A domestically developed Gaussian database replacement method for electricity consumption information collection systems

Abstract

This application relates to the field of domestic database substitution technology, and in particular to a domestic Gaussian database substitution method for electricity consumption information collection systems. The method includes: modifying the electricity consumption information collection system's program and upgrading the program version, and using a DRS synchronization tool to fully synchronize data from an Oracle database to a Gaussian database; performing corresponding functional tests on both the electricity consumption information collection system and the Gaussian database; after both functional tests pass, performing performance tests on the DRS synchronization tool and the Gaussian database, and based on the performance test results, performing configuration optimization and a secondary evaluation and confirmation of production resources; conducting a dual-track trial run using a parallel architecture based on the Gaussian database and the Oracle database; if the dual-track trial run results meet preset requirements, then performing a single-track split on the parallel architecture, switching all business and services to run on the Gaussian database.

Description

Technical Field

[0001] The embodiments of this application relate to the field of domestic database substitution technology, and in particular to a domestic Gaussian database substitution method for electricity information collection systems. Background Technology

[0002] The Electricity Information Collection System is a crucial information system built by the State Grid Corporation of China to achieve the goals of "full coverage, full collection, and full cost control." It primarily performs the following functions: automatic meter reading, replacing manual meter reading by remotely collecting user electricity meter data; load monitoring, real-time monitoring of user electricity load to support demand-side management; line loss analysis, supporting line loss calculation and mitigation through transformer area and line-level data; electricity billing and prepaid management, supporting the remote cost control function of smart meters; and anomaly alarms and fault location, identifying abnormal events such as electricity theft, phase loss, and communication interruptions. The Electricity Information Collection System consists of a main station system, communication network, collection terminals, and smart meters, characterized by large data volume, high concurrency, strong real-time performance, and high reliability requirements.

[0003] GaussData is an enterprise-grade distributed relational database independently developed by a Chinese company. GaussData possesses the following core characteristics: high performance, supporting parallel computing, columnar storage engines, and vectorized execution, making it particularly suitable for massive data analysis; high availability, supporting multiple replicas, strong consistency, and automatic failover; high compatibility, compatible with mainstream SQL (Structured Query Language) standards and the MySQL ecosystem, reducing migration costs; and a distributed architecture, supporting scale-out to meet petabyte (PB) level data processing needs. With these advantages, GaussData has been widely applied in key industries such as finance, government affairs, energy, and telecommunications.

[0004] Against the backdrop of my country's vigorous promotion of the information technology innovation strategy and the independent control of key infrastructure, it is essential to migrate the electricity information collection system from traditional commercial databases or open-source databases to the domestically produced Gaussian database.

[0005] However, the migration and replacement of the database is not something that can be done overnight. The electricity consumption information collection system also differs from other information systems in many ways due to its business characteristics. In the process of replacing the electricity consumption information collection system with the domestic Gaussian database, there are problems such as difficulty in compatibility and adaptation, complexity in data migration and consistency assurance, performance optimization and stability risks, difficulty in rebuilding the ecosystem tools and operation and maintenance system, and business continuity and rollback risks. Summary of the Invention

[0006] To address the aforementioned technical issues, embodiments of this application propose a domestic Gaussian database replacement method for electricity consumption information collection systems. This method aims to comprehensively advance the process from four dimensions: technology adaptation, data governance, operation and maintenance restructuring, and organizational collaboration. This will enable a smooth, safe, and efficient domestic transformation, thereby meeting the development needs of electricity consumption information collection systems and enhancing the resilience of the power industry's supply chain.

[0007] To achieve the above objectives, embodiments of this application propose a method for replacing a domestically produced Gaussian database in an electricity consumption information collection system. The method includes: modifying the electricity consumption information collection system's programs to include stream computing services, batch computing services, micro-application services, interface programs, and an interactive sharing platform; simultaneously upgrading the Gaussian environment base program, big data statistics program, and micro-application service program; and using the DRS synchronization tool (Data Replication). The Data Replication Service (DRS) fully synchronizes data from the Oracle database to the Gaussian database. Functional tests are performed on the electricity information collection system, including big data statistics, micro-application services, and query efficiency. Functional tests are also performed on the Gaussian database, including the operation of the Gaussian environment base program and data consistency verification. After the functional tests of both the electricity information collection system and the Gaussian database pass, performance tests are conducted on the DRS synchronization tool and the Gaussian database. Based on the performance test results, configuration optimization and a second assessment of production resources are performed. A parallel architecture based on the Gaussian database and the Oracle database is used for dual-track trial operation. All business and services of the electricity information collection system are still based on the Oracle database, but real-time data synchronization and reproduction of all business and services in the Gaussian database are achieved through the DRS synchronization tool. If the dual-track trial operation meets the preset performance requirements, the parallel architecture is split into single tracks, switching all business and services to run on the Gaussian database, with the Oracle database used as a hot backup to support rapid rollback and emergency switching.

[0008] To achieve the above objectives, embodiments of this application also propose a domestically developed Gaussian database replacement system for electricity consumption information collection systems. The system includes: a replacement preparation module, a functional testing module, a performance testing module, a dual-track trial operation module, and a single-track operation module. The replacement preparation module is used to modify the electricity consumption information collection system's programs, including those for stream computing, batch computing, micro-application services, interface programs, and interactive sharing platforms. Simultaneously, it upgrades the versions of the Gaussian environment base program, big data statistics program, and micro-application service program, and uses the DRS synchronization tool to fully synchronize data from the Oracle database to the Gaussian database. The functional testing module is used to perform functional tests on the electricity consumption information collection system, including big data statistics functions, micro-application service functions, and query efficiency, and to perform tests on the Gaussian database, including the operation of the Gaussian environment base program and data consistency verification. The system includes: a functional testing module; a performance testing module, used to perform performance testing and verification of the DRS synchronization tool and Gaussian database after both the functional tests of the electricity information collection system and the Gaussian database have passed, and to perform configuration optimization and secondary evaluation and confirmation of production resources based on the performance test verification results; a dual-track trial operation module, used to conduct dual-track trial operation using a parallel architecture based on Gaussian database and Oracle database. All business and services of the electricity information collection system are still based on the Oracle database, but real-time data synchronization and reproduction of all business and services in the Gaussian database are achieved through the DRS synchronization tool; and a single-track operation module, used to perform single-track splitting of the parallel architecture after the dual-track trial operation results meet the preset performance requirements, switching all business and services to run in the Gaussian database, and using the Oracle database as a hot backup to support rapid rollback and emergency switchover.

[0009] To achieve the above objectives, embodiments of this application also propose an electronic device, including a processor and a memory, wherein the memory stores instructions executable by the processor, and the processor is configured to execute the instructions such that the electronic device can implement a domestic Gaussian database replacement method for an electricity consumption information collection system as described above.

