Power grid safety supervision service system risk monitoring method and device, terminal and medium

Abstract

The invention discloses a power grid safety supervision service system risk monitoring method and device, a terminal and a medium, according to the scheme, when a network state is abnormal, timestamp information of each task in different task stages is extracted according to historical task records of a power grid safety supervision service system, and actual response time of each task stage is calculated; calculating a phase response time deviation index based on the actual response time, then counting the fault occurrence frequency of various faults and the average fault occurrence frequency of a plurality of unit time periods according to the historical fault record of the power grid safety supervision service system, and calculating a fault processing efficiency fluctuation index; and determining a risk assessment result of the power grid safety supervision service system. According to the scheme, the network connection state, the task response time, the fault processing efficiency and other key indexes of the power grid safety supervision service system are monitored in real time, abnormal conditions existing in the system can be found and processed in time, and therefore the stability and safety of the system are effectively improved.

Description

Technical Field

[0001] This application relates to the field of power grid safety monitoring technology, and in particular to a risk monitoring method, device, terminal and medium for a power grid safety monitoring business system. Background Technology

[0002] The scheduling and notification of power grid safety supervision tasks refers to the process by which safety supervision departments within the power system schedule and notify various safety inspection tasks. Firstly, power grid safety supervision requires advance scheduling of specific inspection times so that relevant departments can coordinate personnel and resources to ensure the smooth conduct of the inspection. The scheduling process typically involves communication with the inspected unit to determine a suitable inspection time, ensuring that normal production and operation are not affected; this is a crucial step in maintaining power system stability.

[0003] Since power grid safety monitoring operations typically rely on information systems for scheduling and notification, malfunctions in these systems can have serious consequences. For example, system crashes, data loss, or transmission errors can prevent the correct communication of inspection times and content, causing the overall inspection plan to fail and consequently impacting power grid safety monitoring. Summary of the Invention

[0004] This application provides a risk monitoring method, device, terminal, and medium for a power grid safety monitoring business system, which addresses the technical problem of low reliability in existing power grid safety monitoring business systems.

[0005] To address the aforementioned technical problems, the first aspect of this application provides a risk monitoring method for a power grid safety monitoring business system, comprising:

[0006] The network status monitoring results of the power grid safety monitoring system are obtained by using the monitoring probes preset in the power grid safety monitoring system.

[0007] When the network status monitoring result indicates that the network status is abnormal, the timestamp information of each task at different task stages is extracted according to the historical task records of the power grid safety supervision business system. Based on the timestamp information of the task stages, the actual response time of each task stage is calculated. Then, based on the actual response time and the predicted response time, the stage response time deviation index is calculated by time series analysis. The predicted response time is obtained by predicting the actual response time using the ARIMA model.

[0008] Based on the historical fault records of the power grid safety monitoring system, the frequency of occurrence of various types of faults and the average frequency of occurrence of faults over multiple time periods are statistically analyzed. Then, based on the frequency of occurrence of faults and the average frequency of occurrence of faults, the fault handling efficiency fluctuation index is calculated.

[0009] The risk assessment results of the power grid safety monitoring system are determined based on the stage response time deviation index and the fault handling efficiency fluctuation index.

[0010] Preferably, based on the actual response time and the predicted response time, the stage response time deviation index is calculated using time series analysis, including:

[0011] Based on the actual response time, calculate the mean response time and standard deviation of the response time for each task stage, and then obtain the response time difference within the stage based on the difference between the actual response time and the mean response time.

[0012] Based on the comparison between the actual response time and the control limit threshold, a response time determination result is determined, wherein the control limit threshold is determined based on the response time difference within the stage and the response time standard deviation, and the response time determination result is used to determine the task stage with abnormal response time.

[0013] Based on the actual response time and the predicted response time, the stage response time deviation index is calculated using time series analysis.

[0014] Preferably, the formula for calculating the stage response time deviation index is:

[0015]

[0016] In the formula, The stage response time deviation index is given. The actual response time is... For the predicted response time, The standard deviation of the response time.

