Visual presentation method, system and equipment for digital power grid protection efficiency evaluation result and medium

Abstract

The invention discloses a digital power grid protection efficiency evaluation result visual presentation method, system and device and a medium, and belongs to the technical field of digital power grids, and the method comprises the steps: obtaining the evaluation frequency, operation environment data and historical efficiency data of each target power grid region, and carrying out the analysis to generate a protection efficiency evaluation demand degree index of each region; determining the evaluation priority level of each region according to the demand degree index, screening out a key evaluation region, and further combining the evaluation frequency and historical data of the key region to analyze and obtain a protection efficiency evaluation demand index; and according to the demand quantity index, planning and executing performance evaluation implementation for the key area, and finally carrying out visual quality evaluation and feedback on an evaluation result. According to the method, the power grid operation environment and protection historical data are dynamically and quantitatively analyzed, the evaluation requirements are identified, key areas are screened, and optimal configuration and scale matching of evaluation resources are achieved.

Description

Technical Field

[0001] This invention relates to the field of digital power grid technology, and specifically to a method, system, device, and medium for visually presenting the evaluation results of digital power grid protection effectiveness. Background Technology

[0002] With the increasing intelligence and complexity of digital power grids, their protection effectiveness assessment has become a core element in ensuring the safe and stable operation of the power grid. Existing digital power grid protection effectiveness assessment systems generally suffer from the following technical bottlenecks: First, they lack systematic analysis of the dynamic characteristics of the power grid operating environment. Traditional methods rely solely on static historical data or single-dimensional indicators, making it difficult to respond in real time to the comprehensive impact of dynamic factors such as equipment load fluctuations, network bandwidth changes, and the rise and fall of external threat levels on protection effectiveness. Second, the matching degree between assessment needs and resource allocation is insufficient. The lack of a hierarchical quantitative model for assessment needs leads to insufficient assessment coverage of high-risk areas or resource redundancy in low-risk areas. Third, the visualization of assessment results lacks a dynamic quality control mechanism. Existing visualization solutions mostly focus on data charts and graphs, failing to incorporate time efficiency, data integrity, and indicator fluctuation characteristics into the quality assessment system, making it difficult to guarantee the accuracy and decision-making reference value of the visualization results. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention provides a method, system, device and medium for visualizing the evaluation results of digital power grid protection effectiveness.

[0004] Therefore, the technical problem solved by this invention is to address the existing digital power grid protection effectiveness assessment system's shortcomings in analyzing the dynamic characteristics of the power grid operating environment, the low matching degree between assessment needs and resource allocation, and the lack of a dynamic quality control mechanism for visualizing assessment results.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a method for visualizing the evaluation results of digital power grid protection effectiveness, comprising, The scope of digital power grid protection effectiveness analysis is obtained, marked as each target power grid area, and the number of protection effectiveness data assessments, power grid operating environment statistics, and protection effectiveness data of each assessment are obtained for each target power grid area.

[0006] Based on the number of protection effectiveness data assessments, power grid operating environment statistics, and protection effectiveness data from each assessment, a comprehensive analysis is conducted to obtain the protection effectiveness assessment requirement index for each target power grid area.

[0007] Based on the protection effectiveness assessment demand index, the assessment priority level of each target power grid area is determined. Based on the number of protection effectiveness assessments and the protection effectiveness data of each key performance assessment area, the protection effectiveness assessment demand index of each key performance assessment area is obtained through comprehensive analysis.

[0008] Based on the required indicators for protective effectiveness assessment, plan and implement the effectiveness assessment, and provide visual quality assessment feedback on the assessment results.

[0009] As a preferred embodiment of the method for visualizing the evaluation results of digital power grid protection effectiveness described in this invention, the comprehensive analysis yields the following protection effectiveness evaluation demand indicators for each target power grid region: Based on statistical data of the power grid operating environment, allowable deviation equipment load rate, allowable deviation network bandwidth utilization rate, and allowable deviation external threat level are extracted from the visualization system database, and the operating environment change characteristic values ​​of each target power grid area are obtained through comprehensive analysis.

[0010] Based on the number of protection effectiveness assessments for each target power grid region and the protection effectiveness data from each assessment, and by extracting the critical number of assessments from the visualization system database, a comprehensive analysis is conducted to obtain the protection effectiveness assessment requirement index for each target power grid region.

[0011] As a preferred embodiment of the method for visualizing the evaluation results of digital power grid protection effectiveness as described in this invention, the step of determining the evaluation priority level of each target power grid area based on the protection effectiveness evaluation demand index includes: Priority criteria for each target power grid region are extracted from the visualization system database.

[0012] The protection effectiveness assessment needs of each target power grid area are compared with the priority determination benchmark, and key effectiveness assessment areas are identified based on the comparison results.

[0013] As a preferred embodiment of the method for visualizing the evaluation results of digital power grid protection effectiveness described in this invention, the comprehensive analysis yields the protection effectiveness evaluation demand indicators for each key effectiveness evaluation area, including: Obtain key characterization information of power grid assets in key performance assessment areas.

[0014] By combining the number of protective effectiveness assessments and the protective effectiveness data from each assessment in each key effectiveness assessment area, a comprehensive analysis was conducted to obtain the protective effectiveness assessment demand index for each key effectiveness assessment area.

[0015] As a preferred embodiment of the method for visualizing the evaluation results of digital power grid protection effectiveness according to the present invention, the step of planning and executing the effectiveness evaluation includes: The range of protective effectiveness assessment demand indicators for each key effectiveness assessment area is extracted from the visualization system database.

[0016] The protective effectiveness assessment demand indicators for each key effectiveness assessment area are compared with the range of protective effectiveness assessment demand indicators for each key effectiveness assessment area.

[0017] The effectiveness assessment values ​​of each key effectiveness assessment area are matched and obtained. Based on the effectiveness assessment values ​​of each key effectiveness assessment area, the protective effectiveness of each key effectiveness assessment area is assessed.

[0018] The beneficial effect of this preferred technical solution is that by establishing a preset interval mapping rule between the protection effectiveness assessment demand index and the effectiveness assessment quantity, the abstract assessment demand index is automatically transformed into an operable and measurable effectiveness assessment quantity, thus solving the problem of the accuracy of dynamic allocation and scheduling of assessment resources.

[0019] As a preferred embodiment of the method for visualizing the evaluation results of digital power grid protection effectiveness according to the present invention, the step of providing visual quality evaluation feedback on the evaluation results includes: Obtain the performance evaluation time and the number of key equipment actually covered by the evaluation in each key performance evaluation area, and match the expected performance evaluation time of each key performance evaluation area with the performance evaluation volume of each key performance evaluation area.

