Task semantic and capability constraint based job task allocation method and system

Abstract

The application provides a task semantic and capability constraint-based work task allocation method and system. The work task allocation method is characterized in that: a task-personnel knowledge graph for a special work scene is constructed by structurally modeling multiple source heterogeneous elements such as tasks, personnel, capability qualifications and work environments; and a subgraph construction and multi-hop correlation reasoning based on task semantic driving are implemented to realize the collaborative matching of task semantic requirements and personnel qualification conditions, and to solve the problems of insufficient task semantic expression, difficulty in reasoning of correlation relationships of heterogeneous elements and lack of explainability and dynamic adaptability of allocation basis in the existing personnel allocation method through the visual expression of reasoning paths and the dynamic updating mechanism of the graph.

Description

Technical Field

[0001] This invention proposes a method and system for assigning job tasks based on task semantics and capability constraints, belonging to the field of artificial intelligence technology. Background Technology

[0002] Special operations refer to work activities with high risks, high professional thresholds, and strict qualification requirements, such as working at heights, confined space operations, handling hazardous media, and maintenance of complex equipment. In actual execution, these operations often involve complex environmental conditions, multi-stage work processes, and strong constraints on personnel qualifications and safety regulations. Achieving a reasonable match between work tasks and personnel while meeting safety and compliance requirements is an important research direction in the field of intelligent recommendation. Currently, the allocation of personnel for special operations is typically modeled as a constrained assignment or scheduling problem. By constructing constraint models of tasks, personnel, and resource conditions, optimization algorithms such as integer programming and heuristic search, or machine learning models trained based on historical allocation data, are used to make matching decisions regarding the recommendation relationship between tasks and personnel. For example, Existing technology one (Hu M, Wang Y, Tian W. Adaptive hybrid optimization for integrated project scheduling and staffing problem with time / resource trade-offs[J]. Operations Research Perspectives, 2025, 15: 100346.) discloses a method that models task-personnel allocation as a bi-objective optimization problem with time and resource constraints. By constructing a multi-objective mathematical model and employing an adaptive hybrid optimization algorithm, it achieves joint optimization of task execution order and personnel allocation scheme. Existing technology two is a patent with publication number CN119599404A entitled "Task Allocation Method Based on Current and Historical Needs," which discloses a task allocation method based on current and historical needs. By performing topic modeling on the requirements document and combining it with personnel historical ratings, it predicts the suitability of personnel for new tasks, thereby generating task allocation results.

[0003] While the aforementioned existing technologies can achieve automatic allocation of tasks and personnel to a certain extent, they focus on matching decisions based on numerical parameters or statistical similarity, making it difficult to express the complex semantic requirements implicit in special operations tasks, especially the complex on-site conditions faced in actual special operations. Secondly, they fail to uniformly model multi-source heterogeneous elements such as tasks, personnel, qualifications, and working environment, lack reasoning on multi-hop relationships between elements, making it difficult to adapt to accurate personnel matching in complex special operation scenarios, and unable to dynamically update data when task and personnel status changes.

[0004] In the existing technology, the task and personnel are modeled as optimization problems with time and resource constraints, and matching decisions are made based on numerical parameters. Its task representation focuses on the abstract modeling at the scheduling level, which makes it difficult to express the complex semantic structure and multi-stage operation requirements contained in special operation tasks.

[0005] In the second existing technology, task allocation results are generated by thematic modeling of the requirements document and combining personnel historical scores. Its allocation logic mainly relies on text similarity and historical statistical information. It does not uniformly model heterogeneous elements such as tasks, personnel, skills and working environment, lacks the ability to reason about multi-hop relationships between elements, and is difficult to dynamically update the allocation basis when the status of tasks or personnel changes. Summary of the Invention

[0006] This invention provides a method and system for task allocation based on task semantics and capability constraints. By structurally modeling multi-source heterogeneous elements such as tasks, personnel, capabilities, and work environment, a task-personnel knowledge graph for special operation scenarios is constructed. Based on task semantics-driven subgraph construction and multi-hop association reasoning, the method achieves coordinated matching of task semantic requirements and personnel qualification conditions. Furthermore, through the visualization of reasoning paths and a dynamic graph update mechanism, it addresses the problems of insufficient task semantic expression, difficulty in reasoning about the relationships between heterogeneous elements, and lack of interpretability and dynamic adaptability in existing personnel allocation methods. The technical solution adopted is as follows:

[0007] A task allocation method based on task semantics and capability constraints, the task allocation method comprising:

[0008] Raw data for special operations personnel allocation is collected, and the raw data is cleaned and preprocessed to generate processed raw data. The raw data is then combined with manual annotation and expert knowledge embedding to construct a special operations task corpus.

[0009] Based on the constructed special operation task corpus, the few-shot learning mechanism of the large language model is used to automatically perform semantic parsing on the input special operation task to obtain the ability and qualification constraints required to complete the feature operation task.

[0010] The aforementioned competency and qualification constraints are combined with the personnel and their semantic elements related to the input special operation tasks to construct a knowledge graph of task personnel in special operation scenarios;

[0011] A semantically driven candidate subgraph is constructed centered on the currently input special operation task, and multi-hop association reasoning is performed. The task semantic requirements and personnel ability and qualification conditions are comprehensively considered in the candidate subgraph to generate the task allocation result for special operation personnel.

