Standardized electrical switch cabinet detection method based on Internet of Things

Abstract

The invention discloses a standardized electrical switch cabinet detection method based on the Internet of Things, and relates to the technical field of electrical switch cabinet detection, and the method specifically comprises the following steps: obtaining the core information of all abnormal data generated by all sensors in an electrical switch cabinet in real time, and carrying out the analysis after obtaining, evaluating the priority of each abnormal data in the electrical switch cabinet with the high current leakage phenomenon, and dividing each abnormal data into low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data according to an evaluation result; and according to the division result of each abnormal data, constructing an abnormal data hierarchical sorting mechanism, and respectively adopting different sorting and processing measures for low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data. According to the method, the problem of unreasonable priority processing of the abnormal data in a high-current leakage scene is solved, quick response and accurate detection are realized, and the fault processing efficiency and the system reliability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical switch cabinet detection, and in particular to a standardized electrical switch cabinet detection method based on the Internet of Things. Background Art

[0002] Electrical switchgear is a device used to receive, distribute and control electric energy in the power system. It is usually used for the connection and protection of high-voltage or low-voltage power equipment. Its main function is to control and protect the circuit through components such as switches, circuit breakers, and relays. Due to the key role of electrical switchgear in the power system, once a failure occurs, it may cause large-scale power outages and even bring safety hazards such as equipment damage or casualties. Therefore, it is very important to ensure the stable operation of the electrical switchgear. It is necessary to regularly inspect and maintain it to detect potential fault hazards in advance and ensure the safety and reliability of the power system. Traditional electrical switchgear detection usually relies on manual inspections, which has the disadvantages of untimely detection, low efficiency, and inability to detect problems in real time. The standardized electrical switchgear detection method based on the Internet of Things installs IoT sensors in the electrical switchgear to collect key data such as current, voltage, and temperature in real time, and transmits the data to the cloud platform for remote analysis through wireless communication. It can monitor the operating status of the electrical switchgear in real time, automatically identify potential faults, and issue early warnings, thereby achieving more accurate, efficient, and standardized detection. This IoT-based detection method can not only improve the operational reliability of electrical switchgear and reduce human errors and omissions, but also provide a scientific basis for the maintenance of power equipment, extend the service life of equipment, and improve the safety and economy of the entire power system.

[0003] The existing standardized electrical switchgear detection technology based on the Internet of Things collects multiple data related to the operating status of the equipment in real time by installing various sensors (such as current sensors, voltage sensors, temperature sensors, gas sensors, etc.) in the electrical switchgear. These sensors transmit the collected data to the cloud platform or local data center through wireless communication technologies (such as Wi-Fi, LoRa, ZigBee, etc.) for remote monitoring and analysis. In the specific detection process, the system first monitors the current, voltage, temperature and other parameters inside the electrical switchgear in real time through sensors to ensure that the equipment operates within the normal range. If a parameter exceeds the set threshold, the system will automatically trigger an alarm and send an early warning message to prompt the operation and maintenance personnel to conduct inspections and maintenance. In addition, the Internet of Things platform will also conduct big data analysis, establish a health record and fault prediction model for the electrical switchgear through long-term operation data, and realize fault prediction and early warning of the equipment. The system can also regularly generate detection reports based on the actual operating status of the switchgear to help the operation and maintenance personnel evaluate the operating performance and maintenance needs of the equipment, thereby realizing scientific, efficient and standardized management and maintenance, and significantly improving the operating reliability and safety of the electrical switchgear.

[0004] The prior art has the following deficiencies: During the operation of electrical switchgear, when a high current burst leakage occurs (for example, leakage current increases rapidly due to insulation failure), multiple sensors (such as current sensors, temperature sensors, and humidity sensors) will simultaneously generate abnormal data to reflect changes in the equipment status. In this case, since the leakage current poses the most direct threat to equipment safety, the system needs to quickly prioritize the current-related data to determine the leakage source and fault location. However, due to the large amount of burst data and limited data processing resources, the system needs to prioritize and dynamically evaluate multiple sensor data, but the existing standardized electrical switchgear detection technology based on the Internet of Things cannot effectively establish a priority processing mechanism in high current leakage scenarios, which may cause the processing of critical data (such as leakage current values) to be delayed. At the same time, the priority processing of non-critical data (such as changes in ambient humidity) consumes system resources, further reducing the efficiency of fault response. The occurrence of this problem may result in the inability to identify the leakage source in a timely manner and take protective measures. The equipment may continue to be exposed to the risk of high current leakage, which in turn leads to serious consequences such as equipment damage, expansion of the fault range, and even system shutdown.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a standardized electrical switch cabinet detection method based on the Internet of Things to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a standardized electrical switch cabinet detection method based on the Internet of Things, specifically comprising the following steps: During the operation of the electrical switch cabinet, real-time detection of whether high current leakage occurs in the electrical switch cabinet, and in the event of high current leakage, determination of abnormal data generated by all sensors in the electrical switch cabinet to reflect changes in equipment status; Acquire the core information of each abnormal data generated by all sensors in the electrical switch cabinet in real time, analyze it after acquisition, evaluate the priority of each abnormal data in the electrical switch cabinet with high current leakage, and classify each abnormal data into low priority abnormal data, medium priority abnormal data and high priority abnormal data according to the evaluation results; According to the classification results of each abnormal data, a hierarchical sorting mechanism for abnormal data is constructed, and different sorting and processing measures are taken for low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data respectively; In the process of the abnormal data hierarchical sorting mechanism processing each abnormal data, the dynamic feedback information of each abnormal data is obtained in real time, and analyzed after acquisition to evaluate whether the sorting effect of the abnormal data hierarchical sorting mechanism on each abnormal data meets expectations, and the abnormal data hierarchical sorting mechanism is optimized according to the evaluation results; Real-time monitoring and comprehensive analysis of abnormal data, evaluation results, adjustment records of the hierarchical sorting mechanism and treatment effects obtained during the entire high-current leakage processing process are carried out, and the detection strategy and sorting mechanism are continuously optimized to improve the system's response efficiency and detection accuracy.

[0008] Preferably, the core information of each abnormal data generated by all sensors in the electrical switch cabinet is obtained in real time, and analyzed after acquisition, the priority of each abnormal data in the electrical switch cabinet with high current leakage phenomenon is evaluated, and each abnormal data is divided into low priority abnormal data, medium priority abnormal data and high priority abnormal data according to the evaluation result, which specifically includes the following steps: Acquire the core information of each abnormal data generated by all sensors in the electrical switch cabinet in real time and perform preprocessing after acquisition; Extracting fluctuation information and fault-related information from the core information of each abnormal data after preprocessing, and analyzing them after extraction to generate abnormal fluctuation coefficient and fault sensitivity index of each abnormal data respectively; A priority evaluation model is constructed for the abnormal fluctuation coefficient and fault sensitivity index of each abnormal data generated, and a priority coefficient of each abnormal data is generated. After the generation, it is compared with the preset priority coefficient threshold range, and the priority of each abnormal data in the electrical switch cabinet with high current leakage phenomenon is evaluated according to the comparison result, and each abnormal data is divided into low priority abnormal data, medium priority abnormal data and high priority abnormal data according to the evaluation result.

[0009] Preferably, the logic for obtaining the abnormal fluctuation coefficient and the fault sensitivity index of each abnormal data is as follows: Extract the fluctuation information from the core information of each abnormal data after preprocessing, including the actual value of each abnormal data at different times within a period of time when the electrical switch cabinet has a high leakage phenomenon, the maximum and minimum values ​​of each abnormal data in the historical time period, and mark them as , and , Indicates that the electrical switch cabinet has a high leakage phenomenon for a period of time Moment The actual value of the abnormal data, Indicates that in the historical period The maximum value of abnormal data, Indicates that in the historical period The minimum value of abnormal data, , , and are all positive integers; Calculate the abnormal fluctuation coefficient of each abnormal data. The specific calculation formula is as follows: In the formula, For the The abnormal fluctuation coefficient of abnormal data; Extract the fault-related information from the core information of each preprocessed abnormal data, including the leakage current values ​​of the electrical switch cabinet at different times within a period of time when high leakage occurs and the average leakage current of the electrical switch cabinet in the historical time period, and mark them as and , Indicates that the electrical switch cabinet has a high leakage phenomenon for a period of time The leakage current value at the moment, Indicates the average leakage current of the electrical switchgear in the historical period; Calculate the fault sensitivity index of each abnormal data. The specific calculation formula is as follows: In the formula, For the The fault sensitivity index of abnormal data.

[0010] Preferably, the abnormal fluctuation coefficient of each abnormal data generated is and fault sensitivity index Construct a priority evaluation model and generate the priority coefficient of each abnormal data through weighted summation , and after generation, it is compared with the preset priority coefficient threshold interval Compare and evaluate the priority of each abnormal data in the electrical switch cabinet with high current leakage according to the comparison results, and divide each abnormal data into low priority abnormal data, medium priority abnormal data and high priority abnormal data according to the evaluation results. The specific comparison analysis and classification are as follows: like , the priority of the abnormal data in the electrical switch cabinet where the high current leakage phenomenon occurs is low priority, and the abnormal data is classified as low priority abnormal data; like , the priority of the abnormal data in the electrical switch cabinet with high current leakage is medium priority, and the abnormal data is classified as medium priority abnormal data; like The priority of the abnormal data in the electrical switch cabinet where the high current leakage phenomenon occurs is high priority, and the abnormal data is classified as high priority abnormal data.

