Scientific research talent and enterprise information matching method and system

Abstract

The invention relates to the technical field of information technology, and discloses a scientific research talent and enterprise information matching method and system, and the method comprises the steps: obtaining enterprise demand information, and constructing an enterprise demand feature set; obtaining scientific research achievement information, constructing an achievement feature set, and generating an application scene prediction result; obtaining enterprise historical technology information, constructing an enterprise technology layout feature set, and identifying a potential technology demand direction; obtaining scientific research talent information and constructing a scientific research talent ability feature set; mapping the feature set to a unified feature space to form an enterprise demand vector, an enterprise technology layout vector and a scientific research talent ability vector; and performing matching analysis on the scientific research talent capability vector, the enterprise demand vector and the enterprise technology layout vector based on the unified feature space to generate a matching result set, and outputting matched scientific research talent information and related scientific research achievement recommendation or cooperation docking information. The method can improve the industrialization efficiency of scientific achievements.

Description

Technical Field

[0001] This invention relates to the field of information technology, and more specifically, to a method and system for matching scientific research talent with enterprise information. Background Technology

[0002] As global technological innovation accelerates, the reliance of industrial technology upgrades on the transformation of scientific research results continues to deepen. Enterprises face an urgent need for technological iteration and product innovation, while a large number of scientific research talents and their achievements are scattered in universities and research institutions, forming significant information barriers. Although current scientific research achievement matching platforms can centrally display enterprise needs, scientific research results, and talent information, their matching mechanisms have fundamental flaws. Existing systems generally use keyword matching or tag mapping methods, separating enterprise needs, scientific research results, and talent information into isolated data points. They fail to construct a structured expression system that includes the logical chain of demand formation, the path chain of achievement verification, and the chain of talent capability evolution. As a result, the matching results only reflect shallow semantic relationships and cannot assess the feasibility of actual cooperation.

[0003] For example, the technological needs proposed by enterprises often implicitly contain multiple layers of logical relationships, such as the causal constraints between technological objects and application scenarios, and the correlation between maturity requirements and verification conditions. Traditional methods cannot parse such structured semantics, causing recommendation results to deviate from actual technology implementation scenarios. Simultaneously, enterprise technology deployments have dynamic evolutionary characteristics, with historical technology keyword sequences implying future technology gaps. However, existing solutions lack the ability to analyze the temporal sequence of enterprise technology evolution segments, making it difficult to deduce potential demand directions from historical patents and research achievements. This results in the systematic omission of high-value research talent aligned with the enterprise's strategic development direction. In the research talent evaluation process, over-reliance on superficial indicators such as the number of research directions or patent counts neglects the comprehensive quantification of technological stability, the completeness of verification elements, and the consistency of application scenarios. This distorts talent capability profiles and fails to distinguish between short-term hot research and sustainable technological development capabilities.

[0004] Furthermore, the lack of structured evidence in the matching results makes it difficult for companies to trace key evidence chains in the recommendation logic. User feedback is not incorporated into the closed-loop optimization mechanism, preventing the system from improving matching accuracy through the accumulation of historical pairing relationships, which has long hampered the efficiency of technology transfer. Summary of the Invention

[0005] In view of this, the present invention proposes a method and system for matching scientific research talents with enterprise information, which aims to effectively analyze the structured semantics of enterprise needs, scientific research results and talent information, construct a unified feature space for accurate matching, thereby improving the matching efficiency and cooperation feasibility between scientific research talents and enterprises.

[0006] In one aspect, this invention proposes a method for matching scientific research talent with enterprise information, comprising the following steps: Obtain enterprise demand information of the target enterprise. The enterprise demand information includes at least technical field information, industry direction information, application scenario description information, and technology maturity requirement information. Semantic parsing and structural processing are performed on the enterprise demand information to construct a set of enterprise demand features. Acquire scientific research results information, which includes at least a brief introduction of the results technology, information on the technical field to which the results belong, information on the technology maturity evaluation, and information on the technology development trend. Analyze the scientific research results information, construct a set of results features, and generate application scenario prediction results based on the set of results features. The historical technology information of the target enterprise is obtained. The historical technology information includes at least the technology keywords that the enterprise has deployed, the existing technology direction information, and the publicly disclosed scientific research results or patent information. The historical technology information is analyzed to construct a set of enterprise technology layout characteristics, and potential technology demand directions are identified based on the set of enterprise technology layout characteristics. Information on scientific research talents is obtained, including at least information on research direction, research achievements or patents, and research experience. The information on scientific research talents is then analyzed to construct a set of ability characteristics of scientific research talents. The enterprise demand feature set, achievement feature set, enterprise technology layout feature set, and scientific research talent capability feature set are mapped to a unified feature space to form an enterprise demand vector, an enterprise technology layout vector, and a scientific research talent capability vector. Based on the unified feature space, a matching analysis is performed on the scientific research talent capability vector, the enterprise demand vector, and the enterprise technology layout vector to generate a set of matching results between scientific research talents and target enterprises. Based on the matching result set, the system outputs scientific research talent information that matches the target enterprise, and generates scientific research achievement recommendation information or cooperation matching information associated with the scientific research talent information.

[0007] Furthermore, constructing a set of enterprise demand characteristics includes: Enterprise demand information is broken down into a set of demand fragments, which includes at least technology object fragments, application scenario fragments, constraint fragments, and maturity fragments. For each requirement fragment in the requirement fragment set, generate a fragment evidence item. The fragment evidence item includes at least the fragment's position index in the enterprise requirement information, the fragment's trigger word type, and the fragment's context boundary. The enterprise demand evidence chain is constructed based on fragment evidence items. The enterprise demand evidence chain connects the technology object fragment, application scenario fragment, and constraint condition fragment in the order of position index and records the connection type between adjacent fragments. The connection type includes at least causal connection, parallel connection, and conditional connection. When the same technical object fragment corresponds to multiple different application scenario fragments in the enterprise demand evidence chain, the enterprise demand evidence chain is split into multiple demand sub-chains, and each demand sub-chain forms a set of candidate demand feature groups in the enterprise demand feature set.

[0008] Furthermore, identifying the technology field and industry direction information within the enterprise's demand information includes: Extract the core phrase of the technical object based on the technical object fragment in each demand subchain, and extract the core phrase of the scenario object based on the application scenario fragment; Perform domain mapping on the core phrases of technology objects and the core phrases of scenario objects respectively to obtain the candidate set of technology fields and the candidate set of industry directions; When there are multiple candidates in the candidate set of technical fields and a unique primary candidate cannot be formed, the connection type of the requirement subchain is invoked for disambiguation determination. The disambiguation determination includes: when the connection type is causal connection, candidates that are consistent with the core phrase mapping of the technical object are selected first; when the connection type is conditional connection, candidates that are consistent with the core phrase mapping of the scene object are selected first. The disambiguated technology sector information and industry direction information are bound to the corresponding demand sub-chains to form a set of enterprise demand characteristics.

[0009] Furthermore, the set of outcome characteristics includes: The scientific research results information is broken down into a set of results fragments, which at least include a technical introduction fragment, a verification condition fragment, a verification result fragment, and an application orientation fragment. A chain of evidence for the results is constructed based on a set of result fragments. The chain of evidence for the results is connected in the order of technical introduction fragment, verification condition fragment, verification result fragment and application-oriented fragment. The maturity evaluation information of the results is generated based on the chain of evidence of the results. The maturity evaluation information of the results is jointly determined by the verification condition fragment and the verification result fragment. Under the condition that the verification condition fragment has test environment constraints and the verification result fragment lacks corresponding indicator description, the maturity evaluation information of the results is marked as an incomplete maturity state and the incomplete maturity state is written into the result feature set. Application scenario prediction results are generated based on application-oriented fragments. The application scenario prediction results include at least three types of scenario elements: application object, deployment form, and operation constraints.

[0010] Furthermore, constructing a set of enterprise technology layout characteristics and identifying potential technology demand directions includes: Extract a set of technical keywords from historical technical information and form a sequence of enterprise technology evolution in chronological order; The enterprise's technology evolution sequence is divided into continuous evolutionary segments, and each evolutionary segment corresponds to an aggregation result of a technology direction; By comparing each demand subchain in the enterprise demand feature set with the evolution segment group, we obtain the demand coverage item set and the demand gap item set. The demand gap item set consists of technical directions or scenario elements that exist in the demand subchain but not in the evolution segment group. The set of demand gap items is decomposed into two types based on the source of the gap: the first type is the technology direction gap, and the second type is the scenario element gap. The first type and the second type of gap are jointly identified as potential technology demand directions.

[0011] Furthermore, constructing a set of research talent capability characteristics includes: Obtain research results or patent information from scientific research talent information, construct corresponding achievement evidence chain or patent evidence chain for each research result or patent information, and extract technical direction elements, verification elements and scenario elements within the chain. Multiple evidence chains corresponding to scientific research talents are merged according to scenario elements to form a set of scientific research talent scenario clusters; For each set of scientific research talent scenario clusters, a capability indication vector is generated. The capability indication vector includes at least a directional stability component and a verification completeness component. The directional stability component is determined by the consistency of technical direction elements within the scenario cluster, and the verification completeness component is determined by the completeness of verification elements. By integrating capability indicator vectors with research direction information, a set of capability characteristics for research talents is formed.

[0012] Furthermore, mapping the aforementioned set of enterprise demand characteristics, set of achievement characteristics, set of enterprise technology layout characteristics, and set of scientific research talent capability characteristics to a unified feature space includes: Map each subchain of demand in the enterprise demand feature set to a demand chain vector, and retain the chain segment boundary index of the demand chain vector; Map the achievement evidence chain in the achievement feature set to an achievement chain vector, and retain the verification segment boundary index of the achievement chain vector; Map the capability indicator vectors in the set of scientific research talent capability characteristics to talent chain vectors, and retain the scene cluster boundary index of the talent chain vectors; In a unified feature space, a comparable chain alignment structure is formed by aligning the chain segment boundary index with the validation segment boundary index as a constraint, which is then used for subsequent matching analysis.

[0013] Furthermore, the matching analysis and generation of a set of matching results includes: Based on the chain alignment structure, chain segment matching is performed between the capability vector of scientific research talents and the demand vector of enterprises. Chain segment matching includes direction segment matching, scenario segment matching and verification segment matching. When the direction segment and the scene segment are matched successfully, but the verification segment is not matched, the corresponding scientific research talent will be included in the supplementary scientific research talent set, and a supplementary reason label will be generated. The supplementary reason label shall include at least the insufficient maturity label and the missing evidence label. When the direction segment is matched successfully but the scene segment is matched unsuccessfully, the second type of gap in the potential technical requirement direction is called to replay and verify the scene segment. The replay verification is to re-execute the scene segment matching with the second type of gap as the replacement scene element, and when the replay verification is successful, the corresponding scientific research talent is included in the potential cooperative scientific research talent set. When a directional segment fails to match, the corresponding research talent is directly removed and not included in the matching result set.

[0014] Furthermore, outputting scientific research talent information and generating related recommendation information includes: For each scientific research talent in the matching result set, generate matching basis description information. The matching basis description information includes at least the index of the hit demand sub-chain, the index of the hit achievement evidence chain, the index of the hit talent scenario cluster, and the corresponding supplementary reason mark or replay verification mark. The matching criteria information and the scientific research talent information will be output together; Receive feedback from enterprises on the output results, and update the priority combination library and the exclusion combination library respectively. The priority combination library is used to record the valid pairing relationship between the demand subchain index and the talent scenario cluster index, and the exclusion combination library is used to record the invalid pairing relationship between the demand subchain index and the talent scenario cluster index. In subsequent matching analysis, valid pairings in the priority combination library are used first to generate a set of candidate scientific research talents, and invalid pairings recorded in the combination library are removed during the candidate screening stage.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: by decomposing enterprise demand information and scientific research achievement information into demand fragment sets and achievement fragment sets respectively, and constructing enterprise demand evidence chains and achievement evidence chains, the matching process is transformed from "keyword correspondence" to "evidence chain structure alignment", reducing the probability of mismatch caused by relying solely on superficial semantic similarity.

[0016] When there are multiple candidates in the candidate set of the technical field and a unique main candidate cannot be formed, the connection type of the demand sub-chain is introduced to perform disambiguation judgment, realize differentiated selection path based on causal connection and conditional connection, and improve the consistency and interpretability of the determination results of technical field information and industry direction information.

[0017] By jointly generating technology maturity assessment information through verification condition fragments and verification result fragments, and marking incomplete maturity status when the verification result fragment lacks corresponding indicator descriptions, the maturity assessment of results has the ability to "identify completeness" and avoids directly matching results with insufficient evidence with high maturity requirements.

[0018] Based on the enterprise's technology evolution sequence and evolution fragment group, the set of demand coverage items and the set of demand gap items are obtained by comparing with the demand sub-chain. The gap is then decomposed into technology direction gap and scenario element gap, realizing the structured reverse inference of the enterprise's potential technology demand direction and reducing the omissions in recommendations caused by relying solely on explicit demand descriptions.

[0019] By constructing evidence chains for scientific research talent achievements or patents and grouping them into scenario clusters according to scenario elements, a capability indicator vector containing directional stability and verification completeness components is further generated. This enables the set of scientific research talent capability characteristics to reflect the consistency of research direction and the completeness of achievement verification, thereby improving the distinguishability of talent profiles.

[0020] A chain segment matching mechanism is adopted, which includes direction segment matching, scenario segment matching, and verification segment matching. When the verification segment matching fails, a supplementary reason flag is generated. When the scenario segment matching fails, a replay verification is performed based on the second type of gap. This enables the hierarchical output of supplementary and potential collaborative scientific research talents, improving the usability and scalability of matching.

[0021] On the other hand, the present invention also provides a system for matching scientific research talent with enterprise information, comprising: The first acquisition and processing module is configured to acquire enterprise demand information of the target enterprise. The enterprise demand information includes at least technical field information, industry direction information, application scenario description information, and technology maturity requirement information. The module performs semantic parsing and structured processing on the enterprise demand information to construct a set of enterprise demand features. The second acquisition and processing module is configured to acquire scientific research results information, which includes at least a brief introduction of the results technology, information on the technical field to which the results belong, information on the maturity of the technology, and information on the development trend of the technology. The module parses the scientific research results information, constructs a set of results features, and generates application scenario prediction results based on the set of results features. The historical information processing module is configured to acquire the historical technology information of the target enterprise. The historical technology information includes at least the enterprise's deployed technology keyword information, existing technology direction information, and publicly disclosed scientific research results or patent information. The module analyzes the historical technology information, constructs a set of enterprise technology layout characteristics, and identifies potential technology demand directions based on the set of enterprise technology layout characteristics. The third data acquisition and processing module is configured to acquire scientific research talent information, which includes at least scientific research direction information, scientific research achievements or patent information and scientific research experience information, and to parse the scientific research talent information to construct a set of scientific research talent capability characteristics. The matching and recommendation module is configured to map the enterprise demand feature set, achievement feature set, enterprise technology layout feature set, and scientific research talent capability feature set to a unified feature space to form an enterprise demand vector, an enterprise technology layout vector, and a scientific research talent capability vector. Based on the unified feature space, a matching analysis is performed on the scientific research talent capability vector, the enterprise demand vector, and the enterprise technology layout vector to generate a set of matching results between scientific research talents and target enterprises. Based on the matching result set, the system outputs scientific research talent information that matches the target enterprise, and generates scientific research achievement recommendation information or cooperation matching information associated with the scientific research talent information.

