A high voltage equipment maintenance data processing and analysis system

Abstract

The present invention relates to the technical field of high-voltage equipment data processing, and in particular to a high-voltage equipment maintenance data processing and analysis system. The system analyzes the maintenance data of the high-voltage equipment to determine the active operation and maintenance value within each cycle time, and then sets the active maintenance level. The active data within the cycle time is classified according to the data interval of the active maintenance level, and the capacity frequency value of the active maintenance level is calculated. Based on the capacity frequency value and the active operation and maintenance value, the equipment maintenance value within the cycle time is determined, and then the correlation value between the number of training times within the cycle time and the equipment maintenance value is calculated, and a normal training signal is determined based on the correlation value. The real-time maintenance value is calculated from the normal training signal, and a normal operation and maintenance signal and a training optimization signal are generated according to the real-time maintenance value. The training work of the high-voltage equipment is adjusted according to the signal, which is beneficial to optimizing the maintenance data of the equipment, further improving the maintenance efficiency and reducing the maintenance cost.

Description

Technical Field

[0001] The present invention relates to the technical field of high-voltage equipment data processing, and in particular to a high-voltage equipment maintenance data processing and analysis system. Background Art

[0002] High-voltage equipment refers to equipment that can handle high-voltage electrical energy. They play an important role in power systems, industrial production, transportation and other fields.

[0003] The prior art CN117528596A discloses a fault detection and maintenance method and data processing equipment. By embedding an identifier in the link data and adopting a distributed link, the distributed link tracking can record and display the request process and call link of the entire system in real time, and can provide accurate fault location to help administrators quickly locate and solve faults. It can also provide detailed fault analysis information to help administrators deeply analyze the causes and impacts of faults, improve the fault tolerance of the system, and reduce the impact range of faults, so as to help administrators analyze the performance bottlenecks and optimization points of the system and improve the performance of the system to troubleshoot faults.

[0004] However, when the equipment is being maintained, the professional knowledge and skills of the employees are one of the factors affecting the maintenance data of the high-voltage equipment. When the professional knowledge and skills of the employees are insufficient, it will affect the maintenance data of the high-voltage equipment, making the maintenance efficiency of the high-voltage equipment low. Regular training is one of the ways to improve the professional knowledge and skills of employees. However, when the training plan and direction of the employees are wrong, the maintenance efficiency of the high-voltage equipment is not improved. At this time, training the employees will not only waste time, but also occupy corporate resources. Summary of the invention

[0005] The purpose of the present invention is to solve the problems in the background technology and to propose a high-voltage equipment maintenance data processing and analysis system.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A high-voltage equipment maintenance data processing and analysis system, comprising:

[0008] The data collection module is used to collect the maintenance work orders and training lists of high-voltage equipment and transmit them to the operation and maintenance analysis module;

[0009] The operation and maintenance analysis module is used to obtain operation and maintenance data, and divide the operation and maintenance data into active data and passive data according to the maintenance reasons of the operation and maintenance data. At the same time, the operation and maintenance frequency is calculated based on the number of times the high-voltage equipment is maintained in the maintenance work order within the cycle time. Then, based on the operation and maintenance frequency, the number of times active data appears, and the number of times passive data appears, the active operation and maintenance value of the high-voltage equipment within the cycle time is determined;

[0010] A level processing module is used to set an active maintenance level for a fault area of ​​a high-voltage device, including determining a normal operation value and a regional fault threshold of the fault area according to a maintenance work order, then obtaining active data of the fault area, and counting the regional active quantity, multiplying the regional active quantity by a first coefficient and a second coefficient, determining a first boundary value and a second boundary value, then arranging the active data of the fault area, determining an active sequence, and determining a first end value and a second end value based on the first boundary value and the second boundary value, combining the normal operation value and the regional fault threshold with the first end value and the second end value, and determining a data interval of the active maintenance level;

[0011] The correlation analysis module is used to classify the active maintenance data within the cycle time according to the data interval of the active maintenance level, and calculate the capacity frequency value in each active maintenance level, then take the active operation and maintenance value within the corresponding cycle time, and combine it with the capacity frequency value to calculate the equipment maintenance value within the cycle time, and then obtain the training list, identify the number of trainings within the cycle time, calculate the correlation value between the equipment operation and maintenance value and the number of trainings, and determine the normal training signal according to the correlation value;

[0012] The real-time analysis module is used to calculate the equipment maintenance value within the real-time cycle time according to the normal training signal, and mark it as the real-time maintenance value. At the same time, it generates the normal operation and maintenance signal and the training optimization signal according to the real-time maintenance value;

[0013] The terminal display module is used to display training abnormality signals, normal operation and maintenance signals and training optimization signals on the terminal device respectively. Relevant management personnel adjust the training work of high-voltage equipment based on the signals displayed on the terminal device.

