Comprehensive consulting services and project management platform for the transformation of scientific and technological achievements

Abstract

The present invention discloses a comprehensive consulting service and project management platform for the transformation of scientific and technological achievements, which relates to the technical field of consulting on the transformation of scientific and technological achievements, including: starting the registration and metadata extraction program for the scientific and technological achievement information uploaded for the first time, automatically generating a unique and non-repeatable achievement metadata tag, and associating the achievement information with the tag and storing it in the achievement database, forming an initial achievement entity model with the tag as the core. The present invention achieves unified identification and centralized management of achievements by assigning a unique metadata tag to the achievements uploaded for the first time and establishing a main achievement entity model, thereby avoiding the fragmentation of achievement information; at the same time, with the help of natural language recognition and semantic aggregation algorithms, the platform can perform intelligent comparison and fine-grained merging of subsequently uploaded achievement information, which not only retains the valuable supplementary information uploaded by each team, but also eliminates conflicting fields caused by inconsistent descriptions, thereby ensuring the integrity and consistency of the achievement content.

Description

Technical Field

[0001] The present invention relates to the field of scientific and technological achievement transformation consulting technology, and in particular to a comprehensive consulting service and project management platform for scientific and technological achievement transformation. Background Art

[0002] This comprehensive consulting service and project management platform for the transformation of scientific and technological achievements is a digital system that integrates scientific and technological achievement docking, transformation implementation, project management, and professional consulting services. By integrating the resources of universities, research institutions, enterprises, and investment and financing institutions, the platform provides comprehensive consulting services, including scientific and technological achievement information release, technology evaluation, market analysis, policy interpretation, intellectual property services, business model design, and investment and financing docking. It also provides full-lifecycle project management capabilities, including project establishment, schedule control, resource coordination, risk assessment, and achievement evaluation. It aims to improve the efficiency of scientific and technological achievements from laboratory to industrialization and promote the deep integration of science, technology, and the economy.

[0003] The existing technology has the following deficiencies:

[0004] In the current technological landscape, universities still have room for improvement in managing scientific and technological achievements. If the same scientific achievement is uploaded to the platform by different team members or research groups, resulting in multiple, similar but inconsistent versions of the achievement, businesses may mistakenly choose to collaborate with less mature or incomplete versions due to information confusion. This situation not only hinders the efficient transfer of achievements but also severely undermines the credibility and professional image of universities and platforms.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a comprehensive consulting service and project management platform for the transformation of scientific and technological achievements. By assigning unique metadata tags to the achievements uploaded for the first time and establishing a main achievement entity model, the unified identification and centralized management of the achievements are achieved, avoiding the fragmentation of achievement information; at the same time, with the help of natural language recognition and semantic aggregation algorithms, the platform can perform intelligent comparison and fine-grained merging of subsequently uploaded achievement information, which not only retains the valuable supplementary information uploaded by each team, but also eliminates conflicting fields caused by inconsistent descriptions, ensuring the integrity and consistency of the achievement content, so as to solve the problems in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a comprehensive consulting service and project management platform for the transformation of scientific and technological achievements, including an achievement archiving and identification module, a semantic analysis and feature extraction module, an achievement similarity determination and attribution judgment module, an achievement aggregation and version fusion module, and a version management and authority review module;

[0008] The achievement archiving and identification module starts the registration and metadata extraction process for the scientific and technological achievement information uploaded for the first time, automatically generates a unique and non-repeatable achievement metadata tag, and associates the achievement information with the tag and stores it in the achievement database, forming an initial achievement entity model with the tag as the core;

[0009] The semantic parsing and feature extraction module performs text preprocessing on the subsequently uploaded scientific research results information. It uses a pre-trained natural language recognition model to convert the newly uploaded results information into feature vectors, and then compares and analyzes the feature vectors of all scientific and technological results stored in the results database one by one, and calculates the semantic similarity value between the two.

