Resource data integration system based on digital transformation enterprise

Abstract

The invention relates to the technical field of data management and data integration, and discloses a digital transformation enterprise-based resource data integration system, which comprises a data acquisition module, a data calculation module, a result evaluation module, a feedback optimization module and an execution module, the data acquisition module is responsible for collecting resource data and internal sub-architecture data from a digital transformation enterprise data source and monitoring system operation data and equipment online parameters at the same time, and the data calculation module is responsible for processing the collected resource data and providing a calculation formula to assist in analyzing problems. The result evaluation module is responsible for evaluating an output result of the data calculation module, judging whether an expected target is reached or not and providing a basis for the feedback optimization module, and the feedback optimization module discovers problems and provides optimization suggestions according to the output result of the result evaluation module and needs to confirm whether the system runs well or not; and the execution module is responsible for converting measures of the feedback optimization module into actual operation and promoting continuous improvement of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of data management and data integration, and in particular to a resource data integration system based on digital transformation enterprises. Background Art

[0002] During the digital transformation process, enterprise resource data integration systems face numerous challenges. First, different data sources may differ in how they define, interpret, and encode the same data, leading to inconsistent data semantics and increasing integration difficulties. For example, the same concept may have different names and definitions in different sub-architectures, making it prone to ambiguity when sharing and analyzing data across sub-architectures.

[0003] Secondly, system integration and compatibility are also major challenges facing enterprises. Different systems and devices may use different technical standards, protocols, and interfaces, leading to integration difficulties. This obstacle is particularly evident when integrating traditional systems with emerging technologies such as cloud computing and big data.

[0004] Furthermore, data silos are prevalent, with data from various sub-architectures within an enterprise isolated and difficult to share and integrate, limiting data flow and utilization efficiency. This not only hinders the unified management and analysis of enterprise-wide data but also affects the timeliness and accuracy of decision-making.

[0005] To sum up, enterprises need to overcome problems such as inconsistent data semantics, difficulties in system integration, and data silos during digital transformation, and achieve effective integration and efficient utilization of resource data by introducing advanced technologies and management strategies. Summary of the Invention

[0006] (1) Technical problems solved

[0007] In response to the shortcomings of the existing technology, the present invention provides a resource data integration system based on digital transformation enterprises, which has the advantages of semantic unification, efficient integration, and breaking data silos, solving the problems of inconsistent data semantics, difficult system integration, and data silos.

[0008] (2) Technical solution

[0009] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a resource data integration system based on digitally transformed enterprises, comprising a data acquisition module, a data calculation module, a result evaluation module, a feedback optimization module, and an execution module;

[0010] The data acquisition module is responsible for collecting resource data and internal sub-architecture data from the digital transformation enterprise data source, while monitoring system operation data and equipment online parameters to provide a basis for subsequent processing;

[0011] The data calculation module is responsible for processing the collected resource data and providing calculation formulas to assist in problem analysis;

[0012] The result evaluation module is responsible for evaluating the output results of the data calculation module, judging whether the expected goals are achieved, and providing a basis for the feedback optimization module;

[0013] The feedback optimization module finds problems and proposes optimization suggestions based on the output results of the result evaluation module, and also needs to confirm whether the system is running well;

[0014] The execution module is responsible for converting the measures of the feedback optimization module into actual operations to promote continuous improvement of the system.

[0015] Preferably, the data acquisition module is the input end of the entire system, and the data acquisition module includes three data acquisition units, namely: a semantic data acquisition unit, a system integration data acquisition unit and a data sharing data acquisition unit.

[0016] Preferably, the semantic data acquisition unit acquires semantic data through an internal image dataset and a text dataset of an enterprise, and the semantic data includes semantic elements and matching degrees between semantic elements.

[0017] Preferably, the system integration data acquisition unit integrates system integration data through an enterprise system application program interface, and the system integration data includes a system internal sub-architecture and a system internal sub-interface.

[0018] Preferably, the data sharing data acquisition unit obtains shared data by connecting to the enterprise's internal network through a network. The shared data includes the total time required for the enterprise to complete the task, the amount of tasks that the enterprise assumes to be completed, and the amount of tasks actually completed by the enterprise.

[0019] Preferably, the data calculation module includes a semantic consistency analysis unit, a system integration analysis unit and a data sharing analysis unit.