[0020] The number of devices covered by the allowable assessment time difference and allowable deviation assessment is extracted from the visualization system database. Based on the specific values ​​of each protection effectiveness index and the effectiveness assessment quantity of each key effectiveness assessment area, the quality index of the assessment results of each key effectiveness assessment area is obtained through comprehensive analysis.

[0021] The beneficial effects of this preferred technical solution are that it not only compares the actual and expected evaluation time with the actual and planned number of covered equipment for verification, but also combines the evaluation data itself with the preset performance evaluation quantity for comprehensive analysis, thereby generating a comprehensive evaluation result quality index. This achieves dual closed-loop verification of the evaluation process and result data, and can effectively identify low-quality results caused by insufficient evaluation execution, incomplete data collection, or abnormalities in the process.

[0022] As a preferred embodiment of the method for visualizing the evaluation results of digital power grid protection effectiveness as described in this invention, the evaluation result quality index thresholds for each key effectiveness evaluation area are extracted from the visualization system database, and the evaluation result quality indexes for each key effectiveness evaluation area are compared with the evaluation result quality index thresholds for each key effectiveness evaluation area.

[0023] If the quality index of the assessment result of a key performance assessment area is higher than or equal to the threshold of the quality index of the assessment result of that key performance assessment area, then the quality of the assessment result of that key performance assessment area is assessed as qualified, and the assessment results of each key performance assessment area are presented visually.

[0024] If the quality index of the assessment result of a key performance evaluation area is lower than the threshold of the quality index of the assessment result of the key performance evaluation area, the quality of the assessment result of the key performance evaluation area will be deemed unqualified, and the protection performance of the key performance evaluation area will be reassessed.

[0025] The beneficial effect of this preferred technical solution is that it transforms the abstract quality index into a clear binary judgment of pass / fail, and directly links it to the subsequent presentation or re-evaluation actions, thereby realizing the final quality inspection and automatic process control before visualization output.

[0026] This invention provides a system for visualizing the evaluation results of digital power grid protection effectiveness.

[0027] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a visualization system for evaluating the effectiveness of digital power grid protection, comprising: a data acquisition and processing module, an evaluation demand analysis module, a region screening module, an evaluation planning module, an evaluation execution and scheduling module, a quality evaluation and feedback control module, a visualization system database, and a visualization module.

[0028] The data acquisition and processing module is responsible for acquiring or inputting the analysis scope, target area markers, number of protection effectiveness data assessments for each area, power grid operating environment statistics, and specific protection effectiveness data for each assessment.

[0029] The assessment needs analysis module is responsible for calculating the degree of need index for protection effectiveness assessment. It combines data on changes in the operating environment with historical assessment data to generate the degree of need index through analysis.

[0030] The region screening module compares the calculated protection effectiveness assessment requirement index with the corresponding threshold retrieved from the database to identify and mark key effectiveness assessment areas that need to be assessed.

[0031] The assessment planning module, for the selected key areas, further combines the number of key equipment, historical assessment data, and critical values ​​obtained from the database to analyze and calculate the required indicators for protective effectiveness assessment to guide specific assessment work.

[0032] The assessment execution and scheduling module queries the index interval mapping relationship in the database according to the protection effectiveness assessment demand index, and matches and determines the specific effectiveness assessment quantity required for each key area.

[0033] The quality assessment and feedback control module calculates the quality index of the assessment result by comparing the actual assessment time, coverage and planned value, and combining the assessment data itself. The quality index is then compared with the quality threshold in the database to determine whether the assessment result is qualified, whether it can be visualized, or whether a reassessment needs to be initiated.

[0034] The visualization system database stores the allowable deviation values ​​of various operating indicators, the number of critical assessments, the threshold values ​​of demand indicators, the number of critical key equipment, the range of assessment demand indicators, the allowable assessment time difference, the allowable coverage equipment deviation, and the threshold values ​​of the assessment result quality index.

[0035] The visualization module is responsible for displaying the final results.

[0036] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for visualizing the evaluation results of digital power grid protection effectiveness.

[0037] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for visualizing the evaluation results of digital power grid protection effectiveness.

[0038] The beneficial effects of this invention are as follows: By dynamically and quantitatively analyzing the power grid operating environment and historical protection data, this invention identifies assessment needs and selects key areas, thereby achieving optimized allocation and scale matching of assessment resources. Simultaneously, it introduces a closed-loop quality assessment and feedback mechanism based on timeliness, coverage, and data fluctuations, ensuring the rigor of the assessment process and solving the technical problems of existing digital power grid protection effectiveness assessment systems, such as insufficient analysis of the dynamic characteristics of the power grid operating environment, low matching degree between assessment needs and resource allocation, and lack of dynamic quality control mechanisms for visualization of assessment results. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 The above is a flowchart of a method for visualizing the evaluation results of digital power grid protection effectiveness, provided as an embodiment of the present invention.

[0041] Figure 2This is a schematic diagram of a scheme for a digital power grid protection effectiveness evaluation result visualization system provided in one embodiment of the present invention. Detailed Implementation

[0042] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0043] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a method for visualizing the evaluation results of digital power grid protection effectiveness, including: S1. Obtain the scope of digital power grid protection effectiveness analysis, mark it as each target power grid area, and obtain the number of protection effectiveness data assessments, power grid operating environment statistics, and protection effectiveness data of each assessment for each target power grid area; S2. Based on the number of protection effectiveness data assessments, power grid operating environment statistics, and protection effectiveness data from each assessment, a comprehensive analysis is conducted to obtain the protection effectiveness assessment demand index for each target power grid area. S3. Determine the assessment priority level of each target power grid area based on the protection effectiveness assessment demand index, and comprehensively analyze the protection effectiveness assessment demand index of each key performance assessment area based on the number of protection effectiveness assessments and the protection effectiveness data of each assessment.

[0044] S4. Based on the required indicators for protective effectiveness assessment, plan and implement the effectiveness assessment, and provide visual quality assessment feedback on the assessment results.

[0045] This invention constructs a complete intelligent decision-making system by dynamically analyzing changes in the power grid operating environment and historical protection effectiveness data. This system encompasses demand quantification, regional classification, assessment planning, and quality feedback. It enables accurate identification and dynamic prioritization of digital power grid protection effectiveness assessment needs, ensuring that assessment resources are intelligently focused on high-risk, high-demand areas. Through a closed-loop quality assessment and feedback mechanism, it effectively guarantees the accuracy and timeliness of assessment results, ultimately outputting high-quality, highly reliable, and visualized protection effectiveness assessment results.