[0012] An interpretable mechanism is constructed based on multiple reachable paths formed during the reasoning process, and the key correlation path between the requirements of the special operation task and the personnel's capabilities is output.

[0013] After the completion of the special operation task, execution feedback information is collected, and the knowledge graph of the task personnel is dynamically updated. The status and / or weight of relevant entities and relationships in the knowledge graph of the task personnel are adjusted according to the task execution results and personnel performance corresponding to the special operation task.

[0014] Furthermore, raw data for the allocation of special operations personnel is collected, and the raw data is cleaned and preprocessed to generate processed raw data. This raw data is then combined with manually labeled data and expert knowledge embedding to construct a special operations task corpus, including:

[0015] Collect raw data for the allocation of special operations personnel, wherein the raw data includes task data and personnel data;

[0016] The task data is uniformly represented as a text-tag correspondence pattern and stored using a structured tag format;

[0017] The task data is cleaned and preprocessed to form a standardized representation of the task text data. The task text data is then manually annotated to generate a set of task semantic tags with task semantic tags.

[0018] By combining the aforementioned task semantic tag set with an expert knowledge embedding mechanism, empirical knowledge in the field of special operation scheduling is transformed into structured constraint relationships;

[0019] The structured constraint relationship corresponding to the transformation of the empirical knowledge in the special operation scheduling field is the special operation task corpus contained in the special operation task corpus.

[0020] Furthermore, based on the constructed special task corpus, the few-shot learning mechanism of a large language model is used to automatically perform semantic parsing on the input special task to obtain the ability and qualification constraints required to complete the special task, including:

[0021] When a special task with new input is obtained, a few-shot prompt based on a large language model is constructed for automatic semantic parsing of the task.

[0022] By using the structure corresponding to the few-sample prompts, the large language model performs semantic understanding on the newly input special operation task under the given distribution of labeled task examples and generates candidate labels aligned with the controlled vocabulary; wherein the labeled task examples are from the special operation task corpus.

[0023] The task semantic label set corresponding to the candidate label results is mapped to the corresponding personnel ability label set according to the expert knowledge mapping rules, thereby obtaining the ability and qualification constraints that need to be met to complete the feature task.

[0024] Furthermore, the few-sample prompt includes at least the following:

[0025] The task semantic parsing instruction is used to constrain the output of a large language model to a controlled set of labels rather than free text;

[0026] Several labeled task examples are presented, each in the form of "task description - tag set";

[0027] The normalized text representation of the special operation task corresponding to the current input to be parsed.

[0028] Furthermore, by combining the aforementioned competency constraints with the personnel and their semantic elements related to the input special operation task, a knowledge graph of task personnel in the special operation scenario is constructed, including:

[0029] For a feature task that has completed the acquisition of competency and qualification constraints, a task entity corresponding to it in the task personnel knowledge graph is constructed, and the semantic tag corresponding to the feature task is introduced as a task semantic tag entity. The task entity and the task semantic tag entity are connected through semantic association.

[0030] Each special operations personnel is constructed as a personnel entity, and the ability attributes and historical behavioral characteristics formed in historical tasks of each special operations personnel are introduced into the task personnel knowledge graph in the construction process along with the personnel entity in the form of attribute entities;

[0031] The capability and qualification constraints required for the featured task are retrieved and introduced into the task personnel knowledge graph during the construction process to characterize the relationship between the task and personnel capabilities.

[0032] Furthermore, each special operations personnel is constructed as a personnel entity, and the ability attributes and historical behavioral characteristics formed in historical tasks of each special operations personnel are introduced into the task personnel knowledge graph during the construction process along with the personnel entity in the form of attribute entities, including:

[0033] Retrieve the ability attributes of each special operations personnel;

[0034] The ability attributes possessed by each special operations personnel are constructed into a corresponding personnel ability tag entity;

[0035] Retrieve the performance records of each special operations personnel in historical missions;

[0036] The performance of each special operations personnel in historical tasks is constructed into a personnel behavior statistical entity.

[0037] The personnel capability tag entity and personnel behavior statistics entity are combined with the personnel entity and introduced into the task personnel knowledge graph during the construction process.

[0038] Furthermore, the aforementioned competency constraints are incorporated into the task personnel knowledge graph during the construction process to characterize the relationship between task requirements and personnel capabilities, including:

[0039] When the expert knowledge mapping rule determines that the combination of task semantic tags has a corresponding personnel ability tag entity, a requirement relationship is established between the task entity and the corresponding personnel ability tag entity.

[0040] The historical task execution records of special operations personnel are introduced into the task personnel knowledge graph structure. When a special operations personnel has performed a task that is the same as or similar to the input special operations task in the historical record, a historical execution relationship is established between the personnel entity corresponding to the special operations personnel and the task entity.

[0041] By adding basic attribute information to task entities, personnel entities, personnel ability tag entities, and personnel behavior statistics entities and their corresponding relationships, a task personnel knowledge graph is constructed.

[0042] Furthermore, a semantically driven candidate subgraph is constructed centered on the currently input special operation task, and multi-hop association reasoning is performed. The task semantic requirements and personnel competency qualifications are comprehensively considered within the candidate subgraph to generate a task allocation result for the special operation personnel, including:

[0043] The candidate subgraph is represented as a heterogeneous graph structure, and a relation-aware graph neural network is used to perform multi-level message passing on the candidate subgraph. In the process of multi-level message passing, the representation vector of each node in each layer is updated according to its neighboring nodes and relation types.

