System and method for checking OBIS data consistency of DLMS intelligent electric energy meter

Abstract

The invention provides a DLMS intelligent electric energy meter OBIS data consistency checking system and method. A protocol standard format import module converts OBIS object data in a to-be-tested DLMS intelligent electric energy meter design document into a test protocol file according to a protocol format; the data storage module stores the unified protocol version of the test protocol file in a database; the test module performs OBIS data consistency check on the to-be-tested DLMS intelligent electric energy meter according to the test protocol file and the historical test data in the database; and the result display module stores the test result in a database, and a user can query and export the test result through the module.

Description

Technical Field

[0001] This invention belongs to the field of electricity meter communication technology, and particularly relates to a DLMS smart electricity meter OBIS data consistency verification system and method. Background Technology

[0002] DLMS is a set of standards developed by the European Organization for Standardization (CEN / TC 145) to provide a unified communication protocol and data model for utility sectors such as electricity, water, and gas. The core idea of ​​DLMS is to enable standardized communication between devices by defining a common message format and data model.

[0003] With the widespread application of DLMS smart meters, the consistency and reliability of their data have become key factors in ensuring the efficient operation of power systems. However, existing mainstream DLMS consistency testing tools have significant shortcomings, especially in verifying OBIS (Object Identification System) data, lacking comprehensive verification capabilities for data volume, access permissions, and data types. This deficiency leads to potential issues such as data inconsistency, permission conflicts, or type errors in DLMS smart meters during practical applications, seriously affecting the stability and security of the system.

[0004] The prior art patent with publication number CN117909124A discloses a method, system, and storage medium for verifying electricity meter parameters. The method includes: reading all meter parameters of the sample meter under test and confirming that all parameters are correct; sending a CRC calculation command; the meter under test receiving the CRC calculation command, traversing a preset OBIS table, reading attribute data, and calculating the CRC value; reading the CRC value and filling it into a parameter comparison library as a standard value; and comparing the CRC value of all meters in the batch inspection process. Summary of the Invention

[0005] Existing consistency checking tools for DLMS smart meters cannot fully test all OBIS data within the DLMS smart meter. They lack comprehensive verification capabilities for data volume, access permissions, and data types, often failing to determine consistency with design documents. Furthermore, they are complex to operate and inefficient to use.

[0006] To address the aforementioned technical problems, the present invention provides the following technical solution: a DLMS smart meter OBIS data consistency verification system, comprising a protocol standard format import module, which converts the OBIS object data in the design document of the DLMS smart meter under test into a test specification file in the form of an Excel spreadsheet or XML file according to the specification format; a data storage module, which stores the test specification file in a database with a unified specification version; a testing module, which performs OBIS data consistency verification on the DLMS smart meter under test based on the content in the database; and a result display module, which saves the test results, allowing users to query and export the test results.

[0007] Specifically, after the protocol standard format import module converts the pre-designed OBIS object data in the design document according to the specification, it performs a data self-check, verifies the format of all data, and automatically repairs any abnormal formats. Only after all data format verifications pass will the converted test specification file be output to the data storage module.

[0008] Specifically, the data storage module is compatible with multiple versions of the DLMS protocol. When storing data, this module converts the test protocol file output by the standard protocol import module into the same DLMS protocol version.

[0009] Specifically, when the test module performs OBIS data consistency verification, it logs into the DLMS meter with different identities, performs read and write operations on each attribute of the OBIS object according to the test specification file in the database, and records the results of the read and write operations. It then compares the permissions, read and write results, and quantities with the test specification file in the database, records any extra or missing OBIS objects in the meter, OBIS objects whose access permissions in the meter are inconsistent with those in the database, and OBIS objects whose data types of each attribute are inconsistent with those in the test specification file in the database, and saves the test results in the database.

[0010] Specifically, the testing module includes a dynamic test decision tree, which is trained based on historical test data stored in the database. The dynamic test decision tree model can dynamically adjust the test order. At the same time, fuzz testing technology is introduced to automatically generate outlier inputs for permission boundary values.

