Intellectual property achievement transformation supervision system and method based on data analysis

Abstract

The invention relates to the technical field of achievement transformation supervision, in particular to an intellectual property achievement transformation supervision system and method based on data analysis, the system comprises a scheme association fusion module, the scheme association fusion module combines extracted information and generated content scheme fragments, analyzes the association between different content scheme fragments, and stores the association between the content scheme fragments; and fusing the content scheme fragments with relevance to obtain new content scheme fragments, and generating different rehearsal schemes of the project. According to the invention, the scheme integration and review management before the generation of the technical scheme are carried out, the influence of the segmentation position of the research record data on the subsequent to-be-reviewed technical scheme is considered, and the content of the generated intellectual property achievement conversion scheme and the historical review record of each expert in the expert database are combined. The screening of the review experts corresponding to the generated intellectual property conversion scheme is realized, the pertinence and accuracy of the review result are ensured, and the intellectual property achievement conversion risk of an enterprise is reduced.

Description

Technical Field

[0001] This invention relates to the field of technology for the supervision of the transformation of research results, specifically to a data analysis-based system and method for supervising the transformation of intellectual property research results. Background Technology

[0002] The commercialization of intellectual property rights refers to the practical activities by which research institutions and individuals transform their scientific and technological achievements into new technologies, new materials, new products, or new services that can be used in social production and daily life. This is a transition from implicit to explicit productivity, and a systematic project in which technological input constitutes the initial cost, while technological output constitutes the subsequent benefits.

[0003] Existing data-driven intellectual property (IP) commercialization supervision systems often only monitor the business process of IP commercialization (application status and transaction status), without addressing the integration and review management of solutions before technical solutions are generated. Furthermore, they fail to consider the impact of the segmentation of research record data on subsequent technical solutions awaiting review, and do not combine the content of the generated IP commercialization solution with the historical review records of each expert in the expert database to select the appropriate review experts. This leads to significant biases in the review results, impacting the IP commercialization rate of enterprises. Therefore, existing technologies have significant shortcomings. Summary of the Invention

[0004] The purpose of this invention is to provide a data analysis-based intellectual property achievement transformation supervision system and method to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a data analysis-based method for supervising the transformation of intellectual property achievements, the method comprising the following steps: S1. With user authorization, collect chat logs between various users within the project team, extract information from each collected chat log, segment the chat log content based on the extracted information, and generate different content scheme fragments. S2. Combining the extracted information and the generated content plan fragments, analyze the correlation between different content plan fragments, merge the content plan fragments with correlation to obtain new content plan fragments, and generate different pre-rehearsal plans for the project. S3. Analyze the set of prerequisites corresponding to each pre-simulation scheme of the project, combine the experimental results of the project and the retrieval results of the corresponding pre-simulation schemes with the union of the sets of prerequisites, analyze the transformation demand assessment value of each pre-simulation scheme, and generate a recommended scheme for the transformation of technological achievements. S4. Feed the generated technology transfer recommendation scheme back to the technology transfer evaluation terminal, and combine the technical characteristics of the technology transfer recommendation scheme with the domain characteristics of each person in the expert database of the technology transfer evaluation terminal to generate the best recommended review expert bound to the technology transfer recommendation scheme.

[0006] Furthermore, when extracting information from each of the collected chat records, the interval between two adjacent chat records is determined. Chat records with an interval longer than the first preset interval are divided into two different chat segments, while those with an interval shorter than the preset interval are divided into the same chat segment. Keywords within each chat segment are extracted, and the set of keywords extracted from the same chat segment is taken as an information extraction result. When generating different content scheme fragments, the following steps are included: Step 1: Obtain the information extraction results corresponding to different chat segments; Step 2: Record the information extraction results corresponding to two adjacent chat segments as the first information extraction result and the second information extraction result, respectively. Obtain the intersection between the first information extraction result and the second information extraction result. Record the ratio of the total number of times the elements in the first information extraction result appear in the corresponding chat segment to the total number of times the elements in the first information extraction result appear in the corresponding chat segment as the first information percentage. The ratio of the total number of times the elements in the intersection of the results appear in the corresponding chat segments of the second information extraction results to the total number of times the elements in the corresponding chat segments of the second information extraction results appear is recorded as the second information percentage. Step 3: Calculate the connection influence value between the first information extraction result and the second information extraction result, denoted as X. , Where B1 represents the proportion of the first information; B2 represents the proportion of the second information; r represents the influence factor and r is a preset constant in the database; Step 4: Based on X, determine the content segment corresponding to the chat segment to which the first information extraction result and the second information extraction result belong. When X is greater than or equal to the first preset value, it is determined that the chat segments to which the first information extraction result and the second information extraction result belong to the same content scheme segment. The chat segments to which the first information extraction result and the second information extraction result belong are integrated according to the order of chat time to obtain a new integrated chat segment. The information extraction result of the new integrated chat segment is the union of the first information extraction result and the second information extraction result. When X is less than the first preset value, it is determined that the chat segments to which the first information extraction result and the second information extraction result belong do not belong to the same content scheme segment, and the chat segments to which the first information extraction result and the second information extraction result belong cannot be integrated. The system retrieves individual chat segments from the collected chat history that cannot be integrated, segments the collected chat history according to the retrieved chat segments, and uses each segmented result as a content scheme segment.

