Method, system and device for achievement transformation management for high-level enterprise management and medium

Abstract

The invention discloses an achievement conversion management method, system and device for high-level enterprise management and a medium, relates to the technical field of scientific and technological achievement information management, and completes association and coupling of achievement conversion information through event-driven decomposition and hierarchical clustering to form achievement conversion driving data. Mining an association rule between the achievement conversion driving data from the achievement conversion driving data to form an achievement conversion association rule for associating the research table, the project state table and the intellectual property table so as to realize association and coupling between achievement information tables; and constructing a high-enterprise affirmation detection model, performing affirmation detection on the high-enterprise affirmation evidence chain by using the high-enterprise affirmation detection model to determine whether the high-enterprise affirmation evidence chain is correct and complete, and feeding back event-driven decomposition of the result conversion information and perfecting the high-enterprise affirmation evidence chain by using an affirmation detection result.

Description

Technical Field

[0001] The present invention relates to the technical field of scientific and technological achievement information management, and in particular to an achievement conversion management method, system, equipment and medium for high-tech enterprise management. Background Art

[0002] With the rapid development of science and technology and the intensification of market competition, the identification and management of high-tech enterprises have become an important means to promote the innovation ability and core competitiveness of enterprises. The identification of high-tech enterprises (hereinafter referred to as "high-tech enterprises") not only involves the R&D investment, quantity and quality of intellectual property rights of enterprises, but also requires enterprises to have efficient management and application capabilities in the transformation of scientific and technological achievements. However, enterprises are currently facing many challenges in the process of achievement transformation management, especially in the management and coordination of R&D tables, project status tables and intellectual property tables.

[0003] In the existing high-tech enterprise management system, the R&D table, project status table and intellectual property table are important components of enterprise management and high-tech enterprise certification. The R&D table mainly records the enterprise's R&D investment, the work content of R&D personnel and the progress of R&D projects; the project status table focuses on the overall progress and phased results of the project; the intellectual property table records the company's patents, trademarks and other intellectual property rights; the R&D table and project status table are usually managed by the R&D department, while the intellectual property table is managed by the legal or intellectual property management department. This departmental division leads to data isolation, making it difficult to achieve information sharing and collaborative management.

[0004] Although the R&D table and project status table record R&D activities and project progress, they fail to clearly reflect how these activities are transformed into intellectual property results; although the intellectual property table records intellectual property information such as patents, it fails to trace its R&D background and project sources, resulting in the output of intellectual property lacking the support of R&D activities. In the process of high-tech enterprise certification, enterprises need to provide direct evidence of the connection between R&D activities and intellectual property results. However, due to the weak coupling relationship between the R&D table, project status table and intellectual property table, enterprises often find it difficult to provide a complete chain of evidence, affecting the pass rate of high-tech enterprise certification.

[0005] The existing results conversion management methods of high-tech enterprises have obvious deficiencies in the coupling relationship between R&D tables, project status tables and intellectual property tables, making it difficult to form a complete chain of evidence to support the identification of high-tech enterprises. Summary of the invention

[0006] Based on the problems raised by the above background technology, the purpose of the present invention is to provide an achievement conversion management method, system, equipment and medium for high-tech enterprise management, which realizes data sharing, logical association and dynamic management by integrating the relationship between the R&D table, project status table and intellectual property table, so as to improve the achievement conversion efficiency and the success rate of high-tech enterprise certification, and solves the problem that the achievement conversion management method of existing high-tech enterprises has obvious deficiencies in the coupling relationship between the R&D table, project status table and intellectual property table, and it is difficult to form a complete chain of evidence to support the certification of high-tech enterprises.

[0007] The present invention is achieved through the following technical solutions: The first aspect of the present invention provides a method for managing the achievement conversion of high-tech enterprises, comprising the following steps: Step S1, obtaining high-tech enterprise certification information, extracting achievement conversion information from the high-tech enterprise certification information, and generating an achievement information table; the achievement information table includes: a research and development table, a project status table, and an intellectual property table; Step S2, performing event-driven decomposition on the achievement conversion information to obtain event-driven data, and performing hierarchical clustering on the event-driven data to obtain achievement conversion driven data; Step S3, performing association rule mining on the achievement conversion driving data to generate achievement conversion association rules, and associating the R&D table, the project status table and the intellectual property table based on the achievement conversion association rules to generate a high-tech enterprise identification evidence chain; Step S4: construct a high-tech enterprise identification detection model, use the high-tech enterprise identification detection model to perform identification detection on the high-tech enterprise identification evidence chain, and obtain the high-tech enterprise identification result.