[0011] Preferably, according to the division results of each abnormal data, a hierarchical sorting mechanism for abnormal data is constructed, specifically: according to the division results of low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data, different sorting rules and processing measures are set respectively to form a hierarchical sorting mechanism for abnormal data; the hierarchical sorting mechanism is based on the priority of each abnormal data and the real-time allocation status of system resources, and automatically determines the sorting strategy and processing method of each abnormal data through pre-set rules; Different sorting and processing measures are taken for low-priority exception data, medium-priority exception data and high-priority exception data, specifically: for high-priority exception data, the fast processing strategy in the hierarchical sorting mechanism is used to prioritize the allocation of computing resources, perform real-time processing and analysis on the high-priority exception data, and quickly respond to and generate fault location and cause judgments; for medium-priority exception data, the delayed processing strategy in the hierarchical sorting mechanism is used to queue and store the medium-priority exception data, and gradually analyze it after the high-priority data is processed; for low-priority exception data, the archiving strategy in the hierarchical sorting mechanism is used to archive the low-priority exception data records.

[0012] Preferably, in the process of the abnormal data hierarchical sorting mechanism processing each abnormal data, dynamic feedback information of each abnormal data is obtained in real time, and analyzed after acquisition to evaluate whether the sorting effect of the abnormal data hierarchical sorting mechanism on each abnormal data meets expectations, and optimize the abnormal data hierarchical sorting mechanism according to the evaluation results, which specifically includes the following steps: In the process of processing each abnormal data by the abnormal data hierarchical sorting mechanism, dynamic feedback information of each abnormal data is obtained in real time, and preprocessed after acquisition; Extracting the sorting response information and the dynamic processing information from the preprocessed dynamic feedback information of each abnormal data, and analyzing them after extraction to generate the sorting response deviation coefficient and the dynamic processing result coefficient of each abnormal data respectively; A sorting effect evaluation model is constructed for the sorting response deviation coefficient and dynamic processing result coefficient of each generated abnormal data to generate a sorting evaluation coefficient for each abnormal data. After the generation, the coefficient is compared with the pre-set sorting evaluation coefficient threshold for each abnormal data. According to the comparison results, it is evaluated whether the sorting effect of each abnormal data by the abnormal data hierarchical sorting mechanism meets the expectation, and the abnormal data hierarchical sorting mechanism is optimized according to the evaluation results.

[0013] Preferably, the logic for obtaining the sorting response deviation coefficient and the dynamic processing result coefficient of each abnormal data is as follows: The sorting response information in the dynamic feedback information of each abnormal data after preprocessing is extracted, including the time point when each abnormal data is identified by the abnormal data hierarchical sorting mechanism and enters the queue, the time point when each abnormal data starts to be processed, and the average waiting time of all data in the queue where each abnormal data is located, and they are marked as , and , Indicates The time point when an abnormal data is identified by the abnormal data hierarchical sorting mechanism and enters the queue, Indicates The time point when abnormal data begins to be processed, Indicates The average waiting time of all data in the queue where the abnormal data is located, , is a positive integer; Calculate the sort response deviation coefficient of each abnormal data. The specific calculation formula is as follows: In the formula, For the The sorting response deviation coefficient of abnormal data; The dynamic processing information in the dynamic feedback information of each abnormal data after preprocessing is extracted, including the time taken for each abnormal data to be processed from the beginning to the end, the amount of effective data per unit time generated by each abnormal data during the processing, and the total amount of effective data generated by each abnormal data during the processing, and the information is marked as , and , Indicates The time taken from the start to the completion of the processing of abnormal data. Indicates The amount of valid data per unit time generated during the processing of abnormal data, Indicates The total amount of valid data during the processing of abnormal data; Calculate the dynamic processing result coefficient of each abnormal data. The specific calculation formula is as follows: In the formula, For the The dynamic processing result coefficient of abnormal data.

[0014] Preferably, the sort response deviation coefficient for each abnormal data generated is and dynamic processing result coefficient Construct a ranking effect evaluation model and generate ranking evaluation coefficients for each abnormal data through weighted summation , and after generation, the sorting evaluation coefficient threshold of each abnormal data is set in advance Compare and evaluate whether the abnormal data stratification and sorting mechanism has achieved the expected sorting effect on each abnormal data according to the comparison results, and optimize the abnormal data stratification and sorting mechanism according to the evaluation results. The specific comparison and analysis are as follows: like , the abnormal data hierarchical sorting mechanism achieves the expected sorting effect on the abnormal data, and there is no need to optimize the abnormal data hierarchical sorting mechanism; like The hierarchical sorting mechanism for abnormal data did not achieve the expected sorting effect on the abnormal data, and it is necessary to optimize the hierarchical sorting mechanism for abnormal data, including: adjusting the queue management rules in the hierarchical sorting mechanism for abnormal data, optimizing the sorting priority strategy according to the evaluation results, and allocating resources to process high-priority abnormal data first; dynamically modifying the processing delay parameters of low-priority abnormal data to reduce the occupation of system resources; reallocating processing resources, improving resource utilization efficiency, and ensuring that data of different priorities can be reasonably sorted and processed.

[0015] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention realizes efficient monitoring of the operating status of the electrical switch cabinet by detecting high current leakage phenomena in real time and accurately identifying abnormal data. When high current leakage occurs, the system can quickly obtain the core information of each abnormal data, and generate abnormal fluctuation coefficients and fault sensitivity indexes based on the extracted fluctuation information and fault-related information, which makes the evaluation of the priority of abnormal data more scientific and accurate. The hierarchical sorting mechanism after priority division can dynamically allocate resources and implement hierarchical processing strategies, thereby ensuring that high-priority abnormal data is processed quickly, significantly improving the system's response efficiency to critical fault data. In addition, during the operation of the hierarchical sorting mechanism, the real-time feedback mechanism enables the system to dynamically monitor the sorting effect, and further optimizes the sorting mechanism by calculating and evaluating the sorting response deviation coefficient and the dynamic processing result coefficient, ultimately achieving accurate judgment and rapid response to high current leakage scenarios.

[0016] 2. The present invention expands the priority evaluation of abnormal data from a single dimension to multiple dimensions by constructing a multi-parameter evaluation model, and uses a combination of abnormal fluctuation coefficient and fault sensitivity index, which not only improves the accuracy of priority division, but also enhances the system's adaptability to complex data scenarios. Secondly, the hierarchical sorting mechanism realizes the dynamic optimization of resource utilization, which not only gives priority to high-priority abnormal data, but also delays or archives low-priority abnormal data. This resource allocation strategy effectively avoids the waste of system resources and ensures processing efficiency under high-load scenarios. Finally, through the dynamic optimization of the sorting effect by the real-time feedback mechanism, the system can adjust the sorting rules and resource allocation strategies according to real-time data, significantly improving the flexibility and responsiveness of the entire detection process.

[0017] 3. The present invention forms an efficient closed-loop technical system by combining real-time monitoring, data analysis, priority assessment, sorting strategy and dynamic feedback optimization. This design effectively solves the problems of key data processing delay and resource waste in traditional IoT-based electrical switchgear detection technology, and is particularly suitable for complex and urgent scenarios such as high current leakage. While achieving high-precision data analysis, the system improves processing efficiency and detection accuracy by dynamically adjusting the sorting mechanism, thereby providing higher protection for the safe operation of the electrical switchgear. These technical effects and advantages make the present invention significantly innovative and practical in the relevant technical fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0019] Figure 1 The present invention is a flowchart of a standardized electrical switch cabinet detection method based on the Internet of Things. DETAILED DESCRIPTION

[0020] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0021] The present invention provides Figure 1 A standardized electrical switch cabinet detection method based on the Internet of Things is shown, which specifically includes the following steps: During the operation of the electrical switch cabinet, real-time detection of whether high current leakage occurs in the electrical switch cabinet, and in the event of high current leakage, determination of abnormal data generated by all sensors in the electrical switch cabinet to reflect changes in equipment status; During the operation of the electrical switchgear, real-time detection of whether high current leakage occurs can be achieved by combining the real-time monitoring function of the current sensor with the data analysis algorithm. The current sensor is deployed at the key nodes of the electrical switchgear, which can continuously collect the real-time value of the leakage current and record the trend of the current change over time. Through the Internet of Things network, these data will be transmitted to the central processing unit in real time. The system uses advanced data processing algorithms to analyze the change rate and abnormal amplitude of the leakage current, and generates two parameters: the current change rate coefficient and the leakage current abnormal factor. These two parameters are combined to generate a leakage detection evaluation coefficient, which is compared with the preset leakage threshold. If the evaluation coefficient exceeds the normal range, the system immediately determines that a high current leakage phenomenon has occurred and issues an alarm signal, while triggering the subsequent abnormal data collection and processing process. This method not only ensures the real-time detection, but also effectively improves the accuracy of judging high current leakage phenomena.