[0044] For each person entity, identify multiple reachable paths between it and the input special operation task in the candidate subgraph, and represent each reachable path as a sequence of nodes and relations;

[0045] The paths are encoded using the relation types and node representations contained in each reachable path, and a path score is calculated for each reachable path.

[0046] The path scores of each reachable path included in the multiple reachable paths corresponding to each personnel entity are aggregated to form a comprehensive matching score between the personnel entity and the special operation task.

[0047] The candidate special operations personnel corresponding to the candidate subgraphs are sorted according to the comprehensive matching score, and the candidate special operations personnel or combination of candidate special operations personnel with the highest score is selected as the recommended allocation result for the current task.

[0048] Furthermore, based on the multiple reachable paths formed during the reasoning process, an interpretable mechanism is constructed to output the key correlation paths between the requirements of the special operation task and the personnel capabilities, including:

[0049] The system retrieves the personnel entity corresponding to any recommended special operations personnel from the recommended allocation results, and retrieves the corresponding set of high-contribution inference paths from the candidate subgraph. The high-contribution inference path is determined as follows: all reachable paths corresponding to the personnel entity are sorted in descending order of path score, and paths with scores higher than a preset threshold are selected as high-contribution inference paths. The preset threshold is set by the dispatcher according to actual needs.

[0050] The reachable paths are sorted according to the path score of each reachable path included in the set of high contribution inference paths, and the top-ranked paths are selected as explanatory paths, that is, the key correlation paths between the requirements of the special operation task and the personnel's capabilities.

[0051] Furthermore, upon completion of the special operation task, execution feedback information is collected, and the task personnel knowledge graph is dynamically updated. Based on the task execution results and personnel performance corresponding to the current special operation task, the state and / or weights of relevant entities and relationships in the task personnel knowledge graph are adjusted, including:

[0052] Real-time collection of execution data information during the execution of special operations tasks; wherein, the execution data information includes whether the task is completed as planned, the actual execution time, abnormal or risk events during the execution process, personnel cooperation status, and the dispatcher's subjective evaluation of the execution effect;

[0053] The execution data information is associated with the corresponding task entities and personnel entities in a structured form;

[0054] The personnel behavior statistics entity of special operation personnel is dynamically adjusted based on the execution data information of special operation tasks during the execution process and the preset update rules.

[0055] Based on the actual performance of special operations personnel in special operations tasks, the weights of the relationship edges in the personnel knowledge graph are adjusted.

[0056] When existing task semantic tags or capability tags cannot fully describe the actual needs during the execution of special operations tasks, the situation is marked as a "semantic insufficiency" or "capability deficiency" event and submitted to the manual review process.

[0057] A task allocation system based on task semantics and capability constraints, the task allocation system comprising:

[0058] The data acquisition and preprocessing module is used to collect raw data assigned to special operation personnel, clean and preprocess the raw data to generate processed raw data, and combine the raw data with manual annotation and expert knowledge embedding to construct a special operation task corpus.

[0059] The competency and qualification constraint acquisition module is used to automatically perform semantic parsing on the input special operation task based on the constructed special operation task corpus and using the few-shot learning mechanism of the large language model to obtain the competency and qualification constraints required to complete the feature operation task.

[0060] The knowledge graph construction module is used to combine the ability and qualification constraints with the personnel and their semantic elements related to the input special operation tasks to construct a task personnel knowledge graph in the special operation scenario;

[0061] The task allocation result acquisition module is used to construct a semantically driven candidate subgraph centered on the currently input special operation task and perform multi-hop association reasoning. In the candidate subgraph, the task semantic requirements and personnel ability and qualification conditions are comprehensively considered to generate the task allocation result of special operation personnel.

[0062] The explanation mechanism construction module is used to construct an explainable mechanism based on multiple reachable paths formed during the reasoning process, and output the key correlation path between the requirements of the special operation task and the personnel's capabilities.

[0063] The information feedback and graph adjustment module is used to collect execution feedback information after the completion of the current special operation task, and to dynamically update the task personnel knowledge graph. Based on the task execution results and personnel performance corresponding to the current special operation task, the module adjusts the status and / or weight of relevant entities and relationships in the task personnel knowledge graph.

[0064] Beneficial effects of this invention:

[0065] This invention provides a method and system for task allocation based on task semantics and capability constraints. By performing structured modeling of the semantics of special operations tasks, personnel capabilities and qualifications, and their correlations, and introducing a knowledge graph reasoning mechanism driven by task semantics, it can more accurately express the complex semantic requirements implicit in special operations tasks, realize multi-hop correlation reasoning between task semantics and personnel capability conditions, thereby improving the matching accuracy and applicability of special operations personnel allocation results in complex scenarios.