[0017] Preferably, the formula for calculating the fault handling efficiency fluctuation index is:

[0018]

[0019] In the formula, This is the index for fluctuations in fault handling efficiency. Let i be the frequency of occurrence of the i-th type of fault. The average failure frequency for each time period, where x is the time period number and s is the total number of time periods.

[0020] Preferably, the risk assessment results of the power grid safety monitoring system are determined based on the stage response time deviation index and the fault handling efficiency fluctuation index, including:

[0021] Feature vectors are extracted from the stage response time deviation index and the fault handling efficiency fluctuation index. The feature vectors are then input into a preset risk prediction model to determine the risk assessment result of the power grid safety monitoring business system through the calculation of the risk prediction model. The risk prediction model is a multinomial regression model with the training objective of minimizing the sum of prediction errors. Specifically, the sum of prediction errors is the sum of the prediction errors of the risk value labels of all power grid safety monitoring business systems that have experienced faults.

[0022] Preferably, the risk assessment results include: high risk level, medium risk level, and low risk level.

[0023] Preferably, after determining the risk assessment results of the power grid safety monitoring system, the process further includes:

[0024] When the risk assessment result is of medium risk level, obtain the inspection instructions sent by the power grid safety supervision business system and the execution records of the inspection instructions, and determine the accuracy rate of the inspection instructions execution of the power grid safety supervision business system;

[0025] The accuracy rate of the inspection instruction execution and the risk assessment results are mapped to a fuzzy set constructed based on historical data for fuzzy inference operations. Based on the fuzzy inference operation results, task risk decision support information for the power grid safety supervision business system is determined. Meanwhile, a second aspect of this application provides a risk monitoring device for a power grid safety supervision business system, comprising:

[0026] The network status monitoring unit is used to obtain the network status monitoring results of the power grid safety supervision business system through the monitoring probes preset in the power grid safety supervision business system;

[0027] The response time deviation determination unit is used to extract the timestamp information of each task at different task stages based on the historical task records of the power grid safety monitoring business system when the network status is abnormal. Based on the start and end times of the task stages, the actual response time of each task stage is calculated. Then, based on the actual response time and the predicted response time, the stage response time deviation index is calculated by time series analysis. The predicted response time is obtained by predicting the actual response time using the ARIMA model.

[0028] The fault handling fluctuation determination unit is used to calculate the fault occurrence frequency of various types of faults and the average fault occurrence frequency of multiple unit time periods based on the historical fault records of the power grid safety monitoring business system, and then calculate the fault handling efficiency fluctuation index based on the fault occurrence frequency and the average fault occurrence frequency.

[0029] The system risk assessment unit is used to determine the risk assessment results of the power grid safety monitoring business system based on the stage response time deviation index and the fault handling efficiency fluctuation index.

[0030] A third aspect of this application provides a risk monitoring terminal for a power grid safety monitoring business system, comprising: a memory and a processor;

[0031] The memory is used to store program code, which is used to implement a risk monitoring method for a power grid safety monitoring business system as provided in the first aspect of this application.

[0032] The processor is used to read and execute the program code.

[0033] The fourth aspect of this application provides a computer-readable storage medium storing program code, which is read and executed by a processor to implement a risk monitoring method for a power grid safety monitoring business system as provided in the first aspect of this application.

[0034] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0035] The solution provided in this application first obtains the network status monitoring results of the power grid safety supervision business system. When an anomaly occurs in the network status, it extracts the timestamp information of each task at different task stages based on the historical task records of the power grid safety supervision business system, calculates the actual response time of each task stage, and calculates the stage response time deviation index based on the actual response time. Then, based on the historical fault records of the power grid safety supervision business system, it statistically analyzes the fault occurrence frequency of various types of faults and the average fault occurrence frequency of multiple unit time periods, calculates the fault handling efficiency fluctuation index, and determines the risk assessment result of the power grid safety supervision business system. This solution, by real-time monitoring, analysis, and prediction of key indicators such as network connection status, task response time, and fault handling efficiency of the power grid safety supervision business system, can promptly detect and handle anomalies in the system, thereby effectively improving the stability and security of the system. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating an embodiment of a risk monitoring method for a power grid safety monitoring business system provided in this application.