[0022] After confirming the occurrence of high current leakage, the system needs to quickly identify the abnormal data generated by all sensors in the electrical switch cabinet to reflect the change of equipment status. Through the Internet of Things communication protocol, the system sends collection requests to multiple types of sensors (such as temperature sensors, humidity sensors, etc.) in the electrical switch cabinet, and the sensors immediately return real-time data at the current moment, including temperature fluctuation values, humidity change rates, and other key parameters of equipment operation. After receiving this data, the central system uses a denoising algorithm to eliminate irrelevant or redundant data, and marks data that exceeds the set safety threshold as abnormal data. Then, the system further analyzes the burstiness and correlation of this data, screens out data that may be associated with high current leakage, and generates a list of abnormal data markers for subsequent priority analysis and processing. In this way, the system can accurately lock in data that has a critical impact on the equipment status in the high current leakage scenario, avoiding delays in the response of key data due to data overload.

[0023] The reason why it is necessary to filter abnormal data in the above way after the high current leakage phenomenon occurs is that multiple sensors will generate a large amount of data at the same time in an emergency scenario, and the system resources are limited. If these data are processed without screening, key data may be delayed or even ignored, which will lead to the failure to handle the fault in time. High current leakage is one of the most dangerous forms of failure in electrical switchgear, which is directly related to the safety and stability of the equipment. By detecting leakage phenomena in real time and filtering related abnormal data, the system can optimize resource allocation at the data level to ensure priority response to data closely related to fault location. This not only can quickly and accurately determine the source of the fault, but also provide high-quality data input for subsequent priority evaluation, fundamentally improving the system's response speed and fault handling efficiency, while solving the response delay and resource waste caused by data priority confusion mentioned in the technical problem.

[0024] Acquire the core information of each abnormal data generated by all sensors in the electrical switch cabinet in real time, analyze it after acquisition, evaluate the priority of each abnormal data in the electrical switch cabinet with high current leakage, and classify each abnormal data into low priority abnormal data, medium priority abnormal data and high priority abnormal data according to the evaluation results; In this embodiment, the core information of each abnormal data generated by all sensors in the electrical switch cabinet is obtained in real time, and analyzed after acquisition, the priority of each abnormal data in the electrical switch cabinet with high current leakage phenomenon is evaluated, and each abnormal data is divided into low priority abnormal data, medium priority abnormal data and high priority abnormal data according to the evaluation result, which specifically includes the following steps: Acquire the core information of each abnormal data generated by all sensors in the electrical switch cabinet in real time and perform preprocessing after acquisition; During the operation of the electrical switch cabinet, the core information of each abnormal data generated by all sensors can be obtained in real time by combining multiple types of sensors deployed in the electrical switch cabinet with the IoT communication module. Specifically, various sensors (such as current sensors, temperature sensors, humidity sensors, etc.) continuously collect data at a preset sampling frequency, and the collected raw data is preliminarily packaged and processed through the local data aggregator, and the abnormal data is classified and stored according to fields such as timestamp and sensor type. Subsequently, these data are transmitted to the central data processing unit in real time through the IoT communication protocol (such as MQTT or CoAP protocol). After receiving the data, the central data processing unit ensures the integrity of the data transmission through a verification mechanism, and extracts the core data that meets the preset characteristics (such as current fluctuation value, temperature mutation value, etc.). This method realizes the real-time acquisition and accurate transmission of multi-sensor data, providing a complete information basis for subsequent data analysis.

[0025] The purpose of preprocessing is to improve the quality and reliability of abnormal data and ensure the accuracy and stability of subsequent analysis. Noise, redundant information or missing values ​​may occur during data acquisition. If preprocessing is not performed, it may lead to deviations in analysis results and affect the system's ability to respond to high current leakage. Preprocessing includes denoising, data formatting and data consistency verification. The specific implementation methods are as follows: For denoising, filtering algorithms (such as mean filtering or median filtering) can be used to eliminate high-frequency noise in the data; for data formatting, the data uploaded by different sensors are normalized into a unified format (such as a unified timestamp and unit); for consistency verification, outliers are eliminated by comparing the logical consistency between multiple sensor data at the same time point. For example, if the temperature value of a sensor far exceeds the measurement range of other sensors, it is marked as invalid data and eliminated. Through these preprocessing operations, the data entering the priority evaluation process is ensured to be of high quality and credibility.

[0026] Extracting fluctuation information and fault-related information from the core information of each abnormal data after preprocessing, and analyzing them after extraction to generate abnormal fluctuation coefficient and fault sensitivity index of each abnormal data respectively; Extracting the fluctuation information and fault-related information from the core information of each abnormal data after preprocessing can be achieved through data feature analysis and correlation modeling. Specifically, the system first performs feature decomposition on the preprocessed data, extracts key feature values ​​related to the fluctuation information, such as the current data value, the historical maximum value, and the historical minimum value, and forms the fluctuation information by calculating the degree of deviation between the current data and the historical range. In addition, the system uses the current value of the leakage current, the current value of the abnormal data, and the historical average value of the leakage current for the fault-related information, and calculates the dynamic correlation between the abnormal data and the leakage current through mathematical modeling. For example, the system evaluates the correlation strength between the abnormal data change and the leakage current change through a correlation analysis algorithm (such as the Pearson correlation coefficient or the weighted linear regression model), and generates fault-related information. The entire extraction process is completed by the central data processing unit. The software automatically matches the data features and correlation relationships, screens out information that meets specific conditions, forms an independent feature set of fluctuation information and fault-related information, and provides accurate input for subsequent parameter calculations.

[0027] A priority evaluation model is constructed for the abnormal fluctuation coefficient and fault sensitivity index of each abnormal data generated, and a priority coefficient of each abnormal data is generated. After the generation, it is compared with the preset priority coefficient threshold range, and the priority of each abnormal data in the electrical switch cabinet with high current leakage phenomenon is evaluated according to the comparison result, and each abnormal data is divided into low priority abnormal data, medium priority abnormal data and high priority abnormal data according to the evaluation result.

[0028] The pre-set priority coefficient threshold can be dynamically determined by historical data analysis and multi-scenario simulation. Specifically, the system first collects historical operation data, including the abnormal fluctuation coefficient, fault sensitivity index and corresponding actual fault response results (such as fault type, impact range, response time, etc.) of each abnormal data in different high current leakage scenarios. Using these data, the system classifies the priority coefficients of abnormal data through clustering analysis algorithms (such as K-means clustering) to identify the key distribution characteristics of various types of data in high current leakage scenarios. Then, based on the clustering results, the system uses a multi-objective optimization algorithm (such as a genetic algorithm or a particle swarm optimization algorithm) to optimize the classification threshold of the priority coefficient to ensure that the threshold can strike a balance between the minimum misjudgment rate and the maximum accuracy. For example, by simulating the impact of different priority divisions on the fault response time, the upper and lower limits of the threshold interval are automatically adjusted, and finally the priority coefficient threshold intervals of low priority, medium priority and high priority suitable for the current electrical switchgear operating environment are generated. The entire process is automatically completed by the software to ensure the accuracy and adaptability of the dynamic adjustment of the threshold, and provide a scientific basis for the subsequent priority division of abnormal data.

[0029] In this embodiment, the logic for obtaining the abnormal fluctuation coefficient and fault sensitivity index of each abnormal data is as follows: Extract the fluctuation information from the core information of each abnormal data after preprocessing, including the actual value of each abnormal data at different times within a period of time when the electrical switch cabinet has a high leakage phenomenon, the maximum and minimum values ​​of each abnormal data in the historical time period, and mark them as , and , Indicates that the electrical switch cabinet has a high leakage phenomenon for a period of time Moment The actual value of the abnormal data, Indicates that in the historical period The maximum value of abnormal data, Indicates that in the historical period The minimum value of abnormal data, , , and are all positive integers; When the electrical switch cabinet has a high current leakage phenomenon, the actual value of each abnormal data at different times within a period of time, and the maximum and minimum values ​​of each abnormal data in the historical time period can be obtained by combining sensor collection and data analysis. First, various sensors (such as current sensors, temperature sensors, and humidity sensors) collect data in real time at a fixed sampling frequency during the operation of the electrical switch cabinet, and these data are transmitted to the central processing unit through the Internet of Things communication protocol (such as MQTT or CoAP protocol). For the actual values ​​at different times within a period of time when the high leakage phenomenon occurs, the system sets an event trigger mechanism. When the leakage current exceeds the set threshold, the system automatically records the sensor data within the set time window after the fault occurs, and sorts them by time points and stores them as the actual value sequence of the current time period. For the maximum and minimum values ​​of the historical time period, the system extracts all records of the corresponding sensor within the specified time range from the historical database, and obtains the maximum and minimum values ​​by statistically analyzing these records. In this way, the acquisition of real-time data ensures that the dynamic state of the current high leakage phenomenon is reflected, while the acquisition of historical maximum and minimum values ​​provides a comparison benchmark for analyzing the abnormality and fluctuation of the data.