[0066] Meanwhile, the task allocation method and system based on task semantics and capability constraints provided by this invention clearly demonstrate the basis for personnel allocation to the scheduler through an interpretable mechanism based on reasoning paths. It also supports interactive review and intervention by the scheduler during the allocation process, and dynamically updates the knowledge graph based on task execution feedback, enabling the personnel allocation system to continuously evolve. Compared to existing static allocation methods, this invention improves the interpretability, credibility, and dynamic adaptability of personnel allocation decisions while ensuring allocation efficiency, making it more suitable for the actual needs of special operation scenarios. Attached Figure Description

[0067] Figure 1 This is a flowchart of the task allocation method based on task semantics and capability constraints described in this invention;

[0068] Figure 2 This is a flowchart illustrating the special task corpus preprocessing and construction process of the present invention;

[0069] Figure 3 This is a flowchart of the task semantic-driven personnel allocation reasoning method of the present invention. Detailed Implementation

[0070] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0071] This invention proposes a method and system for job task allocation based on task semantics and capability constraints, such as... Figure 1 As shown, the task allocation method includes:

[0072] Raw data for special operations personnel allocation is collected, and the raw data is cleaned and preprocessed to generate processed raw data. The raw data is then combined with manual annotation and expert knowledge embedding to construct a special operations task corpus.

[0073] Based on the constructed special operation task corpus, the few-shot learning mechanism of the large language model is used to automatically perform semantic parsing on the input special operation task to obtain the ability and qualification constraints required to complete the feature operation task.

[0074] The aforementioned competency and qualification constraints are combined with the personnel and their semantic elements related to the input special operation tasks to construct a knowledge graph of task personnel in special operation scenarios;

[0075] A semantically driven candidate subgraph is constructed centered on the currently input special operation task, and multi-hop association reasoning is performed. The task semantic requirements and personnel ability and qualification conditions are comprehensively considered in the candidate subgraph to generate the task allocation result for special operation personnel.

[0076] An interpretable mechanism is constructed based on multiple reachable paths formed during the reasoning process, and the key correlation path between the requirements of the special operation task and the personnel capabilities is output to support the interpretation and review of the personnel allocation results.

[0077] After the completion of the special operation task, execution feedback information is collected and the task personnel knowledge graph is dynamically updated. The status and / or weight of relevant entities and relationships in the task personnel knowledge graph are adjusted according to the task execution results and personnel performance corresponding to the special operation task, so as to improve the applicability of the special operation personnel allocation system.

[0078] The technical solution described in this embodiment constructs a task-personnel knowledge graph for special operation scenarios by structurally modeling multi-source heterogeneous elements such as tasks, personnel, competency qualifications, and working environment. Based on task semantic-driven subgraph construction and multi-hop association reasoning, it achieves coordinated matching of task semantic requirements and personnel qualification conditions. Furthermore, through the visualization of reasoning paths and a dynamic graph update mechanism, it addresses the problems of insufficient task semantic expression, difficulty in reasoning about heterogeneous element relationships, and lack of interpretability and dynamic adaptability in existing personnel allocation methods. Moreover, by combining the aforementioned structured modeling with the introduction of a task semantic-driven knowledge graph reasoning mechanism, it can more accurately express the complex semantic requirements implicit in special operation tasks, achieving multi-hop association reasoning between task semantics and personnel competency conditions, thereby improving the matching accuracy and applicability of special operation personnel allocation results in complex scenarios.

[0079] Meanwhile, the task allocation method and system based on task semantics and capability constraints provided in this embodiment clearly demonstrate the basis for personnel allocation to the scheduler through an interpretable mechanism based on reasoning paths. It also supports interactive review and intervention by the scheduler during the allocation process, and dynamically updates the knowledge graph based on task execution feedback, enabling the personnel allocation system to continuously evolve. Compared to existing static allocation methods, this invention improves the interpretability, credibility, and dynamic adaptability of personnel allocation decisions while ensuring allocation efficiency, making it more suitable for the actual needs of special operation scenarios.

[0080] One embodiment of the present invention involves collecting raw data for the allocation of special operations personnel, cleaning and preprocessing the raw data to generate processed raw data, and combining the raw data with manual annotation and expert knowledge embedding to construct a special operations task corpus, including:

[0081] Collect raw data for the allocation of special operations personnel, wherein the raw data includes task data and personnel data;

[0082] The task data is uniformly represented as a text-tag correspondence pattern and stored using a structured tag format;

[0083] The task data is cleaned and preprocessed to form a standardized representation of the task text data. The task text data is then manually annotated to generate a set of task semantic tags with task semantic tags.

[0084] By combining the aforementioned task semantic tag set with an expert knowledge embedding mechanism, empirical knowledge in the field of special operation scheduling is transformed into structured constraint relationships;

[0085] The structured constraint relationship corresponding to the transformation of the empirical knowledge in the special operation scheduling field is the special operation task corpus contained in the special operation task corpus.

[0086] This embodiment's technical solution collects raw data for special operations personnel allocation and constructs a high-quality special operations task corpus through standardized cleaning, manual annotation, and expert knowledge embedding. The collected data includes two categories: task data and personnel data. Task data originates from the scheduling system or historical operation records and includes: natural language description of the task, task category, task execution stage, and task execution result; personnel data includes basic personnel identity information, ability attribute information, and historical task execution behavior data. The special operations task corpus preprocessing and construction process is as follows: Figure 2 As shown.

[0087] To facilitate subsequent processing, the system uniformly represents all task samples as text-label pairs and stores them using a structured record format. The original representation of a single task sample is as follows:

[0088] ;

[0089] Wherein, id is the unique identifier of the task, text is the task description text entered by the scheduler, and meta is the task-related meta information, such as the task time window and the number of workers.

