Intelligent substation patrol system and method based on trusted WLAN (Wireless Local Area Network)

Abstract

The invention discloses an intelligent substation inspection system and method based on a trusted WLAN (Wireless Local Area Network), and relates to the technical field of substation inspection. The system is composed of a plurality of patrol terminal devices and a substation monitoring device, the patrol terminal devices are responsible for collecting working behavior data of patrol personnel, and the substation state monitoring device is used for monitoring operation state data of a substation in real time. The intelligent patrol method is characterized by comprising the following steps: extracting historical behavior data of any patrol terminal device based on the patrol terminal device, and acquiring current abnormal behavior data from any patrol terminal device which detects abnormal patrol behaviors. According to the invention, by introducing intelligent patrol and state monitoring equipment and combining wireless signal trust factor analysis, dynamic adjustment and monitoring of patrol personnel and substation equipment are realized, so that the anomaly detection capability is improved, resource utilization is optimized, and safe operation and efficient management of a substation are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of xxxx, specifically to a substation intelligent inspection system and method based on a trusted WLAN. Background Technique

[0002] A substation refers to a place in the power system that transforms voltage and current, receives electric energy, and distributes electric energy. The substation in a power plant is a step-up substation, and its function is to step up the electric energy generated by the generator and feed it into the high-voltage power grid.

[0003] With the increasing complexity and security requirements in the operation dehyas16dn area of substations, traditional detection and maintenance methods face challenges such as low efficiency, susceptibility to external interference, and insufficient reliability. The substation intelligent inspection system based on a trusted WLAN realizes data collection and analysis through a wireless network, which can effectively improve the inspection efficiency, simplify the equipment layout, and also has the characteristics of strong anti-interference ability. Summary of the Invention

[0004] The purpose of the present invention is to provide a substation intelligent inspection system and method based on a trusted WLAN to solve the problems raised in the above background technique.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A substation intelligent inspection method based on a trusted WLAN, which is applied to a substation intelligent inspection system. The system consists of multiple inspection terminal devices and substation monitoring devices. The inspection terminal devices are responsible for collecting the work behavior data of inspectors, and the substation status monitoring devices are used to monitor the operation status data of the substation in real time. The intelligent inspection method includes the following steps:

[0006] Extract its historical behavior data from any one of the inspection terminal devices, and obtain its current abnormal behavior data from any inspection terminal device that detects abnormal inspection behavior;

[0007] Extract historical status data from any one of the status monitoring devices, and obtain its current abnormal status data from any status monitoring device that detects abnormal operation status of the substation.

[0008] As a specific solution of the technical solution of the present application, the wireless signal strength between the inspection terminal and the status monitoring device includes:

[0009] Obtain the first wireless signal strength between the first inspection terminal device and the second inspection terminal device;

[0010] Obtain the second wireless signal strength between the first status monitoring device and the second status monitoring device;

[0011] Calculate a first trust factor based on the first wireless signal strength, which is used to evaluate the reliability of the first patrol terminal device;

[0012] Calculate a second trust factor based on the second wireless signal strength, which is used to evaluate the reliability of the second status monitoring device;

[0013] Calculate the current patrol frequency based on the first trust factor and the historical behavior data of the first patrol terminal device;

[0014] Calculate its current monitoring frequency based on the second trust factor and the historical status data of the second status monitoring device;

[0015] Adjust the patrol frequency of the first patrol terminal device according to the calculated current patrol frequency;

[0016] Adjust the monitoring frequency of the second status monitoring device according to the calculated current monitoring frequency.

[0017] As a specific solution of the technical solution of this application, the method for calculating the trust factor based on the wireless signal strength includes the following steps:

[0018] Obtain the total wireless signal strength of all patrol terminal devices and status monitoring devices;

[0019] Obtain the first wireless signal strength between the first patrol terminal device and the second patrol terminal device;

[0020] Calculate a first trust factor based on the total wireless signal strength and the first wireless signal strength, which is used to evaluate the reliability of the first patrol terminal device;

[0021] Calculate a second trust factor based on the total wireless signal strength and the second wireless signal strength, which is used to evaluate the reliability of the second status monitoring device;

[0022] Compare the calculated first trust factor and second trust factor with a preset trust factor database, and adjust the first trust factor and second trust factor according to the comparison result to obtain a more accurate evaluation of the device reliability.