[0030] Calculate the abnormal fluctuation coefficient of each abnormal data. The specific calculation formula is as follows: In the formula, For the The abnormal fluctuation coefficient of abnormal data; This calculation formula is designed to comprehensively measure the fluctuation range and severity of each abnormal data over a period of time, thereby reflecting its deviation from the historical operating status. and Indicates the current data value The absolute deviation from the historical maximum and minimum values ​​reflects the data at a certain point in time. The degree of deviation from the boundary of the historical fluctuation range. The greater the deviation, the stronger the abnormality of the data. It is the historical fluctuation range, which is used for normalization processing to eliminate the influence of different data dimensions and scale differences and make the results consistent; the cumulative sum represents the aggregation of deviations at all time points to capture the fluctuation trend of abnormal data in the entire time period; It means taking the average of the deviations within the time period to smooth the impact of single-point outliers, thereby more comprehensively reflecting the overall volatility of abnormal data. This calculation method ensures that the abnormal fluctuation coefficient can accurately and stably quantify the degree of fluctuation of abnormal data, providing a scientific basis for subsequent priority evaluation.

[0031] No. Abnormal fluctuation coefficient of abnormal data There is a positive correlation between the size of and the priority of each abnormal data in the electrical switch cabinet where the high current leakage phenomenon occurs. The larger the abnormal fluctuation coefficient, the more significant the fluctuation amplitude of the data during the high current leakage phenomenon, and the more its actual value deviates from the historical operating range, indicating that the abnormal data may reflect abnormal behavior that is more closely related to the fault state, and therefore has a higher priority; conversely, if the abnormal fluctuation coefficient is small, the fluctuation of the data is not significant, and its state is closer to the historical normal operating range, indicating that its abnormality is low, and the possibility of association with the current high current leakage phenomenon is small, and the priority is relatively low. By using the abnormal fluctuation coefficient, it is possible to quantitatively judge the importance of each abnormal data in the fault scenario, thereby achieving scientific division of priorities and reasonable allocation of resources.

[0032] Extract the fault-related information from the core information of each preprocessed abnormal data, including the leakage current values ​​of the electrical switch cabinet at different times within a period of time when high leakage occurs and the average leakage current of the electrical switch cabinet in the historical time period, and mark them as and , Indicates that the electrical switch cabinet has a high leakage phenomenon for a period of time The leakage current value at the moment, Indicates the average leakage current of the electrical switchgear in the historical period; The leakage current values ​​at different times within a period of time when the electrical switch cabinet has a high leakage phenomenon and the average leakage current value of the electrical switch cabinet in the historical time period can be acquired and generated in real time by combining sensor acquisition and data processing. Specifically, the high-precision current sensor installed inside the electrical switch cabinet can continuously monitor the real-time value of the leakage current at a fixed sampling frequency (for example, multiple times per second), and the collected data is transmitted to the central processing unit in real time through the Internet of Things communication module. At the moment when the leakage current exceeds the set threshold, the software triggers the event response mechanism, records the timestamp when the fault occurs, and extracts the leakage current data at all times within the set time window from the continuous sampling values ​​of the current sensor, and saves it as a "sequence of leakage current values ​​in the fault time period". At the same time, for the average leakage current value of the historical time period, the system extracts the long-term sampling data of the current sensor from the historical database, and uses the built-in statistical analysis function of the software (such as the mean calculation module) to calculate the extracted data to generate the average leakage current value of the historical time period. This process can be run in real time in the background by setting an automated calculation task to ensure that accurate historical reference data can be provided immediately when high leakage occurs. Through this automated processing link of sensor-communication-statistical analysis, the system can obtain leakage current data and historical average values ​​during the fault period in real time, providing accurate input for the calculation of the fault sensitivity index.

[0033] Calculate the fault sensitivity index of each abnormal data. The specific calculation formula is as follows: In the formula, For the The fault sensitivity index of abnormal data.

[0034] This calculation formula is designed to comprehensively evaluate the dynamic correlation between each abnormal data and the high current leakage phenomenon. In the formula, Indicates the leakage current value at the current moment Compared with the historical leakage current average The deviation reflects the abnormal degree of leakage current under the current fault condition; It is the current moment The actual value of the abnormal data is multiplied by the leakage current deviation Indicates the response intensity of the dynamic change of abnormal data to the leakage current deviation, reflecting the correlation between the two; the denominator The entire calculation result is normalized to eliminate the influence of the magnitude of the leakage current data on the index calculation, making different data comparable. Finally, the correlation of all time points is accumulated to indicate the comprehensive correlation between the abnormal data and the leakage current during the entire fault period. The accumulated values ​​are averaged to balance the impact of single-point abnormal fluctuations and ensure that the results are more stable and reliable. In this way, the fault sensitivity index can scientifically quantify the dynamic relationship between abnormal data and fault status, providing an accurate basis for priority assessment.

[0035] No. Fault sensitivity index of abnormal data The size of directly affects its priority division when evaluating the occurrence of high current leakage. The larger the fault sensitivity index, the stronger the dynamic correlation between the actual change of the abnormal data and the abnormal deviation of the leakage current, that is, the abnormal data may more directly reflect the fault state or key environmental characteristics in the high current leakage phenomenon, so it has a higher priority; conversely, if the fault sensitivity index is small, it indicates that the change of the abnormal data is weakly correlated with the fluctuation of the leakage current, its importance and correlation with the fault state are low, and its priority is relatively low. Through this indexed quantification method, the system can accurately evaluate the importance of each abnormal data, thereby giving priority to data closely related to the high current leakage phenomenon, and realizing reasonable allocation of resources and rapid response.

[0036] In this embodiment, the abnormal fluctuation coefficient of each abnormal data generated is and fault sensitivity index Construct a priority evaluation model and generate the priority coefficient of each abnormal data through weighted summation , and after generation, it is compared with the preset priority coefficient threshold interval Compare and evaluate the priority of each abnormal data in the electrical switch cabinet with high current leakage according to the comparison results, and divide each abnormal data into low priority abnormal data, medium priority abnormal data and high priority abnormal data according to the evaluation results. The specific comparison analysis and classification are as follows: like , the priority of the abnormal data in the electrical switch cabinet where the high current leakage phenomenon occurs is low priority, and the abnormal data is classified as low priority abnormal data; This situation means that the abnormal volatility and fault sensitivity of the abnormal data are low, and its dynamic correlation with the current high current leakage phenomenon is weak. In other words, the fluctuation range of the data is close to the normal operating state, and its changes have not significantly deviated from the historical range. It may be a slight fluctuation caused by environmental noise or irrelevant factors. Classifying such data as low-priority abnormal data can effectively avoid unnecessary analysis and processing of irrelevant data under limited resources, reduce system resource waste, and ensure that processing resources are concentrated on more critical data analysis.

[0037] like , the priority of the abnormal data in the electrical switch cabinet with high current leakage is medium priority, and the abnormal data is classified as medium priority abnormal data; This means that the abnormal volatility or fault sensitivity of the abnormal data is related to the high current leakage phenomenon to a certain extent, but the correlation is at a medium level. Such data may reflect changes in the status of the equipment, but it does not directly indicate the source of leakage or the key fault point. By classifying such data as medium-priority abnormal data, the system can gradually analyze it after the high-priority data is processed, thereby ensuring that key data is responded to first while not ignoring potential fault hazards and ensuring the comprehensiveness of fault analysis.

[0038] like The priority of the abnormal data in the electrical switch cabinet where the high current leakage phenomenon occurs is high priority, and the abnormal data is classified as high priority abnormal data.

[0039] This means that the abnormal data has high abnormal volatility and fault sensitivity, and has a significant dynamic correlation with the high current leakage phenomenon. Such data usually directly reflects the drastic changes in leakage current or serious abnormalities in the equipment operating environment, and may be a direct representation of the fault source or critical fault area. Classifying such data as high-priority abnormal data, the system will immediately respond and process it, quickly locate the fault source and take targeted measures, which will help avoid the spread of faults or equipment damage, and improve the overall response efficiency and safety assurance capabilities of the system.

[0040] A priority evaluation model is constructed for the abnormal fluctuation coefficient and fault sensitivity index of each abnormal data generated, and the priority coefficient of each abnormal data is generated by weighted summation. This can be achieved in the following way: the system first calculates the abnormal fluctuation coefficient of each abnormal data. and fault sensitivity index Standardize them separately to ensure that they are within the same numerical range to eliminate the impact of different dimensions on the calculation results; then, the system sets two weight coefficients based on historical data and simulation analysis results. and , respectively represent the importance of abnormal fluctuation coefficient and fault sensitivity index in priority assessment. The weight coefficient is usually adjusted through optimization algorithms (such as genetic algorithms or gradient descent methods) so that the model can accurately distinguish the priority of abnormal data in different scenarios. For example, in the high current leakage scenario of electrical switch cabinets, the fault sensitivity index usually better reflects the direct correlation between data and faults, so The value may be large, and the abnormal fluctuation coefficient reflects the severity of the data. The value is determined according to its contribution to the fault correlation. Finally, the priority coefficient is calculated as Through this weighted sum model, the system can comprehensively measure the importance of each abnormal data and provide a scientific basis for priority division.

