Talent evaluation report generation method and system

Abstract

The embodiment of the invention discloses a talent evaluation report generation method and system, and the method comprises the steps: obtaining a complete-cycle interaction text data set of a target user cluster, and comprehensively capturing the interaction information of a user at different stages; entity relationship triple extraction is carried out on a complete-cycle interaction text data set through a deep semantic understanding model, a complicated text can be converted into a structured knowledge graph, and entity association in the structured knowledge graph is clearly presented; the association strength of entity nodes is quantitatively evaluated based on a dynamic weight distribution algorithm, a collaborative matching quantitative index set is generated, and the collaborative degree between entities can be accurately reflected; a talent evaluation report is automatically generated based on the collaborative matching quantitative index set; wherein the visual relation network visually displays relation venation, the decision basis is matched to provide reliable support for evaluation, and output from an original text to an accurate and visual talent evaluation report with the decision basis is integrally achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of text data processing, and in particular to a method and system for generating a talent evaluation report. Background Art

[0002] The rapid development of artificial intelligence (AI) technology has led to significant progress in text generation. AI has made it possible to process and analyze large amounts of text data, and a variety of algorithms and models have emerged, providing powerful tools for mining the potential information within text. Through text analysis and processing, decision support and other services can be provided in a wide range of fields.

[0003] However, within the context of existing artificial intelligence and text generation technologies, there are significant flaws in text processing related to talent evaluation. Traditional methods often rely on simple statistical analysis of text, failing to delve deeply into the complex entity relationships within it. Large amounts of interactive text are difficult to effectively integrate and transform into structured information with practical guidance. Quantitative assessments also lack precise and flexible methods to measure the strength of correlations between various factors. Ultimately, the generated reports often lack intuitiveness and comprehensive decision-making support. Achieving high-quality output from raw text into accurate, intuitive, and decision-supported talent evaluation reports is a critical technical challenge that needs to be addressed. Summary of the Invention

[0004] The embodiments of the present invention provide a talent evaluation report generation method and system for realizing high-quality output of a talent evaluation report from original text that is accurate, intuitive, and decision-making.

[0005] In a first aspect, an embodiment of the present invention provides a talent evaluation report generation method, which is applied to a talent evaluation report generation system. The method includes: obtaining a full-cycle interactive text dataset of a target user cluster; using a deep semantic understanding model to extract entity relationship triples from the full-cycle interactive text dataset to generate a structured knowledge graph; using a dynamic weight allocation algorithm to quantitatively evaluate the association strength of entity nodes in the structured knowledge graph to generate a set of collaborative matching quantitative indicators; and automatically generating a report based on the set of collaborative matching quantitative indicators to output a talent evaluation report containing a visual relationship network and a basis for matching decisions.

[0006] In a second aspect, an embodiment of the present invention provides a talent evaluation report generation system, comprising:

[0007] processor;

[0008] a storage device having a computer program stored thereon,

[0009] When the computer program is executed by the processor, the processor implements the talent evaluation report generating method.

[0010] An embodiment of the present invention provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the talent evaluation report generation method are implemented.

[0011] It can be seen that the embodiments of the present invention have the following beneficial effects: by obtaining the full-cycle interactive text dataset of the target user cluster, the interactive information of users at different stages can be fully captured; the entity relationship triples of the full-cycle interactive text dataset are extracted through the deep semantic understanding model, and the complex text can be converted into a structured knowledge graph, clearly presenting the entity associations therein; based on the dynamic weight allocation algorithm, the association strength of entity nodes is quantitatively evaluated, and a set of collaborative matching quantitative indicators is generated, which can accurately reflect the degree of collaboration between entities; a talent evaluation report is automatically generated based on the collaborative matching quantitative indicator set; among them, the visualized relationship network intuitively displays the relationship context, and the matching decision basis provides reliable support for the evaluation, and the overall output of the talent evaluation report from the original text is realized, which is accurate, intuitive and decision-based. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a flow chart of a talent evaluation report generation method provided by an embodiment of the present invention.

[0013] Figure 2 This is a schematic diagram of the basic structure of a talent evaluation report generation system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0015] See also Figure 1 As shown in FIG, this figure is a flow chart of a talent evaluation report generation method provided by an embodiment of the present invention, which can be applied to a talent evaluation report generation system. Figure 1 As shown, the method includes steps 120 to 180.

[0016] Step 120: Obtain a full-cycle interaction text dataset of the target user cluster.

[0017] In the talent evaluation application scenario involved in the embodiments of this invention, the goal is to comprehensively and deeply understand the interaction information of the target user cluster at all stages, thereby providing rich and accurate data support for subsequent talent evaluation. The full-cycle interactive text dataset includes, but is not limited to, industry announcement texts, talent collaboration request texts, and historical conversation records. These texts reflect the behavior, needs, and communication of the target user cluster in industry activities from different perspectives.

[0018] For example, industry announcements describe key information such as the industry's goals, plans, and resource needs at key stages of development. Talent collaboration requests describe the capabilities and desired goals of users participating in industry collaboration. Historical conversation logs document discussions, decision-making processes, and feedback between users at different times around industry-related topics. By comprehensively collecting these different types of text data, we can create a complete and rich dataset of full-cycle interactive text.

[0019] Step 140: Use a deep semantic understanding model to extract entity relationship triples from the full-cycle interactive text dataset to generate a structured knowledge graph.

[0020] In this embodiment of the present invention, the deep semantic understanding model first performs a detailed semantic understanding and analysis of the words and sentences in the full-cycle interactive text dataset to identify key entities. For example, it identifies the resource demand entities represented by the industry-side resource demand keywords from industry announcement texts; accurately extracts capability attribute entities describing user capabilities from talent collaboration request texts; and mines market-related entities related to industry market dynamics from historical conversation records.

[0021] Then, through in-depth analysis and reasoning of semantic relationships in the text, the relationships between these entities are determined. For example, it is determined whether there is a supply-demand matching relationship between the resource demand entity and the capability attribute entity, and whether there is a relationship between the capability attribute entity and the market-related entity that adapts to market changes.

[0022] Finally, the identified entities and their relationships are organized and represented as triples: (entity 1, relationship, entity 2). By processing all relevant text in the full-cycle interactive text dataset, a large number of entity-relationship triples are integrated and organized, ultimately generating a structured knowledge graph. This knowledge graph presents the relationships between different entities in an intuitive and orderly manner, providing a framework and information foundation for subsequent quantitative evaluation and decision analysis.

[0023] In an optional embodiment, the full-cycle interactive text dataset includes industry announcement texts, talent collaboration request texts, and historical conversation records. Step 140 includes:

[0024] Step 141: Perform domain-adaptive word segmentation processing on the industry announcement text to identify the industry-side resource demand keyword set and constraint description phrases.

[0025] In an embodiment of the present invention, a domain-adaptive word segmentation algorithm is first applied to industry announcement texts. This algorithm accurately segments the text based on the industry's expertise and language conventions. For example, in industry announcement texts, for sentences describing resource requirements, the algorithm considers the industry's commonly used terminology and expressions and rationally segments the sentences into individual lexical units. By studying and analyzing a large number of industry announcement texts, the algorithm is able to adapt to the linguistic characteristics of different industry sectors and accurately identify keywords representing industry-side resource requirements. For example, in one industry, terms such as "professional and technical personnel," "specific equipment," and "R&D funding" may be identified as resource requirement keywords, forming the industry-side resource requirement keyword set. The algorithm also focuses on the description of resource requirement constraints in the text, such as phrases such as "complete the project within the specified timeframe" and "meet specific quality standards." These are identified as constraint description phrases. These constraint description phrases are important for clarifying the specific requirements and limitations of industry resource requirements. Together with the resource requirement keyword set, they comprehensively reflect the detailed information about resource requirements in the industry announcement text.