[0007] This invention introduces the concept of connection influence value. By analyzing the relationship between the information extraction results corresponding to different chat segments, it realizes the dynamic division of content scheme segments, providing data reference for subsequent steps to analyze the correlation between different content scheme segments and generate pre-simulation schemes.

[0008] Furthermore, the method for analyzing the correlation between different content scheme fragments includes the following steps: S21. Obtain the generated content scheme fragment, and denote the i-th content scheme fragment as Ai; obtain the extraction information corresponding to Ai, wherein the extraction information corresponding to Ai is the union of the information extraction results corresponding to each chat fragment within Ai; S22. Obtain the association analysis feature information corresponding to Ai, wherein the association analysis feature information corresponding to Ai includes the extraction information corresponding to Ai and the set of analysis directions corresponding to Ai; When obtaining the set of analysis directions corresponding to Ai, the membership degree of the extracted information corresponding to Ai relative to the keyword library corresponding to each analysis direction is retrieved from the database preset form. The set of all analysis directions with a membership degree greater than the second preset value is selected as the set of analysis directions. Let Fij denote the membership degree of the extracted information corresponding to Ai relative to the keyword library corresponding to the j-th analysis direction in the database preset form. , Among them, g ij This represents the number of keywords in the extracted information corresponding to Ai that belong to the keyword library corresponding to the j-th analysis direction in the database preset form; h （i，j，k） This indicates the sequence number of the k-th keyword in the keyword library corresponding to the j-th analysis direction in the database preset form, as represented by the extracted information for Ai; gz ij This indicates the total number of keywords in the extracted information corresponding to Ai; S23. Determine whether the intersection of the analysis direction sets in the correlation analysis feature information corresponding to different content scheme fragments is an empty set. Divide each content scheme fragment whose intersection between corresponding analysis direction sets is not an empty set into a group. Divide each content scheme fragment whose intersection between corresponding analysis direction sets is an empty set into different groups. The same content scheme fragment belongs to one or more groups. The intersection of the analysis direction sets corresponding to all content scheme fragments in the same group is not an empty set. S24. Determine the correlation between different content scheme fragments within the same group; When merging related content segments, the segments are spliced ​​together according to the chronological order of the corresponding chat records; the spliced ​​result is used as the new content segment after merging. Take any one of the content scheme fragments that are unrelated to each other as a pre-rehearsal scheme.

[0009] This invention categorizes content scheme fragments into groups by analyzing the intersection of analysis direction sets corresponding to each content scheme fragment. This method can integrate different chat contents across chat fragments, thereby achieving the integrity of the pre-show scheme while avoiding the bloat of the pre-show scheme content.