[0022] It is understandable that the aforementioned system and method for matching scientific research talent with enterprise information have the same beneficial effects, and will not be elaborated upon here. Attached Figure Description

[0023] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a method for matching scientific research talent with enterprise information, provided as an embodiment of the present invention; Figure 2 This is a functional block diagram of a scientific research talent and enterprise information matching system provided in an embodiment of the present invention. Detailed Implementation

[0024] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] See Figure 1 As shown, this application proposes a method for matching scientific research talent with enterprise information, including: In the process of matching research talent with enterprise information, enterprise demand information, research achievement information, and research talent information are usually processed as independent semantic units, lacking a structured expression mechanism for the logic of demand formation, the path of achievement verification, and the evolution of research talent capabilities. Furthermore, the matching algorithm mainly relies on keyword matching or tag mapping, resulting in matching results that only reflect superficial semantic similarity and cannot accurately assess the feasibility of actual cooperation. Moreover, existing methods, when processing enterprise demands, do not incorporate historical technology layout information for dynamic analysis, making it difficult to identify potential technology demand directions. In addition, the research talent capability assessment process does not comprehensively consider the completeness of research achievement verification, consistency of application scenarios, and stability of research directions, resulting in a single-dimensional capability profile. After the matching results are generated, there is a lack of structured matching basis explanations, and enterprise feedback information is not effectively integrated into subsequent matching processes, thus preventing the system from forming an iteratively optimizable matching closed loop.

[0026] For example, in a technology upgrade project of a new energy battery manufacturing company, the company released a demand seeking "the development of high cycle life solid electrolyte materials suitable for low-temperature environments." The existing matching system extracts keywords from this demand, identifying terms such as "solid electrolyte" and "low-temperature environment," and matches relevant researchers. However, the company's historical technology information shows that it possesses a large number of published patents in the field of liquid electrolytes, and its technology evolution sequence indicates a shift towards solid-state technology. Yet, the system failed to identify the potential technology demand direction as "compatibility materials for the transition from liquid to solid-state" based on the company's technology layout characteristics. Furthermore, among the recommended researchers, some individuals lacked actual low-temperature testing environment constraints in their research results verification fragments, and their verification result fragments did not include descriptions of cycle life indicators, but the system did not mark these as incomplete maturity states. The matching results only output a recommended list, without providing indexes of the matched demand sub-chains or achievement evidence chains, making it difficult for the company to understand the recommendation logic. Moreover, the company's feedback was not used to update the priority and exclusion combination libraries.

[0027] If these issues are not addressed, the credibility of matching results will decrease, making it difficult for companies to identify research talent whose technological evolution paths align closely with their own, leading to missed collaboration opportunities. The lack of interpretability in the system prevents companies from verifying the matching criteria, reducing trust in the platform. Simultaneously, the absence of a feedback mechanism prevents the matching algorithm from iteratively optimizing based on actual collaboration results, causing a continuous deterioration in the overall system performance. Ultimately, the efficiency of commercializing research results will be constrained, and the information asymmetry between technology supply and demand will not be effectively alleviated.

[0028] In response, this application proposes a method for matching scientific research talent with enterprise information, including the following steps: S1: Obtain the target company's enterprise demand information, which includes at least technical field information, industry direction information, application scenario description information, and technology maturity requirements information. Then, perform semantic parsing and structured processing on the enterprise demand information to construct a set of enterprise demand features. S2: Obtain scientific research results information, which includes at least the results' technical overview, the technical field to which the results belong, the technology maturity assessment, and the technology development trend. Analyze the scientific research results information, construct a set of results features, and generate application scenario prediction results based on the set of results features. S3: Obtain the historical technology information of the target company. This historical technology information includes at least the technology keywords that the company has deployed, the existing technology directions, and the publicly disclosed scientific research results or patent information. Analyze this historical technology information, construct a set of technology layout characteristics of the company, and identify potential technology demand directions based on the set of technology layout characteristics of the company. S4: Obtain information on scientific research talents, which includes at least information on research direction, research achievements or patents, and research experience. Analyze this information to construct a set of scientific research talent capability characteristics. S5: Map the enterprise's demand feature set, achievement feature set, enterprise technology layout feature set, and scientific research talent capability feature set to a unified feature space to form an enterprise demand vector, an enterprise technology layout vector, and a scientific research talent capability vector. S6: Based on this unified feature space, perform matching analysis between the scientific research talent's capability vector and the enterprise's demand vector and technology layout vector to generate a set of matching results between scientific research talent and target enterprise. Based on this matching result set, the system outputs information on scientific research talents that match the target company, and generates recommendations for scientific research achievements or cooperation opportunities associated with this information.

[0029] For ease of understanding, the following explains some key terms in this embodiment: Enterprise needs information refers to the specific technical requirements and business objectives of a target enterprise during its technological upgrades, product development, or industrial layout. This information typically includes the enterprise's required technological fields, desired industry direction, descriptions of specific application scenarios, and specific requirements for technological maturity. In-depth analysis of this information helps to accurately grasp the enterprise's true needs.

[0030] Semantic parsing and structuring refers to the automated analysis of unstructured or semi-structured text information, extracting key entities, relationships, and events, and transforming them into a machine-understandable and processable structured data format. This process eliminates the ambiguity of natural language, laying the foundation for subsequent feature extraction and matching.

[0031] A set of enterprise demand features refers to a series of structured features that comprehensively and accurately describe enterprise demands, formed through semantic parsing and structuring of enterprise demand information. This set can include multiple dimensions such as technology objects, application scenarios, constraints, and technology maturity, and is used to represent enterprise demands in a unified feature space.

[0032] Scientific research findings information refers to the knowledge, technologies, products, or solutions acquired by researchers or institutions during scientific research activities. This information typically includes a technical summary of the findings, the relevant technical field, a technology maturity assessment, and future technological development trends. Analyzing this information helps in evaluating the value and applicability of the findings.

[0033] The set of research achievement features refers to a series of structured features that comprehensively describe research achievements after analyzing their information. This set can include multiple dimensions such as technical principles, verification conditions, verification results, and application directions, and is used to represent research achievements in a unified feature space.

[0034] Application scenario prediction results refer to the predictions made based on the characteristics of scientific research findings, through analysis and reasoning, regarding the specific application environments, deployment methods, and operating conditions that the findings may be applicable to. These predictions help to link scientific research findings with the actual application needs of enterprises.

[0035] Historical technology information refers to the technological assets and development trajectory accumulated by a target company over a past period. This information includes at least the company's established technological keywords, existing technological directions, and publicly disclosed research results or patents. Analyzing this information can reveal the company's technological evolution path and potential technological needs.

[0036] A company's technology layout feature set refers to a series of structured features that reflect its current technological strength and future development direction, formed by analyzing the company's historical technology information. This set can include technology evolution sequences, technology direction aggregation results, etc., and is used to represent the company's technology layout in a unified feature space.

[0037] Potential technology demand refers to the technology needs that a company may have in the future, but which have not yet been explicitly stated, based on its existing technology layout, development goals, and market trends. These implicit technology gaps can be identified by analyzing the characteristics of a company's technology layout.

[0038] Information on research talent refers to a researcher's personal background, professional abilities, and research output. This information includes at least their research direction, research achievements or patent information, and research experience. Analyzing this information helps to comprehensively assess a researcher's professional abilities and research potential.

[0039] The set of research talent capability characteristics refers to a series of structured features that comprehensively describe the professional capabilities of research talents after analyzing their information. This set may include components such as directional stability and verification completeness, and is used to represent the capabilities of research talents in a unified feature space.

[0040] A unified feature space refers to a standardized and normalized multi-dimensional space where feature information from different sources (such as enterprise needs, scientific research results, enterprise technology layout, and scientific research talent capabilities) can be mapped into comparable vector representations. In this space, the similarity or correlation between different entities can be measured by the distance or angle between vectors.

[0041] Enterprise demand vector, enterprise technology layout vector, and scientific research talent capability vector are numerical representations of the sets of enterprise demand features, enterprise technology layout features, and scientific research talent capability features, respectively, within a unified feature space. These vectors carry structured information about the corresponding entities and support subsequent quantitative matching analysis.

[0042] Matching analysis refers to assessing the fit between research talent and corporate needs and technological strategies within a unified feature space by calculating similarity, distance, or performing other algorithms between different vectors. This analysis aims to identify the research talent best suited to a company's requirements.

[0043] The matching result set refers to a list of research talents that meet specific matching criteria, generated by the system after matching analysis. Each research talent in this set has a certain matching relationship with the target company, and may include matching evidence.

[0044] Research achievement recommendation information refers to providing details of research achievements related to a talent and highly aligned with the company's needs when outputting matching information about research personnel. This information helps companies gain a more comprehensive understanding of the talent's output and technical capabilities.

[0045] Collaboration matching information refers to providing matching information on scientific research talent while simultaneously offering channels or suggestions to facilitate further communication and cooperation between enterprises and these talents. This information aims to promote the practical transformation and industrialization of scientific research results.

[0046] This embodiment provides a method for matching research talent with enterprise information. The method first obtains the target enterprise's demand information, which includes at least technical field information, industry direction information, application scenario description information, and technology maturity requirements. To gain a deeper understanding of the enterprise's demand information, various processing methods can be employed. For example, a team of human experts can read and analyze the demand documents submitted by the enterprise, manually extracting key technical fields, industry directions, application scenarios, and technology maturity requirements, and organizing them into a structured table format. Alternatively, a rule-based text processing system can be used, with pre-defined keyword and phrase matching rules, to identify predetermined technical field terms, industry direction terms, application scenario description patterns, and technology maturity level indicators from the enterprise's demand text. Through these methods, a preliminary set of enterprise demand characteristics can be constructed.

[0047] Furthermore, this method acquires information about scientific research achievements, which includes at least a brief description of the technology, the technical field to which the achievement belongs, a technology maturity assessment, and information on technological development trends. This information is then analyzed to construct a set of achievement features, and application scenario predictions are generated based on this feature set. Specifically, the submitter of the research achievement or the platform administrator can manually fill in the technical description, the technical field to which the achievement belongs, the technology maturity level, and the judgment on future development trends. Alternatively, a keyword-based indexing method can be used to extract core keywords from the technical description of the achievement and map them to a pre-defined technical field classification system to determine the technical field to which the achievement belongs. The technology maturity assessment can be conducted manually based on the achievement's experimental reports or test data. The application scenario prediction can be based on expert judgment of the achievement's technical characteristics and existing market applications.

[0048] Furthermore, this method acquires the target company's historical technology information, which includes at least the company's existing technology keywords, current technology directions, and publicly available research results or patent information. This historical technology information is analyzed to construct a set of company technology layout characteristics, and potential technology demand directions are identified based on this set. For example, publicly available patent documents and research project reports can be collected periodically, key technology keywords and technology classification tags can be extracted, and these can be listed in chronological order to form a rough record of the company's technology evolution. A simple statistical analysis of these keywords and tags can provide an overview of the company's existing technology directions. The identification of potential technology demand directions can be based on a simple comparison between the company's current technology layout and general industry technology development trends. For example, if the company lacks patent coverage in a certain general technology field, it can be marked as a potential demand.

[0049] This method also acquires information on research talent, which includes at least research direction, research achievements or patent information, and research experience. This information is then analyzed to construct a set of research talent capability characteristics. Specifically, researchers can be required to manually fill in their main research direction, a list of representative research achievements or patents, and detailed research experience during registration. Alternatively, information such as research field keywords, number of published papers, and number of projects participated in can be extracted from researchers' resumes or academic homepages through manual reading or simple text matching, and these can be incorporated into the research talent capability characteristics.

[0050] Subsequently, the enterprise's demand feature set, achievement feature set, technology layout feature set, and scientific research talent capability feature set are mapped to a unified feature space, forming enterprise demand vector, enterprise technology layout vector, and scientific research talent capability vector, respectively. For example, the text information in each of the above feature sets can be converted into numerical vectors using the Bag-of-Words model or TF-IDF (Term Frequency-Inverse Document Frequency) method, and then these numerical vectors are concatenated to form a high-dimensional feature vector. For categorical features, one-hot encoding can be used for conversion. In this way, structured features and text features from different sources are uniformly represented as numerical vectors to facilitate subsequent computational processing.

[0051] Based on this unified feature space, a matching analysis is performed between the research talent's capability vector and the enterprise's demand vector and technology layout vector, generating a set of matching results between research talents and target enterprises. Specifically, the cosine similarity algorithm can be used to calculate the similarity between the research talent's capability vector and the enterprise's demand vector, as well as the similarity between the research talent's capability vector and the enterprise's technology layout vector. These similarity scores are then weighted and summed to obtain a comprehensive matching score. Based on this comprehensive matching score, the research talents are ranked, and those with scores higher than a preset threshold are selected as members of the matching result set.

[0052] Finally, based on this matching result set, the system outputs information on research talents matching the target company and generates recommended research achievements or collaboration opportunities associated with this talent information. For example, the system can directly list the names, institutions, and contact information of several research talents with the highest matching scores. Simultaneously, it can filter out research achievements from these talents that have a high degree of overlap with the company's technical field or application scenario keywords, and output these as recommended research achievements. Collaboration opportunities can be a general contact email address or a platform messaging function.

[0053] The following example will provide a more detailed explanation of the above technical solution: Suppose a company called "Innovation Manufacturing Company" whose core business is manufacturing industrial automation equipment. The company currently faces a technical challenge: it needs to develop an intelligent sensor capable of long-term stable operation in high-temperature, high-humidity, and highly corrosive environments to monitor the status of critical equipment on the production line, enabling predictive maintenance. The company requires the sensor to reach the pilot or small-batch production maturity stage (e.g., TPL 6-7).

[0054] First, the system obtains the enterprise demand information from the innovative intelligent manufacturing company. The company's submitted demand document explicitly states its need for "intelligent sensors in high-temperature, high-humidity, and highly corrosive environments," and mentions keywords such as "predictive maintenance," "industrial IoT," and requirements for "high reliability" and "TPL 6-7." The system performs semantic parsing and structured processing on this demand document. For example, by identifying "high-temperature, high-humidity, and highly corrosive" as a constraint segment, "intelligent sensors" as a technology object segment, "predictive maintenance" as an application scenario segment, and "TPL 6-7" as a maturity segment, the system constructs a set of enterprise demand features containing these structured elements.

[0055] Next, the system acquires a massive amount of scientific research information. For example, the database contains a research achievement titled "Self-Powered Sensor Technology for Harsh Environments Based on Novel Ceramic Materials," whose technical description details the sensor's performance in high-temperature and corrosive gas environments, and includes laboratory verification data. The system analyzes this information, extracting elements such as the technical principle, verification conditions (high temperature, corrosive gas), and verification results (long-term stable operation), constructing a feature set for the achievement. Furthermore, based on the characteristics of this achievement, the system predicts that its application scenarios may include "petrochemical equipment monitoring" and "metallurgical production line monitoring," among others.

[0056] Simultaneously, the system acquires historical technological information about the innovative manufacturing company. By analyzing the company's past patents and project records, the system found that it has strong technological accumulation in traditional sensor manufacturing and data acquisition, but there are technological gaps in new material applications, self-powered technology, and adaptability to harsh environments. The system constructs a set of characteristics of the company's technological layout and identifies "new corrosion-resistant material technology" and "self-powered sensor technology" as potential technological demand directions.

[0057] Subsequently, the system retrieves information on research talents. For example, the database contains a "Professor Li" whose research focus is "special environment sensor materials and devices." He holds multiple patents related to "high-temperature corrosion environment sensors" and has published numerous related papers. His research experience includes several projects related to industrial applications. The system analyzes Professor Li's information to construct a set of research talent capability characteristics, reflecting the depth and breadth of Professor Li's expertise in specific technical fields.