[0010] The achievement similarity determination and attribution judgment module sets several similarity determination thresholds based on the semantic similarity values ​​obtained from the comparison, specifically including a high threshold interval, a medium threshold interval, and a low threshold interval, to determine whether the subsequently uploaded scientific and technological achievements belong to the same scientific and technological achievements as the first uploaded scientific and technological achievements;

[0011] The achievement aggregation and version fusion module starts the automatic aggregation and version information fusion process for cases where the same scientific and technological achievements are identified. Based on the version information of the first uploaded achievement, it uses natural language recognition and semantic aggregation algorithms to perform a fine-grained field-level comparison between the subsequently uploaded scientific and technological achievements and the first uploaded achievements. For cases where new scientific and technological achievements are identified, they are added and assigned new metadata tags.

[0012] The version management and authority review module generates a new version number for scientific and technological achievements after the information is automatically merged, and records the difference information and corresponding modification records between all versions to form a complete version tracking and historical record system for the achievement entity model; at the same time, a unique achievement manager is designated for each achievement entity, and is given the authority to make final confirmation and review of key fields.

[0013] Preferably, the achievement metadata tag is one of an achievement unique identification code, an achievement digital object unique identifier, and an achievement semantic fingerprint code.

[0014] Preferably, the steps for calculating the semantic similarity value are as follows:

[0015] Call the pre-trained natural language recognition model to perform word segmentation, word vector conversion, and contextual semantic modeling on the newly uploaded text information, and output a feature vector u with a dimension of D, where D represents the vector dimension produced by the pre-trained language model;

[0016] For each achievement information stored in the achievement database, the same or compatible semantic model is called to obtain the respective feature vector v i , where i represents the number of each record in the achievement database;

[0017] After the vectors are generated, all vectors are aligned in dimension.

[0018] Preferably, after completing the feature vector acquisition and alignment, the following similarity formula is used to calculate the newly uploaded result vector u=(u1,…,u D ) and the i-th result vector v in the database i =(v i1 ,…,v iD ) performs exponential weighted calculation of dimension-by-dimension differences and obtains a semantic similarity value:

[0019]

[0020] , where: S im (u,v i ) represents the semantic similarity value, α is the sensitivity adjustment parameter, α>0, which is used to control the influence weight of local dimension difference in the overall similarity; β is the smoothing factor, β>0; It means that the relative difference of each vector dimension is weighted and accumulated to form the final distance metric, which is then mapped to the similarity range of (0, 1] by the exponential function. k represents the kth component of the semantic vector u corresponding to the newly uploaded scientific and technological achievement text, v ik The feature vector v representing the i-th existing achievement in the achievement database i The kth component of , k represents the dimension index of the semantic vector, that is, the kth dimension is currently being calculated.

[0021] Preferably, the semantic similarity values ​​obtained by comparison are compared and analyzed with the high threshold interval, the medium threshold interval, and the low threshold interval to determine whether the scientific and technological achievements uploaded for the first time and the scientific and technological achievements uploaded subsequently belong to the same scientific and technological achievements. The specific determination steps are as follows:

[0022] If the semantic similarity value is within the high threshold range, it is determined that the subsequently uploaded scientific and technological achievements are the same as the first uploaded scientific and technological achievements;

[0023] If the semantic similarity value is in the medium range, it is determined that the subsequently uploaded scientific and technological achievements are pending confirmation from the first uploaded scientific and technological achievements, and the administrator or the person who uploaded the achievements is prompted to conduct manual review and final confirmation;

[0024] If the semantic similarity value is in the low threshold range, it is determined that the subsequently uploaded scientific and technological achievements are not the same as the first uploaded scientific and technological achievements, and the subsequently uploaded scientific and technological achievements are determined to be new scientific and technological achievements and added.

[0025] Preferably, based on the version information of the first uploaded achievement, natural language recognition and semantic aggregation algorithms are used to perform a fine-grained field-level comparison of the subsequently uploaded scientific and technological achievements with the first uploaded achievements. The specific steps are as follows:

[0026] Perform field-level data extraction and standardization operations on the first uploaded achievement information and the subsequent uploaded achievement information;

[0027] The pre-trained language model is used to embed the text content of each field and convert it into a multi-dimensional semantic vector representation.