[0010] To achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program that, when executed by a processor, enables the implementation of a domestically developed Gaussian database replacement method for electricity consumption information collection systems as described above.

[0011] Optionally, the program modification of the electricity consumption information collection system, including modifications for stream computing services, batch computing services, micro-application services, interface programs, and interactive sharing platforms, includes:

[0012] For stream computing services, based on resource availability, the existing Oracle production environment will continue to handle all stream computing services within its jurisdiction. A data offloading program will be added to offload the original data collected in the pilot area to the Gaussian test environment, which will independently complete the stream computing service based on the adapted Flink program and write it to the Gaussian database. Then, a consistency comparison will be performed with the Oracle production environment.

[0013] For batch computing tasks, ensure that the Oracle production environment and the Gaussian test environment each have a complete and fundamentally consistent set of files. The Oracle production environment continues to handle all batch computing tasks within its full jurisdiction, while the Gaussian test environment independently completes batch computing tasks based on the adapted Spark program and writes them to the Gaussian database, and then performs a consistency comparison with the Oracle production environment.

[0014] For micro-application services, a new Gaussian data access service has been added, and the calling strategy of all micro-application services has been modified to support access to different data access services. At the same time, a global switch and a single application switch have been added to enable real-time switching of micro-application services. For data change operations of micro-application services, a DRS data synchronization task has been added.

[0015] For the interface program, modify the interface program to support access to the Gaussian database, and add a DRS data synchronization task for operations involving changes to interface data.

[0016] For the interactive sharing platform, the platform was modified to support querying and writing to the Gaussian database, so as to realize two-way data interaction;

[0017] The process involves upgrading the versions of the Gaussian environment base program, big data statistics program, and micro-application service program, and using the DRS synchronization tool to fully synchronize the data from the Oracle database to the Gaussian database, including:

[0018] Upgrade all versions of the Gaussian Environment Base Program, Big Data Statistics Program, and Micro Application Service Program to the latest version, and fully upgrade the standardized data model of the Gaussian Database to the latest version.

[0019] The DRS synchronization tool is used to fully synchronize archive data, equipment operating parameters, measurement data, and statistical data from the Oracle database to the Gaussian database.

[0020] Optionally, the function test on query efficiency involves clicking on each function menu one by one to test the function response speed. For regular function pages, the average query efficiency is less than or equal to 3 seconds for 10 consecutive queries, and less than 10 seconds for fuzzy and large batch data queries.

[0021] The functional test for data consistency verification involves comparing and verifying the same day's data using a program version based on Gaussian database and a program version based on Oracle database, checking whether the query results are consistent, and ensuring that there are no logical errors in the modification of the query statements in Gaussian database.

[0022] Optionally, performance testing and verification of the DRS synchronization tool are performed, including:

[0023] The data insertion performance test was conducted by performing simulated data insertions in an Oracle database, based on single form field, single table multiple fields, and multiple table multiple fields, with data volumes of 10,000, 100,000, 1 million, 5 million, and 10 million. First, the data was synchronized to the Oracle database using the OGG synchronization tool, and the data insertion accuracy and time consumption statistics were recorded. Then, the same volume of insertion jobs was performed, and the data was synchronized to the Gaussian database using the DRS synchronization tool, and the data insertion accuracy and time consumption statistics were recorded. Finally, the quality and efficiency results of OGG (Oracle Golden Gate) synchronization and DRS synchronization were compared.

[0024] Data deletion performance testing was conducted using simulated data deletion operations on Oracle databases, with data volumes of 10,000, 100,000, 1 million, 5 million, and 10 million, based on single-table field, single-table multi-field, and multi-table multi-field scenarios. First, data was synchronized to the Oracle database using the OGG synchronization tool, and the accuracy and time taken for data deletion were recorded. Then, a deletion operation of the same volume was performed, and the data was synchronized to the Gaussian database using the DRS synchronization tool, with the accuracy and time taken for data deletion also recorded. Finally, the performance and efficiency results of OGG synchronization and DRS synchronization were compared.

[0025] Data change performance testing was conducted using simulated data changes in an Oracle database, categorized by single-table field, single-table multi-field, and multi-table multi-field scenarios, with data volumes of 10,000, 100,000, 1 million, 5 million, and 10 million. First, the data was synchronized to the Oracle database using the OGG synchronization tool, with the accuracy and time taken recorded. Then, a similarly sized change operation was performed, synchronizing to the Gaussian database using the DRS synchronization tool, with the accuracy and time taken recorded. Finally, the performance of OGG and DRS synchronization was compared.

[0026] Performance testing of table structure changes was conducted on Oracle databases with data volumes of thousands, tens of thousands, millions, tens of millions, and hundreds of millions, categorized by single-field and multi-field data. Changes included field data format, field name, addition of new fields, and addition of index information. First, the tables were synchronized to the Oracle database using OGG synchronization, with the accuracy and time taken recorded. Then, tables of the same size were synchronized to the Gaussian database using DRS synchronization, with the accuracy and time taken recorded again. Finally, the performance of OGG and DRS synchronization was compared.

[0027] Data format differences are synchronously verified, and performance tests are performed on data changes for cases where Gaussian database and Oracle database do not correspond in terms of data format.

[0028] Optionally, performance testing and verification of Gaussian Database can be performed. Specifically, typical business scenarios can be selected to complete the read and write pressure load performance test of Gaussian Database, including batch data entry and update, file fusion, failure list, and power consumption calculation.

[0029] Batch data entry and updates are performed by using data collected from the Kafka base in the production environment of the test base. The data entry service completes the writing of all daily frozen user data, while the power consumption calculation and data entry test are completed through stream computing. The performance metrics of writing the same amount of data to Gaussian database and Oracle database are compared.

[0030] Archive fusion involves reading archive information from the Gaussian database through a base archive fusion big data program, integrating and writing the information into the Gaussian database, and includes wide table generation and downloading cached archive operations.

[0031] The failure list is generated by reading Hive and HBase data through the failure list generation service of the base, generating a failure list and writing it to the Gaussian database. The failure list is refreshed by consuming the collected data from the production environment base Kafka through the test base.

[0032] The configuration optimization and secondary evaluation and confirmation of production resources based on the results of performance testing and verification include:

[0033] Based on the performance test results, in collaboration with the Gaussian Database developers, a second assessment of production resources was conducted on the DRS synchronization tool and Gaussian Database. Based on the results of the second assessment of production resources, the requirements for the DRS synchronization tool were adjusted and the configuration was optimized. The storage resource requirements for Gaussian Database were determined according to the preset user volume, and resource expansion was completed.

[0034] Optionally, the dual-track trial run using a parallel architecture based on Gaussian database and Oracle database includes:

[0035] The Oracle production environment will remain unchanged, and all business and services within the full jurisdiction of the electricity information collection system will still be carried out based on the Oracle database.