[0008] In the above technical scheme, firstly, the high-tech enterprise certification information is obtained from the high-tech enterprise management module in the enterprise management system. The high-tech enterprise certification information is collected by the enterprise management system, including R&D data, project status information, intellectual property information, equipment information, high-tech enterprise information and other data. Data related to the achievement conversion information is extracted from the high-tech enterprise certification information, which includes R&D data, project status data and intellectual property data, and an achievement information table is formed, which includes an R&D table, a project status table and an intellectual property table.

[0009] Since the R&D table, project status table and intellectual property table are under the charge of different departments, their filling specifications, filling formats and naming methods are not quite the same. Therefore, in this method, the three tables of R&D table, project status table and intellectual property table are not directly coupled. Instead, event-driven decomposition is performed through the extracted achievement conversion information. Based on each achievement conversion information and driven by achievement conversion events, data is associated and clustered through hierarchical clustering to form achievement conversion driven data. This achievement conversion driven data avoids data distortion caused by the filling formats of various departments, and prevents errors caused by data distortion when the three tables are subsequently associated.

[0010] Through event-driven decomposition and hierarchical clustering, the association and coupling of achievement conversion information is completed to form achievement conversion driving data. Then, the association rules between achievement conversion driving data are mined from the achievement conversion driving data to form achievement conversion association rules for associating R&D tables, project status tables, and intellectual property tables, so as to achieve the association and coupling between achievement information tables.

[0011] Construct a high-tech enterprise recognition and detection model, and use it to conduct recognition and detection on the high-tech enterprise recognition evidence chain to determine whether the high-tech enterprise recognition evidence chain is correct and complete. The results of the recognition and detection will be used to provide feedback on the event-driven decomposition of the results conversion information and to improve the high-tech enterprise recognition evidence chain.

[0012] In an optional embodiment, event-driven decomposition of the achievement conversion information includes the following steps: Step S21, extracting achievement information fields from the achievement conversion information, and integrating the extracted achievement information fields into an achievement information field set; Step S22, traversing the achievement information field set, performing dependency analysis on the achievement information field set, and constructing an achievement dependency graph based on the dependency analysis result; Step S23: performing event-driven analysis on the achievement dependency graph, and forming an event-driven relationship chain according to the result of the event-driven analysis.

[0013] In an optional embodiment, performing dependency analysis on the achievement information field set and constructing an achievement dependency graph based on the dependency analysis result includes the following steps: Step S221: extracting an achievement information field from the achievement information field set as an information node, and calculating the correlation between the information node and other achievement information fields in the achievement information field set; Step S222: establishing an initial dependency relationship between the information node and other achievement information fields in the achievement information field set based on the correlation; Step S223, repeating step S221 to step S222 until the traversal of the achievement information field set is completed, and an initial achievement field dependency graph is generated; Step S224: performing dependency structure analysis on the initial achievement field dependency graph, and performing dependency relationship calculation on the initial achievement field dependency graph according to the result of the dependency structure analysis to obtain a dependency relationship value; Step S225: Utilize the dependency values ​​to perform dependency optimization on the initial achievement field dependency graph to obtain an achievement dependency graph.

[0014] In an optional embodiment, the calculation process of the dependency calculation is as follows: ; In the above formula, For the The result information field is the same as the The dependency of each outcome information field; For the The result information field is the same as the The field similarity of the result information fields; For the The result information field is the same as the The dependency value of each achievement information field; For the The result information field is the same as the The number of dependent connection channels between the result information fields; For the The dependency structure value of the dependent connection channel; For the The number of result information fields on the dependent connection channel; For the A set of result information fields on a dependent connection channel; For the A collection of result information fields on a dependent connection channel.

[0015] In an optional embodiment, event-driven analysis is performed on the achievement dependency graph, including the following steps: Step S231, dividing the achievement dependency graph into nodes based on the scientific and technological achievement process to generate a plurality of achievement dependency subgraphs; Step S232, vectorizing and representing a plurality of achievement dependency subgraphs to generate a plurality of vectorized achievement dependency subgraphs; Step S233, extracting features from a plurality of vectorized result dependency subgraphs to obtain event-driven features; Step S234: utilizing the event-driven feature to perform event-driven association on a plurality of vectorized outcome dependency subgraphs to form an event-driven relationship chain.