[0026] Step 142: Utilize a bidirectional attention mechanism to parse the nested semantic structure in the talent collaboration request text and extract the user-side capability description entity.

[0027] In this step, a bidirectional attention mechanism is applied to the analysis of talent collaboration request text. The bidirectional attention mechanism provides a deep understanding of the text from two perspectives. When processing the talent collaboration request text, the mechanism first conducts a preliminary understanding of the overall semantics of the text. Then, through forward and backward attention flows, it focuses on key information within the text. For complex nested semantic structures within the text, such as sentences containing multiple layers of modifiers and qualifiers, the bidirectional attention mechanism can gradually parse and clarify the relationships between each semantic level. For example, in a talent collaboration request text, "Professionals with extensive project experience and proficiency in various advanced technologies are looking to participate in challenging projects," the bidirectional attention mechanism first identifies the core entity "professionals." It then uses forward attention to focus on the descriptions of the personnel's abilities, such as "extensive project experience and proficiency in various advanced technologies," and uses backward attention to confirm the connection between these descriptions and the collaboration goal, "participating in challenging projects." Through the above method, entities describing user-side capabilities, such as "rich project experience" and "proficient in the types of technologies", can be accurately extracted. These user-side capability description entities comprehensively reflect the capabilities and characteristics of users requesting collaboration, and provide important information for the subsequent evaluation of the match between users and industry needs.

[0028] Step 143: Calculate the semantic coherence index between the industry demand expression and the user feedback expression in the historical conversation record through a preset cross-text entity alignment matrix.

[0029] The pre-set cross-text entity alignment matrix can be understood as a data structure that provides an effective tool for calculating the semantic coherence between industry demand statements and user feedback statements in historical conversation records. In this step, each industry demand statement and corresponding user feedback statement in the historical conversation record is first converted into a semantic vector representation that can be processed by a computer. For example, a word vector model is used to map the vocabulary in the statement into vector form, and then the entire statement is converted into a comprehensive semantic vector using a common text encoding algorithm. Next, the two semantic vectors are matched and calculated according to the rules and methods defined by the cross-text entity alignment matrix. For example, the cosine similarity between them is calculated, which measures the cosine value of the angle between the two vectors in space to determine the degree of semantic similarity. Alternatively, a dynamic time warping algorithm is used to calculate the optimal matching path between the two sequences, taking into account the order and rhythm of the text statements. This results in a semantic coherence index. This semantic coherence index can quantitatively represent the closeness of the semantic connection between the industry demand statement and the user feedback statement, providing an important quantitative basis for subsequent comprehensive analysis and helping to determine the user's understanding of and response to the industry demand.

[0030] Step 144: Based on the resource requirement keyword set, the user-side capability description entity and the semantic coherence index, a fusion processing based on time evolution characteristics is performed to obtain and generate the structured knowledge graph; wherein the structured knowledge graph includes resource requirement entities, capability attribute entities and market association entities.

[0031] In this step, the resource demand keyword set, user-side capability description entity, and semantic coherence index obtained previously are fused, and the above fusion fully considers the time evolution characteristics. In the process of industrial development, resource demand, user capabilities, and the relationship between them at different stages will change over time. Therefore, when fusion is performed, the data is first sorted and classified in chronological order. For example, the resource demand keyword set, user-side capability description entity, and corresponding semantic coherence index in different time periods are sorted separately.

[0032] Then, using time series analysis and fusion algorithms, we comprehensively process this data. For resource demand keyword sets, we analyze their changing trends over time to determine core resource requirements at different stages. For user-side capability description entities, we observe whether new capabilities emerge or existing capabilities improve over time. For semantic coherence indicators, we analyze their fluctuations at different time points to determine the stability of the relationship between industry demand and user feedback.

[0033] Through the aforementioned fusion processing based on time evolution, these different types of data are organically combined to form a complete structured knowledge graph. In this knowledge graph, resource demand entities, capability attribute entities, and market-related entities are interconnected, collectively presenting the dynamic relationship between talent, resources, and markets in industrial activities. This provides a comprehensive and accurate information foundation for subsequent quantitative evaluation and decision-making.

[0034] Step 160: Quantitatively evaluate the association strength of entity nodes in the structured knowledge graph using a dynamic weight allocation algorithm to generate a set of collaborative matching quantitative indicators.

[0035] In an embodiment of the present invention, a dynamic weight allocation algorithm is used to accurately measure the strength of association between entity nodes in a structured knowledge graph. The algorithm fully takes into account the complexity and dynamic nature of entity relationships in the knowledge graph. First, the algorithm conducts a comprehensive analysis of each entity node in the knowledge graph to examine its role and importance in different relationships. For example, for resource-demand entities, its degree of dependence on various resources at different stages of industrial activities will be analyzed; for capability attribute entities, its role in meeting different resource needs will be evaluated; for market-related entities, its impact on industrial activities and talent supply and demand will be considered.

[0036] Based on these analysis results, each entity node is then assigned initial weights in different relationships. These initial weights reflect the strength of the underlying associations between the entity nodes. The algorithm then dynamically adjusts these weights based on real-time data and dynamic factors. For example, as the industry market environment shifts and the urgency of certain resource demands increases, the association weights between the corresponding resource demand entities and other entities will increase. Alternatively, as the capabilities of a particular type of talent are more fully utilized in a new project, the association weights of their capability attribute entities will also be adjusted accordingly.

[0037] Through the above-mentioned dynamic weight allocation algorithm, the association strength of all entity nodes in the structured knowledge graph is quantitatively evaluated, and finally a set of collaborative matching quantitative indicators is generated. These indicator sets comprehensively and accurately reflect the degree of collaborative matching between different entities, providing a quantitative basis for subsequent talent evaluation and decision-making.

[0038] In an optional embodiment, step 160 includes:

[0039] Step 161: Based on the co-occurrence frequency of entity nodes in the structured knowledge graph, the initial association weights corresponding to the resource demand entity, capability attribute entity and market association entity are generated respectively.

[0040] In this step, the co-occurrence frequency of each entity node in the structured knowledge graph is first counted. The co-occurrence frequency refers to the number of times two or more entity nodes appear at the same time in the relationship network of the knowledge graph. For example, for resource requirement entity A and capability attribute entity B, if they frequently appear at the same time in multiple different triple relationships (such as (A, need, B), (A, dependency, B), etc.), their co-occurrence frequency is higher. By traversing and counting all possible entity node combinations in the knowledge graph, the co-occurrence frequency of resource requirement entity, capability attribute entity and market-related entity with other entities is obtained. Then, the initial association weight is generated based on the co-occurrence frequency.

[0041] A common method is to normalize the co-occurrence frequency so that its value range is between 0 and 1. For example, the co-occurrence frequency of resource requirement entity A and capability attribute entity B is f AB , f AB Divide by the sum of all co-occurrence frequencies related to A, and the result is used as part of the initial association weight between A and B. By performing similar processing on the co-occurrence frequencies between all entity nodes, initial association weights corresponding to resource demand entities, capability attribute entities, and market-related entities are generated. These initial association weights preliminarily reflect the closeness of the association between entity nodes and lay the foundation for subsequent weight adjustment and quantitative evaluation.

[0042] Step 162: Based on the sentiment polarity features analyzed in the talent collaboration request text, the initial association weight corresponding to the capability attribute entity is corrected by the sentiment coefficient to obtain a corrected capability attribute weight.