[0058] Next, the system maps the aforementioned sets of enterprise demand characteristics, achievement characteristics, enterprise technology layout characteristics, and scientific research talent capability characteristics to a unified feature space. In this space, the demand of the innovative manufacturing company is represented as an enterprise demand vector, Professor Li's capability is represented as a scientific research talent capability vector, and the achievement "self-powered sensor technology for harsh environments based on novel ceramic materials" is also represented as an achievement vector.

[0059] Based on this unified feature space, the system performs a matching analysis between Professor Li's research talent capability vector and the enterprise demand vector and technology layout vector of the Innovation Manufacturing Company. The system finds that Professor Li's research direction in "special environment sensor materials" highly aligns with the Innovation Manufacturing Company's need for "intelligent sensors in high-temperature, high-humidity, and highly corrosive environments." Verification data of Professor Li's research achievements indicates that his technology maturity has reached TPL 6, meeting the company's technology maturity requirements. Furthermore, Professor Li's expertise in "new corrosion-resistant material technology" precisely fills a gap in the Innovation Manufacturing Company's potential technology needs. Through this multi-dimensional matching analysis, the system generates the matching results between Professor Li and the Innovation Manufacturing Company.

[0060] Finally, based on the matching result set, the system outputs Professor Li's detailed information to the innovative manufacturing company. Simultaneously, the system generates research achievement recommendations associated with Professor Li's information, such as recommending Professor Li's achievement "Self-Powered Sensor Technology for Harsh Environments Based on Novel Ceramic Materials," and providing a detailed technical report of this achievement. Furthermore, the system generates cooperation and matchmaking information, such as providing Professor Li's contact information or suggesting initiating cooperation negotiations through the platform. This example demonstrates that this method, through structured analysis and unified spatial mapping of multi-source heterogeneous information, achieves accurate and multi-dimensional matching between research talent and enterprise needs, and provides interpretable recommendation criteria.

[0061] The overall technical concept of this embodiment makes a significant contribution to existing technologies. In the example above, the innovative manufacturing company's need for "intelligent sensors in high-temperature, high-humidity, and highly corrosive environments" might, in existing technologies, only lead to keyword matching with experts in the "sensor" field, but fail to deeply identify its specific requirements regarding "harsh environments" and "high reliability." This method, by semantically parsing and structuring the company's demand information, constructs a set of company demand features, capturing multiple dimensions of the demand, such as the technical field, industry direction, application scenario description, and technology maturity requirements, thereby avoiding matching biases caused by superficial semantic similarity.

[0062] Existing technologies, when addressing potential enterprise needs, typically rely solely on static demand descriptions for matching. For example, an innovative manufacturing company may not explicitly state a need for "new corrosion-resistant material technology," but its historical technology portfolio analysis may reveal gaps in this area. This method, by acquiring historical technology information of target companies and identifying potential technology demand directions based on a set of company technology portfolio characteristics, proactively identifies unexpressed but crucial technology gaps for the company's future development. This allows for the recommendation of research talent highly relevant to these potential needs, avoiding the omission of research talent and achievements related to the company's future development direction.

[0063] Furthermore, current methods for evaluating the capabilities of scientific research personnel often rely primarily on the number of research directions, achievements, or patents, lacking a comprehensive analysis of the completeness of research achievement verification, consistency of application scenarios, and stability of research directions. In the example above, Professor Li's research achievements are not only numerous, but more importantly, the verification data and application scenario predictions of his "self-powered sensor technology for harsh environments based on novel ceramic materials" highly align with the needs of the innovative manufacturing company. This method, by constructing a set of characteristics for scientific research personnel capabilities, comprehensively considers the verification status of research achievements, application orientation, and stability of research directions, providing a more refined and comprehensive profile of scientific research personnel capabilities and supporting highly reliable matching.

[0064] Furthermore, after the matching results are generated, existing systems typically only output a recommended list or ranking results, lacking a structured explanation of the matching criteria. This method, based on the matching result set, outputs information on research talents matching the target company and generates recommended research achievements or collaboration opportunities associated with that talent information. For example, the system not only recommends Professor Li but also clearly points out Professor Li's expertise in "special environment sensor materials," the technological maturity of his achievements, and the points of convergence with the company's potential needs. This allows the innovative manufacturing company to clearly understand the recommendation logic, improving the interpretability of the matching results. Through a structured, multi-dimensional information analysis and matching mechanism, this method achieves a highly credible, highly interpretable, and sustainably optimized matching process between research talents and companies, thereby improving the efficiency and quality of research achievement industrialization.

[0065] In some of the above implementation methods, semantic parsing and structuring of enterprise demand information are proposed to construct a set of enterprise demand features. However, in practice, enterprise demand information often contains multi-dimensional and complexly intertwined technical elements. For example, a technical object may correspond to multiple application scenarios or have multiple constraints. If only simple parsing and structuring are performed, it is difficult to accurately capture these complex relationships, which may result in an insufficiently refined and comprehensive construction of the enterprise demand feature set, thereby affecting the accuracy of subsequent matching analysis.

[0066] To address this, this application further proposes the following steps for constructing a set of enterprise demand features: decomposing enterprise demand information into a set of demand fragments, which includes at least technology object fragments, application scenario fragments, constraint condition fragments, and maturity fragments; generating fragment evidence items for each demand fragment in the set, which includes at least the fragment's position index in the enterprise demand information, the fragment's trigger word type, and the fragment's context boundary; constructing an enterprise demand evidence chain based on the fragment evidence items, where the enterprise demand evidence chain connects the technology object fragments, application scenario fragments, and constraint condition fragments in order of position index, and records the connection types between adjacent fragments, including at least causal connections, parallel connections, and conditional connections; and, where the same technology object fragment corresponds to multiple different application scenario fragments in the enterprise demand evidence chain, splitting the enterprise demand evidence chain into multiple demand sub-chains, and forming each demand sub-chain into a set of candidate demand feature groups in the enterprise demand feature set.

[0067] Specifically, enterprise requirements are broken down into a set of requirement fragments. The technology object fragment refers to the core technology or product entity explicitly mentioned in the enterprise requirements. Its function is to identify the core content of the required technology, for example, by using named entity recognition technology in Natural Language Processing (NLP) to identify technical terms or product names from the enterprise requirement text. The application scenario fragment refers to the environment, field, or specific context in which the technology or product described in the enterprise requirements will be applied. Its function is to clarify the background and purpose of the technology's use, for example, by using event extraction or relation extraction in NLP to identify verb phrases or prepositional phrases related to the technology object. The constraint fragment refers to the restrictions or requirements imposed on the performance, cost, time, compliance, etc., of the technology or product in the enterprise requirements. Its function is to limit the feasibility of the technical solution, for example, by using rule matching to identify keywords such as "must," "must not," and "should meet," and their subsequent content. The maturity fragment refers to the requirements regarding the development stage of the required technology or product, such as the Technology Readiness Level (TRL) or commercialization level. Its function is to assess the applicability of the technical solution, for example, by identifying keywords such as "prototype stage" and "small batch production."

[0068] For each requirement fragment in the requirement fragment set, a fragment evidence item is generated. This fragment evidence item is a metadata annotation for each identified requirement fragment to provide richer contextual information. Its purpose is to provide precise location and semantic clues for subsequent construction of the evidence chain. In implementation, during text parsing, the start and end character positions of each fragment in the original text are recorded as position indices; simultaneously, keywords or phrases that led to the fragment's identification are identified as trigger word types, such as "need" potentially triggering a technology object fragment, and "applied to" potentially triggering an application scenario fragment; and words or sentences within a certain range surrounding the fragment are extracted as contextual boundaries to preserve local semantic information.

[0069] A chain of evidence for enterprise needs is constructed based on fragmented evidence items. This chain is a structured representation that organizes discrete demand fragments according to their logical relationships and order in the original text. Its purpose is to reveal the inherent logic and relevance of enterprise needs. In implementation, the positional indexes in the fragmented evidence items are analyzed to connect the technology object fragments, application scenario fragments, and constraint condition fragments according to their order of appearance in the original text. Simultaneously, natural language processing techniques are used to analyze the semantic relationships between adjacent fragments. For example, "because A, therefore B" indicates a causal connection; "A and B" indicates a parallel connection; and "if A, then B" indicates a conditional connection. These connection types are then recorded.

[0070] When a single technology object fragment corresponds to multiple different application scenario fragments within the enterprise requirement evidence chain, the enterprise requirement evidence chain is split into multiple requirement sub-chains, and each requirement sub-chain forms a group of candidate requirement features in the enterprise requirement feature set. When a technology object can be applied to multiple different scenarios, to avoid information confusion and improve matching accuracy, the original complex requirement chain needs to be decomposed. Its purpose is to decompose complex requirements into more granular and clearly defined independent requirement units, ensuring that each requirement sub-chain represents a clear technology-scenario combination. In implementation, this can be achieved by traversing the constructed enterprise requirement evidence chain to identify situations where a "one-to-many" relationship exists between a technology object fragment and an application scenario fragment. Once identified, an independent requirement sub-chain is generated, using the technology object fragment as the core and combining it with each different application scenario fragment and its related constraint condition fragments.

[0071] This application's solution first decomposes the original enterprise demand information into basic semantic fragments such as technology objects, application scenarios, constraints, and maturity levels, ensuring comprehensive identification of the demand information. Then, it generates fragment evidence items for each fragment, including location indexes, trigger word types, and contextual boundaries, providing precise metadata support for subsequent structured processing. Based on this, these fragments are constructed into an enterprise demand evidence chain according to their logical order and semantic relationships in the original text, explicitly recording the connection types between fragments, thus organizing the discrete demand information into a logically coherent whole. When a technology object is found to correspond to multiple application scenarios, the evidence chain is intelligently split into multiple demand sub-chains, each representing an independent and clearly defined technology-scenario combination. This layer-by-layer refinement and structured processing effectively addresses the challenges posed by the complexity and ambiguity of enterprise demand information, ensuring that the construction of the enterprise demand feature set is refined, accurate, and unambiguous, providing high-quality input for subsequent matching of research talent with enterprise information.

[0072] The following is a concrete example to illustrate this. Suppose the company's requirements are: "Our company needs a high-efficiency image recognition technology for facial recognition in intelligent security systems, requiring an accuracy rate of over 99%; simultaneously, this technology can also be applied to customer flow analysis in smart retail scenarios, requiring an accuracy rate of over 95%. Both applications need to run in real time on edge devices, and we currently hope the technology is in the prototype verification stage." First, by breaking down the company's requirements into a set of requirement fragments, we can obtain: the technology object fragment includes "efficient image recognition technology", "facial recognition", and "customer flow analysis"; the application scenario fragment includes "intelligent security system", "smart retail scenario", and "real-time operation on edge devices"; the constraint condition fragment includes "recognition accuracy rate of over 99%" and "customer flow analysis accuracy rate of over 95%"; and the maturity fragment includes "prototype verification stage".

[0073] Next, fragment evidence items are generated for these requirement fragments. For example, the fragment evidence item for "efficient image recognition technology" may include its position index in the original text, the trigger word type "requirement", and its context boundaries.

[0074] Then, an evidence chain for the enterprise's needs is constructed based on these fragmented pieces of evidence. In this example, "efficient image recognition technology" serves as the core technology, relating both to "facial recognition" in the "intelligent security system" and "customer flow analysis" in the "smart retail scenario." Therefore, the constructed evidence chain will demonstrate the connection between "efficient image recognition technology" and multiple application scenarios.

[0075] Finally, because the evidence chain of enterprise requirements includes the technological object segment "efficient image recognition technology" corresponding to two different application scenarios: "intelligent security system" and "smart retail scenario," this evidence chain is split into multiple requirement sub-chains. Specifically, it can be divided into two requirement sub-chains: the first requirement sub-chain focuses on the requirement of "efficient image recognition technology" for "face recognition" in "intelligent security system," and includes its corresponding constraints (recognition accuracy rate of over 99%), operational requirements (real-time operation on edge devices), and maturity requirements (prototype verification stage); the second requirement sub-chain focuses on the requirement of "efficient image recognition technology" for "customer flow analysis" in "smart retail scenario," and includes its corresponding constraints (customer flow analysis accuracy rate of over 95%), operational requirements (real-time operation on edge devices), and maturity requirements (prototype verification stage). Each requirement sub-chain will serve as a group of candidate requirement features in the enterprise requirement feature set.

[0076] Through the aforementioned technical solution, this application systematically decomposes complex enterprise needs, meticulously annotates fragmented evidence items, constructs a logically clear chain of evidence, and intelligently splits the sub-chains of needs when ambiguity exists. This ensures that the construction of the enterprise need feature set is highly accurate, comprehensive, and unambiguous. This significantly improves the structuring level and semantic accuracy of enterprise need information, avoiding inaccurate matching problems caused by misunderstandings of needs. Therefore, it lays a solid foundation for the accurate matching of subsequent research talent and enterprise information, effectively improving the efficiency and success rate of matching.

[0077] In some embodiments described above, this application proposes a method for constructing a set of enterprise demand features. This method decomposes enterprise demand information into a set of demand fragments, constructs an enterprise demand evidence chain based on fragment evidence items, and further decomposes it into multiple demand sub-chains. Each demand sub-chain forms a group of candidate demand features in the enterprise demand feature set. However, in this process, when it is necessary to accurately determine the technical field information and industry direction information corresponding to each demand sub-chain from these candidate demand feature groups, especially when the field mapping may generate multiple technical field candidates or industry direction candidates, and it is not possible to directly determine a unique primary candidate, ambiguity may arise, thereby affecting the accuracy of the enterprise demand feature set and reducing the accuracy of subsequent matching of scientific research talent and enterprise information.

[0078] To address this, this application further proposes a method for determining the technical field information and industry direction information in enterprise demand information. The steps include: extracting core phrases for technical objects based on technical object fragments in each demand subchain, and extracting core phrases for scenario objects based on application scenario fragments; performing domain mapping on the core phrases for technical objects and scenario objects respectively to obtain a candidate set of technical fields and a candidate set of industry directions; when multiple candidates exist in the candidate set of technical fields and a unique primary candidate cannot be formed, invoking the connection type of the demand subchain for disambiguation determination. The disambiguation determination includes: when the connection type is causal connection, prioritizing candidates whose mapping is consistent with the core phrase of the technical object; when the connection type is conditional connection, prioritizing candidates whose mapping is consistent with the core phrase of the scenario object; and binding the disambiguated technical field information and industry direction information to their respective demand subchains to form a set of enterprise demand features.

[0079] The core phrases of a technology object refer to concise words or phrases identified from the technology object fragments of the demand subchain that represent the core content or key entities of the technology. Their function is to transform unstructured technical descriptions into standardized concepts that can be further analyzed and mapped. Extraction methods may include, but are not limited to: using Natural Language Processing (NLP) techniques, such as keyword extraction algorithms or noun phrase extraction based on dependency parsing; or matching and identification through a predefined domain dictionary. The core phrases of a scenario object refer to concise words or phrases identified from the application scenario fragments of the demand subchain that represent the key elements or environment of the application scenario. Their function is to clarify the specific application environment or conditions in which the technology exists, providing contextual information for subsequent domain mapping. Extraction methods may include, but are not limited to: using Named Entity Recognition (NER) models to identify entities such as location, equipment, and environment; or matching through the construction of a scenario dictionary.

