A power grid data digitization operation platform

Abstract

The application relates to the technical field of data management, and discloses a power grid data digital operation platform, which comprises an integral management module, a scene type management module, a scene operation monitoring module, a scene quality monitoring module, a scene development management module and a user preference analysis module; the scene type management module, the scene operation monitoring module, the scene quality monitoring module and the scene data analysis module are used for analyzing and managing monitoring data of platform scenes; the scene development management module is used for detecting interfaces, data, pages and permissions of platform scenes, and performing optimization according to detection results; and the user preference analysis module is used for obtaining scene use preferences of users by performing multidimensional analysis on access data of users in platform scenes. It can be seen that the application can effectively solve problems such as insufficient basic support capability of a power grid platform, thereby improving the visualization degree of power grid data and improving the management efficiency and accuracy of the power grid data.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and in particular to a digital operation platform for power grid data. Background Technology

[0002] With the deepening construction and application of the current power grid data platform and its thematic scenarios, employees at all levels of the company have an increasingly urgent need for digital business innovation using application data. This puts enormous pressure on the platform in terms of high concurrency and high availability, leading to problems such as the need to improve platform performance, iterate platform application functions, insufficient platform basic support capabilities, and inadequate infrastructure resources. To achieve the integration of monitoring and display systems and applications, the current power grid data platform needs to undergo a technical architecture upgrade, improve platform functions, enhance platform and scenario application performance, provide support for increasing the practical application across the entire network, and improve employees' ability to perceive business status.

[0003] Therefore, a digital operation platform for power grid data is currently being provided, which can effectively solve problems such as insufficient basic support capabilities of power grid platforms, insufficient infrastructure resources, and insufficient data management efficiency, thereby improving the visualization of power grid data and improving the management efficiency and accuracy of power grid data. Summary of the Invention

[0004] This invention provides a platform for the digital operation of power grid data, which can effectively solve problems such as insufficient basic support capabilities of power grid platforms, insufficient infrastructure resources, and insufficient data management efficiency, thereby improving the visualization of power grid data and improving the management efficiency and accuracy of power grid data.

[0005] To address the aforementioned technical problems, the first aspect of this invention discloses a power grid data digitization operation platform, the platform comprising at least: a scenario type management module, a scenario operation monitoring module, a scenario quality monitoring module, a scenario data analysis module, the scenario development management module, and a user preference analysis module; The scenario type management module is used to customize and configure platform scenarios corresponding to users with business domain roles, and to classify the platform scenarios. The scenario operation monitoring module is used to monitor the access data of the platform scenario, including the user's behavior trajectory, frequency, duration and time period when accessing the platform scenario; The scenario quality monitoring module is used to evaluate the completeness, timeliness, and rationality of the power grid data in the platform scenario, obtain the evaluated indicator results, and issue warnings for the evaluated indicator results. The power grid data includes at least the access data, document data, and indicator data of the platform scenario. The scenario development management module is used to detect the scenario interface, scenario data, scenario page and scenario permissions of the platform scenario, obtain the detection results, and optimize the platform scenario based on the detection results; The user preference analysis module is used to obtain the user's scenario usage preferences by performing multi-dimensional analysis on the user's access data in the scenario of the platform.

[0006] As an optional implementation, in the first aspect of the present invention, the scene type management module includes: a homepage configuration module and a scene category management module; The specific functions of the homepage configuration module include: For the user needs corresponding to the business domain roles, customize the platform scene interface corresponding to the user; when the user is detected to be logged into the platform, the platform will redirect to the platform scene interface. The specific functions of the scene classification management module include: The platform scenarios are classified according to the scenario classification information form to obtain multiple classification types of the platform scenarios. The identification information of each classification type includes: category ID, category name, category icon, category description, parent category, sort number, status, creator, creation time, last modifier, and last modification time. Associate each category with the role or user group that matches the category, and set corresponding access permissions for each associated role or user group, including the visibility range of the category. The platform scenes are sorted according to the classification type corresponding to the platform scenes, and the sorting of the platform scenes in the scene map is modified by moving the position of the platform scenes.

[0007] As an optional implementation, in the first aspect of the present invention, the specific functions of the scene operation monitoring module include: The access data of the platform scene is monitored. The access data includes the user's behavior trajectory, frequency, duration and time period when accessing the platform scene. Based on the access data and using a big data algorithm model, the operation detection dashboard, user behavior trajectory, scene operation duration and scene access time period in the platform scene are analyzed for intent to obtain the user's behavioral intent results. It also captures scene behavior information from multiple dimensions and analyzes the user's access pattern based on the scene behavior information. The scene behavior information includes at least one of the following: page inactivity duration, dwell time, number of visits, visit time period, number of clicks on scene subpages, and dwell time.

[0008] As an optional implementation, in the first aspect of the present invention, the scene quality monitoring module includes: a power grid data integrity detection module, a timeliness detection module, a reasonableness detection module, and a lighting warning module; The specific functions of the integrity detection module, the timeliness detection module, and the rationality detection module include: defining detection standards, classifying and managing detection rules, and evaluating indicators for the integrity, timeliness, and rationality of the power grid data in the platform scenario, respectively. The specific functions of the light warning module include: A lighting warning model is defined based on the indicator results obtained after evaluating the completeness, timeliness, and rationality of the power grid data in the platform scenario. The lighting warning model includes lighting rules defined for the indicator results of the completeness, timeliness, and rationality. The lighting rules defined in the lighting warning model include: When the compliance rate corresponding to the indicator result is higher than or equal to the preset first threshold, the indicator result is defined as the first lighting state; When the compliance rate corresponding to the indicator result is lower than the preset first threshold and higher than or equal to the preset second threshold, the indicator result is defined as the second lighting state. When the compliance rate corresponding to the indicator result is lower than the preset second threshold, the indicator result is defined as the third lighting state; Record the historical lighting change data for each user's platform scenario, and generate a quality change trend chart of the indicators corresponding to the completeness, timeliness, and rationality of the power grid data for the platform scenario.

[0009] As an optional implementation, in the first aspect of the present invention, the scene data analysis module includes: an analysis report management module, a data analysis display module, and a data display rule configuration module; The specific functions of the analysis report management module include: Report generation rule management is specifically used to construct the generation rules for the monitoring and analysis report based on the report template, the report statistical period, and the data scope covered by the report; The report classification management is specifically used for querying and classifying the generated monitoring and analysis reports; The specific functions of the data analysis and display module include: data rule management, dynamic data binding, and data model recognition.