[0016] In an optional embodiment, performing hierarchical clustering on the event-driven data includes the following steps: Step S24, calculating the node dependency of the event-driven relationship chain, and determining a cluster center from the event-driven relationship chain according to the node dependency; Step S25: using a k-nearest neighbor clustering algorithm to perform clustering calculation on the event-driven relationship chain with the cluster center as the starting point, and updating the event-driven relationship chain according to the clustering calculation result to obtain an updated event-driven relationship chain; Step S26: Perform a stop clustering condition analysis on the update event driven relationship chain. If the stop clustering condition is not met, repeat steps S24 to S26 by taking the update event driven relationship chain as the event driven relationship chain.

[0017] In an optional embodiment, performing association rule mining on the achievement conversion driving data includes the following steps: Step S31, obtaining a historical high-tech enterprise identification evidence chain, and using the historical high-tech enterprise identification evidence chain to establish a historical association rule; Step S32, calculating the frequent values ​​in the achievement conversion driving data, arranging the achievement conversion driving data from large to small according to the frequent values, and generating a frequent driving data set; Step S33, selecting the first N achievement conversion driving data from the frequent driving data set to form an association rule data set; Step S34: performing association rule matching on the association rule data set and the historical association rule, and determining the achievement conversion association rule according to the result of the association rule matching.

[0018] The second aspect of the present invention provides an achievement conversion management system for high-tech enterprise management, including: An information extraction module is used to obtain high-tech enterprise certification data, extract achievement conversion information from the high-tech enterprise certification data, and generate an achievement information table; the achievement information table includes: a research and development table, a project status table, and an intellectual property table; An event-driven module, used for performing event-driven decomposition on the achievement conversion information to obtain event-driven data, and performing hierarchical clustering on the event-driven data to obtain achievement conversion driven data; A data association module is used to perform association rule mining on the achievement conversion driving data to generate achievement conversion association rules, and to associate the R&D table, the project status table and the intellectual property table based on the achievement conversion association rules to generate a high-tech enterprise identification evidence chain; The identification and detection module is used to build a high-tech enterprise identification and detection model, and use the high-tech enterprise identification and detection model to perform identification and detection on the high-tech enterprise identification evidence chain to obtain the high-tech enterprise identification results.

[0019] A third aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a results conversion management method for high-tech enterprise management when executing the computer program.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a results conversion management method for high-tech enterprise management.

[0021] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. Through event-driven decomposition and hierarchical clustering, the association and coupling of achievement conversion information are completed from the data level; 2. Mining the association rules between the achievement conversion driving data from the achievement conversion driving data, forming the achievement conversion association rules for associating the R&D table, project status table and intellectual property table, and completing the association and coupling between the achievement information tables at the table level; 3. Use the high-tech enterprise recognition detection model to conduct recognition detection on the high-tech enterprise recognition evidence chain to determine whether the high-tech enterprise recognition evidence chain is correct and complete, and use the recognition detection results to provide dual feedback on the event-driven decomposition of achievement conversion information and the high-tech enterprise recognition evidence chain. Through the dual feedback mechanism, the event-driven decomposition process is improved and the missing evidence in the high-tech enterprise certification evidence chain is supplemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 A schematic diagram of a process flow of a method for converting achievements into management for high-tech enterprise management provided in Embodiment 1 of the present invention; Figure 2 A schematic diagram of the structure of the achievement conversion management system for high-tech enterprise management provided by Embodiment 2 of the present invention; Figure 3 A schematic diagram of the structure of an electronic device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0024] Figure 1 The flowchart of the achievement conversion management method for high-tech enterprise management provided in Example 1 of the present invention is as follows: Figure 1 As shown in the figure, the results conversion management method for high-tech enterprise management includes the following steps: Step S1, obtaining high-tech enterprise certification information, extracting achievement conversion information from the high-tech enterprise certification information, and generating an achievement information table; the achievement information table includes: a research and development table, a project status table, and an intellectual property table; Step S2, performing event-driven decomposition on the achievement conversion information to obtain event-driven data, and performing hierarchical clustering on the event-driven data to obtain achievement conversion driven data; Step S3, performing association rule mining on the achievement conversion driving data to generate achievement conversion association rules, and associating the R&D table, the project status table and the intellectual property table based on the achievement conversion association rules to generate a high-tech enterprise identification evidence chain; Step S4: construct a high-tech enterprise identification detection model, use the high-tech enterprise identification detection model to perform identification detection on the high-tech enterprise identification evidence chain, and obtain the high-tech enterprise identification result.