[0028] Preferably, after completing the semantic vectorization, for each corresponding field, the semantic similarity between the first uploaded version and the subsequent uploaded versions is calculated using an improved semantic distance function to measure the semantic consistency of each field;

[0029] Fields with a similarity greater than or equal to the set threshold are considered semantically duplicated or highly consistent and do not require modification. Fields with a similarity less than the threshold are identified as information differences or potential conflicts, and are classified as "incremental information" or "conflicting information" and marked in the difference analysis table.

[0030] Preferably, the fields identified as "incremental information" are directly appended to the content structure of the corresponding fields in the main achievement entity, integrated as "additional information", and marked with the source and version number in the database;

[0031] For field contents that are identified as conflicting, they are preprocessed according to preset strategies to generate a recommended retention value for each conflicting field, while retaining all historical content as version records.

[0032] Preferably, a new version is generated based on the original main achievement entity model, and the incremental information and processed field content are unified into the new version data package, and the additional metadata is bound to the version model; if it is judged that there is a high risk or key field conflict, the "audit flow mechanism" is automatically triggered, and the integration result is submitted to the "sole manager" of the achievement for manual review and final confirmation.

[0033] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0034] The present invention achieves unified identification and centralized management of achievements by assigning unique metadata tags to the first uploaded achievements and establishing a main achievement entity model, thus avoiding the fragmentation of achievement information. At the same time, with the help of natural language recognition and semantic aggregation algorithms, the platform can intelligently compare and fine-grainedly merge the achievement information uploaded subsequently, which not only retains the valuable supplementary information uploaded by each team, but also eliminates the conflicting fields caused by inconsistent descriptions, ensuring the integrity and consistency of the achievement content. In addition, the introduction of the version management mechanism and the achievement manager system enables universities to conduct traceable and controllable dynamic supervision of the evolution process of each achievement, improves the accuracy, credibility and conversion efficiency of the achievement display, and truly realizes the transformation from "extensive uploading" to "intelligent aggregation and precise release". It builds a new model of intelligent, structured and trustworthy scientific and technological achievement management for universities, significantly enhancing the professional image and external cooperation attractiveness of universities in the process of achievement transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0036] Figure 1 This is a module diagram of the comprehensive consulting service and project management platform for the transformation of scientific and technological achievements of the present invention. DETAILED DESCRIPTION

[0037] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0038] The present invention provides Figure 1 The comprehensive consulting service and project management platform for the transformation of scientific and technological achievements shown includes:

[0039] In a university or scientific and technological achievement management platform, when a team member or research group member uploads a brand-new scientific and technological achievement information for the first time, the system first initiates the registration and metadata extraction process of the achievement information, specifically including the parsing and structured storage of key information fields such as the achievement name, the subject area to which it belongs, the research content summary, the information of the research members and team, the intellectual property status (such as the patent or software copyright number), and the technology maturity; on this basis, the system automatically generates a unique and non-repeatable achievement metadata tag (i.e., the achievement main identification code), and associates the above-mentioned achievement information with the tag and stores it in the achievement database, forming an initial achievement entity model with the tag as the core;

[0040] Through this step, we can ensure that each scientific and technological achievement has a unique identification and tracking mark, lay the foundation for unified and standardized management of achievement information, and effectively avoid information redundancy problems caused by multiple uploads of subsequent achievements.

[0041] To ensure that each achievement is uniquely identified in the scientific and technological achievement management platform, the system automatically generates an achievement metadata tag for each achievement uploaded for the first time. This tag is not only used to uniquely identify the achievement, but also serves as a core index for subsequent version management, data tracking, and information aggregation. The following are three common forms of achievement metadata tags, each of which is explained in detail:

[0042] 1. Unique Identifier Code (UID)

[0043] The unique identification code for an achievement is a unique, non-repeatable string number generated based on automatic numbering rules. It typically uses a combination of letters and numbers, for example, "GZ2025-TF000123." It can include the abbreviation of the university (e.g., GZ for the university code), the year (2025), the achievement type identifier (TF: scientific and technological achievement), and a serial number. This type of tag is easy for machines to read and sort, and is suitable for the unified archiving and rapid indexing of large-scale achievement information. Using this achievement UID, the system can quickly locate the achievement entity model and achieve a unique mapping in the database. It is the most basic and commonly used type of achievement metadata tag.

