Power grid equipment security management platform based on RPA

Abstract

The present invention relates to the technical field of power grid security management, and discloses a power grid equipment security management platform based on RPA, including a data analysis unit and an RPA control unit. The data analysis unit can perform detailed abnormality analysis on the operation data of the power grid equipment, and timely discover potential safety hazards by monitoring the characteristics of each device one by one, so as to ensure that the power grid equipment is always in a normal operating state, thereby improving the reliability and stability of the equipment. The RPA control unit also performs operation and maintenance transmission analysis based on abnormal signals, and can quickly determine to send the abnormal signals to the most appropriate operation and maintenance personnel, thereby greatly shortening the fault response time and improving the efficiency of safety management.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid security management, and in particular to a power grid equipment security management platform based on RPA. Background Art

[0002] With the continuous expansion of the scale of power grids and the improvement of their intelligence, the safety management of power grid equipment has become extremely important. Traditional power grid equipment safety management methods mainly rely on manual monitoring and analysis, which has problems such as low efficiency and prone to human errors. At the same time, with the increase in the number of power grid equipment and the massive growth of operating data, how to quickly and accurately analyze the operating status of equipment and perform operation and maintenance in a timely manner has become an urgent problem to be solved.

[0003] In recent years, Robotic Process Automation (RPA) technology has gradually emerged, providing new ideas and methods for the safe management of power grid equipment. RPA technology can simulate human operations, automatically perform repetitive and regular tasks, and improve work efficiency and accuracy. By applying RPA technology to the safe management of power grid equipment, it is possible to automatically collect, analyze and process equipment operation data, detect equipment abnormalities in a timely manner, and quickly assign them to relevant operation and maintenance personnel for repair, thereby improving the safety and reliability of power grid equipment.

[0004] Therefore, the present invention proposes a power grid equipment security management platform based on RPA, aiming to solve the problems existing in the traditional power grid equipment security management method and improve the security management level of power grid equipment. Summary of the invention

[0005] The purpose of the present invention is to provide a power grid equipment security management platform based on RPA, which solves the technical problems raised in the background technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The RPA-based power grid equipment security management platform includes:

[0008] A data collection unit, used to collect operation data of power grid equipment, the operation data including operation values ​​of multiple equipment characteristics;

[0009] A preprocessing unit, used for preprocessing the running data, and then determining an abnormal value in the running data through the preprocessing result, and replacing the abnormal value;

[0010] The data analysis unit is used to perform abnormal analysis on each device feature of the power grid equipment according to the pre-processed operation data, and determine whether each device feature of the power grid equipment is normal according to the abnormal analysis result, and generate an abnormal signal of the corresponding device feature for the abnormal device feature;

[0011] The RPA control unit is used to receive the abnormal signal of the corresponding equipment characteristics transmitted by the data analysis unit, and then perform operation and maintenance transmission analysis based on the abnormal signal. Based on the operation and maintenance transmission analysis results, it determines the transmission instructions to be transmitted to the relevant operation and maintenance personnel, and then sends the abnormal signal to the corresponding management personnel according to the transmission instructions.

[0012] As a further solution of the present invention: the preprocessing method of the preprocessing unit is as follows:

[0013] Step SF1, select a device feature;

[0014] In the specified period, obtain the running value of the device feature on n standard acquisition nodes, and mark the running value of the device feature as T0 i , where i=1, 2, ... n;

[0015] Step SF2: Obtain all T0 i The mean and standard deviation of the data are labeled TP and TB respectively;

[0016] Step SF3: Then verify each T0 using the mean value TP and standard deviation TB. i Whether there are abnormal values ​​in , the verification method is as follows:

[0017] If |T0 i -TP|≤3×TB, it means that T0 i is the normal value;

[0018] If |T0 i -TP|>3×TB, it means that T0 i is an outlier;

[0019] Step SF4: extract all T0 values ​​that are determined to be normal. i , and calculate its average value T1P;

[0020] Step SF5: extract all T0 values ​​that are determined to be abnormal values. i , and then replace the running value obtained on the corresponding standard collection node with T1P;

[0021] As a further solution of the present invention: the abnormality analysis method of the data analysis unit is as follows:

[0022] Step SK1: Select device features

[0023] Select a device feature, obtain the pre-processed running value of the device feature on n standard acquisition nodes within a specified period, and re-mark the running value of the device feature as T1, T2, T3, ... T in turn according to the time trend. n ;

[0024] Step SK2: Calculate the difference determination factor

[0025] pass:

[0026]

[0027] Calculate the first-order difference B1, second-order difference B2 and third-order difference B3 of the characteristic operation value of the corresponding equipment;

[0028] Then extract the first-order difference B1, the second-order difference B2 and the third-order difference B3;

[0029] And through:

[0030]

[0031] Calculate the stage difference determination factor X of the characteristic operation value of the corresponding equipment;

[0032] Step SK3: Compare the decision factor with the threshold

[0033] Compare the stage difference determination factor X with the preset stage difference determination threshold Xy:

[0034] If the determination factor X> the stage difference determination threshold value Xy, it is determined that the device characteristic of the power grid device is abnormal, and an abnormal signal of the corresponding characteristic is generated accordingly;

[0035] If the determination factor X≤the stage difference determination threshold value Xy, it is determined that the device characteristic of the power grid device is normal, and no abnormal signal of the corresponding characteristic is generated.

[0036] As a further solution of the present invention: the operation and maintenance transmission analysis method of the RPA control unit is as follows:

[0037] Step SR1: Obtain location and workload information

[0038] Obtain the location information of each operation and maintenance personnel and the power grid equipment that generates abnormal signals;

[0039] At the same time, the current workload of each operation and maintenance personnel is obtained, and the workload is expressed as the number of maintenance tasks of the operation and maintenance personnel;

[0040] Step SR2: Determine distance value

[0041] According to the location information of each operation and maintenance personnel and the power grid equipment, the distance value between each operation and maintenance personnel and the power grid equipment is determined;

[0042] Step SR3: Determine the load factor

[0043] Extracting a preset maximum workload, where the maximum workload is expressed as the upper limit of the number of maintenance tasks that the operation and maintenance personnel can undertake;

[0044] Determine the load rate of operation and maintenance personnel based on workload and maximum workload;

[0045] Step SR4: Calculate execution priority value

[0046] Select an operation and maintenance personnel, through , calculate the execution priority value L of the operation and maintenance personnel;

[0047] Where β1 and β2 are preset weight coefficients, which are used to adjust the weights of load rate and distance value when calculating the execution priority value of relevant operation and maintenance personnel;

[0048] Step SR5: Generate a transmission instruction

[0049] The execution priority values ​​of all operation and maintenance personnel are extracted, and the operation and maintenance personnel corresponding to the maximum execution priority value are extracted, and then a transmission instruction is generated according to the operation and maintenance personnel, which means that the power grid equipment that generates abnormal signals will be assigned to the operation and maintenance personnel for repair.

[0050] As a further solution of the present invention: wherein the distance value is obtained by: Make sure to get;

[0051] Where D is the distance between an operation and maintenance personnel and the power grid equipment that generates abnormal signals, (X1, Y1) is the location coordinate of the operation and maintenance personnel, X1 is its latitude coordinate, Y1 is its longitude coordinate, (X2, Y2) is the location coordinate of the power grid equipment, X2 is its latitude coordinate, and Y2 is its longitude coordinate.

[0052] As a further solution of the present invention: wherein the load index is obtained by Make sure to get;

[0053] Where F is the load rate of an operation and maintenance personnel, Fmax is the maximum workload, and Fd is the current workload of the operation and maintenance personnel.

[0054] As a further solution of the present invention: the operating data also includes parameter values ​​of environmental characteristics corresponding to the ambient temperature and ambient humidity.

[0055] As a further solution of the present invention: the data analysis unit is further used to control and analyze the environment in which the power grid equipment is located according to the operation data corresponding to the environmental characteristics, and determine whether the environment in which the power grid equipment is located is normal according to the control and analysis results, and for abnormal environmental characteristics, an abnormal signal of the corresponding environmental characteristics is generated, and then the abnormal signal of the corresponding environmental characteristics is transmitted to the RPA control unit;

[0056] The RPA control unit is also used to receive control signals of corresponding characteristics transmitted by the data analysis unit, and combine them with predetermined execution instructions to adjust the status of the power grid equipment.

