Configuration data consistency evaluation device and method based on multi-dimensional verification

Abstract

The invention relates to the technical field of configuration data verification, in particular to a configuration data consistency evaluation device and method based on multi-dimensional verification, and the device comprises a data collection layer, a core verification layer and a result output layer which are connected in sequence; the data acquisition layer is used for acquiring configuration data; the core verification layer comprises a structure verification module, a content verification module and a relation verification module which are connected in parallel; the structure verification module is used for carrying out structure verification on the structure compliance, the field integrity, the data type and the data format of the configuration data; the content verification module is used for carrying out content verification on key fields, enumeration values and business logic conformity of the configuration data; the relationship verification module is used for performing relationship verification on the dependency relationship, the reference integrity and the time sequence consistency of the configuration data; and the result output layer is used for receiving the verification result of the core verification layer and generating a comprehensive consistency score through a weighted aggregation algorithm. The integrity and accuracy of configuration data verification are improved, and potential safety hazards are reduced.

Description

Technical Field

[0001] This invention relates to the field of configuration data verification technology, specifically to a configuration data consistency evaluation device and method based on multi-dimensional verification. Background Technology

[0002] In the fields of safety control such as rail transit and chemical production, the Industrial Internet of Things (IIoT) data configuration management platform (CMP) is the core hub connecting physical devices and upper-level applications. Its functions run through the entire chain, including device access, data acquisition, configuration management, data processing, storage, analysis and application interaction. The correctness of the platform's configuration data is directly related to the stable operation and production safety of the industrial production system.

[0003] Currently, industrial production processes still rely heavily on manual methods for CMP configuration data entry and verification. Configuration errors introduced by manual operation (such as incorrect parameter filling, field omissions, logical conflicts, etc.) can easily lead to equipment malfunctions, instruction execution deviations, and other problems, resulting in serious consequences such as production stoppages and safety accidents. Therefore, conducting secondary verification of the correctness of configuration data is a key step in reducing potential safety hazards in production.

[0004] However, existing methods for verifying configuration data have significant limitations, primarily relying on manual post-hoc comparison. These inherent flaws are manifested in the following ways: Limited verification dimensions: Verification can only target a few explicit indicators of the configuration data, failing to conduct systematic verification across multiple dimensions. Incomplete coverage: Manual comparison struggles to cover the full scope of massive amounts of configuration data, especially for cross-device or cross-system configuration data, easily leading to omissions and undetected potential configuration errors. The consistency and reliability of verification results are difficult to guarantee, and post-hoc verification is inherently delayed, posing a high security risk. Summary of the Invention

[0005] The purpose of this invention is to provide a configuration data consistency evaluation device and method based on multi-dimensional verification. By using a hierarchical architecture, three-dimensional parallel automated verification, and quantitative scoring, the completeness and accuracy of configuration data verification are improved, security risks are reduced, and the stable operation of industrial Internet of Things systems is ensured.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: This invention provides a configuration data consistency evaluation device based on multi-dimensional verification, comprising a data acquisition layer, a core verification layer, and a result output layer connected in sequence. The data acquisition layer is used to collect configuration data. The core verification layer includes a structure verification module, a content verification module, and a relationship verification module connected in parallel. The structure verification module is used to perform structure verification on the structural compliance, field integrity, data type, and format of the configuration data to obtain a structure verification result. The content verification module is used to perform content verification on the key fields, enumeration values, and business logic compliance of the configuration data to obtain a content verification result. The relationship verification module is used to perform relationship verification on the dependencies, reference integrity, and temporal consistency of the configuration data to obtain a relationship verification result. The result output layer is used to receive the structure verification result, content verification result, and relationship verification result from the core verification layer, and to perform weighted aggregation on the structure verification result, content verification result, and relationship verification result to generate a comprehensive consistency score.

[0007] Optionally, the structure verification results include structure compliance results, field integrity results, and data type and format results. The structure verification module performs structure compliance verification on the configuration data by loading a predefined schema template to check if the structure of the configuration data conforms to the specifications, thus obtaining a structure compliance result. It also performs structure verification on the field integrity of the configuration data by checking if the required fields exist, thus obtaining a field integrity result. Finally, it performs structure verification on the data type and format of the configuration data by verifying whether the value of each field conforms to the defined data type and format requirements, thus obtaining a data type and format result.