[0044] 2. Digital Object Identifier (DOI)

[0045] DOI is an international digital identifier system commonly used for academic papers and data resources. It can also be used to mark scientific and technological achievements. In the scientific and technological achievement management system, the platform can generate exclusive DOI numbers for key achievements, such as "10.12345 / univ.tech.2025.008", which are used for external citations, achievement publication and permanent links. DOI has global uniqueness, traceability and long-term stability, and is particularly suitable for high-value achievements with clear achievement forms (such as data packages, patent reports, transformation reports, etc.). Through DOI, achievements can be tracked and cited across platforms, enhancing the dissemination and authority of scientific and technological achievements. It is particularly suitable for external display of achievement transformation, literature citation and intellectual property tracking.

[0046] 3. Semantic Fingerprint Code of the results;

[0047] The semantic fingerprint coding of achievements is a hash identification code generated by extracting natural language features from the achievement content. For example, it uses the achievement's technical name, keywords, research abstract, and other information, and generates a unique code through semantic vectorization and hash compression, such as "SFP-B3F7C9D812." This type of tag has "content relevance" and can be used as a "semantic signature" of the achievement content, helping the platform to quickly perform semantic matching, similarity recognition, and classification aggregation in subsequent uploads. This code not only achieves unique identification but also has strong content recognition capabilities. It is suitable for building intelligent achievement comparison and clustering systems and is a commonly used auxiliary identification method in achievement semantic recognition and machine learning algorithms.

[0048] Through the generation and application of these three types of achievement metadata tags, the scientific and technological achievement management system can realize the unique identification, version tracking, semantic analysis and cross-platform citation of achievement information, providing accurate, efficient and intelligent basic support for the transformation of scientific and technological achievements in universities.

[0049] When other teams or project team members subsequently upload new scientific and technological achievements, the system first performs text preprocessing on the uploaded scientific research achievement information (such as achievement name, technical abstract, patent data, technology maturity, etc.); then, the system uses a pre-trained natural language recognition model (such as BERT, RoBERTa or other semantic models) to convert the newly uploaded achievement information into a feature vector, and compares and analyzes the feature vectors of all scientific and technological achievements stored in the achievement database one by one, and calculates the semantic similarity value between the two;

[0050] Through the above steps, the platform can automatically and in real time determine the initial similarity between the subsequently uploaded results information and the existing results on the platform, providing objective and scientific data support for further judgment, ensuring the accurate matching of subsequent results with the original results, and reducing errors or omissions caused by manual comparison.

[0051] Text preprocessing specifically includes natural language word segmentation, stop word removal, named entity recognition, and feature word vector extraction.

[0052] The steps for calculating the semantic similarity value are as follows:

[0053] First, a pre-trained natural language recognition model (such as BERT or RoBERTa) is called to perform word segmentation, word vector conversion, and contextual semantic modeling on the newly uploaded text information, and finally output a feature vector u with a dimension of D. Among them, D represents the vector dimension produced by the pre-trained language model; then, for each achievement information stored in the achievement database, the system also calls the same or compatible semantic model to obtain its respective feature vector v i , where i represents the number of each record in the results database. After generating the vectors, the system aligns all vectors in terms of dimension to ensure that they belong to the same model and the same dimension, ensuring the comparability and consistency of subsequent similarity calculations;

[0054] Through this step, the system not only completes the high-dimensional semantic representation of the new input text, but also lays the foundation for vector alignment for subsequent similarity analysis, enabling feature vectors to be accurately compared in the same embedding space.

[0055] After completing the feature vector acquisition and alignment, the system uses the following similarity formula to calculate the newly uploaded result vector u=(u1,…,u D ) and the i-th result vector v in the database i =(v i1 ,…,v iD ) performs exponential weighted calculation of dimension-by-dimension differences and obtains a semantic similarity value:

[0056]