[0057] As a further solution of the present invention: the control analysis method is as follows:

[0058] Among them, the control analysis method of each environmental feature is the same, and one feature is selected;

[0059] Step SU1: within a specified period, obtain the parameter values ​​of the environmental feature on m standard collection nodes, and mark the parameter values ​​of the environmental feature as H in turn according to the time trend. j ;

[0060] Step SU2, by: and

[0061] Find all H j The mean value HP and standard deviation HB;

[0062] Step SU3, then compare the average value HP and the standard deviation HB with the preset average value threshold HPy and the standard deviation threshold HBy respectively:

[0063] If HP>HPy and HB>HBy are both true, the environment of the power grid equipment is abnormal, and a control signal of corresponding characteristics is generated accordingly;

[0064] The control signals of corresponding characteristics include a temperature control signal obtained according to an ambient temperature parameter value of the environment in which the power grid equipment is located, and a humidity control signal obtained according to an ambient humidity parameter value of the environment in which the power grid equipment is located.

[0065] As a further solution of the present invention: wherein the execution instruction is pre-set by the power grid related personnel in the control state according to the corresponding characteristics of the power grid equipment;

[0066] The execution instructions include but are not limited to a control signal for adjusting the current ambient temperature of the power grid equipment according to a temperature control signal, and a control signal for adjusting the current ambient humidity of the power grid equipment according to a humidity control signal.

[0067] As a further solution of the present invention: the state adjustment method is as follows:

[0068] When a temperature control signal is received, the cooling device arranged at the corresponding position of the power grid equipment is controlled to start according to the control signal for adjusting the current ambient temperature of the power grid equipment, so as to reduce the current ambient temperature;

[0069] When a humidity control signal is received, the dehumidification equipment arranged at the corresponding position of the power grid equipment is controlled to start according to the control signal for adjusting the current ambient humidity of the power grid equipment, so as to reduce the current ambient humidity.

[0070] Beneficial effects of the present invention:

[0071] The data analysis unit of the present invention can perform detailed abnormal analysis on the operation data of the power grid equipment, and timely discover potential safety hazards by monitoring the characteristics of each device one by one, thereby ensuring that the power grid equipment is always in a normal operating state and improving the reliability and stability of the equipment.

[0072] In the present invention, the RPA control unit performs operation and maintenance transmission analysis based on the abnormal signal, and can quickly determine to send the abnormal signal to the most appropriate operation and maintenance personnel, which greatly shortens the fault response time and improves the efficiency of safety management.

[0073] In the present invention, the abnormal analysis method of the data analysis unit can accurately determine whether each device feature of the power grid device is normal by calculating the stage difference determination factor and comparing it with the preset threshold value, thereby providing a clear target for subsequent maintenance work.

[0074] In the present invention, the preprocessing unit preprocesses the operating data, can effectively determine and replace abnormal values ​​in the operating data, improve the accuracy and reliability of the data, and further ensure the precision of abnormal analysis.

[0075] In the present invention, the operation and maintenance transmission analysis method of the RPA control unit comprehensively considers the location information of the operation and maintenance personnel and the power grid equipment and the workload of the operation and maintenance personnel. By calculating the execution priority value, the power grid equipment that generates abnormal signals can be allocated to the most suitable operation and maintenance personnel for maintenance. This allocation method takes into account the distance factor to ensure that the operation and maintenance personnel can reach the fault site as soon as possible, and also takes into account the workload factor, avoiding allocating too many maintenance tasks to the already heavily burdened operation and maintenance personnel, thereby improving the efficiency and quality of maintenance work.

[0076] The present invention, based on the RPA power grid equipment security management platform, realizes the automated process of data collection, analysis, abnormality determination and operation and maintenance personnel allocation, reduces manual intervention and improves the intelligent level of management; the platform can monitor the operating status of power grid equipment in real time and send out abnormal signals in time, providing a strong guarantee for the safe management of power grid equipment.

[0077] The present invention, by accurately locating abnormal equipment characteristics and optimizing the allocation of operation and maintenance personnel, can ensure that maintenance personnel arrive at the fault site in the shortest time and perform accurate maintenance work, thereby improving the timeliness and accuracy of power grid equipment maintenance; this helps to reduce the impact of equipment failures on power grid operation and improve the reliability and stability of the power grid.