[0010] As an optional implementation, in the first aspect of the present invention, the scene development management module includes: a scene interface detection module, a scene data detection module, a scene page detection module, and a scene permission detection module; The specific functions of the scene interface detection module include: Obtain the API request corresponding to the page in the platform scenario, and determine whether the API request corresponding to the page conforms to the preset format specification; When it is determined that the API request corresponding to the page does not conform to the preset format specification, it is determined that the API request corresponding to the page is not configured correctly, and the API request is reconfigured; and / or, The API request corresponding to the page is input into a preset environment detection model, and the configuration parameters in the API request corresponding to the page are determined to match the detection parameters of the environment detection model based on the detection parameters of the environment detection model. When it is determined that the configuration parameters in the API request corresponding to the page do not match the detection parameters of the environment detection model, it is determined that the API request corresponding to the page is not adapted to the current running environment of the platform scenario, and the API request is reconfigured. The format specifications include: HTTP method type, status code type, URL path format, and response format; the detection parameters of the environment detection model include: environment variables, cross-domain configuration parameters, protocol parameters, and timeout setting parameters. The specific functions of the scene data detection module include: The power grid data of the platform scenario is input into a preset scenario data detection model, and the power grid data of the platform scenario is judged to meet the detection indicators of the scenario data detection model according to the detection indicators of the scenario data detection model. When it is determined that the power grid data of the platform scenario does not meet the detection indicators of the scenario data detection model, it is determined that the power grid data of the platform scenario is not configured correctly, and the power grid data of the platform scenario is optimized. The scenario data detection model includes: a data cache detection model, a duplicate data detection model, a data default request detection model, and a data query compliance detection model. The data cache detection model is used to detect whether the platform scenario correctly enables the caching of dataset analysis results. The data default request detection model is used to detect whether the data default request is correctly configured when the page of the platform scenario is in the initialization state. The data query compliance detection model is used to detect whether the dataset query conditions corresponding to the power grid data in the platform scenario meet the performance specifications.

[0011] As an optional implementation, in the first aspect of the present invention, the specific functions of the scene page detection module include: The page layout parameters of the platform scene are input into the preset scene page detection model, and the page layout parameters of the platform scene are determined to match the preset layout parameters of the scene page detection model based on the preset layout parameters of the scene page detection model. When it is determined that the page layout parameters of the platform scene do not match the preset layout parameters of the scene page detection model, it is determined that the page layout of the platform scene is not configured correctly, and the page layout parameters of the platform scene are readjusted. The scenario page detection model includes: page layout compliance detection model, page content size detection model, page adaptation mode detection model, debugging information detection model, and chart registration detection model. The specific functions of the scene permission detection module include: The permission data of the platform scenario is input into a preset scenario permission detection model, and the permission data of the platform scenario is determined to meet the permission detection conditions of the scenario permission detection model based on the permission conditions of the scenario page detection model. When it is determined that the permission data of the platform scenario does not meet the permission detection conditions of the scenario permission detection model, it is determined that the permission data of the platform scenario is not configured correctly, and the permission data of the platform scenario is reconfigured. The permission detection conditions of the scene permission detection model include: the user script correctly calls the permission initialization function after the scene page is loaded; permission data is correctly stored in the global state or cache; the current user's permission ID is carried as a filtering condition when querying permission data; the scene page data is automatically refreshed when the user switches permissions; and lazy loading or virtual scrolling is used when the permission data is too large.

[0012] As an optional implementation, in the first aspect of the present invention, the specific functions of the user preference analysis module include: By using a rule engine and / or big data model, the identity information, access time, participation, scenario relevance, and business indicator relevance of users accessing the platform are analyzed from multiple dimensions to obtain the access tags and access profiles of the users. Based on the user's access tags and access profile, the system generates the user's scenario access preference results and recommends matching platform scenarios to the user based on these results.

[0013] A second aspect of the present invention discloses another power grid data digitization operation device, the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the power grid data digital operation platform disclosed in the first aspect of the present invention.

[0014] The third aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the power grid data digitization operation platform disclosed in the first aspect of the present invention.

[0015] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention provides a digital operation platform for power grid data. The platform includes: an points management module, a scenario type management module, a scenario operation monitoring module, a scenario quality monitoring module, a scenario data analysis module, a document management module, and an indicator management module. The scenario type management module, scenario operation monitoring module, scenario quality monitoring module, and scenario data analysis module are used to analyze and manage monitoring data for platform scenarios. The document management module is used to classify and manage document data in the platform's help center. The points management module is used for managing points rules for users corresponding to platform scenarios, importing points information, reviewing and monitoring abnormal points information. The indicator management module is used for managing the format and standardization of indicator data for the platform. Therefore, implementing this invention can effectively solve problems such as insufficient basic support capabilities, inadequate infrastructure resources, and insufficient data management efficiency in power grid platforms, thereby improving the visualization of power grid data and enhancing the management efficiency and accuracy of power grid data. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in 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.

[0017] Figure 1 This is a schematic diagram of the structure of the first power grid data digitization operation platform disclosed in the embodiments of the present invention; Figure 2 This is a schematic diagram of the structure of the second type of power grid data digitization operation platform disclosed in the embodiments of the present invention; Figure 3 This is a schematic diagram of the structure of the third type of power grid data digital operation platform disclosed in the embodiments of the present invention; Figure 4 This is a schematic diagram of the structure of the first type of power grid data digital operation device disclosed in the embodiments of the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] This invention discloses a digital operation platform for power grid data. The platform includes: an points management module, a scenario type management module, a scenario operation monitoring module, a scenario quality monitoring module, a scenario data analysis module, a document management module, and an indicator management module. The scenario type management module, scenario operation monitoring module, scenario quality monitoring module, and scenario data analysis module are used to analyze and manage monitoring data for platform scenarios. The document management module is used to classify and manage document data in the platform's help center. The points management module is used for managing points rules for users corresponding to platform scenarios, importing points information, reviewing and monitoring abnormal points information. The indicator management module is used to manage the format of indicator data for the platform. This effectively solves problems such as insufficient basic support capabilities, inadequate infrastructure resources, and insufficient data management efficiency of power grid platforms, thereby improving the visualization of power grid data and enhancing the management efficiency and accuracy of power grid data. These are described in detail below.

