A power transformation maintenance test data automatic acquisition control method and system

Abstract

The application discloses a kind of power transformation maintenance test data automatic acquisition control method and system, it is related to data acquisition technical field, including obtaining target power transformation equipment information, based on target power transformation equipment information, obtain equipment historical operation data, according to equipment historical operation data, based on power transformation maintenance test demand analysis, obtain power transformation maintenance test demand information.The application solves the problem that the prior art ignores the difference of data characteristics, adopts a unified standard to cause abnormal identification deviation, improves the accuracy of historical operation data analysis, provides a data basis for subsequent data analysis through benchmark operation data boundary information, makes the boundary more fit the actual operation characteristics of equipment, realizes the accurate setting of maintenance test through maintenance test demand information, improves the maintenance efficiency, through data-based acquisition parameter information and data acquisition parameter information, ensures the timeliness and accuracy of test data.

Description

Technical Field

[0001] This invention relates to the field of data acquisition technology, specifically to an automated acquisition and control method and system for substation maintenance and testing data. Background Technology

[0002] As a core component of the power system, the operating status of substation equipment directly determines the safety, stability, and reliability of the power grid. Whether it's 500kV and above main grid transformers, 220kV hub substation circuit breakers, or 10kV distribution network equipment, all require regular maintenance and testing to identify potential hazards. The quality of the collected maintenance and testing data is crucial for assessing the degree of equipment performance degradation and pinpointing fault risks. In-depth analysis of historical operating data from substation maintenance and testing provides data support for developing maintenance and testing plans, thereby shortening maintenance cycles, reducing operation and maintenance costs, and ensuring the stability of the power system.

[0003] Currently, the acquisition and control of substation maintenance and testing data suffers from several limitations. It cannot accurately analyze historical operating data, nor can it accurately assess the operating status of substation equipment based on historical data. This leads to inaccurate selection of maintenance and testing items, reduced maintenance efficiency, and increased maintenance time costs. Existing technologies often directly determine initial boundaries based on equipment rated parameters without dynamically correcting for statistical characteristics of historical normal operating data. This can result in misjudging normal historical fluctuations as exceeding boundaries, or overlooking minor anomalies caused by equipment performance degradation due to overly broad boundaries. Furthermore, the analysis of abnormal data often involves applying fixed weights to all data, reducing the accuracy of data analysis. Additionally, data acquisition is often conducted at a fixed frequency, failing to collect abnormal equipment data in a timely manner, thus reducing the timeliness and accuracy of data acquisition and affecting data quality. Summary of the Invention

[0004] To address the aforementioned technical problems, this paper provides an automated data acquisition and control method and system for substation maintenance and testing. This solution resolves the issues raised in the background section regarding the inability to accurately analyze historical operating data and assess the operational status of substation equipment based on this data. This leads to inaccurate selection of maintenance and testing items, reduced maintenance efficiency, and increased maintenance time costs. Existing technologies often directly determine initial boundaries based on equipment rated parameters without dynamically correcting for statistical characteristics of historical normal operating data. This can result in misjudging normal historical fluctuations as exceeding boundaries or overlooking minor anomalies caused by equipment performance degradation due to overly broad boundaries. Furthermore, the analysis of abnormal data often involves applying fixed weights to all data, reducing the accuracy of data analysis. Additionally, data acquisition is often performed at a fixed frequency, failing to collect abnormal equipment data in a timely manner, thus reducing the timeliness and accuracy of data acquisition and affecting data quality.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] An automated data acquisition and control method for substation maintenance and testing includes:

[0007] Obtain target substation equipment information, which includes substation equipment type information and substation equipment parameter information to be inspected and tested;

[0008] Based on the target substation equipment information, historical operating data of the equipment is obtained, including equipment voltage data, equipment current data, equipment operating power data, equipment operating temperature data, and equipment vibration frequency data.

[0009] Based on historical equipment operation data and substation maintenance and testing needs analysis, substation maintenance and testing needs information is obtained.

[0010] Based on the substation maintenance and testing requirements, and using historical equipment operating data, we obtain basic data acquisition parameters.

[0011] Based on the information on substation maintenance and testing requirements, maintenance and testing are carried out on the target substation equipment.

[0012] Based on the data acquisition parameters, substation maintenance test data are collected to obtain maintenance data for the target equipment.

[0013] Based on the target equipment maintenance data and historical equipment operation data, the basic data acquisition parameters are adjusted to obtain the data acquisition parameter information.

[0014] Preferably, the step of obtaining substation maintenance and testing requirement information based on historical equipment operating data and substation maintenance and testing requirement analysis specifically includes:

[0015] Based on the historical operating data of the equipment, the data is divided according to the data type, and the corresponding historical operating data analysis group for each type of equipment is obtained;

[0016] Based on the parameter information of the power equipment, obtain the baseline operating data boundary information corresponding to the historical operating data of each type of equipment;

[0017] The baseline operating data boundary is compared with the equipment historical operating data analysis group, and the number of times the equipment historical operating data in each equipment historical operating data analysis group exceeds the baseline operating data boundary is used as the operating deviation coefficient of the equipment historical operating data analysis group.

[0018] The sum of the operating durations of the historical operating data of each equipment in the historical operating data analysis group that exceed the boundary of the baseline operating data is taken as the baseline operating time deviation value of the historical operating data analysis group of that equipment.

[0019] The mean value of the boundary difference between the historical operating data of the equipment and the benchmark operating data in each group of historical operating data analysis is taken as the characteristic operating deviation value of the historical operating data analysis group of the equipment.

[0020] Based on historical equipment operation data and data sensitivity analysis, the weights corresponding to the operation deviation coefficient, the baseline operation time deviation value, and the characteristic operation deviation value are obtained.

[0021] Based on the analysis of historical equipment operation data, the operation deviation coefficient, the baseline operation time deviation value, and the characteristic operation deviation value are normalized.

[0022] Based on the normalized operating deviation coefficient, the baseline operating time deviation value, and the characteristic operating deviation value, the operating anomaly coefficient corresponding to each group of historical operating data analysis is obtained using a weighted summation formula.

[0023] Based on equipment operation risk analysis, obtain the operation anomaly coefficient threshold corresponding to the historical operation data of each type of equipment;

[0024] Based on the operation anomaly coefficient and operation anomaly coefficient threshold corresponding to the historical operation data of each type of equipment, it is determined whether there is anomaly in the historical operation data of that type of equipment. If the operation anomaly coefficient exceeds the operation anomaly coefficient threshold, then the historical operation data of that type of equipment is abnormal, and information on the types of abnormal operation data is obtained.

[0025] Based on the abnormal operation data type information, substation maintenance and testing requirements information is obtained, which includes maintenance and testing information corresponding to the abnormal operation data type.

[0026] Preferably, the step of obtaining the baseline operating data boundary information corresponding to the historical operating data of each type of equipment based on the substation equipment parameter information specifically includes:

[0027] S100: Based on the power equipment parameter information, obtain the rated parameter information corresponding to the historical operating data of each type of equipment. The rated parameter information includes rated parameter range information and rated parameter value information.

[0028] S200: Based on the rated parameter information corresponding to the historical operating data of each type of equipment, the rated parameter range corresponding to the historical operating data of each type of equipment is used as the initial operating data boundary;

[0029] S300: Based on the equipment historical operation data analysis group, the average of all equipment historical operation data in the equipment historical operation data analysis group is used as the calibration operation data of the equipment historical operation data analysis group;

[0030] S400: Use the difference between the calibration operation data and the rated parameter values ​​of the equipment's historical operation data analysis group as the benchmark analysis deviation value;

[0031] S500: The average difference between the historical operating data and the calibration operating data of each device in the historical operating data analysis group is used as the operating analysis deviation value;

[0032] S600: If the operational analysis deviation value exceeds the benchmark analysis deviation value, remove the historical operational data corresponding to the maximum difference between the historical operational data and the calibration operational data in the historical operational data analysis group. Repeat steps S300-S500 until the operational analysis deviation value does not exceed the benchmark analysis deviation value, and obtain the historical operational data correction analysis group.