[0038] Figure 2 This is a schematic diagram of an embodiment of a risk monitoring device for a power grid safety monitoring business system provided in this application.

[0039] Figure 3 This is a schematic diagram of the structure of a risk monitoring terminal embodiment of a power grid safety monitoring business system provided in this application. Detailed Implementation

[0040] This application provides a risk monitoring method, device, terminal, and medium for a power grid safety monitoring business system, which addresses the technical problem of low reliability in existing power grid safety monitoring business systems.

[0041] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0042] Firstly, this application provides a risk monitoring method for a power grid safety monitoring business system, specifically including:

[0043] Please see Figure 1 The first aspect of this application provides a risk monitoring method for a power grid safety monitoring business system, comprising:

[0044] Step 101: Obtain the network status monitoring results of the power grid safety supervision business system through the preset monitoring probes in the power grid safety supervision business system.

[0045] It should be noted that the power grid safety monitoring system is divided into several monitoring points, with these locations preferably being key nodes and important network junctions within the system. Key system nodes and network junctions can be identified by reviewing the overall architecture and coverage of the power grid safety monitoring system; these points typically include data centers, server rooms, and critical network equipment. Then, the same type of monitoring probe is configured at the corresponding node location of each monitoring point. The network connection status of the system is monitored through these probes, and the monitoring results are used to determine if any anomalies have occurred.

[0046] Step 102: When the network status monitoring results indicate that the network status is abnormal, extract the timestamp information of each task at different task stages according to the historical task records of the power grid safety supervision business system. Calculate the actual response time of each task stage based on the timestamp information of the task stages. Then, calculate the stage response time deviation index by using time series analysis based on the actual response time and the predicted response time.

[0047] The predicted response time is obtained by using the ARIMA (Autoregressive Integrated Moving Average) model based on the actual response time.

[0048] It should be noted that when the system's network connection status is abnormal, the average response time and standard deviation of each stage over a past period are recorded as data support for the baseline time. Real-time monitoring is conducted from task creation to assignment. Time data for each task assignment is collected, and the start and end timestamps of assignment are recorded. Real-time monitoring is conducted from task receipt to task completion. Time data for task execution is collected, and the timestamps of task receipt and completion are recorded. Real-time monitoring is conducted from task completion to report generation and transmission. Time data for report generation is collected, and the start and end timestamps of report generation are recorded. For each stage of each task, the actual response time within that stage is obtained, and the actual response time is analyzed to calculate the stage response time deviation index. This assesses the degree of anomaly in the response time of tasks assigned and executed by the power grid safety monitoring business system. The method for obtaining the stage response time deviation index is as follows:

[0049] Collect task assignment and execution data for each task at each stage, generate timestamps for the reports, mark the start and end times of each stage, calculate the actual response time for each stage, and mark it as... And standardize the actual response time data;

[0050] Using historical data, calculate the mean and standard deviation of response time for each stage as a reference for the baseline time and fluctuation range. Calculate the difference between the actual response time and the baseline time, i.e., calculate the response time difference within the stage, and label it as MK.

[0051] Draw a control chart for each stage, set control limits (UCL and LCL), and immediately mark any actual response time exceeding the control limits as abnormal. , In the formula, k is a constant. For the standard deviation of response time, To control the upper limit of the chart, This is the lower limit of the control chart;

[0052] The ARIMA model is used to predict the actual response time trend. The predicted response time values ​​within the time period s are obtained and labeled as follows. The actual response time value and the predicted response time value are analyzed, and the stage response time deviation index is calculated by combining the control chart and time series analysis results. The specific calculation expression is as follows: In the formula, This is the stage response time deviation index.

[0053] A larger stage response time deviation index indicates that the actual response time is significantly higher than the predicted value. This means that a more significant delay occurred during task execution. It suggests that some departments or personnel may be overloaded, leading to task delays. Additionally, network latency, server overload, and other issues may also cause slow task execution. The obtained stage response time deviation index can be further compared with a reference threshold. If the stage response time deviation index is greater than or equal to the reference threshold, an abnormal task response time signal is generated; if the stage response time deviation index is less than the reference threshold, a normal task response time signal is generated. The reference threshold for the stage response time deviation index can be determined by using the stage response time deviation index calculated from the abnormal task stages within the control limits.