[0036] All services and businesses within the pilot scope of the electricity information collection system will be synchronized and reproduced in real time in the Gaussian database using the DRS synchronization tool. At the business layer, dynamic distribution will be achieved through middleware, forming a parallel architecture based on Gaussian database and Oracle database for dual-track trial operation.

[0037] For the pilot scope of the electricity information collection system, it supports dual-active mode of Oracle database and Gaussian database. When Gaussian database fails, it can quickly switch to Oracle database to ensure business continuity. It also supports batch migration and function upgrade by module and has a fast rollback mechanism to ensure that the migration process is safe and controllable.

[0038] Within the preset dual-track trial operation period, performance verification of stability, timeliness, and accuracy of the dual-track trial operation was conducted to obtain the dual-track trial operation results.

[0039] Optionally, if the dual-track trial operation results meet the preset performance requirements, the parallel architecture is split into a single track, all services are switched to run on the Gaussian database, and the Oracle database is used as a hot backup to support rapid rollback and emergency switchover, including:

[0040] If the dual-track trial operation results meet the preset performance requirements, then based on the results of the dual-track trial operation, we will work with the developers of Gaussian Database to complete the construction of Gaussian Database.

[0041] Using the DRS synchronization tool, archive data, equipment operating parameters, measurement data, and statistical data from the Oracle database are fully synchronized to the Gaussian database, and real-time synchronization is enabled.

[0042] Switch the Oracle database configuration to Gaussian Database, complete system partitioning, perform dual-track environment rollback for the parallel architecture, and use the Oracle database as a hot backup to support rapid rollback and emergency switchover.

[0043] This application proposes a domestic Gaussian database replacement method for electricity information collection systems. It promotes the process from four dimensions: technology adaptation, data governance, operation and maintenance reconstruction, and organizational collaboration. Through full-process design and multi-dimensional protection, it achieves multiple key technical effects.

[0044] First, this application ensures energy security through the localization of the database. It successfully replaces the traditional open-source database with Gaussian Database, avoiding dependence on foreign technology, completely eliminating the risk of being held back by foreign technologies, aligning with the strategic requirement of independent control over critical infrastructure, strengthening the security and reliability of the electricity information collection system, building a solid security barrier for core business data in the power industry, and ensuring the safe operation of power businesses.

[0045] Secondly, this application solves the data migration challenge, achieving a smooth transition and compatibility. Addressing the characteristics of the electricity information collection system—large data volume, high concurrency, strong real-time requirements, and high reliability—this application effectively improves the compatibility of database migration by modifying and upgrading programs for various business applications, including stream computing, batch computing, and micro-application services, ensuring a deep match between system functions and business scenarios. Utilizing the DRS synchronization tool, it achieves full data synchronization of the Oracle database and real-time synchronization during the dual-track trial operation phase. Combined with a data consistency verification mechanism, it ensures no data loss or deviation during the migration process.

[0046] Third, this application improves the operational efficiency and capacity of the electricity consumption information collection system. Through performance testing and configuration optimization, it significantly enhances the business processing efficiency of the electricity consumption information collection system, meeting the system's demands for processing massive amounts of data and handling high-concurrency access. A secondary assessment of production resources confirms the rational allocation of resources, reduces operational costs, achieves the goal of cost reduction and efficiency improvement, and adapts to the needs of business expansion.

[0047] Fourth, it effectively ensures business continuity and reduces operational risks and rollback costs. This application designs a migration path from a parallel architecture dual-track trial run to a single-track split. During the dual-track trial run, all business operations and services are still based on the Oracle database and simultaneously replicated in the Gaussian database, ensuring uninterrupted business operations and no service degradation during the migration process, effectively avoiding the risk of interruption. After the single-track split, the Oracle database is used as a hot backup of data, establishing a rapid rollback and emergency switchover mechanism to provide reliable rollback assurance and reduce migration risks and failure losses. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies of this application will be briefly introduced below. Obviously, the following drawings are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. The drawings described herein are only used to explain this application and are not intended to limit this application.

[0049] Figure 1This is a flowchart of a domestic Gaussian database replacement method for an electricity consumption information collection system provided in one embodiment of this application;

[0050] Figure 2 This is a schematic diagram of a dual-track trial operation provided in one embodiment of this application;

[0051] Figure 3 This is a schematic diagram of single-rail operation provided in one embodiment of this application;

[0052] Figure 4 This is a schematic diagram of the structure of a domestically produced Gaussian database replacement system for an electricity consumption information collection system provided in another embodiment of this application;

[0053] Figure 5 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. Those skilled in the art will understand that many technical details have been presented in the embodiments of this application to facilitate better understanding. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of this application. The following embodiments can be combined with and referenced by each other without contradiction.

[0055] One embodiment of this application proposes a domestic Gaussian database replacement method for electricity consumption information collection systems, applied to electronic devices, wherein the electronic devices can be terminals or servers. This embodiment and the following embodiments will use servers as an example for description. The implementation details of the domestic Gaussian database replacement method for electricity consumption information collection systems proposed in this embodiment are described below. The following implementation details are provided for ease of understanding only and are not necessary for implementing this solution.

[0056] The specific process of the domestic Gaussian database replacement method for electricity consumption information collection systems proposed in this embodiment can be described as follows: Figure 1 As shown, it includes:

[0057] Step 101 involves modifying the electricity information collection system, including stream computing services, batch computing services, micro-application services, interface programs, and interactive sharing platforms. Simultaneously, it involves upgrading the versions of the Gaussian environment base program, big data statistics program, and micro-application service program, and using the DRS synchronization tool to fully synchronize the data from the Oracle database to the Gaussian database.

[0058] In the specific implementation, the server first needs to perform preparatory work before replacement. This includes modifying the electricity information collection system to include stream computing services, batch computing services, micro-application services, interface programs and interactive sharing platforms. At the same time, it is necessary to upgrade the program versions of the Gaussian environment base program, big data statistics program and micro-application service program, upgrade the data model, and use the DRS synchronization tool to fully synchronize the data in the Oracle database to the Gaussian database.

[0059] In one example, for stream computing services, the server needs to consider resource availability. The existing Oracle production environment continues to handle all stream computing tasks across its entire scope. A data offloading program is added to divert the original data collected within the pilot scope to the Gaussian test environment. This environment independently completes the stream computing tasks based on an adapted Flink program and writes the data to the Gaussian database. A consistency comparison is then performed with the Oracle production environment. The pilot scope refers to the area specified for Gaussian database testing.

[0060] In one example, for batch computing tasks, the server needs to ensure that the Oracle production environment and the Gaussian test environment each have a complete and fundamentally consistent set of files. The Oracle production environment continues to handle all batch computing tasks within its full jurisdiction, while the Gaussian test environment independently completes batch computing tasks based on an adapted Spark program and writes them to the Gaussian database, and then performs a consistency comparison with the Oracle production environment.