[0025] It should be noted that the high-tech enterprise certification process requires the integration of R&D table, project status table and intellectual property table data to form a complete high-tech enterprise certification evidence chain. However, in enterprises, the R&D table, project status table and intellectual property table are respectively responsible for and filled out by different departments. The integration process is often accompanied by difficulties in data alignment and data loss, resulting in an incomplete evidence chain for high-tech enterprise certification and difficulty in forming a complete evidence chain to support the certification of high-tech enterprises.

[0026] Therefore, it is necessary to analyze and couple the relevant data of the R&D table, project status table and intellectual property table in the high-tech enterprise management to form a complete evidence chain for high-tech enterprise identification. Therefore, in this method, the high-tech enterprise identification data is first obtained from the high-tech enterprise management module in the enterprise management system. The high-tech enterprise identification data is collected by the enterprise management system, including R&D data, project status data, intellectual property data, equipment data, high-tech enterprise information and other data. The data related to the achievement conversion information is extracted from the high-tech enterprise identification data, which includes R&D data, project status data and intellectual property data, and the achievement information table is formed, which includes the R&D table, project status table and intellectual property table.

[0027] Since the R&D table, project status table and intellectual property table are under the charge of different departments, their filling specifications, filling formats and naming methods are not quite the same. Therefore, in this method, the three tables of R&D table, project status table and intellectual property table are not directly coupled. Instead, event-driven decomposition is performed through the extracted achievement conversion information. Based on each achievement conversion information and driven by achievement conversion events, data is associated and clustered through hierarchical clustering to form achievement conversion driven data. This achievement conversion driven data avoids data distortion caused by the filling formats of various departments, and prevents errors caused by data distortion when the three tables are subsequently associated.

[0028] Through event-driven decomposition and hierarchical clustering, the association and coupling of achievement conversion information is completed to form achievement conversion driving data. Then, the association rules between achievement conversion driving data are mined from the achievement conversion driving data to form achievement conversion association rules for associating R&D tables, project status tables, and intellectual property tables, so as to achieve the association and coupling between achievement information tables.

[0029] Construct a high-tech enterprise recognition and detection model, and use it to conduct recognition and detection on the high-tech enterprise recognition evidence chain to determine whether the high-tech enterprise recognition evidence chain is correct and complete. The results of the recognition and detection will be used to provide feedback on the event-driven decomposition of the results conversion information and to improve the high-tech enterprise recognition evidence chain.

[0030] In an optional embodiment, event-driven decomposition of the achievement conversion information includes the following steps: Step S21, extracting achievement information fields from the achievement conversion information, and integrating the extracted achievement information fields into an achievement information field set; Step S22, traversing the achievement information field set, performing dependency analysis on the achievement information field set, and constructing an achievement dependency graph based on the dependency analysis result; Step S23: performing event-driven analysis on the achievement dependency graph, and forming an event-driven relationship chain according to the result of the event-driven analysis.

[0031] In this embodiment, the coupling between data depends largely on the dependency relationship between data. To achieve the coupling of achievement conversion information, it is necessary to perform dependency analysis between its fields. Therefore, the achievement information field is first extracted from the achievement conversion information, and then the dependency relationship between the achievement information fields is preliminarily analyzed to form a preliminary achievement dependency graph. For example: the data generated in the research and development stage include the name, goal, start and end time, budget, person in charge, etc. of the project, and the data generated in the intellectual property stage include the name, application date, application status, and authorization date of the intellectual property. Among them, the goal, start and end time, budget, and person in charge of the project depend on the name of the project, and the application date, application status, and authorization date of the intellectual property depend on the name of the intellectual property. There is also a certain dependency relationship between the project name and the intellectual property name. Therefore, in this embodiment, there is a dependency relationship between the above data, and based on the dependency relationship, the achievement dependency graph is constructed with the data fields as nodes and the dependency relationship between the data fields as edges.

[0032] Since there is a sequence and driving relationship between project development, project status and intellectual property rights, the event-driven nature of the data needs to be considered in this embodiment, and based on the event-driven nature, an event-driven analysis is performed on the outcome dependency graph to form an event-driven relationship chain. The event-driven relationship chain not only includes the surface relationship between the data, but also includes the process logical relationship between the data in the process of outcome conversion, further overcoming the current defect of weak coupling between data.