[0054] If this happens frequently, it can lead to a backlog of tasks, affecting the efficiency and reliability of the overall inspection plan. Delays may cause critical inspection windows to be missed, increasing potential risks and impacting the safe operation of the power grid.

[0055] Step 103: Based on the historical fault records of the power grid safety monitoring business system, calculate the fault occurrence frequency of various types of faults and the average fault occurrence frequency of multiple unit time periods. Then, calculate the fault handling efficiency fluctuation index based on the fault occurrence frequency and the average fault occurrence frequency.

[0056] It should be noted that historical fault report data is collected, organized, and categorized. Fault reports are classified into different fault types based on their descriptions, including network faults, equipment faults, and software faults. For each fault type i, its quantity Ni is calculated, expressed as: Ni = count(fault type = i). The quantity of each fault type is counted by time period, expressed as: N(i,j) = count(fault type = i, time period = j). For each fault type i, its occurrence frequency Fi is calculated, with the specific calculation expression as follows: In the formula, Nj represents the time point for each type of failure;

[0057] Observe the frequency changes of each type of fault over time periods, perform time series analysis for each type of fault, and then smooth the data using a moving average method. Analyze the average fault frequency and actual fault frequency for each time period to calculate the fault handling efficiency fluctuation index. The specific calculation expression is as follows: In the formula, This is the index for fluctuations in fault handling efficiency. The average failure frequency is represented by x, where x is the time period number and s is the total number of time periods. A larger failure handling efficiency fluctuation index indicates a greater deviation between the actual response time and the target response time. This means that the system's failure handling efficiency fluctuates significantly, i.e., the system's response speed to failures may exhibit marked instability across different time periods.

[0058] Step 104: Determine the risk assessment results of the power grid safety monitoring business system based on the stage response time deviation index and the fault handling efficiency fluctuation index.

[0059] It should be noted that by comprehensively analyzing the abnormality of the response time of the power grid safety monitoring system in scheduling and executing tasks and the stability of the power grid safety monitoring system's fault handling efficiency, the risk of failure of the power grid safety monitoring system is assessed and predicted, thereby obtaining the risk assessment result of the power grid safety monitoring system.

[0060] More specifically, based on the phase response time deviation index and the fault handling efficiency fluctuation index, the risk assessment results of the power grid safety monitoring business system are determined, including:

[0061] Feature vectors are extracted from the stage response time deviation index and the fault handling efficiency fluctuation index. The feature vectors are then input into a preset risk prediction model. The risk prediction model is used to determine the risk assessment results of the power grid safety supervision business system. The risk prediction model is a multinomial regression model with the training objective of minimizing the sum of prediction errors of the risk value labels of all power grid safety supervision business systems that have faults.

[0062] It should be noted that the stage response time deviation index and fault handling efficiency fluctuation index are converted into the first feature vector. The first feature vector is used as the input of the machine learning model. The machine learning model uses the prediction of the risk value label of the power grid safety supervision business system for each set of first feature vectors as the prediction objective. The training objective is to minimize the sum of prediction errors of the risk value labels of all power grid safety supervision business systems. The machine learning model is trained until the sum of prediction errors converges and the model training stops. The risk coefficient of the power grid safety supervision business system failure is determined based on the model output. The machine learning model is a multinomial regression model.

[0063] Furthermore, the risk assessment results mentioned in this embodiment can be divided into: high-risk level, medium-risk level, and low-risk level. Specific examples of determination can be found in the following examples:

[0064] The risk coefficient of failure of the power grid safety monitoring business system is compared with the gradient risk threshold. The gradient standard threshold includes a first risk threshold and a second risk threshold, and the first risk threshold is less than the second risk threshold. The risk coefficient of failure of the power grid safety monitoring business system is compared with the first risk threshold and the second risk threshold respectively.