[0008] Optionally, the configuration data can be in JSON or XML format; data types can be integers or strings; and format requirements include IP address and date / time formats.

[0009] Optionally, the content verification results include key field results, enumeration value results, and business logic compliance results. The content verification module performs content verification on the key fields of the configuration data, specifically: verifying whether the key configuration items in the source and destination data match to obtain key field results; key configuration items include device address and threshold parameters. It also performs content verification on the enumeration values ​​of the configuration data, specifically: verifying whether the values ​​of enumeration fields are within a predefined legal set, and whether numeric fields are within a set reasonable range, to obtain enumeration value results. Finally, it performs content verification on the business logic compliance of the configuration data, specifically: verifying whether the device type and communication protocol configurations are compatible, to obtain business logic compliance results.

[0010] Optionally, the relationship verification results include dependency results, referential integrity results, and temporal consistency results. The relationship verification module verifies the dependencies of configuration data by: verifying whether the dependencies declared in the configuration are satisfied based on the configuration item dependency graph to obtain dependency results; verifying the referential integrity of configuration data by: verifying whether there are valid corresponding items in the target system for the reference identifiers in the configuration to obtain referential integrity results; and verifying the temporal consistency of configuration data by: verifying whether the effective time order of configuration changes with timing requirements conforms to the business logic to obtain temporal consistency results.

[0011] The configuration data includes first configuration data from the source end and second configuration data from the destination end; the data acquisition layer is used to acquire the first configuration data from the source end through the CMP data probe and to acquire the second configuration data from the destination end through the lightweight data probe.

[0012] Optionally, the results output layer is also used to output a verification report and alarm information for the overall consistency score.

[0013] This invention also provides a configuration data consistency assessment method based on multi-dimensional verification, applied to the aforementioned configuration data consistency assessment device based on multi-dimensional verification, comprising: collecting first configuration data from a source end and second configuration data from a destination end; the configuration data includes first configuration data and second configuration data; performing structural verification on the configuration data's structural compliance, field integrity, data type, and format to obtain structural verification results; performing content verification on the configuration data's key fields, enumeration values, and business logic compliance to obtain content verification results; performing relationship verification on the configuration data's dependencies, reference integrity, and temporal consistency to obtain relationship verification results; and aggregating the structural verification results, content verification results, and relationship verification results using a weighted aggregation algorithm to generate a comprehensive consistency score for the configuration data.

[0014] Optionally, the first configuration data at the source end is collected via a CMP data probe, and the second configuration data at the destination end is collected via a lightweight data probe.

[0015] Optionally, after generating the overall consistency score of the configuration data, the system may also include: outputting a verification report and alarm information for the overall consistency score.

[0016] The present invention also provides a storage medium storing a computer program, which is invoked and executed by a computer to implement the method described above.

[0017] The beneficial effects of this invention are: 1. The core verification layer adopts a parallel connection of structure verification module, content verification module and relationship verification module, which improves verification efficiency compared with serial verification. The three types of verification can be executed synchronously without waiting for the previous step to complete. It is especially suitable for batch verification of massive configuration data, such as device configuration of multiple sources and multiple destinations, reducing verification time and providing high real-time performance.

[0018] 2. In structural verification, data type and format requirements are distinguished. In content verification, both precise value matching and business logic compliance are taken into account. In relation verification, dependency graphs, reference integrity and temporal consistency are covered. Comprehensive verification is achieved from format compliance to logical closed loop, reducing the risk of failure caused by inconsistent configurations and ensuring that configuration differences under different business scenarios can be accurately identified, making the verification results more targeted.

[0019] 3. The data acquisition layer supports dual-probe acquisition of CMP data probes and lightweight data probes, which can flexibly adapt to different deployment environments of the source and destination. It can collect first configuration data in complex scenarios and second configuration data in lightweight scenarios. No modification to the existing system is required, which has strong compatibility and low deployment cost.