[0014] As a further solution of the present invention, a method for determining an active operation and maintenance value includes:

[0015] S1: Set the cycle time and divide the maintenance work order according to the cycle time, then obtain the number of times the high-voltage equipment is maintained in the maintenance work order within each cycle time, divide the number of times maintained by the cycle time, and mark the calculated result as the operation and maintenance frequency FZi, where i represents the time number of the cycle time;

[0016] S2: Obtain maintenance work orders for high-voltage equipment, and divide the operation and maintenance data into active data and passive data based on the maintenance reasons in the maintenance work orders. Active data refers to abnormal conditions that are actively discovered by staff during daily maintenance. At this time, the high-voltage equipment has not yet experienced obvious faults during operation. Passive data refers to faults that directly occur during the operation of the high-voltage equipment.

[0017] Count the number of active data and passive data in each cycle time, and then based on the formula The active operation and maintenance value DZi in the i-th cycle time is obtained, where LZi represents the number of times active data appears in the i-th cycle time, and LBi represents the number of times passive data appears in the i-th cycle time.

[0018] As a further solution of the present invention, the active maintenance level includes capability level 1, capability level 2, capability level 3 and capability level 4. The specific method for setting the active maintenance level includes:

[0019] SS1: Based on the maintenance work order, a fault area is randomly selected in the high-voltage equipment and marked as a single target area. At the same time, the operating data of the single target area in normal state is collected and averaged. The calculation result is then marked as the normal operating value of the single target interval.

[0020] SS2: Identify the operation and maintenance data of the single target area in the maintenance work order, obtain the passive data in the operation and maintenance data, average the passive data, and mark the result as the regional fault threshold;

[0021] SS3: Then, in the operation and maintenance data, the active data of the single target area is extracted, and the number of occurrences of the active data is identified and marked as the regional active quantity AC;

[0022] Multiply the regional active quantity by the first coefficient k1 and the first coefficient k2 respectively, and mark the obtained data results as the first threshold value and the second threshold value, wherein the first coefficient k1 and the first coefficient k2 are both threshold values, and k2>k1;

[0023] SS4: Arrange the active data of the monomer target area in order to obtain the active sequence;

[0024] Then, based on the first threshold value and the second threshold value, a first end value and a second end value are set in the active sequence, wherein in the active sequence, according to the active arrangement position, the active data corresponding to the position of the first threshold value is marked as the first end value, and the active data corresponding to the position of the second threshold value is marked as the second end value;

[0025] SS5: Then mark the active data in the data interval between the normal operation value and the first end value as capability level one, mark the active data in the data interval between the first end value and the second end value as capability level two, mark the active data in the data interval between the second end value and the regional fault threshold as capability level three, and directly mark the passive data as capability level four.

[0026] As a further solution of the present invention, if the first threshold value and the second threshold value are not integers, the integer parts of the first threshold value and the second threshold value are taken, and 1 is added to the integer parts, and the obtained results are then used as the final first threshold value and the second threshold value.

[0027] As a further solution of the present invention, when calculating the first threshold value and the second threshold value, the regional active quantity AC corresponding to the single target area must reach the minimum number of samples. If the minimum number of samples is not reached, an interpolation algorithm is used to supplement the data in the existing active data of the single target area so that the regional active quantity reaches the minimum number of samples.

[0028] As a further solution of the present invention, when the active data is arranged in order, wherein the arrangement method includes descending order and ascending order, if the normal operation value is less than the regional fault threshold, the arrangement method selects descending order to obtain the active sequence, otherwise, if the normal operation value is greater than the regional fault threshold, the arrangement method selects ascending order to obtain the active sequence.

