Metering box edge computing security gateway system

Abstract

The invention relates to the technical field of intelligent management of electric power metering equipment, and discloses a metering box edge computing security gateway system which comprises an edge computing processing unit, a data acquisition and transmission module, a security protection judgment module, a system state monitoring module and an interactive management feedback module. The data acquisition and transmission module acquires real-time data, historical data and environmental parameters of the metering box in stages and generates abnormal signals; the safety protection judgment module dynamically adjusts a protection strategy; and the system state monitoring module records the response duration to evaluate the protection effect. The system further comprises a system reliability evaluation module, a data cycle summarization module and a regional safety abnormity evaluation module, abnormity reminding signals are generated through multi-dimensional analysis, and the system reliability is optimized. According to the invention, accurate acquisition, dynamic protection and regional safety grading evaluation of the data of the metering box are realized, and the real-time performance of data processing and the system safety are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology for power metering equipment, specifically to a metering box edge computing security gateway system. Background Technology

[0002] With the rapid development of smart grids, metering boxes, as key equipment in power systems, directly impact the stability and management efficiency of the power grid through the accuracy and security of their operational data. Traditional metering boxes primarily rely on centralized processing for data acquisition and transmission, requiring data to be uploaded to the cloud or central server for analysis. This approach suffers from high latency, high bandwidth consumption, and poor real-time performance. Furthermore, metering boxes operate in complex environments, frequently encountering external factors such as temperature fluctuations, humidity changes, and electromagnetic interference. Traditional systems struggle to dynamically adjust data acquisition strategies, leading to data loss or frequent anomalies.

[0003] In existing technologies, the safety protection functions of metering boxes are typically based on fixed rules or threshold judgments, lacking the ability to dynamically respond to abnormal signals. For example, when data acquisition deviates, the system can only issue a simple alarm, unable to automatically optimize the acquisition process or adjust the protection strategy. Furthermore, system status monitoring is often limited to a single link, failing to comprehensively assess the protection effect and system reliability, making it difficult to detect potential risks in a timely manner. In addition, traditional methods do not conduct graded assessments of the regional safety status of the metering box, making it impossible to accurately locate high-risk areas, further increasing the difficulty of operation and maintenance.

[0004] The rise of edge computing technology offers a new approach to solving the aforementioned problems. By offloading computing power to the local metering box, data processing efficiency and real-time performance can be significantly improved. However, the application of existing edge computing solutions in the metering box field is still in its early stages, particularly lacking in the collaborative optimization of data acquisition, anomaly detection, security protection, and system evaluation. Therefore, a gateway system integrating edge computing and intelligent security detection functions is needed to achieve accurate acquisition, dynamic protection, and comprehensive evaluation of metering box operating data. Summary of the Invention

[0005] The purpose of this invention is to provide a metering box edge computing security gateway system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a metering box edge computing security gateway system, the system comprising: an edge computing processing unit, a data acquisition and transmission module, a security protection discrimination module, a system status monitoring module, and an interactive management feedback module; The data acquisition and transmission module collects data during the operation of the metering box. The operation of the metering box includes a real-time data acquisition stage, a historical data retrieval stage, and an environmental parameter monitoring stage. During the real-time data acquisition stage, the current operating data of the metering box is read and the transmitted data is recorded synchronously. After the real-time acquisition is completed, the historical data retrieval stage is entered, where the past operating data of the metering box is extracted and the transmitted data is continuously recorded. After the retrieval is completed, the environmental parameter monitoring stage is entered to collect the transmission data of the surrounding environment. The data acquisition and transmission module analyzes the data to determine whether an abnormal acquisition signal, retrieval signal, or monitoring signal has been generated. This abnormal signal is then sent via the edge computing processing unit to the security protection discrimination module and the interactive management feedback module. The interactive management feedback module displays and issues alerts for the abnormal acquisition, retrieval, or monitoring signals. Upon receiving these signals, the security protection discrimination module adaptively adjusts the data acquisition process to optimize the protection effect. The system status monitoring module tracks the protection process of the security protection discrimination module, records the moment when the security protection discrimination module receives, retrieves, or monitors an abnormal signal, and marks it as the abnormal reception moment. The abnormal reception moment is used as the starting point for timing until the protection process returns to stability to obtain the response time. If the response time does not exceed the preset response time threshold, the corresponding protection process is judged to be effective and the number of effective times is accumulated. If the response time exceeds the preset response time threshold, the corresponding protection process is judged to be ineffective and the number of ineffective times is accumulated.

[0007] Preferably, the specific operation process of the data acquisition and transmission module is as follows: During the real-time data acquisition phase, the integrity, continuity, and accuracy values ​​of the metering box data are acquired. The difference between the integrity value and the corresponding standard value is marked as the integrity deviation value. Similarly, the continuity deviation value and accuracy deviation value are obtained. If the integrity deviation value, continuity deviation value, or accuracy deviation value exceeds the corresponding preset range, an acquisition anomaly signal is generated. During the historical data retrieval phase, the deviation of the timestamp information of the metering box data from the standard time is marked as the time offset value. The storage format and data volume of the historical data are collected, and the difference between the storage format and the corresponding standard format is marked as the format deviation value. Similarly, the data volume deviation value is obtained. If the time offset value, format deviation value, or data volume deviation value exceeds the corresponding preset range, a retrieval abnormality signal is generated. During the environmental parameter monitoring phase, the temperature gradient, humidity fluctuation, and electromagnetic interference values ​​around the metering box are collected. The difference between the temperature gradient value and the corresponding standard value is marked as the temperature deviation value. Similarly, the humidity deviation value and interference deviation value are obtained. If the temperature deviation value, humidity deviation value, or interference deviation value exceeds the corresponding preset range, a monitoring abnormality signal is generated.

[0008] Preferably, the system further includes a system reliability assessment module that is communicatively connected to the edge computing processing unit. The system reliability assessment module collects the frequency and intensity of abnormal signals generated, retrieves, and monitors abnormal signals during the corresponding operation process and marks them as abnormal frequency values. It also collects the number of failures and the number of effective failures during the corresponding operation process and marks the ratio of the number of failures to the number of effective failures as a protection deviation value. The abnormal frequency value and the protection deviation value are numerically calculated to obtain the system evaluation value. If the system evaluation value exceeds the preset system evaluation threshold, a system abnormality reminder signal is generated. If the system evaluation value does not exceed the preset system evaluation threshold, a system normality reminder signal is generated. The system abnormality reminder signal is then sent to the interactive management terminal via the edge computing processing unit.

[0009] Preferably, the system further includes a data periodic aggregation module that is communicatively connected to the edge computing processing unit. The data periodic aggregation module is used to set a statistical period, summarize and analyze all the operating processes of the metering box within the statistical period, generate a summary reminder signal or a summary normal signal through analysis, and send the summary reminder signal to the interactive management feedback module via the edge computing processing unit. When the interactive management feedback module receives the summary reminder signal, it issues a corresponding reminder.