[0023] As a specific solution of the technical solution of this application, the method for dynamically adjusting the patrol and monitoring frequencies based on abnormal behavior and status data includes the following steps:

[0024] Classify based on the current abnormal behavior data and current abnormal status data to determine the abnormal type;

[0025] According to the abnormal type, obtain the corresponding third trust factor and fourth trust factor from a preset trust factor database;

[0026] Comprehensively evaluate the reliability of the device by combining the first trust factor, the second trust factor, the third trust factor, and the fourth trust factor;

[0027] According to the evaluation results, further adjust the inspection frequency of the inspection terminal and the monitoring frequency of the status monitoring device.

[0028] As a specific solution of the technical solution of the present application, the steps of realizing real-time monitoring and feedback through the first inspection terminal device and the first status monitoring device include:

[0029] Real-time collect the current monitoring data of the first inspection terminal device and the first status monitoring device;

[0030] According to the obtained monitoring data, calculate the work performance of the inspection personnel and the operation status score of the substation respectively;

[0031] Based on the calculated work performance score and operation status score, determine the third and fourth trust factors;

[0032] According to the results of the trust factors, adjust the inspection or monitoring frequency, and send early warnings or notifications when necessary.

[0033] The intelligent inspection system of the substation based on a trusted WLAN includes multiple inspection terminal devices and substation status monitoring devices, where each inspection terminal device is used to record the work behavior data of the inspection personnel, and each substation status monitoring device is used to monitor the operation status data of the substation. The system also includes a data processing center, and the data processing center includes the following steps:

[0034] A data reader, used to obtain the historical data and current abnormal data of the inspection terminal device and the substation status monitoring device;

[0035] A processor, used to calculate the first trust factor and the second trust factor according to the obtained data, and calculate the current inspection or monitoring frequency based on the trust factors and historical data;

[0036] A controller, used to adjust the actual inspection or monitoring frequency of the inspection terminal device and the substation status monitoring device according to the calculated inspection or monitoring frequency.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] The intelligent inspection system and method of the substation based on a trusted WLAN. By introducing intelligent inspection and status monitoring devices and combining wireless signal trust factor analysis, the system realizes dynamic adjustment monitoring of inspection personnel and substation equipment, thereby improving the abnormal detection ability, optimizing resource utilization, and ensuring the safe operation and efficient management of the substation.

[0039] Meanwhile, the device trust factor is calculated based on the strength of the transmitted wireless signal to evaluate the device reliability. The system dynamically adjusts the inspection and monitoring frequencies according to the trust factor and historical data, giving priority to normal devices and reducing the impact of abnormal devices. When an abnormal event occurs, the system sends a warning or notification to ensure environmental safety. Brief Description of the Drawings

[0040] Figure 1 It is a schematic diagram of the wireless signal strength process between the inspection terminal and the status monitoring device of the present invention;

[0041] Figure 2 It is a schematic diagram of the process of the intelligent inspection method of the present invention;

[0042] Figure 3 It is a schematic diagram of the method for dynamically adjusting the inspection and monitoring frequencies of abnormal behaviors and status data of the present invention. Detailed Embodiments

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] It should be noted that in the description of the present invention, the orientation or positional relationship indicated by the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.

[0045] In addition, it should be understood that, for the sake of convenience of description, the sizes of the various components shown in the accompanying drawings are not drawn in actual proportional relationships. For example, the thickness or width of some layers may be exaggerated relative to other layers.

[0046] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined or described in one drawing, it will not be necessary to further discuss and describe it specifically in the description of the subsequent drawings.