[0029] As a further solution of the present invention, a method for determining a device maintenance value includes:

[0030] ST1: Obtain the operation and maintenance data in each cycle time and its corresponding active maintenance level, arbitrarily select a cycle time and mark it as the target time area, and take this cycle time as an example to obtain the total amount of operation and maintenance data in the target time area, and then classify the operation and maintenance data in the target time area according to the active maintenance level, and identify the amount of operation and maintenance data in each active maintenance level, and then divide the amount of data in each active maintenance level by the total amount of operation and maintenance data in the target time, and mark the calculation result as the capability frequency value PSj, j represents the active maintenance level, where j=1, 2, 3, 4, respectively representing capability level 1, capability level 2, capability level 3 and capability level 4 in the active maintenance level;

[0031] ST2: Get the active operation and maintenance value DZi of the target time zone and use the formula The equipment maintenance value WHi in the target time zone is obtained, where aj represents the weight factor of the active maintenance level j. Furthermore, a1<a2<a3<a4, and All are proportional coefficients.

[0032] As a further solution of the present invention, a method for generating a training normal signal includes:

[0033] ST3: Get the training list and obtain the number of trainings in each cycle time according to the cycle time in the maintenance work order. Then bind the equipment maintenance value in a cycle time with the number of trainings and mark them as the target processing group.

[0034] Arrange the target treatment groups in chronological order, and calculate the correlation value PR between the equipment maintenance value and the number of training times in the target data group based on the Pearson correlation coefficient algorithm;

[0035] ST4: Set the correlation threshold. If the correlation value PR≤the correlation threshold, a training abnormality signal is generated and transmitted to the terminal display module. Otherwise, if the correlation value PR>the correlation threshold, a training normal signal is generated and transmitted to the terminal display module.

[0036] As a further solution of the present invention, the real-time analysis module is used to calculate the equipment maintenance value of the high-voltage equipment within the real-time cycle time under the normal training signal, and mark it as the real-time maintenance value. If the real-time maintenance value is less than or equal to the maintenance threshold, a normal operation and maintenance signal is generated. Conversely, if the real-time maintenance value is greater than the maintenance threshold, a training optimization signal is generated.

[0037] As a further solution of the present invention, the method for adjusting the training work of the high-voltage equipment by the terminal display module includes:

[0038] If a training abnormality signal is displayed, the training content will be modified. If a normal operation and maintenance signal is displayed, the original training work will be carried out. If a training optimization signal is displayed, the training frequency needs to be increased.

[0039] Compared with the prior art, the advantages of the present invention are:

[0040] The present invention analyzes the maintenance data of high-voltage equipment and divides the maintenance data into active data and passive data, then calculates the active operation and maintenance value within each cycle time, then sets an active maintenance level for each fault area, and classifies the active data within the cycle time according to the data interval of the active maintenance level, calculates the capacity frequency value in each active maintenance level, determines the equipment maintenance value within the cycle time based on the capacity frequency value and the active operation and maintenance value, then calculates the correlation value between the number of trainings within the cycle time and the equipment maintenance value, and determines the normal training signal and the abnormal training signal based on the correlation value, and then calculates the real-time maintenance value according to the normal training signal, and generates the normal operation and maintenance signal and the training optimization signal according to the real-time maintenance value. Relevant management personnel adjust the training work of the high-voltage equipment based on the normal operation and maintenance signal and the training optimization signal, which, on the one hand, improves the skill level of employees and the equipment maintenance effect, thereby ensuring the safe production and stable operation of the enterprise, and on the other hand, is conducive to optimizing the maintenance data of the equipment, including improving the maintenance efficiency and reducing the maintenance cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0043] Reference Figure 1 , a high-voltage equipment maintenance data processing and analysis system, including a data acquisition module, an operation and maintenance analysis module, a level processing module, a correlation analysis module, a real-time analysis module and a terminal display module;

[0044] The data collection module is used to collect maintenance work orders and training lists of high-voltage equipment. The maintenance work order includes the operation and maintenance data, operation and maintenance time and maintenance reasons of the high-voltage equipment. The training list refers to the training time and training content of the department for employees. The data collection module and the operation and maintenance analysis module are connected in a one-way manner.