[0090] Task text cleaning and preprocessing aim to reduce noise and expression differences in the scheduler's natural language descriptions, making similar tasks exhibit more stable consistency in the semantic space, and providing calculable standardized inputs for subsequent manual annotation, few-sample prompt construction, and graph storage. Since task descriptions typically contain colloquial expressions, redundant embellishments, inconsistent formats, and diverse synonyms, the system performs standardization processing on the task text.

[0091] The cleaning process is roughly as follows: The system first performs basic cleaning on the task text, removing characters and formatting noise that do not contribute semantically, including abnormally encoded characters, invisible control characters, repeated spaces, meaningless consecutive punctuation marks, and templated prefixes and suffixes unrelated to the task content (such as uniform greetings or system prompts). Simultaneously, it standardizes the expression of numbers and units, unifying equivalent expressions such as "2 hours / two hours / 120 minutes" into a single representation, and extracts structured information such as time windows and number of people from the text and writes it into the meta information field.

[0092] After basic text cleaning, the system performs semantic granular normalization, transforming long sentences into shorter, more suitable segments for annotation and parsing. Specifically, when a task description contains multiple actions, objects, or constraints, the system uses punctuation and conjunction pattern recognition to segment it into several semantic segments while maintaining their original order, allowing subsequent annotations to be aligned to "task phase" or "constraint." For example, for a description like "Repair a broken power line on a rooftop within 2 hours," three core semantic segments—"task location / scene," "task action + object," and "time constraint"—can be retained, enabling subsequent annotations to be aligned to more specific label evidence.

[0093] To reduce the impact of synonyms and industry slang on semantic consistency, the above-mentioned technical solution in this embodiment introduces a controlled vocabulary-driven synonym unification mechanism. This mechanism merges common synonyms, near-synonyms, and professional terms with different spellings in the field of special operations into a unified expression. For example, expressions such as "power line / cable / line" and "inspection / repair / maintenance" are mapped to unified terms without changing the semantics of the task. Similarly, words with obvious abbreviations or colloquialisms are standardized through a mapping table. The purpose of this process is to align key trigger words in the text with the controlled tagging system, thereby improving the consistency of subsequent manual annotation and the reusability of tags.

[0094] Through the above text cleaning and semantic normalization processes, the original task samples are transformed from their initial natural language descriptions into normalized task text representations. At this point, a single task sample is represented by the original text d. raw Convert to normalized representation d norm Its form is:

[0095] ;

[0096] In obtaining the normalized task text representation d norm Then, the task corpus is manually annotated to construct high-quality few-shot learning samples. Manual annotation refers to mapping the task text representation into a discrete label system, where each task label reflects a part of the task semantics.

[0097] Simultaneously, the system annotates tasks based on a pre-built controlled tagging system. This tagging system covers common semantic dimensions in special operations, including the task object, task type, task method or context, and risk-related characteristics. Each tag corresponds to a clear semantic definition and scope of application. During manual annotation, the annotator uses standardized text... norm Based on this, several labels that best match the semantics of each task are selected, thus forming a structured semantic representation of the task. The labeled task sample can be represented as:

[0098] ;

[0099] in, This represents the i-th semantic label related to the task.

[0100] Building upon the completion of task semantic labeling, an expert knowledge embedding mechanism is further introduced to transform experiential knowledge in the special operations scheduling domain into structured constraints. Specifically, domain experts analyze the correspondence between "task semantic labels—personnel capability labels," clarifying the personnel capability requirements typically needed to complete tasks with specific semantic features in actual operations. For example, when the task label set contains both a job type label and a job context label, experts can identify the corresponding capability label set, which characterizes the basic capability requirements for completing that type of task. This expert knowledge is embedded in the system in the form of mapping rules to characterize the relationship between task semantics and personnel capabilities. This mapping relationship can be formally represented as:

[0101] ;

[0102] in, A set of semantic tags for the task. Given a set of capability labels derived from expert knowledge, the function... The mapping rules are established. At this point, a high-quality corpus of special operations tasks has been constructed.

[0103] The technical solution described in this embodiment improves the core performance indicators of the special operation task corpus, enhances the quality and processing efficiency of the corpus data, reduces the error rate of subsequent personnel allocation operations, and strengthens the practicality and adaptability of the corpus. Through standardized cleaning and preprocessing of the original task data, noise interference in the task text is reduced, improving the consistency and standardization of the text data. This increases the semantic similarity of similar tasks, reduces semantic deviations caused by synonymous expressions, colloquialisms, and format differences, and improves the accuracy and consistency of corpus annotation. Manual annotation combined with a controlled labeling system further improves the accuracy and standardization of task semantic label annotation, reducing annotation costs. The system reduces errors, improves label reuse, and minimizes efficiency losses caused by repeated labeling. The expert knowledge embedding mechanism transforms domain experience into structured constraints, improving the matching accuracy of task-personnel capability associations in the corpus, enhancing the corpus's support for subsequent personnel allocation decisions, and reducing adaptation errors in the decision-making process. Simultaneously, the comprehensiveness of data collection and the structured nature of storage improve the corpus's data integrity and retrieval efficiency, shortening the response time for corpus retrieval and parsing. This provides efficient, accurate, and reliable performance support for subsequent few-shot learning, graph entry, and intelligent scheduling and allocation, ultimately improving the overall operational efficiency and decision-making accuracy of the special operations personnel allocation system.