[0022] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a power grid data digitization operation platform disclosed in an embodiment of the present invention. Wherein, Figure 1The described power grid data digitization operation platform can be applied as a power grid data digitization operation cloud platform or a power grid data digitization operation cloud system. Optionally, the platform can be deployed on local terminal devices or cloud terminal devices. Local terminal devices can include at least one of PC terminals, local servers, etc., while cloud devices can include at least one of cloud hosts, cloud servers, cloud virtual devices, etc. This embodiment of the invention does not impose limitations. Figure 1 As shown, the power grid data digitization operation platform may include the following modules: The system comprises the following modules: Scene Type Management Module 101, Scene Operation Monitoring Module 102, Scene Quality Monitoring Module 103, Scene Data Analysis Module 104, Scene Development Management Module 105, User Preference Analysis Module 106, Points Management Module 107, Data Management Module 108, and Indicator Management Module 109. Among these, Points Management Module 107, Data Management Module 108, and Indicator Management Module 109 are optional. The scenario type management module 101 is used to customize the platform scenario corresponding to the user for the business domain role, and to classify the platform scenario.

[0023] The scenario operation monitoring module 102 is used to monitor the access data of the platform scenario. The access data includes the user's behavior trajectory, frequency, duration and time period when accessing the platform scenario.

[0024] The scenario quality monitoring module 103 is used to assess the completeness, timeliness, and rationality of the power grid data in the platform scenario, obtain the assessed indicator results, and issue warnings based on the assessed indicator results. The power grid data includes at least the platform scenario access data, document data, and indicator data.

[0025] The scenario data analysis module 104 is used to manage the monitoring and analysis reports of platform scenarios.

[0026] The scenario development management module 105 is used to detect the scenario interfaces, scenario data, scenario pages and scenario permissions of the platform scenario, obtain the detection results, and optimize the platform scenario based on the detection results.

[0027] The user preference analysis module 106 is used to obtain the user's usage preferences in different scenarios by performing multi-dimensional analysis on the user's access data in the platform scenario.

[0028] The points management module 107 is used to manage the points that users obtain through business in the corresponding platform scenarios.

[0029] The data management module 108 is used to classify and manage the document data corresponding to the help center of the platform scenario.

[0030] The indicator management module 109 is used to manage the format of indicator data in the platform scenario.

[0031] In embodiments of the present invention, optionally, such as Figure 2 As shown, the scene type management module 101 may include: homepage configuration module 1011, scene release approval module 1012, and scene category management module 1013.

[0032] The specific functions of the homepage configuration module 1011 include: Customize the platform scenario interface for each user based on their business domain roles and user needs; when a user logs into the platform, the platform will redirect to the platform scenario interface.

[0033] The specific functions of the scenario release approval module 1012 include: The system reviews and approves user-submitted release applications based on the scenario-based release approval information form. It also determines whether the release scenario meets the release requirements. If the release scenario meets the release requirements, the application will be released to the production environment. The approval action includes at least one of the following: approval or rejection.

[0034] The specific functions of the scene classification management module 1013 include: The platform scenarios are categorized based on the scenario classification information form, resulting in multiple category types. The identification information for each category type includes: category ID, category name, category icon, category description, parent category, sort number, status, creator, creation time, last modifier, and last modification time.

[0035] Associate each category with a corresponding role or user group, and set corresponding access permissions for each associated role or user group, including the visibility scope of the category.

[0036] The platform scenarios are sorted according to their corresponding category types, and their sorting on the scene map is modified by moving the positions of the platform scenarios.

[0037] By customizing the homepage configuration, user efficiency and experience are improved; the scene release approval function enhances the standardization and quality of scene releases, ensuring system stability and reliability; and the scene classification management function makes the scene map's directory structure more flexible and configurable, improving the efficiency and flexibility of scene management.

[0038] In this embodiment of the invention, the specific functions of the scene operation monitoring module 102 may optionally include: The monitoring platform's access data includes user behavior patterns, frequency, duration, and time periods. Based on this access data, big data algorithm models (such as association algorithm models, clustering algorithm models, KNN algorithm models, etc.) are used to perform intent analysis on the platform's operational monitoring dashboards, user behavior patterns, scene operation durations, and scene access periods to obtain the user's behavioral intent results.

[0039] It also captures scene behavior information from multiple dimensions and analyzes the user's access patterns based on the scene behavior information. The scene behavior information includes at least one of the following: inactive time, dwell time, number of visits, visit time period, number of clicks on scene subpages, and dwell time.

[0040] By monitoring and analyzing scene data from multiple dimensions, users can quickly grasp scene access and abnormal situations, thereby helping to optimize scene design and user experience, and improve the practicality of the scene.

[0041] In embodiments of the present invention, optionally, such as Figure 2 As shown, the scene quality monitoring module 103 includes: a power grid data integrity detection module 1031, a timeliness detection module 1032, a reasonableness detection module 1033, a lighting warning module 1034, a detection rule management module 1035, a quality detection processing module 1036, a quality detection analysis module 1037, a problem work order management module 1038, and a problem work order synchronization module 1039.

[0042] The specific functions of the integrity detection module 1031 include: Completeness standards are defined to allow for adding, modifying, or deleting conditions that must be met to achieve completeness.

[0043] Completeness rule management is used to classify and manage the completeness rules of power grid data.

[0044] Completeness timed analysis is used to evaluate the completeness of power grid data daily through scheduled tasks, and output the evaluation results of the power grid data completeness indicators.

[0045] The specific functions of the timeliness detection module 1032 include: The timeliness standard definition is used to add, modify, or delete the conditions that must be met to achieve timeliness.

[0046] Timeliness rule management is used to classify and manage the timeliness rules of power grid data.

[0047] Timeliness timed analysis is used to evaluate and analyze the timeliness of power grid data on a daily basis through timed tasks, and output the evaluation results of the timeliness indicators of power grid data.

[0048] The specific functions of the rationality detection module 1033 include: Reasonableness standards are defined and used to add, modify, or delete conditions that must be met to achieve reasonableness.

[0049] Reasonableness rule management is used to define and configure reasonableness rules, and to define reasonable and unreasonable indicators for power grid data respectively.

[0050] Reasonableness timed analysis is used to evaluate the reasonableness of power grid data on a daily basis through scheduled tasks, and output the evaluation results of the reasonableness indicators of power grid data.

[0051] The specific functions of the light warning module 1034 include: A lighting warning model is defined based on the indicator results obtained after evaluating the completeness, timeliness, and rationality of the power grid data in the platform scenario. The lighting warning model includes lighting rules defined for the indicator results of completeness, timeliness, and rationality.

[0052] The lighting rules defined in the lighting warning model include: When the compliance rate corresponding to the indicator result is higher than or equal to the preset first threshold, the indicator result is defined as the first lighting state (e.g., green light state).