[0047] As Figures 1 - 3As shown in the figure, the present invention provides a technical solution: a substation intelligent patrol method based on a trusted WLAN, which is applied to a substation intelligent patrol system based on a trusted WLAN. The system consists of multiple patrol terminal devices and substation monitoring devices. The patrol terminal devices are responsible for collecting the work behavior data of the patrol personnel, and the substation status monitoring devices are used to monitor the operation status data of the substation in real time. The intelligent patrol method includes the following steps:

[0048] Extract its historical behavior data from any one of the patrol terminal devices, and obtain its current abnormal behavior data from any patrol terminal device that detects an abnormal patrol behavior. It should be clear that the system needs to be able to access any one of the patrol terminal devices, which are hardware devices for monitoring, inspection, or data collection, such as cameras, sensors, and drones, etc. The system should have the communication ability with these devices, be able to connect to the devices through wired or wireless networks, and obtain their data. After accessing the patrol terminal devices, the system needs to extract their historical behavior data from the devices. The data includes the operation status, operation records, inspection paths, and collected data of the devices in the past period of time. Then, through the analysis of the historical behavior data, the system can identify the normal behavior patterns of the devices. When the current behavior of the device shows a significant deviation from the normal pattern, the system will determine it as an abnormal behavior. The abnormal behaviors include sudden changes in the device operation status, deviation of the inspection path, and abnormalities in data collection, etc. Once an abnormal behavior is detected, the system needs to obtain its current abnormal behavior data from this patrol terminal device. These data include the operation parameters, error logs, and real-time collected data of the device under abnormal conditions. The system further analyzes the obtained current abnormal behavior data to determine the specific cause of the abnormality. Through the analysis, the system can judge whether the abnormality is caused by device failure, external interference, operation error, or other reasons. According to the analysis results, the system can take corresponding response measures, such as sending an alarm to the operator, automatically adjusting the device operation parameters, starting standby devices, etc. At the same time, the system can also feedback the analysis results to relevant personnel for further decision-making and processing.

[0049] Extract historical status data from any one of the status monitoring devices, and obtain its current abnormal status data from any status monitoring device that detects the abnormal operation status of the substation. It should be clear that in the embodiments of this application, the system needs to be able to access any one of the status monitoring devices, which are deployed in the substation for real-time monitoring of the operation status of the substation. For example, parameters such as temperature, current, voltage, vibration, and oil level of key devices such as transformers, circuit breakers, buses, and capacitors. The system is connected to the devices through wired or wireless networks and obtains their data. At the same time, it should also be clear that after accessing the status monitoring devices, the system needs to extract their historical status data from the devices. The data includes operation parameter records, status change logs, alarm records, etc. of the devices in the past period of time. The analysis of historical status data is the basis for analyzing the normal operation mode of the devices, used to establish a reference status model of the devices, and help identify the behavior characteristics of the devices under normal conditions. The system needs to have the ability to detect the abnormal operation status of the substation. Through the analysis of historical status data, the system can identify the normal operation mode of the substation and its devices. When the currently monitored status data shows a significant deviation from the normal mode, the system will determine it as an abnormal operation status. The abnormal status may be manifested as device parameters exceeding the threshold (such as too high temperature, abnormal current), sudden changes in the device operation status (such as frequent tripping of the circuit breaker), or abnormal linkage with other devices, etc. Once the abnormal operation status of the substation is detected, the system needs to obtain its current abnormal status data from the relevant status monitoring devices. These data include real-time operation parameters, fault codes, alarm information, environmental data (such as temperature, humidity), etc. of the devices in the abnormal status. Obtaining these data helps to further analyze the specific manifestations and possible causes of the abnormality.

[0050] Based on the wireless signal strength between the patrol terminal and the status monitoring device includes:

[0051] Obtain the first wireless signal strength between the first patrol terminal device and the second patrol terminal device. It should be clear that the system measures the wireless signal strength between the two to be -65 dBm. dBm is the unit of signal strength, and the closer the value is to 0, the stronger the signal;

[0052] Obtain the second wireless signal strength between the first status monitoring device and the second status monitoring device. The system measures the wireless signal strength between the two to be -75 dBm

[0053] Based on the first wireless signal strength, calculate the first trust factor to evaluate the reliability of the first patrol terminal device. It should be clear that the signal strength range is from -100 dBm (weakest) to -50 dBm (strongest), and the trust factor range is from 0 (unreliable) to 1 (fully reliable). Use the linear mapping formula to calculate the first trust factor: , therefore, the first trust factor is 0.7.