[0011] Specifically, the testing module also includes a dynamic anomaly diagnosis engine, which embeds a multi-level anomaly diagnosis tree. When an OBIS permission or data type anomaly is detected, such as... Figure 3 As shown, the root cause of the anomaly is located through object inheritance relationship analysis and binary-level data parsing, and a root cause diagnosis report is output.

[0012] Specifically, after each test, the results display module generates a real-time data quality profile and a test report, which are then saved in the database. Operators can view the display data of the current test and historical test results at any time through the results display module via the interactive interface.

[0013] Specifically, the real-time data quality profile includes a three-dimensional quality assessment matrix. It assesses the data completeness of the test object based on the percentage of missing OBIS in the test results, the permission compliance of the test object based on the risk level of unauthorized access, and the type consistency of the test object based on the similarity of binary structure.

[0014] This invention also provides a method for verifying the consistency of OBIS data in a DLMS smart energy meter, which utilizes the aforementioned DLMS smart energy meter OBIS data consistency verification system and includes the following steps: S1. Convert the OBIS object data in the design document of the DLMS smart energy meter to be tested into a test specification file in the form of an Excel spreadsheet or XML file according to the specification format. S2. Store the unified specification version of the test specification file in the database; S3. Based on the contents of the database, perform OBIS data consistency verification on the DLMS smart energy meter under test; S4. Save the test results in the database.

[0015] Specifically, when performing OBIS data consistency verification in S3, log in to the DLMS meter with different identities, and perform OBIS object permission tests, OBIS object quantity consistency tests, and OBIS data type and structure comparisons with design requirements in sequence according to the test specification documents in the database.

[0016] The beneficial effects of this invention are: comprehensive data verification capabilities and protocol adaptability. This system can automatically adapt across versions, comprehensively verifying the OBIS data of DLMS smart meters, covering three key dimensions: data volume, access permissions, and data type; it also balances automation and efficiency: adopting a risk-driven dynamic test sequence, it can adaptively generate test strategies based on the test history records stored in the database, reducing repetitive and unnecessary steps, and prioritizing the testing of error-prone parts of the meter; it can quickly identify anomalies in OBIS data, significantly improving verification efficiency; and during the testing process, it can accurately locate and report anomalies, providing accurate basis for subsequent repairs based on the output root cause diagnosis report. Attached Figure Description

[0017] Figure 1 This is a system framework diagram of the present invention.

[0018] Figure 2This is a flowchart of the method of the present invention.

[0019] Figure 3 This is a flowchart illustrating the process of dynamic anomaly diagnosis.

[0020] Figure 4 Flowchart for OBIS read / write permission judgment. Detailed Implementation

[0021] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] Example 1: A DLMS smart energy meter OBIS data consistency verification system, such as Figure 1 As shown, the system includes a protocol standard format import module, which converts the OBIS object data in the design document of the DLMS smart meter under test into a test specification file in the form of an Excel spreadsheet or XML file according to the specification format; a data storage module, which stores the test specification file in a unified specification version in the database; a testing module, which performs OBIS data consistency verification on the DLMS smart meter under test based on the contents of the database; and a results display module, which saves the test results and allows users to query and export the test results.

[0023] Specifically, after the protocol standard format import module converts the pre-designed OBIS object data in the design document according to the specification, it performs a data self-check, verifies the format of all data, and automatically repairs any abnormal formats. Only after all data format verifications pass will the converted test specification file be output to the data storage module.

[0024] The specific application process is as follows: The protocol standard format import module first converts the pre-designed OBIS objects of the DLMS energy meter under test into test specification files in the form of Excel spreadsheets or XML files according to the specification format. The test specification files describe all permissions of the DLMS smart energy meter and all OBIS objects. The unified format makes it easy for software to extract the corresponding data from Excel and XML. The unified format has the following aspects: the fixed Excel and XML spreadsheets must have a Cover (for recording the author's name and specification file version), a Change Log (for recording the modification history), a Class Version (for recording the interface class version), a DLMS cfg (DLMS configuration, recording user permissions, variable definitions and custom data structures), and other OBIS sheets with different functions. Each sheet (a node in XML) has specific requirements, such as separating the six fields of the OBIS code with a specified delimiter (e.g., 1-0:1.8.0*255). Standardize the format of commonly used data types to avoid using different expressions for the same data type. For example, define "An ordered sequence of ASCII characters, tag=10, visible-string" as "visible_string".