[0043] In this step, the sentiment polarity analysis algorithm is first used to process the talent collaboration request text to analyze its sentiment polarity characteristics. The sentiment polarity analysis algorithm analyzes the vocabulary, sentence structure, and semantic connotations in the text to determine whether the sentiment expressed in the text is positive, negative, or neutral. For example, in a talent collaboration request text, if the statement "I am very much looking forward to participating in this project and I believe that my abilities can make a positive contribution to the project" appears, the sentiment polarity analysis algorithm will identify its positive sentiment; if the statement "I have doubts about certain project requirements and worry that my abilities cannot fully meet them" appears, the sentiment polarity analysis algorithm will identify its negative sentiment.

[0044] Then, based on the analyzed sentiment polarity characteristics, the corresponding sentiment coefficient is determined. For example, for positive sentiment tendencies, a sentiment coefficient greater than 1 is set, such as 1.2; for negative sentiment tendencies, a sentiment coefficient less than 1 is set, such as 0.8; and for neutral sentiment tendencies, the sentiment coefficient is 1. These sentiment coefficients are then applied to the initial association weights corresponding to the capability attribute entities for correction. For example, if the initial association weight between capability attribute entity C and other entities is wC, and the sentiment polarity analysis results indicate a positive sentiment tendency with a corresponding sentiment coefficient of 1.2, then the corrected capability attribute weight is wC × 1.2.

[0045] Through the above method, the initial association weights of the capability attribute entities are adjusted by taking into account the emotional factors in the talent collaboration request text, and the revised capability attribute weights are obtained that can more accurately reflect the actual situation.

[0046] Step 163: In combination with the timeliness parameter of the industry announcement text, the initial association weight of the market-related entity is dynamically updated using a time decay function to obtain the updated market association weight.

[0047] In this step, the timeliness parameter of the industry announcement text is a key factor. The timeliness parameter reflects the release time of the industry announcement text and the effectiveness and influence of its content at different time points. For example, a certain industry announcement text is released at time t0, and its timeliness will gradually decrease as time t passes. The time decay function is a mathematical model used to describe the gradual weakening of the influence of certain factors over time. In an embodiment of the present invention, the initial association weight of the market-related entity is combined with the timeliness parameter of the industry announcement text, and is dynamically updated using the time decay function. For example, the initial association weight of the market-related entity D is w D, the timeliness parameter can be expressed as a time-related variable, such as τ(t), where t is the current time. The time decay function can be expressed as f(τ(t)), for example, f(τ(t)) = e -k×(t-t0) , where k is the decay coefficient, then the market correlation weight after time update is w D ×f(τ(t)). Through the above method, as time changes, according to the timeliness of industry announcement texts, the initial association weights of market-related entities are dynamically adjusted to obtain more realistic updated market association weights, which can accurately reflect the changes in the strength of association between market-related entities and other entities at different time points.

[0048] Step 164: Based on the initial association weight of the resource demand entity, the modified capability attribute weight and the market association weight after timeliness update, the multi-dimensional association strength is calculated through the fuzzy logic reasoning system, and a set of collaborative matching quantitative indicators reflecting real-time matching needs is generated according to the multi-dimensional association strength.

[0049] In this step, the initial association weights of the resource requirement entities, the revised capability attribute weights, and the updated market association weights are provided as input parameters to the fuzzy logic inference system. These weights reflect the associations between entities from different perspectives, but they themselves have a certain degree of fuzziness and uncertainty. For example, the strength of the association between the resource requirement entity and the capability attribute entity may not be absolutely clear and unambiguous, but may have a certain degree of ambiguity.

[0050] The fuzzy logic reasoning system performs comprehensive analysis and reasoning on these input parameters based on pre-set fuzzy rules and logical relationships. For example, it sets fuzzy rules such as "If the initial association weight of the resource demand entity is high, the modified capability attribute weight is also high, and the market association weight after time-based updates is medium, then the multi-dimensional association strength is high." By reasoning through all possible weight combinations, the multi-dimensional association strength is calculated. This multi-dimensional association strength is the result of comprehensive consideration of multiple factors and comprehensively reflects the closeness of the connections between different entities across multiple dimensions.

[0051] Finally, based on the calculated multi-dimensional correlation strength, a set of collaborative matching quantitative indicators is generated to reflect real-time matching needs. These indicator sets present the collaborative matching between different entities in a quantitative form, which can provide an intuitive and accurate basis for talent evaluation and decision-making.

[0052] Step 180: Automated report generation is performed based on the collaborative matching quantitative indicator set, and a talent evaluation report including a visualized relationship network and matching decision basis is output.

[0053] In an embodiment of the present invention, a comprehensive and intuitive talent evaluation report is automatically generated using a set of collaborative matching quantitative indicators. The report will include a visualized relationship network and matching decision basis.

[0054] First, we conduct an in-depth analysis of the set of quantitative indicators of collaborative matching to understand the meaning of each indicator and the relationship between them. These indicator sets reflect the collaborative matching between resource demand entities, capability attribute entities and market-related entities. By analyzing them, we can gain insight into the fit between talents and industry needs.

[0055] Then, based on these analysis results, we begin constructing a visual relationship network. Leveraging existing visualization tools and algorithms, we graphically represent the entity relationships within the knowledge graph. For example, we represent resource requirement entities, capability attribute entities, and market-related entities with nodes of different shapes. The relationships between entities are connected by lines, whose thickness or color indicates the strength of the relationship.

[0056] This visualization method intuitively displays the complex relationships between different entities, helping decision makers quickly understand the alignment between talent and industry needs. Furthermore, based on the set of quantitative indicators for collaborative matching and the visualized relationship network, the basis for matching decisions can be refined. For example, if the correlation strength between certain resource demand entities and capability attribute entities is high, it indicates that these capabilities are well-suited to meet the corresponding resource needs, which can be used as a basis for decision making. Alternatively, if market-related entities demonstrate a trend in demand for certain talent capabilities, this can also be incorporated into decision making.

[0057] Finally, the visualized relationship network and matching decision basis are integrated into a report to form a talent evaluation report. This report not only provides an intuitive visual display, but also gives a clear decision basis, providing strong support for industry decision makers.

[0058] In an optional embodiment, step 180 includes:

[0059] Step 181: Determine a key decision dimension based on the collaborative matching quantitative indicator set, and determine a visualization component matching the key decision dimension from a preset industry report template library.

[0060] In this step, the set of quantitative indicators for collaborative matching is first carefully analyzed and screened to determine the key decision dimensions. Key decision dimensions refer to those factors or indicator dimensions that have a decisive influence on talent evaluation and decision-making. For example, the set of quantitative indicators for collaborative matching may include multiple dimensions such as the matching degree between resource requirements and capabilities, the fit between market demand trends and talent capabilities, etc. By evaluating the importance of these indicators, analyzing their correlation, and judging the degree of influence on the decision-making results, the most critical dimensions are determined. For example, if in the current industry environment, the matching degree between resource requirements and capabilities is the core factor in determining whether a talent is suitable, then this dimension is determined as a key decision dimension. Then, based on the determined key decision dimensions, select the corresponding visualization components from the preset industry report template library.

[0061] The preset industry report template library is a pre-built resource library containing a variety of visualization templates suitable for different industries and decision-making scenarios. For example, for the key decision-making dimension of resource demand and capability alignment, a bar chart might be used to visually display the talent capability alignment corresponding to different resource demands, or a line chart might be used to show the trend of capability-demand alignment over time. Each visualization component in the template library is carefully designed to accurately and clearly present data information of a specific dimension, allowing decision makers to quickly understand and analyze it.

[0062] Through the above methods, the foundation is laid for the subsequent generation of visual relationship networks and talent evaluation reports, ensuring that the visual display is closely aligned with key decision-making dimensions and can effectively convey key information.