[0010] Furthermore, the set of preconditions includes the set of influencing parameters, the experimental results of the corresponding pre-simulation scheme, and the retrieval results of the corresponding pre-simulation scheme. The experimental results refer to whether the experimental data of the pre-simulation scheme is superior to the experimental data of the existing technology. If the experimental data of the pre-simulation scheme is superior to the experimental data of the existing technology, the corresponding experimental result coefficient is obtained and recorded as 1; otherwise, the corresponding experimental result coefficient is obtained and recorded as 0. The set of influencing parameters is a set of parameter categories or factor categories that affect the results of the technical scheme contained in the chat records corresponding to the corresponding pre-simulation scheme. The retrieval results of the simulation scheme are the maximum values ​​of the interference percentages in the comparison files where the corresponding set of influencing parameters intersects with the set of influencing parameters of the simulation scheme. The interference percentage of the set of influencing parameters in the comparison file compared to the set of influencing parameters in the simulation scheme is equal to the quotient of the number of elements in the union of the set of influencing parameters in the comparison file and the set of influencing parameters in the simulation scheme divided by the maximum number of elements in the set of influencing parameters in the comparison file and the set of influencing parameters in the simulation scheme. When analyzing the conversion demand assessment value of each pre-simulation scheme, the conversion demand assessment value of the nth pre-simulation scheme is denoted as Wn, where Wn = XSn·QSn. Where XSn represents the experimental result coefficient corresponding to the experimental result of the corresponding pre-rehearsal scheme in the precondition set corresponding to the nth pre-rehearsal scheme; QSn represents the retrieval result of the corresponding pre-rehearsal scheme in the precondition set corresponding to the nth pre-rehearsal scheme. When generating recommended schemes for the transformation of technological achievements, the preliminary schemes are sorted in descending order of the transformation demand assessment value, and the resulting sequence is recorded as the recommended schemes for the transformation of technological achievements.

[0011] If there are cases where the corresponding conversion demand evaluation values ​​are the same during the sorting process of this invention, the order of each pre-simulation scheme with the same conversion demand evaluation value will be randomly arranged.

[0012] Furthermore, when the generated technology transfer recommendation scheme is fed back to the technology transfer evaluation terminal, the generated technology transfer recommendation scheme is bound to the corresponding project and stored in the database.

[0013] Furthermore, in the process of obtaining the membership degree between the technical features of the technology transfer recommendation scheme and the domain features of each person in the expert database of the technology transfer evaluation terminal, the technical features of the technology transfer recommendation scheme are obtained. The technical features include multiple elements, and each element corresponds to a set of analysis directions for a pre-simulation scheme. The domain characteristics of the personnel in the expert database are the union of the analytical directions involved in the technical solutions that the corresponding personnel have published or reviewed; Let Lm be the degree of membership between the technical characteristics of the technology transfer recommendation scheme and the domain characteristics corresponding to the m-th person in the expert database of the technology transfer evaluation terminal. , Where E represents the number of elements within the technical features of the recommended technology transfer scheme; Pm d The quotient is the number of intersection elements between the technical features of the recommended scheme for technology transfer and the domain features corresponding to the m-th person in the expert database of the technology transfer evaluation terminal, divided by the total number of elements in the set of analysis directions corresponding to the d-th element in E. The expert pool members with the highest affiliation to the technology transfer recommendation scheme will be selected as the best recommendation and review experts for the corresponding technology transfer recommendation scheme. The serial number corresponding to each pre-trial scheme in the technology transfer recommendation scheme is the review serial number of the corresponding best recommended review expert.

[0014] A data analysis-based intellectual property rights commercialization supervision system, the system comprising the following modules: The content dynamic segmentation module, with user authorization, collects chat logs between various users within the project team, extracts information from each collected chat log, segments the chat log content based on the extracted information, and generates different content segmentation plans. The scheme association and fusion module combines the extracted information and the generated content scheme fragments, analyzes the correlation between different content scheme fragments, merges the content scheme fragments with correlation, obtains new content scheme fragments, and generates different pre-rehearsal schemes for the project. The technology transfer recommendation and analysis module analyzes the set of prerequisites corresponding to each pre-project scheme, combines the experimental results of the project with the retrieval results of the corresponding pre-project schemes, and analyzes the transformation demand evaluation value of each pre-project scheme to generate a technology transfer recommendation scheme. The intelligent recommendation module for technology transfer schemes feeds back the generated technology transfer recommendation schemes to the technology transfer evaluation terminal. It also combines the technical characteristics of the technology transfer recommendation schemes with the domain characteristics of each person in the expert database of the technology transfer evaluation terminal to generate the best recommended review experts for the technology transfer recommendation schemes.

[0015] Furthermore, the intelligent recommendation module for conversion solutions includes a conversion result feedback unit and a solution matching analysis unit. The transformation result feedback unit feeds back the generated technology achievement transformation recommendation scheme to the achievement transformation evaluation terminal. The scheme matching and analysis unit combines the technical characteristics of the technology transfer recommendation scheme with the domain characteristics of each person in the expert database of the technology transfer evaluation terminal to generate the best recommended review experts to be bound to the technology transfer recommendation scheme.