[0025] In practical applications, the protocol standard format import module also performs self-correction checks on the test specification file, checking and correcting the Excel format. This includes two steps: format anomaly detection and automatic repair. Utilizing the Aspose.Cells.dll dynamic link library, after loading the Excel spreadsheet, it checks the OBIS attribute names, attribute data type descriptions, and access permission descriptions of different interface class instances. For those that are unqualified, it returns the corresponding error reason and displays it in the operation window. After passing the format check, it modifies the pre-stored data on the server. OBIS verification can only proceed after the format check is passed.

[0026] Specifically, the data storage module is compatible with multiple versions of the DLMS protocol. When storing data, this module converts the test protocol file output by the standard protocol import module into the same DLMS protocol version.

[0027] In practical applications, after the test specification file completes the self-correction check, the data storage module saves it to the database. The data storage module includes protocol middleware to ensure compatibility with different DLMS versions and resolve differences between DLMS versions. Since the DLMS energy meter under test may use different versions of the DLMS protocol, the data storage module will convert the test specification files of different versions of different energy meters into a unified version and store it in the database.

[0028] Specifically, when the test module performs OBIS data consistency verification, it logs into the DLMS meter with different identities, performs read and write operations on each attribute of the OBIS object according to the test specification file in the database, and records the results of the read and write operations. It then compares the permissions, read and write results, and quantities with the test specification file in the database, records any extra or missing OBIS objects in the meter, OBIS objects whose access permissions in the meter are inconsistent with those in the database, and OBIS objects whose data types of each attribute are inconsistent with those in the test specification file in the database, and saves the test results in the database.

[0029] Specifically, the testing module includes a dynamic test decision tree, which is trained based on historical test data stored in the database. The dynamic test decision tree model can dynamically adjust the test order. At the same time, fuzz testing technology is introduced to automatically generate outlier inputs for permission boundary values.

[0030] Specifically, the testing module also includes a dynamic anomaly diagnosis engine, which embeds a multi-level anomaly diagnosis tree. When an OBIS permission or data type anomaly is detected, the root cause of the anomaly is located through object inheritance relationship analysis and binary-level data parsing, and a root cause diagnosis report is output.

[0031] In practical applications, the dynamic anomaly diagnosis engine of the testing module is implemented by embedding a multi-level anomaly diagnosis tree within the testing module. When an OBIS permission or data type anomaly is detected, root cause localization is achieved through object inheritance relationship analysis and binary-level data parsing. After localization, a root cause diagnosis report is automatically output. In this embodiment, the specific adaptive testing strategy generation is based on training a decision tree model using historical test data, dynamically adjusting the test order, and introducing fuzzing technology to automatically generate anomaly value inputs for permission boundary values.

[0032] Specifically, after each test, the results display module generates a real-time data quality profile and a test report, which are then saved in the database. Operators can view the display data of the current test and historical test results at any time through the results display module via the interactive interface.

[0033] In practical applications, after the testing module tests all the energy meters under test, the results display module queries and exports the test results. It includes two parts: a real-time data quality profile and a generated test report. After the test is completed, the operator can view the real-time data quality profile and the results of this test through an interactive window, as well as the results of all tests that were not conducted and the reasons for failure. All results are stored in the database, and historical test results can also be queried.

[0034] Specifically, the real-time data quality profile includes a three-dimensional quality assessment matrix. It assesses the data completeness of the test object based on the percentage of missing OBIS in the test results, the permission compliance of the test object based on the risk level of unauthorized access, and the type consistency of the test object based on the similarity of binary structure.