[0045] The operation and maintenance analysis module is used to obtain maintenance work orders for high-voltage equipment. At the same time, based on the maintenance reasons in the maintenance work orders, the operation and maintenance data is divided into active data and passive data. Active data refers to abnormal conditions actively discovered by staff during daily maintenance, including abnormal parts and abnormal operation data. At this time, the high-voltage equipment has not yet shown obvious fault phenomena during operation. Passive data refers to fault phenomena that directly occur during the operation of high-voltage equipment, including support insulator breakage in circuit breaker failure, relay protection tripping or inter-turn short circuit in transformer failure, and single-phase ground short circuit in cable failure. After that, the operation and maintenance data is analyzed to determine the active operation and maintenance value. Specifically, the calculation method of the active operation and maintenance value includes:

[0046] S1: Set the cycle time, divide the maintenance work order according to the cycle time, then obtain the number of times the high-voltage equipment is maintained in the maintenance work order within each cycle time, and then divide the number of times maintained by the cycle time, and mark the calculated result as the operation and maintenance frequency FZi, where i represents the time number of the cycle time. In this embodiment, the cycle time is set to 3 months;

[0047] S2: Then divide the operation and maintenance data in each cycle into active data and passive data, and count the number of active data and passive data in each cycle, and then calculate the number of active data and passive data based on the formula Obtain the active operation and maintenance value DZi within the i-th cycle time, where LZi represents the number of times active data appears within the i-th cycle time, and LBi represents the number of times passive data appears within the i-th cycle time;

[0048] The operation and maintenance analysis module is connected to the correlation analysis module and the level processing module in a one-way communication manner;

[0049] The level processing module is used to analyze the operation and maintenance data of the high-voltage equipment and determine the active maintenance level of the high-voltage equipment. The active maintenance level includes capability level 1, capability level 2, capability level 3 and capability level 4. The specific method for determining the active maintenance level includes:

[0050] SS1: Based on the maintenance work order, a fault area is randomly selected in the high-voltage equipment and marked as a single target area. At the same time, the operating data of the single target area in normal state is collected and averaged. The calculation result is then marked as the normal operating value of the single target interval.

[0051] SS2: Identify the operation and maintenance data of the single target area in the maintenance work order, obtain the passive data in the operation and maintenance data, average the passive data, and mark the result as the regional fault threshold;

[0052] SS3: Then, in the operation and maintenance data, the active data of the single target area is extracted, and the number of occurrences of the active data is identified and marked as the regional active quantity AC;

[0053] The regional active quantity is multiplied by the first coefficient k1 and the first coefficient k2 respectively, and the obtained data results are marked as the first threshold value and the second threshold value, wherein the first coefficient k1 and the first coefficient k2 are both threshold values, and k2>k1, and the specific values ​​of k1 and k2 are obtained by technicians in this field after big data calculation;

[0054] If the first threshold and the second threshold are not integers, the integer parts of the first threshold and the second threshold are taken, and 1 is added to the integer parts, and the obtained results are used as the final first threshold and the second threshold. For example, if the result after the first threshold is calculated is 4.2, the integer part of the first threshold is 4, and then 1 is added to the integer part 4 to obtain a result of 5, and 5 is used as the final result of the first threshold;

[0055] It should be further explained that when calculating the first threshold and the second threshold, the regional active quantity AC corresponding to the single target area should reach the minimum sample number. If the minimum sample number is not reached, the interpolation algorithm is used to supplement the data in the existing active data of the single target area, so that the regional active quantity reaches the minimum sample number, wherein the minimum sample number is a threshold value, and the specific value is obtained by those skilled in the art after big data calculation. The interpolation algorithm is a prior art and will not be described in detail here.

[0056] SS4: Arrange the active data of the monomer target area in order to obtain the active sequence;

[0057] Then, based on the first threshold value and the second threshold value, a first end value and a second end value are set in the active sequence, wherein in the active sequence, according to the active arrangement position, the active data corresponding to the position of the first threshold value is marked as the first end value, and the active data corresponding to the position of the second threshold value is marked as the second end value;

[0058] It should be further explained that when the active data is arranged in order, the arrangement method includes descending and ascending. If the normal operation value is less than the regional fault threshold, the arrangement method is descending, that is, the active data is arranged from small to large to obtain an active sequence. On the contrary, if the normal operation value is greater than the regional fault threshold, the arrangement method is ascending, that is, the active data is arranged from large to small to obtain an active sequence.

