A method, system, device and medium for power enterprise architecture difference analysis of multi-dimensional quantitative evaluation

Abstract

The application discloses a kind of multi-dimensional quantitative evaluation electric power enterprise architecture difference analysis method, system, equipment and medium, comprising: obtaining and pre-processing present situation and blueprint architecture snapshot, evolution mode library and project and investment plan, the pre-processed present situation snapshot is used as architecture asset data;According to the hierarchical block processing asset data of business domain, distributes computing power through task dynamic fragmentation scheduling mechanism, executes difference identification operation in parallel to generate architecture difference list;Based on evolution mode library, match each gap, calculate the evolution workload and cost, calculate weighted score in combination with preset evaluation index to determine rectification priority, fuse project and investment plan to generate architecture rectification plan and land;In the rectification process, the latest present situation snapshot is dynamically synchronized through polling and event triggering mechanism, real-time calculation architecture alignment degree, when identifying high priority gap or alignment degree is lower than preset threshold, trigger early warning. Realize the automation closed loop support from difference identification to priority decision.

Description

Technical Field

[0001] This invention relates to the technical field of power information technology, and in particular to a method, system, equipment, and medium for multi-dimensional quantitative assessment of power enterprise architecture differences. Background Technology

[0002] As power companies deepen their digital transformation, ensuring consistency between their enterprise architecture blueprint and the current state has become crucial. However, existing architecture discrepancy verification technologies are mostly general-purpose tools or manual analysis methods, which are ill-suited to the power company's five-layer architecture ("business-application-data-technology-security") and the industry's high compliance, real-time, and confidentiality requirements. Research reveals the following core shortcomings of existing technologies: First, they can only identify the existence of assets, failing to delve into deeper architectural differences such as dispatch system versions and performance indicators, business data flow specifications, and equipment technical protocol compliance, thus hindering refined management. Second, general-purpose tools cannot effectively connect to power-specific architecture data sources or support the power industry's unique architecture evolution patterns, leading to analysis results that are detached from actual business needs. Third, with massive amounts of architecture assets, traditional manual analysis or simple script-based traversal methods are too time-consuming to support the performance requirements of high-frequency architecture monitoring and rapid decision-making.

[0003] Existing technologies only output a static gap list, which cannot quantify the workload and cost of evolution based on industry evolution patterns, and it is difficult to prioritize rectification items in conjunction with project and investment plans. At the same time, they cannot dynamically calculate the architecture alignment and trigger warnings when key deviations occur, causing architecture evolution to lag behind business development.

[0004] In summary, there is an urgent need for an efficient, intelligent, and dynamically responsive architecture consistency management method tailored to the characteristics of the power industry, in order to achieve automated closed-loop support from difference identification to priority decision-making. Summary of the Invention

[0005] In view of the aforementioned existing problems, this invention is proposed. Therefore, this invention provides a multi-dimensional quantitative assessment method, system, equipment, and medium for power enterprise architecture difference analysis, addressing the problems of existing methods such as single difference identification dimensions, insufficient comparative calculation performance, lack of quantitative evolution assessment methods, weak security control of confidential data, and lack of visualization and closed-loop management capabilities.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a method for multi-dimensional quantitative evaluation of differences in the architecture of power enterprises, including: Acquire and preprocess current architecture snapshots, blueprint architecture snapshots, power industry evolution model library, project plan data and investment plan data of power enterprises, and use the preprocessed current architecture snapshots as architecture asset data to be analyzed; The architecture asset data is processed by business domain layering and block segmentation to form several data shards. Computing resources are allocated through a task dynamic sharding scheduling mechanism. Difference identification operations are performed in parallel on each data shard to generate an architecture difference list between each data shard and the blueprint architecture snapshot. Based on the power industry evolution model library, for each gap in the architecture difference list, the corresponding evolution model is matched, and the evolution workload and estimated cost of each evolution model are calculated. The weighted scores of the preset evaluation indicators are calculated based on the evolution workload and estimated costs. The rectification priorities of each gap are determined based on the weighted scores. The structural rectification plan is generated by combining the project plan data and investment plan data to implement the rectification of the power company's structure. During the rectification process, the latest current architecture snapshot is dynamically synchronized through a polling mechanism and an event triggering mechanism, and its architecture alignment with the blueprint architecture snapshot is calculated in real time. When a gap with a high rectification priority is detected or the architecture alignment is lower than a preset threshold, an early warning message is triggered.

[0007] As a preferred embodiment of the multi-dimensional quantitative assessment method for power enterprise architecture difference analysis described in this invention, the architecture asset data is processed by business domain hierarchical segmentation to form several data fragments, including: Using the business layer, application layer, data layer, technology layer, and security layer of the power enterprise architecture as boundaries, the overall architecture asset dataset is horizontally layered to isolate architecture asset data at different architecture levels; Within each horizontal layer, based on at least one dimension of the power business domain boundary, asset business coupling relationship and system deployment cluster scope, the architecture asset data is granularly segmented, and the architecture asset data belonging to different business domains under the same architecture layer that have no data interaction, no business dependency and no topological association are divided into several independent data fragments. Among them, all the data shards formed have no overlapping assets, no data associations, and no business dependencies, so that each data shard can be processed independently in parallel.

[0008] As a preferred embodiment of the multi-dimensional quantitative evaluation method for power enterprise architecture difference analysis described in this invention, the method involves: allocating computing resources through a dynamic task sharding scheduling mechanism, performing difference identification operations in parallel on each data shard, and generating an architecture difference list between each data shard and the blueprint architecture snapshot, including: For each data shard, generate a one-to-one corresponding shard subtask. Based on the data volume of the architecture assets, the number of field dimensions, and the complexity of the associated topology of each data shard, calculate the computing power consumption level of each corresponding shard subtask in real time. Based on the preset computing power resource pool quota, idle computing nodes are dynamically allocated. Sharded subtasks with computing power consumption levels lower than the preset level and asset data volume less than the preset data volume are aggregated to the same computing node. Sharded subtasks with computing power consumption levels not lower than the preset level or associated topological complexity higher than the preset complexity are allocated to dedicated computing nodes. This enables each computing node to synchronously distribute sharded subtasks corresponding to the same data shard to the four preset difference recognition sub-engines to execute multidimensional comparison tasks. It also controls the operation process of sharded subtasks corresponding to different data shards to ensure that they do not compete for resources or interfere with each other. The task heartbeat monitoring mechanism monitors the running status of each computing node in real time. For sharded subtasks that time out or are abnormally interrupted, retry scheduling is automatically triggered, and failed computing nodes are masked. At the same time, the remaining tasks to be processed on the failed computing nodes are migrated to other normal computing nodes. After all sharding subtasks and sub-engine tasks are completed, the difference data of each dimension are aggregated in a unified manner, and duplicate differences are deduplicated and the validity of differences is verified twice to output a standardized list of architectural differences.