[0104] In one embodiment of the present invention, based on the constructed special operation task corpus, the few-shot learning mechanism of a large language model is used to automatically perform semantic parsing on the input special operation task to obtain the ability and qualification constraints required to complete the special operation task, including:

[0105] When a special task with new input is obtained, a few-shot prompt based on a large language model is constructed for automatic semantic parsing of the task.

[0106] By using the structure corresponding to the few-sample prompts, the large language model performs semantic understanding on the newly input special operation task under the given distribution of labeled task examples and generates candidate labels aligned with the controlled vocabulary; wherein the labeled task examples are from the special operation task corpus.

[0107] The task semantic label set corresponding to the candidate label results is mapped to the corresponding personnel ability label set according to the expert knowledge mapping rules, thereby obtaining the ability and qualification constraints that need to be met to complete the feature task.

[0108] In this embodiment, the system, based on a high-quality special operation task corpus, utilizes a few-shot learning mechanism based on a large language model to automatically perform semantic parsing on newly input special operation tasks and generate the capability and qualification constraints required to complete the task. This reduces the cost of manual modeling and improves the system's adaptability to complex and fuzzy task descriptions.

[0109] When a new task description is input, the system constructs a few-sample prompt for automatic task semantic parsing. The prompt consists of an instruction description, a small number of labeled task examples, and the task to be parsed. The labeled examples come from the special operation task corpus constructed in the above embodiments. Each example contains the task normalization text and its corresponding set of semantic tags, which are used to clarify the target form and output structure of task semantic parsing for the large language model.

[0110] The few-sample prompt includes at least the following:

[0111] The task semantic parsing instruction is used to constrain the output of a large language model to a controlled set of labels rather than free text;

[0112] Several labeled task examples are presented, each in the form of "task description - tag set";

[0113] The normalized text representation of the special operation task corresponding to the current input to be parsed.

[0114] This prompting structure guides the large language model to perform semantic understanding of new tasks and generate candidate labels aligned with the controlled vocabulary, given the example distribution.

[0115] During model inference, the system calls a large language model to process the above prompts, obtaining a set of candidate labels related to the task's semantics. The model output can be represented as a list of labels sorted by relevance:

[0116] ;

[0117] To enhance the controllability and reliability of the scheduling process, the system retains the text trigger fragments corresponding to each tag in the model output while generating task semantic tags. These fragments are then presented to the scheduler in a highlighted or quoted manner, allowing them to intuitively understand the basis for the model's tag generation. When the scheduler has objections to the automatic parsing results, they can confirm, delete, or supplement and correct the tags. Tag results that have been manually confirmed will be recorded and written back to the task corpus for incremental updates of the controlled tag system.

[0118] Based on the set of task semantic tags, the system further generates the capability and qualification constraints required to complete the task. Specifically, the system uses embedded expert knowledge mapping rules to map the set of task semantic tags to the corresponding set of personnel capability tags.

[0119] This embodiment, based on an existing corpus of special operations tasks, leverages a few-shot learning mechanism within a large language model to achieve automatic semantic parsing of newly input special operations tasks. It efficiently obtains the personnel competency and qualification constraints required for the task, demonstrating outstanding core effectiveness and aligning with practical application needs. This reduces the cost of manual modeling and parsing, minimizes errors caused by human intervention, and improves the efficiency and standardization of task semantic parsing. By constructing standardized few-shot prompts and combining them with labeled task examples in the corpus, the large language model is guided to generate candidate labels aligned with a controlled vocabulary, ensuring the accuracy and standardization of the parsing results and enhancing the system's adaptability to complex and ambiguous task descriptions. During model inference, the text trigger fragments corresponding to the labels are retained, improving the controllability and reliability of the scheduling process. Simultaneously, manual correction and result rewriting are supported, enabling incremental updates to the controlled label system and corpus, continuously optimizing parsing accuracy. By relying on expert knowledge mapping rules, task semantic tags are transformed into personnel ability and qualification constraints, providing accurate and direct decision-making basis for the allocation of special operations personnel. This connects the corpus construction and personnel allocation process, further improving the intelligence level and decision-making efficiency of the special operations scheduling system, and enhancing the overall practicality and adaptability of the system.

[0120] In one embodiment of the present invention, when the task semantic tags or capability tags generated based on the large language model are not fully included in the existing controlled tag system, the system triggers a supplementary parsing process involving an expert model. Specifically, the system submits candidate tags that cannot be matched in the existing tag system to the expert model for judgment. The expert model performs semantic confirmation on the tag and determines whether it should be included in the tag system as a new tag, or mapped or merged into an existing tag system. The tag results confirmed by the expert model are recorded as new expert mapping rules or tag extensions, and the task semantic tag system and capability tag system are updated synchronously.

[0121] The technical solution described in this embodiment addresses the mismatch between tags generated by the large language model and the existing controlled tag system, improving the completeness and adaptability of task semantic parsing and capability tag mapping. When generated tags are not fully included in the existing tag system, an expert model supplementary parsing process is triggered. Through semantic discrimination by the expert model, the attribution of unmatched tags is clarified, enabling reasonable inclusion, mapping, or merging of tags and avoiding parsing omissions or deviations. Simultaneously, the confirmed tag results are updated to the tag system and expert mapping rules, achieving dynamic iterative optimization of the tag system, continuously improving the adaptability of the corpus and parsing model, further enhancing the accuracy and comprehensiveness of subsequent task parsing, reducing manual intervention costs, strengthening the system's adaptability to new and special special operation tasks, ensuring the completeness of personnel capability and qualification constraints, and providing more reliable support for the accurate allocation of special operation personnel.