[0061] In one example, for micro-application services, the server will add a Gaussian data access service and modify the calling strategy of all micro-application services to support access to different data access services. At the same time, a global switch and a single application switch will be added to enable real-time switching of micro-application services. For data change operations of micro-application services, the server will add a DRS data synchronization task.

[0062] In one example, for the interface program, the server will modify the interface program to support access to the Gaussian database, and add a DRS data synchronization task for operations that change interface data.

[0063] In one example, for the interactive sharing platform, the server will modify the interactive sharing platform to support querying and writing to the Gaussian database to achieve bidirectional data interaction.

[0064] In one example, after the server completes the program modification, it needs to upgrade all programs—Gaussian Environment Base Program, Big Data Statistics Program, and Micro-application Service Program—to the latest versions, and also fully upgrade the standardized data model of the Gaussian database to the latest version. Finally, the DRS synchronization tool is used to fully synchronize the archive data, device operating parameters, measurement data, and statistical data from the Oracle database to the Gaussian database.

[0065] In one example, the server needs to be adapted to the electricity information collection system (de-adapted to Oracle database). First, driver and connection pool adaptation is performed to ensure stable connection of the micro-application service to the Gaussian database. This includes driver and connection pool adaptation modifications. Driver adaptation includes driver replacement, connection method adjustment, and Maven coordinate examples. Connection pool adaptation includes feature comparison with Oracle database, investigation of adaptation reasons, and connection pool keep-alive adaptation. Second, data model adaptation is performed to minimize differences in table creation statements and data types between Oracle and Gaussian databases, facilitating rapid data model adaptation. This includes object adaptation, index creation statement adaptation, adaptation of tables without primary keys, and adaptation of partitioned tables automatically created by Oracle database. Next, non-standard syntax adaptation is performed to understand the differences in CRUD SQL statements between Oracle and Gaussian databases. This allows ISVs (Independent Software Vendors) to manually judge and individually correct the recommendations of UGO (Gaussian Database's accompanying tools). This includes creating a common function comparison table, explaining package and stored procedure syntax compatibility, and handling paginated non-sequential query statements. Finally, there is distributed adaptation. The goal is to understand the distributed version of Gaussian Database, how to choose the distribution key, and the performance impact of an unreasonable distribution key. This includes determining the distribution method and applicable scenarios, determining the basis for selecting the distribution key, analyzing the performance impact of an unreasonable distribution key, and adapting table creation statements and primary keys.

[0066] In one example, the cluster size of the electricity information collection system is configured according to the data capacity, involving 3 categories and 7 values, namely the survey value (including the source database capacity). Annual growth coefficient Reserved number of years for growth ), experience points (capacity limit percentage) Coefficient of expansion Data skewness coefficient Deployment value (number of data replicas) The total capacity is calculated using the following formula:

[0067] ;

[0068] in, Indicates the total capacity. Typically, the database should be read-only if it exceeds 85% of its capacity, and the minimum number of data replicas should be three. This refers to high-concurrency updates and modifications, meaning the proportion of junk data that hasn't been cleaned up yet, generally less than 20%. The uneven distribution of data caused by unreasonable sharding is generally less than 20%, and 150 is the local log space reserved by Gaussian.

[0069] In one example, calculating the number of Gaussian servers required by the electricity information collection system involves three parameters: total capacity, total capacity, and total capacity. Maximum capacity of a single machine and expansion coefficient .

[0070] ;

[0071] in, The number of Gaussian servers required for the electricity information collection system. It is generally set to 12. 7.68TB This represents each expansion as... Multiple expansion, This indicates rounding up to the nearest integer.

[0072] Step 102: Perform functional tests on the electricity information collection system, including big data statistics, micro-application service, and query efficiency, and perform functional tests on the Gaussian database, including the operation of the Gaussian environment base program and data consistency verification.

[0073] In practice, after the server completes the preparatory work before replacement, it can perform functional tests on the electricity information collection system, including big data statistics, micro-application service functions and query efficiency, and on the Gaussian database, including Gaussian environment base program operation and data consistency verification. Only after both the functional tests of the electricity information collection system and the Gaussian database pass can subsequent performance testing be carried out.

[0074] In one example, the functional test on query efficiency involved clicking on each function menu one by one to test the function response speed. For regular function pages, the average query efficiency was less than or equal to 3 seconds after 10 consecutive queries, while the query efficiency for fuzzy and large batch data was less than 10 seconds.

[0075] In one example, the functional test for data consistency verification involves comparing and verifying the same day's data using a Gaussian database-based program version and an Oracle database-based program version, checking whether the query results are consistent, and ensuring that the modification of the Gaussian database query statements is free of logical errors.

[0076] Step 103: After the functional tests of the electricity information collection system and the Gaussian database are passed, the performance of the DRS synchronization tool and the Gaussian database is verified, and the configuration is optimized and the production resources are evaluated and confirmed based on the results of the performance test.

[0077] In practice, once the functional tests of the electricity information collection system and the Gaussian database are passed, the performance of the DRS synchronization tool and the Gaussian database can be tested and verified. Based on the results of the performance test and verification, configuration optimization and secondary evaluation and confirmation of production resources can be carried out.

[0078] In one example, performance testing and verification of the DRS synchronization tool includes the following.

[0079] The data insertion performance test was conducted by performing simulated data insertions in an Oracle database, based on single-table field, single-table multi-field, and multi-table multi-field data volumes of 10,000, 100,000, 1 million, 5 million, and 10 million data entries. First, the data was synchronized to the Oracle database using the OGG synchronization tool, and the accuracy and time taken for data insertion were recorded. Then, the same volume of insertion jobs was performed, and the data was synchronized to the Gaussian database using the DRS synchronization tool, and the accuracy and time taken for data insertion were recorded. Finally, the performance results of OGG synchronization and DRS synchronization were compared.

[0080] Data deletion performance testing was conducted using simulated data deletion operations on Oracle databases, with data volumes of 10,000, 100,000, 1 million, 5 million, and 10 million, based on single-table field, single-table multi-field, and multi-table multi-field scenarios. First, data was synchronized to the Oracle database using the OGG synchronization tool, with data deletion accuracy and time consumption recorded. Then, a similarly sized deletion operation was performed, synchronizing to the Gaussian database using the DRS synchronization tool, with data deletion accuracy and time consumption recorded. Finally, the performance and efficiency results of OGG and DRS synchronization were compared.

[0081] Data change performance testing was conducted using simulated data changes in an Oracle database, categorized by single-table field, single-table multi-field, and multi-table multi-field scenarios, with data volumes of 10,000, 100,000, 1 million, 5 million, and 10 million. First, the data was synchronized to the Oracle database using the OGG synchronization tool, with the accuracy and time taken recorded. Then, a similarly sized change operation was performed, synchronizing to the Gaussian database using the DRS synchronization tool, with the accuracy and time taken recorded again. Finally, the performance of OGG and DRS synchronization was compared.