[0009] As a preferred embodiment of the multi-dimensional quantitative evaluation method for power enterprise architecture difference analysis described in this invention, the method further includes: before allocating computing resources through a dynamic task sharding scheduling mechanism, performing difference identification operations in parallel on each data shard, and generating an architecture difference list between each data shard and the blueprint architecture snapshot, the method further includes: A multi-engine task mutual exclusion scheduling strategy is constructed to control the four types of difference identification sub-engines—existence comparison, maturity / performance comparison, relationship / integration comparison, and standard compliance comparison—to be decoupled from each other and run as independent processes.

[0010] As a preferred embodiment of the multi-dimensional quantitative assessment method for power enterprise architecture difference analysis described in this invention, the method includes: based on the power industry evolution model library, matching the corresponding evolution model for each gap in the architecture difference list, and calculating the evolution workload and estimated cost of each evolution model, including: Based on the power industry evolution model library, according to the gap type, asset level and asset scale of each gap in the difference list, the evolution model corresponding to each gap is matched, and the benchmark parameters of the corresponding evolution model are retrieved; wherein, the benchmark parameters include: industry standard basic workload, unit labor cost, equipment purchase unit price and deployment and commissioning cost. Based on the basic workload, the preset asset complexity coefficient, and the preset business coupling coefficient, the evolution workload corresponding to each gap is calculated. Based on the unit labor cost, equipment purchase price, and deployment and debugging cost, the estimated cost corresponding to each gap is calculated.

[0011] As a preferred embodiment of the multi-dimensional quantitative assessment method for power enterprise architecture difference analysis described in this invention, the preset assessment indicators include: technical difficulty level, business impact scope, rectification cost, and project urgency; a weighted score of the preset assessment indicators is calculated based on the evolution workload and estimated cost; and the rectification priority of each gap is determined based on the weighted score, including: Based on the aforementioned evolution workload and estimated costs, the technical difficulty level, business impact scope, rectification cost, and project urgency are quantitatively scored, resulting in corresponding technical difficulty scores, business impact scores, rectification cost scores, and project urgency scores. The business impact scope includes single-system impact and full business domain impact, and the technical difficulty level includes three levels: low, medium, and high. A weighted algorithm is used to calculate the overall score based on the scores for technical difficulty, business impact, rectification cost, and project urgency. Based on the overall score, the rectification priorities are divided into high priority, medium priority and low priority, and high priority rectification tasks are automatically linked to the existing project investment plan.

[0012] As a preferred embodiment of the multi-dimensional quantitative assessment method for power enterprise architecture difference analysis described in this invention, the following is included: During the rectification process, a polling mechanism and an event triggering mechanism are used to dynamically synchronize the latest current architecture snapshot, and the architecture alignment degree between the snapshot and the blueprint architecture snapshot is calculated in real time. When a gap with a high rectification priority is detected or the architecture alignment degree is lower than a preset threshold, an early warning message is triggered, including: Based on the power architecture operation and maintenance cycle, the data center asset database is polled periodically to obtain the latest current architecture snapshot and architecture asset change events, and real-time data synchronization is automatically triggered when an architecture asset change event occurs; Based on the latest current architecture snapshot obtained synchronously, the difference identification sub-engine is invoked to compare it with the blueprint architecture snapshot in real time. According to the number of compliant assets, the total number of assets and the weight of each architecture level, the alignment contribution value of each architecture level is calculated and the overall architecture alignment is obtained by summarizing. The alignment value of the entire network architecture is calculated in real time iteratively. When a gap with a high priority rectification level is detected or the overall alignment of the architecture is lower than a preset threshold, a deviation warning is pushed to the administrator, and the rectification progress is tracked. After rectification is completed, the difference comparison engine is called back for verification and the rectification tracking log is updated until the architectural consistency goal is achieved.

[0013] Secondly, this invention provides a multi-dimensional quantitative assessment system for analyzing differences in the architecture of power enterprises, comprising: The multi-source data access module is used to acquire and preprocess the current architecture snapshot, blueprint architecture snapshot, power industry evolution model library, project plan data and investment plan data of power enterprises, and use the preprocessed current architecture snapshot as the architecture asset data to be analyzed. The multi-dimensional difference comparison module is used to process the architecture asset data into business domain hierarchical blocks to form several data fragments. It allocates computing resources through a task dynamic fragment scheduling mechanism, performs difference identification operations on each data fragment in parallel, and generates an architecture difference list between each data fragment and the blueprint architecture snapshot. The evolution quantitative assessment module is used to match the corresponding evolution mode for each gap in the list of architectural differences based on the power industry evolution mode library, and calculate the evolution workload and estimated cost of each evolution mode. The architecture rectification implementation module is used to calculate the weighted score of preset evaluation indicators based on the evolution workload and estimated cost, determine the rectification priority of each gap based on the weighted score, and generate an architecture rectification plan by combining the project plan data and investment plan data, so as to implement the rectification of the power company's architecture. The closed-loop monitoring and tracking module is used to dynamically synchronize the latest current architecture snapshot during the rectification process through a polling mechanism and an event triggering mechanism, and calculate its architecture alignment with the blueprint architecture snapshot in real time. When a gap with a high rectification priority is detected or the architecture alignment is lower than a preset threshold, an early warning message is triggered.

[0014] Thirdly, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the power enterprise architecture difference analysis method for multi-dimensional quantitative evaluation are implemented.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the multi-dimensional quantitative assessment method for power enterprise architecture difference analysis.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: By optimizing parallel computing algorithms, this invention improves the efficiency of analyzing massive and complex interconnected power architecture assets, meeting the performance requirements of high-frequency architecture monitoring and rapid decision-making, and ensuring the efficient completion of analysis tasks; by specifically connecting to the power industry evolution model library, it deeply adapts to the power-specific evolution models and industry standards; furthermore, it constructs a complete closed-loop mechanism for analysis, evaluation, tracking, early warning, and review, ensuring that IT investment and architecture blueprints remain synchronized by real-time monitoring of architecture alignment and dynamically pushing deviation warnings, guaranteeing the completion rate of rectification of high-priority gaps, and helping power companies steadily advance their digital transformation. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the 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. Wherein: Figure 1 This is a schematic diagram of the process flow for a multi-dimensional quantitative assessment method for analyzing the differences in the architecture of power enterprises, as described in one embodiment of the present invention. Figure 2 This is a business process diagram of a multi-dimensional quantitative assessment method for power enterprise architecture difference analysis according to an embodiment of the present invention. Figure 3 This is an overall framework diagram of a multi-dimensional quantitative evaluation system for analyzing differences in the architecture of power enterprises, as described in one embodiment of the present invention. Figure 4 This is a schematic diagram illustrating the implementation process of a multi-dimensional quantitative assessment method for power enterprise architecture difference analysis, as described in one embodiment of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, 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, and 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.