[0065] If the risk coefficient of a failure in the power grid safety monitoring system exceeds the second risk threshold, the risk is classified as high-risk, and a Level 1 warning signal is generated. Immediate emergency measures are taken, such as deploying backup systems, allocating more human resources, and notifying relevant departments for emergency response. Monitoring and inspection frequency of the system is increased to ensure timely detection and resolution of potential problems. Emergency plans are activated to quickly restore system functionality and minimize the impact of the failure on operations.

[0066] If the risk coefficient of a failure in the power grid safety monitoring system is greater than or equal to the first risk threshold and less than or equal to the second risk threshold, the risk of the failure is classified as medium risk, and a level-two warning signal is generated. The monitoring level is then upgraded, and the system's monitoring and anomaly handling capabilities are strengthened. The scheduling and coordination of the fault handling team are also enhanced to ensure rapid response and problem resolution.

[0067] If the risk coefficient of a failure in the power grid safety monitoring system is less than the first risk threshold, the risk of such a failure is classified as low-risk, and a level-three warning signal is generated. System performance optimization and problem investigation are then conducted, and potential problems are promptly fixed to prevent them from escalating into more serious failures. Monitoring and analysis of the system's operational status are strengthened, and resource allocation is adjusted in a timely manner to ensure the system maintains stable operation even under high load.

[0068] It's important to note that Level 1 warning signals are more significant than Level 2 warning signals, and Level 2 warning signals are more significant than Level 3 warning signals. Level 1 warning signals represent the highest level of importance, indicating a potential serious risk of system failure requiring immediate emergency measures to prevent or minimize its impact. Level 2 warning signals indicate a potential medium risk, requiring timely attention and intervention to prevent further escalation. Level 3 warning signals indicate lower priority issues that still require attention and resolution to ensure stable system operation. The benefit of warning signals lies in their real-time monitoring and alerting of system health, which helps prevent failures, reduce downtime, improve productivity, and prompts organizations to take timely action to maintain and optimize the system.

[0069] By comprehensively utilizing monitoring probes to monitor the system's network connection status in real time, monitor the execution process of safety inspection tasks, and statistically analyze historical fault report data, the operational status and fault handling capabilities of the power grid safety supervision system can be fully assessed. Through comprehensive analysis of data such as system network status, task response time, and fault handling trends, the risk of system failures can be accurately assessed and classified into different levels for appropriate handling measures. This predictive and tiered approach helps to identify and resolve potential problems in advance, thereby improving system stability and reliability, reducing losses and impacts caused by failures, and providing effective protection for the normal operation and sustainable development of the power grid safety supervision system.

[0070] Furthermore, after determining the risk assessment results of the power grid safety monitoring system in step 104, the process also includes:

[0071] Step 105: When the risk assessment result is at a medium risk level, obtain the inspection instructions sent by the power grid safety supervision business system and the execution records of the inspection instructions, and determine the accuracy rate of the inspection instructions execution of the power grid safety supervision business system;

[0072] Step 106: Map the inspection instruction execution accuracy and risk assessment results to a fuzzy set constructed based on historical data for fuzzy inference calculation. Based on the fuzzy inference calculation results, determine the task risk decision support information of the power grid safety supervision business system. This task risk decision support information contains basic task management decision schemes, which can be used as a reference for maintenance personnel. This enables maintenance personnel to better adjust the task execution order and resource allocation according to priority and urgency, so as to achieve efficient management of the power grid safety supervision business dispatch time.

[0073] It should be noted that when the system is at a medium risk level, the probability of failure is between low and high risk. The traditional threshold judgment method, with its "either / or" judgment logic, is difficult to accurately judge this ambiguous state. Therefore, this embodiment provides a method that, when the risk assessment result is at a medium risk level, considers the non-linear relationship between the accuracy of the inspection instructions (such as the instruction error rate) and the system failure risk, and comprehensively weighs the impact of both on task management.

[0074] Collect data on all inspection commands sent by the system and their execution status. This data includes the total number of commands and the number of commands executed correctly. By filtering and analyzing the collected data, the number of correctly executed commands is determined. These can be commands confirmed to have been executed without errors, or commands confirmed to have been executed correctly through system logs or other recording methods. The accurate command ratio is calculated using the following expression: Accurate command ratio = (Number of correctly executed commands / Total number of commands). Based on the calculated accurate command ratio, the system's accuracy is interpreted and evaluated. A higher ratio indicates higher command execution accuracy; conversely, a lower ratio suggests a greater possibility of execution errors, requiring further investigation and improvement.