[0020] 4. The results output layer generates a comprehensive consistency score through a weighted aggregation algorithm, transforming abstract verification results into quantitative indicators. This allows users to intuitively judge the consistency level of configuration data, providing a more accurate assessment of consistency weaknesses compared to traditional solutions that only output pass or fail results. The verification report provides detailed results for each module, facilitating rapid troubleshooting by operations and maintenance personnel. Alarm information provides real-time alerts for high-risk inconsistencies, preventing configuration data transmission interruptions and anomalies caused by configuration inconsistencies, thus reducing operational risks and troubleshooting costs. Attached Figure Description

[0021] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. The drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0022] Figure 1 This is an architecture diagram of a configuration data consistency evaluation device based on multi-dimensional verification according to the present invention; Figure 2 This is a flowchart of a configuration data consistency evaluation method based on multi-dimensional verification in this invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only one preferred embodiment of this invention and are only used to explain this invention. They do not limit the scope of protection of this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0024] As one implementation method, such as Figure 1 As shown, a configuration data consistency evaluation device based on multi-dimensional verification includes a data source layer, a data acquisition layer, a core verification layer, and a result output layer connected in sequence. The core of this invention lies in constructing a three-dimensional verification engine composed of a structure verification module, a content verification module, and a relationship verification module. These three modules receive configuration data from the source and destination ends through a unified data model and execute their respective dimension verification tasks in parallel, generating a comprehensive consistency score through a weighted aggregation algorithm.

[0025] The data source layer includes two systems that provide raw data: the CMP configuration management platform (i.e., the source end) and the distribution execution system (i.e., the destination end). The data acquisition layer is used to collect configuration data. The configuration data is in JSON and XML formats and includes first configuration data and second configuration data. The data acquisition layer is used to collect the first configuration data from the source end through the CMP data probe and the second configuration data from the destination end through the lightweight data probe.

[0026] This layer serves as a bridge connecting the data source and the verification layer. Data collection is accomplished through dedicated probes: CMP Data Probe: Specifically interfaces with the CMP configuration management platform, collecting its latest configuration data on a scheduled or triggered basis and synchronizing the data to the core verification layer. Distribution Data Probe: Specifically interfaces with the distribution execution system, collecting the distribution data it generates. This distribution data includes configuration content and distribution process data, and is also synchronized to the core verification layer. The purpose of these two probes is to ensure that the verification layer receives complete data from both the configuration and execution ends, preparing for subsequent verification.

[0027] The core verification layer, also known as the verification engine, employs a microservice architecture to verify the consistency of configuration data. Data is collected by lightweight probes deployed at both the source and destination ends and then uniformly sent to the multi-dimensional verification engine. This engine is the core of the system, responsible for coordinating and executing verification tasks across three dimensions. Upon receiving the data, the verification engine launches verification threads in parallel for the structure, content, and relationship dimensions to achieve maximum verification efficiency.

[0028] The core verification layer comprises a parallel-connected structure verification module, content verification module, and relationship verification module. The structure verification module performs structural verification on the configuration data, checking for structural compliance, field integrity, and data type and format. The verification results include structural compliance results, field integrity results, and data type and format results. Specifically, the structure verification module verifies the configuration data's structure compliance by loading a predefined schema template to determine if the data's structure conforms to the specifications, thus obtaining a structural compliance result. It also verifies the field integrity by checking the existence of required fields, resulting in a field integrity result. Finally, it verifies the data type and format by checking whether the value of each field conforms to the defined data type and format requirements, resulting in a data type and format result. Data types include integers and strings, and format requirements include IP addresses and date / time formats.

[0029] Specifically, this dimension aims to ensure the correctness of the data skeleton, primarily implementing the following verifications: Schema compliance verification: For configuration data in JSON / XML format, a predefined schema template is loaded, and its structure is strictly verified to conform to the specifications. Field integrity check: Verify that all required fields in the configuration data exist without omission. Data type and format matching: Verify that the value of each field conforms to the defined data type (e.g., integer, string) and format requirements (e.g., IP address, date and time format).