[0010] Preferably, the specific analysis process of the data periodic summary module is as follows: The protection effectiveness is analyzed to obtain the protection efficiency value, and the number of times the system abnormal reminder signal is generated within the statistical period is collected. The ratio of the number of system abnormal reminder signals to the total number of processes within the statistical period is marked as the abnormality ratio. The protection efficiency value and the abnormality ratio are numerically calculated to obtain the data summary value. If the data summary value exceeds the preset data summary threshold, a summary reminder signal is generated; if the data summary value does not exceed the preset data summary threshold, a summary normal signal is generated.

[0011] The preferred method for analyzing the effectiveness of safety protection is as follows: By analyzing and determining whether the corresponding operation process is a substandard protection process, the ratio of the number of substandard protection processes to the total number of operation processes within the statistical period is marked as the substandard protection rate. The difference between the abnormal detection value of the corresponding substandard protection process and the preset abnormal detection threshold is marked as the detection deviation value. All detection deviation values ​​within the statistical period are summed and averaged to obtain the average deviation value. All abnormal detection values ​​within the statistical period are summed and averaged to obtain the average detection value. The substandard protection rate, average deviation value, and average detection value are numerically calculated to obtain the protection effectiveness value.

[0012] In the analysis of security protection effectiveness, when determining whether a corresponding operational process is a substandard protection process, the number of standard anomalies and the actual number of protection anomalies in that process are collected. The ratio of the actual number of protection anomalies to the number of standard anomalies is used as the anomaly detection value. If the anomaly detection value does not exceed the preset anomaly detection threshold, it indicates that the security protection discrimination module has failed to detect a sufficient number of anomalies as expected, and the operational process is marked as a substandard protection process. The ratio of the number of substandard protection processes to the total number of operational processes within the statistical period is the substandard protection rate. This ratio reflects the overall proportion of system protection failures within the statistical period. The larger the ratio, the more prevalent the substandard protection situation, and the worse the overall system protection effect. It is inversely correlated with the protection effectiveness value.

[0013] For each process where protection fails to meet standards, the difference between its abnormal detection value and the preset abnormal detection threshold is calculated, i.e., the detection deviation value. This deviation value reflects the degree of deviation between the abnormal detection capability and the standard requirements during that process. The average deviation value is obtained by summing all detection deviation values ​​within the statistical period. This average value comprehensively reflects the overall degree of deviation of the abnormal detection capability from the standard during the process where protection fails to meet standards. The larger the average deviation value, the higher the overall deviation and the worse the protection effect, and it is inversely correlated with the protection effectiveness value.

[0014] The average detection value is obtained by summing all abnormal detection values ​​within the statistical period and taking the mean. This mean reflects the average level of actual abnormal detection capability during the process of substandard protection. Since substandard protection is determined when the abnormal detection value does not exceed the preset threshold, the lower the average detection value, the fewer the actual number of detected abnormalities relative to the standard number of abnormalities, and the weaker the abnormal detection capability of the safety protection discrimination module. It is inversely correlated with the protection effectiveness value.

[0015] The protection effectiveness value is obtained by numerically calculating the non-compliance protection rate, average deviation value, and average detection value. This calculation comprehensively considers the proportion of non-compliant processes, the degree of deviation in anomaly detection capability, and the actual detection level to form a quantitative indicator that can comprehensively measure the system's security protection effect. The higher the protection effectiveness value, the stronger the system's security protection capability within the statistical period; conversely, it indicates that the protection effect is insufficient, and targeted optimization of protection strategies or improvement of anomaly detection capability is needed.

[0016] The preferred method for determining if protection is inadequate is as follows: The system collects the number of standard anomalies and the number of actual protection anomalies in the corresponding operation process. The ratio of the number of actual protection anomalies to the number of standard anomalies is marked as the anomaly detection value. If the anomaly detection value does not exceed the preset anomaly detection threshold, the corresponding operation process is marked as a process with substandard protection.

[0017] Preferably, the system further includes a regional safety anomaly assessment module. The data periodic aggregation module sends the aggregated normal signal to the regional safety anomaly assessment module through the data storage unit. When the regional safety anomaly assessment module receives the aggregated normal signal, it analyzes the safety status of each area during the operation of the meter box, and determines whether a meter box safety anomaly signal is generated through the analysis. The meter box safety anomaly signal is then sent to the interactive management feedback module through the data storage unit.

[0018] The preferred approach is as follows: The specific analysis process of the regional security anomaly assessment module is as follows: Several detection zones are set in the metering box operating area. During operation, the safety deviation values ​​of the corresponding detection zones are collected. The safety deviation values ​​of all detection zones are summed and the average value is taken to obtain the safety reference value. The difference between the safety deviation value of the corresponding detection zone and the safety reference value is calculated and the absolute value is taken to obtain the area deviation value. If the area deviation value exceeds the preset area deviation threshold, the corresponding detection zone is marked as an abnormal area. The system collects the total runtime of the metering box within a statistical period, as well as the number of times the corresponding detection area was marked as an abnormal area within the statistical period, and marks these as abnormal frequency values. The ratio of the abnormal frequency value to the total runtime is marked as the abnormal frequency ratio. Through comparative analysis, the corresponding detection area is defined as a high-risk area, a medium-risk area, or a low-risk area. If a high-risk area exists in the metering box's operating area, a metering box safety abnormality signal is generated. If no high-risk area exists in the metering box's operating area, the ratio of the number of medium-risk areas to the number of low-risk areas is marked as the area assessment value. If the area assessment value exceeds the preset area assessment threshold, a metering box safety abnormality signal is generated.

[0019] The preferred comparative analysis process is as follows: If the abnormal frequency ratio exceeds the maximum value of the preset abnormal frequency range, the corresponding detection area is defined as a high-risk area; if the abnormal frequency ratio does not exceed the minimum value of the preset abnormal frequency range, the corresponding detection area is defined as a low-risk area; if the abnormal frequency ratio is within the preset abnormal frequency range, the corresponding detection area is defined as a medium-risk area.

[0020] Compared with the prior art, the beneficial effects of the present invention are: This invention effectively solves the problems of high data acquisition latency and insufficient protection capabilities in traditional metering boxes by combining edge computing and intelligent discrimination technology. The data acquisition and transmission module performs real-time data acquisition, historical data retrieval, and environmental parameter monitoring in stages, and generates anomaly signals through deviation analysis to ensure the integrity and accuracy of data acquisition. The safety protection discrimination module dynamically adjusts the protection strategy based on the anomaly signals, significantly improving the system's adaptability to complex environments. The system status monitoring module records response time and protection effectiveness, providing quantitative basis for system optimization and avoiding the problem of difficulty in timely detection of protection failures in traditional methods.

[0021] The system reliability assessment module analyzes anomaly frequency and protection deviation values ​​to generate system assessment signals, helping maintenance personnel quickly locate potential risks. The data periodic summary module comprehensively analyzes operational data within a statistical period and generates summary alert signals, further improving the precision of system management. The regional safety anomaly assessment module divides areas into high-risk, medium-risk, and low-risk zones, enabling accurate assessment of the safety status of metering box areas and providing a scientific basis for targeted maintenance.