[0016] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: In the process of supervising the transformation of intellectual property achievements, this invention integrates and manages the schemes before the technical solutions are generated, taking into account the impact of the segmentation position of research record data on the technical solutions to be reviewed subsequently, and combining the content of the generated intellectual property achievement transformation scheme itself with the historical review records of each expert in the expert database, thereby enabling the screening of review experts corresponding to the generated intellectual property transformation scheme, ensuring the relevance and accuracy of the review results, reducing the risk of intellectual property achievement transformation for enterprises, and providing better intellectual property management and services for enterprises and research institutions. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the structure of a data analysis-based intellectual property achievement transformation supervision system according to the present invention; Figure 2 This is a flowchart illustrating a data analysis-based method for supervising the transformation of intellectual property achievements according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 The present invention provides a technical solution: a data analysis-based intellectual property achievement transformation supervision system, the system comprising the following modules: The content dynamic segmentation module, with user authorization, collects chat logs between various users within the project team, extracts information from each collected chat log, segments the chat log content based on the extracted information, and generates different content segmentation plans. The scheme association and fusion module combines the extracted information and the generated content scheme fragments, analyzes the correlation between different content scheme fragments, merges the content scheme fragments with correlation, obtains new content scheme fragments, and generates different pre-rehearsal schemes for the project. The technology transfer recommendation and analysis module analyzes the set of prerequisites corresponding to each pre-project scheme, combines the experimental results of the project with the retrieval results of the corresponding pre-project schemes, and analyzes the transformation demand evaluation value of each pre-project scheme to generate a technology transfer recommendation scheme. The intelligent recommendation module for technology transfer schemes feeds back the generated technology transfer recommendation schemes to the technology transfer evaluation terminal. It also combines the technical characteristics of the technology transfer recommendation schemes with the domain characteristics of each person in the expert database of the technology transfer evaluation terminal to generate the best recommended review experts for the technology transfer recommendation schemes.

[0020] The intelligent recommendation module for conversion solutions includes a conversion result feedback unit and a solution matching analysis unit. The transformation result feedback unit feeds back the generated technology achievement transformation recommendation scheme to the achievement transformation evaluation terminal. The scheme matching and analysis unit combines the technical characteristics of the technology transfer recommendation scheme with the domain characteristics of each person in the expert database of the technology transfer evaluation terminal to generate the best recommended review experts to be bound to the technology transfer recommendation scheme.

[0021] like Figure 2 As shown, a data analysis-based method for supervising the commercialization of intellectual property achievements includes the following steps: S1. With user authorization, collect chat logs between various users within the project team, extract information from each collected chat log, segment the chat log content based on the extracted information, and generate different content scheme fragments. When extracting information from each collected chat record, the interval between two adjacent chat records is determined. If the interval is longer than the first preset interval, the two chat records are divided into two different chat segments. Otherwise, the two chat records are divided into the same chat segment. Keywords within each chat segment are extracted, and the set of keywords extracted from the same chat segment is taken as an information extraction result. When generating different content scheme fragments, the following steps are included: Step 1: Obtain the information extraction results corresponding to different chat segments; Step 2: Record the information extraction results corresponding to two adjacent chat segments as the first information extraction result and the second information extraction result, respectively. Obtain the intersection between the first information extraction result and the second information extraction result. Record the ratio of the total number of times the elements in the first information extraction result appear in the corresponding chat segment to the total number of times the elements in the first information extraction result appear in the corresponding chat segment as the first information percentage. The ratio of the total number of times the elements in the intersection of the results appear in the corresponding chat segments of the second information extraction results to the total number of times the elements in the corresponding chat segments of the second information extraction results appear is recorded as the second information percentage. Step 3: Calculate the connection influence value between the first information extraction result and the second information extraction result, denoted as X. , Where B1 represents the proportion of the first information; B2 represents the proportion of the second information; r represents the influence factor and r is a preset constant in the database; Step 4: Based on X, determine the content segment corresponding to the chat segment to which the first information extraction result and the second information extraction result belong. When X is greater than or equal to the first preset value, it is determined that the chat segments to which the first information extraction result and the second information extraction result belong to the same content scheme segment. The chat segments to which the first information extraction result and the second information extraction result belong are integrated according to the order of chat time to obtain a new integrated chat segment. The information extraction result of the new integrated chat segment is the union of the first information extraction result and the second information extraction result. When X is less than the first preset value, it is determined that the chat segments to which the first information extraction result and the second information extraction result belong do not belong to the same content scheme segment, and the chat segments to which the first information extraction result and the second information extraction result belong cannot be integrated. The system retrieves individual chat segments from the collected chat history that cannot be integrated, segments the collected chat history according to the retrieved chat segments, and uses each segmented result as a content scheme segment.