[0063] Step 182: Using a text generative adversarial network, a natural language description paragraph that matches the semantics of the industry field is generated based on the logical association relationship of the collaborative matching quantitative indicator set.

[0064] In this step, the text generation adversarial network consists of a generator and a discriminator, which compete with each other and evolve together. First, the generator receives the logical association relationship of a set of collaborative matching quantitative indicators as input information. These logical association relationships contain complex connections between different entities, such as the degree of match between resource demand entities and capability attribute entities, and the impact of market-related entities on talent supply and demand. Based on its understanding of this information, the generator attempts to generate natural language descriptions. For example, it might generate "In the current industrial environment, for commonly used resource demands, talents with corresponding capability attributes show a high degree of match. This situation interacts with market-related factors and jointly promotes industrial development."

[0065] However, the initial descriptions generated by the generator may contain semantic inaccuracies or logical incoherence. This is when the discriminator comes into play. It evaluates the natural language descriptions generated by the generator, determining whether they align with the semantics of the industry domain and accurately reflect the logical relationships within the set of collaborative matching quantitative indicators. If the discriminator finds any problems, it provides feedback to the generator, which then makes adjustments and improvements based on the feedback and generates new descriptions.

[0066] After multiple rounds of the above-mentioned adversarial training, the generator is gradually able to generate natural language description paragraphs that are semantically accurate, logically coherent, and highly matched with the semantics of the industry field. These paragraphs can clearly and accurately explain the meaning and logical relationship represented by the set of collaborative matching quantitative indicators, providing rich and accurate text description support for talent evaluation reports.

[0067] Step 183: Perform cross-modal fusion processing on the visualization component and the natural language description paragraph to generate a fusion layout corresponding to the decision logic relationship.

[0068] This step aims to organically combine the visualization components and natural language descriptions to form a complete and coherent presentation. During cross-modal fusion processing, it's crucial to first clarify the respective characteristics and advantages of the visualization components and natural language descriptions. Visualization components present data and relationships in an intuitive graphical format, enabling decision makers to quickly access key information. Natural language descriptions, on the other hand, provide detailed and accurate textual explanations of logical relationships and analytical results, helping decision makers gain a deeper understanding.

[0069] Then, based on the decision logic, the two are rationally integrated. For example, for a chart element in a visualization component, find the corresponding detailed explanation in the natural language description paragraph, and associate and integrate them in the layout. Perhaps the chart is placed at the top of the page, followed by a detailed text description of the content immediately below. This allows readers to immediately understand the meaning and underlying logic of the chart through the text description.

[0070] Through the above-mentioned cross-modal fusion processing, a fusion layout that closely corresponds to the decision logic is generated. This fusion layout can not only give full play to the advantages of visualization and text description, but also make the two complement each other and bring out the best in each other, providing decision makers with a comprehensive, clear and easy-to-understand information presentation method, which helps them make decisions more accurately.

[0071] Step 184: Based on the fusion layout, resolution adaptive mapping is performed according to the display status characteristics of the industry decision-making terminal to generate a talent evaluation report including a visualized relationship network and matching decision basis.

[0072] In this step, the display characteristics of the industrial decision-making terminal are a key consideration. Different industrial decision-making terminals, such as computer monitors, tablets, and mobile phones, have different screen resolutions, sizes, and display ratios. First, the display characteristics of the industrial decision-making terminal are detected and analyzed to obtain parameter information such as its resolution and screen size. For example, if the decision-making terminal is a computer monitor, its resolution may be 1920×1080 pixels; if it is a mobile phone, the resolution may be 2400×1080 pixels, etc.

[0073] Then, based on these display state characteristics, the fused layout is adaptively mapped to the resolution. This means adjusting and adapting the visual components and natural language description paragraphs in the fused layout to ensure perfect display on terminals with different resolutions. For example, for charts in the visual components, their size and proportions may need to be adjusted according to the screen size to ensure that the chart elements are clearly visible and the layout is reasonable. For natural language description paragraphs, the font size and line spacing may need to be adjusted to make them easy to read on different terminals.

[0074] Through the above-mentioned resolution adaptive mapping, a talent evaluation report that can present the best effect on various industry decision-making terminals is finally generated. The report contains a clear and intuitive visual relationship network and detailed and accurate matching decision basis. Whether on a large-screen computer or a small-screen mobile phone, decision makers can easily view and analyze the report content and make scientific and reasonable decisions.

[0075] In an alternative embodiment, the training process of the deep semantic understanding model includes:

[0076] Step 220: Obtain a multi-field industrial cooperation corpus, perform resource requirement annotation, capability attribute annotation, and market association annotation processing on the multi-field industrial cooperation corpus, and generate a training data set including a resource requirement annotation entity set, a capability attribute annotation entity set, and a market association annotation entity set.

[0077] In an embodiment of the present invention, a multi-field industrial cooperation corpus is an important basis for model training. The corpus collects cooperation-related text data from multiple different industrial fields, covering a wealth of industrial scenarios and language expressions.

[0078] First, the texts in the corpus are carefully analyzed and understood. For each text segment, the annotator needs to identify the resource demand information, capability attribute information, and market-related information involved based on the text content. For example, in a text segment about a certain science and technology industry cooperation project, "professional R&D equipment" and "personnel with specific algorithm development capabilities" are annotated as resource demand annotation entities; "algorithm development capabilities" and "teamwork capabilities" are annotated as capability attribute annotation entities; and "market demand trends" and "industry competition situation" are annotated as market-related annotation entities. By comprehensively annotating the entire multi-field industry cooperation corpus, the text data is converted into structured annotation information.

[0079] Finally, a training dataset is generated that includes a set of resource requirement labeled entities, a set of capability attribute labeled entities, and a set of market association labeled entities. This training dataset provides rich and accurate samples for the training of the deep semantic understanding model, enabling the model to learn the characteristics and relationships of various entities in different industrial fields, laying a solid foundation for the subsequent accurate extraction of entity relationship triples.

[0080] Step 240: Input the training data set into the initial NLP model, and synchronously parse the industry demand semantic features of the resource demand labeled entity set, the capability description semantic features of the capability attribute labeled entity set, and the dynamic association semantic features of the market association labeled entity set through a multi-task learning framework to generate a cross-scale semantic feature vector set.

[0081] In this step, the training dataset is fed into the initial NLP model. The multi-task learning framework enables the model to simultaneously handle multiple related tasks, improving learning efficiency and generalization. For the set of labeled resource requirement entities, the model deeply analyzes the semantic characteristics of these industry requirements. For example, from the entity "professional R&D equipment," the model learns semantic information such as the specific use of this resource requirement within the industry, its importance, and its connection to other industrial activities.

[0082] For sets of entities labeled with capability attributes, the model focuses on the semantic features of capability descriptions, such as the skill range and technical level requirements of "algorithm development capabilities," as well as how they are applied in different projects. For sets of entities labeled with market associations, the model focuses on analyzing dynamic association semantic features, such as the changing patterns of "market demand trends," their impact on industrial development, and their connection to the supply and demand of talent. By analyzing the semantic features of these different types of entity sets, the model converts the semantic information of each entity into a vector form that can be processed by computers. These vectors reflect the semantic features of the entity at different scales, such as from the lexical level, sentence level, to the paragraph level.

[0083] Finally, these semantic feature vectors from different entity sets are integrated together to generate a cross-scale semantic feature vector set, which contains rich semantic information and provides key data support for further training and optimization of subsequent models.