[0041] According to the classification results of each abnormal data, a hierarchical sorting mechanism for abnormal data is constructed, and different sorting and processing measures are taken for low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data respectively; In this embodiment, according to the division results of each abnormal data, a hierarchical sorting mechanism for abnormal data is constructed, specifically: according to the division results of low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data, different sorting rules and processing measures are set respectively to form a hierarchical sorting mechanism for abnormal data; the hierarchical sorting mechanism is based on the priority of each abnormal data and the real-time allocation status of system resources, and automatically determines the sorting strategy and processing method of each abnormal data through pre-set rules; The construction of a hierarchical sorting mechanism for abnormal data can be achieved by combining a priority grading algorithm with a dynamic resource scheduling strategy. Specifically, the system first divides the data into low priority, medium priority, and high priority categories according to the priority coefficient of each abnormal data and its comparison with the preset threshold interval, and assigns different processing priority labels to each type of data; then, the system monitors the available status of the system computing resources in real time through a resource scheduling algorithm (such as a priority-based dynamic queue management algorithm), and dynamically adjusts the sorting strategy and execution process of data processing according to the priority label. For example, high-priority abnormal data is directly pushed to the real-time processing queue, and computing resources are immediately allocated to quickly analyze the fault; medium-priority abnormal data enters the delayed processing queue and is processed in turn according to the resource idleness; low-priority abnormal data is stored in the database, waiting for subsequent non-real-time analysis. Through preset rules and dynamic optimization, the system can prioritize critical data under resource-constrained conditions while maintaining reasonable management of non-critical data. This design can maximize the use of limited computing resources, ensure the system's rapid response to high current leakage phenomena, and take into account the long-term use of secondary and historical data, providing comprehensive protection for the safe operation of electrical switchgear.

[0042] Different sorting and processing measures are taken for low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data, specifically: for high-priority abnormal data, the fast processing strategy in the hierarchical sorting mechanism is used to prioritize the allocation of computing resources, perform real-time processing and analysis on the high-priority abnormal data, and quickly respond to and generate fault location and cause judgments; for medium-priority abnormal data, the delayed processing strategy in the hierarchical sorting mechanism is used to queue and store the medium-priority abnormal data, and gradually analyze it after the high-priority data is processed; for low-priority abnormal data, the archiving strategy in the hierarchical sorting mechanism is used to record and archive the low-priority abnormal data for long-term trend analysis and reference processing in subsequent non-emergency scenarios.

[0043] Different sorting and processing measures are taken for low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data, which can be achieved through a task scheduling algorithm based on priority queue management. Specifically, the system first stores the divided abnormal data into high-priority queues, medium-priority queues and low-priority archive databases according to priority tags, and sets different processing rules and trigger conditions for each queue. For high-priority abnormal data, the system prioritizes computing resources through real-time processing queues, uses parallel computing frameworks to quickly analyze data, and generates fault location and cause judgment reports; priority abnormal data is stored in delayed processing queues, and the queue scheduling strategy is set to "wait for the high-priority queue to be empty" and then processed in sequence to ensure the sequential and efficient use of resources; low-priority abnormal data is directly archived to long-term storage units, recording key information for trend analysis or retrieval processing in non-emergency situations. The management rules of each queue are driven by a preset task scheduling strategy, such as a dynamic resource monitoring module that senses the available status of system resources in real time and automatically adjusts the processing order and execution frequency of tasks in combination with priority tags. This design ensures that high-priority abnormal data is quickly analyzed and processed in fault response, while taking into account the delayed analysis of medium-priority data and the archiving management of low-priority data, thereby achieving a balance between system response speed, resource utilization efficiency and comprehensive data management under conditions of limited resources, and effectively ensuring the safety and operating efficiency of electrical switchgear.

[0044] In the process of the abnormal data hierarchical sorting mechanism processing each abnormal data, the dynamic feedback information of each abnormal data is obtained in real time, and analyzed after acquisition to evaluate whether the sorting effect of the abnormal data hierarchical sorting mechanism on each abnormal data meets expectations, and the abnormal data hierarchical sorting mechanism is optimized according to the evaluation results; In this embodiment, in the process of the abnormal data hierarchical sorting mechanism processing each abnormal data, dynamic feedback information of each abnormal data is obtained in real time, and analyzed after acquisition to evaluate whether the sorting effect of the abnormal data hierarchical sorting mechanism on each abnormal data meets expectations, and optimize the abnormal data hierarchical sorting mechanism according to the evaluation results, which specifically includes the following steps: In the process of processing each abnormal data by the abnormal data hierarchical sorting mechanism, dynamic feedback information of each abnormal data is obtained in real time, and preprocessed after acquisition; Real-time acquisition of dynamic feedback information of abnormal data can be achieved through the dynamic monitoring module in the hierarchical sorting mechanism. When processing each abnormal data, the system automatically collects processing status information, including the start time and end time of the task, the amount of data generated during the processing, and resource usage. This information is captured in real time by sensors or logging modules in the system and transmitted to the central processing unit through IoT communication protocols (such as MQTT or HTTP). To ensure real-time and accuracy, the system uses a combination of event triggering mechanisms (such as task completion or resource usage peak) and time interval collection to aggregate dynamic feedback information into a central database. All collected information is automatically marked with timestamps and identifiers of abnormal data to ensure that each data point is accurately associated with the corresponding abnormal data instance.

[0045] The purpose of preprocessing dynamic feedback information is to improve the consistency and accuracy of the data, thereby ensuring the reliability of subsequent analysis. During the data collection process, problems such as noise, incompleteness or inconsistent formats may occur, which may lead to biased evaluation results if used directly for analysis. Preprocessing includes the following steps: first, use filtering algorithms (such as median filtering) to remove random noise in the collected data to ensure data accuracy; second, perform formatting processing to normalize data such as timestamps and numerical ranges into a unified format; third, use interpolation to fill in missing data points to ensure data integrity; finally, implement logical checks to check the continuity of the time series and the uniqueness of the task identifier, and remove abnormal data points. These preprocessing steps are automatically completed in the central processing unit through the data cleaning algorithm to ensure that the dynamic feedback information can become high-quality input and provide a solid data foundation for the evaluation of sorting effects.

[0046] Extracting the sorting response information and the dynamic processing information from the preprocessed dynamic feedback information of each abnormal data, and analyzing them after extraction to generate the sorting response deviation coefficient and the dynamic processing result coefficient of each abnormal data respectively; Extracting the sorting response information and dynamic processing information from the dynamic feedback information of each abnormal data after preprocessing can be achieved through the data parsing module combined with the feature extraction algorithm. Specifically, the system first uses the standardized dynamic feedback information stream output by the preprocessing module, including fields such as timestamp, processing time, generated data volume, queue position, etc. The data parsing module automatically matches the data related to the sorting response (such as task trigger time, start processing time, average queue waiting time, etc.) and the data related to dynamic processing (such as actual processing time, generated data volume per unit time, total generated data volume, etc.) according to the preset field labels and data rules, and groups and classifies them into sorting response information and dynamic processing information. During the extraction process, the system verifies and filters the fields of each record through the feature mapping algorithm to ensure that the extracted data is complete, valid and non-duplicate. In addition, for the core features such as the time field in the sorting response information and the generated data volume in the dynamic processing information, the parsing module verifies the format consistency and performs time series analysis to further enhance the accuracy and reliability of the extraction results. The extracted information is automatically stored in the corresponding database table, and associations are established according to the identifiers of the abnormal data to provide high-quality basic data for subsequent analysis and parameter generation.

[0047] A sorting effect evaluation model is constructed for the sorting response deviation coefficient and dynamic processing result coefficient of each generated abnormal data to generate a sorting evaluation coefficient for each abnormal data. After the generation, the coefficient is compared with the pre-set sorting evaluation coefficient threshold for each abnormal data. According to the comparison results, it is evaluated whether the sorting effect of each abnormal data by the abnormal data hierarchical sorting mechanism meets the expectation, and the abnormal data hierarchical sorting mechanism is optimized according to the evaluation results.

[0048] The pre-set sorting evaluation coefficient thresholds for each abnormal data can be determined by combining historical data analysis with multi-scenario simulation. Specifically, the system first extracts feedback data containing sorting response information and dynamic processing information from historical operation records, including the sorting response deviation coefficient and dynamic processing result coefficient of abnormal data, as well as the actual sorting effect corresponding to these parameters (such as response delay, processing efficiency, etc.). Then, the historical data is grouped by clustering analysis algorithms (such as K-means clustering) to distinguish the parameter distribution characteristics under different sorting effects, and preliminarily divide the low, medium, and high evaluation coefficient ranges. Subsequently, the system uses multi-scenario simulation tools to perform regression analysis on a large amount of sorting result data generated for different sorting effects (such as extreme loads, high priority ratios, etc.), optimize the threshold range of the evaluation coefficient, and ensure its applicability in different operating environments. Finally, the genetic algorithm or particle swarm optimization algorithm is used to globally optimize the threshold range so that the distribution of low priority, medium priority, and high priority evaluation results has the minimum error rate and maximum discrimination. This process is completely completed automatically by the software, and the generated evaluation coefficient threshold range can dynamically adapt to different system states, providing a reliable benchmark for the accurate evaluation of the sorting effect of abnormal data.