[0022] This invention, through the collaborative work of multiple modules, not only improves the efficiency and real-time performance of metering box data processing but also enhances the system's security and reliability. The introduction of an edge computing processing unit reduces data transmission bandwidth consumption and cloud dependency, making it particularly suitable for distributed power metering scenarios. The real-time alert function of the interactive management feedback module simplifies the operation and maintenance process and reduces manual intervention costs. The overall system design is flexible and highly scalable, adaptable to the security requirements of metering boxes in different environments, and provides strong support for the stable operation of the smart grid. Attached Figure Description

[0023] Figure 1 This is a schematic diagram illustrating the working principle of the metering box edge computing security gateway system described in this invention. Figure 2 This is a design diagram of the data acquisition and transmission module's operation process; Figure 3 Design diagram for the system reliability assessment module; Figure 4 Design diagram for the data periodic summary module; Figure 5This is a design diagram for the regional security anomaly assessment module. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Please see Figures 1-5 This invention relates to an edge computing security gateway system for a metering box. The system includes: an edge computing processing unit, a data acquisition and transmission module, a security protection discrimination module, a system status monitoring module, and an interactive management feedback module. The specific implementation steps are as follows: The data acquisition and transmission module collects data during the operation of the metering box, which includes a real-time data acquisition phase, a historical data retrieval phase, and an environmental parameter monitoring phase. During the real-time data acquisition phase, the module reads the current operating data of the metering box and simultaneously records the transmitted data. After completion, it enters the historical data retrieval phase, extracting past operating data and continuously recording the transmitted data. After retrieval, it enters the environmental parameter monitoring phase to collect transmission data from the surrounding environment.

[0026] The data acquisition and transmission module analyzes and determines whether abnormal acquisition signals are generated, retrieved, or monitored, and sends these abnormal signals to the security protection discrimination module and the interactive management feedback module via the edge computing processing unit. The interactive management feedback module displays the abnormal signals and issues alerts. When the security protection discrimination module receives an abnormal signal, it adaptively adjusts the data acquisition process to optimize the protection effect.

[0027] The system status monitoring module tracks the protection process of the security protection judgment module, records the moment when it receives an abnormal signal and marks it as the abnormal reception moment. It uses this moment as the starting point for timing until the protection process returns to stability, thus obtaining the response duration. If the response duration does not exceed the preset response duration threshold, the corresponding protection process is judged to be effective and the number of effective responses is accumulated; if it exceeds the threshold, the corresponding protection process is judged to be ineffective and the number of ineffective responses is accumulated.

[0028] Example 1: The specific operation process of the data acquisition and transmission module in this example covers three stages: real-time data acquisition, historical data retrieval, and environmental parameter monitoring. Each stage achieves comprehensive monitoring of the metering box's operating data and generation of abnormal signals by setting specific parameter acquisition and anomaly judgment logic.

[0029] During the real-time data acquisition phase, the data acquisition and transmission module first reads the current operating data of the metering box in real time, simultaneously recording the transmitted data. This phase focuses on acquiring three key values: data integrity, continuity, and accuracy. For integrity values, the module compares them with the corresponding standard values, and the difference is clearly marked as the integrity deviation value. Similarly, the difference between the continuity value and the standard value is marked as the continuity deviation value, and the difference between the accuracy value and the standard value is marked as the accuracy deviation value. The standard values ​​here are benchmark parameters pre-set based on historical data from the metering box during normal operation and industry standards, used as the basis for judging whether the current data is normal. When any of the integrity deviation value, continuity deviation value, or accuracy deviation value exceeds its corresponding preset range, the module will immediately generate a data acquisition anomaly signal. It should be noted that the preset ranges are set to fully consider the normal fluctuation range that may occur during the operation of the metering box. For example, the preset range for integrity values ​​may be set to ±5% of the standard value. If the actual integrity deviation value exceeds this range, it indicates that the currently acquired metering box data has an incompleteness anomaly.

[0030] Entering the historical data retrieval phase, the data acquisition and transmission module primarily extracts past operational data from the metering box, continuously recording transmitted data during the extraction process. The core acquisition content at this stage includes the metering box's timestamp information, the storage format of the historical data, and the data volume. For the timestamp information, the module compares it with a standard time; any deviation is marked as a time offset. The standard time is typically based on the time of the National Time Service Center to ensure accurate time comparison. Regarding the storage format, the module compares it with a pre-defined standard format; this difference is marked as a format deviation. The standard format is set according to relevant data storage industry standards to ensure data compatibility and readability. The difference between the data volume and the standard data volume is marked as a data volume deviation. When the time offset, format deviation, or data volume deviation exceeds its respective preset range, the module generates a retrieval anomaly signal. For example, the preset range of time offset value may be set to ±10 seconds. If the timestamp of a certain historical data deviates from the standard time by more than 10 seconds, it is considered that the timestamp of the historical data is abnormal, which may affect the time series analysis and tracing of the data, thereby triggering the retrieval of the abnormal signal.

[0031] During the environmental parameter monitoring phase, the data acquisition and transmission module focuses on collecting environmental parameters around the metering box, specifically including temperature gradient values, humidity fluctuation values, and electromagnetic interference values. For temperature gradient values, the module compares them with corresponding standard values, marking the difference as a temperature deviation value. The standard value is set based on the ambient temperature range required for the metering box to operate normally, such as 20℃ ± 5℃. Similarly, the difference between humidity fluctuation values ​​and standard humidity values ​​is marked as a humidity deviation value, and the difference between electromagnetic interference values ​​and standard interference values ​​is marked as an interference deviation value. When the temperature deviation value, humidity deviation value, or interference deviation value exceeds its corresponding preset range, the module generates a monitoring anomaly signal. For example, the preset range for humidity deviation values ​​might be set to ±10% of the standard humidity. If the actual measured humidity fluctuation value causes the humidity deviation value to exceed this range, it indicates that the ambient humidity around the metering box is abnormal, which may affect the normal operation of the equipment, such as causing internal components to become damp and damaged. In this case, a monitoring anomaly signal is generated.

[0032] Throughout the data acquisition and transmission process, the module performs real-time analysis and processing of the data collected at each stage. The analysis and judgment logic is based on the parameter thresholds set for each stage. When the deviation of a certain parameter exceeds the preset range, the corresponding abnormal signal generation mechanism is immediately triggered. These abnormal signals are synchronously sent to the security protection discrimination module and the interactive management feedback module through the edge computing processing unit. Abnormal signals sent to the interactive management feedback module are displayed in real-time on the module's interface, and alerts are issued via sound and light to ensure that relevant operators can promptly detect any abnormalities during the metering box's operation. Abnormal signals sent to the security protection discrimination module serve as the basis for the module to adaptively adjust the data acquisition process. The security protection discrimination module will take corresponding protective strategy adjustments based on the type and severity of the received abnormal signals, such as strengthening data encryption and increasing data verification frequency, to optimize the protection effect and ensure the security and stability of the metering box's data acquisition and transmission process.