[0084] Step 260: Based on the cross-scale semantic feature vector set, the course learning strategy is used to perform explicit relationship model training on the industry demand semantic features and the capability description semantic features to generate explicit relationship model parameter update results; according to the explicit relationship model parameter update results, the dynamic association semantic features are implicitly mapped to generate an implicit association mapping feature set.

[0085] In this step, the curriculum learning strategy is an effective training method that trains the model in a specific order and difficulty level. First, explicit relationship patterns are trained for the semantic features of industry demand and capability descriptions. This means explicitly teaching the model to learn the direct relationship between these two semantic features. For example, during training, the model is shown the direct connection between the resource requirements of "professional R&D equipment" and the capability attributes of "personnel with equipment operation capabilities," allowing the model to learn this explicit supply and demand relationship pattern.

[0086] By continuously providing these training samples, the model adjusts its parameters to better fit these explicit relationships, generating updated explicit relationship model parameters. These updated model parameters are then used to map dynamic semantic features to implicit features. Dynamic semantic features are often hidden and complex, unlike explicit relationships, which are directly evident. For example, there may be an indirect, implicit relationship between "market demand trends" and "demand for professional R&D equipment," linked by multiple factors.

[0087] Based on the knowledge and patterns learned from the results of updating the parameters of the explicit relationship model, the model attempts to mine and map these implicit association relationships, converting dynamic association semantic features into a set of implicit association mapping features. This set enriches the model's understanding of the relationship between different semantic features and improves the model's ability to handle complex semantic relationships.

[0088] Step 280: Generate an adversarial sample set containing non-standard expression text based on the training data set, input the implicit association mapping feature set and the adversarial sample set into the initial NLP model for multiple rounds of robustness optimization, and output a deep semantic understanding model with an entity extraction error rate lower than a preset threshold; wherein, the multiple rounds of robustness optimization use the adversarial sample set to perform perturbation verification on the dynamic association semantic features in the implicit association mapping feature set.

[0089] In this step, to improve the model's robustness and generalization, we first generate a set of adversarial examples containing text with non-standard expressions based on the training dataset. These examples employ expressions that differ from the standard expressions in the training dataset and may include colloquial expressions, ambiguous vocabulary, and non-standard grammar. For example, "a person with professional skills" might be replaced with "someone with specialized skills in that area." The purpose of generating these adversarial examples is to challenge the model's understanding capabilities and enable it to cope with a variety of complex language situations. The implicit association mapping feature set and the adversarial examples are then fed into the initial NLP model for multiple rounds of robustness optimization.

[0090] During the optimization process, the adversarial example set perturbs and verifies the dynamic association semantic features in the implicit association mapping feature set. Specifically, the non-standard expressions in the adversarial examples interfere with the dynamic association semantic features already learned by the model. The model needs to readjust and optimize its parameters to correctly understand and process these semantic features. Through multiple rounds of this optimization process, the model continuously improves its resistance to various complex language situations and semantic interference.

[0091] When the model's entity extraction error rate is lower than the preset threshold, it means that the model has achieved good performance and robustness. At this time, the deep semantic understanding model is output. This model can accurately extract entity relationship triplets when faced with text data in various real scenarios, providing reliable support for subsequent talent evaluation-related tasks.

[0092] In an alternative embodiment, the method further comprises:

[0093] Step 320: Monitor the actual application data of the talent evaluation report, collect a set of optimization indicators including report adoption rate and decision response time, input the set of optimization indicators into a preset inverse mapping model, and analyze the nonlinear control relationship between the set of optimization indicators and the sentiment coefficient correction parameters and time attenuation function parameters in the dynamic weight allocation algorithm.

[0094] In order to continuously optimize the talent evaluation method and related algorithms, the actual application of the talent evaluation report needs to be monitored. By collecting and analyzing actual application data, the effectiveness of the report in the actual decision-making process and any problems that exist can be understood.

[0095] First, focus on collecting two optimization metrics: report adoption rate and decision response time. The report adoption rate reflects the degree of recognition and use of talent evaluation reports by decision makers. A low report adoption rate may indicate deficiencies in the report's content or format. Decision response time reflects the speed with which decision makers make decisions based on the report. A long decision response time may indicate that the information in the report is not clear enough or that the decision basis is not well-defined.

[0096] The collected optimization indicator set containing these two indicators is then input into a preset inverse mapping model. This model analyzes the optimization indicator set and reversely derives the nonlinear regulatory relationship between these indicators and the sentiment coefficient correction parameters and time decay function parameters in the dynamic weight allocation algorithm. For example, a nonlinear relationship may be found between the report adoption rate and the sentiment coefficient correction parameter. When the sentiment coefficient correction parameter is adjusted within a certain range, the report adoption rate will change significantly. A similar nonlinear relationship also exists between decision response time and the time decay function parameters.

[0097] By analyzing the above nonlinear control relationship, we can provide direction and basis for the subsequent optimization of algorithm parameters.

[0098] Step 340: Based on the nonlinear control relationship, the Bayesian optimization algorithm is used to iteratively adjust the emotion coefficient correction parameter and the time attenuation function parameter to generate an optimized parameter set, the optimized parameter set is injected into the parameter storage queue of the dynamic weight allocation algorithm, and the parameter version comparator is synchronously started to verify the deviation value of the collaborative matching quantitative indicator set before and after the parameter update.

[0099] In this step, based on the nonlinear regulatory relationship analyzed in the previous step, a Bayesian optimization algorithm is used to iteratively adjust the sentiment coefficient correction parameters and the time decay function parameters. Bayesian optimization is an optimization method based on a probabilistic model that efficiently finds optimal parameter values in the search space.

[0100] The algorithm first constructs a probabilistic model based on known nonlinear regulatory relationships. This model predicts the performance of optimization metrics for different parameter values. Then, by continuously sampling and evaluating new parameter combinations, it gradually approaches the optimal parameter values. For example, a new set of sentiment coefficient correction parameters and time decay function parameters are selected based on the probabilistic model. The optimized metric values for this set of parameters are calculated and compared with the previous results. If the new parameter combination yields better optimization metric performance, it is retained and the iterative search continues. If the performance is poor, the search direction is adjusted based on the probabilistic model. Through multiple iterations of these adjustments, an optimized parameter set is generated.

[0101] Next, the optimized parameter set is injected into the parameter storage queue of the dynamic weight allocation algorithm, effectively applying the new parameter values to the algorithm. Simultaneously, a parameter version comparator is launched to verify the deviation between the collaborative matching quantization metric set before and after the parameter update. By comparing the metric sets before and after the update, the impact of the parameter adjustment on the collaborative matching quantization results can be understood, providing a basis for subsequent judgment of the effectiveness of the parameter adjustment.

[0102] Step 360: When the deviation value of the collaborative matching quantitative indicator set is lower than the preset fault tolerance threshold, the incremental learning module is activated to update the algorithm parameters; when the deviation value of the collaborative matching quantitative indicator set is not lower than the preset fault tolerance threshold, the historical parameter version rollback is triggered to restore the original parameter configuration.

[0103] In this step, the preset tolerance threshold is a key criterion for determining the success of parameter adjustments. When the deviation value of the collaborative matching quantitative indicator set calculated by the parameter version comparator is lower than the preset tolerance threshold, it indicates that the current parameter adjustment has an acceptable impact on the collaborative matching quantitative indicator set and may have a positive effect. At this point, the incremental learning module is activated. This module uses the new parameters and relevant data to further learn and update the algorithm, enabling it to better adapt to actual conditions and improve performance.