[0035] In this specific application, real-time data quality profiling is achieved by adding a three-dimensional quality assessment matrix. The data completeness of the test object is represented by the percentage of missing OBIS entries in the test results; the permission compliance of the test object is represented by the unauthorized access risk level; and the type consistency of the test object is represented by the binary structure similarity. The percentage of missing OBIS entries is calculated as 1 - (number of missing entries / total number of entries in the design); the unauthorized access risk level is calculated by weighted entropy values ​​of each unauthorized access risk level; and the binary structure similarity index is obtained through SimHash comparison.

[0036] This embodiment also provides a method for verifying the consistency of OBIS data in a DLMS smart energy meter, applying the aforementioned DLMS smart energy meter OBIS data consistency verification system, such as... Figure 2 As shown, it includes the following steps: S1. Convert the OBIS object data in the design document of the DLMS smart energy meter to be tested into a test specification file in the form of an Excel spreadsheet or XML file according to the specification format. S2. Store the unified specification version of the test specification file in the database; S3. Based on the contents of the database, perform OBIS data consistency verification on the DLMS smart energy meter under test; S4. Save the test results in the database.

[0037] Specifically, when performing OBIS data consistency verification in S3, log in to the DLMS meter with different identities, and perform OBIS object permission tests, OBIS object quantity consistency tests, and OBIS data type and structure comparisons with design requirements in sequence according to the test specification documents in the database.

[0038] This embodiment demonstrates significant technical effects in practical applications: Comprehensive data verification capabilities and protocol adaptability: Traditional software tools support only a single protocol, while this system can automatically adapt across versions, performing a comprehensive verification of OBIS data from DLMS smart meters, covering three key dimensions: data volume, access permissions, and data type. Through precise design matching to requirements, it ensures the integrity, consistency, and compliance of OBIS data, filling a gap in existing tools. Automation and Efficiency: Traditional software tools use a fixed testing sequence, while this invention employs a risk-driven dynamic testing sequence and advanced automated algorithms. It adaptively generates testing strategies based on historical test records stored on the server, reducing repetitive and unnecessary steps and prioritizing testing of error-prone components of the meter. It can quickly identify anomalies in OBIS data, significantly improving verification efficiency. Compared to traditional manual verification methods, this system not only saves time but also avoids human error, significantly improving detection accuracy. Precise anomaly localization and reporting: Traditional software tools only record test failure results, while this invention outputs root cause diagnostic reports. Once issues such as inconsistent data volume, access permission conflicts, or incorrect data types are detected, a detailed report can be generated immediately, providing accurate evidence for subsequent repairs. This precise feedback mechanism greatly improves the efficiency of problem-solving. Highly compatible and adaptable to a wide range of scenarios: This system supports multiple DLMS protocol versions and meter models, enabling flexible adaptation to the needs of different application scenarios. Whether it's factory inspection of new equipment or maintenance and upgrades of existing equipment, reliable quality assurance is provided. Improving product quality and user experience: Through comprehensive OBIS data consistency verification, this system effectively reduces the failure rate of DLMS smart meters in actual operation, and improves the overall product quality and user satisfaction.

[0039] Example 2: A DLMS smart energy meter OBIS data consistency verification system, such as Figure 1 As shown, the system includes a protocol standard format import module, which converts the OBIS object data in the design document of the DLMS smart meter under test into a test specification file in the form of an Excel spreadsheet or XML file according to the specification format; a data storage module, which stores the test specification file in a unified specification version in the database; a testing module, which performs OBIS data consistency verification on the DLMS smart meter under test based on the contents of the database; and a results display module, which saves the test results and allows users to query and export the test results.

[0040] Specifically, after the protocol standard format import module converts the pre-designed OBIS object data in the design document according to the specification, it performs a data self-check, verifies the format of all data, and automatically repairs any abnormal formats. Only after all data format verifications pass will the converted test specification file be output to the data storage module.