[0053] When the compliance rate corresponding to the indicator result is lower than the preset first threshold but higher than or equal to the preset second threshold, the indicator result is defined as the second lighting state (e.g., yellow light state).

[0054] When the compliance rate corresponding to the indicator result is lower than the preset second threshold, the indicator result is defined as the third lighting state (e.g., red light state).

[0055] Record historical lighting change data for each user's platform scenario, and generate a quality change trend chart of the indicators corresponding to the completeness, timeliness, and rationality of the power grid data for the platform scenario.

[0056] The integrity indicator warning management is used to add, delete, modify, and query the warning rules, conditions, and colors based on the integrity indicator assessment results of the power grid data integrity indicator warning model. The integrity indicator warning model can be trained using a rule engine and decision tree algorithm, by using parameters such as the data field integrity rate (e.g., the proportion of non-empty fields), the number of missing key fields, the data table record integrity (e.g., the number of records that should be reported daily versus the number that are actually reported), the data source connection success rate, and the data extraction task completion status in the power grid data.

[0057] The timely lighting warning management system is used to manage the addition, deletion, modification, and query of lighting rules, lighting conditions, and lighting colors based on the timely lighting warning model of power grid data. Specifically, the reasonableness lighting warning model can utilize time series models and threshold alarm algorithms, taking into account factors such as data update time difference (e.g., current time - last update time), data delay duration (e.g., planned time vs. actual arrival time), and data refresh frequency in the power grid data. The parameters, such as data stream processing latency and task scheduling execution time deviation, were obtained through training.

[0058] The Reasonableness Lighting Warning Management is used to add, delete, modify, and query lighting rules, lighting conditions, and lighting colors based on the reasonableness indicator evaluation results of the reasonableness lighting warning model based on power grid data. The reasonableness lighting warning model can be trained using a rule engine and anomaly detection (e.g., isolated forest) algorithms by using parameters such as the compliance rate of numerical range (e.g., whether the voltage value is within a reasonable range), the consistency rate of logical relationship (e.g., total score = sum of each item), the proportion of outliers (e.g., using box plots or Z-score identification), the consistency of data distribution (e.g., whether year-on-year and month-on-month fluctuations are reasonable), and the number of business rule violations in the power grid data.

[0059] The specific functions of the detection rule management module 1035 include: The detection rule classification management is used to add, delete, modify, and query the rule categories, types, and applicable scopes for the integrity detection, timeliness detection, and rationality detection of power grid data.

[0060] The detection rule template management function is used to add, delete, modify, and query the name, applicable conditions, and configuration items of rule templates.

[0061] The specific functions of the quality inspection and processing module 1036 include: Data quality inspection result management is used to add, delete, modify, and query data results corresponding to the quality inspection of power grid data.

[0062] Abnormal indicator management is used to manage abnormal indicators in power grid data.

[0063] The specific functions of the quality inspection and analysis module 1037 include: Data monitoring analysis is used to perform statistical analysis on indicators for monitoring the quality of power grid data.

[0064] Data quality monitoring and analysis is used to perform statistical analysis on the quality inspection results of power grid data.

[0065] The specific functions of the issue work order management module 1038 include: Automatic generation of quality issue work orders: The work order generation engine generates corresponding quality issue work orders based on the data results obtained from data quality inspection, as well as the indicator name, scene number, and scene responsible person of the abnormal indicators.

[0066] The quality issue work order management function is used to add, delete, modify, and query work orders based on their status and number.

[0067] The specific functions of the issue work order synchronization module 1039 include: iOS Issue Ticket Synchronization Task Management: Based on the iOS synchronization task form information, issue tickets are synchronized to the iOS system through synchronization tasks, and issue ticket processing information is obtained from the iOS system. Operations such as adding, modifying, deleting, and / or querying, starting, or stopping the execution time and task name of the synchronization task can be performed.

[0068] Task execution result management is used to query the execution time, execution status, and execution details of synchronized tasks, generate feedback forms for synchronized task execution results, and re-execute matching synchronized tasks for failed synchronized tasks.

[0069] Based on the completeness, timeliness, and rationality of the detection results, the lighting levels are automatically calculated according to preset rules. This transforms abstract data quality into intuitive visual signals, lowering the monitoring threshold and effectively improving the accuracy, reliability, and timeliness of data. It also reduces the risks associated with erroneous data and enhances the quality of decision-making. Simultaneously, the automated work order system and rule template management improve data processing efficiency, reduce maintenance costs, and make the entire data quality management process more standardized and efficient.

[0070] In embodiments of the present invention, optionally, such as Figure 2 As shown, the scene data analysis module 104 includes: analysis report management module 1041, data analysis display module 1042, and data display rule configuration module 1043.

[0071] The specific functions of the analysis report management module 1041 include: Report generation rule management is used to construct rules for generating monitoring and analysis reports based on report templates, report statistical periods, and the scope of data covered by the report.

[0072] Report classification management is used to query and classify the generated monitoring and analysis reports.

[0073] The specific functions of the data analysis and display module 1042 include: data rule management, dynamic data binding, and data model recognition.

[0074] The specific functions of the data display rule configuration module 1043 include: Parameter settings are used to add, delete, modify, and query the layout, elements, borders, margins, cards, text, and tables of the data display interface.

[0075] The element content scaling settings are used to add, delete, modify, and query operations on layout adaptive scaling, layout container storage status, element and layout conflict verification, and position verification based on element content adaptive scaling information.

[0076] The layout adaptive rules are used to configure the layout of the data display interface, freely add layouts, adjust layouts, optimize layout details, and delete layouts in an adaptive manner.

[0077] This scenario analysis module provides comprehensive and flexible data analysis services, helping users gain deep insights into business scenarios, accurately formulate decision-making strategies, and improve the efficiency of power grid operations.

[0078] In embodiments of the present invention, optionally, such as Figure 2 As shown, the scene development management module 105 includes: scene interface detection module 1051, scene data detection module 1052, scene page detection module 1053 and scene permission detection module 1054.

[0079] The specific functions of the scene interface detection module 1051 include: Obtain the API requests corresponding to the pages in the platform scenario, and determine whether the API requests corresponding to the pages conform to the preset format specifications.

[0080] When it is determined that the API request corresponding to the page does not conform to the preset format specification, it is determined that the API request corresponding to the page is not configured correctly, and the API request is reconfigured; and / or, The API request corresponding to the page is input into the preset environment detection model, and the configuration parameters in the API request corresponding to the page are determined to match the detection parameters of the environment detection model based on the detection parameters of the environment detection model.