[0104] When the deviation value of the collaborative matching quantitative indicator set is not lower than the preset fault tolerance threshold, it indicates that the current parameter adjustment may have caused major problems, resulting in unacceptable changes in the collaborative matching quantitative results. At this time, the historical parameter version rollback is triggered, which will restore the parameters of the dynamic weight allocation algorithm to the original configuration to ensure the stability and accuracy of the algorithm, and avoid affecting the normal operation of the entire system and the reliability of talent evaluation due to improper parameter adjustment.

[0105] In an alternative embodiment, the method further comprises:

[0106] Step 420: Embed a data traceability tag sequence into the structured knowledge graph to establish a bidirectional index relationship between each entity node and the source text paragraph in the full-cycle interactive text dataset.

[0107] In this step, to achieve data traceability, a data traceability tag sequence is embedded in each entity node of the structured knowledge graph. Each tag sequence corresponds to a source text paragraph in the full-cycle interactive text dataset. By establishing a bidirectional index relationship, it is possible to quickly locate the source text paragraph from the entity node, and vice versa. For example, for a resource demand entity in the knowledge graph, the index can quickly find the specific paragraph describing the demand in the industry announcement text, facilitating data accuracy verification and further analysis.

[0108] Step 440: extract the key decision description paragraphs in the talent evaluation report, and calculate the semantic consistency hash value sequence between the key decision description paragraphs and the corresponding source text paragraphs.

[0109] This step first identifies key decision paragraphs from the talent evaluation report. These paragraphs contain important decision rationales and conclusions. Next, the corresponding source text paragraphs are found in the full-cycle interactive text dataset. Using a hashing algorithm, the key decision paragraphs and the source text paragraphs are processed separately to calculate a semantically consistent hash value sequence. This sequence can be used to measure the degree of semantic consistency between the two, providing a quantitative indicator for subsequent verification.

[0110] Step 460: Write the semantically consistent hash value sequence into the blockchain distributed ledger node to generate an unalterable evidence chain containing timestamp verification information.

[0111] After obtaining the semantically consistent hash value sequence, it is written to the blockchain's distributed ledger node. The characteristics of the blockchain ensure that the data cannot be tampered with and is traceable. The writing process is accompanied by a timestamp verification information, recording the exact time when the hash value sequence was generated. This forms an immutable chain of evidence. Any modification to the data will be detected, ensuring the authenticity and integrity of the data related to the talent evaluation report.

[0112] Step 480: Monitor the integrity check results of the semantically consistent hash value sequence in real time. When a hash value check failure is detected, trigger the knowledge graph repairer to locate the abnormal entity node. Re-extract the entity relationship triples of the abnormal entity node from the full-cycle interactive text dataset based on the bidirectional index relationship to complete the online repair of the structured knowledge graph.

[0113] In this step, the system performs a real-time integrity check on the semantically consistent hash value sequence. If a hash value check failure is detected, it indicates that the data may have been tampered with or contains errors. This triggers the knowledge graph repairer, which uses bidirectional index relationships to locate the abnormal entity node. The entity relationship triples associated with the abnormal entity node are then re-extracted from the full-cycle interactive text dataset and corrected to ensure the accuracy and consistency of the structured knowledge graph.

[0114] In a preferred embodiment, step 480 includes:

[0115] Step 481: When it is detected that the integrity check result of the semantic consistency hash value sequence fails, the knowledge graph repairer is triggered to traverse the entity node topology network of the structured knowledge graph, and locate the abnormal entity node according to the tag verification result of the data traceability tag sequence.

[0116] When the integrity check failure signal appears, the knowledge graph repairer starts and begins to traverse the entity node topology network of the structured knowledge graph. It verifies based on the tag information of the data traceability tag sequence, compares the tags of each node with the preset rules, finds the entity node associated with the abnormal hash value, determines it as an abnormal entity node, and clarifies the target for subsequent repair work.

[0117] Step 482: Call the source text paragraph address pointer corresponding to the abnormal entity node in the bidirectional index relationship, and extract the original text segment set associated with the abnormal entity node from the full-cycle interactive text dataset.

[0118] After locating the anomalous entity node, the bidirectional index relationship is used to obtain the source text paragraph address pointer corresponding to the node. Based on the source text paragraph address pointer, the original text fragments associated with the anomalous entity node are accurately extracted from the full-cycle interactive text dataset. These fragments contain the original information of the entity node, providing a basis for re-extracting the entity relationship triples.

[0119] Step 483: Input the original text fragment set into the deep semantic understanding model to perform incremental entity relationship parsing, generate an updated entity relationship triple set that matches the semantics of current industry needs through the entity relationship triple extraction processing, and dynamically recalibrate the adjacent edge weights of the abnormal entity nodes in the structured knowledge graph based on the entity node attribute values of the updated entity relationship triple set using a node attribute conflict resolution algorithm to generate a repaired adjacent edge weight set, reconstruct multiple association paths of the abnormal entity node in the structured knowledge graph based on the repaired adjacent edge weight set, and synchronously update the tag verification code associated with the abnormal entity node in the data traceability tag sequence.

[0120] Optionally, the original text fragment set is fed into a deep semantic understanding model, which performs incremental entity relationship parsing, re-extracts entity relationship triples, and generates an updated set of semantics that meets current industry needs. Based on the entity node attribute values in the updated set, a node attribute conflict resolution algorithm is used to recalibrate the weights of the adjacent edges of the abnormal entity nodes to obtain a repaired set of adjacent edge weights. Based on this set, the association path of the abnormal entity nodes is reconstructed, and the relevant tag verification codes in the data traceability tag sequence are updated to ensure data consistency and traceability.

[0121] Step 484: Inject the reconstructed multiple association paths into the rendering pipeline of the visual relationship network, trigger the blockchain distributed ledger node to re-execute the semantic consistency hash value sequence calculation and evidence verification on the updated entity relationship triple set, and output the repaired structured knowledge graph.

[0122] Furthermore, the reconstructed association paths are integrated into the rendering pipeline of the visual relationship network, ensuring accurate presentation in the visualization. Simultaneously, the blockchain distributed ledger nodes are prompted to recalculate semantically consistent hash values and perform evidence verification on the updated set of entity-relationship triples. After this process, a repaired structured knowledge graph is output, ensuring its accuracy and reliability.

[0123] In an independent embodiment, the method also includes: collecting interactive feedback text data for the talent evaluation report, using a sentiment polarity classifier to identify positive optimization suggestion paragraphs and negative conflict description paragraphs in the interactive feedback text data; extracting the ability matching keyword set in the positive optimization suggestion paragraph and the demand deviation keyword set in the negative conflict description paragraph to generate a feedback semantic feature vector; based on the feedback semantic feature vector, performing gradient back propagation optimization on the sentiment coefficient correction parameter in the dynamic weight allocation algorithm to generate an adaptive sentiment coefficient matrix; injecting the adaptive sentiment coefficient matrix into the generation logic of the collaborative matching quantitative indicator set, and iteratively updating the semantic expression strength of the natural language description paragraph in the talent evaluation report and the alignment with industry demand.

[0124] In an embodiment of the present invention, interactive feedback text data for talent evaluation reports is first collected. A sentiment polarity classifier is used to divide them into positive optimization suggestion paragraphs and negative conflict description paragraphs. A set of capability matching keywords is extracted from the positive paragraphs, and a set of demand deviation keywords is extracted from the negative paragraphs to generate a feedback semantic feature vector. Based on this vector, gradient backpropagation is used to optimize the sentiment coefficient correction parameters in the dynamic weight allocation algorithm to obtain an adaptive sentiment coefficient matrix. This matrix is integrated into the collaborative matching quantitative indicator set generation logic, and the natural language description paragraphs in the talent evaluation report are iteratively updated to better align their semantic expressions with industry needs.