[0059] SS5: Then the active data in the data interval between the normal operation value and the first end value is marked as capability level 1, the active data in the data interval between the first end value and the second end value is marked as capability level 2, the active data in the data interval between the second end value and the regional fault threshold is marked as capability level 3, and the passive data is directly marked as capability level 4;

[0060] Then, the maintenance work order of the high-voltage equipment is obtained, and the operation and maintenance data in the maintenance work order is obtained, and the operation and maintenance data is marked with the corresponding active maintenance level according to the data interval of the active maintenance level;

[0061] It should be further explained that, for the active maintenance level, capability level 1> capability level 2> capability level 3> capability level 4, and the higher the active maintenance level of the active data, the stronger the professional ability of the relevant staff regarding high-voltage equipment;

[0062] There is a one-way communication connection between the level processing module and the association analysis module;

[0063] The correlation analysis module is used to obtain the training list and combine it with the active operation and maintenance value and the active maintenance level to determine the effective value of the training. The specific method for determining the effective value of the training includes:

[0064] ST1: Obtain the operation and maintenance data in each cycle time and its corresponding active maintenance level, arbitrarily select a cycle time and mark it as a target time area, and take this cycle time as an example to obtain the total amount of operation and maintenance data in the target time area, and classify the operation and maintenance data in the target time area according to the active maintenance level, and identify the amount of operation and maintenance data in each active maintenance level, and then divide the amount of data in each active maintenance level by the total amount of operation and maintenance data in the target time, and mark the calculation result as the capability frequency value PSj, where j represents the active maintenance level. In this embodiment, j=1, 2, 3, 4, respectively representing capability level 1, capability level 2, capability level 3, and capability level 4 in the active maintenance level;

[0065] ST2: Then obtain the active operation and maintenance value DZi of the target time zone, and use the formula The equipment maintenance value WHi in the target time zone is obtained, where aj represents the weight factor of the active maintenance level j. Furthermore, a1<a2<a3<a4, and are proportional coefficients, aj, and The specific values ​​of are obtained by technicians in this field after big data calculation;

[0066] It should be further explained that when the equipment maintenance value is larger, it means that the frequency of equipment abnormalities is higher, and the frequency of equipment abnormalities being actively discovered is lower. At this time, the corresponding management personnel's maintenance ability for high-voltage equipment is lower. Conversely, when the equipment maintenance value is smaller, it means that the frequency of equipment abnormalities is lower, and the frequency of equipment abnormalities being actively discovered is higher. At this time, the corresponding management personnel's maintenance ability for high-voltage equipment is higher.

[0067] ST3: Get the training list and obtain the number of trainings in each cycle time according to the cycle time in the maintenance work order. Then bind the equipment maintenance value in a cycle time with the number of trainings and mark them as the target processing group.

[0068] Then, the target processing groups are arranged in chronological order, and based on the Pearson correlation coefficient algorithm, the correlation value PR between the equipment maintenance value and the number of training times in the target data group is calculated, wherein the Pearson correlation coefficient algorithm is a prior art and will not be described in detail here;

[0069] ST4: Set the correlation threshold. If the correlation value PR≤the correlation threshold, a training abnormality signal is generated and transmitted to the terminal display module. The training abnormality signal indicates that there is an abnormality in the training method. After the training, the professional maintenance ability of the employees for high-voltage equipment is not greatly improved. On the contrary, if the correlation value PR>the correlation threshold, a training normal signal is generated and transmitted to the terminal display module. The specific value of the correlation threshold is obtained by technicians in this field after big data calculation.

[0070] The real-time analysis module is used to obtain the normal training signal, and under the normal training signal, calculate the equipment maintenance value of the high-voltage equipment within the real-time cycle time, and mark it as the real-time maintenance value. If the real-time maintenance value is less than or equal to the maintenance threshold, a normal operation and maintenance signal is generated. On the contrary, if the real-time maintenance value is greater than the maintenance threshold, a training optimization signal is generated, wherein the specific value of the maintenance threshold is obtained by technicians in this field after big data calculation;

[0071] Afterwards, the real-time analysis module transmits the generated normal operation and maintenance signal and training optimization signal to the terminal display module. The terminal display module is used to display the training abnormality signal, normal operation and maintenance signal and training optimization signal on the terminal device respectively. The relevant management personnel adjust the training work of the high-voltage equipment based on the signal displayed on the terminal device, thereby improving the maintenance efficiency of the high-voltage equipment. Furthermore, the adjustment method includes: if a training abnormality signal is displayed, the training content is modified; if a normal operation and maintenance signal is displayed, the original training work is carried out; if a training optimization signal is displayed, the training frequency needs to be increased.