[0033] Furthermore, the data acquisition and transmission module maintains continuous data recording and transmission throughout each stage of its operation. Whether it's the synchronous recording and transmission during real-time data acquisition or the continuous recording and transmission during historical data retrieval, data integrity and traceability are ensured. This continuous data recording and transmission mechanism provides accurate and comprehensive data support for subsequent modules such as system status monitoring and reliability assessment. This enables the entire metering box edge computing security gateway system to form a complete closed-loop monitoring system, achieving comprehensive, real-time monitoring and management of the metering box's operational status.

[0034] Through the detailed data acquisition, parameter comparison, anomaly detection, and signal generation and transmission mechanisms described above, the data acquisition and transmission module can accurately identify potential data acquisition anomalies, historical data retrieval anomalies, and abnormal environmental parameters during the operation of the metering box. It promptly transmits this anomaly information to relevant modules, providing a solid foundation for the system's security protection and anomaly handling. This phased, multi-parameter monitoring approach fully considers various factors that may be involved in the metering box's operation, ensuring the system's sensitivity and accuracy in responding to anomalies and effectively improving the reliability and stability of the metering box edge computing security gateway system.

[0035] Example 2: In this example, the system reliability assessment module establishes a communication connection with the edge computing processing unit. By quantitatively analyzing the frequency, intensity, and effectiveness of protection processes for abnormal signals during system operation, it achieves an assessment of the overall system reliability and provides anomaly alerts. The module's operation mechanism covers multiple stages, including data acquisition, parameter labeling, numerical calculation, and signal generation. These stages are closely integrated to form a complete reliability assessment system.

[0036] The system reliability assessment module collects abnormal signals generated during operation in real time, including acquired, retrieved, and monitored signals. During acquisition, it not only records the number of times abnormal signals are generated but also quantifies their intensity. The frequency of abnormal signals refers to the number of various abnormal signals generated per unit time, while the intensity is classified according to the severity of the abnormal situation reflected by the signal. For example, the intensity can be divided into three levels: mild, moderate, and severe, each corresponding to a different numerical range to facilitate subsequent numerical calculations. The module combines the collected abnormal signal frequency and intensity into an abnormal frequency value, which intuitively reflects the frequency and severity of abnormal situations occurring during system operation.

[0037] Meanwhile, the system reliability assessment module also collects the number of failures and effective times during the corresponding operation. These failure and effective times are recorded by the system status monitoring module when tracking the protection process of the security protection judgment module. Specifically, during the protection process, when the response time exceeds a preset response time threshold, the system status monitoring module determines that the corresponding protection process has failed and accumulates the failure count; when the response time does not exceed the preset response time threshold, the corresponding protection process is determined to be effective and the effective count is accumulated. After obtaining this data from the system status monitoring module, the system reliability assessment module marks the ratio of the number of failures to the number of effective times as the protection deviation value. The protection deviation value reflects the proportional relationship between the effectiveness and failure rate of the protection process. When this ratio is large, it indicates that there are more failures during the protection process, and the system's protection effect needs to be improved.

[0038] After marking the abnormal frequency values ​​and protection deviation values, the system reliability assessment module performs numerical calculations on these two parameters to obtain a system evaluation value. The specific method of numerical calculation can be set according to actual needs. For example, a weighted summation method can be used, assigning different weights to the abnormal frequency values ​​and protection deviation values. The weights are set based on the different degrees of impact of the two on system reliability; typically, the weight of the abnormal frequency value is relatively large because it directly reflects the occurrence of abnormal situations. Through this numerical calculation, the two parameters from different dimensions are combined into a system evaluation value that comprehensively reflects the system reliability.

[0039] The system reliability assessment module compares the system assessment value with a preset system assessment threshold. The preset system assessment threshold is a benchmark value pre-set based on historical assessment data during normal system operation and industry standards, used to determine the current reliability status of the system. If the system assessment value exceeds the preset system assessment threshold, it indicates that the frequency and intensity of abnormal situations occurring during system operation are high, and the failure rate of the protection process is relatively high, posing a significant risk to the system's reliability. In this case, the module generates a system anomaly alert signal. If the system assessment value does not exceed the preset system assessment threshold, it indicates that the system is operating relatively stably and its reliability is within the normal range, and the module generates a system normal alert signal.

[0040] The generated system anomaly alert signal is sent to the interactive management terminal via the edge computing processing unit. Upon receiving the signal, the interactive management terminal alerts the operator in various ways, such as displaying an anomaly alert window on the screen showing the specific anomaly type and assessment results, accompanied by audible alarms or flashing lights, so that the operator can promptly detect system abnormalities. After receiving the alert, the operator can use the information provided by the system anomaly alert signal to further inspect and maintain the system, such as troubleshooting frequently occurring anomaly signals and optimizing or adjusting parts with poor protection, thereby ensuring that the system can quickly return to normal operation and improving system reliability and stability.

[0041] Throughout the entire operation, the system reliability assessment module maintains real-time communication with the edge computing processing unit to ensure timely data transmission and processing. Simultaneously, the module periodically updates and analyzes the collected data to dynamically reflect changes in system reliability. For example, the module can be set to a fixed assessment cycle, such as performing a reliability assessment hourly or daily, or it can be dynamically adjusted based on the system's operating status. When an anomaly occurs in the system, a reliability assessment is automatically triggered to promptly grasp the system's reliability status.

[0042] By comprehensively analyzing abnormal frequency values ​​and protection deviation values ​​through the system reliability assessment module, the system's reliability status can be comprehensively and accurately evaluated. This assessment method not only considers the occurrence of abnormal situations but also takes into account the effectiveness of the protection process, making the assessment results more scientific and reasonable. When a reliability problem occurs in the system, the module can promptly generate anomaly alert signals, providing operators with decision-making basis, helping them quickly locate the problem and take corresponding solutions, thereby effectively reducing the system failure rate and improving the system's operational stability and reliability. The configuration of this module provides strong support for the reliable operation of the metering box edge computing security gateway system, ensuring that the system can respond promptly to various abnormal situations, guaranteeing the normal operation of the metering box and the secure transmission of data.

[0043] Example 3: In this example, the data periodic aggregation module is communicatively connected to the edge computing processing unit. By setting a statistical period, it aggregates and analyzes the metering box's operating data, generating corresponding alert signals to achieve periodic monitoring of the system's operating status. The module's operation process covers statistical period setting, operating data aggregation, security protection effect analysis, anomaly occurrence ratio calculation, numerical calculation, and signal generation. These stages cooperate with each other to form a systematic periodic evaluation mechanism.

[0044] The data periodic aggregation module first requires setting the statistical period. This period can be adjusted according to actual application needs, such as setting it to 1 day, 7 days, or 30 days. Operators can also customize the period length based on the metering box's operating characteristics and management requirements. Setting the statistical period provides a clear time frame for subsequent data aggregation and analysis, ensuring the phased nature and comparability of the data.