[0020] Preferably, the semantic consistency analysis unit calculates the semantic similarity Sim(W i ,W r ), which is calculated as follows:

[0021]

[0022] In the formula, Sim(W i ,W r ) represents semantic similarity, and Represents the data source W i and W r The xth pair of semantic elements in It represents the matching degree between the xth pair of semantic elements, and its value range is [0,1]. The closer the value is to 1, the more similar the semantics are. x represents the weight of the xth pair of semantic elements, and N represents the total number of semantic elements.

[0023] Preferably, the system integration analysis unit calculates the system compatibility score Lz based on the system integration data, and the calculation formula is:

[0024]

[0025] In the formula, Lz represents the system compatibility score, and Represents the system architecture W i and W r The internal sub-architecture of the yth system, Indicates the matching degree between the internal sub-architectures of the y-th system, b y represents the weight of the y-th system internal sub-architecture, U represents the total number of system internal sub-architectures, Indicates the similarity of sub-architectures within the system, and Respectively represent the system internal sub-interface W i and W r The zth system internal sub-interface, Indicates the matching degree between the sub-interfaces within the z-th system, c z represents the weight of the zth internal sub-interface of the system, K represents the total number of internal sub-interfaces of the system, Indicates the similarity of sub-interfaces within the system.

[0026] Preferably, the data sharing analysis unit calculates the cross-departmental collaboration efficiency Ql based on the shared data, and the calculation formula is:

[0027]

[0028] In the formula, Ql represents the efficiency of cross-departmental collaboration, T represents the total time required for the enterprise to complete the task, and S j S represents the amount of tasks that the enterprise assumes it plans to complete. s Indicates the actual amount of tasks completed by the enterprise. represents the enterprise task completion rate, and P represents the enterprise preset data sharing quality score.

[0029] Preferably, the result evaluation module is based on the semantic similarity Sim(W i ,W r), system compatibility score Lz and cross-departmental collaboration efficiency Ql evaluate the overall performance and integration effect of the system, judge whether the semantic consistency of data, compatibility of system architecture and interface, and efficiency of cross-departmental collaboration meet the expected goals, and provide a basis for feedback optimization module;

[0030] The feedback optimization module provides feedback by receiving the evaluation results of the result evaluation module. When the evaluation results show that the overall performance or integration effect of the system does not meet the expected goals, it indicates that the feedback optimization module needs to intervene, identify problems and make optimization suggestions, and at the same time confirm whether the system is running well.

[0031] Compared with the existing technology, the present invention provides a resource data integration system based on digital transformation enterprises, which has the following beneficial effects:

[0032] 1. The present invention calculates the semantic similarity Sim(W i ,W r ), the system can identify and solve the semantic inconsistency problem between data sources, thereby achieving data standardization and unified management. Semantic similarity can be used to quantify the semantic consistency between different data sources. When the semantic similarity Sim(W i ,W r ) is close to 1, indicating a semantic element and The semantics of the data sources are highly consistent, and the system can automatically confirm that the semantics between the data sources match well without further adjustment. i ,W r ) is significantly lower than 1, indicating that the semantic element and There are large differences in the semantics of data. The system can automatically identify and troubleshoot semantic inconsistencies and take measures such as unifying the data dictionary or optimizing the semantic matching algorithm to optimize them.

[0033] 2. The present invention calculates the system compatibility score Lz, which is used to evaluate the compatibility between different systems or modules for system integration. When the system compatibility score Lz is high (close to the full score), it indicates that the matching degree between the system architecture and the interface is high, the compatibility between the systems is good, and they can be directly integrated without additional adjustment. When the system compatibility score Lz is low (significantly lower than the full score), it indicates that there is a compatibility problem between the system architecture and the interface. At this time, the system will automatically optimize the system compatibility score Lz by adjusting the system architecture, optimizing the interface equipment, and introducing adapters or middleware. Therefore, by calculating the system compatibility score Lz, it is helpful to comprehensively evaluate the compatibility between different systems, provide a scientific basis for system integration, and reduce the risks and costs in the integration process.