[0081] When it is determined that the configuration parameters in the API request corresponding to the page do not match the detection parameters of the environment detection model, it is determined that the API request corresponding to the page is not adapted to the current platform scenario's operating environment, and the API request is reconfigured. The format specifications include: HTTP method type, status code type, URL path format, and response format. The detection parameters of the environment detection model include: environment variables, cross-domain configuration parameters, protocol parameters, and timeout settings.

[0082] The specific functions of the scene data detection module 1052 include: The power grid data of the platform scenario is input into the preset scenario data detection model, and the power grid data of the platform scenario is judged to meet the detection indicators of the scenario data detection model based on the detection indicators of the scenario data detection model.

[0083] When it is determined that the power grid data of the platform scenario does not meet the detection indicators of the scenario data detection model, it is determined that the power grid data of the platform scenario is not configured correctly, and the power grid data of the platform scenario is optimized.

[0084] The scenario data detection models include: data cache detection model, duplicate data detection model, data default request detection model, and data query compliance detection model. The data cache detection model is used to detect whether the platform scenario correctly enables the caching of dataset analysis results. The data default request detection model is used to detect whether the default data request is correctly configured when the page of the platform scenario is in the initialization state. The data query compliance detection model is used to detect whether the dataset query conditions corresponding to the power grid data in the platform scenario meet the performance specifications.

[0085] The specific functions of the scene page detection module 1053 include: The page layout parameters of the platform scene are input into the preset scene page detection model. Based on the preset layout parameters of the scene page detection model, it is determined whether the page layout parameters of the platform scene match the preset layout parameters of the scene page detection model.

[0086] When it is determined that the page layout parameters of the platform scene do not match the preset layout parameters of the scene page detection model, it is determined that the page layout of the platform scene is not configured correctly, and the page layout parameters of the platform scene are readjusted.

[0087] The scenario page detection models include: page layout compliance detection model, page content size detection model, page adaptation mode detection model, debugging information detection model, and chart registration detection model. The page layout compliance detection model is used to detect whether the page layout in the platform scenario is reasonable. The page content size detection model is used to detect whether the page content size in the platform scenario is within the preset range. The page adaptation mode detection model is used to detect whether the page in the platform scenario is correctly adapted to the display device (e.g., PC, large screen, mobile terminal). The debugging information detection model is used to detect and clean up the legacy debugging code in the development process. The chart registration detection model is used to detect whether the ECharts chart is correctly initialized.

[0088] The specific functions of the scene permission detection module 1054 include: The platform scene permission data is input into the preset scene permission detection model, and the permission detection conditions of the scene page detection model are used to determine whether the platform scene permission data meets the permission detection conditions of the scene permission detection model.

[0089] When it is determined that the permission data of the platform scenario does not meet the permission detection conditions of the scenario permission detection model, it is determined that the permission data of the platform scenario is not configured correctly, and the permission data of the platform scenario is reconfigured.

[0090] The permission detection conditions of the scene permission detection model include: the user script correctly calls the permission initialization function after the scene page is loaded; permission data is correctly stored in the global state or cache; the current user's permission ID is carried as a filtering condition when querying permission data; the scene page data is automatically refreshed when the user switches permissions; and lazy loading or virtual scrolling is used when the permission data is too large.

[0091] By automating the detection of multiple dimensions such as interface calls, data processing, page layout, organizational structure adaptation, and resource addresses, this helps platform administrators to promptly identify and fix problems during the development phase, ensuring that the launched scenarios have high performance, high compatibility, and high standardization.

[0092] In embodiments of the present invention, optionally, such as Figure 2 As shown, the specific functions of the user preference analysis module 106 include: By using rule engines and / or big data models, we can conduct multi-dimensional analysis of users' identity information, access time, participation, scenario relevance, and business indicator relevance in the platform access scenarios to obtain user access tags and access profiles.

[0093] Based on the user's corresponding access tags and access profile, the system generates the user's scenario access preference results and recommends matching platform scenarios to the user based on these results.

[0094] The identity information can include the user's organizational level, job role, and business domain affiliation. Access time periods can include statistics based on daily time periods, weekly periods, monthly periods, quarterly periods, or holidays. Engagement can include the user's access depth, interaction behavior, frequency of function usage, and number of activity participations. Scenario relevance can include the strength of association between scenarios accessed simultaneously by the user in a single session, the user's typical access path from entering the platform to leaving, user scenario preference clustering, and the probability of user scenario transitions. Business metric relevance can include the probability of multiple metrics being viewed simultaneously by the user, the order in which metrics are viewed by the user, the frequency of metric viewing, average dwell time, and collection / follow rate. User access tags can include basic attribute tags (e.g., organizational level, job role, region, registration time). User profiles can include: points and badge levels, behavioral preference tags (e.g., access preferences: high-frequency access periods, common scenarios, common metrics, access terminals; content preferences: focus on safety production, focus on operational metrics, focus on data analysis), business characteristic tags (e.g., business domain, common business scenarios, focus on metric categories), and value tags (e.g., highly active users, dormant users). User profiles can also include: basic information cards (e.g., user ID, name, organization, position, registration time, last active time), behavioral overview (e.g., number of visits in the last 7 days, average dwell time, top 3 common scenarios, top 3 common metrics), tag cloud (displaying user tags in word cloud format, tag size representing weight), and similar users (finding other users with similar behavior for group expansion).

[0095] In this embodiment of the invention, optionally, generating the user's scenario access preference result based on the user's corresponding access tags and access profile may include: User feature information is extracted from user tags and profiles. This feature information includes: normalized discrete features (such as organization ID, job ID), continuous features (such as access frequency, stay duration), and statistical features (such as the distribution of access scenario categories in the past 7 days).

[0096] Extract scene feature information from the metadata of the platform scene. This feature information includes: scene classification, tags, applicable terminals, quality score, popularity features (total visits, recent visits), and content features (such as text vectors extracted from scene descriptions).

[0097] User feature information and scene feature information are mapped to the same vector space, and cosine similarity is calculated.

[0098] The cosine similarity is calculated as score(u, s)=sim(vector_u, vector_s), where u represents the user, s represents the platform scenario, vector_u represents the user feature vector, and vector_s represents the scenario feature vector.

[0099] Construct a co-occurrence matrix for each platform scenario, calculate the similarity between the user's historical access scenarios and each platform scenario, and calculate the user's preference value for each platform scenario based on the similarity between the historical access scenarios and each platform scenario.