[0045] Within the set statistical period, the data periodic summary module summarizes and analyzes all operational processes of the metering box. These processes include real-time data acquisition, historical data retrieval, environmental parameter monitoring, and the protection processes of the safety protection judgment module, covering the operational status of each module in the entire system. The module communicates with the edge computing processing unit to obtain real-time operational data from each module within the statistical period, ensuring data integrity and accuracy.

[0046] During the summary analysis, the module first obtains the protection effectiveness value through safety protection effect analysis. This analysis involves judging and quantifying processes that fail to meet protection standards within the statistical period. Specifically: it determines whether a corresponding operating process is a non-compliant process, marking the ratio of the number of non-compliant processes to the total number of operating processes within the statistical period as the non-compliant protection rate; it marks the difference between the abnormal detection value of the corresponding non-compliant process and the preset abnormal detection threshold as the detection deviation value, summing and averaging all detection deviation values ​​within the statistical period to obtain the average deviation value, and simultaneously summing and averaging all abnormal detection values ​​within the statistical period to obtain the average detection value; finally, the non-compliant protection rate, average deviation value, and average detection value are numerically calculated to obtain the protection effectiveness value. The judgment criteria for non-compliant processes are: collecting the standard number of abnormalities and the actual number of protective abnormalities for the corresponding operating process, calculating the ratio of the actual number of protective abnormalities to the standard number of abnormalities as the abnormal detection value; if the abnormal detection value does not exceed the preset abnormal detection threshold, the operating process is marked as a non-compliant process.

[0047] Meanwhile, the data periodic aggregation module collects the number of system anomaly alert signals generated within the statistical period. These signals are generated by the system reliability assessment module and are triggered when the system assessment value exceeds a preset system assessment threshold. The module marks the ratio of the number of system anomaly alert signals to the total number of processes running within the statistical period as the anomaly occurrence ratio. This ratio reflects the frequency of system anomalies within the statistical period, and combined with the total number of processes running, it allows for a more objective assessment of system stability.

[0048] After obtaining the protection effectiveness value and the anomaly occurrence ratio, the data periodic aggregation module performs numerical calculations on these two parameters to obtain a data aggregation value. The numerical calculation method can be set according to actual needs. For example, a weighted combination method can be used to comprehensively consider the impact of the protection effectiveness value and the anomaly occurrence ratio on the overall system status, assigning different weight coefficients to each. The weight coefficients are set based on the importance of system security protection and the impact of anomalies. Usually, the protection effectiveness value has a higher weight to highlight the core position of security protection effectiveness in system evaluation.

[0049] The module then compares the aggregated data value with a preset data aggregation threshold. This preset threshold is a baseline value pre-set based on historical data from normal system operation and industry standards, used to determine whether the system's operating status is normal within the statistical period. If the aggregated data value exceeds the preset threshold, it indicates that the system's security protection effectiveness is insufficient within the statistical period, and abnormal situations occur frequently, suggesting potential risks to the system's operation. In this case, the module generates an aggregation alert signal. If the aggregated data value does not exceed the preset threshold, it indicates that the system is operating relatively stably within the statistical period, and a normal aggregation signal is generated.

[0050] The generated summary alert signal is sent to the interactive management feedback module via the edge computing processing unit. Upon receiving the summary alert signal, the interactive management feedback module displays the specific summary analysis results through a visual interface, including key parameters such as protection effectiveness value, anomaly occurrence ratio, and data summary value. It also presents the system's operational status and existing problems within the statistical period in a visual format, using text and charts. Simultaneously, the module issues corresponding alerts, such as audible prompts or visual warnings, to attract the operator's attention. Based on the information provided by the summary alert signal, operators can perform targeted maintenance and optimization of the system, such as strengthening security protection strategies and investigating high-frequency anomalies, thereby improving the system's operational stability and security.

[0051] During operation, the data periodic aggregation module maintains data interaction with other modules through the data storage unit. For example, it obtains the number of system anomaly alert signals generated from the system reliability assessment module, and relevant data on the protection process from the security protection discrimination module and the system status monitoring module, ensuring the comprehensiveness and accuracy of the data required for aggregation and analysis. Simultaneously, the module sends the generated aggregated normal signals to the regional security anomaly assessment module through the data storage unit, providing basic data support for regional security assessment.

[0052] Periodic analysis via the data periodic aggregation module enables the timely detection of potential recurring problems or periodic anomalies during system operation, preventing the accumulation and escalation of issues. For example, if aggregation alerts are triggered for multiple consecutive statistical periods, it may indicate a systemic fault requiring comprehensive overhaul. Conversely, a sudden increase in the anomaly rate during a particular statistical period allows for targeted investigation of the operational data within that period to pinpoint the source of the anomaly. This periodic aggregation and analysis mechanism provides an effective monitoring tool for the long-term stable operation of the metering box edge computing security gateway system. It helps managers grasp the system's operational status from a macro perspective, enabling preventative maintenance and refined management, thereby improving system reliability and security while reducing operational costs.

[0053] Example 4: In this example, the security protection effect analysis is the core step in the data periodic aggregation module's data aggregation. It identifies processes that fail to meet protection standards, quantifies relevant parameters, and performs comprehensive calculations to obtain a protection effectiveness value that reflects the system's security protection capabilities, providing crucial evidence for evaluating the system's operational status. This analysis process encompasses multiple steps, including judging processes that fail to meet protection standards, calculating multi-dimensional parameters, and performing comprehensive numerical calculations. Each step is interconnected, forming a scientific protection effect evaluation system.

[0054] The first step in analyzing the effectiveness of security protection is to determine whether the corresponding operating process is a substandard protection process. In practice, the system collects the standard number of anomalies and the actual number of protected anomalies for the corresponding operating process. The standard number of anomalies is preset based on historical data from the metering box during normal operation and industry standards, and is used to measure the number of anomalies that should be detected under ideal protection conditions. The actual number of protected anomalies is the number of anomalies actually detected by the security protection discrimination module during the operating process. The system marks the ratio of the actual number of protected anomalies to the standard number of anomalies as the anomaly detection value. If the anomaly detection value does not exceed the preset anomaly detection threshold, the corresponding operating process is marked as a substandard protection process. For example, if the standard number of anomalies for a certain operating process is set to 10, the actual number of protected anomalies is 8, and the anomaly detection value is 0.8, and the preset anomaly detection threshold is 0.9, then the operating process is marked as a substandard protection process, indicating that the security protection discrimination module's ability to detect anomalies in this process has not met the expected standard, and there is a security vulnerability.

[0055] Within the statistical period, after identifying all processes that fail to meet protection standards, the system marks the ratio of the number of processes failing to meet protection standards to the total number of processes running within the statistical period as the failure rate. The failure rate directly reflects the overall proportion of system protection failures within the statistical period. For example, if the total number of processes running within the statistical period is 100, and 15 of them fail to meet protection standards, then the failure rate is 15%. The higher this value, the more problems the system has in the overall protection process, and the worse the protection effect.