[0122] In one embodiment of the present invention, the capability and qualification constraints are combined with personnel and their semantic elements related to the input special operation task to construct a task personnel knowledge graph in the special operation scenario, including:

[0123] For a feature task that has completed the acquisition of competency and qualification constraints, a task entity corresponding to it in the task personnel knowledge graph is constructed, and the semantic tag corresponding to the feature task is introduced as a task semantic tag entity. The task entity and the task semantic tag entity are connected through semantic association.

[0124] Each special operations personnel is constructed as a personnel entity, and the ability attributes and historical behavioral characteristics formed in historical tasks of each special operations personnel are introduced into the task personnel knowledge graph in the construction process along with the personnel entity in the form of attribute entities;

[0125] The capability and qualification constraints required for the featured task are retrieved and introduced into the task personnel knowledge graph during the construction process to characterize the relationship between the task and personnel capabilities.

[0126] In the above technical solution of this embodiment, the system, based on completing the semantic parsing of tasks and the generation of capability and qualification constraints, performs unified modeling of tasks, personnel and their related semantic elements, and constructs a task-personnel knowledge graph for special operation scenarios to support subsequent semantic-driven reasoning and personnel allocation decisions.

[0127] The system first models the special operation tasks using a graph structure. For each processed special operation task, the system constructs a corresponding task entity in the knowledge graph to represent a specific task instance. Simultaneously, the semantic tags corresponding to the task are introduced as task semantic tag entities, used to characterize the semantic attributes of the task, such as the operation object, operation type, operation method, and risk characteristics. Task entities and task semantic tag entities are connected through semantic association relationships, which can be represented as triples: .in, Represents the task entity. This represents a semantic label entity related to the task.

[0128] In the personnel information modeling process, the system constructs a personnel entity for each special operations worker and integrates the personnel's ability attributes and historical behavioral characteristics into the knowledge graph in the form of attribute entities. Personnel entities are connected to their ability tag entities and behavioral statistics entities through corresponding semantic relationships, which can be represented as follows:

[0129] ;

[0130] ;

[0131] in, Represents a person entity. This represents an entity that labels personnel capabilities. This represents a statistical entity representing personnel behavior. Through the above modeling method, the system forms a personnel-centric substructure of capabilities and behaviors in the knowledge graph, used to depict a comprehensive capability profile of personnel.

[0132] After completing the basic modeling of task entities and personnel entities, the system further introduces the capability and qualification constraints generated in the above embodiments into the knowledge graph to characterize the relationship between task requirements and personnel capabilities.

[0133] To support subsequent reasoning and subgraph extraction operations, the system assigns type identifiers to different types of entities and relations when constructing the knowledge graph, and attaches basic attribute information to relation edges to distinguish relationships from different semantic sources. For example, the `performed` relation generated from historical execution records and the `requires_ability` relation generated from expert rules have different relation type identifiers in the graph, so as to distinguish and combine them in subsequent reasoning processes.

[0134] In one embodiment of the present invention, each special operations worker is constructed as a personnel entity, and the ability attributes and historical behavioral characteristics formed in historical tasks of each special operations worker are introduced into the task personnel knowledge graph during the construction process along with the personnel entity in the form of attribute entities, including:

[0135] Retrieve the ability attributes of each special operations personnel;

[0136] The ability attributes possessed by each special operations personnel are constructed into a corresponding personnel ability tag entity;

[0137] Retrieve the performance records of each special operations personnel in historical missions;

[0138] The performance of each special operations personnel in historical tasks is constructed into a personnel behavior statistical entity.

[0139] The personnel capability tag entity and personnel behavior statistics entity are combined with the personnel entity and introduced into the task personnel knowledge graph during the construction process. Specifically, the capability attributes possessed by personnel are modeled as personnel capability tag entities, which are used to represent their capability characteristics such as qualification certificates, work skills, operational experience, solution capabilities, risk perception capabilities, and collaboration capabilities; the execution performance formed by personnel in historical tasks is modeled as personnel behavior statistics entities, which are used to represent measurable indicators such as task completion rate, response efficiency, and collaboration frequency.

[0140] The technical solution described in this embodiment achieves multi-dimensional improvements, enhancing the accuracy, efficiency, and reliability of personnel matching and decision-making for special operations tasks, and optimizing the construction efficiency and application support capabilities of the knowledge graph. Through unified modeling of special operations tasks, personnel, and related semantic elements, the structuring level of task and personnel information is improved, information redundancy is reduced, and the entity recognition accuracy and relation mapping precision of the knowledge graph are increased, ensuring accurate mapping and association of core information such as task semantic tags, personnel ability attributes, and historical behavioral characteristics. The method of constructing a comprehensive personnel capability profile enhances the objectivity and comprehensiveness of personnel capability assessment, reducing the need for human evaluation. Subjective errors improve the accuracy of matching personnel capabilities with task requirements. The introduction of capability and qualification constraints further strengthens the correspondence between task requirements and personnel capabilities, enhancing the rationality and scientific nature of subsequent personnel allocation decisions. Furthermore, the setting of type identifiers for different types of entities and relationships, and the addition of basic attribute information to relationship edges, improves the efficiency of subsequent semantic-driven reasoning and subgraph extraction, shortens reasoning response time, and reduces the misjudgment rate during reasoning. This ensures the stability and accuracy of decision outputs, thereby improving the overall efficiency and quality of personnel allocation decisions in special operation scenarios and providing strong performance support for the safe and efficient conduct of special operations.