[0054] Calculate a second trust factor based on the second wireless signal strength for evaluating the reliability of the second status monitoring device. Use the same formula to calculate the second trust factor: , so the second trust factor is 0.5.

[0055] Calculate the current inspection frequency based on the first trust factor and the historical behavior data of the first inspection terminal device. It should be clear that in the embodiment of this application, the default inspection frequency of the first inspection terminal device is once per hour. If the first trust factor is 0.7, the system adjusts the inspection frequency according to the trust factor. For example: the current inspection frequency = default inspection frequency * first trust factor = 1 * 0.7 = 0.7 times per hour. Therefore, the current inspection frequency is adjusted to 0.7 times per hour, that is, an inspection is carried out every 1.43 hours.

[0056] Calculate its current monitoring frequency based on the second trust factor and the historical status data of the second status monitoring device. If the second trust factor is 0.5, the system can adjust the monitoring frequency according to the trust factor. For example: the current monitoring frequency = default monitoring frequency × second trust factor = 1 × 0.5 = 0.5 times per minute. Therefore, the current monitoring frequency is adjusted to 0.5 times per minute (that is, a monitoring is carried out every 2 minutes). Adjust the inspection frequency of the first inspection terminal device according to the calculated current inspection frequency; the system adjusts the inspection frequency of the first inspection terminal device from the default 1 time per hour to 0.7 times per hour to adapt to the current communication quality. Adjust the monitoring frequency of the second status monitoring device according to the calculated current monitoring frequency. The system adjusts the monitoring frequency of the second status monitoring device from the default 1 time per minute to 0.5 times per minute to adapt to the current communication quality. It should also be clear that in this application, assuming that the wireless signal strength between the first inspection terminal device and the second inspection terminal device increases from -65 dBm to -55 dBm, then the first trust factor is: , at this time, the current inspection frequency is adjusted to: the current inspection frequency = 1 * 0.9 = 0.9 times per hour, and the system increases the inspection frequency from 0.7 times per hour to 0.9 times per hour. If the signal strength deteriorates, assuming that the wireless signal strength between the second status monitoring device and the second status monitoring device decreases from -75 dBm to -85 dBm, then the second trust factor is: , at this time, the current monitoring frequency is adjusted to: current monitoring frequency = 1 * 0.3 = 0.3 times per minute, and the system reduces the monitoring frequency from 0.5 times per minute to 0.3 times per minute. Assume that the task completion rate of the first patrol terminal device in the past week is 95% and the failure rate is 2%. The system can further optimize the trust factor by combining these historical data. For example, if the task completion rate is high and the failure rate is low, the weight of the trust factor can be appropriately increased: adjusted first trust factor = 0.7 × 1.1 = 0.77. At this time, the current patrol frequency is adjusted to: current patrol frequency = 1 × 0.77 = 0.77 times per hour. Assume that the data integrity rate of the second status monitoring device in the past week is 90% and the number of abnormal event records is 5. The system can further optimize the trust factor by combining these historical data. For example, if the data integrity rate is low or the number of abnormal events is large, the weight of the trust factor can be appropriately reduced: adjusted second trust factor = 0.5 × 0.9 = 0.45. At this time, the current monitoring frequency is adjusted to: current monitoring frequency = 1 × 0.45 = 0.45 times per minute.

[0057] The method for calculating the trust factor based on the wireless signal strength includes the following steps:

[0058] Obtain the total wireless signal strength of all patrol terminal devices and status monitoring devices;

[0059] Obtain the first wireless signal strength between the first patrol terminal device and the second patrol terminal device;

[0060] Calculate the first trust factor based on the total wireless signal strength and the first wireless signal strength, which is used to evaluate the reliability of the first patrol terminal device;

[0061] Calculate the second trust factor based on the total wireless signal strength and the second wireless signal strength, which is used to evaluate the reliability of the second status monitoring device;