[0078] In summary, the RPA-based power grid equipment security management platform has many beneficial effects, such as improving management efficiency, accurately locating anomalies, optimizing the allocation of operation and maintenance personnel, improving the level of intelligence, and improving the timeliness and accuracy of maintenance. It is of great significance to ensure the safe and stable operation of power grid equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] The present invention will be further described below in conjunction with the accompanying drawings.

[0080] Figure 1 It is the system framework of the power grid equipment security management platform based on RPA of the present invention. Figure 1 .

[0081] Figure 2 It is the system framework of the power grid equipment security management platform based on RPA of the present invention. Figure 2 .

[0082] Figure 3 It is a flow chart of the data analysis unit in the RPA-based power grid equipment security management platform of the present invention.

[0083] Figure 4 It is a flow chart of the RPA control unit in the RPA-based power grid equipment security management platform of the present invention. DETAILED DESCRIPTION

[0084] 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.

[0085] Embodiment 1

[0086] See also Figure 1 and Figure 4 As shown, the present invention is a power grid equipment security management platform based on RPA, including:

[0087] A data collection unit, used to collect operating data of power grid equipment, including operating values ​​of multiple equipment characteristics;

[0088] In this embodiment, the data acquisition unit uses various sensors to collect basic operation data, and uses wireless sensor network technology to achieve interconnection and data sharing between sensor nodes;

[0089] Each sensor node can work together to monitor power grid equipment in multiple dimensions. For example, for large transformer equipment, multiple wireless sensors can be deployed in different locations to monitor parameters such as temperature, vibration, and partial discharge at the same time. The data can be aggregated and transmitted through a self-organizing network to improve the comprehensiveness and real-time nature of data collection.

[0090] At the same time, the frequency of data collection is dynamically adjusted according to the importance and operating status of the power grid equipment;

[0091] For key equipment or equipment that is in high-load operation or has a high incidence of failures, increase the collection frequency, such as from once every minute to once every 30 seconds;

[0092] For relatively unimportant equipment with stable operating status, appropriately reduce the acquisition frequency to reduce the pressure of data transmission and processing, while saving system resources;

[0093] A preprocessing unit, used for preprocessing the running data, and then determining an abnormal value in the running data through the preprocessing result, and replacing the abnormal value;

[0094] The preprocessing method is as follows:

[0095] Step SF1, wherein the control analysis method of each device feature is the same, and one device feature is selected as an example;

[0096] In the specified period, obtain the running value of the device feature on n standard acquisition nodes, and mark the running value of the device feature as T0 i , where i=1, 2, ... n;

[0097] Step SF2: Obtain all T0 i The mean and standard deviation of the data are labeled TP and TB respectively;

[0098] It is calculated as follows:

[0099]

[0100] Where T0 i For all T0 i The average value of all T0 i The standard deviation of

[0101] Step SF3: Then verify each T0 using the mean value TP and standard deviation TB. i Whether there are abnormal values ​​in , the verification method is as follows:

[0102] Taking three times the standard deviation TB as the threshold, when the absolute value of the difference between the operating value T0i of the equipment feature and the average value TP exceeds three times the standard deviation TB, the operating value T0i of the equipment feature is an abnormal value; otherwise, it is not an abnormal value;

[0103] Specifically:

[0104] If |T0 i -TP|≤3×TB, it means that T0 i is the normal value;

[0105] If |T0 i -TP|>3×TB, it means that T0 i is an outlier;

[0106] Step SF4: extract all T0 values ​​that are determined to be normal. i , and calculate its average value T1P;

[0107] Step SF5: extract all T0 values ​​that are determined to be abnormal values. i , and then replace the running value obtained on the corresponding standard collection node with T1P;

[0108] The data analysis unit is used to perform abnormal analysis on each device feature of the power grid equipment according to the preprocessed operation data, and determine whether each device feature of the power grid equipment is normal according to the abnormal analysis result. For abnormal device features, an abnormal signal of the corresponding device feature is generated, and then the abnormal signal of the corresponding device feature is transmitted to the RPA control unit;

[0109] Take a device feature as an example;

[0110] Step SK1: within a specified period, obtain the pre-processed running values ​​of the device feature on n standard acquisition nodes, and re-mark the running values ​​of the device feature as T1, T2, T3, ..., T according to the time trend. n ;