[0033] S700: Based on the historical operating data of the equipment, the correction analysis group adjusts the initial operating data boundary according to the range of historical operating data of the equipment to obtain the baseline operating data boundary information;

[0034] Specifically, if the minimum value of the equipment's historical operating data is greater than the lower limit of the initial operating data boundary, then the minimum value of the equipment's historical operating data will be used as the lower limit of the benchmark operating data boundary; if the maximum value of the equipment's historical operating data is less than the upper limit of the initial operating data boundary, then the maximum value of the equipment's historical operating data will be used as the upper limit of the benchmark operating data boundary.

[0035] Preferably, the step of obtaining the weights corresponding to the operating deviation coefficient, the baseline operating time deviation value, and the characteristic operating deviation value based on the equipment's historical operating data and data sensitivity analysis specifically includes:

[0036] Based on the baseline operating data boundary information, obtain the timestamp information corresponding to the operating duration of each time the historical operating data of the equipment in each group of equipment historical operating data analysis group exceeds the baseline operating data boundary;

[0037] Based on timestamp information, obtain the equipment characteristic operating time period information corresponding to each group of historical operating data analysis groups. The equipment characteristic operating time period represents the duration of operation of historical operating data of equipment that exceeds the boundary of baseline operating data.

[0038] Data alignment is performed based on timestamps. Any historical operating data of any device is used as the target data. If the device characteristic operating time period corresponding to the target data overlaps with the device characteristic operating time period corresponding to any historical operating data of any device, the overlapping device characteristic operating time period is used as the device operating deviation transmission time period of the target data.

[0039] The ratio of the number of time segments in which the equipment operation deviation of the target data is transmitted to the operation deviation coefficient is used as the operation expression coefficient of the target data.

[0040] The ratio of the sum of the durations corresponding to the transmission time periods of the equipment operation deviation of the target data to the deviation value of the baseline operation time is used as the time expression coefficient of the target data;

[0041] Based on the equipment operation deviation transmission time period of the target data, obtain the historical operation data corresponding to the equipment operation deviation transmission time period of the target data;

[0042] The average of the boundary differences between the historical operating data and the benchmark operating data corresponding to the equipment operating deviation propagation period of the target data is taken as the equipment operating deviation propagation value of the target data.

[0043] The ratio of the equipment operation deviation transfer value to the characteristic operation deviation value of the target data is used as the transfer expression coefficient of the target data;

[0044] The sum of the runtime expression coefficient, the time expression coefficient, and the transfer expression coefficient is used as the baseline value for data characteristics.

[0045] The ratio of the running expression coefficient to the data feature benchmark value is used as the weight of the running deviation coefficient; the ratio of the time expression coefficient to the data feature benchmark value is used as the weight of the benchmark running time deviation value; and the ratio of the transmission expression coefficient to the data feature benchmark value is used as the weight of the feature running deviation value.

[0046] Preferably, the step of obtaining basic data acquisition parameter information based on substation maintenance and testing requirements and historical equipment operating data specifically includes:

[0047] Based on the substation maintenance and testing requirements, obtain information on the types of abnormal operation data;

[0048] Based on the historical operating data of the equipment, the historical operating data of the equipment corresponding to the abnormal operating data type is sorted by time series to obtain the historical time series information of the equipment historical operating data of that type.

[0049] Based on historical time series information, obtain the fluctuation difference of historical operating data of adjacent devices in the historical time series;

[0050] Obtain the fluctuation time interval of historical operating data of adjacent devices in the historical time series;

[0051] The ratio of the fluctuation difference to the fluctuation time interval of the historical operating data of adjacent devices in the historical time series is used as the data fluctuation rate of the historical operating data of the adjacent devices.

[0052] Based on the fluctuation difference of historical operating data of adjacent devices in the historical time series, the standard deviation of the fluctuation difference of historical operating data of adjacent devices is obtained based on the mean of the fluctuation difference;

[0053] The standard deviation of the fluctuation difference between historical operating data of adjacent devices is used as the data fluctuation identification value.

[0054] The ratio of the identified data fluctuation value to the maximum data fluctuation rate is used as the benchmark data acquisition time.

[0055] The reciprocal of the baseline data acquisition time corresponding to the historical operating data of each type of equipment is used as the data acquisition frequency of the operating data of that type of equipment to obtain the basic data acquisition parameter information.

[0056] Preferably, the step of adjusting the basic data acquisition parameters based on the target equipment maintenance data and historical equipment operation data to obtain the data acquisition parameter information specifically includes:

[0057] Based on the historical operating data of the equipment corresponding to the types of abnormal operating data, a coordinate system is established with time as the horizontal axis and equipment operating data value as the vertical axis to obtain the historical operating curve of the equipment corresponding to each type of abnormal operating data.

[0058] Based on the baseline operating data boundary information corresponding to each type of abnormal operating data, the historical operating curve of the equipment that exceeds the baseline operating data boundary is taken as the equipment operating characteristic curve.

[0059] The first derivative of the equipment operation characteristic curve is taken to obtain the data mutation coefficient corresponding to each data point in the equipment operation characteristic curve;

[0060] Data points in the equipment operation characteristic curve whose data mutation coefficient exceeds the data fluctuation identification value are considered as equipment operation mutation points;

[0061] The minimum value of the data mutation coefficient corresponding to the sudden change point in equipment operation is used as the data acquisition characteristic coefficient;

[0062] Based on the maintenance data of the target equipment, and using time series as a basis, the product of the difference between the maintenance data of adjacent target equipment and the data collection frequency is used as the fluctuation coefficient of the maintenance data of the target equipment.

[0063] Based on the fluctuation coefficient and data acquisition characteristic coefficient of the target equipment maintenance data, determine whether to adjust the basic data acquisition parameters and obtain the data acquisition parameter information;

[0064] If the fluctuation coefficient of the target equipment maintenance data exceeds the data acquisition characteristic coefficient, the ratio of the data fluctuation identification value to the fluctuation coefficient of the target equipment maintenance data will be used as the data acquisition frequency. If the fluctuation coefficient of the target equipment maintenance data does not exceed the data acquisition characteristic coefficient, the basic data acquisition parameters will remain unchanged.

[0065] Furthermore, an automated data acquisition and control system for substation maintenance and testing is proposed to implement the acquisition and control method described above, including:

[0066] The main control module is used to normalize the operating deviation coefficient, the baseline operating time deviation value, and the characteristic operating deviation value based on the historical operating data analysis group of the equipment. Based on the normalized operating deviation coefficient, the baseline operating time deviation value, and the characteristic operating deviation value, and using a weighted summation formula, it obtains the operating anomaly coefficient corresponding to each group of historical operating data analysis. Based on the operating anomaly coefficient and operating anomaly coefficient threshold corresponding to each type of historical operating data, it determines whether the historical operating data of that type of equipment is abnormal. If the operating anomaly coefficient exceeds the operating anomaly coefficient threshold, then the historical operating data of that type of equipment is abnormal. It obtains information on the types of abnormal operating data, and based on this information, obtains information on substation maintenance and testing requirements. Based on this information, it conducts maintenance and testing on the target substation equipment. According to the historical operating data of the equipment, it sorts the historical operating data corresponding to the types of abnormal operating data by time sequence to obtain the historical time sequence information of that type of historical operating data. Based on the historical time sequence information, it obtains basic data acquisition parameter information. Based on the target equipment maintenance data and the equipment historical operating data, it adjusts the basic data acquisition parameter information to obtain the data acquisition parameter information.

[0067] The information acquisition module is used to acquire target substation equipment information, including substation equipment type information and substation equipment parameter information to be inspected and tested. Based on the target substation equipment information, the module acquires historical operating data of the equipment, including equipment voltage data, equipment current data, equipment operating power data, equipment operating temperature data, and equipment vibration frequency data. According to the substation equipment parameter information, the module acquires rated parameter information corresponding to the historical operating data of each type of equipment, including rated parameter range information and rated parameter value information. Based on the data-based parameter information, the module collects substation maintenance test data to acquire target equipment maintenance data.