[0022] S2. Combining the extracted information and the generated content plan fragments, analyze the correlation between different content plan fragments, merge the content plan fragments with correlation to obtain new content plan fragments, and generate different pre-rehearsal plans for the project. The method for analyzing the correlation between different content scheme fragments includes the following steps: S21. Obtain the generated content scheme fragment, and denote the i-th content scheme fragment as Ai; obtain the extraction information corresponding to Ai, wherein the extraction information corresponding to Ai is the union of the information extraction results corresponding to each chat fragment within Ai; S22. Obtain the association analysis feature information corresponding to Ai, wherein the association analysis feature information corresponding to Ai includes the extraction information corresponding to Ai and the set of analysis directions corresponding to Ai; When obtaining the set of analysis directions corresponding to Ai, the membership degree of the extracted information corresponding to Ai relative to the keyword library corresponding to each analysis direction is retrieved from the database preset form. The set of all analysis directions with a membership degree greater than the second preset value is selected as the set of analysis directions. Let Fij denote the membership degree of the extracted information corresponding to Ai relative to the keyword library corresponding to the j-th analysis direction in the database preset form. , Among them, g ij This represents the number of keywords in the extracted information corresponding to Ai that belong to the keyword library corresponding to the j-th analysis direction in the database preset form; h （i，j，k） This indicates the sequence number of the k-th keyword in the keyword library corresponding to the j-th analysis direction in the database preset form, as represented by the extracted information for Ai; gz ijThis indicates the total number of keywords in the extracted information corresponding to Ai; S23. Determine whether the intersection of the analysis direction sets in the correlation analysis feature information corresponding to different content scheme fragments is an empty set. Divide each content scheme fragment whose intersection between corresponding analysis direction sets is not an empty set into a group. Divide each content scheme fragment whose intersection between corresponding analysis direction sets is an empty set into different groups. The same content scheme fragment belongs to one or more groups. The intersection of the analysis direction sets corresponding to all content scheme fragments in the same group is not an empty set. S24. Determine the correlation between different content scheme fragments within the same group; When merging related content segments, the segments are spliced ​​together according to the chronological order of the corresponding chat records; the spliced ​​result is used as the new content segment after merging. Take any one of the content scheme fragments that are unrelated to each other as a pre-rehearsal scheme.

[0023] In this embodiment, if there is a phenomenon where the intersection of the analysis direction sets in the associated analysis feature information of multiple different content scheme fragments is not an empty set, it is necessary to continue splicing and fusion. Even if one or more content scheme fragments belong to the result of splicing and fusion, it is necessary to perform splicing and fusion operation until there is no correlation between the obtained content scheme fragments before generating the pre-playout scheme.

[0024] S3. Analyze the set of prerequisites corresponding to each pre-simulation scheme of the project, combine the experimental results of the project and the retrieval results of the corresponding pre-simulation schemes with the union of the sets of prerequisites, analyze the transformation demand assessment value of each pre-simulation scheme, and generate a recommended scheme for the transformation of technological achievements. The set of preconditions includes the set of influencing parameters, the experimental results of the corresponding pre-simulation scheme, and the retrieval results of the corresponding pre-simulation scheme. The experimental results refer to whether the experimental data of the pre-simulation scheme is superior to the experimental data of the existing technology. If the experimental data of the pre-simulation scheme is superior to the experimental data of the existing technology, the corresponding experimental result coefficient is obtained and recorded as 1; otherwise, the corresponding experimental result coefficient is obtained and recorded as 0. The set of influencing parameters is a set of parameter categories or factor categories that affect the results of the technical scheme contained in the chat records corresponding to the corresponding pre-simulation scheme. The retrieval results of the simulation scheme are the maximum values ​​of the interference percentages in the comparison files where the corresponding set of influencing parameters intersects with the set of influencing parameters of the simulation scheme. The interference percentage of the set of influencing parameters in the comparison file compared to the set of influencing parameters in the simulation scheme is equal to the quotient of the number of elements in the union of the set of influencing parameters in the comparison file and the set of influencing parameters in the simulation scheme divided by the maximum number of elements in the set of influencing parameters in the comparison file and the set of influencing parameters in the simulation scheme. When analyzing the conversion demand assessment value of each pre-simulation scheme, the conversion demand assessment value of the nth pre-simulation scheme is denoted as Wn, where Wn = XSn·QSn. Where XSn represents the experimental result coefficient corresponding to the experimental result of the corresponding pre-rehearsal scheme in the precondition set corresponding to the nth pre-rehearsal scheme; QSn represents the retrieval result of the corresponding pre-rehearsal scheme in the precondition set corresponding to the nth pre-rehearsal scheme. When generating recommended schemes for the transformation of technological achievements, the preliminary schemes are sorted in descending order of the transformation demand assessment value, and the resulting sequence is recorded as the recommended schemes for the transformation of technological achievements.