[0019] Example 1, referring to Figures 1-2 As one embodiment of the present invention, this embodiment provides a multi-dimensional quantitative assessment method for analyzing differences in the architecture of power enterprises, such as... Figure 1 As shown, it includes: S100: Acquire and preprocess the current architecture snapshot, blueprint architecture snapshot, power industry evolution model library, project plan data and investment plan data of power enterprises, and use the preprocessed current architecture snapshot as the architecture asset data to be analyzed; S200: The architecture asset data is processed by business domain layering and block division to form several data shards. Computing resources are allocated through a task dynamic sharding scheduling mechanism. Difference identification operations are performed in parallel on each data shard to generate an architecture difference list between each data shard and the blueprint architecture snapshot. S300: Based on the power industry evolution model library, for each gap in the architecture difference list, match the corresponding evolution model, and calculate the evolution workload and estimated cost of each evolution model. S400: Calculate the weighted score of the preset evaluation indicators based on the evolution workload and estimated cost, determine the rectification priority of each gap based on the weighted score, and generate an architecture rectification plan by combining project plan data and investment plan data to implement the rectification of the power company's architecture. S500: During the rectification process, the latest current architecture snapshot is dynamically synchronized through a polling mechanism and an event triggering mechanism, and its architecture alignment with the blueprint architecture snapshot is calculated in real time. When a gap with a high rectification priority is detected or the architecture alignment is lower than a preset threshold, an early warning message is triggered.

[0020] It should be noted that existing discrepancy analyses are limited to a single dimension, merely comparing the surface-level "presence" or "absence," lacking adaptability to the power industry, unable to connect with power-specific architectural assets, exhibiting low analytical efficiency, failing to meet high-performance comparison requirements, lacking quantitative assessment capabilities, lacking objective basis for evolutionary decisions, exhibiting weak control over classified data, failing to meet power safety and compliance requirements, lacking closed-loop management capabilities, and making discrepancy rectification difficult to implement. This invention, combined with the specific needs of power enterprise architectural management, breaks through the limitations of "presence-absence comparison," achieving a systematic and intelligent identification of four types of discrepancies in power enterprise architecture: existence, maturity / performance, relationships / integration, and standards compliance. Specifically, it optimizes comparison algorithms for massive power architecture assets (including complex relationships), ensuring that 95% of analysis tasks are completed within 5 minutes, balancing efficiency and accuracy; it constructs a quantitative assessment model adapted to the power industry, combining evolution standards and project plans to achieve objective assessment and prioritization of evolution workload, cost, and risk; it designs a refined access control system to ensure access security for core confidential data such as power architecture blueprints and current status data, meeting the compliance requirements of the power industry; and it achieves interactive visual analysis and real-time consistency monitoring, constructing a closed-loop control mechanism for analysis, assessment, tracking, and early warning to support the steady evolution of the architecture.

[0021] Furthermore, in step S100 of this embodiment of the invention, the multi-source data access module extracts a snapshot of the current architecture from the power enterprise's data center; imports the blueprint architecture snapshot, the power industry evolution model library, and project and investment plans; completes data format unification, removes redundant data, and stores it in a dedicated database.

[0022] It should be noted that the difference comparison engine calls the four types of sub-engines and adopts an optimized sharding parallel computing algorithm to decouple and split the four types of difference verification tasks, allocate computing power independently, and perform synchronous parallel operations. It uniformly accesses the pre-processed standardized current architecture data and blueprint architecture standard data, and completes automated difference identification based on exclusive comparison judgment rules.

[0023] In this embodiment of the invention, step S200 involves performing business domain-level layering and segmentation of the architecture asset data to form several data fragments, including: Using the business layer, application layer, data layer, technology layer, and security layer of the power enterprise architecture as boundaries, the overall architecture asset dataset is horizontally layered to isolate architecture asset data at different architecture levels; Within each horizontal layer, based on at least one dimension of the power business domain boundary, asset business coupling relationship and system deployment cluster scope, the architecture asset data is granularly segmented, and the architecture asset data belonging to different business domains under the same architecture layer that have no data interaction, no business dependency and no topological association are divided into several independent data fragments. Among them, all the data shards formed have no overlapping assets, no data associations, and no business dependencies, so that each data shard can be processed independently in parallel.

[0024] In this embodiment of the invention, step S200 allocates computing resources through a dynamic task sharding scheduling mechanism, performs difference identification operations in parallel on each data shard, and generates a list of architectural differences between each data shard and the blueprint architecture snapshot, including: For each data shard, generate a one-to-one corresponding shard subtask. Based on the data volume of the architecture assets, the number of field dimensions, and the complexity of the associated topology of each data shard, calculate the computing power consumption level of each corresponding shard subtask in real time. Based on the preset computing power resource pool quota, idle computing nodes are dynamically allocated. Sharded subtasks with computing power consumption levels lower than the preset level and asset data volume less than the preset data volume are aggregated to the same computing node. Sharded subtasks with computing power consumption levels not lower than the preset level or associated topological complexity higher than the preset complexity are allocated to dedicated computing nodes. This enables each computing node to synchronously distribute sharded subtasks corresponding to the same data shard to the four preset difference recognition sub-engines to execute multidimensional comparison tasks. It also controls the operation process of sharded subtasks corresponding to different data shards to ensure that they do not compete for resources or interfere with each other. The task heartbeat monitoring mechanism monitors the running status of each computing node in real time, automatically triggers retry scheduling for sharded subtasks that time out or are abnormally interrupted, and masks failed computing nodes. At the same time, it migrates the remaining tasks to be processed on failed computing nodes to other normal computing nodes. After all sharding subtasks and sub-engine tasks are completed, the difference data of each dimension are aggregated in a unified manner, and duplicate differences are deduplicated and the validity of differences is verified twice to output a standardized list of architectural differences.