[0068] The evaluation module is used to determine the initial operating data boundary based on the rated parameter information corresponding to the historical operating data of each type of equipment. It uses the rated parameter range corresponding to the historical operating data of each type of equipment as the initial operating data boundary. Based on the historical operating data analysis group, it uses the average of the historical operating data of all equipment in the group as the calibration operating data for that group. The difference between the calibration operating data and the rated parameter value in the historical operating data analysis group is used as the benchmark analysis deviation value. The average difference between the historical operating data and the calibration operating data of each equipment in the group is used as the operating analysis deviation value. Based on the benchmark analysis deviation value and the operating analysis deviation value, benchmark operating data boundary information is obtained, and the target number... The ratio of the number of segments in the equipment operation deviation transmission time period to the operation deviation coefficient is used as the operation expression coefficient of the target data. The ratio of the total duration corresponding to the equipment operation deviation transmission time period of the target data to the baseline operation time deviation value is used as the time expression coefficient of the target data. The mean of the boundary differences between the historical operation data and the baseline operation data corresponding to the equipment operation deviation transmission time period of the target data is used as the equipment operation deviation transmission value of the target data. The ratio of the equipment operation deviation transmission value of the target data to the characteristic operation deviation value is used as the transmission expression coefficient of the target data. Based on the operation expression coefficient, time expression coefficient, and transmission expression coefficient, the weights corresponding to the operation deviation coefficient, the baseline operation time deviation value, and the characteristic operation deviation value are obtained.

[0069] The display module interacts with the main control module and is used to output and display baseline operating data boundary information, substation maintenance test requirement information, basic data acquisition parameter information, data acquisition parameter information, and target equipment maintenance data.

[0070] Optionally, the main control module specifically includes:

[0071] The control unit is used to sort the historical operating data of the abnormal operating data type according to the time sequence, obtain the historical time sequence information of the historical operating data of that type of equipment, obtain the basic data acquisition parameter information based on the historical time sequence information, and adjust the basic data acquisition parameter information according to the target equipment maintenance data and the equipment historical operating data to obtain the data acquisition parameter information.

[0072] An information receiving unit interacts with the information acquisition module and the evaluation module to receive data and transmit it to the maintenance and testing unit.

[0073] The maintenance and testing unit is used to analyze historical operating data of the equipment, normalize the operating deviation coefficient, the reference operating time deviation value, and the characteristic operating deviation value, and obtain the operating anomaly coefficient corresponding to each group of historical operating data analysis based on the normalized operating deviation coefficient, the reference operating time deviation value, and the characteristic operating deviation value, using a weighted summation formula. Based on the operating anomaly coefficient and the operating anomaly coefficient threshold corresponding to each type of historical operating data, it is determined whether there is an anomaly in the historical operating data of that type of equipment. If the operating anomaly coefficient exceeds the operating anomaly coefficient threshold, then the historical operating data of that type of equipment is anomaly. The unit obtains information on the types of abnormal operating data, obtains substation maintenance and testing requirements based on the information on the types of abnormal operating data, and conducts maintenance and testing on the target substation equipment based on the substation maintenance and testing requirements.

[0074] Optionally, the information acquisition module specifically includes:

[0075] The first acquisition unit is used to acquire target substation equipment information, which includes substation equipment type information and substation equipment parameter information to be inspected and tested. Based on the target substation equipment information, the first acquisition unit is used to acquire historical operating data of the equipment, which includes equipment voltage data, equipment current data, equipment operating power data, equipment operating temperature data and equipment vibration frequency data.

[0076] The second acquisition unit is used to acquire rated parameter information corresponding to the historical operating data of each type of equipment based on the parameter information of the substation equipment. The rated parameter information includes rated parameter range information and rated parameter value information. Based on the data base acquisition parameter information, the substation maintenance test data is collected to acquire the maintenance data of the target equipment.

[0077] Optionally, the evaluation module specifically includes:

[0078] The first evaluation unit is used to take the rated parameter range corresponding to the historical operating data of each type of equipment as the initial operating data boundary based on the rated parameter information corresponding to the historical operating data of each type of equipment; take the mean of the historical operating data of all equipment in the historical operating data analysis group as the calibration operating data of the historical operating data analysis group; take the difference between the calibration operating data of the historical operating data analysis group and the rated parameter value as the benchmark analysis deviation value; take the mean of the difference between the historical operating data of each equipment in the historical operating data analysis group and the calibration operating data as the operating analysis deviation value; and obtain the benchmark operating data boundary information based on the benchmark analysis deviation value and the operating analysis deviation value.

[0079] The second evaluation unit is used to take the ratio of the number of segments of the equipment operation deviation transmission time period of the target data to the operation deviation coefficient as the operation expression coefficient of the target data, the ratio of the sum of the durations corresponding to the equipment operation deviation transmission time period of the target data to the baseline operation time deviation value as the time expression coefficient of the target data, the mean of the boundary difference between the historical operation data and the baseline operation data corresponding to the equipment operation deviation transmission time period of the target data as the equipment operation deviation transmission value of the target data, and the ratio of the equipment operation deviation transmission value to the characteristic operation deviation value as the transmission expression coefficient of the target data. Based on the operation expression coefficient, the time expression coefficient, and the transmission expression coefficient, the weights corresponding to the operation deviation coefficient, the baseline operation time deviation value, and the characteristic operation deviation value are obtained.

[0080] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0081] This invention proposes an automated data acquisition and control method and system for substation maintenance and testing. By using an operational anomaly coefficient, it solves the problems of existing technologies ignoring differences in data characteristics and using uniform standards to cause deviations in anomaly identification, thereby improving the accuracy of historical operational data analysis. By using benchmark operational data boundary information, it provides a data foundation for subsequent data analysis, making the boundaries more closely match the actual operating characteristics of the equipment. By using maintenance and testing requirement information, it enables accurate setting of maintenance and testing, improving maintenance efficiency. By using data foundation acquisition parameter information and data acquisition parameter information, it ensures the timeliness and accuracy of test data. Attached Figure Description

[0082] Figure 1 This is a flowchart of an automated data acquisition and control method for substation maintenance and testing proposed in this invention.

[0083] Figure 2 This is a flowchart of the process for obtaining the abnormality coefficient in this invention;

[0084] Figure 3This is a flowchart of the process for obtaining baseline running data boundary information in this invention;

[0085] Figure 4 This is a flowchart illustrating the process of acquiring basic data collection parameters in this invention.

[0086] Figure 5 This is a block diagram of an automated data acquisition and control system for substation maintenance and testing proposed in this invention. Detailed Implementation

[0087] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0088] Reference Figure 1 - Figure 4 As shown in the figure, an automated data acquisition and control method for substation maintenance and testing in an embodiment of the present invention includes:

[0089] Obtain target substation equipment information, which includes substation equipment type information and substation equipment parameter information to be inspected and tested;

[0090] Based on the target substation equipment information, historical operating data of the equipment is obtained, including equipment voltage data, equipment current data, equipment operating power data, equipment operating temperature data, and equipment vibration frequency data.

[0091] Based on historical equipment operation data and substation maintenance and testing needs analysis, substation maintenance and testing needs information is obtained.

[0092] Specifically, based on historical equipment operating data and substation maintenance and testing needs analysis, substation maintenance and testing needs information is obtained, including:

[0093] Based on the historical operating data of the equipment, the data is divided according to the data type, and the corresponding historical operating data analysis group for each type of equipment is obtained;

[0094] Based on the parameter information of the power equipment, obtain the baseline operating data boundary information corresponding to the historical operating data of each type of equipment;

[0095] The baseline operating data boundary is compared with the equipment historical operating data analysis group, and the number of times the equipment historical operating data in each equipment historical operating data analysis group exceeds the baseline operating data boundary is used as the operating deviation coefficient of the equipment historical operating data analysis group.

[0096] The sum of the operating durations of the historical operating data of each equipment in the historical operating data analysis group that exceed the boundary of the baseline operating data is taken as the baseline operating time deviation value of the historical operating data analysis group of that equipment.

[0097] The mean value of the boundary difference between the historical operating data of the equipment and the benchmark operating data in each group of historical operating data analysis is taken as the characteristic operating deviation value of the historical operating data analysis group of the equipment.

[0098] Based on historical equipment operation data and data sensitivity analysis, the weights corresponding to the operation deviation coefficient, the baseline operation time deviation value, and the characteristic operation deviation value are obtained.