[0030] The content validation module performs content validation on the key fields, enumeration values, and business logic compliance of configuration data. The validation results include key field results, enumeration value results, and business logic compliance results. Specifically, the module performs content validation on key fields by matching the exact values ​​of key configuration items in the source and destination data to obtain key field results. Key configuration items include device addresses and threshold parameters. It also performs content validation on enumeration values ​​to confirm that the values ​​of enumeration fields are within a predefined set of valid values ​​and that numeric fields are within a set reasonable range, thus obtaining enumeration value results. Finally, it performs content validation on business logic compliance by verifying the configuration compliance of device types and communication protocols, resulting in a business logic compliance result.

[0031] Specifically, this dimension focuses on the correctness of data values, primarily implementing: Key field value comparison: Precise value matching of key configuration items (such as device address, threshold parameters) in the source and destination data. For example, if the resource threshold configured in CMP is 100, is the threshold actually 100 in the distributed data also valid (to avoid inconsistencies between configuration and execution)? Enumerated value and range check: Confirm that the values ​​of all enumerated fields are within the predefined legal set; numeric fields (such as upper temperature limit) are within the set reasonable range. For example, if CMP defines priority as only 1-5, verify whether the priority in the distributed data is within this range (to avoid illegal values). Business rule compliance verification: Integrate a business rule engine to perform complex business logic verification. For example, verify that "when the device type is A, its communication protocol must be configured as B". Or, for example, if CMP is configured to distribute only on weekdays, verify whether the execution time of the distributed data is within the weekday range to avoid violating business logic.

[0032] The relationship verification module is used to verify the dependencies, referential integrity, and timing consistency of configuration data. The verification results include dependency results, referential integrity results, and timing consistency results. Specifically, the module verifies the dependencies of configuration data by checking whether the dependencies declared in the configuration are satisfied based on the configuration item dependency graph, thus obtaining the dependency result. It also verifies the referential integrity of configuration data by ensuring that all reference identifiers in the configuration have valid corresponding items in the target system, thus obtaining the referential integrity result. Finally, it verifies the timing consistency of configuration data by checking whether the effective time sequence of configuration changes with timing requirements conforms to the business logic, thus obtaining the timing consistency result.

[0033] Specifically, this dimension is the highest level of validation, aiming to ensure logical consistency between data and uncover deep-seated errors across configuration items. It primarily implements: Data dependency verification: Building a dependency graph between configuration items and verifying that all declared dependencies are satisfied. For example, verifying that "configuration A, which configuration B depends on, must exist and have been successfully deployed" to avoid missing dependencies. Referential integrity check: Ensuring that all reference identifiers (such as foreign keys) in the configuration have valid counterparts in the target system, with no dangling references. For example, if CMP configures a reference template ID=123, verifying that the template ID referenced in the deployed data exists and is valid to avoid invalid references. Timing consistency judgment: For configuration changes with timing requirements, verifying that their effective time order conforms to business logic to prevent errors such as reverse chronological order. For example, if CMP configures version 2.0 to be deployed after version 1.0, verifying that the version times of the deployed data conform to the chronological order to avoid timing confusion.

[0034] Furthermore, the structure verification module loads predefined schema templates to automate the verification of field integrity, data type, and format. The content verification module integrates a lightweight rule engine to perform key field comparisons, enumeration value checks, and business logic verification. The relationship verification module constructs a configuration item dependency graph based on a graph database to achieve dynamic verification of reference integrity and temporal consistency. Through these technical means, the problems of traditional verification methods being single-dimensional and unable to detect cross-configuration logic errors are solved, realizing a leap from "surface consistency" to "deep semantic consistency" of configuration data.

[0035] The output layer receives the structure, content, and relationship verification results from the core verification layer, and performs weighted aggregation on these results to generate a comprehensive consistency score. The verification results from the three dimensions—structure, content, and relationship—are summarized into a unified result set, which includes pass / fail items and corresponding exception details.

[0036] The output layer also outputs verification reports and alarm information for the overall consistency score. Real-time alarms are triggered for aggregated non-compliance items, allowing relevant personnel to promptly identify problems via email, SMS, and pop-ups on the operations and maintenance platform. Complete verification results (including verification dimensions, anomaly details, timestamps, etc.) are generated into structured reports in PDF or Excel format for auditing, review, or archiving.