[0049] In this embodiment, the logic for obtaining the sorting response deviation coefficient and the dynamic processing result coefficient of each abnormal data is as follows: The sorting response information in the dynamic feedback information of each abnormal data after preprocessing is extracted, including the time point when each abnormal data is identified by the abnormal data hierarchical sorting mechanism and enters the queue, the time point when each abnormal data starts to be processed, and the average waiting time of all data in the queue where each abnormal data is located, and they are marked as , and , Indicates The time point when an abnormal data is identified by the abnormal data hierarchical sorting mechanism and enters the queue, Indicates The time point when abnormal data begins to be processed, Indicates The average waiting time of all data in the queue where the abnormal data is located, , is a positive integer; The quantitative data of "the time point when each abnormal data is identified and enters the queue by the abnormal data hierarchical sorting mechanism, the time point when each abnormal data starts to be processed, and the average waiting time of all data in the queue where each abnormal data is located" can be automatically obtained through the task management module and data monitoring module in the hierarchical sorting mechanism. First, the time point when each abnormal data is identified and enters the queue (entry time point) can be obtained through the timestamp recorded by the task management module when the data is added to the queue. For example, at the moment when the data arrives at the sorting queue, the system automatically triggers the event log function to mark the identification time point of the data. Secondly, the time point when each abnormal data starts to be processed (start time point) is recorded by the task scheduling module when the processing flow of the data is started. The system will generate and store the timestamp through the work log module of the processor while the scheduler calls the processing resources. Finally, the average waiting time of all data in the queue where each abnormal data is located (the average waiting time of the queue) is obtained by the data monitoring module regularly calculating the waiting time of all tasks in the queue. The specific method is: the system accumulates the "start time minus the entry time" of each task in the current queue, and divides it by the total number of tasks to get the average value. The entire process of acquiring these data is automatically completed by the task management and monitoring modules in the system through software, ensuring the real-time and accuracy of the data, and providing a reliable basic support for subsequent analysis and parameter calculation.

[0050] Calculate the sort response deviation coefficient of each abnormal data. The specific calculation formula is as follows: In the formula, For the The sorting response deviation coefficient of abnormal data; Sorting response deviation coefficient The purpose of the calculation formula is to comprehensively measure the response effect of the actual waiting time from the abnormal data entering the queue to being processed relative to the average waiting time of the queue, as well as the impact of response deviation on sorting consistency. The first part of the formula Through normalization, the influence of different queue loads on the waiting time calculation is eliminated, so that the waiting times of different queues can be compared under the same dimension; Part II By normalizing the absolute deviation, the impact of the inconsistency between the sorting response and the average waiting time is magnified, thereby more sensitively reflecting the sorting deviation problem. The overall structure of the formula introduces the deviation into the weighted correction to ensure that when the actual waiting time is close to the average time, The value is close to the ideal value, and when the deviation is large, The values ​​are significantly reduced, making it easier to identify problematic data.

[0051] The calculation of the sorting response deviation coefficient combines three core data: the time point of entering the queue, the time point of starting processing, and the average waiting time of the queue, accurately measuring the sorting response effect of abnormal data. First, the actual waiting time is calculated by simple subtraction of the time point of entering the queue (such as 8:30 in the morning converted to 8.5 hours) and the time point of starting processing (such as 3:15 in the afternoon converted to 15.25 hours), for example Hours, which indicates the time interval from entry to start of processing. Next, the average waiting time of all data in the queue (for example, the waiting time of other data is 6 hours, 7 hours, and 6.5 hours, the average is hours) is used for normalization to eliminate the impact of loads between different queues. The first part of the formula Compare the ratio of actual waiting time to average waiting time to measure response efficiency; Part II The weight of the deviation is magnified to reflect whether the sorting response is consistent with the average. For example, substituting into the formula , indicating that the response deviation is small and close to expectations. Through such a design, It can intuitively reflect the sorting response effect of each abnormal data and provide a basis for optimizing the sorting mechanism.

[0052] No. The rank response deviation coefficient of abnormal data The size of is directly related to whether the sorting effect of the abnormal data stratification sorting mechanism on the data reaches the expected result. The larger the value, the higher the actual waiting time of the abnormal data is, or the sorting deviation is large, which indicates that the sorting mechanism fails to effectively respond to the priority requirements of the data, and the sorting effect does not meet expectations; conversely, The smaller the value, the lower the actual waiting time of the abnormal data is than the average waiting time of the queue or the sorting response is too early, which may lead to unreasonable resource allocation and weaken the overall efficiency of the sorting mechanism. The size of can clearly determine whether the sorting mechanism has reasonably responded to abnormal data according to the expected priority, and identify possible problem data, providing a basis for further optimizing the sorting mechanism.

[0053] The dynamic processing information in the dynamic feedback information of each abnormal data after preprocessing is extracted, including the time taken for each abnormal data to be processed from the beginning to the end, the amount of effective data per unit time generated by each abnormal data during the processing, and the total amount of effective data generated by each abnormal data during the processing, and the information is marked as , and , Indicates The time taken from the start to the completion of the processing of abnormal data. Indicates The amount of valid data per unit time generated during the processing of abnormal data, Indicates The total amount of valid data during the processing of abnormal data; Several types of quantitative data, such as "the time taken from the start to the completion of processing of each abnormal data, the amount of valid data per unit time generated by each abnormal data during the processing, and the total amount of valid data of each abnormal data during the processing" can be automatically obtained through the task management module and the data processing monitoring module of the hierarchical sorting mechanism. Specifically, the time taken from the start to the completion of processing of each abnormal data (processing time) can be calculated from the timestamp recorded by the task management module. For example, when a certain abnormal data enters the processing flow, the system automatically records the start timestamp (such as "10:00" for 10.0 hours), and records the completion timestamp when the processing is completed (such as "10:02" for 10.033 hours). The total processing time (0.033 hours) can be obtained by subtracting the two. The amount of valid data per unit time (generation rate per unit time) generated by each abnormal data during the processing is calculated by the data processing monitoring module by counting the amount of valid data generated per unit time (such as fault feature points, analysis results, etc.). For example, if 10 valid data points are generated within 1 second, the rate is 10 points / second; and the total amount of valid data (total generation) of each abnormal data during the processing is obtained by accumulating the number of valid data points generated during all processing processes. For example, if 100 valid data points are generated in the entire processing flow, the total generation is 100 points. The collection of these data is completely completed automatically by the software, using a combination of log recording, timestamp calculation and real-time data statistics to ensure the real-time and accuracy of the data, providing a reliable basic support for the calculation of dynamic processing result coefficients.

[0054] Calculate the dynamic processing result coefficient of each abnormal data. The specific calculation formula is as follows: In the formula, For the The dynamic processing result coefficient of abnormal data.

[0055] Dynamic processing result coefficient The design of the calculation formula combines the processing time, the data generation rate per unit time and the total amount of generated data, aiming to comprehensively measure the dynamic processing effect of abnormal data. In the formula, By generating data per unit time Divide by the total processing time , evaluates the data generation efficiency. The shorter the processing time and the higher the generation rate, the greater the efficiency evaluation of the coefficient; The total amount of data generated Logarithmic weighting is performed to ensure that its impact gradually increases, while limiting the excessive impact of extreme data, balancing the weights of quality and quantity. For example, if an abnormal data is processed from 10:00 am (time point is 10.0 hours) to 10:02 am (time point is 10.033 hours), the processing time is hours (about two minutes). Assume that the amount of valid data generated per unit time is Data points / hour, total generated data volume Data points, substitute into the formula: This design combines efficiency and effectiveness through the subtraction of time points (total processing time) and logarithmic weighting (total amount of generated data), providing a reliable metric for the performance evaluation of the hierarchical sorting mechanism.

[0056] No. Dynamic processing result coefficient of abnormal data The size of directly reflects the efficiency and effect of the abnormal data hierarchical sorting mechanism in processing the data, which is closely related to the evaluation of whether the sorting effect meets expectations. The larger the value, the higher the amount of valid data generated by the abnormal data in a short period of time, and the higher the processing efficiency and result quality, indicating that the processing effect of the hierarchical sorting mechanism on the data is in line with expectations; otherwise, The smaller the value, the longer the processing time or the less data generated. There may be problems such as unreasonable resource allocation and inefficient processing flow, and the sorting effect does not meet expectations. The size of can quantify the performance of the hierarchical sorting mechanism when processing abnormal data, provide a reliable basis for optimizing the sorting mechanism, and ensure the efficiency and rationality of the overall processing flow.