[0080] Domain mapping refers to associating extracted core phrases of technical objects and scenario objects with corresponding technical fields and industry directions through a pre-constructed knowledge graph, ontology, or classification system. Its purpose is to unify discrete phrase concepts into a standardized domain classification system, laying the foundation for subsequent matching and analysis. Implementation methods may include, but are not limited to: calculating the similarity between phrases and domain labels based on word vector models and selecting the highest similarity phrases as candidates; or classifying phrases into preset technical fields and industry directions through rule matching, expert systems, or machine learning classifiers. The candidate set of technical fields refers to the set of multiple potential technical fields that may be associated with a given core phrase of a technical object through domain mapping. The candidate set of industry directions refers to the set of multiple potential industry directions that may be associated with a given core phrase of a scenario object through domain mapping. The existence of these two sets indicates that ambiguity or generalization may exist in the initial mapping stage.

[0081] Disambiguation determination refers to using additional contextual information (i.e., the connection type of the demand subchain) to eliminate ambiguity when the domain mapping result is not unique, thereby determining the most accurate technology field and industry direction. Its role is to improve the accuracy and specificity of domain identification. Implementation methods can include, but are not limited to: designing a rule-based inference engine that selects based on the pre-defined priority or weight of the connection type; or training a classification model that takes the connection type, core phrase, and candidate set as input and outputs the final domain selection. Connection types (such as causal connections, parallel connections, and conditional connections) are the semantic relationships between adjacent segments in the demand subchain, reflecting the logical connections between the technology object, application scenario, and constraints. A causal connection indicates that the technology object is the cause of or realization of a certain result. In this case, the technology object itself is the core, and therefore its mapped domain is more dominant. A conditional connection indicates that the application scenario or constraint is the prerequisite or limitation for the technology object to function. In this case, the scenario or condition has a stronger limiting effect on the specific implementation path of the technology and the selection of the domain. The priority selection strategy assigns higher weight or decision-making power to the core phrase of the technology object or the core phrase of the scenario object in domain mapping disambiguation based on the semantic characteristics of the connection type. For example, when the connection type is causal connection, the system will focus more on the domain mapping result of the core phrase of the technology object, selecting the candidate domain with the highest semantic relevance to that core phrase; when the connection type is conditional connection, the system will focus more on the domain mapping result of the core phrase of the scene object, selecting the candidate domain with the highest semantic relevance to that core phrase. Binding refers to associating the unique or most important technology field information and industry direction information, determined after disambiguation processing, as attributes or tags to their respective demand sub-chains. Its function is to assign clear domain and direction attributes to each demand sub-chain, thereby improving the construction of the enterprise demand feature set. Implementation methods may include, but are not limited to: adding the determined technology field and industry direction as structured data fields to the data structure of the demand sub-chain; or establishing indexes or pointers to associate and store the demand sub-chains with the corresponding domain and direction information.

[0082] This application's solution accurately determines the technical field and industry direction information of enterprise needs by introducing deep semantic analysis of technical object fragments and application scenario fragments in the demand subchain and combining intelligent disambiguation with the connection types between fragments. Specifically, firstly, from each demand subchain, the system identifies and extracts the most representative core phrases of technical objects and scenario objects. These core phrases are the essence of the demand subchain's semantics, providing the foundation for subsequent domain mapping. Next, the system performs domain mapping operations on these core phrases, associating them with preset technical field and industry direction classification systems, thereby generating their respective candidate sets of technical fields and candidate sets of industry directions. At this stage, because the core phrases may be ambiguous or generalized, multiple candidate domains may appear, leading to uncertainty in the initial mapping results. To resolve this uncertainty, this solution cleverly utilizes the connection types already recorded in the demand subchain. When there are multiple candidates in the technical field candidate set and a unique primary candidate cannot be directly determined, the system performs disambiguation based on the semantic characteristics of the connection types. For example, if the connection type is causal, indicating that the technology object is the core driving factor, the system will prioritize candidate domains that map to the core phrase of the technology object. If the connection type is conditional, indicating that the application scenario or constraints have a decisive impact on the technology implementation, the system will prioritize candidate domains that map to the core phrase of the scenario object. Through this intelligent reasoning based on semantic connections, the system can effectively eliminate ambiguity and accurately identify the technology field and industry direction information that best meet the enterprise's needs from multiple candidate domains. Ultimately, this disambiguated technology field and industry direction information is precisely bound to the corresponding demand sub-chain, thus forming a more accurate, detailed, and unambiguous set of enterprise demand features. This process elevates the construction of the enterprise demand feature set from the initial generation of candidate groups to a refined description with clear domain and direction attributes, significantly enhancing the expressive power of enterprise demand features and the accuracy of subsequent matching.

[0083] The following example illustrates this. Suppose that during the construction of a set of enterprise demand features, by breaking down enterprise demand information and constructing a demand evidence chain, a demand sub-chain is formed, containing the following content: "Develop an AI algorithm to improve battery life, which needs to run on edge devices to reduce power consumption." According to the above description, in this demand sub-chain, the technology object segment is "AI algorithm," the application scenario segment is "run on edge devices," and the connection type between adjacent segments is identified as "conditional connection" (i.e., "run on edge devices" is a condition for the "AI algorithm" to run, and also a condition for "reducing power consumption"). First, based on this demand sub-chain, the system extracts the core phrase of the technology object, such as "AI algorithm," and simultaneously extracts the core phrase of the scenario object, such as "edge devices." Next, the system performs domain mapping on "AI algorithm," potentially obtaining a candidate set of technology fields, such as: {Artificial Intelligence, Machine Learning, Deep Learning, Algorithm Optimization}. Simultaneously, it performs domain mapping on "edge devices," potentially obtaining a candidate set of industry directions, such as: {Edge Computing, Internet of Things, Smart Hardware, Low-Power Technology}. At this point, there are multiple candidates in the technology field candidate set, making it impossible to directly determine a single primary candidate. Under these conditions, the system will invoke the connection type of the demand subchain for disambiguation. Since the connection type of this demand subchain is "conditional connection," according to the disambiguation rules of this scheme, the system will prioritize candidates whose mapping is consistent with the core phrase "edge device" of the scenario object. This means that in the candidate set of technical fields for "AI algorithm," the system will tend to select technical fields more closely related to the application scenario of "edge device." For example, although "deep learning" is a type of AI algorithm, if the mapping result of "edge device" is more biased towards "edge computing" or "embedded systems," then the system may ultimately determine the technical field of "AI algorithm" as related to "edge computing AI algorithm" or "embedded AI algorithm," rather than the general "deep learning." Similarly, industry direction information will also be precisely determined as "edge computing" or "Internet of Things," etc. Finally, the technical field information (e.g., "edge computing AI algorithm") and industry direction information (e.g., "edge computing") determined after disambiguation are bound to this demand subchain respectively, thus forming a feature group with clear domain and direction attributes in the enterprise demand feature set.

[0084] Through the above technical solution, this application effectively addresses the problem of unclear technical field and industry direction information caused by the potential ambiguity of domain mapping when constructing enterprise demand feature sets. By introducing the extraction of core phrases for technical objects and core phrases for scenario objects, and combining this with intelligent disambiguation based on the connection types between segments in the demand sub-chain, this solution can accurately determine the unique or most important technical field and industry direction information corresponding to each demand sub-chain from multiple candidate domains. This refined domain and direction determination significantly improves the accuracy and specificity of enterprise demand feature sets, avoiding matching biases caused by domain ambiguity. Therefore, in subsequent analysis of matching scientific research talent with enterprise information, matching can be performed based on more accurate enterprise demand vectors, thereby generating scientific research talent matching results that highly match the needs of the target enterprise, improving the efficiency and quality of matching.

[0085] In some embodiments described above, this application proposes a method for acquiring scientific research results information, parsing the information, constructing a set of results features, and generating application scenario prediction results based on the set of results features. However, in its implementation, if the parsing and feature construction of the scientific research results information are not detailed and structured enough, it may be difficult to accurately assess the maturity of the scientific research results and their potential application scenarios, thereby affecting the accuracy of matching scientific research talent with enterprise information. For example, a set of results features constructed solely based on keywords or coarse-grained information may not effectively distinguish between the laboratory stage and the engineering verification stage of the results, nor can it clearly depict their specific performance and limitations in a particular application environment, leading to deviations in the matching results.

[0086] In response, this application further proposes a method for constructing a set of achievement features, the steps of which include: decomposing research achievement information into a set of achievement fragments, the set of achievement fragments including at least a technology introduction fragment, a verification condition fragment, a verification result fragment, and an application-oriented fragment; constructing an achievement evidence chain based on the set of achievement fragments, the achievement evidence chain being connected in the order of the technology introduction fragment, the verification condition fragment, the verification result fragment, and the application-oriented fragment; generating achievement maturity evaluation information based on the achievement evidence chain, the achievement maturity evaluation information being jointly determined by the verification condition fragment and the verification result fragment, and under the condition that the verification condition fragment has test environment constraints and the verification result fragment lacks corresponding indicator descriptions, the achievement maturity evaluation information is marked as an incomplete maturity state, and the incomplete maturity state is written into the achievement feature set; generating application scenario prediction results based on the application-oriented fragment, the application scenario prediction results including at least three types of scenario elements: application object, deployment form, and operational constraints.

[0087] Decomposing research findings into sets of fragments involves using automated or semi-automated methods to identify and extract key information units with specific semantics and functions from raw, often unstructured or semi-structured, research text information. These information units are defined as fragments, representing the smallest analyzable units constituting the core content of the research findings. For example, Natural Language Processing (NLP) techniques, such as Named Entity Recognition (NER) and relation extraction, can be used to identify technical details, experimental conditions, performance data, and potential application directions from the descriptive text of the research findings. Alternatively, pre-defined rule bases or pattern matching algorithms can be used to scan and parse the text, extracting sentences or phrases that conform to specific syntactic or semantic patterns as corresponding fragments. The construction of these fragment sets aims to transform complex research information into a structured, machine-understandable data format, laying the foundation for subsequent analysis and chain-like construction.

[0088] A collection of research findings segments includes at least a technical overview segment, a verification conditions segment, a verification results segment, and an application-oriented segment. The technical overview segment typically contains a general description of the core technical principles, innovative points, main functions, or problems solved by the research findings. The verification conditions segment details the constraints such as environment, parameters, equipment, and standards used during the verification or experimentation of the research findings. The verification results segment records the specific data, performance indicators, effect evaluations, or conclusions obtained from the verification or experiment. The application-oriented segment elucidates the potential application areas, target users, deployment methods, or expected benefits of the research findings. These segments characterize the attributes of the research findings from different dimensions, collectively constituting a comprehensive understanding of the research findings.

[0089] Constructing a chain of evidence based on a set of research fragments refers to connecting and organizing the various fragments obtained from the above-mentioned decomposition according to a predetermined logical order, forming a data structure that reflects the complete logical path of scientific research results from technical description to verification and application. The construction of the chain of evidence aims to establish the inherent connections between the fragments, so that the understanding of scientific research results is no longer isolated information points, but a whole with context and causal relationships. For example, a graph structure or linked list structure can be used to represent this connection relationship, where each node represents a fragment of the research result, and the edges represent the order or dependency relationship between the fragments.

[0090] The chain of evidence for research findings is connected in the order of technical introduction, verification conditions, verification results, and application orientation. This means that the construction of the chain of evidence strictly follows the natural development process of scientific research results from conception and experimental verification to practical application. This sequential connection ensures the logicality and completeness of the information flow, enabling a clear tracing of the technical background, verification process, actual effects, and potential applications when analyzing the results. For example, the technical introduction, as the starting point of the chain, provides an overview of the results; the verification conditions and verification results follow closely, jointly describing the verification process and performance of the results; and the application orientation, as the ending point of the chain, indicates the ultimate value and application prospects of the results.

[0091] Generating maturity assessment information based on the chain of evidence refers to evaluating the current maturity stage of a research achievement by analyzing specific segments of the chain of evidence, particularly the verification conditions and verification results. The maturity assessment information is jointly determined by the verification conditions and verification results segments, meaning that maturity evaluation requires a comprehensive consideration of both the rigor of the experiment or verification (reflected in the verification conditions) and the actual results achieved (reflected in the verification results). For example, if the verification conditions describe rigorous testing in a simulated real-world environment, and the verification results show excellent performance indicators, the maturity assessment of the achievement will be high. Conversely, if the verification conditions are limited to basic laboratory testing, or if the verification result data is insufficient, the maturity assessment will be correspondingly lower.

[0092] When a validation condition segment contains testing environment constraints but the validation result segment lacks corresponding indicator descriptions, the maturity evaluation information of the achievement is marked as an incomplete maturity state, and this incomplete maturity state is written into the achievement feature set. This is a refined processing of the maturity assessment of scientific research achievements. When the validation condition segment clearly indicates a specific testing environment or constraint (e.g., requiring performance testing under high temperature and humidity conditions), but the validation result segment fails to provide specific performance indicators or data corresponding to these constraints, it indicates that there is a lack of information in the maturity assessment of the achievement. In this case, marking it as an incomplete maturity state can accurately reflect the current situation of insufficient validation in specific aspects of the achievement, avoiding incorrect maturity judgments due to incomplete information. This marking helps enterprises to more clearly understand the risks of the achievement and the need for further validation when matching results.

[0093] Application scenario prediction results based on application-oriented fragments refer to extracting and structuring specific scenario elements that research results may be applicable to from application-oriented fragments in the chain of evidence. Application scenario prediction results include at least three types of scenario elements: application object, deployment form, and operational constraints. The application object specifies the entity or system to which the research results will be applied, such as "intelligent manufacturing production line" or "medical diagnostic equipment." The deployment form describes the physical or logical form that the research results may take in practical applications, such as "embedded module," "cloud service platform," or "standalone software." Operational constraints indicate the environmental, performance, and security limitations or requirements that the results may face during application, such as "low power consumption," "real-time response," and "high concurrency." Through this structured prediction, the potential application value of research results can be transformed into quantifiable and matchable characteristics, thereby more accurately aligning with enterprise needs.

[0094] This application's solution meticulously breaks down research findings into structured information units such as a technical overview fragment, verification condition fragment, verification result fragment, and application-oriented fragment. These fragments are logically connected to form a chain of evidence, clearly demonstrating the complete trajectory of the research findings from technical principles to the verification process and application prospects. Based on this, the maturity assessment information can be more accurately determined by the verification condition and verification result fragments, especially when verification conditions are clear but verification results are missing. This allows for the identification and marking of an incomplete maturity state, avoiding the blind overestimation of maturity. Simultaneously, the application-oriented fragment is further analyzed into scenario elements such as application targets, deployment models, and operational constraints, making the potential application scenarios of the research findings concrete and structured. This meticulous approach to constructing research findings features fully explores and expresses the intrinsic value and applicability of the research findings, providing high-quality, high-dimensional feature data for subsequent matching analysis with enterprise needs and technology layouts, significantly improving the accuracy and effectiveness of the matching.

[0095] The following is a concrete example. Suppose a research achievement is described as follows: "This research developed a deep learning-based image recognition algorithm. On the standard public dataset ImageNet, it achieved a recognition accuracy of 95% and a processing speed of 100ms / image. The algorithm was trained and tested in a laboratory environment using NVIDIA GPUs. In the future, it can be applied to intelligent security monitoring systems to realize face recognition and behavior analysis. The deployment form is edge computing devices, which need to meet the requirements of low latency and high reliability." First, this research finding information is broken down into a collection of fragmented findings: Technical summary excerpt: "This research developed an image recognition algorithm based on deep learning." Validation condition snippet: "Training and testing were performed on the standard public dataset ImageNet in a laboratory setting using NVIDIA GPUs." Verification result excerpt: "The recognition accuracy reached 95%, and the processing speed was 100ms / image." Application-oriented segment: "In the future, it can be applied to intelligent security monitoring systems to achieve facial recognition and behavior analysis. The deployment form is edge computing devices, which need to meet the requirements of low latency and high reliability." Next, a chain of evidence is constructed based on these fragments of results, in the following order: technical introduction fragment -> verification condition fragment -> verification result fragment -> application-oriented fragment.