[0082] Performance testing of table structure changes was conducted on Oracle databases with data volumes of thousands, tens of thousands, millions, tens of millions, and hundreds of millions of data points, categorized by single-field and multi-field data. Changes included field data format, field name, addition of new fields, and addition of index information. First, the tables were synchronized to the Oracle database using OGG synchronization, with the accuracy and time taken recorded. Then, tables of the same size were synchronized to the Gaussian database using DRS synchronization, with the accuracy and time taken also recorded. Finally, the performance of OGG and DRS synchronization was compared.

[0083] Data format differences are synchronously verified, and performance tests are performed on data changes for cases where Gaussian database and Oracle database do not correspond in terms of data format.

[0084] In one example, performance testing was conducted on Gaussian Database. Specifically, typical business scenarios were selected to perform read / write load capacity tests on Gaussian Database, including batch data entry and updates, file merging, failure list analysis, and power consumption calculation.

[0085] Batch data entry and updates were tested by consuming data collected from the production environment's Kafka database. The data entry service then wrote all daily frozen user data. Simultaneously, power consumption calculations and data entry tests were performed using stream computing. The performance metrics of writing the same amount of data to Gaussian database and Oracle database were compared.

[0086] Archive fusion involves using a base archive fusion big data program to read archive information from the Gaussian database, integrate it, and write it into the Gaussian database. This process includes wide table generation and downloading cached archives.

[0087] The failure list is generated by reading Hive and HBase data through the failure list generation service of the base, writing the failure list to the Gaussian database, and refreshing the failure list by consuming the collected data from the production environment base's Kafka.

[0088] In one example, after the server completes performance testing and verification of the DRS synchronization tool and Gaussian database, it can, based on the results of the performance testing and verification, collaborate with the developers of Gaussian database to conduct a secondary assessment of production resources for the DRS synchronization tool and Gaussian database. Based on the results of the secondary assessment of production resources, it can adjust the strategy and optimize the configuration of the DRS synchronization tool, and formulate the storage resource requirements of Gaussian database according to the preset user volume and complete resource expansion.

[0089] In one example, optimization strategies include: the characteristics of distributed architecture, where data is stored on different nodes in a Gaussian database distributed environment, and each node processes pagination queries in parallel, the final merged results may be inconsistent in order; the default sorting is missing, requiring the addition of `order by`, and when `order by` is not explicitly specified, the system returns results according to the physical storage order of the data; and the uncertainty of the execution plan, where the distributed query optimizer may dynamically adjust the execution plan based on data distribution, load, etc. (such as selecting different shards, scanning methods, scanning order, data redistribution, etc.), resulting in different concatenation orders of the query results for each query.

[0090] Step 104: A dual-track trial operation is conducted using a parallel architecture based on Gaussian database and Oracle database. All business and services of the electricity information collection system are still carried out based on Oracle database, but real-time data synchronization and reproduction of all business and services in Gaussian database are achieved through the DRS synchronization tool.

[0091] In practice, after completing configuration optimization and secondary evaluation and confirmation of production resources, a parallel architecture based on Gaussian database and Oracle database can be adopted for dual-track trial operation. All business and services of the electricity information collection system are still carried out based on Oracle database, but real-time data synchronization and reproduction of all business and services in Gaussian database are realized through DRS synchronization tool.

[0092] In one example, the dual-track trial operation could be as follows: Figure 2As shown. The server needs to maintain the Oracle production environment unchanged, and all business and services within the full jurisdiction of the electricity information collection system will still be based on the Oracle database. All business and services within the pilot scope of the electricity information collection system will be synchronized and reproduced in real-time in the Gaussian database using the DRS synchronization tool. Dynamic data distribution will be implemented at the business layer through middleware, forming a parallel architecture based on both Gaussian and Oracle databases for dual-track trial operation. For the pilot scope of the electricity information collection system, a dual-active mode of Oracle and Gaussian databases is supported. In the event of a Gaussian database failure, the system will quickly switch to the Oracle database to ensure business continuity. It also supports batch migration and functional upgrades by module and has a rapid rollback mechanism (5 minutes) to ensure the migration process is safe and controllable. Finally, within the preset dual-track trial operation period, performance verification of stability, timeliness, and accuracy will be conducted to obtain the dual-track trial operation results.

[0093] Step 105: If the dual-track trial results meet the preset performance requirements, the parallel architecture is split into single tracks, all business and services are switched to run on the Gaussian database, and the Oracle database is used as a hot backup to support rapid rollback and emergency switchover.

[0094] In practice, if the results of the dual-track trial run meet the preset performance requirements, the parallel architecture can be split into a single track, all business and services can be switched to run on the Gaussian database, and the Oracle database can be used as a hot backup to support rapid rollback and emergency switchover.

[0095] In one example, the single-rail operation can be described as follows: Figure 3 As shown. After the server met the preset performance requirements during the dual-track trial run, the Gaussian database was built in collaboration with its developers. Next, using the DRS synchronization tool, all archive data, device operating parameters, measurement data, and statistical data from the Oracle database were fully synchronized to the Gaussian database, with real-time synchronization enabled. Finally, the Oracle database configuration was switched to the Gaussian database, the system was partitioned, and the dual-track environment was cleared for the parallel architecture. The Oracle database was used as a hot backup to support rapid rollback and emergency switchover.

[0096] This embodiment proposes a domestic Gaussian database replacement method for electricity information collection systems. It promotes the process from four dimensions: technology adaptation, data governance, operation and maintenance reconstruction, and organizational collaboration. Through full-process design and multi-dimensional protection, it achieves multiple key technical effects.

[0097] First, this embodiment ensures energy security through the localization of the database. It successfully replaces the traditional open-source database with Gaussian Database, avoiding dependence on foreign technology, completely eliminating the risk of being held back by foreign technologies, aligning with the strategic requirement of independent control over critical infrastructure, strengthening the security and reliability of the electricity information collection system, building a solid security barrier for core business data in the power industry, and ensuring the safe operation of power services.

[0098] Secondly, this embodiment solves the data migration challenge, achieving a smooth transition and compatibility. Addressing the characteristics of the electricity information collection system—large data volume, high concurrency, strong real-time requirements, and high reliability—this embodiment effectively improves the compatibility of database migration by modifying and upgrading programs for various business applications such as stream computing, batch computing, and micro-application services, ensuring a deep match between system functions and business scenarios. Utilizing the DRS synchronization tool, it achieves full data synchronization of the Oracle database and real-time synchronization during the dual-track trial operation phase. Combined with a data consistency verification mechanism, it ensures no data loss or deviation during the migration process.