[0034] 3. The present invention calculates the cross-departmental collaboration efficiency Ql, which is used to compare the collaboration efficiency between different departments or teams to determine the best practices. When the cross-departmental collaboration efficiency Ql is low, the system will automatically analyze the reasons for the low task completion rate. If the system detects that the task completion rate is low, it will be judged that the reason is unreasonable task allocation or insufficient resources. At this time, the feedback optimization module will reallocate tasks to ensure that the resources and capabilities of each department are fully utilized. When the data sharing quality score is low, it is judged that the data is inaccurate or not updated in time. It is recommended to strengthen data governance to ensure the accuracy and real-time nature of the data, and break the data silos through unified data standards and interface specifications. When the cross-departmental collaboration efficiency Ql is high, the result evaluation module is still required to regularly evaluate and feedback the optimization module's optimized collaboration process to ensure that the system can continue to improve, and use data analysis tools to monitor collaboration efficiency in real time and discover potential problems in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] See also Figure 1 , a resource data integration system based on digital transformation enterprises, including data acquisition module, data calculation module, result evaluation module, feedback optimization module and execution module;

[0038] The data acquisition module is responsible for collecting resource data and internal sub-architecture data from digitally transformed enterprise data sources, while monitoring system operation data and equipment online parameters to provide a basis for subsequent processing;

[0039] The data calculation module is responsible for processing the collected resource data and providing calculation formulas to assist in problem analysis;

[0040] The result evaluation module is responsible for evaluating the output results of the data calculation module, judging whether the expected goals are achieved, and providing a basis for the feedback optimization module;

[0041] The feedback optimization module identifies problems and proposes optimization suggestions based on the output of the result evaluation module, and also needs to confirm whether the system is running well;

[0042] The execution module is responsible for converting the measures of the feedback optimization module into actual operations to promote continuous improvement of the system.

[0043] The data acquisition module is the input end of the entire system. The data acquisition module includes three data acquisition units: semantic data acquisition unit, system integration data acquisition unit and data sharing data acquisition unit.

[0044] The semantic data acquisition unit obtains semantic data through the enterprise's internal image data set and text data set. The semantic data includes semantic elements and the matching degrees between semantic elements.

[0045] The system integration data acquisition unit integrates system integration data through the enterprise system application program interface. The system integration data includes the system internal sub-architecture and the system internal sub-interface.

[0046] The data sharing data collection unit obtains shared data through the network connection of the enterprise's internal network. The shared data includes the total time required for the enterprise to complete the task, the amount of tasks the enterprise assumes to be completed, and the amount of tasks actually completed by the enterprise.

[0047] The data computing module includes a semantic consistency analysis unit, a system integration analysis unit and a data sharing analysis unit.

[0048] The semantic consistency analysis unit calculates the semantic similarity Sim(W i ,W r ), which is calculated as follows:

[0049]

[0050] In the formula, Sim(W i ,W r ) represents semantic similarity, and Represents the data source W i and W r The xth pair of semantic elements in It represents the matching degree between the xth pair of semantic elements, and its value range is [0,1]. The closer the value is to 1, the more similar the semantics are. x represents the weight of the xth pair of semantic elements, and N represents the total number of semantic elements;

[0051] The advantage is: by calculating the semantic similarity Sim(W i ,W r ), the system can identify and solve the semantic inconsistency problem between data sources, thereby achieving data standardization and unified management. Semantic similarity can be used to quantify the semantic consistency between different data sources. When the semantic similarity Sim(W i ,W r) is close to 1, indicating a semantic element and The semantics of the data sources are highly consistent, and the system can automatically confirm that the semantics between the data sources match well without further adjustment. i ,W r ) is significantly lower than 1, indicating that the semantic element and There are large differences in the semantics of data. The system can automatically identify and troubleshoot semantic inconsistencies and take measures such as unifying the data dictionary or optimizing the semantic matching algorithm to optimize them.

[0052] The system integration analysis unit calculates the system compatibility score Lz based on the system integration data. The calculation formula is:

[0053]

[0054] In the formula, Lz represents the system compatibility score, and Represents the system architecture W i and W r The internal sub-architecture of the yth system, Indicates the matching degree between the internal sub-architectures of the y-th system, b y represents the weight of the y-th system internal sub-architecture, U represents the total number of system internal sub-architectures, Indicates the similarity of sub-architectures within the system, and Respectively represent the system internal sub-interface W i and W r The zth system internal sub-interface, Indicates the matching degree between the sub-interfaces within the z-th system, c z represents the weight of the zth internal sub-interface of the system, K represents the total number of internal sub-interfaces of the system, Indicates the similarity of sub-interfaces within the system.