[0111] Step SK2: In all T1, T2, T3, ... T n middle;

[0112] First, the average of the absolute values ​​of the differences between all two adjacent operating values ​​is calculated, and recorded as the first-order difference of the corresponding equipment characteristic operating values;

[0113] The calculation formula is as follows:

[0114]

[0115] In the formula, B1 is the first-order difference of the characteristic operating value of the corresponding equipment;

[0116] Next, calculate half of the absolute value of the difference between all the operating values ​​of each other, and then take the average value, which is recorded as the second-order difference of the corresponding equipment characteristic operating value:

[0117] The calculation formula is as follows:

[0118]

[0119] Where B2 is the second-order difference of the characteristic operating value of the corresponding equipment;

[0120] After that, calculate one third of the absolute value of the difference between all two operating values, and then take the average value, which is recorded as the third-order difference of the corresponding equipment characteristic operating value.

[0121] The calculation formula is as follows:

[0122]

[0123] Where B3 is the third-order difference of the characteristic operating value of the corresponding equipment;

[0124] Then, the first-order difference B1, the second-order difference B2 and the third-order difference B3 are extracted, and the sum of the absolute values ​​of the differences between the first-order difference, the second-order difference and the third-order difference is calculated and then divided by 2, and the stage difference determination factor of the characteristic operation value of the corresponding equipment is obtained;

[0125] The calculation formula is as follows:

[0126]

[0127] Where X is the stage difference determination factor of the characteristic operation value of the corresponding equipment;

[0128] Step SK3: compare the stage difference determination factor X with the preset stage difference determination threshold value Xy:

[0129] If the determination factor X> the stage difference determination threshold value Xy, it is determined that the device characteristic of the power grid device is abnormal, and an abnormal signal of the corresponding device characteristic is generated accordingly;

[0130] If the determination factor X≤the stage difference determination threshold value Xy, it is determined that the device characteristic of the power grid device is normal, and no abnormal signal of the corresponding device characteristic is generated;

[0131] In this embodiment, the data analysis unit also grades the analysis results according to the severity of the abnormality;

[0132] For example, the abnormalities of equipment characteristics are divided into three levels: slight abnormality, moderate abnormality and severe abnormality, corresponding to different warning colors and processing priorities;

[0133] Minor abnormalities can be continuously monitored and reported regularly;

[0134] Moderate anomalies require prompt notification of operation and maintenance personnel for preliminary inspection;

[0135] In case of serious abnormalities, the emergency handling process will be initiated immediately, maintenance tasks will be quickly assigned and relevant management personnel will be notified;

[0136] The RPA control unit is used to receive the abnormal signal of the corresponding equipment characteristics transmitted by the data analysis unit, and then perform operation and maintenance transmission analysis based on the abnormal signal, determine the transmission instruction to be transmitted to the relevant operation and maintenance personnel based on the operation and maintenance transmission analysis result, and then send the abnormal signal to the corresponding management personnel according to the transmission instruction;

[0137] The specific method is as follows:

[0138] Step SR1: Obtain location information of each operation and maintenance personnel and the power grid equipment that generates abnormal signals;

[0139] At the same time, the current workload of each operation and maintenance personnel is obtained, and the workload is expressed as the number of maintenance tasks of the operation and maintenance personnel;

[0140] Step SR2: Determine the distance between each operation and maintenance personnel and the power grid equipment according to the location information of each operation and maintenance personnel and the power grid equipment;

[0141] The distance value is obtained by: Make sure to get;

[0142] Where D is the distance between an operation and maintenance personnel and the power grid equipment that generates abnormal signals, (X1, Y1) is the location coordinate of the operation and maintenance personnel, X1 is its latitude coordinate, Y1 is its longitude coordinate, (X2, Y2) is the location coordinate of the power grid equipment, X2 is its latitude coordinate, and Y2 is its longitude coordinate;

[0143] Step SR3: extracting a preset maximum workload, where the maximum workload is represented by an upper limit value of the number of maintenance tasks that the operation and maintenance personnel can undertake;

[0144] Determine the load rate of operation and maintenance personnel based on workload and maximum workload;

[0145] Load index through Make sure to get;

[0146] In the formula, F is the load rate of an operation and maintenance personnel, Fmax is the maximum workload, and Fd is the current workload of the operation and maintenance personnel;

[0147] Step SR4: Take an operation and maintenance personnel as an example.