[0099] Based on the analysis of historical equipment operation data, the operation deviation coefficient, the baseline operation time deviation value, and the characteristic operation deviation value are normalized.

[0100] Based on the normalized operating deviation coefficient, the baseline operating time deviation value, and the characteristic operating deviation value, the operating anomaly coefficient corresponding to each group of historical operating data analysis is obtained using a weighted summation formula.

[0101] Based on equipment operation risk analysis, obtain the operation anomaly coefficient threshold corresponding to the historical operation data of each type of equipment;

[0102] Based on the operation anomaly coefficient and operation anomaly coefficient threshold corresponding to the historical operation data of each type of equipment, it is determined whether there is anomaly in the historical operation data of that type of equipment. If the operation anomaly coefficient exceeds the operation anomaly coefficient threshold, then the historical operation data of that type of equipment is abnormal, and information on the types of abnormal operation data is obtained.

[0103] Based on the abnormal operation data type information, substation maintenance and testing requirements information is obtained, which includes maintenance and testing information corresponding to the abnormal operation data type.

[0104] In this solution, analysis groups are constructed by classifying data types, focusing on a single data type to specifically uncover operational patterns in that dimension. It's understandable that historical operational data for power equipment includes multi-dimensional data such as voltage, current, temperature, and vibration frequency (e.g., historical data for a 500kV transformer may cover over 100,000 records from the past year). Mixed analysis could easily lead to "key anomalies being buried." This solution constructs a three-dimensional anomaly assessment system: "Operational Deviation Coefficient – ​​Baseline Operating Time Deviation Value – Characteristic Operating Deviation Value." This upgrades anomalies from "qualitative description" to "quantitative calculation." The Operational Deviation Coefficient (number of times exceeding the boundary) quantifies the frequency of anomalies; the Baseline Operating Time Deviation Value (total duration exceeding the boundary) measures the sustained impact of anomalies; and the Characteristic Operating Deviation Value (mean of the difference between exceeding the boundary) characterizes the severity of anomalies, avoiding misjudgments caused by a single indicator.

[0105] It is important to note that different types of power equipment and data types exhibit significantly different sensitivities to "abnormal indicators" (e.g., voltage data of main grid transformers is more sensitive than that of distribution network equipment, and the sensitivity of temperature data's out-of-boundary duration is higher than that of vibration frequency). This step dynamically allocates weights through data sensitivity analysis, rather than using fixed weights, to ensure that the anomaly assessment closely reflects the actual characteristics of the equipment. Different types of historical operating data are accurately assessed using an operational anomaly coefficient; a higher value indicates a higher risk. In this embodiment, the operational anomaly coefficient threshold is set for each type of equipment based on its voltage level and importance. For example, for 500kV and above main grid equipment, the operational anomaly coefficient threshold for the equipment voltage is 0.5 (because this type of equipment has a significant impact on grid stability and requires stricter control), while for 220kV and below distribution network equipment, the operational anomaly coefficient threshold is 0.6.

[0106] Specifically, based on the parameter information of the power equipment, the baseline operating data boundary information corresponding to the historical operating data of each type of equipment is obtained, including:

[0107] S100: Based on the power equipment parameter information, obtain the rated parameter information corresponding to the historical operating data of each type of equipment. The rated parameter information includes rated parameter range information and rated parameter value information.

[0108] S200: Based on the rated parameter information corresponding to the historical operating data of each type of equipment, the rated parameter range corresponding to the historical operating data of each type of equipment is used as the initial operating data boundary;

[0109] S300: Based on the equipment historical operation data analysis group, the average of all equipment historical operation data in the equipment historical operation data analysis group is used as the calibration operation data of the equipment historical operation data analysis group;

[0110] S400: Use the difference between the calibration operation data and the rated parameter values ​​of the equipment's historical operation data analysis group as the benchmark analysis deviation value;

[0111] S500: The average difference between the historical operating data and the calibration operating data of each device in the historical operating data analysis group is used as the operating analysis deviation value;

[0112] S600: If the operational analysis deviation value exceeds the benchmark analysis deviation value, remove the historical operational data corresponding to the maximum difference between the historical operational data and the calibration operational data in the historical operational data analysis group. Repeat steps S300-S500 until the operational analysis deviation value does not exceed the benchmark analysis deviation value, and obtain the historical operational data correction analysis group.

[0113] S700: Based on the historical operating data of the equipment, the correction analysis group adjusts the initial operating data boundary according to the range of historical operating data of the equipment to obtain the baseline operating data boundary information;

[0114] Specifically, if the minimum value of the equipment's historical operating data is greater than the lower limit of the initial operating data boundary, then the minimum value of the equipment's historical operating data will be used as the lower limit of the benchmark operating data boundary; if the maximum value of the equipment's historical operating data is less than the upper limit of the initial operating data boundary, then the maximum value of the equipment's historical operating data will be used as the upper limit of the benchmark operating data boundary.

[0115] In this solution, a baseline boundary is constructed by correcting rated parameters and historical data. The "theoretical safe range" is anchored based on the rated parameters to ensure that the boundary does not deviate from the equipment design standards. Combined with the actual fluctuations (such as mean and deviation values) of the historical operating data analysis group, the boundary is adjusted to the "historical normal operating range". The final baseline boundary not only conforms to the equipment design attributes but also fits the actual operating rules, avoiding abnormal misjudgments or omissions caused by the disconnect between theory and practice. The equipment's historical operating data often contains abnormal interference values ​​(such as instantaneous overheating caused by sensor failure or negative values ​​generated by data transmission errors). If these are directly used for boundary adjustment, it will lead to distortion of the baseline boundary. Therefore, the data is removed by analyzing the deviation values ​​of the baseline and the deviation values ​​of the operation, providing a clean data foundation for subsequent anomaly identification.

[0116] Understandably, the rated parameter range usually has a large safety margin (such as the rated voltage range being ±10% of the rated value). If it is directly used as the boundary, it will lead to "minor anomalies that cannot be identified". For example, the rated voltage range of the equipment is 198-242kV (220kV±10%), and the actual normal operating voltage is stable at 215-225kV. If the rated range is used as the boundary, a minor anomaly such as the voltage dropping to 200kV (although within the rated range, it has deviated from the normal operating level) will be ignored. Therefore, by using the equipment historical operating data correction and analysis group, the data boundary is narrowed, which significantly improves the sensitivity of anomaly identification.

[0117] Specifically, based on historical equipment operating data and data sensitivity analysis, the weights corresponding to the operating deviation coefficient, baseline operating time deviation value, and characteristic operating deviation value are obtained, including:

[0118] Based on the baseline operating data boundary information, obtain the timestamp information corresponding to the operating duration of each time the historical operating data of the equipment in each group of equipment historical operating data analysis group exceeds the baseline operating data boundary;

[0119] Based on timestamp information, obtain the equipment characteristic operating time period information corresponding to each group of historical operating data analysis groups. The equipment characteristic operating time period represents the duration of operation of historical operating data of equipment that exceeds the boundary of baseline operating data.

[0120] Data alignment is performed based on timestamps. Any historical operating data of any device is used as the target data. If the device characteristic operating time period corresponding to the target data overlaps with the device characteristic operating time period corresponding to any historical operating data of any device, the overlapping device characteristic operating time period is used as the device operating deviation transmission time period of the target data.

[0121] The ratio of the number of time segments in which the equipment operation deviation of the target data is transmitted to the operation deviation coefficient is used as the operation expression coefficient of the target data.

[0122] The ratio of the sum of the durations corresponding to the transmission time periods of the equipment operation deviation of the target data to the deviation value of the baseline operation time is used as the time expression coefficient of the target data;

[0123] Based on the equipment operation deviation transmission time period of the target data, obtain the historical operation data corresponding to the equipment operation deviation transmission time period of the target data;

[0124] The average of the boundary differences between the historical operating data and the benchmark operating data corresponding to the equipment operating deviation propagation period of the target data is taken as the equipment operating deviation propagation value of the target data.

[0125] The ratio of the equipment operation deviation transfer value to the characteristic operation deviation value of the target data is used as the transfer expression coefficient of the target data;

[0126] The sum of the runtime expression coefficient, the time expression coefficient, and the transfer expression coefficient is used as the baseline value for data characteristics.