[0099] Third, it improves the operational efficiency and carrying capacity of the electricity consumption information collection system. This embodiment, through performance testing and configuration optimization, significantly improves the business processing efficiency of the electricity consumption information collection system, meeting the system's needs for processing massive amounts of data and handling high-concurrency access. Through secondary evaluation and confirmation of production resources, it achieves reasonable resource allocation, reduces operation and maintenance costs, achieves the goal of cost reduction and efficiency improvement, and adapts to the needs of business expansion.

[0100] Fourth, it effectively ensures business continuity and reduces operational risks and rollback costs. This embodiment designs a migration path from parallel architecture dual-track trial operation to single-track splitting. During the dual-track trial operation phase, all business and services are still based on the Oracle database and simultaneously reproduced in the Gaussian database, ensuring uninterrupted business operations and no service degradation during the migration process, effectively avoiding the risk of interruption. After single-track splitting, the Oracle database is used as a hot backup of data, establishing a rapid rollback and emergency switchover mechanism to provide reliable rollback protection and reduce migration risks and failure losses.

[0101] The steps described above are merely for clarity in describing the technical solution. In actual implementation, they can be combined into one step, or certain steps can be broken down into multiple steps, as long as they involve the same logical relationship, they are all within the scope of protection of this application. Any insignificant modifications or designs added to the algorithm or process, as long as they do not change the core of the algorithm or process, are also within the scope of protection of this application.

[0102] Another embodiment of this application proposes a domestic Gaussian database replacement system for electricity consumption information collection systems. The implementation details of the domestic Gaussian database replacement system for electricity consumption information collection systems proposed in this embodiment are described in detail below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution.

[0103] The specific structure of the domestic Gaussian database replacement system for electricity consumption information collection system proposed in this embodiment is as follows: Figure 4 As shown, it includes: a replacement preparation module 201, a functional testing module 202, a performance testing module 203, a dual-track trial operation module 204, and a single-track operation module 205.

[0104] The replacement preparation module 201 is used to modify the electricity information collection system, including stream computing services, batch computing services, micro-application services, interface programs, and interactive sharing platforms. At the same time, it upgrades the program versions of the Gaussian environment base program, big data statistics program, and micro-application service program, and uses the DRS synchronization tool to fully synchronize the data in the Oracle database to the Gaussian database.

[0105] Functional testing module 202 is used to perform functional tests on the electricity information collection system, including big data statistics, micro-application service functions and query efficiency, and to perform functional tests on the Gaussian database, including the operation of the Gaussian environment base program and data consistency verification.

[0106] The performance testing module 203 is used to perform performance testing and verification on the DRS synchronization tool and Gaussian database after the functional tests of the electricity information acquisition system and Gaussian database have passed, and to perform configuration optimization and secondary evaluation and confirmation of production resources based on the results of the performance testing and verification.

[0107] The dual-track trial operation module 204 is used to conduct dual-track trial operation using a parallel architecture based on Gaussian database and Oracle database. All business and services of the electricity information collection system are still carried out based on Oracle database, but real-time data synchronization and reproduction of all business and services in Gaussian database are realized through DRS synchronization tool.

[0108] The single-track operation module 205 is used to perform single-track splitting of the parallel architecture when the results of the dual-track trial operation meet the preset performance requirements, switch all business and services to run on the Gaussian database, and use the Oracle database as a hot backup to support rapid rollback and emergency switchover.

[0109] It is worth noting that all modules involved in this embodiment are logical modules. In practical applications, a logical module can be a physical module, a part of a physical module, or an organic combination of multiple physical modules. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce modules that are not closely related to solving the technical problems proposed in this application. However, this does not mean that other modules are absent from this embodiment.

[0110] It is not difficult to see that this embodiment is a system embodiment corresponding to the above method embodiments, and this embodiment can be implemented in conjunction with the above method embodiments. The relevant technical details and technical effects mentioned in the above method embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above method embodiments.

[0111] Another embodiment of this application provides an electronic device, such as Figure 5 As shown, it includes a processor 301 and a memory 302. The memory 302 stores instructions that the processor 301 can execute. When the processor 301 is configured to execute the instructions, the electronic device can realize a domestic Gaussian database replacement method for an electricity information collection system as described in the above method embodiment.

[0112] The memory and processor are connected via a bus, which includes any number of interconnecting buses and bridges, connecting various circuits of one or more processors and the memory. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0113] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0114] Another embodiment of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, can implement a domestic Gaussian database replacement method for an electricity information collection system as described in the above method embodiments.