[0148] Calculate the inverse of the product of the distance value of the power grid equipment where the operation and maintenance personnel generates the abnormal signal and the weight coefficient 1, and then add the product of the load rate of the operation and maintenance personnel and the weight coefficient 2 to obtain the execution priority value of the operation and maintenance personnel;

[0149] The calculation formula is as follows:

[0150] ;

[0151] In the formula, L is the execution priority value of the operation and maintenance personnel;

[0152] β1 and β2 correspond to the preset weight coefficient 1 and weight coefficient 2, respectively, which are used to adjust the weights of the load rate and the distance value when calculating the execution priority value of the relevant operation and maintenance personnel;

[0153] Step SR5, extracting the execution priority values ​​of all operation and maintenance personnel, and extracting the operation and maintenance personnel corresponding to the maximum execution priority value, and then generating a transmission instruction according to the operation and maintenance personnel, which means that the power grid equipment generating the abnormal signal is assigned to the operation and maintenance personnel for repair;

[0154] In this embodiment, in addition to considering the location and workload of the operation and maintenance personnel, factors such as the skill level of the operation and maintenance personnel and the complexity of the equipment failure are also comprehensively analyzed;

[0155] For example, complex technical failures are given priority in being assigned to experienced and highly skilled maintenance personnel;

[0156] For some simple routine faults, they can be assigned to operation and maintenance personnel who are closer and have a relatively lower workload, thus achieving optimal allocation of operation and maintenance resources;

[0157] In this embodiment, the data acquisition unit collects the operating data of the power grid equipment, including the operating values ​​of multiple equipment characteristics, so as to fully understand the operating status of the power grid equipment; the preprocessing unit preprocesses the operating data, determines and replaces abnormal values, improves the accuracy and reliability of the data, and provides a more reliable basis for subsequent data analysis; the data analysis unit performs abnormal analysis on each equipment characteristic of the power grid equipment, can timely discover the abnormal situation of the equipment characteristics, and generate an abnormal signal to transmit to the RPA control unit; the RPA control unit performs operation and maintenance transmission analysis based on the abnormal signal, determines the transmission instructions to be transmitted to the relevant operation and maintenance personnel, and can quickly assign the abnormal situation to the appropriate operation and maintenance personnel for repair, thereby improving the operation and maintenance efficiency and the safety of the power grid equipment.

[0158] Embodiment 2

[0159] As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from that of the first embodiment only in that:

[0160] The operation data also includes parameter values ​​of the ambient temperature and ambient humidity corresponding to the environmental characteristics;

[0161] The data analysis unit is also used to control and analyze the environment in which the power grid equipment is located according to the operation data corresponding to the environmental characteristics, and determine whether the environment in which the power grid equipment is located is normal according to the control and analysis results. For abnormal environmental characteristics, an abnormal signal of the corresponding environmental characteristics is generated, and then the abnormal signal of the corresponding environmental characteristics is transmitted to the RPA control unit;

[0162] The control analysis method is as follows:

[0163] Among them, the control analysis method of each environmental feature is the same, and one feature is selected as an example;

[0164] Step SU1: within a specified period, obtain the parameter values ​​of the environmental feature on m standard collection nodes, and mark the parameter values ​​of the environmental feature as H in turn according to the time trend. j ;

[0165] Step SU2, by: and

[0166] Find all H j The mean value HP and standard deviation HB;

[0167] Step SU3, then compare the average value HP and the standard deviation HB with the preset average value threshold HPy and the standard deviation threshold HBy respectively:

[0168] If HP>HPy and HB>HBy are both true, the environment of the power grid equipment is abnormal, and a control signal of corresponding characteristics is generated accordingly;

[0169] The control signals of corresponding characteristics include a temperature control signal obtained according to the ambient temperature parameter value of the environment in which the power grid equipment is located, and a humidity control signal obtained according to the ambient humidity parameter value of the environment in which the power grid equipment is located;

[0170] The RPA control unit is also used to receive the control signal of the corresponding characteristics transmitted by the data analysis unit, and combine it with the predetermined execution instruction to adjust the state of the power grid equipment;

