Basic data service intelligent optimization management system

Abstract

The invention relates to the technical field of power grid information management, in particular to a basic data service intelligent optimization management system which comprises a user data acquisition module, a power grid data storage module, a data processing module, a data architecture setting module and a terminal operation module. Calculating platform operation values of various platforms in the power grid system according to the data support degree, then setting positioning coordinates for key data in the output data according to the platform operation values and modules in the platforms, then identifying the corresponding key data and the positioning coordinates thereof according to execution actions of a terminal user, and determining an authority window according to the positioning coordinates; and the permission window is displayed in the terminal equipment according to the data support degree of the parallel data, so that multiple platforms in the power grid are integrated, user-friendly data services are provided, and the use experience of the user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid information management, and in particular to a basic data service intelligent optimization management system. Background Art

[0002] In the context of digital transformation, the level of data management is directly related to the operational efficiency and service level of the enterprise. Therefore, the "data owner system" governance model has emerged. The core concept of the data owner system is "whoever produces it is responsible for it", that is, the producer of the data is also the person responsible for the data, so as to improve data quality and management efficiency.

[0003] The prior art CN117787482A discloses a service management optimization method driven by customer behavior analysis, which includes the following steps: S01, collecting customer electricity usage data; S02, analyzing the collected data through machine learning to identify user behavior patterns; S03, formulating personalized service management strategies based on user behavior patterns; S04, predicting power grid load by user behavior patterns; S05, implementing a real-time monitoring system to continuously track user electricity usage behavior and power grid operation status, and adjusting service strategies and resource allocation based on real-time data.

[0004] However, the power grid system involves multiple application platforms. When users operate on multiple application platforms, they need to manually search for data information in each application platform and confirm permissions, which increases the complexity of system operation and reduces the system's work efficiency. Summary of the invention

[0005] The purpose of the present invention is to solve the problems in the background technology and to propose a basic data service intelligent optimization management system.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A basic data service intelligent optimization management system, comprising a power grid data storage module, a data processing module, a data architecture setting module and a terminal operation module; A power grid information storage module is used to store daily data in the power grid system; The data processing module is used to process the daily data of the terminal users by dividing the daily data into several action data, extracting the key data of the system data and the output data in each action data, and integrating them to form a data set, a subset 1 and a subset 2. The subset 1 is a set of the key data of the output data, and the subset 2 is a set of the key data of the system data. Then, the data occurrence frequency of the key data of the subset 1 is calculated, and then the parallel data corresponding to the key data in the subset 1 is identified in the subset 2, and the data support of the key data in the subset 2 is calculated; The data architecture setting module is used to use the key data of subset one as the sensing layer and the key data of subset two as the identification layer, correspond the key data in the identification layer to various platforms in the power grid system, and then accumulate the data support of the key data in each platform to determine the platform operation value, and then arrange the platforms in the power grid system according to the platform operation value and set the position label, and then each platform is divided into multiple modules according to the platform structure, and the module label number is set, and finally, according to the key data in the sensing layer, the parallel data corresponding to the key data is obtained, and based on the position of the parallel data in the identification layer, the positioning coordinates are set for the key data of the sensing layer; The terminal operation module is used to identify the corresponding key data and its location coordinates in the sensing layer according to the terminal user's execution action, determine the permission window according to the location coordinates, and display the permission window in the terminal device according to the data support of parallel data.

[0007] As a further solution of the present invention, the method for setting the data set, subset one and subset two includes: S1: Extract daily data of terminal users, including system data and output data. Output data refers to the behavioral data input by terminal users into various platforms through terminal devices. System data is mainly generated within the platform to support the operation and function realization of the system. S2: According to the system monitoring time, the daily data of the terminal user is divided into a number of action data. An action data is the daily data of the terminal user operating the power grid system within a time period; S3: separating the system data and the output data in all the action data, and then extracting the key data in the system data and the output data respectively to obtain the key operation information, wherein the key operation information includes the first data and the second data, the first data is the key data extracted from the output data, and the second data is the key data extracted from the system data; S4: Acquire the action data of the terminal user, and summarize the key operation information corresponding to the action data to form a data set, and integrate the first data and the second data in the key operation information respectively to form subset one and subset two, wherein subset one is the set after the first data is integrated, and subset two is the set after the second data is integrated.

[0008] As a further solution of the present invention, a method for extracting key data from system data and output data includes: The TF-IDF algorithm is adopted to obtain information phrases by segmenting the information in the output data and the system data respectively, and then the corresponding TF-IDF value is calculated for each information phrase. At the same time, the information phrases with TF-IDF values ​​greater than or equal to the threshold X1 are marked as key data. When calculating the TF-IDF value, the key data of the output data and the key data of the system data are calculated separately.