[0100] The formula for calculating the user's preference value for each platform scenario is as follows: User preference for the current scene = weighted sum of user's historical visit scenes × similarity between the current scene and historical scenes; The cosine similarity and preference values ​​obtained above are weighted and fused, and a time decay factor (giving higher weight to recent user behavior) is introduced for calculation to obtain the fused score for each platform scenario.

[0101] For each user, all platform scenarios are sorted in descending order based on the scores obtained from the fusion of all platform scenarios to obtain a platform scenario preference list, and platform scenarios with scores above the preference threshold are identified as platform scenarios that the user may be interested in.

[0102] By deeply mining and multi-dimensionally analyzing user behavior data, refined user tags and dynamic user profiles can be constructed, providing platform operators with comprehensive user behavior insight tools, which can improve platform user stickiness, activity, and satisfaction.

[0103] In embodiments of the present invention, optionally, such as Figure 2 As shown, the points management module 107 may include: points system management module 1071, points redemption management module 1072, and medal issuance management module 1073.

[0104] The specific functions of the points system management system 1071 include: Points rules management is used to manage the rules for earning points based on points rule information.

[0105] Points import application is used to import user points based on points import information.

[0106] Points entry verification is used to verify and manage points entry information.

[0107] The points anomaly monitoring is used to monitor abnormal points information according to preset abnormal points standards. When abnormal points information is detected, it reports to the platform and issues an abnormal warning message to the user.

[0108] Points clearing is used to automatically and manually clear expired points based on expired point information, and to automatically calculate the points that are about to expire for each user according to the points clearing rules.

[0109] The specific functions of the points redemption management module 1072 include: Redemption management is used to allow users to redeem items using their points based on the points redemption management information.

[0110] Redemption approval is used to conduct batch reviews, batch rejections, and fill in review comments for users' redemption applications based on points redemption approval information.

[0111] Redemption statistics are used to perform statistical operations on users' point redemption status based on points redemption statistics and according to preset combination filtering conditions.

[0112] The specific functions of the medal awarding management module 1073 include: The medal definition is used to customize the user's medal information based on the medal type and medal configuration information form, and to generate medal application information based on the user's application.

[0113] The medal awarding function is used to calculate the list of users who will receive growth medals based on the medal awarding application information and the growth medal rules, and to automatically generate the corresponding medal awarding documents based on the user list.

[0114] The medal issuance approval function is used to review medal issuance documents based on the medal issuance approval information form. When a medal issuance document is approved, the medal is automatically issued to the personnel matching the medal issuance document.

[0115] This allows users to configure points rules more flexibly and efficiently, enabling automatic points accumulation and record generation, as well as effective handling of expired points, thereby improving user experience and platform operational efficiency; it also enables more efficient points redemption activities and effective management of gift inventory. For approved documents, the system automatically deducts the corresponding points from the user's account, using charts to display redemption quantities, points usage, etc., helping administrators to intuitively understand the activity status; and it enables more flexible and efficient management of badges, enabling automatic generation of badge-earning users, while also supporting administrators to manually add badge issuance documents, improving the user interface display and user experience, and further stimulating user enthusiasm and participation.

[0116] Example 2 Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a power grid data digitization operation platform disclosed in an embodiment of the present invention. Figure 3The described power grid data digitization operation platform can be applied as a power grid data digitization operation cloud platform or a power grid data digitization operation cloud system. Optionally, the platform can be deployed on local terminal devices or cloud terminal devices. Local terminal devices can include at least one of PC terminals, local servers, etc., while cloud devices can include at least one of cloud hosts, cloud servers, cloud virtual devices, etc. This embodiment of the invention does not impose limitations. Figure 3 As shown, the power grid data digitization operation platform may include the following modules: Login Analysis Management Module 201, Data Management Module 202, Indicator Management Module 203, Points Management Module 204, Scene Type Management Module 205, Scene Operation Monitoring Module 206, Scene Quality Monitoring Module 207, Scene Data Analysis Module 208, Scene Development Management Module 209, User Preference Analysis Module 210.

[0117] In embodiments of the present invention, optionally, such as Figure 3 As shown, the specific functions of the login analysis and management module 201 include: The login anomaly dashboard is used to statistically analyze platform login anomalies from different perspectives, such as frequent logins and multiple login attempts.

[0118] Platform login analysis is used to statistically analyze platform login data by recording different dimensions such as the number of logins, login methods, and user levels, and to analyze factors such as the number of logins and peak login periods.

[0119] By using login analysis functionality, platform login anomalies can be analyzed more comprehensively and in-depth, providing strong data support for relevant personnel. At the same time, it enhances the statistics and analysis of login activity, thereby improving platform security and user experience.

[0120] In this embodiment of the invention, optionally, the data management module 202 includes: a data document management module 2021, a data document classification module 2022, and a data document approval module 2023.

[0121] The specific functions of the document management module 2021 include: This function allows you to upload, delete, view, add, edit, publish, download, and remove technical documents, standard guidelines, operational documents, and management regulations from the help center.

[0122] This enriches the platform's technical documentation, standard guidelines, operational documents, management systems, and other documents, helping users efficiently manage various platform documents, ensuring document timeliness, and meeting users' diverse document needs.

[0123] The specific functions of the Document Classification Module 2022 include: classifying and managing documents in the Help Center according to preset document types, including: technical documents, operation documents, standard guidelines and management systems.

[0124] By classifying and managing documents in this way, the organization and retrieval efficiency of documents are effectively improved, making it easier for users to quickly find the information they need, saving users time and energy, and improving work efficiency.

[0125] The specific functions of the document approval module 2023 include: The document to be published is subject to approval. When the approval is granted, the document is uploaded to the help center. The approval process includes document approval, document viewing, viewing approval comments, and filling in approval comments.

[0126] By using the document approval function, the compliance, accuracy, and completeness of published documents are ensured, the quality of published documents is improved, and potential risks caused by document errors or non-compliance are avoided. Through a standardized document approval process, the entire document management process becomes more standardized and efficient.

[0127] In embodiments of the present invention, optionally, such as Figure 3 As shown, the indicator management module 203 may include: a data information management module 2031, an indicator demand management module 2032, an indicator entry management module 2033, a data item management module 2034, a data dashboard management module 2035, a data early warning management module 2036, and a data standard configuration module 2037.

[0128] The specific functions of the data information management module 2031 include: Basic information management, application information management, technical information management, dimensional information management, caliber information management, lineage information management, maintenance record management, indicator information approval and entry management, unit time management, trend model management, data hierarchical control management, data application classification management, and tag management.

[0129] By leveraging data information management functions, a comprehensive data information management system can be built, ensuring data consistency, accuracy, and traceability, and improving the efficiency of data management and the transparency of data use.