[0057] , where: S im (u,v i ) represents the semantic similarity value, α is the sensitivity adjustment parameter, α>0, which is used to control the influence weight of local dimension differences in the overall similarity. The larger the value, the more sensitive it is to local differences. β is the smoothing factor, β>0, which avoids the denominator being zero or too small, which may lead to unstable results. It means that the relative difference of each vector dimension is weighted and accumulated to form the final distance metric, which is then mapped to the similarity range of (0, 1] by the exponential function. k represents the kth component of the semantic vector u corresponding to the newly uploaded scientific and technological achievement text, v ik The feature vector (semantic feature vector) v representing the i-th existing achievement in the achievement database iThe kth component of , k represents the dimension index of the semantic vector, that is, the current calculation is the kth dimension;

[0058] Through the exponential weighting form of this step, the system can delicately capture subtle dimensional differences while taking into account overall semantic differences, achieving accurate similarity assessment of text vectors and providing quantitative criteria for subsequent confirmation of whether two achievements belong to the same technology or similar fields.

[0059] The system sets several similarity determination thresholds based on the semantic similarity values ​​obtained from the above comparison, specifically including a high threshold interval, a medium threshold interval, and a low threshold interval, to determine whether the subsequently uploaded scientific and technological achievements are the same as the first uploaded scientific and technological achievements;

[0060] The semantic similarity values ​​obtained from the comparison are compared and analyzed with the high threshold interval, the medium threshold interval, and the low threshold interval to determine whether the first uploaded scientific and technological achievements and the subsequently uploaded scientific and technological achievements are the same scientific and technological achievements. The specific determination steps are as follows:

[0061] If the semantic similarity value is within the high threshold range, it is determined that the subsequently uploaded scientific and technological achievements are the same as the first uploaded scientific and technological achievements;

[0062] If the semantic similarity value is in the medium range, it is determined that the subsequently uploaded scientific and technological achievements are pending confirmation from the first uploaded scientific and technological achievements, and the administrator or the person who uploaded the achievements is prompted to conduct manual review and final confirmation;

[0063] If the semantic similarity value is in the low threshold range, it is determined that the subsequently uploaded scientific and technological achievements are not the same as the first uploaded scientific and technological achievements, and the subsequently uploaded scientific and technological achievements are determined to be new scientific and technological achievements and added.

[0064] For example: if the similarity exceeds 80%, it is determined to belong to the same scientific and technological achievement and the merging procedure should be initiated; if the similarity is between 60% and 80%, the system will prompt the administrator or the person who uploaded the achievement to conduct manual review and final confirmation; if the similarity is lower than 60%, it will be automatically identified as a new scientific and technological achievement and assigned a new metadata label.

[0065] Through clear and precise division of similarity intervals, this step can achieve rapid and accurate attribution determination of newly uploaded achievements, minimize subjective factors in achievement classification, and greatly improve the efficiency and accuracy of scientific and technological achievement classification.

[0066] If the system identifies the same scientific and technological achievements, the system will start the automatic aggregation and version information fusion program. Based on the version information of the first uploaded achievement, the system will use natural language recognition and semantic aggregation algorithms to perform a fine-grained field-level comparison between the subsequently uploaded scientific and technological achievements and the first uploaded achievements.

[0067] During this process, the incremental information contained in the newly uploaded results (such as new technical parameters, experimental data, or newly applied patent numbers) is automatically supplemented and integrated into the original results information. For information with obvious differences or conflicts (such as technology maturity assessment and patent status), the system uses a preset conflict resolution mechanism (such as field priority scores assigned based on parameters such as upload time, uploader weight, and information source reliability) to determine the final information to be adopted. If the system cannot make a decision automatically, the conflicting fields are marked and manually reviewed and confirmed by the designated sole results manager.

[0068] Based on the version information of the first uploaded achievement, natural language recognition and semantic aggregation algorithms are used to perform a fine-grained field-level comparison of the subsequently uploaded scientific and technological achievements with the first uploaded achievements. The specific steps are as follows:

[0069] The system first performs field-level data extraction and standardization operations on the first uploaded achievement information and the subsequently uploaded achievement information. Each field (such as achievement title, technical summary, application scenario, patent information, technology maturity, core parameters, etc.) will be extracted separately and uniformly cleaned for natural language, including removing redundant formats, standardizing terms, eliminating synonym ambiguity, etc. Subsequently, the system uses pre-trained language models (such as BERT, RoBERTa, etc.) to embed the text content of each field and convert it into a multi-dimensional semantic vector representation, laying a unified vector space foundation for subsequent semantic comparison. The purpose of this step is to ensure that the system has the ability to handle language heterogeneity and semantic ambiguity at the field level, so as to achieve more accurate semantic comparison.