[0009] As a further solution of the present invention, a method for calculating data support includes: Calculate the data occurrence frequency fj of the key data in subset 1, j∈[1,J], j represents different key data, and J represents that there are J key data in the subset; According to the key information of the operation, a key data is randomly selected in subset one and marked as the target data. The key data corresponding to the target data is identified in subset two and marked as parallel data i, where i=1, 2, ..., I, indicating that there are I parallel data for one target data in subset two; Based on the formula The data support Si of parallel data i is obtained, where Bi represents the number of times the ij combination appears in the data set, the ij combination means that the target data j and parallel data i exist simultaneously in the action data, and A is the total value of the action data.

[0010] As a further solution of the present invention, a method for setting positioning coordinates for key data of the sensing layer includes: SS1: Take the key data in subset 1 as the sensing layer and the key data in subset 2 as the recognition layer; SS2: Based on various platforms in the power grid system, the key data in subset 2 are matched with various platforms in the power grid system, all platforms are marked as target platforms in turn, the key data of the target platform is obtained, and the data support of all key data is accumulated to obtain the platform operation value of the target platform; SS3: In the identification layer, various platforms of the power grid system are arranged according to the platform operation values. Further, the platform operation values ​​of various platforms are arranged in descending order, and the position sequence number W is set for the position of the platform arrangement; Match the key data in subset 2 in the module of the platform, and then divide the platform into multiple modules according to the structure in the platform, and set a module label number Q for each module; SS4: According to the key data in the sensing layer, the parallel data corresponding to the key data is obtained, and based on the position of the parallel data in the recognition layer, the positioning coordinates Bn (W, Q) are set for the key data of the sensing layer, where n represents different position coordinates.

[0011] As a further solution of the present invention, the operation method in the terminal operation module includes: Detect the execution actions of the terminal users, match the execution actions of the terminal users in the sensing layer, obtain the corresponding key data and its location coordinates Bn, and then extract the platforms and modules corresponding to the platforms in the recognition layer that are consistent with the location coordinates according to the location coordinates, and mark them as permission windows; Then, according to the data support of the parallel data corresponding to the permission window, the permission windows are arranged according to the priority value with the data support as the priority value, and displayed in the terminal device of the terminal user.

[0012] As a further solution of the present invention, it also includes a user data acquisition module for real-time acquisition of daily data of terminal users in the power grid system, wherein the data acquisition terminal is composed of various platforms of the power grid system in the terminal device, and there is a one-way communication connection between the data acquisition module and the power grid data storage module.