[0025] It should be noted that this invention breaks through the limitations of traditional single comparison methods, achieving comprehensive identification of power enterprise architecture across four dimensions: existence, maturity / performance, relationships / integration, and standards compliance. It can accurately pinpoint deep architectural deviations, significantly improve the identification rate of non-compliant items, and fully meet the refined management and control needs of power enterprises. By optimizing parallel computing algorithms, it improves the efficiency of analyzing massive and complexly interconnected power architecture assets, meeting the performance requirements of high-frequency architecture monitoring and rapid decision-making, and ensuring the efficient completion of analysis tasks.

[0026] Reference Figure 2 In this embodiment of the invention, before allocating computing resources through a task dynamic sharding scheduling mechanism, performing parallel difference identification operations on each data shard, and generating an architectural difference list between each data shard and the blueprint architecture snapshot in step S200, the following steps are also included: A multi-engine task mutual exclusion scheduling strategy is constructed to control the four types of difference identification sub-engines—existence comparison, maturity / performance comparison, relationship / integration comparison, and standard compliance comparison—to be decoupled from each other and run as independent processes.

[0027] Furthermore, the existence comparison uses the blueprint architecture asset list as the base index to establish a unique identifier mapping library for current assets. All architecture assets are traversed and verified through primary key matching. If the blueprint has a corresponding unique identifier for an asset but the current status has no matching data, it is determined to be a missing asset and marked as an asset that needs to be created. If the current status has an asset identifier but the blueprint has no corresponding planning definition, it is determined to be a redundant asset and marked as an asset that needs to be decommissioned, thus completing the matching and verification of the existing architecture assets.

[0028] Furthermore, the maturity / performance comparison targets the same uniquely identified architectural assets in the current state and the blueprint, retrieving core performance indicators such as asset version parameters, hardware capacity, business response time, peak load rate, and concurrent processing volume. The current state's real-time indicator data is precisely compared with the blueprint's preset thresholds, version standards, and performance baselines. If any indicator falls below the blueprint's specified threshold, it is determined that there is an architectural gap where maturity is insufficient and performance cannot meet business load requirements.

[0029] Furthermore, the relationship / integration comparison retrieves the pre-defined blueprint integration topology between various architectural assets, including system interface call directions, cross-system data flow fields, data transmission frequency, interface interaction protocols, and upstream and downstream dependencies. The integration interaction logic of each existing architectural asset is verified. If there are missing interfaces, inconsistent data flows, disordered dependencies, or broken cross-system interaction links, it is determined to be an architectural integration deviation.

[0030] The standard compliance comparison pre-enters official power industry architecture specifications, equipment technical standards, and grid integration guidelines, verifying the technical protocols, deployment specifications, configuration parameters, and interface standards of existing assets against the industry standards bound to the blueprint. Equipment, systems, and interfaces that do not conform to unified power industry technical standards and blueprint-customized specifications are identified as compliance gaps.

[0031] It should be noted that, based on the five-layer power architecture, the overall architecture asset dataset is horizontally layered and vertically segmented into multiple, balanced, and independent asset data shards, avoiding computational bottlenecks caused by excessively large batches of data. Simultaneously, the overall difference comparison task is broken down into sub-computation tasks corresponding to the data shards and four types of difference verification sub-engine tasks, achieving bidirectional decoupling between data sharding and computation tasks. Through a dynamic task scheduling mechanism, the system automatically allocates sharding computation tasks based on the real-time computing load of each computing node, ensuring that all data shards and the four types of comparison sub-engines operate independently and synchronously without blocking each other, allowing for parallel execution of multi-dimensional difference identification without waiting for a single verification task to complete. After all sharding tasks and the four types of comparison verification tasks are completed, all dimensions of difference data are uniformly collected, and result aggregation, duplicate difference deduplication, and secondary difference validity verification operations are performed to remove invalid difference data that has been misjudged or repeatedly marked. Finally, a standardized, high-precision architecture difference list is output, ensuring that 95% of tasks are completed within 5 minutes for massive assets, while guaranteeing no omissions or misjudgments in difference identification.

[0032] Furthermore, the deduplication of duplicate differences includes: constructing a unique fingerprint of the difference based on the unique identifier of the asset, the type of difference, and the location where the difference occurs; merging the duplicate differences from the same source output by different data shards or different difference recognition sub-engines; and retaining only one original difference record for the same asset that is repeatedly identified by multiple computing nodes or multiple difference recognition sub-engines, while removing redundant duplicate data. Furthermore, the secondary verification of the validity of the differences includes: based on the preset invalid difference judgment rules, automatically filtering out the instantaneous misjudgment differences caused by temporary maintenance shutdowns, instantaneous network fluctuations or brief offline equipment, as well as invalid comparison differences caused by the current assets being standby redundant equipment, test environment assets or frozen assets to be decommissioned, and verifying the authenticity of the differences in conjunction with the power architecture maintenance baseline, eliminating invalid difference data that are misjudged or repeatedly marked.

[0033] In this embodiment of the invention, step S300, based on the power industry evolution model library, matches the corresponding evolution model for each gap in the architecture difference list, and calculates the evolution workload and estimated cost of each evolution model, including: Based on the power industry evolution model library, according to the gap type, asset level and asset scale of each gap in the difference list, the evolution model corresponding to each gap is matched, and the benchmark parameters of the corresponding evolution model are retrieved. Among them, the benchmark parameters include: basic workload of industry standards, unit labor cost, equipment purchase price and deployment and commissioning cost. Based on the basic workload, the preset asset complexity coefficient, and the preset business coupling coefficient, calculate the evolution workload corresponding to each gap. Based on the unit labor cost, equipment purchase price, and deployment and debugging cost, calculate the estimated cost corresponding to each gap.

[0034] It should be noted that this embodiment of the invention is specifically designed to interface with the power enterprise's architecture asset library, deeply adapting to the power industry's unique evolutionary patterns and industry standards. By combining project investment plans for precise evaluation, it resolves the issue of general tools being incompatible with local conditions, ensuring that the architecture evolution solution can be directly implemented. Furthermore, a quantitative evaluation model adapted to the power industry is constructed, which can objectively output the evolution workload, costs, and risks. Coupled with an interactive visualization view, it supports both high-level strategic control and facilitates micro-level detail verification, effectively improving the objectivity and efficiency of decision-making.