[0041] The specific application process is as follows: The protocol standard format import module first converts the pre-designed OBIS objects of the DLMS energy meter under test into test specification files in the form of Excel spreadsheets or XML files according to the specification format. The test specification files describe all permissions of the DLMS smart energy meter and all OBIS objects. The unified format makes it easy for software to extract the corresponding data from Excel and XML. The unified format has the following aspects: the fixed Excel and XML spreadsheets must have a Cover (for recording the author's name and specification file version), a Change Log (for recording the modification history), a Class Version (for recording the interface class version), a DLMS cfg (DLMS configuration, recording user permissions, variable definitions and custom data structures), and other OBIS sheets with different functions. Each sheet (a node in XML) has specific requirements, such as separating the six fields of the OBIS code with a specified delimiter (e.g., 1-0:1.8.0*255). Standardize the format of commonly used data types to avoid using different expressions for the same data type. For example, define "An ordered sequence of ASCII characters, tag=10, visible-string" as "visible_string".

[0042] In practical applications, the protocol standard format import module also performs self-correction checks on the test specification file, checking and correcting the Excel format. This includes two steps: format anomaly detection and automatic repair. Utilizing the Aspose.Cells.dll dynamic link library, after loading the Excel spreadsheet, it checks the OBIS attribute names, attribute data type descriptions, and access permission descriptions of different interface class instances. For those that are unqualified, it returns the corresponding error reason and displays it in the operation window. After passing the format check, it modifies the pre-stored data on the server. OBIS verification can only proceed after the format check is passed.

[0043] Specifically, the data storage module is compatible with multiple versions of the DLMS protocol. When storing data, this module converts the test protocol file output by the standard protocol import module into the same DLMS protocol version.

[0044] In practical applications, after the test specification file completes the self-correction check, the data storage module saves it to the database. The data storage module includes protocol middleware to ensure compatibility with different DLMS versions and resolve differences between DLMS versions. Since the DLMS energy meter under test may use different versions of the DLMS protocol, the data storage module will convert the test specification files of different versions of different energy meters into a unified version and store it in the database.

[0045] In the specific application of this embodiment, after the test specification file is stored, the test module performs comprehensive testing based on the contents of the database. Specific test content includes OBIS object permission testing, OBIS object quantity consistency with design testing, and comparison of OBIS data type and structure with design requirements. After the test is completed, the operator can see the real-time data quality profile and the test results on the window, including all non-communication test results and the reasons for failed tests. All results are stored in the database, and historical test results can also be queried. The test module includes a dynamic anomaly diagnosis engine and an adaptive testing strategy. During the test, the OBIS verification system logs into the DLMS meter with different identities, then performs read and write operations on various OBIS attributes according to the design goals in the data, and records the results of the read and write operations. Then, it checks the permissions, read and write results, and quantities against the database, recording any extra or missing OBIS in the meter, inconsistencies between the meter's access permissions and the database, and inconsistencies between the data types of various OBIS attributes and the database, and records these information in the SQL Server database. Specific read and write permission judgments are as follows: Figure 4 As shown, when the testing system fails to obtain permissions for the corresponding OBIS attribute method in Download, there are two possibilities: first, the permissions on the meter under test do not inherently possess the OBIS permissions required in the test specification file; second, all identities in the external specification lack Download permissions, meaning they cannot read data. Similarly, the inability to obtain actual read / write permissions for the meter under test also presents two possibilities: first, the attribute is write-only or neither read nor write, as the system attempts to write data instead of reading it from the meter; second, the execution method's permissions do not attempt to acquire the actual permissions for the meter under test. By testing different meters and obtaining test results, test cases are continuously accumulated in the database, ultimately forming an adaptive testing strategy. The OBIS verification system trains a decision tree model based on historical test data and dynamically adjusts the test order.

[0046] Specifically, the testing module includes a dynamic test decision tree, which is trained based on historical test data stored in the database. The dynamic test decision tree model can dynamically adjust the test order. At the same time, fuzz testing technology is introduced to automatically generate outlier inputs for permission boundary values.

[0047] Specifically, the testing module also includes a dynamic anomaly diagnosis engine, which embeds a multi-level anomaly diagnosis tree. When an OBIS permission or data type anomaly is detected, the root cause of the anomaly is located through object inheritance relationship analysis and binary-level data parsing, and a root cause diagnosis report is output.