[0075] Based on two factors—the accuracy rate of inspection instructions and the risk coefficient of system failure—fuzzy sets are defined, such as "high instruction accuracy", "medium instruction accuracy", "low instruction accuracy" and "high system risk", "medium system risk", "low system risk".

[0076] Based on historical data, fuzzy rules are established to describe the relationship between the accuracy rate of inspection instructions and the risk coefficient of system failure. For example, if the accuracy of inspection instructions is high and the system risk is low, the task priority is high; if the accuracy of inspection instructions is low and the system risk is high, the task priority is low.

[0077] Collect and process the accuracy ratio of inspection instructions and the risk coefficient of system failure, transform them into fuzzy set form, and use the accuracy ratio of inspection instructions and the risk coefficient as input items of fuzzy rules to map them into the corresponding fuzzy sets;

[0078] Based on defined fuzzy rules and input fuzzy sets, fuzzy inference is performed to infer the priority and urgency of tasks. This can be done using a fuzzy logic system, which calculates the corresponding output fuzzy set based on the fuzzy rules and input fuzzy sets.

[0079] The fuzzy output obtained from fuzzy inference is defuzzified to obtain specific task management decisions. Weighted averaging of fuzzy sets or other defuzzification methods can be used to convert the fuzzy output into specific numerical values.

[0080] Based on the task management decisions obtained from defuzzification, the system adjusts and arranges the task execution order and priority. According to the task priority and urgency, the system determines the task execution time and resource allocation, thereby achieving efficient management of the power grid safety monitoring service dispatch time.

[0081] All tasks are categorized according to their priority and urgency. Task priority is divided into high, medium, and low levels, while urgency is divided into urgent and non-urgent categories. The priority and urgency of each task are determined based on its nature and importance. High-priority and urgent tasks may include system fault repair and security vulnerability handling, while low-priority and non-urgent tasks may include routine inspections or maintenance work.

[0082] For high-priority and urgent tasks, schedule them for completion within a shorter timeframe. These tasks can be prioritized to ensure timely resolution and mitigate potential risks. Conversely, low-priority and non-urgent tasks can be scheduled for completion over a longer period to maximize resource utilization and improve efficiency.

[0083] Once the task execution time is determined, the system can send reminders and notifications to relevant personnel via scheduling and notification methods. These notifications may include information such as the task execution time, location, and required resources to ensure that the task is carried out on time and effectively.

[0084] During task execution, it is necessary to continuously monitor the task's progress. If deviations or delays are detected, the task's execution time should be adjusted promptly or its priority should be reassigned to ensure timely completion and achievement of the expected results.

[0085] This invention categorizes tasks according to their priority and urgency, ensuring that high-priority and urgent tasks are processed promptly, thereby reducing potential risks. Simultaneously, for low-priority and non-urgent tasks, execution time is rationally scheduled to fully utilize resources and improve efficiency. A scheduling and notification system allows for timely delivery of task information to relevant personnel, ensuring tasks are completed on time. During task execution, the system continuously monitors progress, adjusting execution times or repricing tasks as needed to guarantee timely completion and maximize the efficiency and accuracy of task management.

[0086] In this embodiment, when the power grid safety monitoring system is at a medium risk level (i.e., the system failure risk coefficient is between the first and second risk thresholds), efficient task management is achieved by combining the accuracy ratio of inspection instructions with the system failure risk coefficient using fuzzy logic. First, data on all inspection instructions sent by the system and their execution status are collected, and the accuracy ratio of the inspection instructions is calculated. The accuracy of instruction execution is evaluated based on the accuracy ratio. Simultaneously, fuzzy rules are established based on historical data to describe the relationship between the accuracy ratio and the system failure risk. The accuracy ratio and risk coefficient are mapped to corresponding fuzzy sets, and fuzzy reasoning is performed to obtain the task priority and urgency. Finally, the fuzzy output is defuzzified to obtain specific task management decisions, and the task execution order and resource allocation are adjusted according to priority and urgency to achieve efficient management of power grid safety monitoring task dispatch time.