[0072] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A high voltage equipment maintenance data processing and analysis system, characterized in that: include: The data collection module is used to collect the maintenance work orders and training lists of high-voltage equipment and transmit them to the operation and maintenance analysis module; The operation and maintenance analysis module is used to obtain operation and maintenance data, and divide the operation and maintenance data into active data and passive data according to the maintenance reasons of the operation and maintenance data. At the same time, the operation and maintenance frequency is calculated based on the number of times the high-voltage equipment is maintained in the maintenance work order within the cycle time. Then, based on the operation and maintenance frequency, the number of occurrences of active data and the number of occurrences of passive data, the active operation and maintenance value of the high-voltage equipment within the cycle time is determined. Specifically, the method for determining the active operation and maintenance value includes: S1: Set the cycle time and divide the maintenance work order according to the cycle time, then obtain the number of times the high-voltage equipment is maintained in the maintenance work order within each cycle time, divide the number of times maintained by the cycle time, and mark the calculated result as the operation and maintenance frequency FZi, where i represents the time number of the cycle time; S2: Obtain maintenance work orders for high-voltage equipment, and divide the operation and maintenance data into active data and passive data based on the maintenance reasons in the maintenance work orders. Active data refers to abnormal conditions that are actively discovered by staff during daily maintenance. At this time, the high-voltage equipment has not yet experienced obvious faults during operation. Passive data refers to faults that directly occur during the operation of the high-voltage equipment. Count the number of active data and passive data in each cycle time, and then based on the formula Obtain the active operation and maintenance value DZi within the i-th cycle time, where LZi represents the number of times active data appears within the i-th cycle time, and LBi represents the number of times passive data appears within the i-th cycle time; A level processing module is used to set an active maintenance level for a fault area of ​​a high-voltage device, including determining a normal operation value and a regional fault threshold of the fault area according to a maintenance work order, then obtaining active data of the fault area, and counting the regional active quantity, multiplying the regional active quantity by a first coefficient and a second coefficient, determining a first boundary value and a second boundary value, then arranging the active data of the fault area, determining an active sequence, and determining a first end value and a second end value based on the first boundary value and the second boundary value, combining the normal operation value and the regional fault threshold with the first end value and the second end value, and determining a data interval of the active maintenance level; The correlation analysis module is used to classify the active maintenance data within the cycle time according to the data interval of the active maintenance level, and calculate the capacity frequency value in each active maintenance level, then take the active operation and maintenance value within the corresponding cycle time, and combine it with the capacity frequency value to calculate the equipment maintenance value within the cycle time, and then obtain the training list, identify the number of trainings within the cycle time, calculate the correlation value between the equipment operation and maintenance value and the number of trainings, and determine the normal training signal according to the correlation value, wherein the method for determining the equipment maintenance value includes: ST1: Obtain the operation and maintenance data in each cycle time and its corresponding active maintenance level, arbitrarily select a cycle time and mark it as the target time area, and take this cycle time as an example to obtain the total amount of operation and maintenance data in the target time area, and then classify the operation and maintenance data in the target time area according to the active maintenance level, and identify the amount of operation and maintenance data in each active maintenance level, and then divide the amount of data in each active maintenance level by the total amount of operation and maintenance data in the target time, and mark the calculation result as the capability frequency value PSj, j represents the active maintenance level, where j=1, 2, 3, 4, respectively representing capability level 1, capability level 2, capability level 3 and capability level 4 in the active maintenance level; ST2: Get the active operation and maintenance value DZi of the target time zone and use the formula The equipment maintenance value WHi in the target time zone is obtained, where aj represents the weight factor of the active maintenance level j. Furthermore, a1<a2<a3<a4, and All are proportionality coefficients; The real-time analysis module is used to calculate the equipment maintenance value within the real-time cycle time according to the normal training signal, and mark it as the real-time maintenance value. At the same time, it generates the normal operation and maintenance signal and the training optimization signal according to the real-time maintenance value; The terminal display module is used to display training abnormality signals, normal operation and maintenance signals and training optimization signals on the terminal device respectively. Relevant management personnel adjust the training work of high-voltage equipment based on the signals displayed on the terminal device.