[0048] In practical applications, the dynamic anomaly diagnosis engine of the testing module is implemented by embedding a multi-level anomaly diagnosis tree within the testing module. When an OBIS permission or data type anomaly is detected, root cause localization is achieved through object inheritance relationship analysis and binary-level data parsing. After localization, a root cause diagnosis report is automatically output. In this embodiment, the specific adaptive testing strategy generation is based on training a decision tree model using historical test data, dynamically adjusting the test order, and introducing fuzzing technology to automatically generate anomaly value inputs for permission boundary values.

[0049] Specifically, after each test, the results display module generates a real-time data quality profile and a test report, which are then saved in the database. Operators can view the display data of the current test and historical test results at any time through the results display module via the interactive interface.

[0050] In practical applications, after the testing module tests all the energy meters under test, the results display module queries and exports the test results. It includes two parts: a real-time data quality profile and a generated test report. After the test is completed, the operator can view the real-time data quality profile and the results of this test through an interactive window, as well as the results of all tests that were not conducted and the reasons for failure. All results are stored in the database, and historical test results can also be queried.

[0051] Specifically, the real-time data quality profile includes a three-dimensional quality assessment matrix. It assesses the data completeness of the test object based on the percentage of missing OBIS in the test results, the permission compliance of the test object based on the risk level of unauthorized access, and the type consistency of the test object based on the similarity of binary structure.

[0052] In this specific application, real-time data quality profiling is achieved by adding a three-dimensional quality assessment matrix. The data completeness of the test object is represented by the percentage of missing OBIS entries in the test results; the permission compliance of the test object is represented by the unauthorized access risk level; and the type consistency of the test object is represented by the binary structure similarity. The percentage of missing OBIS entries is calculated as 1 - (number of missing entries / total number of entries in the design); the unauthorized access risk level is calculated by weighted entropy values ​​of each unauthorized access risk level; and the binary structure similarity index is obtained through SimHash comparison.

[0053] This embodiment also provides a method for verifying the consistency of OBIS data in a DLMS smart energy meter, applying the aforementioned DLMS smart energy meter OBIS data consistency verification system, such as... Figure 2 As shown, it includes the following steps: S1. Convert the OBIS object data in the design document of the DLMS smart energy meter to be tested into a test specification file in the form of an Excel spreadsheet or XML file according to the specification format. S2. Store the unified specification version of the test specification file in the database; S3. Based on the contents of the database, perform OBIS data consistency verification on the DLMS smart energy meter under test; S4. Save the test results in the database.

[0054] Specifically, when performing OBIS data consistency verification in S3, log in to the DLMS meter with different identities, and perform OBIS object permission tests, OBIS object quantity consistency tests, and OBIS data type and structure comparisons with design requirements in sequence according to the test specification documents in the database.

[0055] This embodiment demonstrates significant technical effects in practical applications: Comprehensive data verification capabilities and protocol adaptability: Traditional software tools support only a single protocol, while this system can automatically adapt across versions, performing a comprehensive verification of OBIS data from DLMS smart meters, covering three key dimensions: data volume, access permissions, and data type. Through precise design matching to requirements, it ensures the integrity, consistency, and compliance of OBIS data, filling a gap in existing tools. Automation and Efficiency: Traditional software tools use a fixed testing sequence, while this invention employs a risk-driven dynamic testing sequence and advanced automated algorithms. It adaptively generates testing strategies based on historical test records stored on the server, reducing repetitive and unnecessary steps and prioritizing testing of error-prone components of the meter. It can quickly identify anomalies in OBIS data, significantly improving verification efficiency. Compared to traditional manual verification methods, this system not only saves time but also avoids human error, significantly improving detection accuracy. Precise anomaly localization and reporting: Traditional software tools only record test failure results, while this invention outputs root cause diagnostic reports. Once issues such as inconsistent data volume, access permission conflicts, or incorrect data types are detected, a detailed report can be generated immediately, providing accurate evidence for subsequent repairs. This precise feedback mechanism greatly improves the efficiency of problem-solving. Highly compatible and adaptable to a wide range of scenarios: This system supports multiple DLMS protocol versions and meter models, enabling flexible adaptation to the needs of different application scenarios. Whether it's factory inspection of new equipment or maintenance and upgrades of existing equipment, reliable quality assurance is provided. Improving product quality and user experience: Through comprehensive OBIS data consistency verification, this system effectively reduces the failure rate of DLMS smart meters in actual operation, and improves the overall product quality and user satisfaction.