[0025] If there are cases where the corresponding conversion demand evaluation values ​​are the same during the sorting process of this invention, the order of each pre-simulation scheme with the same conversion demand evaluation value will be randomly arranged.

[0026] S4. Feed the generated technology transfer recommendation scheme back to the technology transfer evaluation terminal, and combine the technical characteristics of the technology transfer recommendation scheme with the domain characteristics of each person in the expert database of the technology transfer evaluation terminal to generate the best recommended review expert bound to the technology transfer recommendation scheme.

[0027] When the generated technology transfer recommendation scheme is fed back to the technology transfer evaluation terminal, the generated technology transfer recommendation scheme is bound to the corresponding project and stored in the database.

[0028] In the process of obtaining the membership degree between the technical features of the technology transfer recommendation scheme and the domain features of each person in the expert database of the technology transfer evaluation terminal, the technical features of the technology transfer recommendation scheme are obtained. The technical features include multiple elements, and each element corresponds to a set of analysis directions for a pre-simulation scheme. The domain characteristics of the personnel in the expert database are the union of the analytical directions involved in the technical solutions that the corresponding personnel have published or reviewed; Let Lm be the degree of membership between the technical characteristics of the technology transfer recommendation scheme and the domain characteristics corresponding to the m-th person in the expert database of the technology transfer evaluation terminal. , Where E represents the number of elements within the technical features of the recommended technology transfer scheme; Pm d The quotient is the number of intersection elements between the technical features of the recommended scheme for technology transfer and the domain features corresponding to the m-th person in the expert database of the technology transfer evaluation terminal, divided by the total number of elements in the set of analysis directions corresponding to the d-th element in E. The expert pool members with the highest affiliation to the technology transfer recommendation scheme will be selected as the best recommendation and review experts for the corresponding technology transfer recommendation scheme. The serial number corresponding to each pre-trial scheme in the technology transfer recommendation scheme is the review serial number of the corresponding best recommended review expert.