[0115] That is, those skilled in the art will understand that all or part of the steps in the above method embodiments can be implemented by a program instructing related hardware. The program is stored in a storage medium and includes several instructions to cause a device (such as a microcontroller, chip, etc.) or processor to execute all or part of the steps of the method described in the method embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0116] It will be understood by those skilled in the art that the above embodiments are specific implementations of this application, and various changes in form and detail can be made in practical applications without departing from the spirit and scope of this application. For those skilled in the art, several improvements and modifications can be made without departing from the principles of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A domestically developed Gaussian database replacement method for electricity consumption information collection systems, characterized in that, The method includes: The electricity information collection system underwent program modifications, including stream computing services, batch computing services, micro-application services, interface programs, and interactive sharing platforms. Simultaneously, the Gaussian environment base program, big data statistics program, and micro-application service program were upgraded. Furthermore, the data in the Oracle database was fully synchronized to the Gaussian database using the DRS synchronization tool. Functional tests were conducted on the electricity consumption information collection system, including big data statistics, micro-application service functions, and query efficiency. Functional tests were also conducted on the Gaussian database, including the operation of the Gaussian environment base program and data consistency verification. After the functional tests of the electricity information collection system and the Gaussian database were passed, the performance of the DRS synchronization tool and the Gaussian database was verified, and the configuration was optimized and the production resources were evaluated and confirmed based on the results of the performance test. The system adopts a parallel architecture based on Gaussian database and Oracle database for dual-track trial operation. All business and services of the electricity information collection system are still based on Oracle database, but real-time data synchronization and reproduction of all business and services in Gaussian database are achieved through DRS synchronization tool. If the dual-track trial results meet the preset performance requirements, the parallel architecture will be split into a single track, all business and services will be switched to run on the Gaussian database, and the Oracle database will be used as a hot backup to support rapid rollback and emergency switchover. The aforementioned program modification of the electricity consumption information collection system, including modifications for stream computing services, batch computing services, micro-application services, interface programs, and interactive sharing platforms, includes: For stream computing services, based on resource availability, the existing Oracle production environment will continue to handle all stream computing services within its jurisdiction. A data offloading program will be added to offload the original data collected in the pilot area to the Gaussian test environment, which will independently complete the stream computing service based on the adapted Flink program and write it to the Gaussian database. Then, a consistency comparison will be performed with the Oracle production environment. For batch computing tasks, ensure that the Oracle production environment and the Gaussian test environment each have a complete and fundamentally consistent set of files. The Oracle production environment continues to handle all batch computing tasks within its full jurisdiction, while the Gaussian test environment independently completes batch computing tasks based on the adapted Spark program and writes them to the Gaussian database, and then performs a consistency comparison with the Oracle production environment. For micro-application services, a new Gaussian data access service has been added, and the calling strategy of all micro-application services has been modified to support access to different data access services. At the same time, a global switch and a single application switch have been added to enable real-time switching of micro-application services. For data change operations of micro-application services, a DRS data synchronization task has been added. For the interface program, modify the interface program to support access to the Gaussian database, and add a DRS data synchronization task for operations involving changes to interface data. For the interactive sharing platform, the platform was modified to support querying and writing to the Gaussian database, so as to realize two-way data interaction; The process involves upgrading the versions of the Gaussian environment base program, big data statistics program, and micro-application service program, and using the DRS synchronization tool to fully synchronize the data from the Oracle database to the Gaussian database, including: Upgrade all versions of the Gaussian Environment Base Program, Big Data Statistics Program, and Micro Application Service Program to the latest version, and fully upgrade the standardized data model of the Gaussian Database to the latest version. The DRS synchronization tool is used to fully synchronize archive data, equipment operating parameters, measurement data and statistical data from the Oracle database to the Gaussian database. The cluster size of the electricity information collection system is configured according to the data capacity, involving 3 categories and 7 values, namely survey values, including source database capacity. Annual growth rate Reserved number of years for growth Experience values, including the maximum capacity ratio. Coefficient of expansion Data skewness coefficient Deployment value, i.e., number of data replicas. The total capacity is calculated using the following formula: ； in, Indicates the total capacity. Set to 80%; if more than 85% is used, the database will be read-only, and the minimum number of data replicas is 3. This means that during high-concurrency updates and modifications, the proportion of junk data that has not yet been cleaned up is less than 20%. The proportion of uneven data distribution caused by unreasonable sharding is less than 20%, and 150 is the local log space reserved by Gaussian. The calculation of the number of Gaussian servers required for the electricity information acquisition system involves three parameters: total capacity, total capacity, and total capacity. Maximum capacity of a single machine and expansion coefficient ; ； in, The number of Gaussian servers required for the electricity information collection system. This represents each expansion as... Multiple expansion, This indicates rounding up to the nearest integer.

2. The method for replacing a domestically produced Gaussian database in an electricity consumption information collection system according to claim 1, characterized in that, The functional test of query efficiency involves clicking on each function menu one by one to test the function response speed. For regular function pages, the average query efficiency is less than or equal to 3 seconds for 10 consecutive queries, and less than 10 seconds for fuzzy and large batch data queries. The functional test for data consistency verification involves comparing and verifying the same day's data using a program version based on Gaussian database and a program version based on Oracle database, checking whether the query results are consistent, and ensuring that there are no logical errors in the modification of the query statements in Gaussian database.

3. The method for replacing a domestically produced Gaussian database in an electricity consumption information collection system according to claim 2, characterized in that, Performance testing and verification of the DRS synchronization tool, including: Data insertion performance testing was conducted using simulated data insertions in an Oracle database, categorized by single form field, single table with multiple fields, and multiple tables with multiple fields, and based on data volumes of 10,000, 100,000, 1 million, 5 million, and 10 million. First, the data was synchronized to the Oracle database using the OGG synchronization tool, with the accuracy and time consumption of data insertion recorded. Then, the same volume of insertion jobs was performed, and the data was synchronized to the Gaussian database using the DRS synchronization tool, with the accuracy and time consumption of data insertion recorded. Finally, the performance results of OGG synchronization and DRS synchronization were compared. Data deletion performance testing was conducted using simulated data deletion operations on Oracle databases, with data volumes of 10,000, 100,000, 1 million, 5 million, and 10 million, based on single-table field, single-table multi-field, and multi-table multi-field scenarios. First, data was synchronized to the Oracle database using the OGG synchronization tool, and the accuracy and time taken for data deletion were recorded. Then, a deletion operation of the same volume was performed, and the data was synchronized to the Gaussian database using the DRS synchronization tool, with the accuracy and time taken for data deletion also recorded. Finally, the performance and efficiency results of OGG synchronization and DRS synchronization were compared. Data change performance testing was conducted using simulated data changes in an Oracle database, categorized by single-table field, single-table multi-field, and multi-table multi-field scenarios, with data volumes of 10,000, 100,000, 1 million, 5 million, and 10 million. First, the data was synchronized to the Oracle database using the OGG synchronization tool, with the accuracy and time taken recorded. Then, a similarly sized change operation was performed, synchronizing to the Gaussian database using the DRS synchronization tool, with the accuracy and time taken recorded. Finally, the performance of OGG and DRS synchronization was compared. Performance testing of table structure changes was conducted on Oracle databases with data volumes of thousands, tens of thousands, millions, tens of millions, and hundreds of millions, categorized by single-field and multi-field data. Changes included field data format, field name, addition of new fields, and addition of index information. First, the tables were synchronized to the Oracle database using OGG synchronization, with the accuracy and time taken recorded. Then, tables of the same size were synchronized to the Gaussian database using DRS synchronization, with the accuracy and time taken recorded again. Finally, the performance of OGG and DRS synchronization was compared. Data format differences are synchronously verified, and performance tests are performed on data changes for cases where Gaussian database and Oracle database do not correspond in terms of data format.

4. The method for replacing a domestically produced Gaussian database in an electricity consumption information collection system according to claim 3, characterized in that, The Gaussian database was tested and verified. Specifically, typical business scenarios were selected to complete the read and write pressure load performance test of the Gaussian database, including batch data entry and update, file fusion, failure list, and power consumption calculation. Batch data entry and updates are performed by using data collected from the Kafka base in the production environment of the test base. The data entry service completes the writing of all daily frozen user data, while the power consumption calculation and data entry test are completed through stream computing. The performance metrics of writing the same amount of data to Gaussian database and Oracle database are compared. Archive fusion involves reading archive information from the Gaussian database through a base archive fusion big data program, integrating and writing the information into the Gaussian database, and includes wide table generation and downloading cached archive operations. The failure list is generated by reading Hive and HBase data through the failure list generation service of the base, generating a failure list and writing it to the Gaussian database. The failure list is refreshed by consuming the collected data from the production environment base Kafka through the test base. The configuration optimization and secondary evaluation and confirmation of production resources based on the results of performance testing and verification include: Based on the performance test results, in collaboration with the Gaussian Database developers, a second assessment of production resources was conducted on the DRS synchronization tool and Gaussian Database. Based on the results of the second assessment of production resources, the requirements for the DRS synchronization tool were adjusted and the configuration was optimized. The storage resource requirements for Gaussian Database were determined according to the preset user volume, and resource expansion was completed.

