the electrical data set when a fault occurs during the operation of the arrester in history; N represents the number of collected data values; They represent the difference averages of the 1st, 2nd, ..., nth types of electrical data respectively; Step S1-3, calculate the difference threshold of electrical data, the formula is: ; Wherein, P_ch is the electrical data difference threshold; They represent the difference averages of the 1st, 2nd, ..., nth types of electrical data respectively; n is the number of types of electrical data; Step S1-4, respectively judge the average values ​​of the n types of electrical data differences, and set the electrical data greater than the electrical data difference threshold P_ch as the characteristic data that affects the operation of the arrester. The characteristic data set is ; Where p1, p2, ..., pm represent the 1st, 2nd, ..., mth characteristic data, m is a positive integer, .

3. The method for online monitoring of lightning arresters based on the Internet of Things according to claim 2 is characterized in that: In step S1-2, the process of extracting the data value of each electrical data within the range according to the collected sample data is specifically as follows: Step S1-2-1, calculate the average value of each type of electrical data collected, and record the average value of each type of electrical data as , , where N represents the number of collected data values; P_i represents the data value; Step S1-2-2: Calculate the deviation degree of different electrical data , ,in, Indicates the degree of deviation of different electrical data; Step S1-2-3, is a positional parameter, As the scale parameter, construct a normal distribution function. According to the normal distribution law, the range of extracted data is .

4. The method for online monitoring of lightning arresters based on the Internet of Things according to claim 3 is characterized in that: In step S2, the process of collecting several groups of characteristic data when the arrester fails in history and training the arrester working state model is specifically as follows: Step S2-1: Collect the characteristic data of the arrester when it is working normally in history according to the set characteristic data type ,in Indicates the first type of feature data list; Indicates the second type of feature data list; Represents the mth feature data list, where the data list stores M collected data values, where M is a positive integer; Step 2-2: Fit the arrester working state curve through the collected data. The working state function is: ; Among them, S work (t) represents the working difference value when the arrester works for t; w represents the arrester working time; t represents the arrester working time, ; p1(t) represents the characteristic data value of p1 corresponding to the characteristic data type of the arrester working time t; M represents the number of characteristic data values, M is a positive integer; p1(t) and p m (t) respectively represent the characteristic data types p1 and p corresponding to the arrester working time t m The characteristic data value of .

5. The method for online monitoring of lightning arresters based on the Internet of Things according to claim 4 is characterized in that: In step S3, the real-time characteristic data of the arrester when working is collected, and a real-time working state curve of the arrester is drawn; at the same time, a historical working state curve of the arrester is drawn, and the state curve is analyzed to calculate the fault threshold of the arrester fault. Specifically, the process is as follows: Step S3-1, collecting characteristic data values ​​of the arrester in real time when it is working; Step S3-2: According to the arrester working state model, the arrester historical working state curve S is fitted respectively. work (t) 1_1 , S work (t) 1_2 ,...,S work (t) 1_c And the real-time working state curve S of the arrester work (t) 2 , where S work (t) 1_1 , S work (t) 1_2 ,...,S work (t) 1_c Respectively represent the historical working state curves of the 1st, 2nd, ..., cth lightning arresters; S work (t) 2 Represent the real-time working state curve of the arrester. Place the curves in the same coordinate system, analyze them at the same time scale t, and calculate the local change rate of the curve corresponding to the same time length t. The calculation formula is: ; Wherein, k(t) represents the local change rate of the working state curve corresponding to the working time of the arrester being t; Indicates the change in the working time of the lightning arrester; Indicates the arrester working time is The working difference value at time Indicates the arrester working time is The working difference value at time Step S3-3, calculate the local change rate k(t) of the curve corresponding to the same time length t, extract the local maximum change rate k_max(t) and the local minimum change rate k_min(t), and calculate the fault threshold of the arrester failure. The calculation formula is: ; ; Among them, x(t)_max represents the upper limit of the fault threshold; x(t)_min represents the lower limit of the fault threshold; k_max(t) represents the local maximum change rate; k_min(t) represents the local minimum change rate.

6. The method for online monitoring of lightning arresters based on the Internet of Things according to claim 5 is characterized in that: In step S4, the process of real-time monitoring of the arrester, real-time warning when exceeding the fault threshold, and inspection and maintenance is as follows: the real-time working state curve S work (t) 2 Calculate the local change rate and judge when When a fault occurs, the system issues a warning in real time and maintenance personnel conduct inspection and repair; Otherwise, no warning is given and the arrester continues to operate.

7. The method for online monitoring of lightning arresters based on the Internet of Things according to claim 6 is characterized in that: In step S5, the electrical data of the arrester collected in real time during operation is backed up, and the collected real-time data is input into the arrester working state model as historical data. The specific steps for optimizing the model are as follows: Step S5-1, backing up the electrical data of the arrester collected in real time when it is working; Step S5-2: input the collected real-time data as historical data into the arrester working state model to optimize the model.

8. An online monitoring system for lightning arresters based on the Internet of Things, characterized in that: The arrester online monitoring system includes a data acquisition module, a data analysis module, a state model training module, a state curve drawing module, a fault threshold calculation module, a real-time monitoring and early warning module, a data backup and model optimization module, a communication module and a user interface module; The data acquisition module is used to collect the working data and historical data of the lightning arrester in real time, and collect the data when the lightning arrester fails for subsequent analysis; The data analysis module is used to process and analyze the collected electrical data and extract characteristic data that affects the working state of the arrester; The state model training module is used to train the working state model of the arrester according to historical data, simulate the working state of the arrester under normal and fault conditions, and is used for online monitoring and prediction; The state curve drawing module is used to draw the working state curve of the lightning arrester according to the real-time and historical data for visual analysis. The curve graph includes the real-time state and historical state curve of the lightning arrester, which is monitored by technicians and dynamically displays the real-time and historical working state curves of the lightning arrester to help technicians understand the working condition of the lightning arrester; The fault threshold calculation module is used to calculate the fault threshold of the arrester by comparing and analyzing the real-time curve and the historical curve, determine whether the current working state of the arrester is normal, analyze the local change rate of the curve, extract the maximum and minimum change rates, and obtain the threshold range of the fault; The real-time monitoring and early warning module is used to monitor the working state of the arrester in real time. When the working state of the arrester exceeds the fault threshold, the system issues an early warning to notify the maintenance personnel to carry out maintenance; The data backup and model optimization module is used to back up the arrester data collected in real time, input it into the state model as new historical data, and optimize the arrester state model; The communication module is used to upload the collected data to the central control system to ensure real-time transmission of the data; The user interface module provides an operation interface for users, allowing technicians to view real-time data, graphs and alarm information, and provides a visual operation interface for maintenance personnel to help monitor the status of lightning arresters, view historical data and receive alarm notifications.

9. The arrester online monitoring system based on the Internet of Things according to claim 8 is characterized in that: The data analysis module includes a data processing unit and a feature extraction unit; The data processing unit is used to perform range extraction on the data according to the normal distribution law; The feature extraction unit extracts key feature data based on comparative analysis of historical normal data and fault data, and calculates the difference average values ​​of different types of electrical data, providing a basis for fault prediction and analysis.

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