[0029] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0030] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for supervising the transformation of intellectual property achievements based on data analysis, characterized in that, The method comprises the following steps: S1, under the authorization of the user, collecting the chat records between each user in the project group, and extracting information from each collected chat record, segmenting the chat record content according to the extracted information, and generating different content scheme fragments; S2, combining the extracted information and the generated content scheme fragments, analyzing the relevance between different content scheme fragments, fusing the content scheme fragments with relevance, obtaining new content scheme fragments, and generating different rehearsal schemes of the project; S3, analyzing the preconditions set corresponding to each rehearsal scheme of the project, combining the experimental results of the project and the corresponding retrieval results of the rehearsal scheme to correspond to the union set of the preconditions, analyzing the transformation demand evaluation value of each rehearsal scheme, and generating a technology achievement transformation recommendation scheme; S4, feeding the generated technology achievement transformation recommendation scheme to the achievement transformation evaluation terminal, and combining the technology characteristics of the technology achievement transformation recommendation scheme with the membership between the field characteristics corresponding to each person in the expert database of the achievement transformation evaluation terminal, to generate the best recommended review expert bound to the technology achievement transformation recommendation scheme. 2.The data analysis based intellectual property achievement transformation supervision method according to claim 1, characterized in that: When extracting information from each collected chat record, the interval duration corresponding to the adjacent two chat records is judged, and the two chat records with an interval duration greater than a first preset duration are divided into two different chat fragments, otherwise, the two chat records are divided into the same chat fragment; the keywords in each chat fragment are extracted, and the set of keywords extracted from the same chat fragment is taken as an information extraction result; When generating different content scheme fragments, the following steps are included: Step one: obtain the information extraction result corresponding to each chat fragment; Step two: the information extraction results corresponding to the adjacent two chat fragments are respectively recorded as the first information extraction result and the second information extraction result, the intersection between the first information extraction result and the second information extraction result is obtained, and the ratio of the total number of elements in the obtained intersection in the corresponding chat fragment in the first information extraction result to the total number of elements in the corresponding chat fragment in the first information extraction result is recorded as the first information proportion; The ratio of the total number of elements in the obtained intersection in the corresponding chat fragment in the second information extraction result to the total number of elements in the corresponding chat fragment in the second information extraction result is recorded as the second information proportion; Step three: calculate the connection influence value between the first information extraction result and the second information extraction result, recorded as X, ， Wherein, B1 represents the first information proportion; B2 represents the second information proportion; r represents the influence factor and r is a constant preset in the database; Step four: according to X, the content scheme fragments corresponding to the chat fragments to which the first information extraction result and the second information extraction result belong respectively are determined, When X is greater than or equal to the first preset value, it is determined that the chat segments to which the first information extraction result and the second information extraction result respectively belong belong to the same content scheme segment, and the chat segments to which the first information extraction result and the second information extraction result respectively belong are integrated in the order of chat time to obtain a new chat segment after integration, and the information extraction result of the new chat segment after integration is the union of the first information extraction result and the second information extraction result; When X is less than the first preset value, it is determined that the chat segments to which the first information extraction result and the second information extraction result respectively belong do not belong to the same content scheme segment, and the chat segments to which the first information extraction result and the second information extraction result respectively belong cannot be integrated; Each chat segment that cannot be integrated in the collected chat record is obtained, and the collected chat record is segmented according to the obtained chat segments, and each segmentation result obtained after segmentation is taken as a content scheme segment. 3.The data analysis based intellectual property achievement transformation supervision method according to claim 1, characterized in that: The method for analyzing the relevance between different content scheme segments comprises the following steps: S21, obtaining the generated content scheme segments, taking the ith content scheme segment as Ai; obtaining the extraction information corresponding to Ai, which is the union of the information extraction results corresponding to each chat segment in Ai; S22, obtaining the relevance analysis feature information corresponding to Ai, which comprises the extraction information corresponding to Ai and the analysis direction set corresponding to Ai; When obtaining the analysis direction set corresponding to Ai, the membership degrees of the extraction information corresponding to Ai with respect to the keyword libraries corresponding to each analysis direction in the database preset form are searched, and a set formed by all analysis directions with membership degrees greater than a second preset value is selected as the analysis direction set; The membership degree of the extraction information corresponding to Ai with respect to the keyword library corresponding to the jth analysis direction in the database preset form is denoted as Fij, ， wherein g ij represents the number of keywords in the extraction information corresponding to Ai that belong to the keyword library corresponding to the jth analysis direction in the database preset form; h （i，j，k） represents the serial number of the kth keyword in the extraction information corresponding to Ai that belongs to the keyword library corresponding to the jth analysis direction in the database preset form in the corresponding keyword library; gz ij represents the total number of keywords in the extraction information corresponding to Ai; S23, obtaining whether the intersection between the analysis direction sets in the relevance analysis feature information corresponding to different content scheme segments is empty, dividing each content scheme segment with a non-empty intersection between the corresponding analysis direction sets into a group, and dividing each content scheme segment with an empty intersection between the corresponding analysis direction sets into different groups, wherein the group to which a content scheme segment belongs is one or more; the intersection between the analysis direction sets corresponding to all content scheme segments in the same group is non-empty; S24, determining that there is relevance between each content scheme segment in the same group; When the content scheme segments with relevance are fused, each content scheme segment is spliced in the order of the corresponding chat record time; and the obtained splicing result is taken as a new content scheme segment after fusion; Any one of the obtained content scheme segments that do not have relevance with each other is taken as a preview scheme.