[0033] In an optional embodiment, performing dependency analysis on the achievement information field set and constructing an achievement dependency graph based on the dependency analysis result includes the following steps: Step S221: extracting an achievement information field from the achievement information field set as an information node, and calculating the correlation between the information node and other achievement information fields in the achievement information field set; Step S222: establishing an initial dependency relationship between the information node and other achievement information fields in the achievement information field set based on the correlation; Step S223, repeating step S221 to step S222 until the traversal of the achievement information field set is completed, and an initial achievement field dependency graph is generated; Step S224: performing dependency structure analysis on the initial achievement field dependency graph, and performing dependency relationship calculation on the initial achievement field dependency graph according to the result of the dependency structure analysis to obtain a dependency relationship value; Step S225: Utilize the dependency values ​​to perform dependency optimization on the initial achievement field dependency graph to obtain an achievement dependency graph.

[0034] In this embodiment, the dependency between the achievement information fields is preliminarily analyzed through the correlation between the fields. For example, the project's goal, start and end time, budget, and person in charge are dependent on the project's name. Then, the correlation between the project's goal, start and end time, budget, person in charge, and the project's name is increased by 1, indicating that there is a correlation between them. Then, all information nodes whose correlation is not 0 are connected to form an initial achievement field dependency graph.

[0035] It should be emphasized that the initial achievement field dependency graph is directional. Therefore, there are multiple structures in the initial achievement field dependency graph. For example, there is a bidirectional dependency between the project name and the intellectual property name. At this time, the two data are in a close dependency structure; if there is a data loop between the data, then the data are in a closed-loop dependency structure; if data A points to data B, data B points to data C, and data A points to data C, then an open-loop dependency structure is formed between data A, data B, and data C; if data A points to data B, and data B points to data C, then a transitive dependency structure is formed between data A, data B, and data C.

[0036] Therefore, a dependency structure analysis is performed on the initial achievement field dependency graph, and dependency calculation is performed on the initial achievement field dependency graph based on the results of the dependency structure analysis, so as to mine the structural dependencies between data and obtain dependency values. The dependency values ​​are then used to optimize the initial achievement field dependency graph, and the dependencies between data are further mined, thus avoiding the defect of poor data coupling caused by a single dependency.

[0037] In an optional embodiment, the calculation process of the dependency calculation is as follows: ; In the above formula, For the The result information field is the same as the The dependency of each outcome information field; For the The result information field is the same as the The field similarity of the result information fields; For the The result information field is the same as the The dependency value of each achievement information field; For the The result information field is the same as the The number of dependent connection channels between the result information fields; For the The dependency structure value of the dependent connection channel; For the The number of result information fields on the dependent connection channel; For the A set of result information fields on a dependent connection channel; For the A collection of result information fields on a dependent connection channel.

[0038] In this embodiment, the dependency value is mainly composed of two aspects, one is the dependency between the achievement information fields, and the other is the field similarity between the achievement information fields. The higher the dependency and field similarity, the higher the dependency value, and the two achievement information fields have a strong dependency. In the subsequent process of improving the chain of evidence, when one achievement information field is needed, the other achievement information field is likely to be needed as well. By calculating the dependency and similarity between the fields, the dependency between the achievement information fields is optimized.

[0039] The number of dependent connection channels refers to the The result information field is the same as the The number of connection relationships between the result information fields, assuming The result information field is data a, The result information field is data e, and the route from data a to data e includes three routes: data a to data b to data c to data e, data a to data e, and data a to data c to data e. The number of dependent connection channels is 3. The dependency structure value of a dependent connection channel refers to the dependency structure formed by the dependent connection channel. In this embodiment, the dependency structure value of a tight dependency structure and a closed-loop dependency structure is set to 10; the dependency structure value of an open-loop dependency structure and a transitive dependency structure is set to 5. The reason for this setting is that the closed-loop structure has a higher degree of dependency than the open-loop structure. Another aspect of the degree of dependency depends on the number of result information fields on the dependent connection channel. The more the number of result information fields on the dependent connection channel, the greater the degree of dependency, and the smaller the degree of dependency.

[0040] The field similarity is used to measure the similarity between the result information fields on the dependent connection channel between two result information fields, and its value is between 0 and 1.

[0041] In an optional embodiment, event-driven analysis is performed on the achievement dependency graph, including the following steps: Step S231, dividing the achievement dependency graph into nodes based on the scientific and technological achievement process to generate a plurality of achievement dependency subgraphs; Step S232, vectorizing and representing a plurality of achievement dependency subgraphs to generate a plurality of vectorized achievement dependency subgraphs; Step S233, extracting features from a plurality of vectorized result dependency subgraphs to obtain event-driven features; Step S234: utilizing the event-driven feature to perform event-driven association on a plurality of vectorized outcome dependency subgraphs to form an event-driven relationship chain.