[0062] Compare the calculated first trust factor and second trust factor with the preset trust factor database, and adjust the first trust factor and second trust factor according to the comparison result to obtain a more accurate evaluation of the device reliability. It should be clear that in the embodiment of the present application, there are 5 patrol terminal devices and 3 status monitoring devices in the system, and their wireless signal strengths are: patrol terminal devices: -60 dBm, -65 dBm, -70 dBm, -75 dBm, -80 dBm, status monitoring devices: -55 dBm, -60 dBm, -65 dBm. Calculate the total wireless signal strength (take the average value): , such as the range of the trust factor is from 0 (unreliable) to 1 (completely reliable), and the calculation formula is: Substitute the values: , therefore, the first trust factor is 0.98. The wireless signal strength between the second status monitoring device and the second status monitoring device is -75 dBm. Use the same formula to calculate the second trust factor: , since the range of the trust factor should not exceed 1, the result needs to be normalized: , therefore, the second trust factor is 1. Then, compare the calculated first trust factor and the second trust factor with the preset trust factor database. The preset trust factor database records the trust factor ranges of the device at different signal strengths: Signal strength ≥ -60 dBm: The trust factor range is 0.9 to 1.0; Signal strength between -60 dBm and -70 dBm: The trust factor range is 0.7 to 0.9; Signal strength < -70 dBm: The trust factor range is 0.0 to 0.7. The comparison results are as follows: The first trust factor is 0.98, which falls within the range of ≥ -60 dBm and conforms to the range of the preset database (0.9 ~ 1.0), so no adjustment is required. The second trust factor is 1, which falls within the range of ≥ -60 dBm and conforms to the range of the preset database (0.9 ~ 1.0), so no adjustment is required. At the same time, it should also be clear that assuming the situation that needs to be adjusted, if the first trust factor is 0.85, which falls within the range of -60 dBm ~ -70 dBm, but the upper limit of the trust factor in this range in the preset database is 0.9, then adjustment is required: The adjusted first trust factor = min(0.9, 0.85) = 0.85. Therefore, the adjusted first trust factor is still 0.85. If the second trust factor is 1.2, which exceeds the range of the preset database (0.9 ~ 1.0), then adjustment is required: The adjusted second trust factor = min(1.0, 1.2) = 1.0. Therefore, the adjusted second trust factor is 1.0. By dynamically adjusting the trust factor and the patrol frequency of the patrol terminal device, the resource utilization rate and communication efficiency can be optimized while ensuring the task completion rate. By dynamically adjusting the trust factor and the monitoring frequency of the status monitoring device, the communication load and device power consumption can be reduced while ensuring the real-time nature of the data. When the trust factor is low, the system can give an early warning to prompt the operation and maintenance personnel to check the communication link or device status, and at the same time ensure the stable operation of the overall system by adjusting the working frequency or switching to standby devices.

[0063] The method for dynamically adjusting the patrol and monitoring frequencies based on abnormal behaviors and status data includes the following steps:

[0064] Classify based on the current abnormal behavior data and the current abnormal status data to determine the type of abnormality;

[0065] Obtain the corresponding third trust factor and fourth trust factor from the preset trust factor database according to the exception type;

[0066] Combine the first trust factor, the second trust factor, the third trust factor, and the fourth trust factor to comprehensively evaluate the reliability of the device;

[0067] According to the evaluation result, further adjust the inspection frequency of the inspection terminal and the monitoring frequency of the status monitoring device. It should be clear that in the embodiment of the present application, the first inspection terminal device detected the following abnormal behaviors: the inspection path deviated from the preset route by more than 10 meters, and the number of communication interruptions reached 3 times within the past 1 hour. The second status monitoring device detected the following abnormal states: the transformer temperature exceeded the threshold of 85 °C. The current fluctuation exceeded 20% of the normal range. According to the preset exception classification rules, the above abnormal behaviors and data are classified as: communication exception (inspection terminal device), device overheat exception (status monitoring device). According to the exception type, obtain the corresponding third trust factor and fourth trust factor from the preset trust factor database. The preset trust factor database records the trust factors corresponding to different exception types: communication exception: the third trust factor is 0.6, device overheat exception: the fourth trust factor is 0.4. Combine the first trust factor, the second trust factor, the third trust factor, and the fourth trust factor to comprehensively evaluate the reliability of the device. The first trust factor is 0.7 (calculated based on the wireless signal strength), and the second trust factor is 0.5 (calculated based on the wireless signal strength). Calculate through the comprehensive evaluation formula: Comprehensive trust factor = w 1 *First trust factor + w 2 *Second trust factor + w 3 *Third trust factor + w 4 *Fourth trust factor, where, w 1 , w 2 , w 3 , w 4 is the weight coefficient, satisfying . The weight coefficients are: (wireless signal strength weight of the inspection terminal device). (wireless signal strength weight of the status monitoring device). (communication exception weight). (device overheat exception weight). Substitute the values to calculate the comprehensive trust factor:

[0068] The comprehensive trust factor = 0.3 * 0.7 + 0.3 * 0.5 + 0.2 * 0.6 + 0.2 * 0.4 = 0.56. Therefore, the comprehensive trust factor is 0.56. According to the evaluation results, the inspection frequency of the inspection terminal and the monitoring frequency of the status monitoring device are further adjusted. In this application, the default inspection frequency is 1 time per hour. The inspection frequency is adjusted according to the comprehensive trust factor: the current inspection frequency = the default inspection frequency * the comprehensive trust factor = 1 * 0.56 = 0.56 times per hour. Therefore, the current inspection frequency is adjusted to 0.56 times per hour (i.e., inspect once every 1.79 hours). The default monitoring frequency is 1 time per minute. The monitoring frequency is adjusted according to the comprehensive trust factor: the current monitoring frequency = the default monitoring frequency * the comprehensive trust factor = 1 * 0.56 = 0.56 times per minute. Therefore, the current monitoring frequency is adjusted to 0.56 times per minute (i.e., monitor once every 1.79 minutes). If the comprehensive trust factor increases, by fixing communication anomalies and device overheating anomalies, the comprehensive trust factor increases from 0.56 to 0.88. At this time, the current inspection frequency is adjusted to: the current inspection frequency = 1 * 0.8 = 0.8 times per hour, and the current monitoring frequency is adjusted to: the current monitoring frequency = 1 * 0.8 = 0.8 times per minute. If the comprehensive trust factor decreases: due to new anomalies (such as device vibration anomalies), the comprehensive trust factor decreases from 0.56 to 0.4. At this time, the current inspection frequency is adjusted to: the current inspection frequency = 1 * 0.4 = 0.4 times per hour. The current monitoring frequency is adjusted to: the current monitoring frequency = 1 * 0.4 = 0.4 times per minute. Therefore, through specific numerical values and calculation formulas, it can be seen that abnormal behavior data and abnormal status data are used to classify abnormal types, and the corresponding trust factors are obtained from a preset database. The comprehensive trust factor is obtained through weighted calculation and is used to comprehensively evaluate the reliability of the device. According to the comprehensive trust factor, the inspection frequency of the inspection terminal and the monitoring frequency of the status monitoring device are dynamically adjusted. When the comprehensive trust factor is relatively high, the working frequency of the device increases to enhance the monitoring ability. When the trust factor is relatively low, the working frequency of the device is relatively low to reduce the communication load and the risk of task failure.

[0069] The steps to achieve real-time monitoring and feedback through the first inspection terminal device and the first status monitoring device include:

[0070] Real-time collect the current monitoring data of the first inspection terminal device and the first status monitoring device;

[0071] According to the obtained monitoring data, calculate the work performance of the inspection personnel and the operation status score of the substation respectively;

[0072] Based on the calculated work performance score and operation status score, determine the third and fourth trust factors;

[0073] According to the results of the trust factor, adjust the frequency of inspections or monitoring, and send early warnings or notifications when necessary. It should be clear that in the embodiments of this application, the monitoring data of the first inspection terminal device is: the distance of the inspection path deviating from the preset route is 5 meters, the number of communication interruptions is 2 times per hour, and the task completion rate is 90%. The monitoring data of the first status monitoring device is: the transformer temperature is 80°C (the threshold is 85°C), the current fluctuation is 15% (the normal range is ±10%), and the data reporting success rate is 95%. Then, based on the obtained monitoring data, calculate the work performance of the inspection personnel and the operation status score of the substation respectively. The calculation is carried out through the work performance score calculation formula:

[0074] Among them, the weight coefficients are: w 1 = 0.5 (task completion rate weight), w 2 = 0.3 (number of communication interruption weights), w 3 = 0.2 (path deviation distance weight). The maximum allowable number of communication interruptions is 3 times per hour, and the maximum allowable path deviation distance is 10 meters. Substitute the values for calculation:

[0075]

[0076] Therefore, the work performance score is 0.65 (with a full score of 1). The operation status score of the substation is:

[0077] Among them, the weight coefficients are: w4 = 0.47 (temperature weight), w5 = 0.3 (current fluctuation weight), w6 = 0.3 (data reporting success rate weight). In this application, the maximum allowable temperature exceedance value is 10°C, and the maximum allowable current fluctuation exceedance value is 20%. Substitute the values for calculation:

[0078]

[0079] Therefore, the operation status score is 0.91 (with a full score of 1). Then, based on the calculated work performance score and operation status score, the third and fourth trust factors are determined. The third trust factor (the trust factor of the patrol terminal device) is directly related to the work performance score, and the third trust factor = work performance score = 0.65. The fourth trust factor (the trust factor of the status monitoring device) is directly related to the operation status score, and the fourth trust factor = operation status score = 0.91. According to the results of the trust factors, the patrol or monitoring frequency is adjusted, and warnings or notifications are sent when necessary. In this application, the default patrol frequency is once per hour. The patrol frequency is adjusted according to the third trust factor: 1 * 0.65 = 0.65 times per hour. Therefore, the current patrol frequency is adjusted to 0.65 times per hour, that is, patrol once every 1.54 hours. The default monitoring frequency is once per minute. The monitoring frequency is adjusted according to the fourth trust factor: 1 * 0.91 = 0.91 times per minute. Therefore, the current monitoring frequency is adjusted to 0.91 times per minute, that is, monitor once every 1.1 minutes. It should also be clear that if the work performance score is lower than 0.6 or the operation status score is lower than 0.8, the system will send a warning or notification. In this application, the work performance score is 0.65 and no warning needs to be sent, and the operation status score is 0.91 and no warning needs to be sent. At the same time, it should be clear again that if the work performance score increases, by optimizing the patrol path and communication quality, the work performance score increases from 0.65 to 0.8, and the third trust factor is updated to 0.8 accordingly. The current patrol frequency is adjusted to: current patrol frequency = 1 * 0.8 = 0.8 times per hour. If the operation status score decreases, assuming that the transformer temperature rises to 88 degrees Celsius, the operation status score decreases from 0.91 to 0.75, and the fourth trust factor is updated to 0.75. The current monitoring frequency is adjusted to: current monitoring frequency = 1 * 0.75 = 0.75 times per minute. Since the operation status score is lower than 0.8, the system will send a warning notification to prompt the operation and maintenance personnel to check the transformer temperature. The monitoring data collected in real time is used to calculate the work performance score and operation status score. The work performance score and operation status score directly determine the third and fourth trust factors. According to the trust factors, the patrol frequency of the patrol terminal and the monitoring frequency of the status monitoring device are dynamically adjusted. When the score is lower than the preset threshold, the system will send a warning or notification to ensure that problems can be handled in a timely manner.

[0080] The substation intelligent patrol system and method based on a trusted WLAN include multiple patrol terminal devices and substation status monitoring devices. Each patrol terminal device is used to record the work behavior data of the patrol personnel, and each substation status monitoring device is used to monitor the operation status data of the substation. The system also includes a data processing center, and the data processing center includes the following steps:

[0081] A data reader for obtaining historical data and current abnormal data of patrol terminal devices and substation status monitoring devices;

[0082] A processor for calculating a first trust factor and a second trust factor based on the obtained data, and calculating the current patrol or monitoring frequency based on the trust factors and historical data;

[0083] A controller for adjusting the actual patrol or monitoring frequency of the patrol terminal device and the substation status monitoring device according to the calculated patrol or monitoring frequency.

[0084] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended embodiments and their equivalents.