[0046] Example 2, an embodiment of the present invention, provides a method for visualizing the evaluation results of digital power grid protection effectiveness based on the previous embodiment, including: The power grid operating environment statistics in S1 include historical average equipment load rate, historical average network bandwidth utilization rate, historical average external threat level, as well as the equipment load rate, network bandwidth utilization rate, and external threat level at the current monitoring time point.

[0047] The protection effectiveness data for each assessment includes the specific values ​​of each protection indicator and the average value of the protection effectiveness indicator values ​​for each assessment. The protection effectiveness indicators include threat detection rate, attack interception success rate, system recovery time, protection strategy update frequency, and vulnerability remediation timeliness.

[0048] Furthermore, the comprehensive analysis in S2 yields the protection effectiveness assessment requirements for each target power grid area, including steps A1-A2: A1. Based on the statistical data of the power grid operation environment, the allowable deviation equipment load rate, allowable deviation network bandwidth utilization rate and allowable deviation external threat level are extracted from the visualization system database, and the characteristic values ​​of the operation environment change of each target power grid area are obtained through comprehensive analysis.

[0049] A2. Based on the number of protection effectiveness assessments for each target power grid area and the protection effectiveness data for each assessment, and extracting the critical number of assessments from the visualization system database, a comprehensive analysis is conducted to obtain the protection effectiveness assessment requirement index for each target power grid area.

[0050] The protection effectiveness assessment demand index can be a comprehensive evaluation parameter that reflects the relative urgency and complexity of the protection effectiveness assessment of the target power grid area under the current operating environment. It can be used to initially screen power grid areas with higher assessment priority and serve as the input basis for subsequent demand calculation.

[0051] In this embodiment of the application, the protection effectiveness assessment requirement index can be calculated by weighted fusion based on the historical assessment frequency of each target power grid area, statistical data of the operating environment, and the degree of abnormal deviation of protection effectiveness data in past assessments.

[0052] Furthermore, the protection effectiveness assessment demand index can be coordinated with power grid operating environment statistics to dynamically respond to equipment load fluctuations and external threat increases and decreases; it can also be coordinated with assessment time efficiency and index fluctuation characteristics to indirectly affect the calculation weight of subsequent demand indicators.

[0053] In this application embodiment, the protection effectiveness assessment demand index of each target power grid area is obtained through comprehensive analysis. This can be achieved by multi-dimensional weighted fusion of the number of assessments, operating environment statistics and historical assessment data anomalies of each area to generate a single demand score.

[0054] In one alternative implementation, the comprehensive analysis of the protection effectiveness assessment requirements of each target power grid area can also be achieved by weighted summation after mapping each input variable to a level using fuzzy membership functions.

[0055] In this embodiment of the application, determining the evaluation priority level of each target power grid area in S3, i.e., screening out each key performance evaluation area, specifically includes: extracting the protection performance evaluation demand level index threshold of each target power grid area from the visualization system database; comparing the protection performance evaluation demand level index of each target power grid area with the protection performance evaluation demand level index threshold of each target power grid area; if the protection performance evaluation demand level index of a target power grid area is higher than or equal to the protection performance evaluation demand level index threshold of that target power grid area, then the target power grid area is marked as a key performance evaluation area.

[0056] If the protection effectiveness assessment requirement index of a target power grid area is lower than the threshold of the protection effectiveness assessment requirement index of the target power grid area, then the target power grid area will not be subject to key protection effectiveness assessment.

[0057] In one optional implementation, determining the assessment priority level of each target power grid area can be based on a priority hierarchical labeling of multi-level threshold intervals. Specifically, multiple preset threshold intervals for protection effectiveness assessment demand indicators are extracted from the visualization system database. The threshold intervals include high demand intervals, medium demand intervals, and low demand intervals. The protection effectiveness assessment demand indicators of each target power grid area are matched sequentially with these threshold intervals. Based on the interval to which they belong, they are dynamically labeled with different priority levels, including first-level key, second-level key, general monitoring, etc., and the allocation ratio and assessment frequency of subsequent assessment resources are determined according to the priority level.

[0058] In another optional implementation, the evaluation priority level of each target power grid area can also be determined by combining the cluster priority judgment of regional topological correlation. Specifically, based on comparing the demand level indicators with preset thresholds, the topological connection relationship and business dependency relationship of the power grid area are further extracted from the visualization system database. If the demand level indicator of a certain area does not reach the independent key threshold, but it has a strong topological connection or key business dependency with one or more key areas that have met the standard, then the area is upgraded to a key performance evaluation area, forming an evaluation cluster based on the risk transmission path, so as to achieve collaborative coverage and evaluation of related risks.

[0059] Furthermore, in S3, determining the assessment priority level for each target power grid area based on the protection effectiveness assessment requirement index includes steps B1-B2: B1. Extract the priority determination criteria for each target power grid area from the visualization system database; B2. Compare the protection effectiveness assessment requirements of each target power grid area with the priority determination benchmark, and identify key effectiveness assessment areas based on the comparison results.

[0060] In the embodiments of this application, the comparison with the priority determination benchmark in B2 is compared with the protection effectiveness assessment requirement index threshold of each target power grid area. Specifically, if the protection effectiveness assessment requirement index of a target power grid area is higher than or equal to the protection effectiveness assessment requirement index threshold of the target power grid area, then the target power grid area is marked as a key effectiveness assessment area.

[0061] If the protection effectiveness assessment requirement index of a target power grid area is lower than the protection effectiveness assessment requirement index threshold of the target power grid area, then the target power grid area will not be subject to key protection effectiveness assessment.

[0062] In one alternative implementation, the comparison with the priority determination benchmark can be based on dynamic ranking and relative priority identification. Specifically, each target power grid area is sorted in descending order according to the numerical value of its protection effectiveness assessment requirement index, and a preset percentage quantile or a fixed number extracted from a database is used as the determination benchmark. Areas ranked higher than this benchmark are dynamically identified as key effectiveness assessment areas for the current period. This benchmark does not depend on a fixed threshold but is adaptively determined based on the relative level of each area's index.

[0063] In another optional implementation, the comparison with the priority determination benchmark can also be achieved by combining a multi-dimensional comprehensive determination with a weight vector. Specifically, a multi-dimensional benchmark weight vector, including indicators of protection effectiveness assessment needs, regional historical failure frequency, and the criticality level of the services carried, as well as the benchmark values ​​corresponding to each dimension, is extracted from the database. The deviation scores of each region on each dimension are calculated and weighted to generate a comprehensive priority score, which is then compared with a preset comprehensive score threshold. If the comprehensive priority score of a region is higher than or equal to the threshold, it is identified as a key effectiveness assessment region.