[0127] The ratio of the running expression coefficient to the data feature benchmark value is used as the weight of the running deviation coefficient; the ratio of the time expression coefficient to the data feature benchmark value is used as the weight of the benchmark running time deviation value; and the ratio of the transmission expression coefficient to the data feature benchmark value is used as the weight of the feature running deviation value.

[0128] In this scheme, by aligning timestamps and analyzing overlapping characteristic time periods, the "deviation transmission relationship" between target data (such as voltage) and other data (such as current) is identified (e.g., overlapping voltage and current out-of-boundary periods indicates a correlation between deviations). The impact of this correlation on various deviation indicators is then quantified using the "operation / time / transmission expression coefficient" (e.g., frequent voltage deviation transmission leads to a higher transmission expression coefficient and a higher weight for the corresponding characteristic operation deviation value). Ultimately, the weights are no longer fixed but dynamically adjusted according to the actual deviation transmission characteristics of the equipment, ensuring that anomaly assessments align with equipment operating patterns. In traditional maintenance, fixed weights (e.g., all allocated at 1 / 3) are often used for "operation deviation coefficient, baseline operating time deviation value, and characteristic operation deviation value," ignoring the "sensitivity differences" of different equipment and data types. For example, for voltage data of 500kV main grid transformers, the "characteristic operation deviation value" (average out-of-boundary difference) has a greater impact on grid safety (significant voltage out-of-boundary may cause insulation breakdown), requiring a higher weight; while for vibration data of 10kV distribution network equipment, the "operation deviation coefficient" (number of out-of-boundary occurrences) better reflects mechanical wear trends and should be prioritized. This solution solves the problems of traditional fixed weights being "detached from equipment characteristics, ignoring risk correlations, and having low evaluation accuracy" by using the logic of "deviation propagation correlation mining - expression coefficient quantification - dynamic weight allocation". It provides a weight basis of "risk coupling and dynamic adaptation" for anomaly assessment, thereby supporting subsequent accurate anomaly identification and maintenance decisions. It is a key link in realizing the automated and intelligent acquisition and control of substation maintenance test data.

[0129] Based on the substation maintenance and testing requirements, and using historical equipment operating data, we obtain basic data acquisition parameters.

[0130] Specifically, based on the substation maintenance and testing requirements and historical equipment operating data, basic data acquisition parameters are obtained, including:

[0131] Based on the substation maintenance and testing requirements, obtain information on the types of abnormal operation data;

[0132] Based on the historical operating data of the equipment, the historical operating data of the equipment corresponding to the abnormal operating data type is sorted by time series to obtain the historical time series information of the equipment historical operating data of that type.

[0133] Based on historical time series information, obtain the fluctuation difference of historical operating data of adjacent devices in the historical time series;

[0134] Obtain the fluctuation time interval of historical operating data of adjacent devices in the historical time series;

[0135] The ratio of the fluctuation difference to the fluctuation time interval of the historical operating data of adjacent devices in the historical time series is used as the data fluctuation rate of the historical operating data of the adjacent devices.

[0136] Based on the fluctuation difference of historical operating data of adjacent devices in the historical time series, the standard deviation of the fluctuation difference of historical operating data of adjacent devices is obtained based on the mean of the fluctuation difference;

[0137] The standard deviation of the fluctuation difference between historical operating data of adjacent devices is used as the data fluctuation identification value.

[0138] The ratio of the identified data fluctuation value to the maximum data fluctuation rate is used as the benchmark data acquisition time.

[0139] The reciprocal of the baseline data acquisition time corresponding to the historical operating data of each type of equipment is used as the data acquisition frequency of the operating data of that type of equipment to obtain the basic data acquisition parameter information.

[0140] This solution achieves a balance between "data timeliness" and "effectiveness" through dynamic frequency calculation. By "targeting abnormal data and quantifying fluctuations to dynamically fix the frequency," it solves the problems of "fuzzy targets, fixed frequencies, and wasted resources" in traditional data acquisition. It provides a data acquisition parameter scheme that is "accurate in data, efficient in resources, and provides strong decision support" for substation maintenance and testing. It is a key technical support for realizing automated data acquisition and control during maintenance, and directly improves the accuracy of maintenance and testing and the efficiency of operation and maintenance.

[0141] Based on the information on substation maintenance and testing requirements, maintenance and testing are carried out on the target substation equipment.

[0142] Based on the data acquisition parameters, substation maintenance test data are collected to obtain maintenance data for the target equipment.

[0143] Based on the target equipment maintenance data and historical equipment operation data, the basic data acquisition parameters are adjusted to obtain the data acquisition parameter information.

[0144] Specifically, based on the target equipment's maintenance data and historical operating data, the basic data acquisition parameters are adjusted to obtain the data acquisition parameter information, which includes:

[0145] Based on the historical operating data of the equipment corresponding to the types of abnormal operating data, a coordinate system is established with time as the horizontal axis and equipment operating data value as the vertical axis to obtain the historical operating curve of the equipment corresponding to each type of abnormal operating data.

[0146] Based on the baseline operating data boundary information corresponding to each type of abnormal operating data, the historical operating curve of the equipment that exceeds the baseline operating data boundary is taken as the equipment operating characteristic curve.

[0147] The first derivative of the equipment operation characteristic curve is taken to obtain the data mutation coefficient corresponding to each data point in the equipment operation characteristic curve;

[0148] Data points in the equipment operation characteristic curve whose data mutation coefficient exceeds the data fluctuation identification value are considered as equipment operation mutation points;

[0149] The minimum value of the data mutation coefficient corresponding to the sudden change point in equipment operation is used as the data acquisition characteristic coefficient;

[0150] Based on the maintenance data of the target equipment, and using time series as a basis, the product of the difference between the maintenance data of adjacent target equipment and the data collection frequency is used as the fluctuation coefficient of the maintenance data of the target equipment.

[0151] Based on the fluctuation coefficient and data acquisition characteristic coefficient of the target equipment maintenance data, determine whether to adjust the basic data acquisition parameters and obtain the data acquisition parameter information;

[0152] If the fluctuation coefficient of the target equipment maintenance data exceeds the data acquisition characteristic coefficient, the ratio of the data fluctuation identification value to the fluctuation coefficient of the target equipment maintenance data will be used as the data acquisition frequency. If the fluctuation coefficient of the target equipment maintenance data does not exceed the data acquisition characteristic coefficient, the basic data acquisition parameters will remain unchanged.

[0153] In this scheme, a "device operation characteristic curve" is first constructed based on historical data (only the abnormal part exceeding the baseline boundary is retained). Then, the "data mutation coefficient" is calculated by first-order differentiation (reflecting the change in the slope of the curve; the larger the slope, the more violent the data mutation). The "device operation mutation point" in history (such as the node where the oil temperature rises sharply before the transformer failure) is accurately located. The "minimum mutation coefficient" corresponding to the mutation point is used as the characteristic coefficient. This coefficient is essentially the "minimum mutation intensity threshold that needs to be paid attention to in history". Based on the time series of the target equipment maintenance data, the "difference between adjacent data × acquisition frequency" is used as the fluctuation coefficient to quantify the real-time change intensity of the data during the maintenance process. The acquisition frequency is adjusted by the real-time change intensity to achieve dynamic control of data acquisition.

[0154] Reference Figure 5 As shown, further, combining the above-mentioned automated acquisition and control method for substation maintenance test data, an automated acquisition and control system for substation maintenance test data is proposed, including:

[0155] The main control module is used to normalize the operating deviation coefficient, the baseline operating time deviation value, and the characteristic operating deviation value based on the historical operating data analysis group of the equipment. Based on the normalized operating deviation coefficient, the baseline operating time deviation value, and the characteristic operating deviation value, and using a weighted summation formula, it obtains the operating anomaly coefficient corresponding to each group of historical operating data analysis. Based on the operating anomaly coefficient and operating anomaly coefficient threshold corresponding to each type of historical operating data, it determines whether the historical operating data of that type of equipment is abnormal. If the operating anomaly coefficient exceeds the operating anomaly coefficient threshold, then the historical operating data of that type of equipment is abnormal. It obtains information on the types of abnormal operating data, and based on this information, obtains information on substation maintenance and testing requirements. Based on this information, it conducts maintenance and testing on the target substation equipment. According to the historical operating data of the equipment, it sorts the historical operating data corresponding to the types of abnormal operating data by time sequence to obtain the historical time sequence information of that type of historical operating data. Based on the historical time sequence information, it obtains basic data acquisition parameter information. Based on the target equipment maintenance data and the equipment historical operating data, it adjusts the basic data acquisition parameter information to obtain the data acquisition parameter information.