[0055] The advantages are: by calculating the system compatibility score Lz, the system compatibility score Lz is used to evaluate the compatibility between different systems or modules for system integration. When the system compatibility score Lz is high (close to the full score), it means that the matching degree between the system architecture and the interface is high, the compatibility between the systems is good, and they can be directly integrated without additional adjustments. When the system compatibility score Lz is low (significantly lower than the full score), it means that there is a compatibility problem between the system architecture and the interface. At this time, the system will automatically optimize the system compatibility score Lz by adjusting the system architecture, optimizing the interface equipment, and introducing adapters or middleware. Therefore, by calculating the system compatibility score Lz, it is helpful to comprehensively evaluate the compatibility between different systems, provide a scientific basis for system integration, and reduce the risk and cost during the integration process.

[0056] The data sharing analysis unit calculates the cross-departmental collaboration efficiency Ql based on the shared data. The calculation formula is:

[0057]

[0058] In the formula, Ql represents the efficiency of cross-departmental collaboration, T represents the total time required for the enterprise to complete the task, and S j S represents the amount of tasks that the enterprise assumes it plans to complete. s Indicates the actual amount of tasks completed by the enterprise. represents the enterprise task completion rate, and P represents the enterprise preset data sharing quality score.

[0059] The advantages are: by calculating the cross-departmental collaboration efficiency Ql, the cross-departmental collaboration efficiency Ql is used to compare the collaboration efficiency between different departments or teams to determine the best practices. When the cross-departmental collaboration efficiency Ql is low, the system will automatically analyze the reasons for the low task completion rate. If the system detects that the task completion rate is low, it is judged that the task allocation is unreasonable or there are insufficient resources. At this time, the feedback optimization module will reallocate tasks to ensure that the resources and capabilities of each department are fully utilized. When the data sharing quality score is low, it is judged that the data is inaccurate or not updated in time. It is recommended to strengthen data governance to ensure the accuracy and real-time nature of the data, and break the data silos through unified data standards and interface specifications. When the cross-departmental collaboration efficiency Ql is high, the result evaluation module is still required to regularly evaluate and feedback the optimization module's optimized collaboration process to ensure that the system can continue to improve, and use data analysis tools to monitor collaboration efficiency in real time and discover potential problems in a timely manner.

[0060] The result evaluation module is based on the semantic similarity Sim(W i ,W r), system compatibility score Lz and cross-departmental collaboration efficiency Ql to evaluate the overall performance and integration effect of the system, determine whether the semantic consistency of data, the compatibility of system architecture and interfaces, and the efficiency of cross-departmental collaboration have reached the expected goals (these indicators comprehensively reflect the system's performance in semantic unification, efficient integration, and breaking down data silos), and provide a basis for the feedback optimization module;

[0061] The feedback optimization module provides feedback by receiving the evaluation results of the result evaluation module. When the evaluation results show that the overall performance or integration effect of the system does not meet the expected goals, it indicates that the feedback optimization module needs to intervene. While making optimization suggestions, the feedback optimization module also needs to confirm whether the system's operating status is good. This is achieved by monitoring the system operating data and equipment online parameters. When there are abnormalities in the system operation, it is necessary to prioritize resolving the operating problems to ensure that the system is stable before optimization.

[0062] The advantages are: the above resource data integration system based on digital transformation helps to achieve closed-loop management of data from collection to optimization and improvement through modular division of labor and collaborative work. Among them, the result evaluation module and feedback optimization module are key links that can assist the continuous improvement of the system to ensure that the system can be continuously optimized so that the system can ultimately achieve the expected digital transformation goals.

[0063] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A resource data integration system based on digital transformation enterprises, characterized by: It includes data acquisition module, data calculation module, result evaluation module, feedback optimization module and execution module; The data acquisition module is responsible for collecting resource data and internal sub-architecture data from the digital transformation enterprise data source, while monitoring system operation data and equipment online parameters to provide a basis for subsequent processing; The data calculation module is responsible for processing the collected resource data and providing calculation formulas to assist in problem analysis; The result evaluation module is responsible for evaluating the output results of the data calculation module, judging whether the expected goals are achieved, and providing a basis for the feedback optimization module; The feedback optimization module finds problems and proposes optimization suggestions based on the output results of the result evaluation module, and also needs to confirm whether the system is running well; The execution module is responsible for converting the measures of the feedback optimization module into actual operations to promote continuous improvement of the system.