[0064] Furthermore, the comprehensive analysis in S3 yields the protective effectiveness assessment requirements for each key effectiveness assessment area, including steps C1-C2: C1. Obtain key characterization information of power grid assets in each key performance assessment area.

[0065] C2. Combining the number of protective effectiveness assessments and the protective effectiveness data of each assessment in key performance assessment areas, a comprehensive analysis is conducted to obtain the protective effectiveness assessment demand index for each key performance assessment area.

[0066] In this embodiment, the key characteristic information of power grid assets in C1 refers to the number of key equipment and the number of critical key equipment. Specifically, the protection effectiveness assessment demand index can be a quantitative parameter representing the scale of assessment resources required for key effectiveness assessment areas, reflecting the comprehensive demand for assessment frequency and data granularity. It can be used to determine the depth and breadth of the actual assessment in key areas, realizing the dynamic allocation of assessment resources. In this embodiment, the protection effectiveness assessment demand index can be generated based on the demand level index, combined with the number of key equipment in the area, the distribution density of historical assessment frequency, and the dispersion of index fluctuation characteristics, through a multi-factor product or weighted linear combination model.

[0067] In one alternative implementation, key characterization information of power grid assets can be the number of key equipment and the fluctuation characteristics of indicators. Specifically, the demand indicators for protection effectiveness assessment can be coordinated with the number of key equipment, with more equipment resulting in higher demand; coordinated with the fluctuation characteristics of indicators, with more severe fluctuations amplifying the demand; and coordinated with assessment time efficiency, affecting the scheduling priority of assessment tasks.

[0068] In another alternative implementation, the key characterization information of power grid assets can also be the business continuity impact level or the centrality in the network topology. Specifically, the business continuity impact level refers to the level label assigned based on the importance of the services supported by the target area in the power grid, including core dispatching, critical power supply, and general monitoring. The higher the level, the stronger its criticality. The network topology centrality is quantified by calculating graph theory indicators such as degree centrality and betweenness centrality of regional nodes in the power grid connection diagram. The higher the centrality, the more core it is in the network structure and the wider the scope of its failure impact. When analyzing demand indicators, this level or centrality value can be used as a key weighting factor and integrated with parameters such as the number of times the protection effectiveness data is evaluated and the volatility of the protection effectiveness data in each evaluation.

[0069] In this embodiment of the application, the performance evaluation in S4 is to conduct a protective performance evaluation on each key performance evaluation area. Specifically, it includes extracting the protective performance evaluation demand index range of each key performance evaluation area from the visualization system database, comparing the protective performance evaluation demand index of each key performance evaluation area with the protective performance evaluation demand index range of each key performance evaluation area one by one to obtain the performance evaluation quantity of each key performance evaluation area, and conducting a protective performance evaluation on each key performance evaluation area based on the performance evaluation quantity of each key performance evaluation area.

[0070] Among them, the range of protective effectiveness assessment demand indicators can be a set of pre-defined value ranges of protective effectiveness assessment demand indicators corresponding to the risk level, used to define the quantitative boundaries of different assessment scale levels.

[0071] The range of demand indicators for protective effectiveness assessment can be generated based on the distribution characteristics of demand indicators in historical assessment data, using clustering or quantile partitioning methods to create multiple mutually exclusive intervals that cover the entire range. Each interval corresponds to an assessment resource allocation mode. The range of demand indicators for protective effectiveness assessment can include, but is not limited to, one or more of the following: low-risk assessment scale range, medium-risk assessment scale range, and high-risk assessment scale range.

[0072] In one alternative implementation, the effectiveness assessment can be dynamically generated based on a linear mapping function. Specifically, a predefined continuous mapping function or coefficient matrix of demand and assessment quantities is extracted from the visualization system database. The protection effectiveness assessment demand indicators for each key effectiveness assessment area are directly input into this function or calculated with the coefficient matrix, dynamically and continuously outputting a specific effectiveness assessment value without needing to match a preset discrete interval.

[0073] In another alternative implementation, performance evaluation can be implemented by adaptively executing evaluation tasks that combine elastic resource pools and queuing models. Specifically, based on the matched or calculated performance evaluation values ​​and the real-time available evaluation resource status, resource scheduling strategies are extracted from the visualization system database. The evaluation task is decomposed into sub-tasks and placed into a central task queue. The scheduler dynamically allocates resources and initiates execution based on the elastic scaling capability of the resource pool, task priority, and dependencies.

[0074] Furthermore, the planning and execution effectiveness assessment implementation in S4 includes steps D1-D3: D1. Extract the range of protection effectiveness assessment demand indicators for each key effectiveness assessment area from the visualization system database.

[0075] D2. Compare the protective effectiveness assessment demand indicators of each key effectiveness assessment area with the range of protective effectiveness assessment demand indicators of each key effectiveness assessment area.

[0076] Specifically, taking the assessment of resource scheduling in a high-density power distribution area as an example, the visualization method of this application embodiment can be that the demand index for the protection effectiveness assessment of a key area is 87.3, which is matched with the preset interval [80, 95) to determine it as a high-risk assessment scale interval, and the full-dimensional in-depth assessment quantity is output as an execution instruction; this instruction automatically triggers the assessment system to call the highest sampling frequency (once every 5 minutes), collect all equipment status fields, and run the complex anomaly detection model; after the assessment is completed, the results are verified by the quality system and a visualization report is output; when the demand index of the area falls back to 62.1, the system automatically matches to the medium-risk assessment scale interval, switches to enhanced sampling assessment quantity, reduces the sampling frequency and model complexity, and realizes dynamic resource contraction.

[0077] D3. Match the effectiveness assessment values ​​of each key effectiveness assessment area, and conduct a protective effectiveness assessment of each key effectiveness assessment area based on the effectiveness assessment values ​​of each key effectiveness assessment area.

[0078] Specifically, the effectiveness assessment quantity can be a specific execution parameter that clearly indicates the scale of resource investment in the assessment, output by comparing demand indicators with demand indicator intervals. It is used to directly drive the execution configuration of the assessment task, such as sampling frequency, data dimensions, and model complexity, thus transforming abstract requirements into concrete operations. In this embodiment, the effectiveness assessment quantity can output the identifier or code of the corresponding interval by matching the demand indicators with preset intervals one-to-one. This identifier is mapped to a preset resource template in the assessment system. For example, the effectiveness assessment quantity can include, but is not limited to, one or more of the following: basic sampling assessment quantity, enhanced sampling assessment quantity, and full-dimensional in-depth assessment quantity. The effectiveness assessment quantity works in conjunction with the protection effectiveness assessment demand indicators as its discretized output result; and works in conjunction with the assessment resource scheduling module as the direct source of instructions for resource allocation.