[0070] After completing the semantic vectorization, the system will calculate the semantic similarity between the first uploaded version and the subsequent uploaded versions for each corresponding field. It usually uses an improved semantic distance function (such as weighted cosine distance, normalized exponential weighted difference, etc.) to accurately measure the degree of semantic consistency of each field. For fields with a similarity greater than or equal to the set threshold (such as more than 95%), the system regards them as semantic duplications or high consistency and does not require modification; for fields with a similarity less than the threshold, the system identifies them as information differences or potential conflicts, and divides them into "incremental information" or "conflict information", and marks them in the difference analysis table. The core function of this step is to identify which information in subsequent versions needs to be supplemented, retained or further confirmed by comparing field by field, so as to accurately capture information differences.

[0071] For the fields identified as "incremental information" in the previous step, the system directly appends them to the content structure of the corresponding fields in the main achievement entity, integrates them as "additional information", and marks the source and version number in the system database. For field content identified as conflicting, the system pre-processes it according to preset strategies, such as field weight sorting (based on factors such as the uploader's role, information completeness, and upload time), credibility assessment (such as whether it is accompanied by patent numbers, experimental reports, and other supporting evidence), etc., to generate a recommended retention value for each conflicting field, while retaining all historical content as version records. The core purpose of this step is to achieve structured aggregation of achievement content while ensuring that information is not lost, and to complete some automatic conflict decisions through rule guidance.

[0072] When the field-level information integration processing is completed, the system will generate a new version (such as v2.0) based on the original main achievement entity model, and will unify the incremental information and processed field content into the new version data package. At the same time, additional metadata such as change logs, difference records, and merge sources will be bound to the version model. If the system determines that there is a high risk or a conflict in key fields (such as major differences in technical maturity or core invention points), it will automatically trigger the "audit flow mechanism" and submit the integration results to the "only manager" of the achievement for manual review and final confirmation. The key to this step is to ensure that the updated version after the system aggregates the achievement information is supported by an audit mechanism to prevent information errors from being automatically uploaded and to ensure the authority, uniformity and accuracy of the achievement information display.

[0073] Through the above four steps, the system realizes the full-chain processing logic from "identifying identical results" to "intelligent aggregation and prudent integration", and builds a scientific and technological achievement version management system that is both efficient and automatic and has credibility assurance.

[0074] Utilizing intelligent natural language recognition and semantic aggregation algorithms, the system identifies that the newly uploaded achievements and existing achievements belong to the same scientific and technological achievements, and then performs detailed, field-by-field intelligent comparison and aggregation and merging of the two versions of information. Taking the information of the first uploaded achievement as the benchmark, the system accurately identifies and extracts the incremental content of key fields such as newly added or more complete technical indicators, experimental data, patent information and application scenarios in subsequent versions, and integrates these more complete and richer information into the existing main achievement entity model, thereby generating a new version of the achievement with more comprehensive, accurate and consistent information. This step can also automatically detect information conflicts or differences between the new and old versions, mark them out for subsequent manual confirmation and processing by management personnel, ensure the uniformity, authority and accuracy of scientific and technological achievement information on the platform, prevent enterprises from mistakenly selecting versions for cooperation due to inconsistent information, effectively enhance the credibility and professional image of university scientific and technological achievements displayed to the outside world, and provide solid data support and guarantee for efficient achievement transformation and precise docking with enterprises.

[0075] Through the above steps, all version information of the same achievement can be automatically and intelligently aggregated, redundant and conflicting information can be eliminated, and the authority, consistency and integrity of the version information displayed to the outside world can be ensured.

[0076] After each automatic merger of scientific and technological achievements, the system generates a new version number (such as V2.0, V3.0) and records the difference information and corresponding modification records between all versions, forming a complete version tracking and historical record system for the achievement entity model. At the same time, the platform designates a unique achievement manager for each achievement entity and grants them the authority to conduct final confirmation and review of key fields to ensure the accuracy, objectivity and standardization of the achievement information released to the outside world. When conflicts arise in subsequent achievement versions or major modifications are required, the system will automatically trigger the review process, and the unique achievement manager will conduct manual review and confirmation before publishing, and record the review process. In addition, the system supports the public display of the latest approved achievement entity model version information, so that enterprises and partners can conduct efficient and barrier-free achievement docking.

