Power transmission and distribution data cross-border operation analysis and optimization management system based on artificial intelligence

Abstract

The invention discloses a power transmission and distribution data cross-border operation analysis and optimization management system based on artificial intelligence, and the system comprises a business data sorting module which is connected with a data processing module through an interface, and the data processing module is connected with a data forwarding transmission module through an interface. The data forwarding transmission module transmits the data to the data receiving and managing module; the business data carding module is used for collecting business data related information; the data processing module comprises three sub-modules, namely a data acquisition module, a data governance module and a data storage module; the data forwarding and transmission module carries out encryption transmission on the data processed by the data governance module to form a mapped data packet and a mapping rule, and carries out cross-border transmission and receiving on the data packet in a text mode through cloud service transmission; and the data receiving and managing module receives the data packet and the mapping rule transmitted by the data transmission module, and performs data organization, data storage and data application after reading the data packet and the mapping rule.

Description

Technical Field

[0001] The present invention relates to the technical field of cross-border data transmission, and in particular to an artificial intelligence-based cross-border operation analysis and optimization management system for power transmission and distribution data. Background Art

[0002] At present, the energy business of large domestic power companies includes a large number of overseas businesses, including the acquisition of some overseas companies to undertake the distribution and sale of electricity, secondary transmission and power generation business of overseas companies; a large number of overseas businesses are affected by their own regional differences, business type differences, development status differences, regulatory differences and technical path differences, which makes it very inconvenient to manage their business-related data; under the existing technology, some overseas business data are still managed through their own independent management systems. When the domestic corporate headquarters needs to aggregate and process data, special personnel are required to collect data and process the corresponding data, so as to integrate the required data with the comprehensive management platform of current domestic enterprises. In the specific operation process, there are many projects, chaotic management and low efficiency; therefore, it is considered to manage data by building a comprehensive management platform involving cross-border data management in the electrical industry; there is no management system for overseas data collection, governance and transmission in the design experience of the data management system of the existing technology. Therefore, it is necessary to design an artificial intelligence-based cross-border operation analysis and optimization management system for power transmission and distribution data to solve the above technical problems. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an artificial intelligence-based cross-border operation analysis and optimization management system for power transmission and distribution data, so as to solve the problem of cross-border management of business data of power enterprises in the prior art. The management system can coordinate the management of various information resources, improve the management capabilities of various internal and external information, improve the efficiency of information resource utilization, and strengthen the communication and coordination capabilities of various business segments.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: The cross-border operation analysis and optimization management system of power transmission and distribution data based on artificial intelligence includes a business data combing module, which is connected to the data processing module through an interface, and the data processing module is connected to the data forwarding transmission module through an interface, and the data forwarding transmission module transmits the data to the data management module; The business data combing module is used to collect business data related information, including company profile, operating data, production data, commercial data, safety data and relative location data of core equipment and facilities related to production; The data processing module includes three sub-modules: data acquisition module, data management module and data storage module; The data forwarding and transmission module encrypts and transmits the data processed by the data governance module, forms mapped data packets and mapping rules, and transmits and receives the data packets in text form through cloud services for cross-border transmission and reception; The data management module receives the data packets and mapping rules transmitted by the data transmission module, and performs data organization, data storage and data application after reading.

[0005] Preferably, the data collection module includes multiple collection interface APIs, and multiple collection structure APIs are connected to different types of data in the business data combing module; the specific implementation method is as follows: S101, define the API interface: This includes defining endpoints, which are URLs in an application that handle specific requests; defining the format of requests and responses, including data types, data structure, and error codes; S102, write data acquisition code: Read files and parse data according to the organization form of data, classify and store data according to the specified data mapping dictionary, and interact with the database; S103, setting data storage: The collected data is saved in the database of the data storage module, and the database is synchronized with hot standby.

[0006] Preferably, the data acquisition module includes a readable database connection, and the readable database connection reads the data in the database of the data storage module by means of database connection configuration; the specific implementation method of configuring the readable database connection is as follows: S201, import the packages related to database connection in Java: This includes importing the java.sql.* package, which contains classes and interfaces for interacting with the database; using Class.forName() to load the driver for the database, loading the bytecode of the specified class and making it instantiable by the JVM; S202, establish database connection: After loading the driver, use the DriverManager.getConnection() method to establish a connection to the database based on the database URL, username, and password as parameters; S203, create a Statement object: After establishing a connection with the database, create a Statement object as a container for executing SQL statements. You can use the Connection.createStatement() method to create a Statement object.