[0042] In this embodiment, the nodes of the achievement dependency graph are divided based on the scientific and technological achievement process. The nodes of the achievement dependency graph are divided into a project development achievement dependency subgraph, a project status achievement dependency subgraph and an intellectual property achievement dependency subgraph according to the three processes of project development, project status and intellectual property rights. The subgraphs are vectorized to represent the basis for feature learning while retaining the original information.

[0043] Feature extraction is performed on the vectorized sub-graph to extract features related to the conversion of results, such as the completion time of R&D tasks, changes in project status, application time of intellectual property rights, etc., to form event-driven features. Other vector data are connected with the event-driven features as the core to establish related event-driven relationship chains.

[0044] In an optional embodiment, performing hierarchical clustering on the event-driven data includes the following steps: Step S24, calculating the node dependency of the event-driven relationship chain, and determining a cluster center from the event-driven relationship chain according to the node dependency; Step S25: using a k-nearest neighbor clustering algorithm to perform clustering calculation on the event-driven relationship chain with the cluster center as the starting point, and updating the event-driven relationship chain according to the clustering calculation result to obtain an updated event-driven relationship chain; Step S26: Perform a stop clustering condition analysis on the update event driven relationship chain. If the stop clustering condition is not met, repeat steps S24 to S26 by taking the update event driven relationship chain as the event driven relationship chain.

[0045] In this embodiment, the selection of cluster centers is not random, but is determined based on the node dependencies in the event-driven relationship chain. Through this dynamic center hierarchical clustering method, it is possible to more effectively cluster the feature vectors and thus cluster the relevant data in the high-tech enterprise identification evidence chain.

[0046] In an optional embodiment, performing association rule mining on the achievement conversion driving data includes the following steps: Step S31, obtaining a historical high-tech enterprise identification evidence chain, and using the historical high-tech enterprise identification evidence chain to establish a historical association rule; Step S32, calculating the frequent values ​​in the achievement conversion driving data, arranging the achievement conversion driving data from large to small according to the frequent values, and generating a frequent driving data set; Step S33, selecting the first N achievement conversion driving data from the frequent driving data set to form an association rule data set; Step S34: performing association rule matching on the association rule data set and the historical association rule, and determining the achievement conversion association rule according to the result of the association rule matching.

[0047] In one embodiment, the R&D table, the project status table and the intellectual property table are associated based on the achievement conversion association rules, including: extracting fields related to achievement conversion driving data from the R&D table, the project status table and the intellectual property table, and associating and combining the fields according to the achievement conversion association rules to form an evidence chain for high-tech enterprise identification.

[0048] In one embodiment, the high-tech enterprise identification detection model includes: a time node authentication module, an event node authentication module, and an integrity authentication module. The output channels of the time node authentication module and the event node authentication module are connected to the input channel of the integrity authentication module.

[0049] Using the above-mentioned high-tech enterprise identification detection model to identify and detect the high-tech enterprise identification evidence chain is to perform time node authentication detection, event node authentication detection and integrity authentication detection on the high-tech enterprise identification evidence chain. Specifically, the time node authentication detection includes the detection of R&D time, achievement conversion time and intellectual property related time. The detection content includes whether the data is complete and whether there are errors in the sequence of time nodes, so as to ensure the accuracy and integrity of the evidence chain at the time node level. The event node authentication detection includes the detection of event data such as the appraisal year, achievement name, project source, appraisal level, appraisal status, and professional field in the project. The detection content includes whether the event data is complete and whether there is a correlation between event data. The correlation can be determined by the dependency value and other related dependent data between the event data. By performing event node authentication detection on the event data, the accuracy and integrity of the evidence chain at the event node level can be ensured. After completing the time node authentication and event node authentication, the evidence chain integrity authentication is performed to check the integrity of the entire evidence chain and the correlation between the time node data and the event node data, so as to ensure the integrity and correctness of the entire evidence chain. At this time, if both completeness and correctness are available, the output is a high-quality evidence chain; if completeness or correctness is lacking, the output content is the data missing in the evidence chain.

[0050] The high-tech enterprise identification results obtained after the test can be used to improve the evidence chain of high-tech enterprise identification and optimize the event-driven decomposition of the results conversion information.

[0051] Specifically, improving the evidence chain for high-tech enterprise identification includes supplementing the missing evidence identified by the high-tech enterprise identification model, or correcting the erroneous evidence.