[0096] Then, maturity assessment information is generated based on the achievement evidence chain. Since the validation condition fragment explicitly specifies the "standard public dataset ImageNet" and the "laboratory environment," and the validation result fragment provides specific metrics such as "recognition accuracy of 95%" and "processing speed of 100ms / image," this information is complete and mutually corresponding. Therefore, the achievement can be assessed as having a high level of technological maturity (e.g., Technology Maturity Level TRL 4-5). If the validation result fragment only describes "good recognition performance" without specific metrics, it will be marked as an incomplete maturity state.

[0097] Finally, application scenario prediction results are generated based on the application-pointing fragment: Applications: "Intelligent security monitoring system", "facial recognition", "behavior analysis".

[0098] Deployment type: "Edge computing device".

[0099] Operational constraints: "Low latency" and "High reliability".

[0100] Through the aforementioned technical solutions, research findings can be understood and represented more comprehensively and deeply. This structured set of findings characteristics not only clearly demonstrates the technical details, verification status, and application potential of research findings, but also identifies information gaps in maturity assessments, thus providing a more accurate and reliable data foundation for matching research talent with enterprise needs. This enables enterprises to more accurately find research findings and talent that meet their technology maturity requirements and application scenario needs, avoiding matching biases caused by ambiguous or incomplete information, and significantly improving matching efficiency and success rate.

[0101] In other embodiments, this application proposes a method for matching research talent with enterprise information. This method, while acquiring and analyzing historical technology information of the target enterprise to construct a set of enterprise technology layout characteristics, further identifies potential technology demand directions. Matching research talent solely based on the enterprise's current needs may fail to fully reveal the enterprise's deep-seated, unmet technological gaps, thus resulting in the matching results failing to fully leverage the strategic value of external research capabilities.

[0102] In response, this application further proposes the following steps for constructing a set of enterprise technology layout characteristics and identifying potential technology demand directions: extracting a set of technology keywords from historical technology information and forming an enterprise technology evolution sequence in chronological order; dividing the enterprise technology evolution sequence into continuous evolution segment groups, with each evolution segment group corresponding to a technology direction aggregation result; comparing each demand sub-chain in the enterprise demand characteristic set with the evolution segment group to obtain a demand coverage item set and a demand gap item set, wherein the demand gap item set consists of technology directions or scenario elements that exist in the demand sub-chain but not in the evolution segment group; decomposing the demand gap item set into a first type of gap and a second type of gap according to the source of the gap, where the first type of gap is a technology direction gap and the second type of gap is a scenario element gap, and jointly identifying the first type of gap and the second type of gap as potential technology demand directions.

[0103] This solution identifies core technological elements and reveals the development trajectory of a company by extracting historical technological data accumulated from its past. Historical technological information can include internal R&D documents, patent applications, product release records, and technology cooperation agreements. Extracting a set of technological keywords can be achieved using Natural Language Processing (NLP) techniques, such as keyword extraction algorithms or entity recognition methods based on pre-trained language models. These keywords represent the technological points that the company focused on or invested in at different times. Forming a chronological sequence of the company's technological evolution means arranging these keywords or their aggregations according to their emergence or activity time, thus visually showcasing the company's technological development path. For example, these technological keywords can be organized based on patent application dates, R&D project initiation dates, or product launch dates. Based on this, the company's technological evolution sequence is structured to better understand the company's technological focus at different stages. Dividing the evolution sequence into continuous evolutionary segments can be achieved using time window sliding, topic model clustering, or graph partitioning algorithms based on technological relevance. For example, every two years can be designated as an evolutionary segment group, or related keywords can be clustered into a technological direction based on their co-occurrence frequency and semantic similarity. Each evolutionary segment group corresponds to a technology direction aggregation result, meaning that each segment group represents one or more technology fields or directions that the company focuses on during a specific period. This aggregation result can be a technology topic term, a technology field category, or a set of related technology keywords. Subsequently, by comparing the company's current needs with its historical technology layout, gaps in the company's technology layout are identified. Each demand sub-chain in the company's demand feature set represents a specific technology demand of the company, which may include specific technology objects, application scenarios, and constraints. Comparing these demand sub-chains with the evolutionary segment groups can be achieved through methods such as semantic similarity calculation, keyword matching, or ontology mapping. The demand coverage set refers to the parts of the demand sub-chains that match the technology directions or scenario elements in the evolutionary segment groups, indicating that the company has already made arrangements or accumulated experience in these areas. The demand gap set refers to the technology directions or scenario elements that exist in the demand sub-chains but cannot be found in the evolutionary segment groups, directly revealing potential technology gaps in the company's ability to meet current needs. Finally, the identified technology gaps are meticulously classified to more accurately understand their nature. Each gap in the set of demand gaps may stem from a lack of technological direction or a lack of application scenario elements. The first type of gap, the technological direction gap, refers to a company's lack of accumulation or strategic planning in a specific technological field or path. The second type of gap, the scenario element gap, refers to a company's lack of experience or technological reserves in a specific application scenario or its constituent elements. By identifying these two types of gaps together as potential technological demand directions, a comprehensive and detailed description can be made of the specific areas and directions in which a company may need to introduce external research and development capabilities in the future.

[0104] This application's solution constructs a set of enterprise technology layout characteristics and identifies potential technology demand directions through refined analysis of the enterprise's historical technology information. First, the system extracts key technology keywords from the enterprise's accumulated historical technology information, such as patents, R&D reports, and product documents, and constructs a technology evolution sequence reflecting the enterprise's technological development trajectory according to the chronological order of these keywords. This sequence visually demonstrates the enterprise's technological focus and investment direction at different historical stages. Based on this, the system further divides this continuous enterprise technology evolution sequence into several discrete, continuous evolutionary segments. Each segment aggregates technology directions within a specific time period, thus forming a snapshot of the enterprise's technology layout at different development stages. Subsequently, to identify the gap between the enterprise's current needs and its historical layout, the system conducts a detailed comparative analysis of each specific demand sub-chain in the enterprise's demand characteristic set with these evolutionary segments. Through this comparison, the system can identify which technology directions or scenario elements in the demand sub-chains have been covered in the enterprise's historical layout, and which have not yet been addressed or are currently unaddressed. To more accurately guide subsequent matching of research talent, the system further categorizes these identified gaps in demand. Specifically, the gaps are broken down into two categories: one is a gap in technical direction, meaning the company lacks experience in a particular technical field or path; the other is a gap in scenario elements, meaning the company lacks experience in a specific application scenario or its components. Through this classification, the system can clearly define the company's potential technical needs, including not only the lack of technology itself but also the gaps in specific application scenarios. Ultimately, these two types of gaps together constitute the company's potential technical demand directions, providing a more targeted and forward-looking basis for subsequent matching of research talent, enabling the matching process to better meet the company's future development and innovative transformation needs.

[0105] The following is a concrete example to illustrate this. When constructing a set of enterprise technology layout characteristics and identifying potential technology needs, the following steps can be taken: First, extract all technology-related noun phrases from the target enterprise's historical patent database, R&D project reports, and product technical specifications using BERT-based named entity recognition technology, as a set of technology keywords. For example, if the enterprise applied for a patent on "lithium-ion battery cathode material" in 2010, released a product on "solid-state battery electrolyte" in 2015, and launched an R&D project on "sodium-ion battery anode material" in 2020, then keywords such as "lithium-ion battery cathode material," "solid-state battery electrolyte," and "sodium-ion battery anode material" can be extracted and arranged according to their first appearance time to form a sequence of the enterprise's technology evolution. Next, this sequence of the enterprise's technology evolution can be divided into five-year time windows, forming continuous evolutionary segments. For example, 2005-2010 could be one segment, 2010-2015 another, and so on. For each evolutionary segment group, the LDA topic model can be used to cluster its contained technical keywords, thereby obtaining the aggregated technical directions corresponding to that segment group, such as "battery material R&D" and "energy storage system integration." Then, assuming there is a demand sub-chain in the enterprise's demand feature set, described as "developing fast-charging solid-state battery technology for electric vehicles, requiring stable operation at -20℃," the system will compare this demand sub-chain with the aforementioned evolutionary segment groups. If it is found that the enterprise, while having accumulated technology in "solid-state battery electrolytes" in historical evolutionary segment groups, lacks the technical directions of "fast-charging" and "electric vehicle applications," and has never involved the scenario element of "stable operation in low-temperature environments," then "fast-charging" and "electric vehicle applications" will be identified as technical direction gaps in the demand gap item set, while "stable operation at -20℃" will be identified as a scenario element gap. Finally, the system will jointly determine these identified technical direction gaps and scenario element gaps as the enterprise's potential technical demand directions. For example, the enterprise's potential technical demand directions might include "electric vehicle fast-charging technology" and "low-temperature solid-state battery technology." These potential technology needs will serve as important inputs for subsequent research talent matching analysis, in order to identify research talents with expertise and experience in these specific fields.

[0106] Through the aforementioned technical solution, this application overcomes the limitations of matching solely based on current enterprise needs. In constructing a set of enterprise technology layout characteristics and identifying potential technology demand directions, by systematically analyzing the enterprise's historical technology evolution sequence and comparing it with the current demand sub-chain, the gaps in the enterprise's technology direction and application scenarios can be accurately identified. This detailed gap analysis, particularly the decomposition of gaps into technology direction gaps and scenario element gaps, allows enterprises to clearly understand which specific technological fields or application scenarios they lack accumulation in. This not only provides enterprises with more forward-looking insights into technology needs but also enables subsequent matching of research talent to move beyond simply meeting explicit needs, proactively identifying and introducing research talent capable of filling these potential technology gaps. Therefore, this solution significantly improves the accuracy and strategic value of matching research talent with enterprises, helping enterprises occupy a favorable position in technological innovation and transformation.

[0107] In some implementations, this application proposes a method for matching research talent with enterprise information. This method acquires enterprise demand information, research achievement information, enterprise historical technology information, and research talent information, and constructs corresponding feature sets for each. These feature sets are then mapped to a unified feature space for matching analysis, ultimately outputting research talent information that matches the target enterprise. However, when constructing the set of research talent capability features, relying solely on the research direction information provided by the research talent themselves may not fully reveal their actual depth of ability in a specific technical field, the focus of their research direction, and the completeness of the verification of their research achievements. This could lead to insufficient accuracy in the matching results, making it difficult to meet the actual needs of enterprises for highly matched talent.

[0108] In response, this application further proposes a method for constructing a set of capabilities of scientific research talents, including: obtaining scientific research results or patent information from scientific research talent information; constructing a corresponding result evidence chain or patent evidence chain for each scientific research result or patent information, and extracting technical direction elements, verification elements, and scenario elements within the chain; merging multiple evidence chains corresponding to scientific research talents according to scenario elements to form a set of scenario clusters for scientific research talents; generating a capability indicator vector for each set of scenario clusters for scientific research talents, wherein the capability indicator vector includes at least a direction stability component and a verification completeness component, wherein the direction stability component is determined by the consistency of technical direction elements within the scenario cluster, and the verification completeness component is determined by the completeness of verification elements; and fusing the capability indicator vector with scientific research direction information to form a set of capabilities of scientific research talents.

[0109] Specifically, after acquiring research achievements or patent information from scientific research talent information, it is necessary to construct a corresponding achievement evidence chain or patent evidence chain for each research achievement or patent. This process aims to delve into the technical details of research achievements or patents, rather than just superficial keywords. For example, Natural Language Processing (NLP) technology can be used to analyze the text of research achievements, such as abstracts, introductions, experimental methods, result analysis, and conclusions, to identify and extract core technical directions (e.g., "deep learning," "biomedicine"), key verification elements (e.g., "experimental data," "simulation results," "test environment," "performance indicators"), and specific application scenario elements (e.g., "medical diagnosis," "intelligent manufacturing," "environmental monitoring"). For patent information, the background technology, invention content, and specific implementation methods of the claims and specification can be analyzed to extract the corresponding technical directions, verification elements, and scenario elements. The achievement evidence chain or patent evidence chain can be represented as a structured data form, such as a graph structure containing multiple nodes and connections, where each node represents a key information fragment, and the connections represent the logical relationships between these fragments.

[0110] Subsequently, multiple achievement evidence chains or patent evidence chains from the same researcher are merged based on the scenario elements they contain, thus forming a set of research talent scenario clusters. The purpose of this step is to aggregate the technical capabilities of researchers demonstrated in different research outputs that have similarities or connections in application scenarios. For example, if a researcher has multiple achievements or patents related to "smart cities" or its subfields (such as "intelligent transportation" or "environmental monitoring"), then the evidence chains corresponding to these achievements or patents will be merged into a "smart city" scenario cluster. The merging process can employ semantic similarity calculation or mapping methods based on predefined scenario ontologies to ensure that highly related scenario elements are effectively aggregated.

[0111] Based on this, a capability indicator vector is generated for each cluster of research talent scenarios. This capability indicator vector includes at least a directional stability component and a verification completeness component. The directional stability component measures the focus and consistency of research talent's research direction within the scenario cluster. For example, it can be determined by calculating the average semantic similarity of technical direction elements in all evidence chains within the scenario cluster, or by statistically analyzing the frequency of occurrence of major technical directions. If the achievements or patents within a scenario cluster are highly focused on one or a few closely related technical directions, its directional stability component is high. The verification completeness component is used to evaluate the sufficiency and reliability of verification of research achievements or patents within the scenario cluster. This can be determined by evaluating the type, quantity, and quality of verification elements in the evidence chain, such as whether it includes multiple verification methods such as experimental data, simulation results, and prototype verification, and whether the verification indicators are detailed and sufficient. Weights can be assigned to different types of verification elements, and weighted scores can be calculated.

[0112] Finally, the generated capability indicator vectors are integrated with the research direction information provided by the researchers themselves to form the final set of research talent capability characteristics. This integration combines quantitative capability indicators extracted from specific achievements and patents with macro-level research direction information declared by the researchers themselves, forming a more comprehensive, accurate, and multi-dimensional profile of the research talent's capabilities. For example, the capability indicator vectors can be used as a supplementary dimension to the research direction information, adding a direction stability component and a verification completeness component to the research direction information (such as keyword vectors) to form a higher-dimensional feature vector.

[0113] This application's solution constructs a structured chain of evidence by deeply analyzing the research achievements and patent information of scientific research talents. It then merging these evidence based on scenario elements to quantify the stability of their research directions and the completeness of their results verification, thereby generating a multi-dimensional and refined set of scientific research talent capability characteristics. This method overcomes the problem of insufficient matching accuracy that may result from relying solely on research direction information. By matching the actual output of scientific research talents with enterprise needs at a deeper level, the accuracy and effectiveness of matching between scientific research talents and target enterprises can be significantly improved. For example, when enterprise needs explicitly require highly mature and fully verified technical solutions, this solution can quickly identify qualified scientific research talents through the verification completeness component, avoiding ineffective connections caused by information asymmetry. Simultaneously, the direction stability component also helps enterprises find scientific research talents with in-depth expertise and strong professionalism in specific fields, ensuring the depth and sustainability of cooperation. Through the above technical solution, enterprises can be provided with more accurate and reliable recommendations for scientific research talents, effectively promoting the success rate of industry-academia-research cooperation.