[0079] The protective effectiveness assessment demand indicators for each key effectiveness assessment area are compared one-to-one with the corresponding intervals. This can be achieved by determining the range of each area's demand indicator value within multiple pre-defined interval boundaries. Furthermore, this comparison can be accomplished by using a binary search algorithm to quickly locate and match ordered interval boundaries, or by constructing an interval membership function and outputting the most suitable interval and confidence level through fuzzy matching. This allows for the automated classification of demand indicators from continuous values ​​to discrete assessment levels, eliminating biases caused by subjectively setting assessment levels.

[0080] The required quantity of protective effectiveness assessment indicators for each key effectiveness assessment area are expressed as follows: in, Let represent the protection effectiveness assessment demand index for the t-th key effectiveness assessment area, where t represents the number of each key effectiveness assessment area, t=1,2,3,…,g, and g represents the total number of key effectiveness assessment areas; This indicates the number of times the protective effectiveness data of the t-th key effectiveness assessment area has been evaluated. Indicates the critical evaluation number; This represents the number of key equipment in the t-th key performance evaluation area. Indicates the number of critical equipment; This represents the value of the nth protective effectiveness index of the t-th key effectiveness assessment area in the r-th effectiveness assessment, where r represents the number of each effectiveness assessment, r=1,2,3,…,h, h represents the total number of effectiveness assessments, and n represents the number of each type of protective effectiveness index, n=1,2,3,…,m, m represents the total number of types of protective effectiveness indicators. This indicates the number of preset protective performance data assessments corresponding to the assessment demand impact factor. This indicates the pre-set number of key equipment items and the corresponding impact factor on the assessed demand. This indicates the impact factor of the assessment demand corresponding to the fluctuation of the preset protective effectiveness index value.

[0081] The critical assessment count can be a preset historical assessment frequency threshold that triggers a significant increase in assessment demand. This threshold is used to identify structural critical points in the assessment response and can serve as a non-linear adjustment anchor for demand indicators. When the number of regional assessments exceeds this value, it triggers a step increase in demand, avoiding assessment lag caused by linear accumulation. In one specific embodiment, the critical assessment count can be determined using piecewise regression or change point detection algorithms based on the inflection point distribution of assessment frequency and subsequent assessment response intensity in historical assessment data. It is understood that the critical assessment count can be coordinated with the number of assessments in protection effectiveness data as a trigger condition for a non-linear increase in demand; and can directly participate in mathematical expression calculations with protection effectiveness assessment demand indicators. For example, the critical assessment count can include, but is not limited to, one or more of the following: critical values ​​triggered by high-frequency assessments, critical values ​​associated with abnormal responses, and critical values ​​associated with resource saturation.

[0082] The critical number of key equipment can be a preset threshold in the digital power grid to trigger a significant increase in assessment demand. This threshold identifies structural leaps in risk levels and can serve as a dynamic calibration parameter for demand indicators. When the number of key equipment in a region exceeds this value, it triggers a non-linear escalation of the assessment scale, avoiding assessment lag caused by linear expansion. In an exemplary embodiment, the critical number of key equipment can be derived from the inflection point distribution of equipment quantity and assessment response intensity extracted from historical assessment data. Piecewise regression or cluster analysis is then used to determine the minimum equipment quantity threshold that causes a significant leap in assessment demand. It is understood that the critical number of key equipment can be used in conjunction with the number of key equipment to determine whether a high-risk scale range has been entered; and it can directly participate in mathematical expression calculations with the protection effectiveness assessment demand indicators. For example, the critical number of key equipment can include, but is not limited to, one or more of the following: critical number based on transformer clusters, critical number based on substation nodes, and critical number based on communication nodes.

[0083] The fluctuation of protective effectiveness indicators can be a statistical representation of the dispersion and anomalous jump characteristics of the values ​​of each protective effectiveness indicator over time in multiple assessments. It can be used to quantify the dynamic instability of regional risk and serve as an enhancing factor for demand indicators. In this embodiment, the fluctuation of protective effectiveness indicators can be measured by calculating the standard deviation, coefficient of variation, or sliding window range of the historical value series of all n indicators in r assessments for region t. For example, the fluctuation of protective effectiveness indicators may include, but is not limited to, one or more of the following: periodic fluctuation intensity, frequency of sudden jumps, and trend drift slope.

[0084] The preset impact factor of the number of protective effectiveness data assessments corresponding to the assessment demand can be a configurable parameter used to adjust the contribution weight of the assessment number to the demand index. It can be used to control the relative importance of historical assessment frequencies in demand calculation and supports adjusting the assessment response sensitivity based on regional characteristics. In a specific embodiment, the preset impact factor of the number of protective effectiveness data assessments corresponding to the assessment demand can be determined through historical data backtracking analysis or expert calibration, and fixed in the system configuration as a hyperparameter of the model. It is understood that the preset impact factor of the number of protective effectiveness data assessments corresponding to the assessment demand can work in conjunction with the number of protective effectiveness data assessments to determine its linear amplification factor to the demand index. For example, the preset impact factor of the number of protective effectiveness data assessments corresponding to the assessment demand can include, but is not limited to, one or more of high-response impact factors, medium-response impact factors, and low-response impact factors.

[0085] The preset key equipment quantity corresponding to the demand impact factor can be a configurable parameter used to adjust the contribution weight of the key equipment quantity to the demand index. It can be used to control the relative importance of equipment scale in demand calculation and support resource allocation strategies for high-density areas. In this embodiment, the preset key equipment quantity corresponding to the demand impact factor can be determined through a correlation analysis of historical assessment resource consumption and equipment scale, and is fixed in the system configuration as a model hyperparameter. It is understood that the preset key equipment quantity corresponding to the demand impact factor can work in conjunction with the key equipment quantity to determine its linear amplification factor on the demand index; and work in conjunction with the critical key equipment quantity to determine the intensity of the impact after exceeding the threshold. For example, the preset key equipment quantity corresponding to the demand impact factor can include, but is not limited to, one or more of the following: high-density sensitive impact factor, medium-density responsive impact factor, and low-density tolerant impact factor.