Claims

1. A substation intelligent patrol method based on trusted WLAN is applied to a substation intelligent patrol system. The system consists of multiple patrol terminal devices and substation monitoring devices. The patrol terminal devices are responsible for collecting the work behavior data of patrol personnel, and the substation status monitoring devices are used to monitor the operation status data of the substation in real time. The system is characterized by: The intelligent patrol method includes the following steps: Based on extracting its historical behavior data from any patrol terminal device, obtaining its current abnormal behavior data from any patrol terminal device that detects abnormal patrol behavior; Extract historical status data from any status monitoring device, and obtain current abnormal status data from any status monitoring device that detects abnormal operation of the substation.

2. According to claim 1, the intelligent substation inspection method based on trusted WLAN is characterized in that: The wireless signal strength between the patrol terminal and the status monitoring device includes: Acquire a first wireless signal strength between the first patrol terminal device and the second patrol terminal device; Acquire a second wireless signal strength between the first status monitoring device and the second status monitoring device; Calculating a first trust factor based on the first wireless signal strength to evaluate the reliability of the first patrol terminal device; Based on the second wireless signal strength, calculating a second trust factor for evaluating the reliability of the second state monitoring device; Calculating a current patrol frequency based on the first trust factor and historical behavior data of the first patrol terminal device; Calculate the current monitoring frequency of the second state monitoring device based on the second trust factor and the historical state data of the second state monitoring device; According to the calculated current patrol frequency, adjusting the patrol frequency of the first patrol terminal device; According to the calculated current monitoring frequency, the monitoring frequency of the second status monitoring device is adjusted.

3. The intelligent substation inspection method based on trusted WLAN according to claim 1 is characterized in that: The method for calculating the trust factor based on wireless signal strength comprises the following steps: Obtain the total wireless signal strength of all patrol terminal devices and status monitoring devices; Acquire a first wireless signal strength between the first patrol terminal device and the second patrol terminal device; Calculate a first trust factor based on the total wireless signal strength and the first wireless signal strength to evaluate the reliability of the first patrol terminal device; Calculating a second trust factor based on the total wireless signal strength and the second wireless signal strength to evaluate the reliability of the second state monitoring device; The calculated first trust factor and the second trust factor are compared with a preset trust factor database, and the first trust factor and the second trust factor are adjusted according to the comparison result to obtain a more accurate device reliability assessment.

4. The intelligent substation inspection method based on trusted WLAN according to claim 1 is characterized in that: The method for dynamically adjusting patrol and monitoring frequency based on abnormal behavior and status data comprises the following steps: Classify the current abnormal behavior data and the current abnormal state data to determine the abnormal type; According to the abnormality type, the corresponding third trust factor and fourth trust factor are obtained from a preset trust factor database; Comprehensively evaluate the reliability of the device by combining the first trust factor, the second trust factor, the third trust factor, and the fourth trust factor; According to the evaluation results, the patrol frequency of the patrol terminal and the monitoring frequency of the status monitoring equipment are further adjusted.

5. The intelligent substation inspection method based on trusted WLAN according to claim 1 is characterized in that: The steps of implementing real-time monitoring and feedback through the first patrol terminal device and the first status monitoring device include: Collecting current monitoring data of the first patrol terminal device and the first status monitoring device in real time; Based on the acquired monitoring data, the work performance of the inspectors and the operating status score of the substation are calculated respectively; Determine the third and fourth trust factors based on the calculated work performance score and operation status score; Based on the results of the trust factor, the frequency of patrols or monitoring is adjusted, and alerts or notifications are sent when necessary.

6. A substation intelligent patrol system based on a trusted WLAN includes multiple patrol terminal devices and substation status monitoring devices, wherein each patrol terminal device is used to record the work behavior data of patrol personnel, and each substation status monitoring device is used to monitor the operation status data of the substation. The system also includes a data processing center, which is characterized by: The data processing center includes the following steps: Data reader, used to obtain historical data and current abnormal data of patrol terminal equipment and substation status monitoring equipment; a processor, configured to calculate a first trust factor and a second trust factor based on the acquired data, and calculate a current patrol or monitoring frequency based on the trust factors and the historical data; The controller is used to adjust the actual patrol or monitoring frequency of the patrol terminal equipment and the substation status monitoring equipment according to the calculated patrol or monitoring frequency.

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