[0057] In this embodiment, the sorting response deviation coefficient of each abnormal data generated is and dynamic processing result coefficient Construct a ranking effect evaluation model and generate ranking evaluation coefficients for each abnormal data through weighted summation , and after generation, the sorting evaluation coefficient threshold of each abnormal data is set in advance Compare and evaluate whether the abnormal data stratification and sorting mechanism has achieved the expected sorting effect on each abnormal data according to the comparison results, and optimize the abnormal data stratification and sorting mechanism according to the evaluation results. The specific comparison and analysis are as follows: like , the abnormal data hierarchical sorting mechanism achieves the expected sorting effect on the abnormal data, and there is no need to optimize the abnormal data hierarchical sorting mechanism; This situation means that the hierarchical sorting mechanism for abnormal data has the expected sorting effect on the abnormal data, that is, the hierarchical sorting mechanism can effectively respond to and process the abnormal data according to the established rules, and its sorting response deviation is small, and the dynamic processing efficiency is high. Specifically, the abnormal data in this case can be reasonably allocated resources and processed in a timely manner, which will neither delay the analysis of key data nor waste system resources, thereby maintaining the overall stability and efficiency of the sorting mechanism. Therefore, there is no need to adjust the hierarchical sorting mechanism, and the system can continue to operate according to the existing logic, saving optimization time and computing costs.

[0058] like The hierarchical sorting mechanism for abnormal data does not achieve the expected sorting effect on the abnormal data, and it is necessary to optimize the hierarchical sorting mechanism for abnormal data, including: adjusting the queue management rules in the hierarchical sorting mechanism for abnormal data, optimizing the sorting priority strategy according to the evaluation results, and allocating resources to process high-priority abnormal data first; dynamically modifying the processing delay parameters of low-priority abnormal data to reduce the occupation of system resources; reallocating processing resources, improving resource utilization efficiency, and ensuring that data of different priorities can be reasonably sorted and processed, thereby improving the sorting effect and meeting expectations.

[0059] This situation means that the hierarchical sorting mechanism for abnormal data has not achieved the expected sorting effect on the abnormal data, that is, there may be problems such as too long sorting response time, unreasonable resource allocation, or low dynamic processing efficiency. Specifically, this situation may cause delays in the processing of high-priority abnormal data, and failure to locate key problems in a timely manner, or too many resources for low-priority data, affecting the overall response speed and accuracy of the system. Such results may weaken the overall performance of the hierarchical sorting mechanism and even have a negative impact on the operating efficiency and reliability of the system. Therefore, it is necessary to optimize the sorting mechanism, such as adjusting queue rules, reallocating resources, or optimizing task scheduling, to improve the sorting effect and ensure the normal operation of the system.

[0060] The optimization of the abnormal data hierarchical sorting mechanism can be achieved by introducing a real-time feedback control module and a resource allocation module. Specifically, the queue management rules and the optimization sorting priority strategy can be adjusted by dynamically monitoring and analyzing the sorting evaluation coefficient of the abnormal data through the real-time feedback control module, and the data in the queue can be automatically reordered based on the evaluation results, such as setting a higher queue priority weight for high-priority abnormal data to ensure that it is scheduled and processed first; at the same time, the processing delay parameters of low-priority abnormal data can be dynamically modified by introducing a delay control mechanism, and the processing time window of low-priority data can be adjusted in real time according to the system resource utilization, such as appropriately delaying the scheduling time of low-priority tasks to avoid occupying key resources. The reallocation of processing resources can be realized by combining the current system load and task priority information through the resource allocation module to dynamically adjust the allocation ratio of computing resources, such as increasing the available resource quota when the number of high-priority tasks is large, while reducing the resource occupation of low-priority tasks. The purpose of this is to ensure that high-priority tasks can be processed quickly, reduce response delays, and effectively utilize system resources to avoid resource waste or scheduling conflicts, and ultimately improve the overall performance and processing efficiency of the abnormal data hierarchical sorting mechanism.

[0061] The ranking effect evaluation model is constructed based on the ranking response deviation coefficient and dynamic processing result coefficient of each abnormal data generated, and the ranking evaluation coefficient of each abnormal data is generated by weighted summation. , can be achieved by building a weight control module combined with data calculation logic. Specifically, first set the sorting response deviation coefficient and dynamic processing result coefficient The weight coefficients are calibrated as and And meet , these weight coefficients are determined by historical data analysis and actual operation requirements. For example, if the system pays more attention to response speed, then will be set relatively high. On the contrary, if you pay more attention to data processing efficiency, Then, the system calculates the value of each abnormal data according to the set weight. and Perform weighted summation and calculate the ranking evaluation coefficient: During the calculation process, the weight coefficient ensures that the relative influence of the two parameters on the evaluation results meets the actual needs. For example, when , When , the ranking evaluation coefficient is more inclined to measure the response speed, and when , This design enables the evaluation model to dynamically reflect the response performance of the sorting mechanism while taking into account the processing efficiency, providing a scientific basis for sorting optimization.

[0062] Real-time monitoring and comprehensive analysis of abnormal data, evaluation results, adjustment records of the hierarchical sorting mechanism and treatment effects obtained during the entire high-current leakage processing process are carried out, and the detection strategy and sorting mechanism are continuously optimized to improve the system's response efficiency and detection accuracy.

[0063] Real-time monitoring of abnormal data, evaluation results, adjustment records of the hierarchical sorting mechanism, and processing effects during the entire high-current leakage treatment process can be achieved through the data acquisition module combined with the real-time analysis module. Specifically, the data acquisition module automatically captures the dynamic changes of abnormal data, the calculation results of the evaluation model, the adjustment operations of the hierarchical sorting mechanism, and the relevant data points of the actual processing effect through comprehensive monitoring of the system operation status. The acquisition module transmits this information to the central analysis system in real time through the Internet of Things communication protocol (such as MQTT) and the database log function. At the same time, the real-time analysis module uses timestamps to synchronize and align various types of monitoring data to ensure that each record can be associated with its corresponding processing stage and operation logic, thereby forming a complete monitoring data link. This method ensures the integrity and accuracy of the monitoring information and provides a reliable data source for subsequent analysis.

[0064] Comprehensive analysis can be achieved through data fusion modules and intelligent analysis algorithms, combining historical data and real-time data to comprehensively evaluate system performance. Specifically, the data fusion module first integrates real-time monitored abnormal data, evaluation results, adjustment records and other information with historical operation data, and eliminates dimensional differences between different data sources through normalization processing and feature mapping algorithms. Subsequently, the intelligent analysis algorithm performs pattern recognition and trend analysis on the fused data, such as identifying the correlation between the processing efficiency of abnormal data and the adjustment of the sorting mechanism through cluster analysis, or predicting the potential impact of the current adjustment strategy on the system response efficiency through regression analysis. This comprehensive analysis method can reveal the deep connection between data and provide a scientific basis for optimization strategies.

[0065] The purpose of real-time monitoring and comprehensive analysis is to improve the response efficiency and detection accuracy of the system in high current leakage scenarios. The high current leakage processing process is complex and changes frequently. Real-time monitoring ensures that the system has instant grasp of abnormal data and processing status, while comprehensive analysis can evaluate the system operation from a global perspective and discover hidden performance bottlenecks or optimization space. For example, if it is found that the evaluation results of a certain stage are continuously high, it may indicate that the adjustment effect of the sorting mechanism is insufficient, and the rules or resource allocation methods need to be optimized; if a certain adjustment record is found to appear repeatedly, there may be unresolved hidden problems. This method that combines real-time monitoring and comprehensive analysis can dynamically optimize the detection strategy and sorting mechanism, so that the system always maintains an efficient operation state and meets the needs of complex scenarios.

[0066] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0067] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0068] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0069] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0070] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0071] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0072] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0073] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A standardized electrical switch cabinet detection method based on the Internet of Things, characterized in that: The specific steps include: During the operation of the electrical switch cabinet, real-time detection of whether high current leakage occurs in the electrical switch cabinet, and in the event of high current leakage, determination of abnormal data generated by all sensors in the electrical switch cabinet to reflect changes in equipment status; Acquire the core information of each abnormal data generated by all sensors in the electrical switch cabinet in real time, analyze it after acquisition, evaluate the priority of each abnormal data in the electrical switch cabinet with high current leakage, and classify each abnormal data into low priority abnormal data, medium priority abnormal data and high priority abnormal data according to the evaluation results; According to the classification results of each abnormal data, a hierarchical sorting mechanism for abnormal data is constructed, and different sorting and processing measures are taken for low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data respectively; In the process of the abnormal data hierarchical sorting mechanism processing each abnormal data, the dynamic feedback information of each abnormal data is obtained in real time, and analyzed after acquisition to evaluate whether the sorting effect of the abnormal data hierarchical sorting mechanism on each abnormal data meets expectations, and the abnormal data hierarchical sorting mechanism is optimized according to the evaluation results; Real-time monitoring and comprehensive analysis of abnormal data, evaluation results, adjustment records of the hierarchical sorting mechanism and treatment effects obtained during the entire high-current leakage processing process are carried out, and the detection strategy and sorting mechanism are continuously optimized to improve the system's response efficiency and detection accuracy.