[0130] The specific functions of the indicator requirement management module 2032 include: adding, deleting, modifying, and querying indicator requirements through indicator requirement filling form information.

[0131] By standardizing the management of indicator requirements, we can improve the efficiency of requirement collection and processing, and ensure the timely updating and accuracy of data indicators.

[0132] The specific functions of the indicator entry management module 2033 include: performing entry operations after the indicator requirement approval process is completed.

[0133] This indicator entry management function ensures that approved indicators can be systematically entered into the data system, maintaining data integrity and standardization.

[0134] The specific functions of the data item management module 2034 include: effectively controlling data items through integrated data management system technology to prevent fragmentation of indicator data management.

[0135] The specific functions of the data dashboard management module 2035 include: Data access management is used to calculate and display in real time the total number of platform indicators, the number of indicators that have been accessed, the indicator access rate, the number and percentage of directly collected data and entered data.

[0136] Data multiplier hierarchical management is used to display differences in data volume by hierarchically classifying and classifying data and binding the multiplier with organizational structure.

[0137] Data catalog index management is used to manage the index of the data catalog according to business domains, scenarios, and other conditions.

[0138] The data dashboard management system provides visual management of data access and usage, making it easier to monitor data status and optimize data display and access.

[0139] This data dashboard management adds visual management of data access and usage, making it easier to monitor data status and optimize data display and access.

[0140] The specific functions of the Data Early Warning Management Module 2036 include: At least one of the following: early warning engine management, early warning rule configuration, early warning template management, target value model management, data association early warning model management, risk scanning management, early warning dashboard, early warning type statistical analysis, early warning detailed data management, and historical early warning analysis management.

[0141] The data early warning management function enables real-time monitoring and early warning of data, timely detection and response to data anomalies, and reduction of risks.

[0142] This data early warning management function enables real-time monitoring and early warning of data, timely detection and response to data anomalies, and reduction of risks.

[0143] The specific functions of the Data Specification Configuration Module 2037 include: configuring and modifying data specifications, and standardizing the management of indicator data formats.

[0144] By using a user-configurable interface, the process of updating data specifications is simplified, and the adaptability and flexibility of data specifications are improved.

[0145] In this embodiment of the invention, for other descriptions of modules 204-210, please refer to the detailed description of modules 101-106 in Embodiment 1. This embodiment of the invention will not repeat the descriptions.

[0146] As can be seen, the power grid data digitization operation platform implementing this invention can more comprehensively and deeply analyze platform login anomalies through the login analysis and management module, while strengthening the statistics and analysis of login status, thereby improving platform security and user experience; through the data management module, it effectively improves document organization and retrieval efficiency, making it easier for users to quickly find the information they need, saving users time and effort, and improving work efficiency; through the indicator management module, it effectively improves data quality management, ensuring data accuracy and timeliness, while also improving the convenience of data use and the real-time nature of early warnings, thereby effectively solving problems such as insufficient basic support capabilities and inadequate infrastructure resources of the power grid platform, and further improving the visualization of power grid data, as well as the management efficiency and accuracy of power grid data.

[0147] It is evident that implementation is as follows Figure 3 The power grid data digitization operation platform shown can manage points rules, import points information, review and monitor abnormal points information through the points management module; it can digitally and intelligently manage platform scenarios through the scenario type management module, scenario operation monitoring module, scenario quality monitoring module, and scenario data analysis module; and it can more comprehensively and deeply analyze platform login anomalies through the login analysis management module, while strengthening the statistics and analysis of login status to improve platform security and user experience. The document management module effectively improves document organization and retrieval efficiency, making it easier for users to quickly find the information they need, saving users time and effort and improving work efficiency. The indicator management module effectively improves data quality management, ensuring data accuracy and timeliness, while also improving the convenience of data use and the real-time nature of early warnings. It can effectively solve problems such as insufficient basic support capabilities and inadequate infrastructure resources of the power grid platform, thereby improving the visualization of power grid data and the management efficiency and accuracy of power grid data.

[0148] Example 3 Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a power grid data digitization operation device disclosed in an embodiment of the present invention. Figure 4 As shown, the power grid data digitization operation device may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the module functions in the power grid data digital operation platform described in Embodiment 1 or Embodiment 2 of the present invention.

[0149] Example 5 This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the module functions in the power grid data digitization operation platform described in Embodiment 1 or Embodiment 2 of this invention.

[0150] Example 6 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the module functions in the power grid data digitization operation platform described in Embodiment 1 or Embodiment 2.

[0151] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0152] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence 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, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0153] Finally, it should be noted that the power grid data digitization operation platform disclosed in the embodiments of the present invention is only a preferred embodiment of the present invention and is only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power grid data digital operation platform, characterized in that, The platform includes at least: a scene type management module, a scene operation monitoring module, a scene quality monitoring module, a scene data analysis module, a scene development management module, and a user preference analysis module; The scenario type management module is used to customize and configure platform scenarios corresponding to users with business domain roles, and to classify the platform scenarios. The scenario operation monitoring module is used to monitor the access data of the platform scenario, including the user's behavior trajectory, frequency, duration and time period when accessing the platform scenario; The scenario quality monitoring module is used to evaluate the completeness, timeliness, and rationality of the power grid data in the platform scenario, obtain the evaluated indicator results, and issue warnings for the evaluated indicator results. The power grid data includes at least the access data, document data, and indicator data of the platform scenario. The scenario development management module is used to detect the scenario interface, scenario data, scenario page and scenario permissions of the platform scenario, obtain the detection results, and optimize the platform scenario based on the detection results; The user preference analysis module is used to obtain the user's scenario usage preferences by performing multi-dimensional analysis on the user's access data in the scenario of the platform.

2. The power grid data digitization operation platform according to claim 1, characterized in that, The scene type management module includes: a homepage configuration module and a scene category management module; The specific functions of the homepage configuration module include: For the user needs corresponding to the business domain roles, customize the platform scene interface corresponding to the user; when the user is detected to be logged into the platform, the platform will redirect to the platform scene interface. The specific functions of the scene classification management module include: The platform scenarios are classified according to the scenario classification information form to obtain multiple classification types of the platform scenarios. The identification information of each classification type includes: category ID, category name, category icon, category description, parent category, sort number, status, creator, creation time, last modifier, and last modification time. Associate each category with the role or user group that matches the category, and set corresponding access permissions for each associated role or user group, including the visibility range of the category. The platform scenes are sorted according to the classification type corresponding to the platform scenes, and the sorting of the platform scenes in the scene map is modified by moving the position of the platform scenes.