[0007] S204, execute SQL query: Use the executeQuery() method of the Statement object to execute the SQL query and return a ResultSet object containing the data in the query result set; S205, obtaining data: By traversing the ResultSet object, the data in the result set is obtained row by row. During the traversal process, in each iteration, the getInt(), getString() and other methods of the ResultSet object are used to retrieve the column values. S206, close the connection: After completing the data retrieval, close the connection to the database to release resources; Because of the try-with-resources statement, the connection will be automatically closed at the end of the code block.

[0008] Preferably, the data acquisition module includes a parsable text data application program, which is used to read the structured text data and unstructured text data in the database of the data storage module and parse the data; the specific implementation method is as follows: S301, obtaining text data of a business data combing module, including unstructured text data and structured text data; S302, text data sorting: By summarizing the format of text data, the program is divided into parsing modules, and the data volume is determined according to the actual data content blocks; S303, using Java language to write a data parsing module to parse the received data; the parsing method is selected and implemented according to the data type and format, including CSV, JSON and XML.

[0009] Preferably, the data governance module includes a data preprocessing module, a data reporting module and an auxiliary coding module; The data preprocessing module further processes the data parsed in step S303, including data cleaning, data standardization and data conversion; data cleaning eliminates invalid, erroneous and duplicated data by screening and deduplication; data standardization normalizes data with different scale features; data conversion includes word segmentation, stem extraction and annotation of text data for feature extraction; The specific execution process of the data reporting module is as follows: Data collection: According to the data connection requirements, collect the corresponding pre-processed data from the data storage module; Data verification: Verify the data after data preprocessing to check the legality and compliance of the data; Data entry: fill the verified data into the specified data table; The auxiliary coding module designs auxiliary coding information for the collected multi-source data by establishing auxiliary coding tools; auxiliary coding includes coding various services used in data processing, storage and transmission; the specific process of auxiliary coding is as follows: Determine the coding rules: formulate corresponding coding rules according to business requirements and data processing procedures, including the format, length and uniqueness of the coding; Coding implementation: Encode various services used in data processing, storage and transmission according to the established coding rules, and record the detailed coding information; Coding verification: Verify the encoded service to check the correctness and consistency of the encoding.

[0010] Preferably, the auxiliary coding module implements data auxiliary coding through a combination of data coding levels and data formats and data types; the data coding levels are divided into the following three levels: a. Raw data layer: includes unprocessed data; the data encoding of the raw data layer uses the natural language description of the raw data, including text and pictures; b. Data cleaning and preprocessing layer: Clean and preprocess the data in the original data layer, including removing duplicate data, correcting erroneous data, and filling in missing data; the data encoding in the data cleaning and preprocessing layer adopts general encoding rules; c. Data mining and advanced processing layer: Perform advanced processing on the cleaned and preprocessed data, including data mining and machine learning; the data encoding of the data mining and advanced processing layer adopts specific encoding rules.

[0011] Preferably, the combination of data format and data type specifically includes: a. Comma-separated value (CSV) format, suitable for storing tabular data, including data in spreadsheets or databases; b. JavaScript object literal JSON format, suitable for cross-platform and cross-language data exchange; c. Extensible Markup Language XML format, suitable for storing structured data, used to define data through tags, and perform data verification and query; d. Spreadsheet Excel format, suitable for storing and analyzing tabular data, including financial data and sales data; e. Portable Document Format (PDF) is suitable for storing and transmitting document data and is used to keep the format and layout of the document unchanged.

[0012] Preferably, the data storage module includes a database, and the database includes a database for data acquisition and storage and a database for data forwarding and storage; the database exists in the form of a local server and a cloud server.

[0013] Preferably, the database for data collection and storage is used to store the data collected by the data collection module from the business data combing module, and the data is stored in the project server for use in the construction of the digital cockpit; the data stored in the data forwarding is used to store data retention and records before cross-border transmission of data, and the incremental or full data is synchronized to the cloud server through regular synchronization and updating of data.

[0014] Preferably, both data acquisition storage and forwarding storage are implemented in a multi-threaded manner.