Claims

1. A DLMS smart energy meter OBIS data consistency verification system, characterized in that, The system includes a protocol standard format import module, which converts the OBIS object data in the design document of the DLMS smart meter under test into a test specification file in Excel spreadsheet or XML format; a data storage module, which stores the test specification file in a unified specification version in the database; a testing module, which performs OBIS data consistency verification on the DLMS smart meter under test based on the test specification file in the database and historical test data; and a results display module, which saves the test results to the database, allowing users to query and export the test results.

2. The DLMS smart energy meter OBIS data consistency verification system according to claim 1, characterized in that, After the protocol standard format import module converts the pre-designed OBIS object data in the design document according to the specification, it performs a data self-check, verifies the format of all data, and automatically repairs any abnormal formats. Only after all data format verifications pass will the converted test specification file be output to the data storage module.

3. The DLMS smart energy meter OBIS data consistency verification system according to claim 1, characterized in that, The data storage module is compatible with multiple versions of the DLMS protocol. When storing data, this module converts the test protocol file output by the standard protocol import module into the same DLMS protocol version.

4. The DLMS smart energy meter OBIS data consistency verification system according to claim 1, characterized in that, When the test module performs OBIS data consistency verification, it logs into the DLMS meter with different identities, performs read and write operations on various attributes of the OBIS object according to the test specification file in the database, and records the results of the read and write operations. The permissions, read / write results, and quantities are then compared with the test specification file in the database. The system records any extra or missing OBIS objects in the meter, OBIS objects whose access permissions in the meter are inconsistent with those in the database, and OBIS objects whose data types for each attribute are inconsistent with those in the test specification file in the database. The test results are then saved in the database.

5. The DLMS smart energy meter OBIS data consistency verification system according to claim 1 or 4, characterized in that, The testing module includes a dynamic test decision tree, which is trained based on historical test data stored in the database. The dynamic test decision tree model dynamically adjusts the test order and introduces fuzz testing technology to automatically generate outlier inputs for permission boundary values.

6. The DLMS smart energy meter OBIS data consistency verification system according to claim 5, characterized in that, The testing module also includes a dynamic anomaly diagnosis engine, which embeds a multi-level anomaly diagnosis tree. When an OBIS permission or data type anomaly is detected, the root cause of the anomaly is located through object inheritance relationship analysis and binary-level data parsing, and a root cause diagnosis report is output.

7. The DLMS smart energy meter OBIS data consistency verification system according to claim 1, characterized in that, After each test, the results display module generates a real-time data quality profile and a test report, which are then saved in the database. Operators can view the data displayed for the current test and historical test results at any time through the interactive interface of the results display module.

8. The DLMS smart energy meter OBIS data consistency verification system according to claim 7, characterized in that, The real-time data quality profile includes a three-dimensional quality assessment matrix. It reflects the data completeness of the test object based on the proportion of missing OBIS in the test results, the permission compliance of the test object based on the risk level of unauthorized access, and the type consistency of the test object based on the similarity of binary structure.

9. A method for verifying the consistency of OBIS data in a DLMS smart energy meter, applied to the DLMS smart energy meter OBIS data consistency verification system according to any one of claims 1-8, characterized in that, It includes the following steps: S1. Convert the OBIS object data in the design document of the DLMS smart energy meter to be tested into a test specification file in the form of an Excel spreadsheet or XML file according to the specification format. S2. Store the unified specification version of the test specification file in the database; S3. Based on the contents of the database, perform OBIS data consistency verification on the DLMS smart energy meter under test; S4. Save the test results in the database.

10. The DLMS smart energy meter OBIS data consistency verification method according to claim 9, characterized in that, When performing OBIS data consistency verification in S3, log in to the DLMS meter with different identities, and perform OBIS object permission tests, OBIS object quantity consistency tests, and OBIS data type and structure comparisons with design requirements in sequence according to the test specification documents in the database.

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