[0171] Among them, the execution instructions are pre-set by the relevant personnel of the power grid in the control state according to the corresponding characteristics of the power grid equipment;

[0172] The execution instructions include but are not limited to a control signal for adjusting the current ambient temperature of the power grid equipment according to the temperature control signal, and a control signal for adjusting the current ambient humidity of the power grid equipment according to the humidity control signal;

[0173] The status adjustment method is as follows:

[0174] When a temperature control signal is received, the cooling device arranged at the corresponding position of the power grid equipment is controlled to start according to the control signal for adjusting the current ambient temperature of the power grid equipment, so as to reduce the current ambient temperature;

[0175] When a humidity control signal is received, the dehumidification equipment arranged at the corresponding position of the power grid equipment is controlled to start according to the control signal for adjusting the current ambient humidity of the power grid equipment, so as to reduce the current ambient humidity.

[0176] In this embodiment, based on the first embodiment, the operating data also includes parameter values ​​of environmental characteristics such as ambient temperature and ambient humidity, so as to more comprehensively understand the environmental conditions of the power grid equipment. The data analysis unit controls and analyzes the environment in which the power grid equipment is located, and can promptly detect environmental abnormalities, and generate abnormal signals of corresponding environmental characteristics to transmit to the RPA control unit. After receiving the control signal, the RPA control unit adjusts the state of the power grid equipment in combination with the predetermined execution instruction, and can promptly adjust the environmental state of the power grid equipment, thereby improving the stability and reliability of the power grid equipment.

[0177] Embodiment 3

[0178] As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments mentioned above for implementation.

[0179] The RPA control unit is also used by operation and maintenance personnel to provide feedback on task execution status, problems encountered, and support required through the platform after receiving maintenance tasks;

[0180] The RPA control unit then adjusts the task allocation strategy or provides corresponding technical guidance in a timely manner based on the feedback information to achieve closed-loop management of the operation and maintenance process;

[0181] At the same time, the RPA control unit is also used to evaluate the performance of operation and maintenance personnel based on multiple factors such as task completion quality, response time, and effectiveness of feedback information, and to provide corresponding rewards or incentives;

[0182] For example, for operation and maintenance personnel with outstanding performance, priority will be given to allocating high-quality training resources, providing promotion opportunities or material rewards to improve their work enthusiasm and sense of responsibility.

[0183] This embodiment combines the solutions of embodiment one and embodiment two for implementation, and can simultaneously perform comprehensive monitoring and analysis of the operating data of the power grid equipment and the environment in which it is located; it improves the comprehensiveness and effectiveness of the safety management of the power grid equipment, can more timely and accurately discover abnormal conditions of the equipment and environment, and take corresponding measures to deal with them, thereby ensuring the safe and stable operation of the power grid equipment.