[0087] The above is a detailed description of an embodiment of a risk monitoring method for a power grid safety supervision business system provided in this application. The following is a detailed description of an embodiment of a risk monitoring device for a power grid safety supervision business system provided in this application.

[0088] Please see Figure 2 The second aspect of this application provides a risk monitoring device for a power grid safety monitoring business system, comprising:

[0089] The network status monitoring unit 201 is used to obtain the network status monitoring results of the power grid safety supervision business system through the monitoring probes preset in the power grid safety supervision business system.

[0090] The response time deviation determination unit 202 is used to extract the timestamp information of each task at different task stages based on the historical task records of the power grid safety monitoring business system when the network status is abnormal. Based on the start and end times of the task stages, the actual response time of each task stage is calculated. Then, based on the actual response time and the predicted response time, the stage response time deviation index is calculated by time series analysis. The predicted response time is obtained by predicting the actual response time using the ARIMA model.

[0091] The fault handling fluctuation determination unit 203 is used to calculate the fault occurrence frequency of various types of faults and the average fault occurrence frequency of multiple unit time periods based on the historical fault records of the power grid safety monitoring business system, and then calculate the fault handling efficiency fluctuation index based on the fault occurrence frequency and the average fault occurrence frequency.

[0092] The system risk assessment unit 204 is used to determine the risk assessment results of the power grid safety monitoring business system based on the stage response time deviation index and the fault handling efficiency fluctuation index.

[0093] Furthermore, it also includes:

[0094] The instruction execution accuracy calculation unit 205 is used to obtain the inspection instructions sent by the power grid safety supervision business system and the execution records of the inspection instructions when the risk assessment result is of medium risk level, and to determine the execution accuracy of the inspection instructions of the power grid safety supervision business system.

[0095] The fuzzy inference unit 206 is used to perform fuzzy inference operations by mapping the inspection instruction execution accuracy and risk assessment results to a fuzzy set constructed based on historical data, so as to determine the task risk decision support information of the power grid safety supervision business system based on the fuzzy inference operation results.

[0096] like Figure 3 As shown, the present application provides an embodiment of a risk monitoring terminal for a power grid safety monitoring business system, which includes: a memory 33 and a processor 31, wherein the memory 33 and the processor 31 can be connected via a communication bus 34;

[0097] The memory 33 is used to store program code, which is used to implement a risk monitoring method for a power grid safety monitoring business system as provided in the above embodiments;

[0098] Processor 31 is used to read and execute program code.

[0099] This application provides an embodiment of a computer-readable storage medium, in which program code is stored. The program code is read and executed by a processor to implement a risk monitoring method for a power grid safety monitoring business system as provided in the above embodiment.

[0100] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the terminals, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0101] In the several embodiments provided in this application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0102] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0103] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0104] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0106] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0107] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A risk monitoring method for a power grid safety monitoring system, characterized in that, include: The network status monitoring results of the power grid safety monitoring system are obtained by using the monitoring probes preset in the power grid safety monitoring system. When the network status monitoring result indicates that the network status is abnormal, the timestamp information of each task at different task stages is extracted according to the historical task records of the power grid safety supervision business system. Based on the timestamp information of the task stages, the actual response time of each task stage is calculated. Then, based on the actual response time and the predicted response time, the stage response time deviation index is calculated by time series analysis. The predicted response time is obtained by predicting the actual response time using the ARIMA model. Based on the historical fault records of the power grid safety monitoring system, the frequency of occurrence of various types of faults and the average frequency of occurrence of faults over multiple time periods are statistically analyzed. Then, based on the frequency of occurrence of faults and the average frequency of occurrence of faults, the fault handling efficiency fluctuation index is calculated. The risk assessment results of the power grid safety monitoring system are determined based on the stage response time deviation index and the fault handling efficiency fluctuation index.