2. The resource data integration system based on digital transformation enterprises according to claim 1 is characterized by: The data acquisition module is the input end of the entire system. The data acquisition module includes three data acquisition units: a semantic data acquisition unit, a system integration data acquisition unit, and a data sharing data acquisition unit.

3. The resource data integration system based on digital transformation enterprises according to claim 2 is characterized by: The semantic data acquisition unit acquires semantic data through an internal image dataset and a text dataset of an enterprise. The semantic data includes semantic elements and matching degrees between semantic elements.

4. The resource data integration system based on digital transformation enterprises according to claim 2 is characterized by: The system integration data acquisition unit integrates system integration data through the enterprise system application program interface, and the system integration data includes a system internal sub-architecture and a system internal sub-interface.

5. The resource data integration system based on digital transformation enterprises according to claim 3 is characterized by: The data sharing data acquisition unit obtains shared data by connecting to the enterprise's internal network through a network. The shared data includes the total time required for the enterprise to complete a task, the amount of tasks the enterprise assumes it plans to complete, and the amount of tasks the enterprise actually completes.

6. The resource data integration system based on digital transformation enterprises according to claim 1 is characterized by: The data calculation module includes a semantic consistency analysis unit, a system integration analysis unit and a data sharing analysis unit.

7. The resource data integration system based on digital transformation enterprises according to claim 6 is characterized by: The semantic consistency analysis unit calculates the semantic similarity Sim(W) according to the semantic data i ,W r ), which is calculated as follows: In the formula, Sim(W i ,W r ) represents semantic similarity, and Represents the data source W i and W r The xth pair of semantic elements in It represents the matching degree between the xth pair of semantic elements, and its value range is [0,1]. The closer the value is to 1, the more similar the semantics are. x represents the weight of the xth pair of semantic elements, and N represents the total number of semantic elements.

8. The resource data integration system based on digital transformation enterprises according to claim 6 is characterized by: The system integration analysis unit calculates the system compatibility score Lz based on the system integration data, and the calculation formula is: In the formula, Lz represents the system compatibility score, and Represents the system architecture W i and W r The internal sub-architecture of the yth system, Indicates the matching degree between the internal sub-architectures of the y-th system, b y represents the weight of the y-th system internal sub-architecture, U represents the total number of system internal sub-architectures, Indicates the similarity of sub-architectures within the system, and Respectively represent the system internal sub-interface W i and W r The zth system internal sub-interface, Indicates the matching degree between the sub-interfaces within the z-th system, c z represents the weight of the zth internal sub-interface of the system, K represents the total number of internal sub-interfaces of the system, Indicates the similarity of sub-interfaces within the system.

9. The resource data integration system based on digital transformation enterprises according to claim 6 is characterized by: The data sharing analysis unit calculates the cross-departmental collaboration efficiency Ql based on the shared data, and the calculation formula is: In the formula, Ql represents the efficiency of cross-departmental collaboration, T represents the total time required for the enterprise to complete the task, and S j S represents the amount of tasks that the enterprise assumes it plans to complete. s Indicates the actual amount of tasks completed by the enterprise. represents the enterprise task completion rate, and P represents the enterprise preset data sharing quality score.

10. The resource data integration system based on digital transformation enterprises according to claim 1 is characterized by: The result evaluation module is based on the semantic similarity Sim(W i ,W r ), system compatibility score Lz and cross-departmental collaboration efficiency Ql evaluate the overall performance and integration effect of the system, judge whether the semantic consistency of data, compatibility of system architecture and interface, and efficiency of cross-departmental collaboration meet the expected goals, and provide a basis for feedback optimization module; The feedback optimization module provides feedback by receiving the evaluation results of the result evaluation module. When the evaluation results show that the overall performance or integration effect of the system does not meet the expected goals, it indicates that the feedback optimization module needs to intervene, identify problems and make optimization suggestions, and at the same time confirm whether the system is running well.

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