[0086] The preset impact factor of the fluctuation in the protective effectiveness index corresponding to the assessment of the demand quantity can be a configurable parameter used to adjust the contribution weight of the index's numerical fluctuation to the demand quantity index. It can be used to control the amplification degree of dynamic risk fluctuations in the demand quantity calculation, supporting enhanced assessment strategies for high-uncertainty regions. In an exemplary embodiment, the preset impact factor of the fluctuation in the protective effectiveness index corresponding to the assessment of the demand quantity can be determined based on the correlation strength analysis between historical fluctuations and subsequent risk events, and is fixed in the system configuration as a model hyperparameter. It is understood that the preset impact factor of the fluctuation in the protective effectiveness index corresponding to the assessment of the demand quantity can work in conjunction with the fluctuation in the protective effectiveness index to determine its nonlinear amplification factor to the demand quantity index. For example, the preset impact factor of the fluctuation in the protective effectiveness index corresponding to the assessment of the demand quantity can be one or more of the following, including but not limited to high-fluctuation-sensitive impact factors, medium-fluctuation-responsive impact factors, and low-fluctuation-tolerant impact factors.

[0087] The matching yields the performance evaluation quantities for each key performance evaluation area. This can be achieved by calling a preset resource template based on the interval to which the demand indicator belongs, and outputting the corresponding evaluation scale instruction. Furthermore, this matching can be implemented by directly associating interval identifiers with evaluation sampling frequencies and data field sets through a key-value mapping table, or by dynamically generating evaluation task configuration scripts based on the matching interval using a rule engine. This transforms abstract risk requirements into resource configuration instructions that can be directly executed by the evaluation system, achieving end-to-end automated conversion from demand to operation.

[0088] Furthermore, the visualization and quality assessment feedback of the evaluation results includes steps E1-E2: E1. Obtain the performance evaluation time and the number of key equipment actually covered by the evaluation in each key performance evaluation area, and match the expected performance evaluation time of each key performance evaluation area with the performance evaluation volume of each key performance evaluation area.

[0089] E2. Extract the allowable assessment time difference and allowable deviation assessment coverage number of equipment from the visualization system database, and comprehensively analyze the assessment result quality index of each key performance assessment area based on the specific values ​​of each protection effectiveness index and the performance assessment quantity of each key performance assessment area.

[0090] The number of devices covered by the allowable assessment time difference and allowable deviation assessment is extracted from the visualization system database. Based on the specific values ​​of each protection effectiveness index and the effectiveness assessment quantity of each key effectiveness assessment area, the quality index of the assessment results of each key effectiveness assessment area is obtained through comprehensive analysis. The quality index of the assessment results of each key effectiveness assessment area is used to quantify the data accuracy and timeliness of the protection effectiveness assessment results of each key effectiveness assessment area, and to provide a basis for the visualization quality assessment feedback of the assessment results.

[0091] The allowable assessment time difference can be the maximum acceptable time deviation range between the actual and expected time of the assessment task, preset by the system. It can serve as a benchmark for assessing time efficiency, used to determine whether the assessment process exceeds the time limit or is completed too early. In this embodiment, the allowable assessment time difference can be dynamically set based on the historical average assessment time and its standard deviation for each key performance assessment area, combined with business tolerance. For example, the allowable assessment time difference may include one or more of the following: allowable time difference for high-risk areas, allowable time difference for medium-risk areas, and allowable time difference for low-risk areas.

[0092] The permissible deviation assessment coverage number can be the maximum acceptable absolute deviation between the actual number of critical devices covered by the assessment task and the number of devices that should be covered, preset by the system. This can be used as a quantitative reference for data integrity to determine whether the assessment results in insufficient or excessive coverage due to missed detections or redundancy. In one specific embodiment, the permissible deviation assessment coverage number can be set as a fixed or proportional deviation threshold based on the total number of critical devices in each region and the historical assessment coverage stability range, combined with a fault-tolerance strategy. For example, the permissible deviation assessment coverage number can include one or more of the following: permissible deviation for densely populated areas, permissible deviation for sparsely populated areas, and permissible deviation for dynamic topology areas.

[0093] The quality index of the assessment results can be a quantitative indicator calculated based on the deviation between the actual implementation of the assessment and the preset tolerance standard. It is used to characterize the credibility of the protection effectiveness assessment results in terms of timeliness and coverage integrity. It can serve as a direct basis for judging the visual quality assessment feedback and determine whether to trigger the reassessment process.

[0094] In this embodiment of the application, the quality index of the evaluation result can be generated by normalizing and weighting the difference between the actual evaluation time and the allowable evaluation time difference, the deviation between the actual number of key equipment covered and the allowable deviation evaluation coverage of equipment, and the abnormal fluctuation range of the protection effectiveness index.

[0095] Furthermore, the assessment result quality index is synergistic with assessment time efficiency, influenced by the relative relationship between actual time consumption and allowable time difference; synergistic with data integrity, influenced by critical equipment coverage deviations; and synergistic with indicator fluctuation characteristics, with the fluctuation amplitude incorporated into the index calculation to enhance sensitivity to abnormal assessment results. For example, the assessment result quality index may include one or more of the following: a quality index based on time deviation, a quality index based on equipment coverage deviation, and a quality index based on abnormal indicator coupling.

[0096] The comprehensive analysis yields the quality index of assessment results for each key performance evaluation area. This quality index can be generated by normalizing the difference between the actual assessment time and the allowable assessment time difference, the deviation between the actual number of covered key equipment and the allowable deviation assessment number of covered equipment, and the abnormal fluctuation amplitude of the protection performance indicators, and then generating a weighted linear combination. Furthermore, the quality index of assessment results for each key performance evaluation area can be calculated using the Euclidean distance method to determine the comprehensive deviation of these three factors in a standardized space, or a piecewise function model can be constructed, setting threshold ranges for time and coverage deviations respectively. If the deviation exceeds the range, the index drops sharply. This allows for a multi-dimensional, non-linear, and quantifiable comprehensive judgment of the assessment result quality, supporting automated feedback triggering.

[0097] Visualizing the evaluation results and providing quality assessment feedback also includes steps F1-F3: F1. Extract the quality index threshold of the evaluation results for each key performance evaluation area from the visualization system database, and compare the quality index of the evaluation results for each key performance evaluation area with the quality index threshold of the evaluation results for each key performance evaluation area. F2. If the quality index of the assessment result of a key performance assessment area is higher than or equal to the threshold of the quality index of the assessment result of that key performance assessment area, then the quality of the assessment result of that key performance assessment area is assessed as qualified, and the assessment results of each key performance assessment area are presented visually. F3. If the quality index of the assessment result of a key performance evaluation area is lower than the threshold of the quality index of the assessment result of the key performance evaluation area, the quality of the assessment result of the key performance evaluation area will be assessed as unqualified, and the protection performance of the key performance evaluation area will be reassessed.