2. The risk monitoring method for a power grid safety monitoring system according to claim 1, characterized in that, Based on the actual response time and the predicted response time, the stage response time deviation index is calculated using time series analysis, including: Based on the actual response time, calculate the mean response time and standard deviation of the response time for each task stage, and then obtain the response time difference within the stage based on the difference between the actual response time and the mean response time. Based on the comparison between the actual response time and the control limit threshold, a response time determination result is determined, wherein the control limit threshold is determined based on the response time difference within the stage and the response time standard deviation, and the response time determination result is used to determine the task stage with abnormal response time. Based on the actual response time and the predicted response time, the stage response time deviation index is calculated using time series analysis.

3. The risk monitoring method for a power grid safety monitoring business system according to claim 2, characterized in that, The formula for calculating the stage response time deviation index is: In the formula, The stage response time deviation index is given. The actual response time is... The predicted response time, The standard deviation of the response time.

4. The risk monitoring method for a power grid safety monitoring business system according to claim 1, characterized in that, The formula for calculating the fault handling efficiency fluctuation index is: In the formula, This is the index for fluctuations in fault handling efficiency. Let i be the frequency of occurrence of the i-th type of fault. The average failure frequency for each time period is given by x, where x is the time period number and s is the total number of time periods.

5. The risk monitoring method for a power grid safety monitoring business system according to claim 1, characterized in that, Based on the stage response time deviation index and the fault handling efficiency fluctuation index, the risk assessment results of the power grid safety monitoring system are determined as follows: Feature vectors are extracted from the stage response time deviation index and the fault handling efficiency fluctuation index. The feature vectors are then input into a preset risk prediction model to determine the risk assessment result of the power grid safety monitoring business system through the calculation of the risk prediction model. The risk prediction model is a multinomial regression model with the training objective of minimizing the sum of prediction errors. Specifically, the sum of prediction errors is the sum of the prediction errors of the risk value labels of all power grid safety monitoring business systems that have experienced faults.

6. The risk monitoring method for a power grid safety monitoring business system according to claim 5, characterized in that, The risk assessment results include: high risk level, medium risk level, and low risk level.

7. The risk monitoring method for a power grid safety monitoring business system according to claim 6, characterized in that, After determining the risk assessment results of the power grid safety monitoring system, the following is also included: When the risk assessment result is of medium risk level, obtain the inspection instructions sent by the power grid safety supervision business system and the execution records of the inspection instructions, and determine the accuracy rate of the inspection instructions execution of the power grid safety supervision business system; The accuracy rate of the inspection instruction execution and the risk assessment results are mapped to a fuzzy set constructed based on historical data for fuzzy inference operations, so as to determine the task risk decision support information of the power grid safety supervision business system based on the fuzzy inference operation results.

8. A risk monitoring device for a power grid safety supervision business system, characterized in that, include: The network status monitoring unit is used to obtain the network status monitoring results of the power grid safety supervision business system through the monitoring probes preset in the power grid safety supervision business system; The response time deviation determination unit is used to extract the timestamp information of each task at different task stages based on the historical task records of the power grid safety monitoring business system when the network status is abnormal. Based on the start and end times of the task stages, the actual response time of each task stage is calculated. Then, based on the actual response time and the predicted response time, the stage response time deviation index is calculated by time series analysis. The predicted response time is obtained by predicting the actual response time using the ARIMA model. The fault handling fluctuation determination unit is used to calculate the fault occurrence frequency of various types of faults and the average fault occurrence frequency of multiple unit time periods based on the historical fault records of the power grid safety monitoring business system, and then calculate the fault handling efficiency fluctuation index based on the fault occurrence frequency and the average fault occurrence frequency. The system risk assessment unit is used to determine the risk assessment result of the power grid safety monitoring business system based on the stage response time deviation index and the fault handling efficiency fluctuation index.

9. A risk monitoring terminal for a power grid safety supervision business system, characterized in that, include: Memory and processor; The memory is used to store program code, which is used to implement a risk monitoring method for a power grid safety monitoring business system as described in any one of claims 1 to 7; The processor is used to read and execute the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that is read and executed by a processor to implement a risk monitoring method for a power grid safety monitoring business system as described in any one of claims 1 to 7.

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