[0184] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0185] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The RPA-based power grid equipment security management platform is characterized by: include: The data analysis unit is used to perform abnormal analysis on each device feature according to the operation value of the device feature in the operation data of the collected power grid equipment, and determine whether each device feature of the power grid equipment is normal according to the abnormal analysis result, and generate an abnormal signal of the corresponding device feature for the abnormal device feature; The abnormality analysis method of the data analysis unit is as follows: Step SK1, select a device feature; In a specified period, the running values ​​of the device feature after preprocessing on n standard acquisition nodes are obtained, and the running values ​​of the device feature are re-marked as T1, T2, T3, ... T in turn according to the time trend. n ; Step SK2: In all T1, T2, T3, ... T n middle; First, the average value of the absolute values ​​of the differences between all two adjacent operating values ​​is calculated, and recorded as the first-order difference of the characteristic operating value of the corresponding equipment; then, half of the absolute values ​​of the differences between all the operating values ​​every other one is calculated, and then the average value thereof is calculated, and recorded as the second-order difference of the characteristic operating value of the corresponding equipment; then, one-third of the absolute values ​​of the differences between all the operating values ​​every other two is calculated, and then the average value thereof is calculated, and recorded as the third-order difference of the characteristic operating value of the corresponding equipment; Finally, the sum of the absolute values ​​of the differences between the first-order difference, the second-order difference, and the third-order difference is calculated and then divided by 2, and the stage difference determination factor of the characteristic operation value of the corresponding equipment is obtained; Step SK3, comparing the stage difference determination factor X with the preset stage difference determination threshold value Xy, if it exceeds the threshold value, it is determined that the device characteristic of the power grid device is abnormal, and then an abnormal signal of the corresponding characteristic is generated; otherwise, it is determined that the device characteristic of the power grid device is normal; The RPA control unit is used to receive the abnormal signal of the corresponding equipment characteristics transmitted by the data analysis unit, and then perform operation and maintenance transmission analysis based on the abnormal signal, determine the transmission instruction to be transmitted to the relevant operation and maintenance personnel based on the operation and maintenance transmission analysis result, and then send the abnormal signal to the corresponding management personnel according to the transmission instruction; The operation and maintenance transmission analysis method of the RPA control unit is as follows: Step SR1: obtaining the location information of each operation and maintenance personnel and the power grid equipment generating the abnormal signal; and obtaining the current workload of each operation and maintenance personnel; Step SR2: Determine the distance between each operation and maintenance personnel and the power grid equipment according to the location information of each operation and maintenance personnel and the power grid equipment; Step SR3: extract the preset maximum workload; determine the load rate of the operation and maintenance personnel according to the workload and the maximum workload; Step SR4, calculate the inverse of the product of the distance value of the power grid equipment where the operation and maintenance personnel generates the abnormal signal and the weight coefficient 1, and then add the product of the load rate of the operation and maintenance personnel and the weight coefficient 2 to obtain the execution priority value of the operation and maintenance personnel; Among them, weight coefficient 1 and weight coefficient 2 are preset values, which are used to adjust the weights of load rate and distance value when calculating the execution priority value of relevant operation and maintenance personnel; Step SR5, extract the execution priority values ​​of all operation and maintenance personnel, and extract the operation and maintenance personnel corresponding to the maximum execution priority value, and then generate a transmission instruction based on the operation and maintenance personnel, which means that the power grid equipment that generates abnormal signals will be assigned to the operation and maintenance personnel for repair.

2. The RPA-based power grid equipment security management platform according to claim 1 is characterized in that: The distance value is calculated by the coordinates of the operation and maintenance personnel and the power grid equipment that generates the abnormal signal.

3. The RPA-based power grid equipment security management platform according to claim 1 is characterized in that: in, The workload is expressed as the number of maintenance tasks for the operation and maintenance personnel, and the maximum workload is expressed as the upper limit of the number of maintenance tasks that the operation and maintenance personnel can undertake.

4. The RPA-based power grid equipment security management platform according to claim 1 is characterized in that: in, Load index through Make sure to get; Where F is the load rate of an operation and maintenance personnel, Fmax is the maximum workload, and Fd is the current workload of the operation and maintenance personnel.

5. The RPA-based power grid equipment security management platform according to claim 1 is characterized in that: Also includes: A data collection unit, used to collect operation data of power grid equipment, the operation data including operation values ​​of multiple equipment characteristics; The preprocessing unit is used to preprocess the running data, and then determine the abnormal values ​​in the running data through the preprocessing results, and replace the abnormal values. Then the preprocessed running data is transmitted to the data analysis unit, and all the running data preprocessed by the data analysis unit are subjected to abnormal analysis.

6. The RPA-based power grid equipment security management platform according to claim 5 is characterized in that: The preprocessing method of the preprocessing unit is as follows: Step SF1, select a device feature; In the specified period, obtain the running value of the device feature on n standard acquisition nodes, and mark the running value of the device feature as T0 i , where i=1, 2, ... n; Step SF2: Obtain all T0 i The mean and standard deviation of the data are labeled TP and TB respectively; Step SF3: Then verify each T0 using the mean value TP and standard deviation TB. i Whether there are outliers in the Step SF4: extract all T0 values ​​that are not abnormal values. i , and calculate its average value T1P; Step SF5: extract all T0 values ​​that are abnormal values. i , and then replace the running value obtained on the corresponding standard collection node with T1P.

7. The RPA-based power grid equipment security management platform according to claim 6 is characterized in that: The verification method in step SF3 is as follows: Taking three times the standard deviation TB as the threshold, when the absolute value of the difference between the operating value T0i of the equipment feature and the average value TP exceeds three times the standard deviation TB, the operating value T0i of the equipment feature is an abnormal value; otherwise, it is not an abnormal value.

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