[0052] Specifically, the event-driven decomposition and optimization of achievement conversion information includes analyzing the missing information or erroneous information identified by the high-tech enterprise recognition model, and optimizing the dependencies between achievement information fields based on the analysis results.

[0053] In summary, the results of the recognition and testing are used for dual feedback on the event-driven decomposition of the achievement conversion information and the evidence chain of the high-tech enterprise recognition. Through the dual feedback mechanism, the event-driven decomposition process is improved and the missing evidence in the high-tech enterprise certification evidence chain is supplemented.

[0054] Figure 2 The schematic diagram of the structure of the achievement conversion management system for high-tech enterprise management provided by Embodiment 2 of the present invention is as follows: Figure 2 As shown in the figure, the results conversion management system used for high-tech enterprise management includes: An information extraction module is used to obtain high-tech enterprise certification data, extract achievement conversion information from the high-tech enterprise certification data, and generate an achievement information table; the achievement information table includes: a research and development table, a project status table, and an intellectual property table; An event-driven module, used for performing event-driven decomposition on the achievement conversion information to obtain event-driven data, and performing hierarchical clustering on the event-driven data to obtain achievement conversion driven data; A data association module is used to perform association rule mining on the achievement conversion driving data to generate achievement conversion association rules, and to associate the R&D table, the project status table and the intellectual property table based on the achievement conversion association rules to generate a high-tech enterprise identification evidence chain; The identification and detection module is used to build a high-tech enterprise identification and detection model, and use the high-tech enterprise identification and detection model to perform identification and detection on the high-tech enterprise identification evidence chain to obtain the high-tech enterprise identification results.

[0055] Figure 3 A schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention is shown in FIG. Figure 3 As shown, the electronic device includes a processor 21, a memory 22, an input device 23 and an output device 24; the number of processors 21 in the computer device can be one or more. Figure 3 A processor 21 is taken as an example; the processor 21, the memory 22, the input device 23 and the output device 24 in the electronic device can be connected by a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0056] The memory 22 is a computer-readable storage medium that can be used to store software programs, computer executable programs, and modules. The processor 21 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 22, that is, the achievement conversion management method for high-tech enterprise management of Example 1 is implemented.

[0057] The memory 22 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 22 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 22 may further include a memory remotely arranged relative to the processor 21, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0058] The input device 23 can be used to receive the ID and password input by the user, etc. The output device 24 is used to output the network configuration page.

[0059] Embodiment 4 of the present invention further provides a computer-readable storage medium, wherein the computer-executable instructions, when executed by a computer processor, are used to implement the achievement conversion management method for high-tech enterprise management provided in Embodiment 1.

[0060] An embodiment of the present invention provides a storage medium containing computer executable instructions, and its computer executable instructions are not limited to the method operations provided in Example 1, and can also execute related operations in the results conversion management method for high-tech enterprise management provided in any embodiment of the present invention.

[0061] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The achievement conversion management method used in high-tech enterprise management is characterized by: The steps include: Step S1, obtaining high-tech enterprise certification information, extracting achievement conversion information from the high-tech enterprise certification information, and generating an achievement information table; the achievement information table includes: a research and development table, a project status table, and an intellectual property table; Step S2, performing event-driven decomposition on the achievement conversion information to obtain event-driven data, and performing hierarchical clustering on the event-driven data to obtain achievement conversion driven data; Step S3, performing association rule mining on the achievement conversion driving data to generate achievement conversion association rules, and associating the R&D table, the project status table and the intellectual property table based on the achievement conversion association rules to generate a high-tech enterprise identification evidence chain; Step S4: construct a high-tech enterprise identification detection model, use the high-tech enterprise identification detection model to perform identification detection on the high-tech enterprise identification evidence chain, and obtain the high-tech enterprise identification result.

2. The achievement conversion management method for high-tech enterprise management according to claim 1 is characterized in that: The event-driven decomposition of the achievement conversion information includes the following steps: Step S21, extracting achievement information fields from the achievement conversion information, and integrating the extracted achievement information fields into an achievement information field set; Step S22, traversing the achievement information field set, performing dependency analysis on the achievement information field set, and constructing an achievement dependency graph based on the dependency analysis result; Step S23: performing event-driven analysis on the achievement dependency graph, and forming an event-driven relationship chain according to the result of the event-driven analysis.