[0056] Next, for each process where protection fails to meet standards, the system calculates the difference between the detected anomaly value and the preset anomaly detection threshold, and marks this difference as the detection deviation value. The preset anomaly detection threshold is a benchmark value set according to the system's security protection requirements, used to determine whether the detected anomaly value meets the requirements. Then, the system sums all the detection deviation values ​​within the statistical period and takes the average of the summation results to obtain the average deviation value. The average deviation value reflects the overall deviation between the detected anomaly value and the preset threshold during the process where protection fails to meet standards within the statistical period. For example, within a certain statistical period, there are 10 processes that fail to meet protection standards. The detection deviation values ​​for each process are 0.1, 0.15, 0.08, 0.2, 0.12, 0.09, 0.18, 0.11, 0.13, and 0.07, respectively. After summing these values ​​and taking the average, the average deviation value is approximately 0.125. The larger this value is, the greater the deviation between the abnormal detection value and the preset threshold in the process of failing to meet protection standards, and the greater the gap between the detection capability of the safety protection discrimination module and the expected standard.

[0057] Simultaneously, the system sums all detected anomalies within the statistical period and averages the sums to obtain the average detection value. The average detection value comprehensively reflects the average level of anomaly detection values ​​during periods of substandard protection. Continuing with the example above, if the anomaly detection values ​​for 10 substandard protection processes are 0.8, 0.75, 0.82, 0.6, 0.78, 0.81, 0.62, 0.79, 0.77, and 0.83 respectively, the average detection value after summing and averaging is approximately 0.75. The smaller this value, the lower the ratio of actual anomalies to standard anomalies during periods of substandard protection, indicating a weaker ability of the safety protection discrimination module to detect anomalies.

[0058] After obtaining the three key parameters—the failure rate, average deviation value, and average detection value—the system performs numerical calculations on them to obtain the final protection effectiveness value. The specific method of numerical calculation is to comprehensively calculate the system's protection effectiveness using a reasonable weighted combination based on the degree of influence of these three parameters on the system's protection effect. For example, the failure rate reflects the overall proportion of processes where protection is substandard and has a significant impact on protection effectiveness, so it can be assigned a higher weight; the average deviation value and average detection value reflect the specific circumstances of processes where protection is substandard from different perspectives, so they can be assigned relatively lower weights. Through this weighted calculation, the three parameters from different dimensions are combined into a protection effectiveness value that comprehensively reflects the system's security protection effect. The magnitude of the protection effectiveness value is positively correlated with the system's protection effect; that is, the higher the protection effectiveness value, the better the system's security protection effect; conversely, the lower the protection effectiveness value, the worse the system's protection effect.

[0059] The protection effectiveness value plays a crucial role in the analysis process of the data periodic summary module. Together with the anomaly occurrence ratio (the ratio of the number of system anomaly alert signals to the total number of processes running within the statistical period), it is used to calculate the data summary value, thereby determining whether the system's operating status is normal within the statistical period. A low protection effectiveness value, combined with a high anomaly occurrence ratio, often leads to the data summary value exceeding the preset data summary threshold, thus triggering a summary alert signal. This alerts operators that the system's security protection effectiveness has issues, requiring targeted optimization and improvement.

[0060] For example, within a certain statistical period, the non-compliance rate is 20%, the average deviation is 0.15, and the average detection value is 0.7. Through weighted calculation, the protection effectiveness value is 65 (assuming a maximum score of 100). Simultaneously, the anomaly occurrence rate is 15%, resulting in a combined data summary value of 70. If the preset data summary threshold is 60, the data summary value exceeds the threshold, generating a summary alert signal. Upon receiving this signal, operators can analyze the specific composition of the protection effectiveness value to determine whether the non-compliance rate is too high, or whether the average deviation or average detection value is too large. This allows for targeted adjustments to the security protection strategy, such as optimizing the anomaly detection algorithm or enhancing the performance of protection modules, to improve the system's security protection effectiveness.

[0061] By analyzing each stage of the security protection effectiveness, the system can comprehensively and deeply evaluate the performance of the security protection judgment module and promptly identify problems in the protection process. This quantitative analysis method based on actual operational data avoids the limitations of subjective judgment, making the evaluation results more objective and accurate. Simultaneously, this analysis process provides clear directions for system optimization and improvement, helping operators to specifically enhance the system's security protection capabilities. This ensures that the metering box edge computing security gateway system can effectively resist various abnormal situations, guaranteeing the normal operation of the metering box and the secure transmission of data.

[0062] Example 5: In this example, the regional safety anomaly assessment module and the data periodic aggregation module establish a communication connection through the data storage unit. After receiving the aggregated normal signal, the module performs multi-dimensional analysis of the safety status of the metering box's operating area, enabling the identification and early warning of regional safety anomalies. The module's operation mechanism covers aspects such as detection area setting, safety deviation value collection, benchmark value calculation, regional deviation judgment, anomaly frequency statistics, and risk level classification. These aspects work together to form a systematic regional safety assessment system.

[0063] The regional safety anomaly assessment module sets up several detection zones within the metering box's operating area. The number and location of these zones can be rationally divided based on the metering box's installation environment and safety management needs. For example, for metering boxes installed in substations, detection zones can be divided according to equipment distribution areas, such as high-voltage zones, low-voltage zones, and the area surrounding the control room. For metering boxes installed outdoors, detection zones can be divided according to geographical location (east, south, west, and north), or based on surrounding environmental characteristics, such as areas with high electromagnetic interference or areas prone to dampness and corrosion. The scientific setting of these detection zones is the foundation for subsequent safety assessments, ensuring comprehensive coverage of areas potentially affected during metering box operation.

[0064] During the operation of the metering chamber, the regional safety anomaly assessment module collects the safety deviation values ​​of the corresponding detection areas in real time. The safety deviation values ​​reflect the degree of difference between the actual safety condition and the standard safety condition of each detection area. The collected data may include, but is not limited to, environmental parameters or equipment status parameters related to the safe operation of the metering chamber, such as temperature, humidity, electromagnetic interference intensity, and mechanical vibration amplitude. For example, if the temperature value collected in a detection area is 40℃, while the standard safe temperature limit for that area is 35℃, then the safety deviation value is 5℃; if the electromagnetic interference intensity collected in another detection area is 80dB, while the standard safety threshold is 60dB, then the safety deviation value is 20dB.

[0065] After collecting the safety deviation values ​​of all detection areas, the module sums these values ​​and takes the average of the sums to obtain the safety benchmark value. The safety benchmark value represents the overall average safety level of the metering chamber's operating area and is an important reference for judging whether each detection area is abnormal. For example, if a metering chamber has 5 detection areas, and the safety deviation values ​​of each area are 5, 8, 3, 6, and 4, the average of these sums yields a safety benchmark value of 5.2.