[0037] By implementing the data consistency device of this invention, the following technical effects can be achieved: Improved verification efficiency: With the help of a parallel multi-threaded architecture, multiple verification dimensions (structural verification, content verification, and relationship verification) can be executed simultaneously, reducing waiting time during the verification process; Timely detection of configuration problems: In conjunction with the CMP platform, full-dimensional verification can be completed quickly after configuration is issued, realizing the synchronous advancement of configuration and verification; Improved verification accuracy: Multi-dimensional verification covers different levels of configuration, which helps to detect various typical configuration errors; Enhanced system operational reliability: It helps to reduce system anomalies caused by configuration errors and improves the system's stable operation capability.

[0038] As one implementation method, such as Figure 2 As shown, the present invention also provides a configuration data consistency evaluation method based on multi-dimensional verification, applied to the above-mentioned configuration data consistency evaluation device based on multi-dimensional verification, comprising: S1. Collect first configuration data from the source end and second configuration data from the destination end; the configuration data includes first configuration data and second configuration data. Collect the first configuration data from the source end using a CMP data probe, and collect the second configuration data from the destination end using a lightweight data probe.

[0039] S2. Perform structural validation on the schema compliance, field integrity, data type, and format of the configuration data to obtain the structural validation results. S21. Perform content validation on the key fields, enumeration values, and business logic compliance of the configuration data to obtain the content validation results. S22. Perform relationship validation on the dependencies, reference integrity, and timing consistency of the configuration data to obtain the relationship validation results. S3. The structure verification results, content verification results, and relationship verification results are aggregated using a weighted aggregation algorithm to generate a comprehensive consistency score for the configuration data.

[0040] The present invention also provides a terminal device, including a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method described above.

[0041] The present invention also provides a storage medium storing a computer program, which is invoked and executed by a computer to implement the method described above.

[0042] Compared with the prior art, the present invention has the following beneficial effects based on the above embodiments: 1. The core verification layer adopts a parallel connection of structure verification module, content verification module and relationship verification module, which improves verification efficiency compared with serial verification. The three types of verification can be executed synchronously without waiting for the previous step to complete. It is especially suitable for batch verification of massive configuration data, such as device configuration of multiple sources and multiple destinations, reducing verification time and providing high real-time performance.

[0043] 2. In structural verification, data type and format requirements are distinguished. In content verification, both precise value matching and business logic compliance are taken into account. In relation verification, dependency graphs, reference integrity and temporal consistency are covered. Comprehensive verification is achieved from format compliance to logical closed loop, reducing the risk of failure caused by inconsistent configurations and ensuring that configuration differences under different business scenarios can be accurately identified, making the verification results more targeted.

[0044] 3. The data acquisition layer supports dual-probe acquisition of CMP data probes and lightweight data probes, which can flexibly adapt to different deployment environments of the source and destination. It can collect first configuration data in complex scenarios and second configuration data in lightweight scenarios. No modification to the existing system is required, which has strong compatibility and low deployment cost.

[0045] 4. The results output layer generates a comprehensive consistency score through a weighted aggregation algorithm, transforming abstract verification results into quantitative indicators. This allows users to intuitively judge the consistency level of configuration data, providing a more accurate assessment of consistency weaknesses compared to traditional solutions that only output pass or fail results. The verification report provides detailed results for each module, facilitating rapid troubleshooting by operations and maintenance personnel. Alarm information provides real-time alerts for high-risk inconsistencies, preventing configuration data transmission interruptions and anomalies caused by configuration inconsistencies, thus reducing operational risks and troubleshooting costs.

[0046] The specific embodiments described above are preferred embodiments of the configuration data consistency evaluation device and method based on multi-dimensional verification of this application, and are not intended to limit the specific implementation scope of this application. The scope of this application includes but is not limited to the specific embodiments described above. All equivalent changes made in accordance with the shape and structure of this application are within the protection scope of this application.

Claims

1. A configuration data consistency evaluation device based on multi-dimensional verification, characterized in that, It includes a data acquisition layer, a core verification layer, and a result output layer connected in sequence; the data acquisition layer is used to collect configuration data; The core verification layer includes a parallel-connected structure verification module, content verification module, and relationship verification module; The structure verification module is used to verify the structural compliance, field integrity, data type and format of configuration data to obtain the structure verification result; the content verification module is used to verify the content compliance of key fields, enumeration values ​​and business logic of configuration data to obtain the content verification result; the relationship verification module is used to verify the dependency relationships, reference integrity and temporal consistency of configuration data to obtain the relationship verification result. The output layer receives the structure verification results, content verification results, and relation verification results from the core verification layer, and performs weighted aggregation on these results to generate a comprehensive consistency score.