[0013] Compared with the prior art, the advantages of the present invention are: The present invention collects and analyzes the daily behavior data of the terminal users, determines the key data of the terminal users' system data and output data and the data support of the key data of the system data, and then calculates the platform operation values ​​of various platforms in the power grid system according to the data support, and then sets the positioning coordinates for the key data in the output data according to the platform operation values ​​and each module in the platform, and then identifies the corresponding key data and its positioning coordinates according to the execution action of the terminal user, and determines the permission window according to the positioning coordinates, and displays the permission window in the terminal device according to the data support of the parallel data, thereby integrating multiple platforms in the power grid, providing user-friendly data services, improving the user experience, and at the same time improving the efficiency and intelligence level of data processing, and shortening the time for users to frequently click and search for various platforms; The present invention calculates the platform operation values ​​of various platforms and arranges the various platforms according to the platform operation values, thereby making the system more flexible, adapting to changes in data access patterns, supporting dynamic optimization, and thus improving data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0016] Reference Figure 1, a basic data service intelligent optimization management system, including a user data acquisition module, a power grid data storage module, a data processing module, a data architecture setting module and a terminal operation module; The user data collection module is used to collect the daily data of the terminal users in the power grid system in real time. The data collection end is composed of various platforms of the power grid system in the terminal equipment, including the unified promotion system and self-built application platforms. The terminal user refers to the operator of the power grid system. After that, the data collection module and the power grid data storage module are connected in a one-way communication manner. The power grid data storage module is used to store basic data in the power grid system, wherein the basic data includes system operation data, user operation data and operation monitoring data, etc. Furthermore, the basic data refers to the daily data of the end user. The power grid data storage module and the data processing module are connected in a one-way communication manner; The data processing module is used to calculate the data support of the data in the power grid database based on the daily data of the terminal users. The data processing module and the data architecture setting module are connected in a one-way manner. The specific data support calculation method includes: S1: Extract daily data of terminal users, including system data and output data. Output data refers to the behavioral data input by terminal users into various platforms through terminal devices, including clicks, browsing records, information input, etc. System data is mainly generated within the platform to support the operation and function realization of the system, including system logs, configuration information, performance monitoring data, etc. S2: Divide the daily data of the end user into several action data according to the system monitoring time; Furthermore, an action data is daily data of the terminal user's operation on the power grid system within a time period, wherein a time period includes 1 minute, 10 minutes, 1 hour, 12 hours and 1 day, etc. The specific time period is set by those skilled in the art based on the actual operation duration of the terminal user; S3: separating the system data and the output data in all the action data, and then extracting the key data in the system data and the output data respectively to obtain the key operation information, wherein the key operation information includes the first data and the second data, the first data is the key data extracted from the output data, and the second data is the key data extracted from the system data; Furthermore, the TF-IDF algorithm is used to extract the key information in the output data and the system data. The information in the output data and the system data is segmented to obtain information phrases, and then the corresponding TF-IDF value is calculated for each information phrase. At the same time, the information phrase with a TF-IDF value greater than or equal to a threshold value X1 is marked as key data. The specific algorithm of the TF-IDF algorithm is a prior art and will not be described here. The threshold value X1 is obtained by those skilled in the art after big data calculation. It should be further explained that when calculating the TF-IDF value, the key data of the output data and the key data of the system data are calculated separately; S4: Acquire the action data of the terminal user, and summarize the key operation information corresponding to the action data to form a data set, and integrate the first data and the second data in the key operation information to form a subset 1 and a subset 2, respectively, wherein the subset 1 is the set after the first data is integrated, and the subset 2 is the set after the second data is integrated; Calculate the data occurrence frequency fj of the key data in subset 1, j∈[1,J], j represents different key data, J represents that there are J key data in the subset. It should be further explained that there is no duplicate data among the J key data; S5: Then, according to the key information of the operation, a key data is randomly selected in the subset 1 and marked as the target data, and the key data corresponding to the target data is identified in the subset 2 and marked as parallel data i, i=1, 2, ..., I, indicating that there are I parallel data for one target data in the subset 2; It should be further explained that one target data and one corresponding parallel data constitute one action data of the terminal user; S6: Then based on the formula Get the data support Si of parallel data i, where Bi represents the number of times the ij combination appears in the data set, and the ij combination means that the target data j and parallel data i exist simultaneously in the action data, and A is the total value of the action data; According to the processing method of steps S5 and S6 above, the data support of each parallel data corresponding to the key data in subset 1 is calculated in turn until the processing is completed; The data architecture setting module is used to set the data architecture layer according to the data support. The specific method of setting the data architecture layer includes: SS1: Take the key data in subset 1 as the sensing layer and the key data in subset 2 as the recognition layer; SS2: Based on various platforms in the power grid system, the key data in subset 2 are matched with various platforms in the power grid system. Taking one platform as an example, this platform is marked as the target platform, the key data of the target platform is obtained, and the data support of all key data is accumulated to obtain the platform operation value of the target platform, and the platform operation values ​​of all platforms are calculated in turn; SS3: In the identification layer, various platforms of the power grid system are arranged according to the platform operation values. Further, the platform operation values ​​of various platforms are arranged in descending order, and the position sequence number W is set for the position of the platform arrangement; Match the key data in subset 2 in the module of the platform, and then divide the platform into multiple modules according to the structure in the platform, and set a module label number Q for each module; SS4: Then, according to the key data in the sensing layer, the parallel data corresponding to the key data is obtained, and based on the position of the parallel data in the recognition layer, the positioning coordinates B (W, Q) are set for the key data in the sensing layer; It should be further explained that, since the key data of the sensing layer will correspond to multiple parallel data in the recognition layer, one key data in the sensing layer corresponds to multiple position coordinates Bn, where n represents different position coordinates; There is a one-way communication connection between the data architecture setting module and the terminal operation module; The terminal operation module is used to detect the execution actions of the terminal user, match the execution actions of the terminal user in the sensing layer, obtain the corresponding key data and its positioning coordinates Bn, and then extract the platform and the corresponding module of the platform in the recognition layer that are consistent with the positioning coordinates according to the positioning coordinates, and mark them as permission windows; Then, according to the data support of the parallel data corresponding to the permission window, the permission windows are arranged according to the priority value with the data support as the priority value, and displayed in the terminal device of the terminal user; Afterwards, the terminal user can directly enter the corresponding platform module according to the displayed permission window, thereby increasing the convenience of work.