[0066] The module calculates the difference between the safety deviation value and the safety benchmark value for each detection area, and takes the absolute value of the difference to obtain the area deviation value. The area deviation value reflects the degree to which the safety status of each detection area deviates from the overall average level. If the safety deviation value of a detection area is 8 and the safety benchmark value is 5.2, then the area deviation value is 2.8; if the safety deviation value of another detection area is 3, then the area deviation value is 2.2. The module compares the area deviation value with a preset area deviation threshold. If the area deviation value exceeds the preset area deviation threshold, the corresponding detection area is marked as an abnormal area. The preset area deviation threshold is pre-set based on historical safety data and equipment safety requirements for the metering box's operating area. For example, it can be set to ±30% of the safety benchmark value. When the area deviation value exceeds this range, it indicates that the safety status of the detection area has significantly deviated from the overall average level, posing a safety hazard.

[0067] Within the statistical period, the regional safety anomaly assessment module also collects the total runtime of the metering box and the number of times the corresponding detection area is marked as an anomaly area, and marks the number of anomalies as an anomaly frequency value. For example, if the statistical period is 7 days, the total runtime of the metering box is 168 hours, and a certain detection area is marked as an anomaly area 10 times during these 7 days, then the anomaly frequency value is 10 times. The module marks the ratio of the anomaly frequency value to the total runtime as the anomaly frequency ratio, i.e., anomaly frequency ratio = anomaly frequency value / total runtime. Taking the above example, the anomaly frequency ratio is 10 times / 168 hours ≈ 0.0595 times / hour.

[0068] The module defines the corresponding detection area as a high-risk, medium-risk, or low-risk zone by comparing and analyzing the relationship between the abnormal frequency ratio and the preset abnormal frequency range. The specific criteria are as follows: if the abnormal frequency ratio exceeds the maximum value of the preset abnormal frequency range, the corresponding detection area is defined as a high-risk zone; if the abnormal frequency ratio does not exceed the minimum value of the preset abnormal frequency range, it is defined as a low-risk zone; if the abnormal frequency ratio is within the preset abnormal frequency range, it is defined as a medium-risk zone. The preset abnormal frequency range is set based on the safety management standards and historical data of the metering box's operating area. For example, if the preset abnormal frequency range is 0.02-0.05 times / hour, and the abnormal frequency ratio of a certain detection area is 0.06 times / hour, exceeding the maximum value, it is defined as a high-risk zone; if it is 0.01 times / hour, below the minimum value, it is defined as a low-risk zone; and if it is 0.03 times / hour, within the range, it is defined as a medium-risk zone.

[0069] After classifying the risk levels of each detection area, the module assesses the overall safety status of the metering box's operating area. If a high-risk zone exists within the operating area, a metering box safety anomaly signal is generated regardless of the risk levels of other areas. If no high-risk zone exists, the module further calculates the ratio of medium-risk zones to low-risk zones, marking it as the area assessment value. For example, if there are two medium-risk zones and three low-risk zones within the operating area, the area assessment value is approximately 2 / 3 ≈ 0.67. The module compares the area assessment value with a preset area assessment threshold. If the area assessment value exceeds the preset threshold, a metering box safety anomaly signal is generated. The preset area assessment threshold is set according to area safety management requirements; for example, if it is set to 0.5, a value of 0.67 exceeds the threshold, generating an anomaly signal.

[0070] The generated safety anomaly signal from the metering box is sent to the interactive management feedback module via the data storage unit. Upon receiving the signal, the interactive management feedback module visually displays the risk level distribution of each detection area on the screen, for example, using color coding to indicate high-risk, medium-risk, and low-risk areas, and displaying key parameters such as the anomaly frequency ratio and area assessment value using charts. Simultaneously, the module will issue audible or visual alerts to draw the operator's attention. Based on the feedback information, the operator can conduct targeted safety checks and rectifications in high-risk or abnormal areas, such as repairing equipment in high-risk areas, strengthening electromagnetic shielding measures, and improving ventilation and heat dissipation conditions, to reduce regional safety risks and ensure the safe operation of the metering box.

[0071] For example, an outdoor metering box operating area is divided into four detection zones with a statistical period of 30 days and a total operating time of 720 hours. Detection zone A repeatedly exceeded the safety benchmark value, with an anomaly frequency ratio of 0.08 times / hour, exceeding the preset maximum anomaly frequency range of 0.05 times / hour, and was defined as a high-risk zone. Detection zones B, C, and D had anomaly frequency ratios of 0.03 times / hour, 0.04 times / hour, and 0.01 times / hour, respectively, and were defined as medium-risk, medium-risk, and low-risk zones. Because high-risk zone A existed within the operating area, the area safety anomaly assessment module immediately generated a metering box safety anomaly signal. Upon receiving the signal, the operator inspected detection zone A and discovered newly installed high-power electrical equipment nearby, causing severe electromagnetic interference exceeding the standard. Electromagnetic shielding measures were then implemented, reducing the electromagnetic interference intensity in the area, lowering the anomaly frequency ratio to 0.04 times / hour, reducing the risk level to medium-risk, and eliminating the safety hazard.

[0072] The operation of the regional safety anomaly assessment module enables refined management of the safety status of the metering box's operating area, timely detection of potential regional safety risks, and provides operators with accurate safety warnings and decision-making basis. This assessment method, based on detection area division and multi-dimensional parameter analysis, fully considers the complexity and regional differences of the metering box's operating environment, improves the accuracy and relevance of safety assessments, effectively ensures the regional security of the metering box edge computing security gateway system, and avoids the impact of localized regional security issues on the normal operation of the entire system.

[0073] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0074] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A metering box edge computing security gateway system, characterized in that, It includes an edge computing processing unit, a data acquisition and transmission module, a security protection and discrimination module, a system status monitoring module, and an interactive management and feedback module. The data acquisition and transmission module collects data during the operation of the metering box. The operation of the metering box includes a real-time data acquisition stage, a historical data retrieval stage, and an environmental parameter monitoring stage. In the real-time data acquisition stage, the current operating data of the metering box is read and the transmitted data is recorded synchronously. After the real-time acquisition is completed, the historical data retrieval stage is entered, where the past operating data of the metering box is extracted and the transmitted data is continuously recorded. After the retrieval is completed, the environmental parameter monitoring stage is entered to collect the transmission data of the surrounding environment. The data acquisition and transmission module analyzes the data to determine whether an abnormal acquisition signal, retrieval signal, or monitoring signal has been generated. This abnormal signal is then sent via the edge computing processing unit to the security protection discrimination module and the interactive management feedback module. The interactive management feedback module displays and issues alerts for the abnormal acquisition, retrieval, or monitoring signals. Upon receiving these signals, the security protection discrimination module adaptively adjusts the data acquisition process to optimize the protection effect. The system status monitoring module tracks the protection process of the security protection discrimination module, records the moment when the security protection discrimination module receives, retrieves, or monitors an abnormal signal, and marks it as the abnormal reception moment. The abnormal reception moment is used as the starting point for timing until the protection process returns to stability to obtain the response time. If the response time does not exceed the preset response time threshold, the corresponding protection process is judged to be effective and the number of effective times is accumulated. If the response time exceeds the preset response time threshold, the corresponding protection process is judged to be ineffective and the number of ineffective times is accumulated.