2. The configuration data consistency evaluation device based on multi-dimensional verification according to claim 1, characterized in that, The structure verification results include structure compliance results, field integrity results, and data type and format results. The structure verification module performs structure compliance verification on the configuration data by loading a predefined schema template to check if the configuration data structure conforms to the specifications, thus obtaining a structure compliance result. It also performs structure verification on the field integrity of the configuration data by checking if the required fields exist, thus obtaining a field integrity result. Finally, it performs structure verification on the data type and format of the configuration data by verifying whether the value of each field conforms to the defined data type and format requirements, thus obtaining a data type and format result.

3. The configuration data consistency evaluation device based on multi-dimensional verification according to claim 2, characterized in that, Configuration data can be in JSON or XML formats; data types can be integers or strings; and format requirements include IP addresses and date / time formats.

4. The configuration data consistency evaluation device based on multi-dimensional verification according to claim 1, characterized in that, Content validation results include key field results, enumeration value results, and business logic compliance results. The content validation module performs content validation on key fields of the configuration data, specifically verifying whether key configuration items in the source and destination data match to obtain key field results. Key configuration items include device address and threshold parameters. It also performs content validation on enumeration values ​​of the configuration data, specifically verifying whether the values ​​of enumeration fields are within a predefined legal set and whether numeric fields are within a set reasonable range to obtain enumeration value results. Finally, it performs content validation on the business logic compliance of the configuration data, specifically verifying whether the device type and communication protocol configurations are compatible to obtain business logic compliance results.

5. The configuration data consistency evaluation device based on multi-dimensional verification according to claim 1, characterized in that, The relationship verification results include dependency results, referential integrity results, and temporal consistency results. The relationship verification module verifies the dependencies of configuration data by: verifying whether the dependencies declared in the configuration are satisfied based on the configuration item dependency graph to obtain dependency results; verifying the referential integrity of configuration data by: verifying whether the reference identifiers in the configuration have valid corresponding items in the target system to obtain referential integrity results; and verifying the temporal consistency of configuration data by: verifying whether the effective time order of configuration changes with timing requirements conforms to the business logic to obtain temporal consistency results.

6. The configuration data consistency evaluation device based on multi-dimensional verification according to claim 1, characterized in that, The configuration data includes first configuration data from the source and second configuration data from the destination. The data acquisition layer is used to acquire the first configuration data from the source end via the CMP data probe, and to acquire the second configuration data from the destination end via the lightweight data probe.

7. The configuration data consistency evaluation device based on multi-dimensional verification according to claim 1, characterized in that, The output layer is also used to output a verification report and alarm information for the overall consistency score.

8. A configuration data consistency evaluation method based on multi-dimensional verification, applied to the configuration data consistency evaluation device based on multi-dimensional verification as described in any one of claims 1 to 6, characterized in that, include: Collect the first configuration data from the source end and the second configuration data from the destination end; The configuration data includes first configuration data and second configuration data; Perform structural validation on the configuration data to ensure its compliance, field integrity, data type, and format, and obtain the structural validation results; Content validation is performed on the key fields and enumeration values ​​of the configuration data to ensure their compliance with business logic, in order to obtain the content validation results; Perform relationship verification on the dependencies, referential integrity, and timing consistency of the configuration data to obtain the relationship verification results; The structure verification results, content verification results, and relationship verification results are aggregated using a weighted aggregation algorithm to generate a comprehensive consistency score for the configuration data.

9. The configuration data consistency evaluation method based on multi-dimensional verification according to claim 8, characterized in that, The first configuration data at the source end is collected using a CMP data probe, and the second configuration data at the destination end is collected using a lightweight data probe.

10. The configuration data consistency evaluation method based on multi-dimensional verification according to claim 8, characterized in that, After generating the overall consistency score for the configuration data, the system also includes: outputting a verification report and alarm information for the overall consistency score.

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