5. A method for replacing a domestically produced Gaussian database in an electricity consumption information collection system according to claim 1, characterized in that, The dual-track trial operation using a parallel architecture based on Gaussian database and Oracle database includes: The Oracle production environment will remain unchanged, and all business and services within the full jurisdiction of the electricity information collection system will still be carried out based on the Oracle database. All services and businesses within the pilot scope of the electricity information collection system will be synchronized and reproduced in real time in the Gaussian database using the DRS synchronization tool. At the business layer, dynamic distribution will be achieved through middleware, forming a parallel architecture based on Gaussian database and Oracle database for dual-track trial operation. For the pilot scope of the electricity information collection system, it supports dual-active mode of Oracle database and Gaussian database. When Gaussian database fails, it can quickly switch to Oracle database to ensure business continuity. It also supports batch migration and function upgrade by module and has a fast rollback mechanism to ensure that the migration process is safe and controllable. Within the preset dual-track trial operation period, performance verification of stability, timeliness, and accuracy of the dual-track trial operation was conducted to obtain the dual-track trial operation results.

6. A method for replacing a domestically produced Gaussian database in an electricity consumption information collection system according to claim 5, characterized in that, If the dual-track trial operation meets the preset performance requirements, the parallel architecture will be split into a single track, switching all business and services to run on the Gaussian database, and using the Oracle database as a hot backup to support rapid rollback and emergency switchover, including: If the dual-track trial operation results meet the preset performance requirements, then based on the results of the dual-track trial operation, we will work with the developers of Gaussian Database to complete the construction of Gaussian Database. Using the DRS synchronization tool, archive data, equipment operating parameters, measurement data, and statistical data from the Oracle database are fully synchronized to the Gaussian database, and real-time synchronization is enabled. Switch the Oracle database configuration to Gaussian Database, complete system partitioning, perform dual-track environment rollback for the parallel architecture, and use the Oracle database as a hot backup to support rapid rollback and emergency switchover.

7. A domestically developed Gaussian database replacement system for electricity consumption information collection systems, characterized in that, The system includes: The replacement preparation module is used to modify the electricity information collection system, including stream computing services, batch computing services, micro-application services, interface programs and interactive sharing platforms. At the same time, it upgrades the program versions of the Gaussian environment base program, big data statistics program and micro-application service program, and uses the DRS synchronization tool to fully synchronize the data in the Oracle database to the Gaussian database. The functional testing module is used to perform functional tests on the electricity information collection system, including big data statistics, micro-application service functions and query efficiency, and to perform functional tests on the Gaussian database, including the operation of the Gaussian environment base program and data consistency verification. The performance testing module is used to perform performance testing and verification on the DRS synchronization tool and Gaussian database after the functional tests of the electricity information acquisition system and Gaussian database have passed, and to perform configuration optimization and secondary evaluation and confirmation of production resources based on the results of the performance testing and verification. The dual-track trial operation module is used to conduct dual-track trial operation using a parallel architecture based on Gaussian database and Oracle database. All business and services of the electricity information collection system are still carried out based on Oracle database, but real-time data synchronization and reproduction of all business and services in Gaussian database are realized through DRS synchronization tool. The single-track operation module is used to perform single-track splitting of the parallel architecture when the results of the dual-track trial operation meet the preset performance requirements, switch all business and services to run on the Gaussian database, and use the Oracle database as a hot backup to support rapid rollback and emergency switchover. The aforementioned program modification of the electricity consumption information collection system, including modifications for stream computing services, batch computing services, micro-application services, interface programs, and interactive sharing platforms, includes: For stream computing services, based on resource availability, the existing Oracle production environment will continue to handle all stream computing services within its jurisdiction. A data offloading program will be added to offload the original data collected in the pilot area to the Gaussian test environment, which will independently complete the stream computing service based on the adapted Flink program and write it to the Gaussian database. Then, a consistency comparison will be performed with the Oracle production environment. For batch computing tasks, ensure that the Oracle production environment and the Gaussian test environment each have a complete and fundamentally consistent set of files. The Oracle production environment continues to handle all batch computing tasks within its full jurisdiction, while the Gaussian test environment independently completes batch computing tasks based on the adapted Spark program and writes them to the Gaussian database, and then performs a consistency comparison with the Oracle production environment. For micro-application services, a new Gaussian data access service has been added, and the calling strategy of all micro-application services has been modified to support access to different data access services. At the same time, a global switch and a single application switch have been added to enable real-time switching of micro-application services. For data change operations of micro-application services, a DRS data synchronization task has been added. For the interface program, modify the interface program to support access to the Gaussian database, and add a DRS data synchronization task for operations involving changes to interface data. For the interactive sharing platform, the platform was modified to support querying and writing to the Gaussian database, so as to realize two-way data interaction; The process involves upgrading the versions of the Gaussian environment base program, big data statistics program, and micro-application service program, and using the DRS synchronization tool to fully synchronize the data from the Oracle database to the Gaussian database, including: Upgrade all versions of the Gaussian Environment Base Program, Big Data Statistics Program, and Micro Application Service Program to the latest version, and fully upgrade the standardized data model of the Gaussian Database to the latest version. The DRS synchronization tool is used to fully synchronize archive data, equipment operating parameters, measurement data and statistical data from the Oracle database to the Gaussian database. The cluster size of the electricity information collection system is configured according to the data capacity, involving 3 categories and 7 values, namely survey values, including source database capacity. Annual growth rate Reserved number of years for growth Experience values, including the maximum capacity ratio. Coefficient of expansion Data skewness coefficient Deployment value, i.e., number of data replicas. The total capacity is calculated using the following formula: ； in, Indicates the total capacity. Set to 80%; if more than 85% is used, the database will be read-only, and the minimum number of data replicas is 3. This means that during high-concurrency updates and modifications, the proportion of junk data that has not yet been cleaned up is less than 20%. The proportion of uneven data distribution caused by unreasonable sharding is less than 20%, and 150 is the local log space reserved by Gaussian. The calculation of the number of Gaussian servers required for the electricity information acquisition system involves three parameters: total capacity, total capacity, and total capacity. Maximum capacity of a single machine and expansion coefficient ; ； in, The number of Gaussian servers required for the electricity information collection system. This represents each expansion as... Multiple expansion, This indicates rounding up to the nearest integer.

8. An electronic device, characterized in that, include: A processor and a memory, wherein the memory stores instructions that the processor can execute, and the processor is configured to, when executing the instructions, enable the electronic device to implement a domestic Gaussian database replacement method for an electricity consumption information collection system as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it can implement a domestic Gaussian database replacement method for electricity information collection systems as described in any one of claims 1 to 6.

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