[0114] In other embodiments, this application proposes constructing sets of enterprise demand features, achievement features, enterprise technology layout features, and scientific research talent capability features, and performing matching analysis based on these feature sets. However, in practice, directly performing matching analysis on these feature sets from different sources, with different structures and granularities may face problems such as semantic inconsistency, low comparison efficiency, and difficulty in fine-grained alignment, thus affecting the accuracy and depth of the matching results. To address this, this application further proposes mapping the aforementioned sets of enterprise demand features, achievement features, enterprise technology layout features, and scientific research talent capability features to a unified feature space. Specifically, this includes: mapping each demand sub-chain in the enterprise demand feature set to a demand chain vector, and retaining the chain segment boundary index of the demand chain vector; mapping the achievement evidence chain in the achievement feature set to an achievement chain vector, and retaining the verification segment boundary index of the achievement chain vector; mapping the capability indicator vector in the scientific research talent capability feature set to a talent chain vector, and retaining the scene cluster boundary index of the talent chain vector; and forming a comparable chain alignment structure in the unified feature space, constrained by the alignment of the chain segment boundary index and the verification segment boundary index, for subsequent matching analysis.

[0115] The process involves mapping each sub-chain of enterprise requirements in the set of enterprise requirements features to a requirement chain vector, while preserving the segment boundary indices of the requirement chain vector. This step aims to transform structured enterprise requirement information into a computable numerical representation while maintaining the traceability of its internal structure. A requirement sub-chain is the basic unit in the set of enterprise requirements features, containing key information such as the technology object, application scenario, and constraints. Mapping it to a requirement chain vector can be achieved using word embedding techniques from natural language processing, such as using pre-trained language models (e.g., BERT, RoBERTa) to encode the text content in the requirement sub-chain, generating a high-dimensional dense vector. Alternatively, feature engineering can be used to manually define different semantic segments (e.g., technology field, application scenario, technology maturity) in the requirement sub-chain, encoding them into different dimensions or sub-vectors. Preserving the segment boundary indices of the requirement chain vector means that during vectorization, the corresponding position or range of each semantic segment (e.g., technology object segment, application scenario segment) in the original requirement sub-chain is recorded in the vector, enabling accurate identification and alignment of these segments during subsequent matching.

[0116] Mapping the achievement evidence chain from the achievement feature set into achievement chain vectors, while retaining the boundary indices of the verification segments within these vectors, aims to transform the detailed descriptions and verification information of research achievements into a unified vector form for effective comparison with enterprise needs. The achievement evidence chain contains key information such as a technical overview, verification conditions, verification results, and application directions, all crucial for evaluating the value and maturity of the achievement. Mapping it to an achievement chain vector allows for the use of sequence models (such as Long Short-Term Memory networks like LSTM or Transformers) to process the sequence structure of the achievement evidence chain, generating vectors that capture its semantic and structural information. Alternatively, features can be extracted from each segment of the achievement evidence chain (such as the technical overview segment, verification condition segment, and verification result segment) and concatenated into a comprehensive vector. Retaining the boundary indices of the verification segments within the achievement chain vector clearly identifies the parts representing verification conditions and results within the vector, providing a crucial basis for subsequent evaluation of the achievement's maturity and for aligning and comparing it with the technology maturity requirements in enterprise needs.

[0117] Mapping the capability indicator vectors from the set of research talent capability characteristics to talent chain vectors, while retaining the scenario cluster boundary indexes of the talent chain vectors, aims to transform the comprehensive capability information of research talent into a standardized vector representation to facilitate matching with enterprise needs and technology layout. The capability indicator vectors already contain information such as directional stability and validation completeness components, reflecting the capability characteristics of research talent in specific scenario clusters. Mapping them to talent chain vectors allows for the direct use of the capability indicator vectors themselves, or further integration with research direction information to form a more comprehensive vector representation. For example, the various components of the capability indicator vector can be used as dimensions of the talent chain vector, combined with word embedding vectors of research directions. Retaining the scenario cluster boundary indexes of the talent chain vectors enables the differentiation and identification of capability information corresponding to different scenario clusters within the talent chain vectors. This allows for precise identification of research talent with the corresponding scenario capabilities for specific application scenarios requiring enterprise needs during matching analysis.

[0118] In a unified feature space, a comparable chain alignment structure is formed by aligning the chain segment boundary index with the validation segment boundary index as a constraint. This structure is used for subsequent matching analysis and is crucial for achieving accurate matching. It ensures that semantically corresponding parts of vectors from different sources can be accurately aligned when compared. The unified feature space is a high-dimensional vector space in which all mapped demand chain vectors, outcome chain vectors, and talent chain vectors reside. Using the chain segment boundary index (from the demand chain vector) and the validation segment boundary index (from the outcome chain vector) as alignment constraints means that when comparing two vectors, the system will use this index information to ensure, for example, that "technical object" in the demand is aligned with "technical description" in the outcome, and "technology maturity requirement" in the demand is aligned with "validation segment" in the outcome. This alignment can be achieved using dynamic programming algorithms (such as Dynamic Time Warping (DTW)) or attention-based matching models, thus forming a structured "chain alignment structure" that can be directly semantically compared.

[0119] This application's solution solves the problem of direct comparison between different types of information by uniformly mapping key information from enterprise demand feature sets, achievement feature sets, enterprise technology layout feature sets, and scientific research talent capability feature sets to a high-dimensional feature space. Specifically, each demand sub-chain in the enterprise demand feature set is transformed into a demand chain vector, and its chain segment boundary index is accurately recorded, making key elements such as the technical objects and application scenarios in the demand identifiable in the vector. Similarly, the achievement evidence chain in the achievement feature set is transformed into an achievement chain vector, and the verification segment boundary index is retained, ensuring that information such as the technical summary, verification conditions, and results of the achievement are reflected in the vector. The capability indicator vector in the scientific research talent capability feature set is mapped to a talent chain vector, and the scenario cluster boundary index is retained to distinguish the capabilities of talents in different scenarios. Based on this, this solution further constructs a comparable chain alignment structure in the unified feature space, using the chain segment boundary index of the demand chain vector and the verification segment boundary index of the achievement chain vector as core constraints. This structure enables the system to ensure precise, point-to-point comparisons between specific semantic segments of enterprise needs (such as technology objects and maturity requirements) and corresponding semantic segments of research achievements or research talent capabilities (such as technology descriptions, verification segments, and scenario clusters) in subsequent matching analyses. For example, when an enterprise's needs focus on the maturity of a specific technology field, the system can directly compare the segments representing technology maturity requirements in the demand chain vector with the regions representing verification segments in the achievement chain vector through the chain alignment structure, thereby efficiently and accurately assessing the matching degree of the achievements. This unified vectorized representation and refined chain alignment mechanism greatly improve the accuracy and efficiency of matching analysis, enabling the deep integration and analysis of heterogeneous information that was originally difficult to compare directly, laying a solid foundation for the accurate matching of research talent and enterprise information.

[0120] As a specific implementation method, assume that there exists a demand sub-chain in the enterprise demand feature set, the content of which is "Develop AI visual inspection technology for intelligent manufacturing, requiring a recognition accuracy of over 99%, and deploying it at the end of the production line". This demand sub-chain can be mapped to a demand chain vector, where the vector segment corresponding to "AI visual inspection technology" is marked as the technology object chain segment, with its boundary index [0, 10]; the vector segment corresponding to "intelligent manufacturing" is marked as the application scenario chain segment, with its boundary index [11, 20]; the vector segment corresponding to "recognition accuracy of over 99%" is marked as the constraint condition chain segment, with its boundary index [21, 30]; and the vector segment corresponding to "deployed at the end of the production line" is marked as the deployment form chain segment, with its boundary index [31, 40]. Simultaneously, assume that there exists an achievement evidence chain in the achievement feature set, the content of which is "A defect detection algorithm based on deep learning developed by a university has achieved a 98% recognition rate for metal surface defects in a laboratory environment, has been preliminarily verified on a small production line, and can be applied to industrial quality inspection". This chain of evidence can be mapped to an achievement chain vector, where the vector segment corresponding to "deep learning-based defect detection algorithm" is marked as the technical introduction segment, with boundary indices [0, 15]; the vector segment corresponding to "98% recognition rate of metal surface defects in laboratory environment" is marked as the verification segment, with boundary indices [16, 35]; the vector segment corresponding to "preliminary verification on small production line" is marked as the verification condition segment, with boundary indices [36, 50]; and the vector segment corresponding to "applicable to industrial quality inspection" is marked as the application direction segment, with boundary indices [51, 60]. Furthermore, in the set of research talent capability characteristics, a research talent's capability indicator vector may reflect characteristics of "high directional stability" and "moderate verification completeness" in the field of "deep learning image recognition," and their main scenario cluster is "industrial automation." This capability indicator vector can be mapped to a talent chain vector, where the vector segment representing the "deep learning image recognition" capability is labeled as a technology direction cluster with boundary indices [0,10]; the vector segment representing the "industrial automation" scenario capability is labeled as a scenario cluster with boundary indices [11, 20]. In a unified feature space, the system uses the boundary indices of the chain segments of the demand chain vector (such as the technology object chain segment [0, 10]) and the boundary indices of the verification segments of the achievement chain vector (such as the verification segment [16, 35]) as constraints to form a comparable chain alignment structure. This means that during matching, the system can semantically align and compare the enterprise's requirements for "AI visual inspection technology" with the research results for "defect detection algorithms based on deep learning," and simultaneously align and compare the enterprise's constraint of "recognition accuracy of over 99%" with the verification result of "recognition rate of 98%" in the achievement.This alignment ensures the accuracy of the matching analysis, enabling the system to accurately assess the fit between research results and enterprise needs, and further match research talents with relevant scenario cluster capabilities with these needs.

[0121] The above technical solution maps the sets of enterprise demand characteristics, achievement characteristics, enterprise technology layout characteristics, and scientific research talent capability characteristics into a unified feature space. During this process, key structural information within each feature set (such as chain segment boundary indices, verification segment boundary indices, and scenario cluster boundary indices) is preserved, thus constructing a comparable chain alignment structure. This significantly improves the accuracy and efficiency of matching analysis, solving the problem of effectively aligning and comparing information from different sources and at different granularities. Based on this, the system can perform more detailed and accurate semantic matching, for example, precisely comparing specific technical elements of enterprise needs with verification details of scientific research achievements, and specific scenario capabilities of scientific research talent. This not only optimizes the accuracy of matching results and reduces the risk of mismatches, but also provides a more solid and interpretable basis for subsequent scientific research talent recommendations and collaborations, enabling the entire method of matching scientific research talent and enterprise information to achieve higher-quality matching results.

[0122] In some of the embodiments described above in this application, a method is proposed to map the sets of enterprise demand characteristics, achievement characteristics, enterprise technology layout characteristics, and scientific research talent capability characteristics to a unified feature space, and to perform matching analysis on the scientific research talent capability vector with the enterprise demand vector and the enterprise technology layout vector based on this space. However, in the actual matching process, simple matching logic may not be able to fully identify the potential relationship between scientific research talent and enterprise needs. For example, when scientific research talent is highly compatible with enterprise needs in terms of technical direction and application scenarios, but has deficiencies in terms of technical maturity or verification, or when there are slight deviations in the application scenario that can be compensated for by potential demand directions, directly judging it as a mismatch may lead to the omission of valuable cooperation opportunities.

[0123] In response, this application further proposes a method for matching analysis and generating a set of matching results, including: performing chain segment matching on the research talent capability vector and the enterprise demand vector based on a chain alignment structure, where chain segment matching includes direction segment matching, scenario segment matching, and verification segment matching; when direction segment matching and scenario segment matching are successful but verification segment matching fails, the corresponding research talent is included in a supplementary research talent set, and a supplementary reason label is generated, which includes at least a maturity deficiency label and an evidence deficiency label; when direction segment matching is successful but scenario segment matching fails, the second type of gap in the potential technology demand direction is invoked to replay and verify the scenario segment, where the replay and verification involves re-performing scenario segment matching with the second type of gap as a substitute scenario element, and when the replay and verification are successful, the corresponding research talent is included in a set of potential collaborative research talents; when direction segment matching fails, the corresponding research talent is directly removed and not included in the matching result set.

[0124] Chain alignment refers to a comparable data organization form formed in a unified feature space by aligning the boundary indices of demand chain vectors with the boundary indices of verification segments of achievement chain vectors. Its function is to provide a structured comparison framework for subsequent matching analysis, ensuring that different types of information (such as enterprise needs, scientific research achievements, and scientific research talent capabilities) can be accurately compared according to preset logic and granularity during matching. For example, it can be a multi-dimensional matrix structure, where rows represent segments of the demand chain and columns represent segments of the scientific research talent capability or achievement chain. Index alignment allows for direct comparison of information in corresponding dimensions. Another implementation method is to project all relevant vectors (demand vectors, talent capability vectors) into a unified feature space and group and associate the projected feature points according to predefined semantic association rules, forming logical "chains." The boundary indices of these chains are used to identify the start and end positions of different semantic segments, thereby achieving structural alignment.

[0125] Chain segment matching, based on chain alignment, refines the matching process between the capability vector of scientific research personnel and the demand vector of enterprises into multiple independent comparisons of specific semantic segments. Its purpose is to improve the accuracy and flexibility of matching, allowing the system to differentiate processing based on the matching results of different segments. For example, a method based on semantic similarity calculation can be used to calculate the cosine similarity or Euclidean distance of the feature vectors of the direction segment, scene segment, and verification segment respectively to determine their matching degree. Another approach is to use pre-trained deep learning models, such as BERT or Transformer, to encode the textual information in the chain segments, and then determine the matching status by calculating the distance between the encoded vectors or by using a classifier.

[0126] Directional segment matching refers to the comparison of research direction information in the capability vector of scientific research personnel with the technical field and industry direction information in the demand vector of enterprises during the chain segment matching process. Its purpose is to determine whether the professional field of scientific research personnel aligns with the technical direction required by the enterprise. For example, it can determine whether the direction matches by comparing the overlap of keywords in the technical directions of both parties, the hierarchical relationship in the technical classification system, or by using ontology for semantic reasoning. Another approach is to encode the direction information into vectors and calculate the similarity between these vectors; when the similarity exceeds a preset threshold, the directional segment is considered to have matched successfully.

[0127] Scene segment matching refers to the comparison of scene elements in the capability vector of scientific research personnel with application scenario descriptions in the demand vector of enterprises during the chain segment matching process. Its purpose is to determine whether the achievements or capabilities of scientific research personnel are applicable to the specific application scenarios of enterprises. For example, this can be achieved by analyzing key entities, behaviors, and environmental elements in the scene description and performing semantic comparison with the application objects, deployment forms, and operational constraints in the application-oriented segments of the scientific research personnel's achievements. Another approach is to construct a knowledge graph of scene elements and evaluate the scene matching degree through the similarity of graph paths or the degree of node correlation.

[0128] Validation segment matching refers to the comparison of validation elements in the capability vector of research personnel with the technology maturity requirements in the demand vector of enterprises during the chain segment matching process. Its purpose is to determine whether the technology maturity or validation completeness of research personnel meets the needs of enterprises. For example, it can compare the correspondence between validation condition segments and validation result segments of research results and enterprise technology maturity requirements, such as whether they have undergone testing in a specific environment or whether they have achieved the expected performance indicators. Another approach is to quantify validation information into maturity levels and directly compare whether the levels meet the requirements, or to use an expert system to evaluate the validation evidence to determine its sufficiency.