[0035] In this embodiment of the invention, the preset evaluation indicators in step S400 include technical difficulty level, business impact scope, rectification cost, and project urgency; a weighted score for the preset evaluation indicators is calculated based on the evolution workload and estimated cost; and the rectification priority for each gap is determined based on the weighted score, including: Based on the evolution workload and estimated costs, the technical difficulty level, business impact scope, rectification cost, and project urgency are quantitatively scored, resulting in corresponding technical difficulty scores, business impact scores, rectification cost scores, and project urgency scores. The business impact scope includes single-system impact and full business domain impact, and the technical difficulty level includes three levels: low, medium, and high. A weighted algorithm is used to calculate the overall score based on the scores for technical difficulty, business impact, rectification costs, and project urgency. Based on the overall score, the rectification priorities are divided into high priority, medium priority and low priority, and high priority rectification tasks are automatically linked to the existing project investment plan.

[0036] Furthermore, the difficulty assessment criteria for the pre-configured power architecture rectification are based on a comprehensive evaluation of four dimensions: asset type, rectification method, degree of architectural coupling, and whether it involves changes to underlying technical protocols. Rectifications involving only front-end configuration modifications, minor parameter adjustments, no interface changes, and no downtime are classified as low difficulty; rectifications involving system version iterations, minor interface modifications, short-term downtime for debugging, and no overall architecture reconstruction are classified as medium difficulty; rectifications involving replacement of underlying technical protocols, reconstruction of multiple system interfaces, changes to core business links, requiring downtime for upgrades, and impacting the overall stability of the architecture are classified as high difficulty.

[0037] Furthermore, the impact is automatically determined based on the topological dependencies, business scope, and number of upstream and downstream related systems of the assets to be rectified. If the impact only affects a single independent business system, has no cross-system data interaction, and the rectification process only affects the function of this system, it is determined to be a single-system impact; if the assets to be rectified belong to the public infrastructure or core middleware assets, and the transformation will have a cascading effect on the operation of multiple business domains and multiple upstream and downstream business systems, affecting the overall power grid business process, it is determined to be a full-business-domain impact. For example, the business impact score is as follows: 10 points for impact on the entire business domain and 5 points for impact on a single system; the technical difficulty score is as follows: 10 points for high difficulty, 6 points for medium difficulty, and 2 points for low difficulty; the rectification cost score is as follows: the higher the cost, the more points are deducted, with 10 points for costs greater than 1 million yuan, 6 points for costs between 500,000 and 1 million yuan, and 2 points for costs less than 500,000 yuan; the project urgency score is as follows: 10 points for projects corresponding to key annual investments and projects requiring rectification within a specified period, 5 points for ordinary reserve projects, and 2 points for projects without binding responsibilities.

[0038] Based on the overall score, tasks are categorized into three priority levels: those with an overall score of ≥8 are the highest priority and require immediate rectification; those with an overall score of 5 ≤ overall score < 8 are of medium priority and will be included in the quarterly rectification plan; and those with an overall score < 5 are of low priority and will be rectified as the architecture iterates. Simultaneously, high-priority tasks are automatically linked to existing power company investment and renovation projects to ensure that core architectural deviations and high-risk compliance issues are addressed and rectified first.

[0039] Furthermore, embodiments of the present invention also output an architecture gap analysis report, including a gap list, heatmap, and detailed description; an evolution impact assessment report containing quantitative data and priority ranking; and a consistency monitoring dashboard including real-time alignment and deviation warnings. The output content is subject to hierarchical control: only administrators can view the complete blueprint's confidential data, architects can edit the analysis results, and ordinary personnel can only view the anonymized report.

[0040] In this embodiment of the invention, during the rectification process in step S500, a polling mechanism and an event triggering mechanism are used to dynamically synchronize the latest current architecture snapshot and calculate its architecture alignment with the blueprint architecture snapshot in real time. When a gap with a high rectification priority or an architecture alignment lower than a preset threshold is detected, an early warning message is triggered, including: Based on the power architecture operation and maintenance cycle, the data center asset database is polled periodically to obtain the latest current architecture snapshot and architecture asset change events, and real-time data synchronization is automatically triggered when an architecture asset change event occurs; Based on the latest current architecture snapshot obtained synchronously, the difference identification sub-engine is invoked to compare it with the blueprint architecture snapshot in real time. According to the number of compliant assets, the total number of assets and the weight of each architecture level, the alignment contribution value of each architecture level is calculated and the overall architecture alignment is obtained by summarizing. The alignment value of the entire network architecture is calculated in real time iteratively. When a discrepancy with a high priority rectification level is detected or the overall architecture alignment is lower than a preset threshold, a deviation warning is pushed to the administrator, and the rectification progress is tracked. After rectification is completed, the difference comparison engine is called back for verification and the rectification tracking log is updated until the architectural consistency goal is achieved.

[0041] It should be noted that the embodiments of the present invention establish a triple security mechanism including hierarchical permissions, data encryption, and operation auditing, strictly controlling access permissions to core confidential information of the power architecture, fully complying with the stringent security and compliance standards of the power industry, and effectively eliminating the risk of data leakage. A complete closed-loop mechanism of analysis, evaluation, tracking, early warning, and review is constructed. By monitoring the architecture alignment in real time and dynamically pushing deviation warnings, it ensures that IT investment and the architecture blueprint remain synchronized, guarantees the completion rate of rectification of high-priority gaps, and helps power companies steadily advance their digital transformation.

[0042] Specifically, a dual mechanism of polling and event triggering is used to update the architecture data. The system periodically polls and retrieves the latest snapshot of the data center asset database according to the power architecture maintenance cycle. Simultaneously, it monitors architecture asset change events. When architecture changes occur, such as system upgrades, parameter modifications, interface iterations, asset decommissioning, or new deployments, it automatically triggers real-time data synchronization without waiting for periodic polling. After continuously capturing the latest architecture data, the system automatically calls a multi-dimensional difference comparison engine to recalculate the differences between the current architecture and the blueprint in real time. Based on the asset weights of each layer of architecture (the security layer has the highest weight, followed by the technology layer, data layer, application layer, and business layer), the system iteratively calculates the alignment value of the entire network architecture in real time using an alignment calculation formula, dynamically refreshing the alignment dashboard data to achieve uninterrupted updates to the architecture fit. Warnings are pushed for high-priority discrepancies to track rectification progress. After rectification is completed, a re-comparison and verification is performed, forming a closed loop of "analysis-rectification-review-update".