[0077] Through the strict management and review system of this step, the authority, credibility and real-time update of the results information can be effectively guaranteed, the overall credibility and professional image of universities and platforms can be significantly improved, and the efficient and orderly transformation and implementation of scientific and technological achievements can be promoted.

[0078] Through the above-mentioned technical solutions for comprehensive consulting services and project management platforms for the transformation of scientific and technological achievements, universities have significantly improved the standardization, uniqueness, and evolutionary controllability of information in the management of scientific and technological achievements, and effectively solved the problems of information confusion and version conflicts caused by multiple team members repeatedly uploading the same achievement. By assigning unique metadata tags to the first uploaded achievements and establishing a master achievement entity model, the system achieves unified identification and centralized management of achievements, avoiding the fragmentation of achievement information. At the same time, with the help of natural language recognition and semantic aggregation algorithms, the platform can intelligently compare and fine-grainedly merge the information of subsequently uploaded achievements, retaining the valuable supplementary information uploaded by each team, and eliminating conflicting fields caused by inconsistent descriptions, ensuring the integrity and consistency of the achievement content. In addition, the introduction of the version management mechanism and the achievement manager system enables universities to conduct traceable and controllable dynamic supervision of the evolution process of each achievement, improving the accuracy, credibility and conversion efficiency of the achievement display, and truly realizing the transformation from "extensive uploading" to "intelligent aggregation and precise release". It has built a new model of intelligent, structured and trustworthy scientific and technological achievement management for universities, and significantly enhanced the professional image and external cooperation attractiveness of universities in the process of achievement transformation.

[0079] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A comprehensive consulting service and project management platform for the transformation of scientific and technological achievements, characterized by: It includes the achievement archiving and identification module, semantic analysis and feature extraction module, achievement similarity determination and attribution judgment module, achievement aggregation and version fusion module, and version management and authority review module; The achievement archiving and identification module starts the registration and metadata extraction process for the scientific and technological achievement information uploaded for the first time, automatically generates a unique and non-repeatable achievement metadata tag, and associates the achievement information with the tag and stores it in the achievement database, forming an initial achievement entity model with the tag as the core; The semantic parsing and feature extraction module performs text preprocessing on the subsequently uploaded scientific research results information. It uses a pre-trained natural language recognition model to convert the newly uploaded results information into feature vectors, and then compares and analyzes the feature vectors of all scientific and technological results stored in the results database one by one, and calculates the semantic similarity value between the two. The achievement similarity determination and attribution judgment module sets several similarity determination thresholds based on the semantic similarity values ​​obtained from the comparison, specifically including a high threshold interval, a medium threshold interval, and a low threshold interval, to determine whether the subsequently uploaded scientific and technological achievements belong to the same scientific and technological achievements as the first uploaded scientific and technological achievements; The achievement aggregation and version fusion module starts the automatic aggregation and version information fusion process for cases where the same scientific and technological achievements are identified. Based on the version information of the first uploaded achievement, it uses natural language recognition and semantic aggregation algorithms to perform a fine-grained field-level comparison between the subsequently uploaded scientific and technological achievements and the first uploaded achievements. For cases where new scientific and technological achievements are identified, they are added and assigned new metadata tags. The version management and authority review module generates a new version number for scientific and technological achievements after the information is automatically merged, and records the differences between all versions and the corresponding modification records, forming a complete version tracking and historical record system for the achievement entity model. At the same time, a unique achievement manager is designated for each achievement entity, giving them the authority to conduct final confirmation and review of key fields. The steps for calculating the semantic similarity value are as follows: Call the pre-trained natural language recognition model to perform word segmentation, word vector conversion and context semantic modeling on the newly uploaded text information, and output a dimension of D The eigenvector of u ,in, D Represents the dimension of the vector produced by the pre-trained language model; For each achievement information stored in the achievement database, the same or compatible semantic model is called to obtain the respective feature vectors ,in, i Indicates the number of each record in the results database; After generating the vectors, all the vectors are aligned in dimension; After completing the feature vector acquisition and alignment, use the following similarity formula to calculate the newly uploaded result vector With the database i Result vector Perform exponential weighted calculation of dimension-by-dimension differences and obtain a semantic similarity value: ,in: Represents the semantic similarity value, is the sensitivity adjustment parameter, , used to control the influence weight of local dimension differences in the overall similarity; is the smoothing factor, ; It means that the relative difference of each vector dimension is weighted and accumulated to form the final distance metric, which is then mapped to The similarity range of Indicates the semantic vector corresponding to the newly uploaded scientific and technological achievements text u No. k A quantity, Indicates the first i Feature vectors of existing results No. k A quantity, k Indicates the dimension index of the semantic vector, that is, the current calculation is the k dimensions.