2. A standardized electrical switch cabinet detection method based on the Internet of Things according to claim 1, characterized in that: The core information of each abnormal data generated by all sensors in the electrical switch cabinet is obtained in real time, and analyzed after acquisition, the priority of each abnormal data in the electrical switch cabinet with high current leakage is evaluated, and each abnormal data is divided into low priority abnormal data, medium priority abnormal data and high priority abnormal data according to the evaluation result, which specifically includes the following steps: Acquire the core information of each abnormal data generated by all sensors in the electrical switch cabinet in real time and perform preprocessing after acquisition; Extracting fluctuation information and fault-related information from the core information of each abnormal data after preprocessing, and analyzing them after extraction to generate abnormal fluctuation coefficient and fault sensitivity index of each abnormal data respectively; A priority evaluation model is constructed for the abnormal fluctuation coefficient and fault sensitivity index of each abnormal data generated, and a priority coefficient of each abnormal data is generated. After the generation, it is compared with the preset priority coefficient threshold range, and the priority of each abnormal data in the electrical switch cabinet with high current leakage phenomenon is evaluated according to the comparison result, and each abnormal data is divided into low priority abnormal data, medium priority abnormal data and high priority abnormal data according to the evaluation result.

3. A standardized electrical switch cabinet detection method based on the Internet of Things according to claim 2, characterized in that: The logic for obtaining the abnormal fluctuation coefficient and fault sensitivity index of each abnormal data is as follows: Extract the fluctuation information from the core information of each abnormal data after preprocessing, including the actual value of each abnormal data at different times within a period of time when the electrical switch cabinet has a high leakage phenomenon, the maximum and minimum values ​​of each abnormal data in the historical time period, and mark them as , and , Indicates that the electrical switch cabinet has a high leakage phenomenon for a period of time Moment The actual value of the abnormal data, Indicates that in the historical period The maximum value of abnormal data, Indicates that in the historical period The minimum value of abnormal data, , , and All are positive integers; Calculate the abnormal fluctuation coefficient of each abnormal data. The specific calculation formula is as follows: In the formula, For the The abnormal fluctuation coefficient of abnormal data; Extract the fault-related information from the core information of each preprocessed abnormal data, including the leakage current values ​​of the electrical switch cabinet at different times within a period of time when high leakage occurs and the average leakage current of the electrical switch cabinet in the historical time period, and mark them as and , Indicates that the electrical switch cabinet has a high leakage phenomenon for a period of time The leakage current value at the moment, Indicates the average leakage current of the electrical switchgear in the historical period; Calculate the fault sensitivity index of each abnormal data. The specific calculation formula is as follows: In the formula, For the The fault sensitivity index of abnormal data.

4. A standardized electrical switch cabinet detection method based on the Internet of Things according to claim 3, characterized in that: The abnormal fluctuation coefficient of each abnormal data generated and fault sensitivity index Construct a priority evaluation model and generate the priority coefficient of each abnormal data through weighted summation , and after generation, it is compared with the preset priority coefficient threshold interval Compare and evaluate the priority of each abnormal data in the electrical switch cabinet with high current leakage according to the comparison results, and divide each abnormal data into low priority abnormal data, medium priority abnormal data and high priority abnormal data according to the evaluation results. The specific comparison analysis and classification are as follows: like , the priority of the abnormal data in the electrical switch cabinet where the high current leakage phenomenon occurs is low priority, and the abnormal data is classified as low priority abnormal data; like , the priority of the abnormal data in the electrical switch cabinet with high current leakage is medium priority, and the abnormal data is classified as medium priority abnormal data; like The priority of the abnormal data in the electrical switch cabinet where the high current leakage phenomenon occurs is high priority, and the abnormal data is classified as high priority abnormal data.

5. A standardized electrical switch cabinet detection method based on the Internet of Things according to claim 4, characterized in that: According to the classification results of each abnormal data, a hierarchical sorting mechanism for abnormal data is constructed, specifically: according to the classification results of low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data, different sorting rules and processing measures are set respectively to form a hierarchical sorting mechanism for abnormal data; the hierarchical sorting mechanism is based on the priority of each abnormal data and the real-time allocation status of system resources, and automatically determines the sorting strategy and processing method of each abnormal data through pre-set rules; Different sorting and processing measures are taken for low-priority exception data, medium-priority exception data and high-priority exception data, specifically: for high-priority exception data, the fast processing strategy in the hierarchical sorting mechanism is used to prioritize the allocation of computing resources, perform real-time processing and analysis on the high-priority exception data, and quickly respond to and generate fault location and cause judgments; for medium-priority exception data, the delayed processing strategy in the hierarchical sorting mechanism is used to queue and store the medium-priority exception data, and gradually analyze it after the high-priority data is processed; for low-priority exception data, the archiving strategy in the hierarchical sorting mechanism is used to archive the low-priority exception data records.

6. A standardized electrical switch cabinet detection method based on the Internet of Things according to claim 5, characterized in that: In the process of the abnormal data hierarchical sorting mechanism processing each abnormal data, the dynamic feedback information of each abnormal data is obtained in real time, and analyzed after acquisition to evaluate whether the sorting effect of the abnormal data hierarchical sorting mechanism on each abnormal data meets the expectations, and optimize the abnormal data hierarchical sorting mechanism according to the evaluation results, which specifically includes the following steps: In the process of processing each abnormal data by the abnormal data hierarchical sorting mechanism, dynamic feedback information of each abnormal data is obtained in real time, and preprocessed after acquisition; Extracting the sorting response information and the dynamic processing information from the preprocessed dynamic feedback information of each abnormal data, and analyzing them after extraction to generate the sorting response deviation coefficient and the dynamic processing result coefficient of each abnormal data respectively; A sorting effect evaluation model is constructed for the sorting response deviation coefficient and dynamic processing result coefficient of each generated abnormal data to generate a sorting evaluation coefficient for each abnormal data. After the generation, the coefficient is compared with the pre-set sorting evaluation coefficient threshold for each abnormal data. According to the comparison results, it is evaluated whether the sorting effect of each abnormal data by the abnormal data hierarchical sorting mechanism meets the expectation, and the abnormal data hierarchical sorting mechanism is optimized according to the evaluation results.

7. A standardized electrical switch cabinet detection method based on the Internet of Things according to claim 6, characterized in that: The logic for obtaining the sorting response deviation coefficient and dynamic processing result coefficient of each abnormal data is as follows: The sorting response information in the dynamic feedback information of each abnormal data after preprocessing is extracted, including the time point when each abnormal data is identified by the abnormal data hierarchical sorting mechanism and enters the queue, the time point when each abnormal data starts to be processed, and the average waiting time of all data in the queue where each abnormal data is located, and they are marked as , and , Indicates The time point when an abnormal data is identified by the abnormal data hierarchical sorting mechanism and enters the queue, Indicates The time point when abnormal data begins to be processed, Indicates The average waiting time of all data in the queue where the abnormal data is located, , is a positive integer; Calculate the sort response deviation coefficient of each abnormal data. The specific calculation formula is as follows: In the formula, For the The sorting response deviation coefficient of abnormal data; The dynamic processing information in the dynamic feedback information of each abnormal data after preprocessing is extracted, including the time taken for each abnormal data to be processed from the beginning to the end, the amount of effective data per unit time generated by each abnormal data during the processing, and the total amount of effective data generated by each abnormal data during the processing, and the information is marked as , and , Indicates The time taken from the start to the completion of the processing of abnormal data. Indicates The amount of valid data per unit time generated during the processing of abnormal data, Indicates The total amount of valid data during the processing of abnormal data; Calculate the dynamic processing result coefficient of each abnormal data. The specific calculation formula is as follows: In the formula, For the The dynamic processing result coefficient of abnormal data.

8. The method for detecting a standardized electrical switch cabinet based on the Internet of Things according to claim 7, characterized in that: The sort response deviation coefficient for each generated abnormal data and dynamic processing result coefficient Construct a ranking effect evaluation model and generate ranking evaluation coefficients for each abnormal data through weighted summation , and after generation, the sorting evaluation coefficient threshold of each abnormal data is set in advance Compare and evaluate whether the abnormal data stratification and sorting mechanism has achieved the expected sorting effect on each abnormal data according to the comparison results, and optimize the abnormal data stratification and sorting mechanism according to the evaluation results. The specific comparison and analysis are as follows: , the abnormal data hierarchical sorting mechanism achieves the expected sorting effect on the abnormal data, and there is no need to optimize the abnormal data hierarchical sorting mechanism; if The hierarchical sorting mechanism for abnormal data did not achieve the expected sorting effect on the abnormal data, and it is necessary to optimize the hierarchical sorting mechanism for abnormal data, including: adjusting the queue management rules in the hierarchical sorting mechanism for abnormal data, optimizing the sorting priority strategy according to the evaluation results, and allocating resources to process high-priority abnormal data first; dynamically modifying the processing delay parameters of low-priority abnormal data to reduce the occupation of system resources; reallocating processing resources, improving resource utilization efficiency, and ensuring that data of different priorities can be reasonably sorted and processed.

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