[0043] It should be noted that this invention is designed for the specific scenarios of power enterprises, and is based on a five-layer architecture to construct a complete process system of "data access - multi-dimensional difference comparison - quantitative evaluation - visualization output - closed-loop tracking". Addressing the characteristics of power enterprise architecture asset data volume, multiple asset layers, complex inter-asset relationships, and numerous individual asset attribute dimensions, this invention abandons the traditional global traversal serial comparison mode. First, the architecture asset data undergoes layered and fragmented preprocessing. Following the power five-layer architecture, assets are divided into independent data fragments for the business layer, application layer, data layer, technology layer, and security layer. Simultaneously, static asset attributes, dynamic performance indicators, relational data, and compliance standard data are separated, achieving data dimension decoupling. Second, a dynamic task fragmentation and scheduling mechanism is introduced, dynamically allocating computing resources based on the data volume and computational complexity of each data fragment, avoiding data accumulation and computational blockage issues on single computing nodes. Simultaneously, a duplicate data filtering and cache comparison mechanism is added. Unchanged architectural assets and fixed industry standard parameters are cached and retained, eliminating the need for repeated traversal comparisons. Difference calculations are only performed on updated, added, and modified architectural assets, significantly reducing unnecessary computation. Finally, the sub-engine comparison tasks are asynchronously and in parallel. The four types of difference comparison sub-engines operate independently and synchronously, and the verification results are aggregated after the comparison is completed. While ensuring the accuracy of architectural difference identification and eliminating omissions, the overall comparison time is greatly reduced. Accurate evaluation is achieved by relying on the power industry evolution model library, and security is ensured by fine-grained permission control. Ultimately, intelligent difference verification and auxiliary improvement of the architectural blueprint and the current situation are realized.

[0044] Example 2, refer to Figure 3 This is one embodiment of the present invention, which differs from the first embodiment in that it provides a multi-dimensional quantitative assessment system for analyzing differences in the architecture of power enterprises, comprising: The multi-source data access module is used to acquire and preprocess the current architecture snapshot, blueprint architecture snapshot, power industry evolution model library, project plan data and investment plan data of power enterprises, and use the preprocessed current architecture snapshot as the architecture asset data to be analyzed. The multi-dimensional difference comparison module is used to process the architecture asset data into business domain hierarchical blocks to form several data shards. It allocates computing resources through a task dynamic sharding scheduling mechanism, performs difference identification operations on each data shard in parallel, and generates an architecture difference list between each data shard and the blueprint architecture snapshot. The evolution quantitative assessment module is used to match the corresponding evolution mode for each gap in the list of architectural differences based on the power industry evolution mode library, and calculate the evolution workload and estimated cost of each evolution mode. The architecture rectification implementation module is used to calculate the weighted score of preset evaluation indicators based on the evolution workload and estimated cost, determine the rectification priority of each gap based on the weighted score, and generate an architecture rectification plan by combining project plan data and investment plan data to implement the rectification of the power company's architecture. The closed-loop monitoring and tracking module is used to dynamically synchronize the latest current architecture snapshot during the rectification process through polling and event triggering mechanisms, and calculate its architecture alignment with the blueprint architecture snapshot in real time. When a gap with high rectification priority is detected or the architecture alignment is lower than a preset threshold, an early warning message is triggered.

[0045] Furthermore, this embodiment of the invention also includes: an interactive visualization module for generating gap heatmaps, alignment dashboards, and hierarchical architecture views, supporting drill-down from a high-level panoramic view to micro-asset details, and supporting one-click export and sharing of reports; and a security access control module for setting role permissions (administrator, architect, general viewer) for blueprint data, current status data, and analysis reports using a hierarchical permission model, coupled with operation auditing and data encryption mechanisms.

[0046] Specifically, each module of the multi-dimensional quantitative assessment power enterprise architecture difference analysis system in this embodiment implements the steps of the multi-dimensional quantitative assessment power enterprise architecture difference analysis method in Embodiment 1 when executed, for example: In one implementation, the power enterprise architecture difference analysis system for multidimensional quantitative assessment can perform the following steps: Using the business layer, application layer, data layer, technology layer, and security layer of the power enterprise architecture as boundaries, the overall architecture asset dataset is horizontally layered to isolate asset data at different architecture levels; Within each horizontal layer, the data is segmented at a granular level based on at least one dimension, including the boundary of the power business domain, the coupling relationship between assets and business, and the scope of the system deployment cluster. Assets belonging to different business domains at the same level that have no data interaction, no business dependence, and no topological association are independently divided into single data shards. Among them, all the data shards formed have no overlapping assets, no data associations, and no business dependencies, so that each data shard can be processed independently in parallel.

[0047] It receives several data shards and generates a one-to-one corresponding shard subtask for each data shard. It performs priority preprocessing and load prediction on all shard subtasks and calculates the task computing power consumption level in real time based on the asset data volume, number of field dimensions and associated topology complexity of each data shard. Based on the preset computing power resource pool quota, idle computing nodes are dynamically allocated. Sharded subtasks with computing power consumption levels lower than the preset threshold and data volume less than the preset data volume are aggregated to the same computing node, while sharded tasks with computing power consumption levels higher than or equal to the preset threshold or associated topological complexity higher than the preset complexity are allocated to dedicated computing nodes. A multi-engine task mutual exclusion scheduling strategy is constructed to control the four types of difference identification sub-engines—existence comparison, maturity / performance comparison, relation / integration comparison, and standard compliance comparison—to be decoupled from each other and run as independent processes. The sub-tasks corresponding to the same data shard are synchronously distributed to the four types of difference identification sub-engines to perform multi-dimensional comparisons. The operation process of the sub-tasks corresponding to different data shards is controlled to ensure that they do not compete for resources or interfere with each other. The task heartbeat monitoring mechanism monitors the running status of each computing node in real time. For sharded subtasks that time out or are abnormally interrupted, retry scheduling is automatically triggered, and failed computing nodes are masked. At the same time, the remaining tasks to be processed on the failed computing nodes are migrated to other normal computing nodes.

[0048] The overall difference comparison task is broken down into sub-computation tasks corresponding to data shards. The four types of difference recognition sub-engine tasks are decoupled from the sub-computation tasks so that each computing node can perform independent and synchronous operations. After all sharding and sub-engine tasks are completed, the difference data of each dimension are aggregated in a unified manner, and duplicate differences are deduplicated and the validity of differences is verified twice to output a standardized list of architectural differences.