2. The comprehensive consulting service and project management platform for the transformation of scientific and technological achievements according to claim 1 is characterized in that: The achievement metadata tag is one of the achievement unique identification code, achievement digital object unique identifier and achievement semantic fingerprint code.

3. The comprehensive consulting service and project management platform for the transformation of scientific and technological achievements according to claim 1 is characterized in that: The semantic similarity values ​​obtained from the comparison are compared and analyzed with the high threshold interval, the medium threshold interval, and the low threshold interval to determine whether the first uploaded scientific and technological achievements and the subsequently uploaded scientific and technological achievements are the same scientific and technological achievements. The specific determination steps are as follows: If the semantic similarity value is within the high threshold range, it is determined that the subsequently uploaded scientific and technological achievements are the same as the first uploaded scientific and technological achievements; If the semantic similarity value is in the medium range, it is determined that the subsequently uploaded scientific and technological achievements are pending confirmation from the first uploaded scientific and technological achievements, and the administrator or the person who uploaded the achievements is prompted to conduct manual review and final confirmation; If the semantic similarity value is in the low threshold range, it is determined that the subsequently uploaded scientific and technological achievements are not the same as the first uploaded scientific and technological achievements, and the subsequently uploaded scientific and technological achievements are determined to be new scientific and technological achievements and added.

4. The comprehensive consulting service and project management platform for the transformation of scientific and technological achievements according to claim 1 is characterized in that: Based on the version information of the first uploaded achievement, natural language recognition and semantic aggregation algorithms are used to perform a fine-grained field-level comparison of the subsequently uploaded scientific and technological achievements with the first uploaded achievements. The specific steps are as follows: Perform field-level data extraction and standardization operations on the first uploaded achievement information and the subsequent uploaded achievement information; The pre-trained language model is used to embed the text content of each field and convert it into a multi-dimensional semantic vector representation.

5. The comprehensive consulting service and project management platform for the transformation of scientific and technological achievements according to claim 4 is characterized in that: After completing semantic vectorization, for each corresponding field, the improved semantic distance function is used to calculate the semantic similarity between the first uploaded version and the subsequent uploaded versions to measure the semantic consistency of each field; Fields with similarity greater than or equal to the set threshold are considered semantically duplicated or highly consistent and require no modification. Fields with similarity less than the threshold are identified as information differences or potential conflicts, classified as "incremental information" or "conflicting information," and marked in the difference analysis table.

6. The comprehensive consulting service and project management platform for the transformation of scientific and technological achievements according to claim 5 is characterized in that: Fields identified as "incremental information" are directly appended to the content structure of the corresponding fields in the main output entity and integrated as "additional information." The source and version number are marked in the database. For field contents that are identified as conflicting, they are preprocessed according to preset strategies to generate a recommended retention value for each conflicting field, while retaining all historical content as version records.

7. The comprehensive consulting service and project management platform for the transformation of scientific and technological achievements according to claim 4 is characterized in that: A new version is generated based on the original master achievement entity model, and the incremental information and processed field content are unified into the new version data package. At the same time, the additional metadata is bound to the version model. If it is judged that there is a high risk or key field conflict, the "audit flow mechanism" is automatically triggered, and the integration results are submitted to the "sole manager" of the achievement for manual review and final confirmation.

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