[0125] In an independent embodiment, the method also includes: performing multi-dimensional semantic quality assessment on the natural language description paragraphs in the talent evaluation report to generate a set of quality indicators including logical coherence index, readability score and domain term density; identifying the semantic mapping deviation items between the visual relationship network and the natural language description paragraphs based on the quality indicator set to generate a paragraph reconstruction priority queue; activating a preset generation strategy adjustment rule library according to the paragraph reconstruction priority queue to perform collaborative optimization on the semantic distribution parameters and sentence template weights of the text generation adversarial network; using the optimized text generation adversarial network to perform secondary analysis on the collaborative matching quantitative indicator set to generate an enhanced talent evaluation report that is deeply matched with the industry field expression specifications.

[0126] Optionally, a multi-dimensional evaluation is first conducted on the natural language description paragraphs in the talent evaluation report to derive quality indicators such as the logical coherence index, readability score, and domain term density. Based on these indicators, the semantic mapping deviation between the visual relationship network and the natural language description paragraph is identified to form a paragraph reconstruction priority queue. According to the queue, the preset rule library is activated to collaboratively optimize the semantic distribution parameters and sentence template weights of the text generation adversarial network. The optimized network is used for secondary parsing and collaborative matching of quantitative indicator sets, ultimately generating an enhanced talent evaluation report that is deeply aligned with the industry's expression standards.

[0127] It is worth mentioning that, based on the technical solutions in the aforementioned specification, those skilled in the art can systematically optimize key links through existing technical means to improve overall performance: The ARIMA model is used to perform multi-period fitting on the timeliness parameters of industry announcement texts, dynamically adjusting the coefficients of the time decay function to ensure that market-related weights accurately match the rhythm of industry activities; a Min-Max and Softmax hybrid normalization algorithm is used to eliminate dimensional differences in resource requirements, capability attributes, and the co-occurrence frequency of market-related entities, thereby enhancing the numerical stability of dynamic weight allocation; BERT's context-aware adversarial sample generation technology is combined to construct non-standard expression texts while preserving semantic coherence, thereby improving the robustness of deep semantic understanding models; based on a reinforcement learning framework, sentiment coefficient correction parameters are linked to report adoption rates to achieve adaptive adjustment of capability attribute weights in response to decision feedback; relying on the CSS Grid responsive layout engine and media query technology, the cross-terminal presentation of visual components and natural language paragraphs is dynamically reorganized to ensure display consistency on multi-resolution devices; and a smart contract-driven automated evidence verification mechanism is introduced to seamlessly integrate knowledge graph repair with blockchain hash recalculation, establishing a closed-loop process for anomaly detection, localization, repair, and verification.

[0128] The above optimization systematically improves or solves possible problems such as time decay parameter adaptation deviation, weight distribution dimensional conflict, insufficient non-standard text parsing, static limitation of sentiment coefficient, multi-terminal display distortion and low repair verification efficiency through time series analysis, data normalization, adversarial training, dynamic parameter learning, visualization adaptation and smart contract technology, and ultimately achieves an all-round enhancement of the embodiments of the present invention in terms of semantic parsing accuracy, algorithm robustness, decision adaptability and user experience.

[0129] It should also be noted that the embodiments of the present invention address technical problems such as semantic understanding deviation, low dynamic matching efficiency and insufficient data credibility in traditional talent evaluation. Through the combined application of specific technical means such as deep semantic understanding models, dynamic weight allocation algorithms, knowledge graph construction and blockchain evidence storage, a full-chain technical solution from data collection to report generation is formed.

[0130] Specifically, first, a deep semantic understanding model is used to extract entity relationship triples from the full-cycle interactive text dataset to generate a structured knowledge graph, solving the semantic parsing problem of unstructured text; then, a dynamic weight distribution algorithm is used in combination with sentiment polarity feature correction, time decay function and Bayesian optimization algorithm to quantify the correlation strength of entity nodes into a collaborative matching indicator, thereby achieving dynamic and accurate matching of industry needs and talent capabilities; further, through the cross-modal fusion technology of text generation adversarial networks and visualization components, a talent evaluation report containing natural language descriptions and visual relationship networks is generated to solve the problem of inconsistent multimodal data expression; at the same time, blockchain technology is introduced to store semantically consistent hash values for key decision-making paragraphs to ensure that the data cannot be tampered with, and the integrity and verifiability of the knowledge graph are guaranteed through traceability marking and online repair mechanisms.

[0131] The technical solutions of the embodiments of the present invention all rely on specific technical implementations such as natural language processing model training, iterative optimization of algorithm parameters, and distributed computing. For example, sentiment polarity correction requires analyzing the sentiment tendency of the text through a sentiment classifier model, and the calculation of semantic coherence indicators relies on the mathematical modeling of the cross-text entity alignment matrix. Knowledge graph repair is automatically completed through incremental entity relationship parsing and node attribute conflict resolution algorithms.

[0132] The technical effects produced by the technical solution of the embodiment of the present invention include improving matching efficiency, enhancing the generalization ability of the model for non-standard expressions, ensuring data credibility, etc., and the technical effects are verified through quantitative indicators such as error rate, hash value, and timeliness parameters. Although the solution involves a logical judgment link, its core lies in the technical realization of the logical judgment process. For example, the text generation adversarial network generates natural language paragraphs that conform to industrial semantics through adversarial training, and the Bayesian optimization algorithm iteratively adjusts parameters through a probability model. Both belong to the technical implementation of rules or methods in the technical field. Therefore, the embodiment of the present invention solves specific problems with technical means as a whole, and the technical features run through the entire process of data processing, model training, and report generation, which is essentially different from the abstractness of "intellectual activity rules".

[0133] The embodiment of the present invention can comprehensively capture the interaction information of users at different stages by acquiring the full-cycle interactive text dataset of the target user cluster; extract entity relationship triples from the full-cycle interactive text dataset through a deep semantic understanding model, and convert complex text into a structured knowledge graph to clearly present the entity associations therein; quantitatively evaluate the association strength of entity nodes based on a dynamic weight allocation algorithm, and generate a set of collaborative matching quantitative indicators, which can accurately reflect the degree of collaboration between entities; automatically generate a talent evaluation report based on the collaborative matching quantitative indicator set; wherein, the visualized relationship network intuitively displays the relationship context, and the matching decision basis provides reliable support for the evaluation, thereby realizing the overall output of a talent evaluation report from the original text that is accurate, intuitive, and has a decision basis.

[0134] See also Figure 2 As shown in FIG. 1 , this figure is a schematic diagram of the basic structure of a talent evaluation report generation system 200 provided by an embodiment of the present invention. The talent evaluation report generation system 200 includes:

[0135] Processor 201;

[0136] a storage device 202 having a computer program 2020 stored thereon;

[0137] When the computer program 2020 is executed by the processor 201 , the processor 201 implements the talent evaluation report generation method.

[0138] Based on the above, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the above method are implemented.

[0139] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems or devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

Claims

1. A method for generating a talent evaluation report, characterized in that: include: Obtain a full-cycle interaction text dataset of the target user cluster; A deep semantic understanding model is used to extract entity relationship triples from the full-cycle interactive text dataset to generate a structured knowledge graph; A dynamic weight allocation algorithm is used to quantitatively evaluate the association strength of entity nodes in the structured knowledge graph to generate a set of collaborative matching quantitative indicators; Automatic report generation is performed based on the collaborative matching quantitative indicator set, and a talent evaluation report including a visual relationship network and matching decision basis is output.