4. The data analysis-based intellectual property achievement transformation supervision method according to claim 2, characterized in that: The precondition set includes a set of influence parameters involved, experimental results of the corresponding pre-performance scheme, and retrieval results of the corresponding pre-performance scheme. The experimental results are whether the experimental data of the pre-performance scheme is better than the experimental data of the existing technology. If the experimental data of the pre-performance scheme is better than the experimental data of the existing technology, a corresponding experimental result coefficient is obtained, denoted as 1, otherwise, a corresponding experimental result coefficient is obtained, denoted as 0. The set of influence parameters involved is a set of parameter categories or factor categories in the chat record corresponding to the corresponding pre-performance scheme which affect the results of the technical scheme. The retrieval result of the pre-performance scheme is the maximum value of the corresponding interference proportion in the contrast file corresponding to the intersection of the corresponding set of influence parameters and the set of influence parameters of the corresponding pre-performance scheme. The interference proportion of the set of influence parameters involved in the contrast file compared with the set of influence parameters of the pre-performance scheme is equal to the quotient of the number of elements in the intersection of the set of influence parameters involved in the contrast file and the set of influence parameters of the pre-performance scheme divided by the maximum value of the number of elements in the set of influence parameters involved in the contrast file and the set of influence parameters of the pre-performance scheme. When analyzing the conversion demand evaluation value of each pre-performance scheme, the conversion demand evaluation value of the nth pre-performance scheme is denoted as Wn, Wn=XSn·QSn, Wherein, XSn represents the experimental result coefficient corresponding to the experimental result of the corresponding pre-performance scheme in the precondition set corresponding to the nth pre-performance scheme; QSn represents the retrieval result of the corresponding pre-performance scheme in the precondition set corresponding to the nth pre-performance scheme. When generating the technical achievement transformation recommendation scheme, the pre-performance schemes are sorted in descending order of the conversion demand evaluation value, and the obtained sequence is denoted as the technical achievement transformation recommendation scheme.

5. The data analysis-based intellectual property achievement transformation supervision method according to claim 1, characterized in that: When feeding back the generated technical achievement transformation recommendation scheme to the achievement transformation evaluation terminal, the generated technical achievement transformation recommendation scheme is bound with the corresponding project and stored in the database.

6. The data analysis-based intellectual property achievement transformation supervision method according to claim 3, characterized in that: In the process of obtaining the membership between the technical features of the technical achievement transformation recommendation scheme and the field features corresponding to each person in the expert library of the achievement transformation evaluation terminal, the technical features of the technical achievement transformation recommendation scheme are obtained, the technical features include multiple elements, and each element corresponds to a set of analysis directions of a pre-performance scheme; The field features corresponding to the personnel in the expert library are the union of the analysis directions involved in the technical schemes published or reviewed by the corresponding personnel; The membership between the technical features of the technical achievement transformation recommendation scheme and the field features corresponding to the mth personnel in the expert library of the achievement transformation evaluation terminal is denoted as Lm, ， Wherein, E represents the number of elements in the technical features of the technical achievement transformation recommendation scheme; Pm d represents the quotient obtained by dividing the number of intersection elements of the technical features of the technical achievement transformation recommendation scheme and the field features corresponding to the mth personnel in the expert library of the achievement transformation evaluation terminal by the total number of elements in the analysis direction set corresponding to the dth element in E. The personnel with the maximum corresponding membership compared with the technical achievement transformation recommendation scheme is regarded as the best recommended review expert bound with the corresponding technical achievement transformation recommendation scheme, The serial number of each pre-performance scheme in the technical achievement transformation recommendation scheme is the review serial number of the corresponding best recommended review expert.

7. A data analysis-based intellectual property achievement transformation supervision system, characterized in that, The system includes the following modules: The content dynamic segmentation module collects chat records between each user in the project group under the authorization of the user, extracts information from each collected chat record, segments the chat record content according to the extracted information, and generates different content scheme fragments. The scheme association fusion module combines the extracted information and the generated content scheme fragments, analyzes the association between different content scheme fragments, fuses the content scheme fragments with association, obtains new content scheme fragments, and generates different rehearsal schemes of the project. The achievement transformation recommendation analysis module analyzes the precondition set corresponding to each rehearsal scheme of the project, combines the experimental results of the project and the search results of the corresponding rehearsal scheme, and analyzes the transformation demand evaluation value of each rehearsal scheme. The transformation scheme intelligent recommendation module feeds back the generated technical achievement transformation recommendation scheme to the achievement transformation evaluation terminal, combines the technical features of the technical achievement transformation recommendation scheme with the membership between the domain features corresponding to each person in the expert database of the achievement transformation evaluation terminal, and generates the best recommended review expert bound to the technical achievement transformation recommendation scheme.

8. The data analysis based intellectual property achievement transformation supervision system according to claim 7, characterized in that: The transformation scheme intelligent recommendation module includes a transformation achievement feedback unit and a scheme matching analysis unit. The transformation achievement feedback unit feeds back the generated technical achievement transformation recommendation scheme to the achievement transformation evaluation terminal. The scheme matching analysis unit combines the technical features of the technical achievement transformation recommendation scheme with the membership between the domain features corresponding to each person in the expert database of the achievement transformation evaluation terminal, and generates the best recommended review expert bound to the technical achievement transformation recommendation scheme.

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