[0156] The information acquisition module is used to acquire target substation equipment information, including substation equipment type information and substation equipment parameter information to be inspected and tested. Based on the target substation equipment information, the module acquires historical operating data of the equipment, including equipment voltage data, equipment current data, equipment operating power data, equipment operating temperature data, and equipment vibration frequency data. According to the substation equipment parameter information, the module acquires rated parameter information corresponding to the historical operating data of each type of equipment, including rated parameter range information and rated parameter value information. Based on the data-based parameter information, the module collects substation maintenance test data to acquire target equipment maintenance data.

[0157] The evaluation module is used to determine the initial operating data boundary based on the rated parameter information corresponding to the historical operating data of each type of equipment. It uses the rated parameter range corresponding to the historical operating data of each type of equipment as the initial operating data boundary. Based on the historical operating data analysis group, it uses the average of the historical operating data of all equipment in the group as the calibration operating data for that group. The difference between the calibration operating data and the rated parameter value in the historical operating data analysis group is used as the benchmark analysis deviation value. The average difference between the historical operating data and the calibration operating data of each equipment in the group is used as the operating analysis deviation value. Based on the benchmark analysis deviation value and the operating analysis deviation value, benchmark operating data boundary information is obtained, and the target number... The ratio of the number of segments in the equipment operation deviation transmission time period to the operation deviation coefficient is used as the operation expression coefficient of the target data. The ratio of the total duration corresponding to the equipment operation deviation transmission time period of the target data to the baseline operation time deviation value is used as the time expression coefficient of the target data. The mean of the boundary differences between the historical operation data and the baseline operation data corresponding to the equipment operation deviation transmission time period of the target data is used as the equipment operation deviation transmission value of the target data. The ratio of the equipment operation deviation transmission value of the target data to the characteristic operation deviation value is used as the transmission expression coefficient of the target data. Based on the operation expression coefficient, time expression coefficient, and transmission expression coefficient, the weights corresponding to the operation deviation coefficient, the baseline operation time deviation value, and the characteristic operation deviation value are obtained.

[0158] The display module interacts with the main control module and is used to output and display baseline operating data boundary information, substation maintenance test requirement information, basic data acquisition parameter information, data acquisition parameter information, and target equipment maintenance data.

[0159] The main control module specifically includes:

[0160] The control unit is used to sort the historical operating data of the abnormal operating data type according to the time sequence, obtain the historical time sequence information of the historical operating data of that type of equipment, obtain the basic data acquisition parameter information based on the historical time sequence information, and adjust the basic data acquisition parameter information according to the target equipment maintenance data and the equipment historical operating data to obtain the data acquisition parameter information.

[0161] An information receiving unit interacts with the information acquisition module and the evaluation module to receive data and transmit it to the maintenance and testing unit.

[0162] The maintenance and testing unit is used to analyze historical operating data of the equipment, normalize the operating deviation coefficient, the reference operating time deviation value, and the characteristic operating deviation value, and obtain the operating anomaly coefficient corresponding to each group of historical operating data analysis based on the normalized operating deviation coefficient, the reference operating time deviation value, and the characteristic operating deviation value, using a weighted summation formula. Based on the operating anomaly coefficient and the operating anomaly coefficient threshold corresponding to each type of historical operating data, it is determined whether there is an anomaly in the historical operating data of that type of equipment. If the operating anomaly coefficient exceeds the operating anomaly coefficient threshold, then the historical operating data of that type of equipment is anomaly. The unit obtains information on the types of abnormal operating data, obtains substation maintenance and testing requirements based on the information on the types of abnormal operating data, and conducts maintenance and testing on the target substation equipment based on the substation maintenance and testing requirements.

[0163] The information acquisition module specifically includes:

[0164] The first acquisition unit is used to acquire target substation equipment information, which includes substation equipment type information and substation equipment parameter information to be inspected and tested. Based on the target substation equipment information, the first acquisition unit is used to acquire historical operating data of the equipment, which includes equipment voltage data, equipment current data, equipment operating power data, equipment operating temperature data and equipment vibration frequency data.

[0165] The second acquisition unit is used to acquire rated parameter information corresponding to the historical operating data of each type of equipment based on the parameter information of the substation equipment. The rated parameter information includes rated parameter range information and rated parameter value information. Based on the data base acquisition parameter information, the substation maintenance test data is collected to acquire the maintenance data of the target equipment.

[0166] The evaluation module specifically includes:

[0167] The first evaluation unit is used to take the rated parameter range corresponding to the historical operating data of each type of equipment as the initial operating data boundary based on the rated parameter information corresponding to the historical operating data of each type of equipment; take the mean of the historical operating data of all equipment in the historical operating data analysis group as the calibration operating data of the historical operating data analysis group; take the difference between the calibration operating data of the historical operating data analysis group and the rated parameter value as the benchmark analysis deviation value; take the mean of the difference between the historical operating data of each equipment in the historical operating data analysis group and the calibration operating data as the operating analysis deviation value; and obtain the benchmark operating data boundary information based on the benchmark analysis deviation value and the operating analysis deviation value.

[0168] The second evaluation unit is used to take the ratio of the number of segments of the equipment operation deviation transmission time period of the target data to the operation deviation coefficient as the operation expression coefficient of the target data, the ratio of the sum of the durations corresponding to the equipment operation deviation transmission time period of the target data to the baseline operation time deviation value as the time expression coefficient of the target data, the mean of the boundary difference between the historical operation data and the baseline operation data corresponding to the equipment operation deviation transmission time period of the target data as the equipment operation deviation transmission value of the target data, and the ratio of the equipment operation deviation transmission value to the characteristic operation deviation value as the transmission expression coefficient of the target data. Based on the operation expression coefficient, the time expression coefficient, and the transmission expression coefficient, the weights corresponding to the operation deviation coefficient, the baseline operation time deviation value, and the characteristic operation deviation value are obtained.

[0169] In summary, the advantages of this invention are as follows: By using anomaly coefficients, it achieves precise classification and analysis of different types of historical operational data, solving the problems of existing technologies ignoring differences in data characteristics and using uniform standards leading to anomaly identification bias, thus improving the accuracy of historical operational data analysis. Through benchmark operational data boundary information, it provides a data foundation for subsequent data analysis, making the boundaries more closely match the actual operating characteristics of the equipment and improving the accuracy of data analysis. Through maintenance and testing requirement information, it enables accurate setting of maintenance and testing, improving maintenance efficiency. Through data foundation acquisition parameter information and data acquisition parameter information, it achieves precise data acquisition, ensuring the timeliness and accuracy of test data.