[0129] A supplementary research talent pool refers to a group of researchers who, during the matching analysis process, highly align with the company's needs in terms of technical direction and application scenarios, but whose technical maturity or validation completeness does not fully meet the company's requirements. Its purpose is to identify research talents with collaboration potential who require further investment or joint development, providing companies with a more comprehensive range of collaboration options. For example, when the system detects a successful match between the direction segment and the scenario segment, but a failure to match the validation segment, the research talent will be added to this pool.

[0130] Supplementary reason markers are labels attached to each researcher in a supplementary research talent pool to explain the specific reasons why they fail to fully match the company's needs. Their purpose is to provide the company with a basis for decision-making, helping it understand potential risks or areas that need to be addressed in the collaboration. For example, when the verification segment mismatch is due to the testing environment constraints of the research results resulting in the missing corresponding indicator description, it can be marked as an "insufficient maturity marker"; when the research talent's verification elements are incomplete, it can be marked as a "missing evidence marker."

[0131] The insufficient maturity marker is a supplementary marker generated when the technology maturity assessment information of research personnel fails to meet the technology maturity requirements of the enterprise. Its purpose is to clearly indicate that the research personnel's technology may still be in the laboratory or prototype stage, requiring further R&D investment to meet the needs of actual enterprise applications. For example, if the enterprise requires a technology maturity level of TPL6 (Technology Prototype System Demonstration), but the research personnel's achievements only reach TPL4 (Laboratory Validation), this marker will be generated.

[0132] The "Lack of Evidence" flag is a supplementary reason flag generated when the verification elements used to demonstrate the technological maturity of a research talent's research achievements or patent information are incomplete or insufficient. Its purpose is to prompt companies to further obtain or verify relevant verification data or reports when evaluating the research talent. For example, this flag will be generated if the verification condition fragment of the research achievement exists, but the verification result fragment is missing, or the description of the verification result is too vague.

[0133] Replay verification refers to the process where, when a research talent's direction segment matches successfully but their scenario segment matches unsuccessfully, the system doesn't directly remove the talent. Instead, it attempts to utilize a second type of gap (scenario element gap) in the company's potential technological needs as an alternative application scenario element and re-executes the scenario segment matching process. Its purpose is to improve the recall rate of the match, identifying research talents whose current application scenarios don't perfectly match but who may have potential in the company's future development direction. For example, if the company's current need scenario is "robot collaboration in intelligent manufacturing," while the research talent's scenario is "robot scheduling in logistics and warehousing," but the company's potential technological needs include "robot scheduling optimization," the system will attempt to use "robot scheduling optimization" as an alternative scenario for replay verification.

[0134] The potential collaborative research talent pool refers to a group of researchers whose technical expertise aligns with a company's needs but failed to initially match. This pool is created through a replay verification mechanism, allowing for a successful re-verification and matching of researchers who initially failed but later found a match by addressing a second type of gap in the company's potential technical needs. Its purpose is to help companies identify research talent who may have future collaborative value and can fill gaps in their potential technical needs. For example, a researcher who successfully completes the replay verification will be included in this pool.

[0135] This application's solution addresses the problem of traditional matching methods potentially missing collaboration opportunities due to simplistic binary judgments by introducing refined chain segment matching, differentiated matching result classification, and an innovative replay verification mechanism. Specifically, within a unified feature space, the system first performs chain segment matching between the research talent capability vector and the enterprise demand vector based on a chain alignment structure. This matching process is subdivided into three levels: direction segment matching, scenario segment matching, and verification segment matching, allowing for independent evaluation of each dimension of the matching. When both the direction and scenario segments match successfully, but the verification segment fails, the system does not directly exclude the research talent but instead includes them in a supplementary research talent set, attaching a maturity insufficiency marker or a lack of evidence marker. This enables enterprises to identify potential collaborators whose technical direction and application scenario align but whose technical maturity still needs improvement, avoiding the loss of talent due to temporary insufficient technical maturity. Furthermore, when the direction segment matches successfully but the scenario segment fails, the system calls upon the second type of gap (i.e., scenario element gap) in the enterprise's potential technical demand direction to replay and verify the scenario segment. This replay verification mechanism allows the system to reassess the scenario matching degree of research talents based on the enterprise's future or potential application scenarios. If the replay verification is successful, the research talent is included in the potential collaborative research talent set. This mechanism greatly broadens the matching scope, enabling the effective identification of research talents even if their current application scenarios deviate from the enterprise's immediate needs, as long as they align with the enterprise's potential development direction. This leads to the discovery of more research talents with forward-looking collaborative value. Only when the most basic direction matching fails is the corresponding research talent directly removed, ensuring the effectiveness and efficiency of the matching. Through the above multi-level and intelligent matching strategy, this application can more comprehensively and accurately identify the matching relationship between research talents and enterprise needs, significantly improving the success rate and collaborative potential of talent-enterprise information matching.

[0136] The following is a concrete example to illustrate this. Suppose a company publishes a requirement. Its requirement vector includes the technical field information "high-precision robot grasping technology in intelligent manufacturing," the application scenario description information "unordered grasping of complex irregularly shaped workpieces," and the technology maturity requirement information "TPL6 (Technology Prototype System Demonstration)." Simultaneously, there are research talent A and research talent B. In research talent A's capability vector, the research direction information is "robot vision and control based on deep learning," the research achievement or patent information shows that their achievement is "an object recognition and localization algorithm based on convolutional neural networks," the verification element indicates that the algorithm has been verified in a laboratory environment for grasping standard workpieces, the technology maturity evaluation information is "TPL4 (Laboratory Verification)," and the scenario element is "robot grasping." In research talent B's capability vector, the research direction information is "industrial robot path planning and scheduling," the research achievement or patent information shows that their achievement is "multi-robot collaborative path planning system," the verification element indicates that the system has been tested in a simulated environment, the technology maturity evaluation information is "TPL5 (System Component Verification)," and the scenario element is "robot scheduling in logistics warehousing." In addition, the analysis of enterprise technology layout characteristics identified potential technology demand directions, among which the second type of gap (scenario element gap) includes "multi-robot collaborative operation optimization".

[0137] During the matching analysis: For research talent A, chain segment matching is performed first. The direction segment matching is successful because "deep learning-based robot vision and control" and "high-precision robot grasping technology in intelligent manufacturing" are highly consistent in technical direction. The scenario segment matching is successful because "robot grasping" and "disordered grasping of complex irregular workpieces" are directly related in application scenarios. However, the verification segment matching fails because research talent A's achievement maturity is TPL4, which fails to meet the company's requirement of TPL6. According to the scheme of this application, since the direction segment matching and scenario segment matching are successful but the verification segment matching fails, research talent A will be included in the supplementary research talent set, and a supplementary reason marker will be generated as "insufficient maturity marker".

[0138] For Research Talent B, the first step is to perform chain segment matching. Directional segment matching is successful because while "industrial robot path planning and scheduling" and "high-precision robot grasping technology in intelligent manufacturing" are not entirely identical, they are related within the broader field of robot technology, thus qualifying as a successful directional segment match. Scenario segment matching fails because "robot scheduling in logistics and warehousing" does not match the company's current need for "disordered grasping of complex, irregularly shaped workpieces." At this point, the system does not directly exclude Research Talent B but instead calls upon the second type of gap in the potential technical demand direction, "multi-robot collaborative operation optimization," to perform a replay verification of the scenario segment. During the replay verification, scenario segment matching is re-executed using "multi-robot collaborative operation optimization" as a substitute scenario element. It is found that Research Talent B's achievement, "multi-robot collaborative path planning system," highly matches this potential scenario element, and the replay verification is successful. Therefore, Research Talent B will be included in the potential collaborative research talent set. Through the above matching process, this application can identify research talents who not only meet current needs but also possess potential collaborative value, avoiding omissions that might occur with simple matching.

[0139] Through the aforementioned technical solution, this application enables refined identification and classification of the matching relationship between scientific research talent and enterprise needs. Specifically, by refining the chain segment matching, it can accurately determine the degree of fit between scientific research talent and different dimensions such as technical direction, application scenario, and technical maturity. When scientific research talent aligns with enterprise needs in core technical direction and application scenario but lacks technical maturity, they are identified as supplementary scientific research talent, and the reasons for their deficiency are clearly marked. This allows enterprises to clearly understand the strengths and weaknesses of potential partners, thereby enabling targeted cooperation planning or technology incubation and avoiding the loss of excellent talent due to temporary insufficient technical maturity. Furthermore, by introducing a replay verification mechanism, the gaps in scenario elements in the enterprise's potential technical needs are used to conduct a secondary evaluation of scientific research talent whose initial scenario matching failed. This effectively uncovers potential collaborative scientific research talent whose current application scenarios are not fully matched but whose future development direction is highly aligned with the enterprise's. This multi-level, intelligent matching strategy significantly improves the recall and accuracy of matching, enabling enterprises to discover and utilize scientific research talent resources more comprehensively and deeply, thereby broadening the scope of cooperation and improving the efficiency and success rate of technological innovation.

[0140] In other embodiments, this application proposes a method for matching scientific research talent with enterprise information. This method can acquire enterprise demand information, scientific research achievement information, historical technology information, and scientific research talent information of the target enterprise. It then analyzes and constructs features from this information, ultimately performing matching analysis in a unified feature space to generate a set of matching results between scientific research talent and the target enterprise. Finally, it outputs scientific research talent information matching the target enterprise, along with associated recommendation information. However, in practical applications, enterprise satisfaction with the matching results may vary, and the system lacks a mechanism to learn and adapt to the personalized preferences of enterprises. This can lead to subsequent matching recommendations failing to continuously optimize and accurately meet the ever-changing needs of enterprises, thus affecting the accuracy of the matching and the user experience.

[0141] In response, this application further proposes the following steps for outputting scientific research talent information and generating associated recommendation information: generating matching basis description information for each scientific research talent in the matching result set, the matching basis description information including at least the index of the hit demand sub-chain, the index of the hit achievement evidence chain, the index of the hit talent scenario cluster, and the corresponding supplementary reason mark or replay verification mark; outputting the matching basis description information and the scientific research talent information together; receiving feedback information from enterprises on the output results, and updating the priority combination library and the exclusion combination library respectively, the priority combination library is used to record the effective pairing relationship between the demand sub-chain index and the talent scenario cluster index, and the exclusion combination library is used to record the invalid pairing relationship between the demand sub-chain index and the talent scenario cluster index; in the subsequent matching analysis, the effective pairing relationship in the priority combination library is called first to generate the candidate scientific research talent set, and the invalid pairing relationship recorded in the exclusion combination library is removed in the candidate screening stage.

[0142] The matching basis explanation information aims to provide enterprises with transparency and interpretability of matching results. It can be generated by the system automatically extracting and integrating key data points involved in the matching process after completing the matching analysis. For example, when a research talent successfully matches a company's needs, the system records which specific sub-chain of needs led to the match, which research talent's achievement evidence chain or talent scenario cluster corresponds to it, and whether supplementary or potential collaboration markers (such as insufficient maturity markers, missing evidence markers, or replay verification markers) were triggered during the matching process. This information can be stored in a structured data format for subsequent display and analysis. Another implementation method is for the system to record the matching path and decision points in real time at each stage of the matching analysis, ultimately summarizing these records to generate a detailed matching report as the matching basis explanation information. Outputting the matching basis explanation information along with the research talent information allows enterprises to simultaneously understand the internal logic and supporting evidence of the recommended research talent. This can be presented as an additional field or associated document of the research talent information. For example, on the user interface, when a company clicks on a recommended research talent, in addition to displaying the talent's basic information, research direction, achievements, etc., a pop-up window or the bottom of the same page will display detailed matching criteria, including specific matching points, matching strength, and any special markers.

[0143] Receiving feedback from enterprises on the output results is a crucial step in establishing a system learning and optimization mechanism. This can be achieved through various interactive methods. For example, after receiving the matching results, the system can provide a user interface allowing enterprises to rate each recommended research talent as "satisfied," "dissatisfied," or "pending," and optionally fill in specific reasons. Another approach is for the system to monitor subsequent actions taken by enterprises regarding the recommendations, such as whether they click to view research talent details, initiate cooperation intentions, or mark a recommendation as "irrelevant," implicitly collecting these actions as feedback. Feedback can include evaluations of the accuracy of the matching, acceptance of the reasons for the recommendations, and the degree to which the research talent's abilities or achievements meet the actual needs. Updating the priority combination library and the exclusion combination library is a core operation that adaptively adjusts the system based on enterprise feedback. When the system receives enterprise feedback, it dynamically maintains these two libraries according to the feedback content. For example, if an enterprise is satisfied with or successfully collaborates on a pairing of a demand subchain index and a talent scenario cluster index, the system will add this index relationship to the priority combination library. Conversely, if a company explicitly expresses dissatisfaction with or rejects a match, the system will add that indexed relationship to the exclusion combination library. These two libraries can be implemented using data structures such as database tables, hash tables, or key-value stores. The key can be a combination of the demand subchain index and the talent scenario cluster index, and the value can be its validity status or confidence level. The priority combination library stores matches that have been verified or approved by the company and have positive significance; these can be assigned higher weight or priority, indicating higher reference value in future matches. The exclusion combination library stores matches that have been verified or rejected by the company and have negative significance; these should be avoided or have their matching scores reduced in future matches.

[0144] This application's solution, based on the aforementioned method for matching research talent with enterprise information, further optimizes the output of matching results and the subsequent matching process. Specifically, after generating a preliminary set of matching results, the system no longer simply outputs research talent information and recommendations, but generates detailed matching basis explanations for each research talent in the set. This explanation includes not only the indexes of the matched demand sub-chains, achievement evidence chains, and talent scenario clusters, but may also include supplementary reason markers or replay verification markers, thus providing enterprises with a transparent explanation of the matching logic. Subsequently, the system outputs this research talent information with detailed basis to the enterprises. Following this, the system introduces a crucial feedback learning mechanism. It can receive feedback from enterprises on these output results, reflecting their satisfaction or dissatisfaction with specific matching relationships. Based on this feedback, the system dynamically updates the priority combination library and the exclusion combination library. The priority combination library records valid pairings between demand sub-chain indices and talent scenario cluster indices that have been recognized or successfully collaborated on by enterprises, while the exclusion combination library records invalid pairings that have been explicitly rejected or are not matched by enterprises. The introduction of this feedback mechanism enables the system to be adaptive and continuously optimize. In subsequent matching analysis, the system no longer simply calculates the match score from scratch; instead, it prioritizes querying and calling upon valid pairings recorded in the priority combination library to quickly generate a high-quality set of candidate research talents. This means that for matching patterns that have been verified as successful, the system can more efficiently identify and recommend corresponding research talents. Simultaneously, during the candidate screening stage, the system utilizes an exclusion combination library to proactively remove research talents corresponding to pairings that have been explicitly marked as invalid by the companies, thereby avoiding the repeated recommendation of talents that do not meet the companies' expectations and significantly improving the accuracy of matching and user experience. In this way, the system can continuously learn and adjust based on actual feedback from companies, making the matching results increasingly aligned with the personalized needs of the companies.