[0015] The beneficial effects of the cross-border operation analysis and optimization management system of power transmission and distribution data based on artificial intelligence provided by the present invention are as follows: The present invention solves the problem of cross-border management of business data of power enterprises in the prior art. The management system can coordinate the management of various information resources, improve the management capabilities of various internal and external information, improve the efficiency of information resource utilization, and strengthen the communication and coordination capabilities of various business sectors. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 is a schematic diagram of a system architecture according to the present invention; Figure 2 is a structural schematic diagram of a data processing module according to the present invention; Figure 3 It is a flow chart of the data governance module in the present invention. DETAILED DESCRIPTION

[0017] like Figure 1~Figure 3 As shown, the cross-border operation analysis and optimization management system of power transmission and distribution data based on artificial intelligence includes a business data combing module, which is connected to the data processing module through an interface, and the data processing module is connected to the data forwarding transmission module through an interface, and the data forwarding transmission module transmits the data to the data management module; The business data combing module is used to collect business data related information, including company profile, operating data, production data, commercial data, safety data and relative location data of core equipment and facilities related to production; The data processing module includes three sub-modules: data acquisition module, data management module and data storage module; The data forwarding and transmission module encrypts and transmits the data processed by the data governance module, forms mapped data packets and mapping rules, and transmits and receives the data packets in text form through cloud services for cross-border transmission and reception; The data management module receives the data packets and mapping rules transmitted by the data transmission module, and performs data organization, data storage and data application after reading.

[0018] Preferably, the data collection module includes multiple collection interface APIs, and multiple collection structure APIs are connected to different types of data in the business data combing module; the specific implementation method is as follows: S101, define the API interface: This includes defining endpoints, which are URLs in an application that handle specific requests; defining the format of requests and responses, including data types, data structure, and error codes; S102, write data acquisition code: Read files and parse data according to the organization form of data, classify and store data according to the specified data mapping dictionary, and interact with the database; S103, setting data storage: The collected data is saved in the database of the data storage module, and the database is synchronized with hot standby.

[0019] Preferably, the data acquisition module includes a readable database connection, and the readable database connection reads the data in the database of the data storage module by means of database connection configuration; the specific implementation method of configuring the readable database connection is as follows: S201, import the packages related to database connection in Java: This includes importing the java.sql.* package, which contains classes and interfaces for interacting with the database; using Class.forName() to load the driver for the database, loading the bytecode of the specified class and making it instantiable by the JVM; S202, establish database connection: After loading the driver, use the DriverManager.getConnection() method to establish a connection to the database based on the database URL, username, and password as parameters; S203, create a Statement object: After establishing a connection with the database, create a Statement object as a container for executing SQL statements. You can use the Connection.createStatement() method to create a Statement object.

[0020] S204, execute SQL query: Use the executeQuery() method of the Statement object to execute the SQL query and return a ResultSet object containing the data in the query result set; S205, obtaining data: By traversing the ResultSet object, the data in the result set is obtained row by row. During the traversal process, in each iteration, the getInt(), getString() and other methods of the ResultSet object are used to retrieve the column values. S206, close the connection: After completing the data retrieval, close the connection to the database to release resources; Because of the try-with-resources statement, the connection will be automatically closed at the end of the code block.

[0021] Preferably, the data acquisition module includes a parsable text data application program, which is used to read the structured text data and unstructured text data in the database of the data storage module and parse the data; the specific implementation method is as follows: S301, obtaining text data of a business data combing module, including unstructured text data and structured text data; S302, text data sorting: By summarizing the format of text data, the program is divided into parsing modules, and the data volume is determined according to the actual data content blocks; S303, using Java language to write a data parsing module to parse the received data; the parsing method is selected and implemented according to the data type and format, including CSV, JSON and XML.

[0022] Preferably, the data governance module includes a data preprocessing module, a data reporting module and an auxiliary coding module; The data preprocessing module further processes the data parsed in step S303, including data cleaning, data standardization and data conversion; data cleaning eliminates invalid, erroneous and duplicated data by screening and deduplication; data standardization normalizes data with different scale features; data conversion includes word segmentation, stem extraction and annotation of text data for feature extraction; The specific execution process of the data reporting module is as follows: Data collection: According to the data connection requirements, collect the corresponding pre-processed data from the data storage module; Data verification: Verify the data after data preprocessing to check the legality and compliance of the data; Data entry: fill the verified data into the specified data table; The auxiliary coding module designs auxiliary coding information for the collected multi-source data by establishing auxiliary coding tools; auxiliary coding includes coding various services used in data processing, storage and transmission; the specific process of auxiliary coding is as follows: Determine the coding rules: formulate corresponding coding rules according to business requirements and data processing procedures, including the format, length and uniqueness of the coding; Coding implementation: Encode various services used in data processing, storage and transmission according to the established coding rules, and record the detailed coding information; Coding verification: Verify the encoded service to check the correctness and consistency of the encoding.