[0098] Specifically, the assessment result quality index can be a quantitative indicator calculated based on the deviation between the actual assessment execution and the preset tolerance standard. It characterizes the credibility of the protection effectiveness assessment results in terms of timeliness and coverage integrity, and can serve as a direct basis for determining whether to trigger a reassessment process in the visualized quality assessment feedback. In this embodiment, the assessment result quality index can generate a single index value by combining the difference between the actual assessment time and the allowable assessment time difference, the deviation between the actual number of covered key equipment and the allowable deviation assessment number of covered equipment, and the abnormal fluctuation amplitude of the protection effectiveness index through normalized weighted fusion. Furthermore, the assessment result quality index can be coordinated with assessment time efficiency, influenced by the relative relationship between actual time and allowable time difference; coordinated with data integrity, influenced by the key equipment coverage deviation; and coordinated with index fluctuation characteristics, with its fluctuation amplitude included in the index calculation to enhance sensitivity to abnormal assessment results. For example, the assessment result quality index can include, but is not limited to, one or more of the following: a quality index based on time deviation, a quality index based on equipment coverage deviation, and a quality index based on abnormal index coupling.

[0099] The assessment result quality index threshold can be a preset minimum acceptable value for the assessment result quality index for each key performance assessment area. It represents a personalized qualification threshold for the credibility of the assessment results in that area, serving as a benchmark for dynamic qualification determination and enabling differentiated quality control for different risk areas. In this embodiment, the assessment result quality index threshold can be generated based on the area's historical assessment qualification rate, the importance level of key equipment, and the intensity of assessment resource investment, through statistical distribution confidence intervals or expert rule mapping. For example, the assessment result quality index threshold may include, but is not limited to, one or more of the following: high-risk area quality threshold, medium-risk area quality threshold, and low-risk area quality threshold.

[0100] The quality index of the assessment results for each key performance evaluation region is compared with its corresponding threshold. This can be done by comparing the currently calculated quality index of each key region with its specific threshold to determine whether it meets the qualification criteria. Furthermore, this comparison can be achieved through a region-by-region parallel comparison mechanism, triggering threshold verification immediately after the assessment results are generated, or by constructing a batch comparison queue to perform comparisons and decisions sequentially according to regional risk priority. This allows for regional differentiation and automation in the determination of assessment result quality, avoiding excessively high tolerance in high-risk regions or wasted resources in low-risk regions due to a uniform threshold.

[0101] If the quality index of a key performance evaluation area falls below its threshold, the area's protective performance will be reassessed. This can be achieved by automatically initiating a new evaluation process when the quality index fails to reach the threshold, including re-collecting data, retrieving the evaluation model, and updating the visualization output. Furthermore, this reassessment can employ an incremental reassessment mode, supplementing and recalculating only the data dimensions that failed the previous evaluation, or a full reassessment mode, forcibly restarting the entire evaluation process and expanding the sampling range. This creates a closed-loop correction mechanism of evaluation-quality judgment-reassessment, ensuring that the visualization output always meets preset quality standards and improving the stability of the results and the reliability of decision-making.

[0102] Example 3, referring to Figure 2 This embodiment of the present invention provides a visualization system for evaluating the effectiveness of digital power grid protection, comprising: a data acquisition and processing module, an evaluation demand analysis module, a region screening module, an evaluation planning module, an evaluation execution and scheduling module, a quality evaluation and feedback control module, a visualization system database, and a visualization module.

[0103] The data acquisition and processing module is responsible for acquiring or inputting the analysis scope, target area markers, number of protection effectiveness data assessments for each area, power grid operating environment statistics, and specific protection effectiveness data for each assessment.

[0104] The assessment needs analysis module is responsible for calculating the degree of need indicators for protective effectiveness assessment. It combines data on changes in the operating environment with historical assessment data to generate the degree of need indicators through analysis.

[0105] The region screening module compares the calculated protection effectiveness assessment requirement index with the corresponding threshold retrieved from the database to identify and mark key effectiveness assessment areas that need to be assessed.

[0106] The assessment and planning module, for the selected key areas, further combines the number of key equipment, historical assessment data, and critical values ​​obtained from the database to analyze and calculate the required indicators for protective effectiveness assessment to guide specific assessment work.

[0107] The assessment execution and scheduling module queries the index interval mapping relationship in the database based on the protection effectiveness assessment demand index, and matches and determines the specific effectiveness assessment quantity required for each key area.

[0108] The quality assessment and feedback control module calculates the quality index of the assessment results by comparing the actual assessment time, coverage and planned values, and combining the assessment data itself. The quality index is then compared with the quality threshold in the database to determine whether the assessment results are qualified, whether they can be visualized, or whether a reassessment needs to be initiated.

[0109] The visualization system database stores the allowable deviation values ​​of various operating indicators, the number of critical assessments, the threshold values ​​of demand indicators, the number of critical critical equipment, the range of assessment demand indicators, the allowable assessment time difference, the allowable deviation of covered equipment, and the threshold values ​​of the assessment result quality index.

[0110] The visualization module is responsible for displaying the final results.

[0111] This embodiment also provides an electronic device applicable to a method for visualizing the evaluation results of digital power grid protection effectiveness, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the method for visualizing the evaluation results of digital power grid protection effectiveness as proposed in the above embodiment.

[0112] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for visualizing the evaluation results of digital power grid protection effectiveness as proposed in the above embodiments.

[0113] The storage medium proposed in this embodiment and the method for visualizing the evaluation results of digital power grid protection effectiveness proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0114] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for visualizing the results of an evaluation of the effectiveness of a digital grid protection, characterized in that: The method comprises the following steps: obtaining the analysis range of the protection effectiveness of the digital power grid, marking each target power grid area, and obtaining the protection effectiveness data evaluation times, power grid operation environment statistical data, and protection effectiveness data of each evaluation of each target power grid area; comprehensively analyzing the protection effectiveness evaluation demand degree index of each target power grid area based on the protection effectiveness data evaluation times, power grid operation environment statistical data, and protection effectiveness data of each evaluation; determining the evaluation priority level of each target power grid area according to the protection effectiveness evaluation demand degree index, and comprehensively analyzing the protection effectiveness evaluation demand quantity index of each key effectiveness evaluation area based on the protection effectiveness data evaluation times and protection effectiveness data of each evaluation of each key effectiveness evaluation area; planning and performing the effectiveness evaluation implementation according to the protection effectiveness evaluation demand quantity index, and performing visual quality evaluation feedback on the evaluation results.