[0170] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. An automated data acquisition and control method for substation maintenance and testing, characterized in that, include: Obtain target substation equipment information, which includes substation equipment type information and substation equipment parameter information to be inspected and tested; Based on the target substation equipment information, historical operating data of the equipment is obtained, including equipment voltage data, equipment current data, equipment operating power data, equipment operating temperature data, and equipment vibration frequency data. Based on historical equipment operation data and substation maintenance and testing needs analysis, substation maintenance and testing needs information is obtained. Based on the substation maintenance and testing requirements, and using historical equipment operating data, we obtain basic data acquisition parameters. Based on the information on substation maintenance and testing requirements, maintenance and testing are carried out on the target substation equipment. Based on the data acquisition parameters, substation maintenance test data are collected to obtain maintenance data for the target equipment. Based on the target equipment maintenance data and historical equipment operation data, the basic data acquisition parameters are adjusted to obtain the data acquisition parameter information; The process of obtaining substation maintenance and testing requirements information based on historical equipment operating data and substation maintenance and testing requirements analysis specifically includes: Based on the historical operating data of the equipment, the data is divided according to the data type, and the corresponding historical operating data analysis group for each type of equipment is obtained; Based on the parameter information of the power equipment, obtain the baseline operating data boundary information corresponding to the historical operating data of each type of equipment; The baseline operating data boundary is compared with the equipment historical operating data analysis group, and the number of times the equipment historical operating data in each equipment historical operating data analysis group exceeds the baseline operating data boundary is used as the operating deviation coefficient of the equipment historical operating data analysis group. The sum of the operating durations of the historical operating data of each equipment in the historical operating data analysis group that exceed the boundary of the baseline operating data is taken as the baseline operating time deviation value of the historical operating data analysis group of that equipment. The mean value of the boundary difference between the historical operating data of the equipment and the benchmark operating data in each group of historical operating data analysis is taken as the characteristic operating deviation value of the historical operating data analysis group of the equipment. Based on historical equipment operation data and data sensitivity analysis, the weights corresponding to the operation deviation coefficient, the baseline operation time deviation value, and the characteristic operation deviation value are obtained. Based on the analysis of historical equipment operation data, the operation deviation coefficient, the baseline operation time deviation value, and the characteristic operation deviation value are normalized. Based on the normalized operating deviation coefficient, the baseline operating time deviation value, and the characteristic operating deviation value, the operating anomaly coefficient corresponding to each group of historical operating data analysis is obtained using a weighted summation formula. Based on equipment operation risk analysis, obtain the operation anomaly coefficient threshold corresponding to the historical operation data of each type of equipment; Based on the operation anomaly coefficient and operation anomaly coefficient threshold corresponding to the historical operation data of each type of equipment, it is determined whether there is anomaly in the historical operation data of that type of equipment. If the operation anomaly coefficient exceeds the operation anomaly coefficient threshold, then the historical operation data of that type of equipment is abnormal, and information on the types of abnormal operation data is obtained. Based on the abnormal operation data type information, substation maintenance and testing requirement information is obtained, which includes maintenance and testing information corresponding to the abnormal operation data type. The step of obtaining the baseline operating data boundary information corresponding to the historical operating data of each type of equipment based on the substation equipment parameter information specifically includes: S100: Based on the power equipment parameter information, obtain the rated parameter information corresponding to the historical operating data of each type of equipment. The rated parameter information includes rated parameter range information and rated parameter value information. S200: Based on the rated parameter information corresponding to the historical operating data of each type of equipment, the rated parameter range corresponding to the historical operating data of each type of equipment is used as the initial operating data boundary; S300: Based on the equipment historical operation data analysis group, the average of all equipment historical operation data in the equipment historical operation data analysis group is used as the calibration operation data of the equipment historical operation data analysis group; S400: Use the difference between the calibration operation data and the rated parameter values ​​of the equipment's historical operation data analysis group as the benchmark analysis deviation value; S500: The average difference between the historical operating data and the calibration operating data of each device in the historical operating data analysis group is used as the operating analysis deviation value; S600: If the operational analysis deviation value exceeds the benchmark analysis deviation value, remove the historical operational data corresponding to the maximum difference between the historical operational data and the calibration operational data in the historical operational data analysis group. Repeat steps S300-S500 until the operational analysis deviation value does not exceed the benchmark analysis deviation value, and obtain the historical operational data correction analysis group. S700: Based on the historical operating data of the equipment, the correction analysis group adjusts the initial operating data boundary according to the range of historical operating data of the equipment to obtain the baseline operating data boundary information; If the minimum value of the equipment's historical operating data is greater than the lower limit of the initial operating data boundary, then the minimum value of the equipment's historical operating data will be used as the lower limit of the benchmark operating data boundary; if the maximum value of the equipment's historical operating data is less than the upper limit of the initial operating data boundary, then the maximum value of the equipment's historical operating data will be used as the upper limit of the benchmark operating data boundary. The step of obtaining the weights corresponding to the operating deviation coefficient, the baseline operating time deviation value, and the characteristic operating deviation value based on the equipment's historical operating data and data sensitivity analysis specifically includes: Based on the baseline operating data boundary information, obtain the timestamp information corresponding to the operating duration of each time the historical operating data of the equipment in each group of equipment historical operating data analysis group exceeds the baseline operating data boundary; Based on timestamp information, obtain the equipment characteristic operating time period information corresponding to each group of historical operating data analysis groups. The equipment characteristic operating time period represents the duration of operation of historical operating data of equipment that exceeds the boundary of baseline operating data. Data alignment is performed based on timestamps. Any historical operating data of any device is used as the target data. If the device characteristic operating time period corresponding to the target data overlaps with the device characteristic operating time period corresponding to any historical operating data of any device, the overlapping device characteristic operating time period is used as the device operating deviation transmission time period of the target data. The ratio of the number of time segments in which the equipment operation deviation of the target data is transmitted to the operation deviation coefficient is used as the operation expression coefficient of the target data. The ratio of the sum of the durations corresponding to the transmission time periods of the equipment operation deviation of the target data to the deviation value of the baseline operation time is used as the time expression coefficient of the target data; Based on the equipment operation deviation transmission time period of the target data, obtain the historical operation data corresponding to the equipment operation deviation transmission time period of the target data; The average of the boundary differences between the historical operating data and the benchmark operating data corresponding to the equipment operating deviation propagation period of the target data is taken as the equipment operating deviation propagation value of the target data. The ratio of the equipment operation deviation transfer value to the characteristic operation deviation value of the target data is used as the transfer expression coefficient of the target data; The sum of the runtime expression coefficient, the time expression coefficient, and the transfer expression coefficient is used as the baseline value for data characteristics. The ratio of the running expression coefficient to the data feature benchmark value is used as the weight of the running deviation coefficient; the ratio of the time expression coefficient to the data feature benchmark value is used as the weight of the benchmark running time deviation value; and the ratio of the transmission expression coefficient to the data feature benchmark value is used as the weight of the feature running deviation value.

2. The automated data acquisition and control method for substation maintenance and testing according to claim 1, characterized in that, The process of acquiring basic data collection parameters based on substation maintenance and testing requirements and historical equipment operating data specifically includes: Based on the substation maintenance and testing requirements, obtain information on the types of abnormal operation data; Based on the historical operating data of the equipment, the historical operating data of the equipment corresponding to the abnormal operating data type is sorted by time series to obtain the historical time series information of the equipment historical operating data of that type. Based on historical time series information, obtain the fluctuation difference of historical operating data of adjacent devices in the historical time series; Obtain the fluctuation time interval of historical operating data of adjacent devices in the historical time series; The ratio of the fluctuation difference to the fluctuation time interval of the historical operating data of adjacent devices in the historical time series is used as the data fluctuation rate of the historical operating data of the adjacent devices. Based on the fluctuation difference of historical operating data of adjacent devices in the historical time series, the standard deviation of the fluctuation difference of historical operating data of adjacent devices is obtained based on the mean of the fluctuation difference; The standard deviation of the fluctuation difference between historical operating data of adjacent devices is used as the data fluctuation identification value. The ratio of the identified data fluctuation value to the maximum data fluctuation rate is used as the benchmark data acquisition time. The reciprocal of the baseline data acquisition time corresponding to the historical operating data of each type of equipment is used as the data acquisition frequency of the operating data of that type of equipment to obtain the basic data acquisition parameter information.

3. The automated acquisition and control method for substation maintenance test data according to claim 2, characterized in that, The process of adjusting the basic data acquisition parameters based on the target equipment maintenance data and historical equipment operation data to obtain the data acquisition parameter information specifically includes: Based on the historical operating data of the equipment corresponding to the types of abnormal operating data, a coordinate system is established with time as the horizontal axis and equipment operating data value as the vertical axis to obtain the historical operating curve of the equipment corresponding to each type of abnormal operating data. Based on the baseline operating data boundary information corresponding to each type of abnormal operating data, the historical operating curve of the equipment that exceeds the baseline operating data boundary is taken as the equipment operating characteristic curve. The first derivative of the equipment operation characteristic curve is taken to obtain the data mutation coefficient corresponding to each data point in the equipment operation characteristic curve; Data points in the equipment operation characteristic curve whose data mutation coefficient exceeds the data fluctuation identification value are considered as equipment operation mutation points; The minimum value of the data mutation coefficient corresponding to the sudden change point in equipment operation is used as the data acquisition characteristic coefficient; Based on the maintenance data of the target equipment, and using time series as a basis, the product of the difference between the maintenance data of adjacent target equipment and the data collection frequency is used as the fluctuation coefficient of the maintenance data of the target equipment. Based on the fluctuation coefficient and data acquisition characteristic coefficient of the target equipment maintenance data, determine whether to adjust the basic data acquisition parameters and obtain the data acquisition parameter information; If the fluctuation coefficient of the target equipment maintenance data exceeds the data acquisition characteristic coefficient, the ratio of the data fluctuation identification value to the fluctuation coefficient of the target equipment maintenance data will be used as the data acquisition frequency. If the fluctuation coefficient of the target equipment maintenance data does not exceed the data acquisition characteristic coefficient, the basic data acquisition parameters will remain unchanged.