[0023] Preferably, the auxiliary coding module implements data auxiliary coding through a combination of data coding levels and data formats and data types; the data coding levels are divided into the following three levels: a. Raw data layer: includes unprocessed data; the data encoding of the raw data layer uses the natural language description of the raw data, including text and pictures; b. Data cleaning and preprocessing layer: Clean and preprocess the data in the original data layer, including removing duplicate data, correcting erroneous data, and filling in missing data; the data encoding in the data cleaning and preprocessing layer adopts general encoding rules; c. Data mining and advanced processing layer: Perform advanced processing on the cleaned and preprocessed data, including data mining and machine learning; the data encoding of the data mining and advanced processing layer adopts specific encoding rules.

[0024] Preferably, the combination of data format and data type specifically includes: a. Comma-separated value (CSV) format, suitable for storing tabular data, including data in spreadsheets or databases; b. JavaScript object literal JSON format, suitable for cross-platform and cross-language data exchange; c. Extensible Markup Language XML format, suitable for storing structured data, used to define data through tags, and perform data verification and query; d. Spreadsheet Excel format, suitable for storing and analyzing tabular data, including financial data and sales data; e. Portable Document Format (PDF) is suitable for storing and transmitting document data and is used to keep the format and layout of the document unchanged.

[0025] Preferably, the data storage module includes a database, and the database includes a database for data acquisition and storage and a database for data forwarding and storage; the database exists in the form of a local server and a cloud server.

[0026] Preferably, the database for data collection and storage is used to store the data collected by the data collection module from the business data combing module, and the data is stored in the project server for use in the construction of the digital cockpit; the data stored in the data forwarding is used to store data retention and records before cross-border transmission of data, and the incremental or full data is synchronized to the cloud server through regular synchronization and updating of data.

[0027] Preferably, both data acquisition storage and forwarding storage are implemented in a multi-threaded manner.

Claims

1. An artificial intelligence-based cross-border operation analysis and optimization management system for power transmission and distribution data, characterized by: It includes a business data combing module, which is connected to the data processing module through an interface, and the data processing module is connected to the data forwarding transmission module through an interface, and the data forwarding transmission module transmits the data to the data management module; The business data combing module is used to collect business data related information, including company profile, operating data, production data, commercial data, safety data and relative location data of core equipment and facilities related to production; The data processing module includes three submodules: data acquisition module, data management module and data storage module; The data forwarding and transmission module encrypts and transmits the data processed by the data governance module, forms mapped data packets and mapping rules, and transmits and receives the data packets in text form through cloud services for cross-border transmission and reception; The data management module receives the data packets and mapping rules transmitted by the data transmission module, and performs data organization, data storage and data application after reading.

2. The cross-border operation analysis and optimization management system for power transmission and distribution data based on artificial intelligence according to claim 1 is characterized in that: The data collection module includes multiple collection interface APIs and multiple collection structure APIs to connect different types of data in the business data combing module; the specific implementation methods are as follows: S101, define the API interface: This includes defining endpoints, which are URLs in an application that handle specific requests; defining the format of requests and responses, including data types, data structure, and error codes; S102, write data acquisition code: Read files and parse data according to the organization form of data, classify and store data according to the specified data mapping dictionary, and interact with the database; S103, setting data storage: The collected data is saved in the database of the data storage module, and the database is synchronized with hot standby.

3. The cross-border operation analysis and optimization management system of power transmission and distribution data based on artificial intelligence according to claim 2 is characterized in that: The data acquisition module includes a readable database connection, which reads the data in the database of the data storage module by means of database connection configuration; the specific implementation method of configuring the readable database connection is as follows: S201, import the packages related to database connection in Java: This includes importing the java.sql.* package, which contains classes and interfaces for interacting with the database; loading the driver for the database, loading the bytecode of the specified class and making it instantiable by the JVM; S202, establish database connection: After loading the driver, establish a connection with the database based on the database URL, username and password as parameters; S203, create a Statement object: After establishing a connection with the database, create a Statement object as a container for executing SQL statements; S204, execute SQL query: Executes a SQL query and returns a ResultSet object containing the data in the query result set; S205, obtaining data: By traversing the ResultSet object, the data in the result set is obtained row by row; during the traversal process, the column value is retrieved in each iteration; S206, close the connection: When you are finished retrieving data, close the connection to the database to free up resources.