2. The method of claim 1, wherein the method further comprises: determining a number of the plurality of digital grid protection performance evaluation results that are within a threshold of the target value; and displaying the number of the plurality of digital grid protection performance evaluation results that are within the threshold of the target value. The comprehensive analysis of the protection effectiveness evaluation demand degree index of each target power grid area comprises the following steps: extracting the allowed deviation device load rate, allowed deviation network bandwidth utilization rate, and allowed deviation external threat level from the visual system database according to the power grid operation environment statistical data, and comprehensively analyzing the operation environment change characteristic value of each target power grid area; comprehensively analyzing the protection effectiveness evaluation demand degree index of each target power grid area according to the protection effectiveness data evaluation times and protection effectiveness data of each evaluation of each target power grid area, and extracting the critical evaluation times from the visual system database.

3. The method of claim 2, wherein the method further comprises: determining a number of the plurality of digital grid protection performance evaluation results that are within a threshold of the target value; and displaying the number of the plurality of digital grid protection performance evaluation results that are within the threshold of the target value. The determination of the evaluation priority level of each target power grid area according to the protection effectiveness evaluation demand degree index comprises the following steps: extracting the priority level judgment benchmark of each target power grid area from the visual system database; comparing the protection effectiveness evaluation demand degree index of each target power grid area with the priority level judgment benchmark, and identifying the key effectiveness evaluation area according to the comparison result.

4. The method of claim 3, wherein the method further comprises: determining a number of the digital grid protection performance evaluation results; and determining a number of the digital grid protection performance evaluation results that are associated with the selected digital grid protection performance evaluation result. The comprehensive analysis of the protection effectiveness evaluation demand quantity index of each key effectiveness evaluation area comprises the following steps: obtaining the key representation information of the power grid assets of each key effectiveness evaluation area; comprehensively analyzing the protection effectiveness evaluation demand quantity index of each key effectiveness evaluation area in combination with the protection effectiveness data evaluation times and protection effectiveness data of each evaluation of each key effectiveness evaluation area.

5. The method of claim 4, wherein the method further comprises: determining a number of the digital grid protection performance evaluation results; and determining a number of the digital grid protection performance evaluation results that are associated with the selected digital grid protection performance evaluation result. The planning and performing of the effectiveness evaluation implementation comprises the following steps: extracting the protection effectiveness evaluation demand quantity index interval of each key effectiveness evaluation area from the visual system database; comparing the protection effectiveness evaluation demand quantity index of each key effectiveness evaluation area with the protection effectiveness evaluation demand quantity index interval of each key effectiveness evaluation area; matching the effectiveness evaluation quantity of each key effectiveness evaluation area, and performing the protection effectiveness evaluation of each key effectiveness evaluation area according to the effectiveness evaluation quantity of each key effectiveness evaluation area.

6. The method of claim 4, wherein the method further comprises: determining a number of the digital grid protection performance evaluation results; and determining a number of the digital grid protection performance evaluation results that are associated with the digital grid protection performance evaluation result. The visual quality evaluation feedback on the evaluation results comprises the following steps: obtaining the effectiveness evaluation time and actual evaluation key device coverage number of each key effectiveness evaluation area, and matching the effectiveness evaluation time of each key effectiveness evaluation area according to the effectiveness evaluation quantity of each key effectiveness evaluation area; The evaluation result quality index of each key performance evaluation area is extracted from the visualization system database, and the evaluation result quality index of each key performance evaluation area is compared with the evaluation result quality index threshold of each key performance evaluation area.

7. The method of claim 4, wherein the method further comprises: determining a number of the digital grid protection performance evaluation results; and determining a number of the digital grid protection performance evaluation results that are associated with the digital grid protection performance evaluation result. The evaluation result quality index of each key performance evaluation area is extracted from the visualization system database, and the evaluation result quality index of each key performance evaluation area is compared with the evaluation result quality index threshold of each key performance evaluation area. If the evaluation result quality index of the key performance evaluation area is higher than or equal to the evaluation result quality index threshold of the key performance evaluation area, the evaluation result quality of the key performance evaluation area is evaluated as qualified, and the evaluation results of each key performance evaluation area are visually presented. If the evaluation result quality index of a key performance evaluation area is lower than the evaluation result quality index threshold of the key performance evaluation area, the evaluation result quality of the key performance evaluation area is evaluated as unqualified, and the protection performance of the key performance evaluation area is re-evaluated.

8. A system for visualizing the results of a digital power grid protection performance evaluation, applying a method for visualizing the results of a digital power grid protection performance evaluation according to any one of claims 1 to 7, characterized in that, It includes: Data acquisition and processing module, evaluation requirement analysis module, region screening module, evaluation planning module, evaluation execution and scheduling module, quality evaluation and feedback control module, visualization system database and visualization module; The data acquisition and processing module is responsible for acquiring or inputting the analysis range, target area mark, protection performance data evaluation times of each area, power grid operation environment statistical data, and specific protection performance data of each evaluation; The evaluation requirement analysis module is responsible for calculating the protection performance evaluation requirement degree index, combining the operation environment change data and evaluation historical data, and generating the requirement degree index through analysis; The region screening module compares the calculated protection performance evaluation requirement degree index with the corresponding threshold value retrieved from the database, thereby identifying and marking the key performance evaluation areas that need to be evaluated; The evaluation planning module further combines the key device quantity, evaluation historical data and critical value obtained from the database for the screened key areas, and calculates the protection performance evaluation requirement quantity index to guide the specific evaluation work through analysis; The evaluation execution and scheduling module queries the index interval mapping relationship in the database according to the protection performance evaluation requirement quantity index, matches and determines the specific performance evaluation quantity required by each key area; The quality evaluation and feedback control module compares the actual evaluation time, coverage range and planned value, and calculates the evaluation result quality index by combining the evaluation data itself, compares the quality index with the quality threshold value in the database, and determines whether the evaluation result is qualified, whether it can be visually presented, or whether it needs to be re-evaluated; The visualization system database stores the allowed deviation value, critical evaluation times, requirement degree index threshold, critical key device quantity, evaluation requirement quantity index interval, allowed evaluation time difference, allowed coverage device deviation and evaluation result quality index threshold of each operation index; The visualization module is responsible for the display of the final result. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-7. The computer program is executed by the processor to implement the steps of the method for visualizing the evaluation result of the protection performance of the digital power grid according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method for visualizing the evaluation result of the protection performance of the digital power grid according to any one of claims 1 to 7.

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