[0145] The following is a concrete example to illustrate this. Suppose that company A publishes a requirement, and its requirement feature set contains a requirement sub-chain that describes the application of "machine learning-based image recognition technology in industrial quality inspection scenarios." Through matching analysis, the system identifies a talent scenario cluster within the research capability feature set of research talent B. This cluster describes the ability to "use deep learning for defect detection in the field of industrial vision," and a match is successful. At this point, the system generates matching basis information for research talent B. This information may include: the index of the matched requirement sub-chain (e.g., requirement sub-chain ID: DCL001), the index of the matched achievement evidence chain (e.g., achievement evidence chain ID: ACL005, corresponding to a paper by research talent B on "surface defect detection based on convolutional neural networks"), and the index of the matched talent scenario cluster (e.g., talent scenario cluster ID: TSC003). If research talent B's achievement has testing environment constraints in the verification conditions and the verification results lack corresponding indicator descriptions, the system may also add a "maturity insufficient marker" at this point. The system outputs the detailed information of research talent B and this matching basis information to company A. After receiving the recommendation, Company A conducts an initial assessment of research talent B. If Company A believes that research talent B's abilities highly match its needs and expresses satisfaction, the system will receive positive feedback from Company A. Based on this feedback, the system will record the pairing relationship between "Requirement Subchain ID: DCL001" and "Talent Scenario Cluster ID: TSC003" in the priority combination library, marking it as a valid pairing. Conversely, if Company A, after assessment, believes that research talent B, although technically compatible, lacks experience in practical application scenarios, or that the maturity of their achievements cannot meet the current needs, and explicitly expresses dissatisfaction, the system will receive negative feedback from Company A. In this case, the system will record the pairing relationship between "Requirement Subchain ID: DCL001" and "Talent Scenario Cluster ID: TSC003" in the exclusion combination library, marking it as an invalid pairing. At some subsequent point in time, Company A publishes another similar requirement, or other companies publish requirements similar to "DCL001". When the system performs a new matching analysis, it will first query the priority combination library. If a valid pairing is found between "DCL001" and "TSC003", the system will prioritize including research talents with the "TSC003" trait (e.g., research talent C) into the candidate research talent set, even if research talent C's matching score may be slightly lower than other talents, because its matching pattern has been verified, it will receive priority recommendation. Meanwhile, during the candidate screening stage, the system will check all research talents who have initially been successfully matched.If a research talent (e.g., research talent D) has a matching relationship with the current demand (e.g., "demand subchain ID: DCL002" and "talent scenario cluster ID: TSC004") in the exclusion combination library, the system will directly remove research talent D and no longer recommend it to enterprises, thereby avoiding invalid recommendations.

[0146] Through the aforementioned technical solution, this application provides enterprises with more transparent and interpretable research talent matching results. Enterprises not only receive recommended research talent information but also clearly understand the underlying logic and supporting evidence of the matching process. More importantly, by introducing an enterprise feedback mechanism, the system can dynamically learn and optimize its matching strategy based on the actual usage and satisfaction of enterprises. The establishment of priority and exclusion combination libraries enables the system to prioritize matching patterns that have been verified as effective and proactively avoid matching patterns that have been proven ineffective in subsequent matching analyses, thereby significantly improving the accuracy, efficiency, and user satisfaction of the matching process. This adaptive learning capability allows the system to better adapt to the ever-changing needs and personalized preferences of enterprises, effectively solving the technical problems of traditional matching methods lacking continuous optimization capabilities and failing to accurately meet the personalized needs of enterprises, thus enhancing the intelligence level and practical value of the entire matching service.

[0147] In another preferred embodiment based on the above embodiments, see [reference] Figure 2 As shown, this embodiment provides a system for matching scientific research talent with enterprise information, including: The first data acquisition and processing module is configured to acquire enterprise demand information of the target enterprise. The enterprise demand information includes at least technical field information, industry direction information, application scenario description information, and technology maturity requirements information. The module performs semantic parsing and structured processing on the enterprise demand information to construct a set of enterprise demand features. The second data acquisition and processing module is configured to acquire scientific research results information, which includes at least a brief introduction of the results, information on the technical field to which the results belong, information on the maturity of the technology, and information on the development trend of the technology. The module analyzes the scientific research results information, constructs a set of results features, and generates application scenario prediction results based on the set of results features. The historical information processing module is configured to acquire the historical technology information of the target enterprise. The historical technology information includes at least the enterprise's deployed technology keywords, existing technology directions, and publicly disclosed scientific research results or patent information. The module analyzes the historical technology information, constructs a set of enterprise technology layout characteristics, and identifies potential technology demand directions based on the set of enterprise technology layout characteristics. The third data acquisition and processing module is configured to acquire scientific research talent information, which includes at least scientific research direction information, scientific research achievements or patent information, and scientific research experience information. The module analyzes the scientific research talent information and constructs a set of scientific research talent capability characteristics. The matching and recommendation module is configured to map the enterprise demand feature set, the achievement feature set, the enterprise technology layout feature set, and the scientific research talent capability feature set to a unified feature space, forming an enterprise demand vector, an enterprise technology layout vector, and a scientific research talent capability vector. Based on a unified feature space, the matching analysis of the ability vector of scientific research talents with the demand vector and technology layout vector of enterprises is performed to generate a set of matching results between scientific research talents and target enterprises. Based on the matching result set, output scientific research talent information that matches the target enterprise, and generate scientific research achievement recommendation information or cooperation matching information associated with the scientific research talent information.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for matching scientific research talent with enterprise information, characterized in that, Includes the following steps: Obtain enterprise demand information of the target enterprise. The enterprise demand information includes at least technical field information, industry direction information, application scenario description information, and technology maturity requirement information. Semantic parsing and structural processing are performed on the enterprise demand information to construct a set of enterprise demand features. Acquire scientific research results information, which includes at least a brief introduction of the results technology, information on the technical field to which the results belong, information on the technology maturity evaluation, and information on the technology development trend. Analyze the scientific research results information, construct a set of results features, and generate application scenario prediction results based on the set of results features. The historical technology information of the target enterprise is obtained. The historical technology information includes at least the technology keywords that the enterprise has deployed, the existing technology direction information, and the publicly disclosed scientific research results or patent information. The historical technology information is analyzed to construct a set of enterprise technology layout characteristics, and potential technology demand directions are identified based on the set of enterprise technology layout characteristics. Information on scientific research talents is obtained, including at least information on research direction, research achievements or patents, and research experience. The information on scientific research talents is then analyzed to construct a set of ability characteristics of scientific research talents. The enterprise demand feature set, achievement feature set, enterprise technology layout feature set, and scientific research talent capability feature set are mapped to a unified feature space to form an enterprise demand vector, an enterprise technology layout vector, and a scientific research talent capability vector. Based on the unified feature space, a matching analysis is performed on the scientific research talent capability vector, the enterprise demand vector, and the enterprise technology layout vector to generate a set of matching results between scientific research talents and target enterprises. Based on the matching result set, the system outputs scientific research talent information that matches the target enterprise, and generates scientific research achievement recommendation information or cooperation matching information associated with the scientific research talent information.

2. The method for matching scientific research talent with enterprise information according to claim 1, characterized in that, Constructing a set of enterprise demand characteristics includes: Enterprise demand information is broken down into a set of demand fragments, which includes at least technology object fragments, application scenario fragments, constraint fragments, and maturity fragments. For each requirement fragment in the requirement fragment set, generate a fragment evidence item. The fragment evidence item includes at least the fragment's position index in the enterprise requirement information, the fragment's trigger word type, and the fragment's context boundary. The enterprise demand evidence chain is constructed based on fragment evidence items. The enterprise demand evidence chain connects the technology object fragment, application scenario fragment, and constraint condition fragment in the order of position index and records the connection type between adjacent fragments. The connection type includes at least causal connection, parallel connection, and conditional connection. When the same technical object fragment corresponds to multiple different application scenario fragments in the enterprise demand evidence chain, the enterprise demand evidence chain is split into multiple demand sub-chains, and each demand sub-chain forms a set of candidate demand feature groups in the enterprise demand feature set.

3. The method for matching scientific research talent with enterprise information according to claim 2, characterized in that, Determining the technology and industry direction information within enterprise demand information includes: Extract the core phrase of the technical object based on the technical object fragment in each demand subchain, and extract the core phrase of the scenario object based on the application scenario fragment; Perform domain mapping on the core phrases of technology objects and the core phrases of scenario objects respectively to obtain the candidate set of technology fields and the candidate set of industry directions; When there are multiple candidates in the candidate set of technical fields and a unique primary candidate cannot be formed, the connection type of the requirement subchain is invoked for disambiguation determination. The disambiguation determination includes: when the connection type is causal connection, candidates that are consistent with the core phrase mapping of the technical object are selected first; when the connection type is conditional connection, candidates that are consistent with the core phrase mapping of the scene object are selected first. The disambiguated technology sector information and industry direction information are bound to the corresponding demand sub-chains to form a set of enterprise demand characteristics.

4. The method for matching scientific research talent with enterprise information according to claim 3, characterized in that, The set of features for constructing results includes: The scientific research results information is broken down into a set of results fragments, which at least include a technical introduction fragment, a verification condition fragment, a verification result fragment, and an application orientation fragment. A chain of evidence for the results is constructed based on a set of result fragments. The chain of evidence for the results is connected in the order of technical introduction fragment, verification condition fragment, verification result fragment and application-oriented fragment. The maturity evaluation information of the results is generated based on the chain of evidence of the results. The maturity evaluation information of the results is jointly determined by the verification condition fragment and the verification result fragment. Under the condition that the verification condition fragment has test environment constraints and the verification result fragment lacks corresponding indicator description, the maturity evaluation information of the results is marked as an incomplete maturity state and the incomplete maturity state is written into the result feature set. Application scenario prediction results are generated based on application-oriented fragments. The application scenario prediction results include at least three types of scenario elements: application object, deployment form, and operation constraints.

5. The method for matching scientific research talent with enterprise information according to claim 4, characterized in that, Constructing a set of enterprise technology layout characteristics and identifying potential technology needs includes: Extract a set of technical keywords from historical technical information and form a sequence of enterprise technology evolution in chronological order; The enterprise's technology evolution sequence is divided into continuous evolutionary segments, and each evolutionary segment corresponds to an aggregation result of a technology direction; By comparing each demand subchain in the enterprise demand feature set with the evolution segment group, we obtain the demand coverage item set and the demand gap item set. The demand gap item set consists of technical directions or scenario elements that exist in the demand subchain but not in the evolution segment group. The set of demand gap items is decomposed into two types based on the source of the gap: the first type is the technology direction gap, and the second type is the scenario element gap. The first type and the second type of gap are jointly identified as potential technology demand directions.

6. The method for matching scientific research talent with enterprise information according to claim 5, characterized in that, The construction of a set of scientific research talent capability characteristics includes: Obtain research results or patent information from scientific research talent information, construct corresponding achievement evidence chain or patent evidence chain for each research result or patent information, and extract technical direction elements, verification elements and scenario elements within the chain. Multiple evidence chains corresponding to scientific research talents are merged according to scenario elements to form a set of scientific research talent scenario clusters; For each set of scientific research talent scenario clusters, a capability indication vector is generated. The capability indication vector includes at least a directional stability component and a verification completeness component. The directional stability component is determined by the consistency of technical direction elements within the scenario cluster, and the verification completeness component is determined by the completeness of verification elements. By integrating capability indicator vectors with research direction information, a set of capability characteristics for research talents is formed.

7. The method for matching scientific research talent with enterprise information according to claim 6, characterized in that, Mapping the aforementioned set of enterprise demand characteristics, set of achievement characteristics, set of enterprise technology layout characteristics, and set of scientific research talent capability characteristics to a unified feature space includes: Map each subchain of demand in the enterprise demand feature set to a demand chain vector, and retain the chain segment boundary index of the demand chain vector; Map the achievement evidence chain in the achievement feature set to an achievement chain vector, and retain the verification segment boundary index of the achievement chain vector; Map the capability indicator vectors in the set of scientific research talent capability characteristics to talent chain vectors, and retain the scene cluster boundary index of the talent chain vectors; In a unified feature space, a comparable chain alignment structure is formed by aligning the chain segment boundary index with the validation segment boundary index as a constraint, which is then used for subsequent matching analysis.

8. The method for matching scientific research talent with enterprise information according to claim 7, characterized in that, Match analysis and generation of a set of matching results include: Based on the chain alignment structure, chain segment matching is performed between the capability vector of scientific research talents and the demand vector of enterprises. Chain segment matching includes direction segment matching, scenario segment matching and verification segment matching. When the direction segment and the scene segment are matched successfully, but the verification segment is not matched, the corresponding scientific research talent will be included in the supplementary scientific research talent set, and a supplementary reason label will be generated. The supplementary reason label shall include at least the insufficient maturity label and the missing evidence label. When the direction segment is matched successfully but the scene segment is matched unsuccessfully, the second type of gap in the potential technical requirement direction is called to replay and verify the scene segment. The replay verification is to re-execute the scene segment matching with the second type of gap as the replacement scene element, and when the replay verification is successful, the corresponding scientific research talent is included in the potential cooperative scientific research talent set. When a directional segment fails to match, the corresponding research talent is directly removed and not included in the matching result set.

9. The method for matching scientific research talent with enterprise information according to claim 8, characterized in that, Outputting research talent information and generating related recommendation information includes: For each scientific research talent in the matching result set, generate matching basis description information. The matching basis description information includes at least the index of the hit demand sub-chain, the index of the hit achievement evidence chain, the index of the hit talent scenario cluster, and the corresponding supplementary reason mark or replay verification mark. The matching criteria information and the scientific research talent information will be output together; Receive feedback from enterprises on the output results, and update the priority combination library and the exclusion combination library respectively. The priority combination library is used to record the valid pairing relationship between the demand subchain index and the talent scenario cluster index, and the exclusion combination library is used to record the invalid pairing relationship between the demand subchain index and the talent scenario cluster index. In subsequent matching analysis, valid pairings in the priority combination library are used first to generate a set of candidate scientific research talents, and invalid pairings recorded in the combination library are removed during the candidate screening stage.

10. A system for matching scientific research talent with enterprise information, used to implement the method for matching scientific research talent with enterprise information as described in any one of claims 1-9, characterized in that, include: The first acquisition and processing module is configured to acquire enterprise demand information of the target enterprise. The enterprise demand information includes at least technical field information, industry direction information, application scenario description information, and technology maturity requirement information. The module performs semantic parsing and structured processing on the enterprise demand information to construct a set of enterprise demand features. The second acquisition and processing module is configured to acquire scientific research results information, which includes at least a brief introduction of the results technology, information on the technical field to which the results belong, information on the maturity of the technology, and information on the development trend of the technology. The module parses the scientific research results information, constructs a set of results features, and generates application scenario prediction results based on the set of results features. The historical information processing module is configured to acquire the historical technology information of the target enterprise. The historical technology information includes at least the enterprise's deployed technology keyword information, existing technology direction information, and publicly disclosed scientific research results or patent information. The module analyzes the historical technology information, constructs a set of enterprise technology layout characteristics, and identifies potential technology demand directions based on the set of enterprise technology layout characteristics. The third data acquisition and processing module is configured to acquire scientific research talent information, which includes at least scientific research direction information, scientific research achievements or patent information and scientific research experience information, and to parse the scientific research talent information to construct a set of scientific research talent capability characteristics. The matching and recommendation module is configured to map the enterprise demand feature set, achievement feature set, enterprise technology layout feature set, and scientific research talent capability feature set to a unified feature space to form an enterprise demand vector, an enterprise technology layout vector, and a scientific research talent capability vector. Based on the unified feature space, a matching analysis is performed on the scientific research talent capability vector, the enterprise demand vector, and the enterprise technology layout vector to generate a set of matching results between scientific research talents and target enterprises. Based on the matching result set, the system outputs scientific research talent information that matches the target enterprise, and generates scientific research achievement recommendation information or cooperation matching information associated with the scientific research talent information.

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