[0049] A quantitative benchmark library for power architecture evolution is pre-built, and industry standard basic workload, unit labor cost, equipment purchase price and deployment and commissioning cost benchmark parameters corresponding to the five types of evolution methods: new construction, upgrade, replacement, decommissioning and integration are entered. Based on the gap type, asset level, and asset size, the corresponding benchmark coefficients are retrieved, and the manpower and man-day data are calculated using the formula: Evolution workload = Basic workload × Asset complexity coefficient × Business coupling coefficient. Using the formula: Estimated evolution cost = manpower / days × unit labor cost + equipment incremental cost + interface modification cost + deployment and maintenance cost, calculate the overall rectification cost of individual gaps.

[0050] Based on the weighted scores of the evaluation indicators, the priority of rectification for each gap is determined, including the evaluation indicators such as technical difficulty, business impact, rectification cost, and project urgency. The scoring is based on four dimensions: technical difficulty, scope of business impact, rectification cost, and project urgency. The scope of business impact includes the impact on a single system and the impact on the entire business domain, and the technical difficulty is divided into three levels: low, medium, and high. The weighted algorithm is used to calculate the overall score, and the formula is: Overall Score = Business Impact Score × 40% + Technical Difficulty Score × 20% + Rectification Cost Score × 20% + Project Urgency Score × 20%; Based on the overall score, the rectification priorities are divided into highest priority, medium priority, and low priority, and the high-priority differentiated tasks are automatically associated with existing projects and investment plans.

[0051] A polling mechanism is used to periodically retrieve the latest status snapshot of the data center asset database, and an event-triggered mechanism is used to automatically synchronize data when an asset change event is detected. Based on the weights of each architecture layer, the overall architecture alignment is calculated in real time using the formula: Overall architecture alignment = Number of compliant assets in each architecture layer / Total number of assets in the corresponding layer × Weight of the corresponding layer. When the overall alignment of the architecture is lower than the preset threshold or a high-priority gap occurs, a deviation warning will be pushed to the administrator. After the rectification is completed, the difference comparison engine is called back for review, the rectification tracking log is updated, and the architectural consistency goal is achieved.

[0052] Example 3, referring to Figure 4 This is one embodiment of the present invention. Unlike the first embodiment, this embodiment uses specific examples to verify the beneficial effects of the present invention.

[0053] In this embodiment, a provincial power grid company has formulated a digital transformation architecture blueprint and plans to build a new generation of dispatch automation system. The system requires application system version V3.0, peak processing performance ≥100,000 messages / second, and system integration using the IEC61850 standard. The current dispatch system is version V2.5 with a performance of 60,000 messages / second, and its integration with the distribution system uses a proprietary protocol.

[0054] The specific implementation process in this embodiment is as follows: The system imports current dispatching data, power distribution system asset data, blueprint target indicators, power industry evolution model library, and annual investment plan. Then, a difference comparison is performed: (1) Maturity gap: The current scheduling system is version V2.5 with a performance of 60,000 messages / second, which does not meet the blueprint V3.0 requirement of 100,000 messages / second; (2) Integration gap: The dispatch and distribution system integration protocol is a proprietary protocol and does not comply with the blueprint IEC61850 standard; (3) Existence gap: The "global data sharing module" required by the blueprint is currently unavailable and needs to be newly built.

[0055] The system assessment concluded that: version upgrade requires 30 person-days and costs 800,000 yuan, with medium technical difficulty; protocol modification requires 20 person-days and costs 500,000 yuan, with low business impact; and building a new module requires 50 person-days and costs 1.2 million yuan, with the highest priority.

[0056] Visual output: Generates a gap heatmap, with red alerts for scheduling domains; dashboard with an overall alignment of 78%, and exports reports that can be directly used for strategic reporting.

[0057] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended 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 multi-dimensional quantitative evaluation method for analyzing differences in the structure of power enterprises, characterized in that, include: Acquire and preprocess current architecture snapshots, blueprint architecture snapshots, power industry evolution model library, project plan data and investment plan data of power enterprises, and use the preprocessed current architecture snapshots as architecture asset data to be analyzed; The architecture asset data is processed by business domain layering and block segmentation to form several data shards. Computing resources are allocated through a task dynamic sharding scheduling mechanism. Difference identification operations are performed in parallel on each data shard to generate an architecture difference list between each data shard and the blueprint architecture snapshot. Based on the power industry evolution model library, for each gap in the architecture difference list, the corresponding evolution model is matched, and the evolution workload and estimated cost of each evolution model are calculated. The weighted scores of the preset evaluation indicators are calculated based on the evolution workload and estimated costs. The rectification priorities of each gap are determined based on the weighted scores. The structural rectification plan is generated by combining the project plan data and investment plan data to implement the rectification of the power company's structure. During the rectification process, the latest current architecture snapshot is dynamically synchronized through a polling mechanism and an event triggering mechanism, and its architecture alignment with the blueprint architecture snapshot is calculated in real time. When a gap with a high rectification priority is detected or the architecture alignment is lower than a preset threshold, an early warning message is triggered.

2. The method for analyzing the differences in the structure of power enterprises through multi-dimensional quantitative evaluation as described in claim 1, characterized in that, The architecture asset data is processed by layering and segmenting into business domains to form several data shards, including: Using the business layer, application layer, data layer, technology layer, and security layer of the power enterprise architecture as boundaries, the overall architecture asset dataset is horizontally layered to isolate architecture asset data at different architecture levels; Within each horizontal layer, based on at least one dimension of the power business domain boundary, asset business coupling relationship and system deployment cluster scope, the architecture asset data is granularly segmented, and the architecture asset data belonging to different business domains under the same architecture layer that have no data interaction, no business dependency and no topological association are divided into several independent data fragments. Among them, all the data shards formed have no overlapping assets, no data associations, and no business dependencies, so that each data shard can be processed independently in parallel.