4. An automated data acquisition and control system for substation maintenance and testing, used to implement the acquisition and control method as described in any one of claims 1-3, characterized in that, include: The main control module is used to normalize the operating deviation coefficient, the baseline operating time deviation value, and the characteristic operating deviation value based on the historical operating data analysis group of the equipment. Based on the normalized operating deviation coefficient, the baseline operating time deviation value, and the characteristic operating deviation value, and using a weighted summation formula, it obtains the operating anomaly coefficient corresponding to each group of historical operating data analysis. Based on the operating anomaly coefficient and operating anomaly coefficient threshold corresponding to each type of historical operating data, it determines whether the historical operating data of that type of equipment is abnormal. If the operating anomaly coefficient exceeds the operating anomaly coefficient threshold, then the historical operating data of that type of equipment is abnormal. It obtains information on the types of abnormal operating data, and based on this information, obtains information on substation maintenance and testing requirements. Based on this information, it conducts maintenance and testing on the target substation equipment. According to the historical operating data of the equipment, it sorts the historical operating data corresponding to the types of abnormal operating data by time sequence to obtain the historical time sequence information of that type of historical operating data. Based on the historical time sequence information, it obtains basic data acquisition parameter information. Based on the target equipment maintenance data and the equipment historical operating data, it adjusts the basic data acquisition parameter information to obtain the data acquisition parameter information. The information acquisition module is used to acquire target substation equipment information, including substation equipment type information and substation equipment parameter information to be inspected and tested. Based on the target substation equipment information, the module acquires historical operating data of the equipment, including equipment voltage data, equipment current data, equipment operating power data, equipment operating temperature data, and equipment vibration frequency data. According to the substation equipment parameter information, the module acquires rated parameter information corresponding to the historical operating data of each type of equipment, including rated parameter range information and rated parameter value information. Based on the data-based parameter information, the module collects substation maintenance test data to acquire target equipment maintenance data. The evaluation module is used to determine the initial operating data boundary based on the rated parameter information corresponding to the historical operating data of each type of equipment. It uses the rated parameter range corresponding to the historical operating data of each type of equipment as the initial operating data boundary. Based on the historical operating data analysis group, it uses the average of the historical operating data of all equipment in the group as the calibration operating data for that group. The difference between the calibration operating data and the rated parameter value in the historical operating data analysis group is used as the benchmark analysis deviation value. The average difference between the historical operating data and the calibration operating data of each equipment in the group is used as the operating analysis deviation value. Based on the benchmark analysis deviation value and the operating analysis deviation value, benchmark operating data boundary information is obtained, and the target number... The ratio of the number of segments in the equipment operation deviation transmission time period to the operation deviation coefficient is used as the operation expression coefficient of the target data. The ratio of the total duration corresponding to the equipment operation deviation transmission time period of the target data to the baseline operation time deviation value is used as the time expression coefficient of the target data. The mean of the boundary differences between the historical operation data and the baseline operation data corresponding to the equipment operation deviation transmission time period of the target data is used as the equipment operation deviation transmission value of the target data. The ratio of the equipment operation deviation transmission value of the target data to the characteristic operation deviation value is used as the transmission expression coefficient of the target data. Based on the operation expression coefficient, time expression coefficient, and transmission expression coefficient, the weights corresponding to the operation deviation coefficient, the baseline operation time deviation value, and the characteristic operation deviation value are obtained. The display module interacts with the main control module and is used to output and display baseline operating data boundary information, substation maintenance test requirement information, basic data acquisition parameter information, data acquisition parameter information, and target equipment maintenance data.

5. The automated data acquisition and control system for substation maintenance and testing according to claim 4, characterized in that, The main control module specifically includes: The control unit is used to sort the historical operating data of the abnormal operating data type according to the time sequence, obtain the historical time sequence information of the historical operating data of that type of equipment, obtain the basic data acquisition parameter information based on the historical time sequence information, and adjust the basic data acquisition parameter information according to the target equipment maintenance data and the equipment historical operating data to obtain the data acquisition parameter information. An information receiving unit interacts with the information acquisition module and the evaluation module to receive data and transmit it to the maintenance and testing unit. The maintenance and testing unit is used to analyze historical operating data of the equipment, normalize the operating deviation coefficient, the reference operating time deviation value, and the characteristic operating deviation value, and obtain the operating anomaly coefficient corresponding to each group of historical operating data analysis based on the normalized operating deviation coefficient, the reference operating time deviation value, and the characteristic operating deviation value, using a weighted summation formula. Based on the operating anomaly coefficient and the operating anomaly coefficient threshold corresponding to each type of historical operating data, it is determined whether there is an anomaly in the historical operating data of that type of equipment. If the operating anomaly coefficient exceeds the operating anomaly coefficient threshold, then the historical operating data of that type of equipment is anomaly. The unit obtains information on the types of abnormal operating data, obtains substation maintenance and testing requirements based on the information on the types of abnormal operating data, and conducts maintenance and testing on the target substation equipment based on the substation maintenance and testing requirements.

6. The automated data acquisition and control system for substation maintenance and testing according to claim 4, characterized in that, The information acquisition module specifically includes: The first acquisition unit is used to acquire target substation equipment information, which includes substation equipment type information and substation equipment parameter information to be inspected and tested. Based on the target substation equipment information, the first acquisition unit is used to acquire historical operating data of the equipment, which includes equipment voltage data, equipment current data, equipment operating power data, equipment operating temperature data and equipment vibration frequency data. The second acquisition unit is used to acquire rated parameter information corresponding to the historical operating data of each type of equipment based on the parameter information of the substation equipment. The rated parameter information includes rated parameter range information and rated parameter value information. Based on the data base acquisition parameter information, the substation maintenance test data is collected to acquire the maintenance data of the target equipment.

7. The automated data acquisition and control system for substation maintenance and testing according to claim 4, characterized in that, The evaluation module specifically includes: The first evaluation unit is used to take the rated parameter range corresponding to the historical operating data of each type of equipment as the initial operating data boundary based on the rated parameter information corresponding to the historical operating data of each type of equipment; take the mean of the historical operating data of all equipment in the historical operating data analysis group as the calibration operating data of the historical operating data analysis group; take the difference between the calibration operating data of the historical operating data analysis group and the rated parameter value as the benchmark analysis deviation value; take the mean of the difference between the historical operating data of each equipment in the historical operating data analysis group and the calibration operating data as the operating analysis deviation value; and obtain the benchmark operating data boundary information based on the benchmark analysis deviation value and the operating analysis deviation value. The second evaluation unit is used to take the ratio of the number of segments of the equipment operation deviation transmission time period of the target data to the operation deviation coefficient as the operation expression coefficient of the target data, the ratio of the sum of the durations corresponding to the equipment operation deviation transmission time period of the target data to the baseline operation time deviation value as the time expression coefficient of the target data, the mean of the boundary difference between the historical operation data and the baseline operation data corresponding to the equipment operation deviation transmission time period of the target data as the equipment operation deviation transmission value of the target data, and the ratio of the equipment operation deviation transmission value to the characteristic operation deviation value as the transmission expression coefficient of the target data. Based on the operation expression coefficient, the time expression coefficient, and the transmission expression coefficient, the weights corresponding to the operation deviation coefficient, the baseline operation time deviation value, and the characteristic operation deviation value are obtained.

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