2. The metering box edge computing security gateway system according to claim 1, characterized in that, The specific operation process of the data acquisition and transmission module is as follows: During the real-time data acquisition phase, the integrity, continuity, and accuracy values ​​of the metering box data are collected. The difference between the integrity value and the corresponding standard value is marked as the integrity deviation value. Similarly, the continuity deviation value and the accuracy deviation value are obtained. If the complete deviation value, continuous deviation value, or accurate deviation value exceeds the corresponding preset range, an abnormal acquisition signal will be generated. During the historical data retrieval phase, the deviation of the timestamp information of the metering box data from the standard time is marked as the time offset value. The storage format and data volume of the historical data are collected, and the difference between the storage format and the corresponding standard format is marked as the format deviation value. Similarly, the data volume deviation value is obtained. If the time offset value, format deviation value, or data volume deviation value exceeds the corresponding preset range, a retrieval abnormality signal is generated. During the environmental parameter monitoring phase, the temperature gradient, humidity fluctuation, and electromagnetic interference values ​​around the metering box are collected. The difference between the temperature gradient value and the corresponding standard value is marked as the temperature deviation value. Similarly, the humidity deviation value and interference deviation value are obtained. If the temperature deviation value, humidity deviation value, or interference deviation value exceeds the corresponding preset range, a monitoring abnormality signal is generated.

3. The metering box edge computing security gateway system according to claim 1, characterized in that, It also includes a system reliability assessment module that communicates with the edge computing processing unit. The system reliability assessment module collects the frequency and intensity of abnormal signals generated during operation, retrieves abnormal signals, and monitors abnormal signals and marks them as abnormal frequency values. It also collects the number of failures and the number of effective times during operation and marks the ratio of the number of failures to the number of effective times as the protection deviation value. The abnormal frequency value and the protection deviation value are numerically calculated to obtain the system evaluation value. If the system evaluation value exceeds the preset system evaluation threshold, a system abnormality reminder signal is generated. If the system evaluation value does not exceed the preset system evaluation threshold, a system normality reminder signal is generated. The system abnormality reminder signal is then sent to the interactive management terminal via the edge computing processing unit.

4. The metering box edge computing security gateway system according to claim 3, characterized in that, It also includes a data periodic aggregation module that communicates with the edge computing processing unit. The data periodic aggregation module is used to set the statistical period, summarize and analyze all the operation processes of the metering box within the statistical period, generate a summary reminder signal or a summary normal signal through analysis, and send the summary reminder signal to the interactive management feedback module through the edge computing processing unit. When the interactive management feedback module receives the summary reminder signal, it issues a corresponding reminder.

5. The metering box edge computing security gateway system according to claim 4, characterized in that, The specific analysis process of the data periodic summary module is as follows: The protection effectiveness value is obtained by analyzing the security protection effect, and the number of times the system abnormal reminder signal is generated within the statistical period is collected. The ratio of the number of times the system abnormal reminder signal occurs to the total number of processes within the statistical period is marked as the abnormal occurrence ratio. The protection effectiveness value and the abnormal occurrence ratio are numerically calculated to obtain the data summary value. If the total data value exceeds the preset data aggregation threshold, an aggregation alert signal will be generated. If the aggregated data value does not exceed the preset aggregated data threshold, a normal aggregated signal is generated.

6. The metering box edge computing security gateway system according to claim 5, characterized in that, The specific analysis process for the safety protection effectiveness analysis is as follows: By analyzing and determining whether the corresponding operation process is a substandard protection process, the ratio of the number of substandard protection processes to the total number of operation processes within the statistical period is marked as the substandard protection rate. The difference between the abnormal detection value of the corresponding substandard protection process and the preset abnormal detection threshold is marked as the detection deviation value. All detection deviation values ​​within the statistical period are summed and averaged to obtain the average deviation value. All abnormal detection values ​​within the statistical period are summed and averaged to obtain the average detection value. The substandard protection rate, average deviation value, and average detection value are numerically calculated to obtain the protection effectiveness value.

7. The metering box edge computing security gateway system according to claim 6, characterized in that, The specific criteria for determining if protection is inadequate are as follows: The system collects the number of standard anomalies and the number of actual protection anomalies in the corresponding operation process. The ratio of the number of actual protection anomalies to the number of standard anomalies is marked as the anomaly detection value. If the anomaly detection value does not exceed the preset anomaly detection threshold, the corresponding operation process is marked as a process with substandard protection.

8. The metering box edge computing security gateway system according to claim 4, characterized in that, It also includes a regional safety anomaly assessment module. The data periodic summary module sends the summary normal signal to the regional safety anomaly assessment module through the data storage unit. When the regional safety anomaly assessment module receives the summary normal signal, it analyzes the safety status of each area during the operation of the meter box. Through analysis, it determines whether a meter box safety anomaly signal is generated, and sends the meter box safety anomaly signal to the interactive management feedback module through the data storage unit.

9. A metering box edge computing security gateway system according to claim 8, characterized in that, The specific analysis process of the regional security anomaly assessment module is as follows: Several detection zones are set in the metering box operating area. During operation, the safety deviation values ​​of the corresponding detection zones are collected. The safety deviation values ​​of all detection zones are summed and the average value is taken to obtain the safety reference value. The difference between the safety deviation value of the corresponding detection zone and the safety reference value is calculated and the absolute value is taken to obtain the area deviation value. If the area deviation value exceeds the preset area deviation threshold, the corresponding detection zone is marked as an abnormal area. The total running time of the metering box within the statistical period is collected, as well as the number of times the corresponding detection area of ​​the statistical period is marked as an abnormal area and marked as an abnormal frequency value. The ratio of the abnormal frequency value to the total running time is marked as the abnormal frequency ratio. By comparing and analyzing, the corresponding detection area is defined as a high-risk area, a medium-risk area, or a low-risk area. If a high-risk area exists in the metering box's operating area, a metering box safety anomaly signal is generated. If no high-risk area exists in the metering box's operating area, the ratio of the number of medium-risk areas to the number of low-risk areas is marked as the area assessment value. If the area assessment value exceeds the preset area assessment threshold, a metering box safety anomaly signal is generated.

10. A metering box edge computing security gateway system according to claim 9, characterized in that, The specific analytical process of the comparative analysis is as follows: If the abnormal frequency ratio exceeds the maximum value of the preset abnormal frequency range, the corresponding detection area is defined as a high-risk area; if the abnormal frequency ratio does not exceed the minimum value of the preset abnormal frequency range, the corresponding detection area is defined as a low-risk area. If the abnormal frequency ratio is within the preset abnormal frequency range, the corresponding detection area is defined as a medium-risk area.

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