3. The achievement conversion management method for high-tech enterprise management according to claim 2 is characterized in that: Performing dependency analysis on the achievement information field set and constructing an achievement dependency graph based on the dependency analysis result includes the following steps: Step S221, extracting an achievement information field from the achievement information field set as an information node, and calculating the correlation between the information node and other achievement information fields in the achievement information field set; Step S222: establishing an initial dependency relationship between the information node and other achievement information fields in the achievement information field set based on the correlation; Step S223, repeating step S221 to step S222 until the traversal of the achievement information field set is completed, and an initial achievement field dependency graph is generated; Step S224: performing dependency structure analysis on the initial achievement field dependency graph, and performing dependency relationship calculation on the initial achievement field dependency graph according to the result of the dependency structure analysis to obtain a dependency relationship value; Step S225: Utilize the dependency values ​​to perform dependency optimization on the initial achievement field dependency graph to obtain an achievement dependency graph.

4. The achievement conversion management method for high-tech enterprise management according to claim 3 is characterized in that: The calculation process of the dependency calculation is as follows: ； In the above formula, For the The result information field is the same as the The dependency of each outcome information field; For the The result information field is the same as the The field similarity of the result information fields; For the The result information field is the same as the The dependency value of each achievement information field; For the The result information field is the same as the The number of dependent connection channels between the result information fields; For the A dependency structure value that depends on the connection channel; For the The number of result information fields on the dependent connection channel; For the A set of result information fields on a dependent connection channel; For the A collection of result information fields on a dependent connection channel.

5. The achievement conversion management method for high-tech enterprise management according to claim 2 is characterized in that: Performing event-driven analysis on the achievement dependency graph includes the following steps: Step S231, dividing the achievement dependency graph into nodes based on the scientific and technological achievement process to generate a plurality of achievement dependency subgraphs; Step S232, vectorizing and representing a plurality of achievement dependency subgraphs to generate a plurality of vectorized achievement dependency subgraphs; Step S233, extracting features from a plurality of vectorized result dependency subgraphs to obtain event-driven features; Step S234: utilizing the event-driven feature to perform event-driven association on a plurality of vectorized outcome dependency subgraphs to form an event-driven relationship chain.

6. The achievement conversion management method for high-tech enterprise management according to claim 2 is characterized in that: Performing hierarchical clustering on the event-driven data includes the following steps: Step S24, calculating the node dependency of the event-driven relationship chain, and determining a cluster center from the event-driven relationship chain according to the node dependency; Step S25, using a k-nearest neighbor clustering algorithm to perform clustering calculation on the event-driven relationship chain with the cluster center as the starting point, and updating the event-driven relationship chain according to the clustering calculation result to obtain an updated event-driven relationship chain; Step S26: Perform a stop clustering condition analysis on the update event driven relationship chain. If the stop clustering condition is not met, repeat steps S24 to S26 by taking the update event driven relationship chain as the event driven relationship chain.

7. The achievement conversion management method for high-tech enterprise management according to claim 1 is characterized in that: Performing association rule mining on the achievement conversion driving data includes the following steps: Step S31, obtaining a historical high-tech enterprise identification evidence chain, and using the historical high-tech enterprise identification evidence chain to establish a historical association rule; Step S32, calculating the frequent values ​​in the achievement conversion driving data, arranging the achievement conversion driving data from large to small according to the frequent values, and generating a frequent driving data set; Step S33, selecting the first N achievement conversion driving data from the frequent driving data set to form an association rule data set; Step S34: performing association rule matching on the association rule data set and the historical association rule, and determining the achievement conversion association rule according to the result of the association rule matching.

8. The achievement conversion management system used for high-tech enterprise management is characterized by: include: An information extraction module is used to obtain high-tech enterprise certification data, extract achievement conversion information from the high-tech enterprise certification data, and generate an achievement information table; The achievement information tables include: R&D table, project status table and intellectual property table; An event-driven module, used for performing event-driven decomposition on the achievement conversion information to obtain event-driven data, and performing hierarchical clustering on the event-driven data to obtain achievement conversion driven data; A data association module is used to perform association rule mining on the achievement conversion driving data to generate achievement conversion association rules, and to associate the R&D table, the project status table and the intellectual property table based on the achievement conversion association rules to generate a high-tech enterprise identification evidence chain; The identification and detection module is used to build a high-tech enterprise identification and detection model, and use the high-tech enterprise identification and detection model to perform identification and detection on the high-tech enterprise identification evidence chain to obtain the high-tech enterprise identification results.

9. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the achievement conversion management method for high-tech enterprise management as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the achievement conversion management method for high-tech enterprise management as described in any one of claims 1 to 7.

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