3. The power grid data digitization operation platform according to claim 1, characterized in that, The specific functions of the scenario operation monitoring module include: The access data of the platform scene is monitored. The access data includes the user's behavior trajectory, frequency, duration and time period when accessing the platform scene. Based on the access data and using a big data algorithm model, the operation detection dashboard, user behavior trajectory, scene operation duration and scene access time period in the platform scene are analyzed for intent to obtain the user's behavioral intent results. It also captures scene behavior information from multiple dimensions and analyzes the user's access pattern based on the scene behavior information. The scene behavior information includes at least one of the following: inactive time, dwell time, number of visits, visit time period, number of clicks on scene subpages, and dwell time.

4. The power grid data digitization operation platform according to claim 1, characterized in that, The scenario quality monitoring module includes: a power grid data integrity detection module, a timeliness detection module, a reasonableness detection module, and a lighting warning module; The specific functions of the integrity detection module, the timeliness detection module, and the rationality detection module include: defining detection standards, classifying and managing detection rules, and evaluating indicators for the integrity, timeliness, and rationality of the power grid data in the platform scenario, respectively. The specific functions of the light warning module include: A lighting warning model is defined based on the indicator results obtained after evaluating the completeness, timeliness, and rationality of the power grid data in the platform scenario. The lighting warning model includes lighting rules defined for the indicator results of the completeness, timeliness, and rationality. Record the historical lighting change data of each user's corresponding platform scenario, and generate a quality change trend chart of the indicator results corresponding to the completeness, timeliness and rationality of the power grid data of the platform scenario; The lighting rules defined in the lighting warning model include: When the compliance rate corresponding to the indicator result is higher than or equal to the preset first threshold, the indicator result is defined as the first lighting state; When the compliance rate corresponding to the indicator result is lower than the preset first threshold and higher than or equal to the preset second threshold, the indicator result is defined as the second lighting state. When the compliance rate corresponding to the indicator result is lower than the preset second threshold, the indicator result is defined as the third lighting state.

5. The power grid data digital operation platform according to claim 1, characterized in that, The scenario data analysis module includes: an analysis report management module, a data analysis display module, and a data display rule configuration module; The specific functions of the analysis report management module include: Report generation rule management is specifically used to construct the generation rules for the monitoring and analysis report based on the report template, the report statistical period, and the data scope covered by the report; The report classification management is specifically used for querying and classifying the generated monitoring and analysis reports; The specific functions of the data analysis and display module include: data rule management, dynamic data binding, and data model recognition.

6. The power grid data digitization operation platform according to claim 1, characterized in that, The scenario development management module includes: a scenario interface detection module, a scenario data detection module, a scenario page detection module, and a scenario permission detection module; The specific functions of the scene interface detection module include: Obtain the API request corresponding to the page in the platform scenario, and determine whether the API request corresponding to the page conforms to the preset format specification; When it is determined that the API request corresponding to the page does not conform to the preset format specification, it is determined that the API request corresponding to the page is not configured correctly, and the API request is reconfigured; and / or, The API request corresponding to the page is input into a preset environment detection model, and the configuration parameters in the API request corresponding to the page are determined to match the detection parameters of the environment detection model based on the detection parameters of the environment detection model. When it is determined that the configuration parameters in the API request corresponding to the page do not match the detection parameters of the environment detection model, it is determined that the API request corresponding to the page is not adapted to the current running environment of the platform scenario, and the API request is reconfigured. The format specifications include: HTTP method type, status code type, URL path format, and response format; the detection parameters of the environment detection model include: environment variables, cross-domain configuration parameters, protocol parameters, and timeout setting parameters. The specific functions of the scene data detection module include: The power grid data of the platform scenario is input into a preset scenario data detection model, and the power grid data of the platform scenario is judged to meet the detection indicators of the scenario data detection model according to the detection indicators of the scenario data detection model. When it is determined that the power grid data of the platform scenario does not meet the detection indicators of the scenario data detection model, it is determined that the power grid data of the platform scenario is not configured correctly, and the power grid data of the platform scenario is optimized. The scenario data detection model includes: a data cache detection model, a duplicate data detection model, a data default request detection model, and a data query compliance detection model. The data cache detection model is used to detect whether the platform scenario correctly enables the caching of dataset analysis results. The data default request detection model is used to detect whether the data default request is correctly configured when the page of the platform scenario is in the initialization state. The data query compliance detection model is used to detect whether the dataset query conditions corresponding to the power grid data in the platform scenario meet the performance specifications.

7. The power grid data digitization operation platform according to claim 6, characterized in that, The specific functions of the scene page detection module include: The page layout parameters of the platform scene are input into the preset scene page detection model, and the page layout parameters of the platform scene are determined to match the preset layout parameters of the scene page detection model based on the preset layout parameters of the scene page detection model. When it is determined that the page layout parameters of the platform scene do not match the preset layout parameters of the scene page detection model, it is determined that the page layout of the platform scene is not configured correctly, and the page layout parameters of the platform scene are readjusted. The scenario page detection model includes: page layout compliance detection model, page content size detection model, page adaptation mode detection model, debugging information detection model, and chart registration detection model. The specific functions of the scene permission detection module include: The permission data of the platform scenario is input into a preset scenario permission detection model, and the permission detection conditions of the scenario page detection model are used to determine whether the permission data of the platform scenario meets the permission detection conditions of the scenario permission detection model. When it is determined that the permission data of the platform scenario does not meet the permission detection conditions of the scenario permission detection model, it is determined that the permission data of the platform scenario is not configured correctly, and the permission data of the platform scenario is reconfigured. The permission detection conditions of the scene permission detection model include: the user script correctly calls the permission initialization function after the scene page is loaded; permission data is correctly stored in the global state or cache; the current user's permission ID is carried as a filtering condition when querying permission data; the scene page data is automatically refreshed when the user switches permissions; and lazy loading or virtual scrolling is used when the permission data is too large.

8. The power grid data digitization operation platform according to claim 1, characterized in that, The specific functions of the user preference analysis module include: By using a rule engine and / or big data model, the identity information, access time, participation, scenario relevance, and business indicator relevance of users accessing the platform are analyzed from multiple dimensions to obtain the access tags and access profiles of the users. Based on the user's access tags and access profile, the system generates the user's scenario access preference results and recommends matching platform scenarios to the user based on these results.

9. A power grid data digitization operation device, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the power grid data digitization operation platform as described in any one of claims 1-8.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the power grid data digitization operation platform as described in any one of claims 1-8.

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