4. The cross-border operation analysis and optimization management system of power transmission and distribution data based on artificial intelligence according to claim 3 is characterized in that: The data acquisition module includes a parsable text data application program, which is used to read the structured text data and unstructured text data in the database of the data storage module and parse the data; the specific implementation method is as follows: S301, obtaining text data of a business data combing module, including unstructured text data and structured text data; S302, text data sorting: By summarizing the format of text data, the program is divided into parsing modules, and the data volume is determined according to the actual data content blocks; S303, using Java language to write a data parsing module to parse the received data; the parsing method is selected and implemented according to the data type and format, including CSV, JSON and XML.

5. The cross-border operation analysis and optimization management system of power transmission and distribution data based on artificial intelligence according to claim 4 is characterized in that: The data governance module includes data preprocessing module, data reporting module and auxiliary coding module; The data preprocessing module further processes the data parsed in step S303, including data cleaning, data standardization and data conversion; data cleaning eliminates invalid, erroneous and duplicated data by screening and deduplication; data standardization normalizes data with different scale features; data conversion includes word segmentation, stem extraction and annotation of text data for feature extraction; The specific execution process of the data reporting module is as follows: Data collection: According to the data connection requirements, collect the corresponding pre-processed data from the data storage module; Data verification: Verify the data after data preprocessing to check the legality and compliance of the data; Data entry: fill the verified data into the specified data table; The auxiliary coding module designs auxiliary coding information for the collected multi-source data by establishing auxiliary coding tools; auxiliary coding includes coding various services used in the process of data processing, storage and transmission; The specific process of auxiliary coding is as follows: Determine the coding rules: formulate corresponding coding rules according to business requirements and data processing procedures, including the format, length and uniqueness of the coding; Coding implementation: Encode various services used in data processing, storage and transmission according to the established coding rules, and record the detailed coding information; Coding verification: Verify the encoded service to check the correctness and consistency of the encoding.

6. The cross-border operation analysis and optimization management system for power transmission and distribution data based on artificial intelligence according to claim 5 is characterized in that: The auxiliary coding module implements data auxiliary coding through the combination of data coding level and data format and data type; the data coding level is divided into the following three levels: a. Raw data layer: includes unprocessed data; the data encoding of the raw data layer uses the natural language description of the raw data, including text and pictures; b. Data cleaning and preprocessing layer: Clean and preprocess the data in the raw data layer, including removing duplicate data, correcting erroneous data, and filling in missing data; The data encoding in the data cleaning and preprocessing layer adopts general encoding rules; c. Data mining and advanced processing layer: advanced processing of cleaned and preprocessed data, including data mining and machine learning; Data encoding in data mining and advanced processing layers uses specific encoding rules.

7. The cross-border operation analysis and optimization management system of power transmission and distribution data based on artificial intelligence according to claim 6 is characterized in that: The combinations of data formats and data types include: a. Comma-separated value (CSV) format, suitable for storing tabular data, including data in spreadsheets or databases; b. JavaScript object literal JSON format, suitable for cross-platform and cross-language data exchange; c. Extensible Markup Language XML format, suitable for storing structured data, used to define data through tags, and perform data verification and query; d. Spreadsheet Excel format, suitable for storing and analyzing tabular data, including financial data and sales data; e. Portable Document Format (PDF) is suitable for storing and transmitting document data and is used to keep the format and layout of the document unchanged.

8. The cross-border operation analysis and optimization management system of power transmission and distribution data based on artificial intelligence according to claim 7 is characterized in that: The data storage module includes a database, which includes a database for data acquisition and storage and a database for data forwarding and storage; the database exists in the form of a local server and a cloud server.

9. The cross-border operation analysis and optimization management system of power transmission and distribution data based on artificial intelligence according to claim 8 is characterized in that: The database for data collection and storage is used to store the data collected by the data collection module from the business data combing module. The data is stored in the project server for use in the construction of the digital cockpit. The data stored in data forwarding is used to store data retention and records before cross-border transmission. By regularly synchronizing and updating the data, the incremental or full data is synchronized to the cloud server.

10. The cross-border operation analysis and optimization management system of power transmission and distribution data based on artificial intelligence according to claim 9 is characterized in that: Data collection storage and forwarding storage are both implemented in a multi-threaded manner.

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