2. A high-voltage equipment maintenance data processing and analysis system according to claim 1, characterized in that: The active maintenance levels include capability level 1, capability level 2, capability level 3 and capability level 4. The specific setting methods of active maintenance levels include: SS1: Based on the maintenance work order, a fault area is randomly selected in the high-voltage equipment and marked as a single target area. At the same time, the operating data of the single target area in normal state is collected and averaged. The calculation result is then marked as the normal operating value of the single target interval. SS2: Identify the operation and maintenance data of the single target area in the maintenance work order, obtain the passive data in the operation and maintenance data, average the passive data, and mark the result as the regional fault threshold; SS3: Then, in the operation and maintenance data, the active data of the single target area is extracted, and the number of occurrences of the active data is identified and marked as the regional active quantity AC; Multiply the regional active quantity by the first coefficient k1 and the first coefficient k2 respectively, and mark the obtained data results as the first threshold value and the second threshold value, wherein the first coefficient k1 and the first coefficient k2 are both threshold values, and k2>k1; SS4: Arrange the active data of the monomer target area in order to obtain the active sequence; Then, based on the first threshold value and the second threshold value, a first end value and a second end value are set in the active sequence, wherein in the active sequence, according to the active arrangement position, the active data corresponding to the position of the first threshold value is marked as the first end value, and the active data corresponding to the position of the second threshold value is marked as the second end value; SS5: Then mark the active data in the data interval between the normal operation value and the first end value as capability level one, mark the active data in the data interval between the first end value and the second end value as capability level two, mark the active data in the data interval between the second end value and the regional fault threshold as capability level three, and directly mark the passive data as capability level four.

3. A high-voltage equipment maintenance data processing and analysis system according to claim 2, characterized in that: If the first threshold value and the second threshold value are not integers, the integer parts of the first threshold value and the second threshold value are taken, and 1 is added to the integer parts, and the obtained results are used as the final first threshold value and the second threshold value.

4. A high-voltage equipment maintenance data processing and analysis system according to claim 2, characterized in that: When calculating the first threshold and the second threshold, the regional active quantity AC corresponding to the single target area must reach the minimum number of samples. If the minimum number of samples is not reached, the interpolation algorithm is used to supplement the data in the existing active data of the single target area so that the regional active quantity reaches the minimum number of samples.

5. A high-voltage equipment maintenance data processing and analysis system according to claim 2, characterized in that: When the active data is arranged in order, the arrangement methods include descending and ascending. If the normal operation value is less than the regional fault threshold, the arrangement method selects the descending method to obtain the active sequence. Conversely, if the normal operation value is greater than the regional fault threshold, the arrangement method selects the ascending method to obtain the active sequence.

6. A high-voltage equipment maintenance data processing and analysis system according to claim 1, characterized in that: The generation method of training normal signal includes: ST3: Get the training list and obtain the number of trainings in each cycle time according to the cycle time in the maintenance work order. Then bind the equipment maintenance value in a cycle time with the number of trainings and mark them as the target processing group. Arrange the target treatment groups in chronological order, and calculate the correlation value PR between the equipment maintenance value and the number of training times in the target data group based on the Pearson correlation coefficient algorithm; ST4: Set the correlation threshold. If the correlation value PR≤the correlation threshold, a training abnormality signal is generated and transmitted to the terminal display module. Otherwise, if the correlation value PR>the correlation threshold, a training normal signal is generated and transmitted to the terminal display module.

7. A high-voltage equipment maintenance data processing and analysis system according to claim 1, characterized in that: The real-time analysis module is used to calculate the equipment maintenance value of the high-voltage equipment within the real-time cycle time under the normal training signal, and mark it as the real-time maintenance value. If the real-time maintenance value is less than or equal to the maintenance threshold, a normal operation and maintenance signal is generated. Conversely, if the real-time maintenance value is greater than the maintenance threshold, a training optimization signal is generated.

8. A high-voltage equipment maintenance data processing and analysis system according to claim 1, characterized in that: The terminal display module adjusts the training work of high-voltage equipment by: If a training abnormality signal is displayed, the training content will be modified. If a normal operation and maintenance signal is displayed, the original training work will be carried out. If a training optimization signal is displayed, the training frequency needs to be increased.

Images