3. The method for analyzing the differences in the structure of power enterprises through multi-dimensional quantitative evaluation as described in claim 2, characterized in that, Computing resources are allocated through a dynamic task sharding scheduling mechanism. Difference identification operations are performed in parallel on each data shard to generate a list of architectural differences between each data shard and the blueprint architecture snapshot, including: For each data shard, generate a one-to-one corresponding shard subtask. Based on the data volume of the architecture assets, the number of field dimensions, and the complexity of the associated topology of each data shard, calculate the computing power consumption level of each corresponding shard subtask in real time. Based on the preset computing power resource pool quota, idle computing nodes are dynamically allocated. Sharded subtasks with computing power consumption levels lower than the preset level and asset data volume less than the preset data volume are aggregated to the same computing node. Sharded subtasks with computing power consumption levels not lower than the preset level or associated topological complexity higher than the preset complexity are allocated to dedicated computing nodes. This enables each computing node to synchronously distribute sharded subtasks corresponding to the same data shard to the four preset difference recognition sub-engines to execute multidimensional comparison tasks. It also controls the operation process of sharded subtasks corresponding to different data shards to ensure that they do not compete for resources or interfere with each other. The task heartbeat monitoring mechanism monitors the running status of each computing node in real time. For sharded subtasks that time out or are abnormally interrupted, retry scheduling is automatically triggered, and failed computing nodes are masked. At the same time, the remaining tasks to be processed on the failed computing nodes are migrated to other normal computing nodes. After all sharding subtasks and sub-engine tasks are completed, the difference data of each dimension are aggregated in a unified manner, and duplicate differences are deduplicated and the validity of differences is verified twice to output a standardized list of architectural differences.

4. The method for analyzing the differences in the structure of power enterprises through multi-dimensional quantitative evaluation as described in claim 3, characterized in that, Before allocating computing resources through a dynamic task sharding scheduling mechanism, performing parallel difference identification operations on each data shard, and generating an architectural difference list between each data shard and the blueprint architecture snapshot, the process also includes: A multi-engine task mutual exclusion scheduling strategy is constructed to control the four types of difference identification sub-engines—existence comparison, maturity / performance comparison, relationship / integration comparison, and standard compliance comparison—to be decoupled from each other and run as independent processes.

5. The method for analyzing the differences in the structure of power enterprises through multi-dimensional quantitative evaluation as described in claim 4, characterized in that, Based on the aforementioned power industry evolution model library, for each gap in the architecture difference list, a corresponding evolution model is matched, and the evolution workload and estimated cost of each evolution model are calculated, including: Based on the power industry evolution model library, according to the gap type, asset level and asset scale of each gap in the difference list, the evolution model corresponding to each gap is matched, and the benchmark parameters of the corresponding evolution model are retrieved; wherein, the benchmark parameters include: industry standard basic workload, unit labor cost, equipment purchase unit price and deployment and commissioning cost. Based on the basic workload, the preset asset complexity coefficient, and the preset business coupling coefficient, the evolution workload corresponding to each gap is calculated. Based on the unit labor cost, equipment purchase price, and deployment and debugging cost, the estimated cost corresponding to each gap is calculated.

6. The method for analyzing the differences in the structure of power enterprises through multi-dimensional quantitative evaluation as described in claim 5, characterized in that, The preset evaluation indicators include technical difficulty level, business impact scope, rectification cost, and project urgency. A weighted score for each preset evaluation indicator is calculated based on the evolving workload and estimated cost. The rectification priority for each gap is determined based on the weighted score, including: Based on the aforementioned evolution workload and estimated costs, the technical difficulty level, business impact scope, rectification cost, and project urgency are quantitatively scored, resulting in corresponding technical difficulty scores, business impact scores, rectification cost scores, and project urgency scores. The business impact scope includes single-system impact and full business domain impact, and the technical difficulty level includes three levels: low, medium, and high. A weighted algorithm is used to calculate the overall score based on the scores for technical difficulty, business impact, rectification cost, and project urgency. Based on the overall score, the rectification priorities are divided into high priority, medium priority and low priority, and high priority rectification tasks are automatically linked to the existing project investment plan.

7. The method for analyzing the differences in the structure of power enterprises through multi-dimensional quantitative evaluation as described in claim 6, characterized in that, During the rectification process, a polling mechanism and an event triggering mechanism are used to dynamically synchronize the latest current architecture snapshot and calculate its architecture alignment with the blueprint architecture snapshot in real time. When a gap with a high rectification priority is detected or the architecture alignment is lower than a preset threshold, an early warning message is triggered, including: Based on the power architecture operation and maintenance cycle, the data center asset database is polled periodically to obtain the latest current architecture snapshot and architecture asset change events, and real-time data synchronization is automatically triggered when an architecture asset change event occurs; Based on the latest current architecture snapshot obtained synchronously, the difference identification sub-engine is invoked to compare it with the blueprint architecture snapshot in real time. According to the number of compliant assets, the total number of assets and the weight of each architecture level, the alignment contribution value of each architecture level is calculated and the overall architecture alignment is obtained by summarizing. The alignment value of the entire network architecture is calculated in real time iteratively. When a gap with a high priority rectification level is detected or the overall alignment of the architecture is lower than a preset threshold, a deviation warning is pushed to the administrator, and the rectification progress is tracked. After rectification is completed, the difference comparison engine is called back for verification and the rectification tracking log is updated until the architectural consistency goal is achieved.

8. A multi-dimensional quantitative assessment system for analyzing differences in the architecture of power enterprises, applied to the method described in any one of claims 1-7, characterized in that, include: The multi-source data access module is used to acquire and preprocess the current architecture snapshot, blueprint architecture snapshot, power industry evolution model library, project plan data and investment plan data of power enterprises, and use the preprocessed current architecture snapshot as the architecture asset data to be analyzed. The multi-dimensional difference comparison module is used to process the architecture asset data into business domain hierarchical blocks to form several data fragments. It allocates computing resources through a task dynamic fragment scheduling mechanism, performs difference identification operations on each data fragment in parallel, and generates an architecture difference list between each data fragment and the blueprint architecture snapshot. The evolution quantitative assessment module is used to match the corresponding evolution mode for each gap in the list of architectural differences based on the power industry evolution mode library, and calculate the evolution workload and estimated cost of each evolution mode. The architecture rectification implementation module is used to calculate the weighted score of preset evaluation indicators based on the evolution workload and estimated cost, determine the rectification priority of each gap based on the weighted score, and generate an architecture rectification plan by combining the project plan data and investment plan data, so as to implement the rectification of the power company's architecture. The closed-loop monitoring and tracking module is used to dynamically synchronize the latest current architecture snapshot during the rectification process through a polling mechanism and an event triggering mechanism, and calculate its architecture alignment with the blueprint architecture snapshot in real time. When a gap with a high rectification priority is detected or the architecture alignment is lower than a preset threshold, an early warning message is triggered.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the power enterprise architecture difference analysis method of multi-dimensional quantitative evaluation as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the power enterprise architecture difference analysis method for multidimensional quantitative assessment as described in any one of claims 1 to 7.

Images