[0017] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A basic data service intelligent optimization management system, characterized in that: It includes a power grid data storage module, a data processing module, a data architecture setting module and a terminal operation module; A power grid information storage module is used to store daily data in the power grid system; The data processing module is used to process the daily data of the terminal users by dividing the daily data into several action data, extracting the key data of the system data and the output data in each action data, and integrating them to form a data set, a subset 1 and a subset 2. The subset 1 is a set of the key data of the output data, and the subset 2 is a set of the key data of the system data. Then, the data occurrence frequency of the key data of the subset 1 is calculated, and then the parallel data corresponding to the key data in the subset 1 is identified in the subset 2, and the data support of the key data in the subset 2 is calculated; The data architecture setting module is used to use the key data of subset one as the sensing layer and the key data of subset two as the identification layer, correspond the key data in the identification layer to various platforms in the power grid system, and then accumulate the data support of the key data in each platform to determine the platform operation value, and then arrange the platforms in the power grid system according to the platform operation value and set the position label, and then each platform is divided into multiple modules according to the platform structure, and the module label number is set, and finally, according to the key data in the sensing layer, the parallel data corresponding to the key data is obtained, and based on the position of the parallel data in the identification layer, the positioning coordinates are set for the key data of the sensing layer; The terminal operation module is used to identify the corresponding key data and its location coordinates in the sensing layer according to the terminal user's execution action, determine the permission window according to the location coordinates, and display the permission window in the terminal device according to the data support of parallel data.

2. A basic data service intelligent optimization management system according to claim 1, characterized in that: The setting methods of the data set, subset 1 and subset 2 include: S1: Extract daily data of terminal users, including system data and output data. Output data refers to the behavioral data input by terminal users into various platforms through terminal devices. System data is mainly generated within the platform to support the operation and function realization of the system. S2: According to the system monitoring time, the daily data of the terminal user is divided into a number of action data. An action data is the daily data of the terminal user operating the power grid system within a time period; S3: separating the system data and the output data in all the action data, and then extracting the key data in the system data and the output data respectively to obtain the key operation information, wherein the key operation information includes the first data and the second data, the first data is the key data extracted from the output data, and the second data is the key data extracted from the system data; S4: Acquire the action data of the terminal user, and summarize the key operation information corresponding to the action data to form a data set, and integrate the first data and the second data in the key operation information respectively to form subset one and subset two, wherein subset one is the set after the first data is integrated, and subset two is the set after the second data is integrated.

3. A basic data service intelligent optimization management system according to claim 2, characterized in that: Methods for extracting key data from system data and output data include: The TF-IDF algorithm is adopted to obtain information phrases by segmenting the information in the output data and the system data respectively, and then the corresponding TF-IDF value is calculated for each information phrase. At the same time, the information phrases with TF-IDF values ​​greater than or equal to the threshold X1 are marked as key data. When calculating the TF-IDF value, the key data of the output data and the key data of the system data are calculated separately.

4. The basic data service intelligent optimization management system according to claim 2, characterized in that: The calculation methods of data support include: Calculate the data occurrence frequency fj of the key data in subset 1, j∈[1,J], j represents different key data, and J represents that there are J key data in the subset; According to the key information of the operation, a key data is randomly selected in subset one and marked as the target data. The key data corresponding to the target data is identified in subset two and marked as parallel data i, where i=1, 2, ..., I, indicating that there are I parallel data for one target data in subset two; Based on the formula The data support Si of parallel data i is obtained, where Bi represents the number of times the ij combination appears in the data set, the ij combination means that the target data j and parallel data i exist simultaneously in the action data, and A is the total value of the action data.

5. The basic data service intelligent optimization management system according to claim 1, characterized in that: Methods for setting positioning coordinates for key data of the sensing layer include: SS1: Take the key data in subset 1 as the sensing layer and the key data in subset 2 as the recognition layer; SS2: Based on various platforms in the power grid system, the key data in subset 2 are matched with various platforms in the power grid system, all platforms are marked as target platforms in turn, the key data of the target platform is obtained, and the data support of all key data is accumulated to obtain the platform operation value of the target platform; SS3: In the identification layer, various platforms of the power grid system are arranged according to the platform operation values. Further, the platform operation values ​​of various platforms are arranged in descending order, and the position sequence number W is set for the position of the platform arrangement; Match the key data in subset 2 in the module of the platform, and then divide the platform into multiple modules according to the structure in the platform, and set a module label number Q for each module; SS4: According to the key data in the sensing layer, the parallel data corresponding to the key data is obtained, and based on the position of the parallel data in the recognition layer, the positioning coordinates Bn (W, Q) are set for the key data of the sensing layer, where n represents different position coordinates.

6. The basic data service intelligent optimization management system according to claim 1, characterized in that: The operation methods in the terminal operation module include: Detect the execution actions of the terminal users, match the execution actions of the terminal users in the sensing layer, obtain the corresponding key data and its location coordinates Bn, and then extract the platforms and modules corresponding to the platforms in the recognition layer that are consistent with the location coordinates according to the location coordinates, and mark them as permission windows; Then, according to the data support of the parallel data corresponding to the permission window, the permission windows are arranged according to the priority value with the data support as the priority value, and displayed in the terminal device of the terminal user.

7. The basic data service intelligent optimization management system according to claim 1, characterized in that: It also includes a user data collection module for real-time collection of daily data of terminal users in the power grid system, wherein the data collection end includes various platforms of the power grid system in the terminal device, and there is a one-way communication connection between the data collection module and the power grid data storage module.

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