2. The method according to claim 1, wherein The full-cycle interactive text dataset includes industry announcement texts, talent collaboration request texts, and historical conversation records. The deep semantic understanding model is used to extract entity relationship triples from the full-cycle interactive text dataset to generate a structured knowledge graph, including: Performing domain-adaptive word segmentation processing on the industry announcement text to identify the industry-side resource demand keyword set and constraint description phrases; A bidirectional attention mechanism is used to parse the nested semantic structure in the talent collaboration request text and extract the user-side capability description entity; Calculating the semantic coherence index between the industry demand expression and the user feedback expression in the historical conversation record through a preset cross-text entity alignment matrix; Based on the resource requirement keyword set, the user-side capability description entity and the semantic coherence index, a fusion processing based on time evolution characteristics is performed to obtain and generate the structured knowledge graph; wherein, the structured knowledge graph includes resource requirement entities, capability attribute entities and market association entities.

3. The method according to claim 2, wherein The dynamic weight allocation algorithm is used to quantitatively evaluate the association strength of entity nodes in the structured knowledge graph to generate a set of collaborative matching quantitative indicators, including: Based on the co-occurrence frequency of entity nodes in the structured knowledge graph, initial association weights corresponding to the resource demand entity, capability attribute entity and market association entity are generated respectively; According to the sentiment polarity characteristics analyzed in the talent collaboration request text, the initial association weight corresponding to the capability attribute entity is corrected by the sentiment coefficient to obtain a corrected capability attribute weight; In combination with the timeliness parameter of the industry announcement text, the initial association weight of the market-related entity is dynamically updated using a time decay function to obtain the market association weight after timeliness update; Based on the initial association weight of the resource demand entity, the revised capability attribute weight and the market association weight after timeliness update, the multi-dimensional association strength is calculated through a fuzzy logic reasoning system, and a set of collaborative matching quantitative indicators reflecting real-time matching needs is generated based on the multi-dimensional association strength.

4. The method according to claim 3, wherein The automated report generation based on the collaborative matching quantitative indicator set outputs a talent evaluation report containing a visual relationship network and matching decision basis, including: Determining key decision dimensions based on the collaborative matching quantitative indicator set, and determining visualization components matching the key decision dimensions from a preset industry report template library; Using a text generative adversarial network, a natural language description paragraph that matches the semantics of the industry field is generated based on the logical association relationship of the collaborative matching quantitative indicator set; Performing cross-modal fusion processing on the visualization component and the natural language description paragraph to generate a fusion layout corresponding to the decision logic relationship; Based on the fusion layout, resolution adaptive mapping is performed according to the display status characteristics of the industry decision-making terminal to generate a talent evaluation report including a visual relationship network and a basis for matching decision-making.

5. The method according to claim 1, wherein The training process of the deep semantic understanding model includes: Obtain a multi-field industrial cooperation corpus, perform resource requirement annotation, capability attribute annotation, and market association annotation processing on the multi-field industrial cooperation corpus, and generate a training data set including a resource requirement annotation entity set, a capability attribute annotation entity set, and a market association annotation entity set; Inputting the training data set into the initial NLP model, and synchronously parsing the industry demand semantic features of the resource demand annotated entity set, the capability description semantic features of the capability attribute annotated entity set, and the dynamic association semantic features of the market association annotated entity set through a multi-task learning framework to generate a cross-scale semantic feature vector set; Based on the cross-scale semantic feature vector set, the course learning strategy is used to perform explicit relationship model training on the industry demand semantic features and the capability description semantic features to generate explicit relationship model parameter update results; according to the explicit relationship model parameter update results, the dynamic association semantic features are implicitly mapped to generate an implicit association mapping feature set; An adversarial sample set containing non-standard expression text is generated based on the training data set, the implicit association mapping feature set and the adversarial sample set are input into the initial NLP model for multiple rounds of robustness optimization, and a deep semantic understanding model with an entity extraction error rate lower than a preset threshold is output.

6. The method according to claim 5, wherein The multiple rounds of robustness optimization perform perturbation verification on the dynamic association semantic features in the implicit association mapping feature set through the adversarial sample set.

7. The method according to claim 1, wherein The method further comprises: Monitor actual application data of the talent evaluation report and collect a set of optimization indicators including report adoption rate and decision response time; Inputting the optimization index set into a preset inverse mapping model, analyzing the nonlinear control relationship between the optimization index set and the emotion coefficient correction parameter and the time decay function parameter in the dynamic weight allocation algorithm; Based on the nonlinear regulation relationship, a Bayesian optimization algorithm is used to iteratively adjust the emotion coefficient correction parameter and the time decay function parameter to generate an optimized parameter set; Injecting the optimized parameter set into the parameter storage queue of the dynamic weight allocation algorithm, and synchronously starting a parameter version comparer to verify the deviation value of the collaborative matching quantitative indicator set before and after the parameter update; When the deviation value of the collaborative matching quantitative indicator set is lower than the preset fault tolerance threshold, the incremental learning module is activated to update the algorithm parameters; when the deviation value of the collaborative matching quantitative indicator set is not lower than the preset fault tolerance threshold, the historical parameter version rollback is triggered to restore the original parameter configuration.

8. The method according to claim 1, wherein The method further comprises: Embed a data traceability tag sequence in the structured knowledge graph to establish a bidirectional index relationship between each entity node and the source text paragraph in the full-cycle interactive text dataset; Extracting a key decision description paragraph from the talent evaluation report, and calculating a semantically consistent hash value sequence between the key decision description paragraph and a corresponding source text paragraph; Writing the semantically consistent hash value sequence into the blockchain distributed ledger node to generate an unalterable evidence chain containing timestamp verification information; The integrity check results of the semantically consistent hash value sequence are monitored in real time. When a hash value check failure is detected, the knowledge graph repairer is triggered to locate the abnormal entity node. The entity relationship triples of the abnormal entity node are re-extracted from the full-cycle interactive text dataset based on the bidirectional index relationship to complete the online repair of the structured knowledge graph.

9. The method according to claim 8, wherein The integrity check result of the semantic consistency hash value sequence is monitored in real time. When a hash value check failure is detected, the knowledge graph repairer is triggered to locate the abnormal entity node. The entity relationship triples of the abnormal entity node are re-extracted from the full-cycle interactive text dataset based on the bidirectional index relationship to complete the online repair of the structured knowledge graph, including: When it is detected that the integrity check result of the semantic consistency hash value sequence fails, the knowledge graph repairer is triggered to traverse the entity node topology network of the structured knowledge graph, and locate the abnormal entity node according to the tag verification result of the data traceability tag sequence; Calling the source text paragraph address pointer corresponding to the abnormal entity node in the bidirectional index relationship, and extracting the original text segment set associated with the abnormal entity node from the full-cycle interactive text dataset; Inputting the original text segment set into the deep semantic understanding model to perform incremental entity relationship parsing, and generating an updated entity relationship triple set that semantically matches current industry needs through the entity relationship triple extraction process; According to the entity node attribute values of the updated entity relationship triple set, a node attribute conflict resolution algorithm is used to dynamically recalibrate the adjacent edge weights of the abnormal entity nodes in the structured knowledge graph to generate a repaired adjacent edge weight set; Reconstructing multiple associated paths of the abnormal entity node in the structured knowledge graph based on the repaired adjacent edge weight set, and synchronously updating the tag verification code associated with the abnormal entity node in the data traceability tag sequence; The reconstructed multiple association paths are injected into the rendering pipeline of the visual relationship network, triggering the blockchain distributed ledger node to re-execute the semantic consistency hash value sequence calculation and evidence verification on the updated entity relationship triple set, and output the repaired structured knowledge graph.

10. A talent evaluation report generation system, characterized in that: include: processor; A storage device stores a computer program thereon, and when the computer program is executed by the processor, the processor implements the talent evaluation report generation method according to any one of claims 1 to 9.

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