[0141] In one embodiment of the present invention, the capability and qualification constraints are introduced into the task personnel knowledge graph during the construction process to characterize the relationship between task requirements and personnel capabilities, including:

[0142] When the expert knowledge mapping rule determines that the combination of task semantic tags has a corresponding personnel ability tag entity, a requirement relationship is established between the task entity and the corresponding personnel ability tag entity.

[0143] The historical task execution records of special operations personnel are introduced into the task personnel knowledge graph structure. When a special operations personnel has performed a task that is the same as or similar to the input special operations task in the historical record, a historical execution relationship is established between the personnel entity corresponding to the special operations personnel and the task entity.

[0144] Basic attribute information is added to task entities, personnel entities, personnel ability tag entities, and personnel behavior statistics entities and their corresponding relationships to distinguish the associations from different semantic sources, thereby completing the construction of the task personnel knowledge graph.

[0145] In the above technical solution of this embodiment, when the expert knowledge mapping rule determines that a certain task semantic tag combination corresponds to a certain capability tag, the system establishes a demand relationship between the task entity and the corresponding capability tag entity, and the relationship can be expressed as follows:

[0146] ;

[0147] Through this relationship, task entities and personnel capability label entities form an indirect association path in the graph, enabling the correspondence between "task semantic requirements - personnel capability conditions" to be expressed in a multi-hop structure.

[0148] Furthermore, the system incorporates personnel's historical task execution records into the graph structure to supplement the experiential connections between tasks and personnel. Unlike the aforementioned historical behavioral characteristics attached to personnel entities in the form of statistical indicators, these refer to measurable statistical indicators formed by special operations personnel in historical tasks, such as task completion rate, response efficiency, and collaboration frequency. These are constructed as "personnel behavior statistical entities" and associated with personnel entities through the `has_metric` relationship. This is a static statistical characterization of personnel capabilities. Historical task execution records refer to records of specific task instances previously performed by special operations personnel. They are used to record the specific task instances that personnel have actually participated in, and their function is to directly establish association edges between personnel entities and task entities to characterize the personnel's actual operational experience.

[0149] When a person has performed a task or a similar task in the historical record, the system establishes a historical execution relationship between the person entity and the task entity. This relationship is represented as follows:

[0150] ;

[0151] After defining and associating the task entities, personnel entities, and their semantic relationships, the system uniformly constructs and formally represents the task-personnel knowledge graph. Specifically, the system organizes various entities and relationships in the special operations personnel allocation scenario into a heterogeneous graph structure, and the knowledge graph can be formally represented as follows: Where V is the set of entity nodes, containing the set of task entities. Personnel entity set Task tag collection The set of personnel capability labels C and the set of personnel behavior statistical entities. E is the set of relation edges, and each relation edge is represented as a triple:

[0152] ;

[0153] Where h represents the head entity, t represents the tail entity, and r represents the relationship type between the two, which includes at least has_semantic, has_ability, has_metric, requires_ability, and performed.

[0154] This embodiment's technical solution, by introducing competency constraints into the task personnel knowledge graph, clearly depicts the relationship between task requirements and personnel capabilities, clarifies the correspondence logic between tasks and personnel capabilities, and provides a clear demand orientation for subsequent personnel matching. By establishing the demand relationship between task entities and corresponding personnel capability tag entities through expert knowledge mapping rules, it achieves an indirect association and multi-hop structure expression of "task semantic requirements—personnel capability conditions," improving the accuracy of matching task requirements with personnel capabilities. Introducing historical task execution records of special operations personnel establishes a historical execution relationship between personnel entities and task entities, supplementing the experiential association between tasks and personnel, and providing experiential support for personnel allocation. By attaching basic attribute information to various entities and their corresponding edges, it distinguishes associations from different semantic sources, ensuring the clarity and recognizability of relationships in the knowledge graph. The final task-oriented personnel knowledge graph was constructed and formalized, integrating various entities and relationships to form a complete task-personnel association system. This improved the structure and usability of the knowledge graph, providing reliable data support for subsequent semantic-driven reasoning and personnel allocation decisions. Overall, it optimized the rationality and efficiency of special operations personnel allocation and ensured the standardization of operations.

[0155] In one embodiment of the present invention, a semantically driven candidate subgraph is constructed centered on the currently input special operation task, and multi-hop association reasoning is performed. The task semantic requirements and personnel competency qualifications are comprehensively considered within the candidate subgraph to generate a task allocation result for special operation personnel, including:

[0156] The candidate subgraph is represented as a heterogeneous graph structure, and a relation-aware graph neural network is used to perform multi-level message passing on the candidate subgraph. In the process of multi-level message passing, the representation vector of each node in each layer is updated according to its neighboring nodes and relation types.

[0157] For each person entity, identify multiple reachable paths between it and the input special operation task in the candidate subgraph, and represent each reachable path as a sequence of nodes and relations;

[0158] The paths are encoded using the relation types and node representations contained in each reachable path, and a path score is calculated for each reachable path.

[0159] The path scores of each reachable path included in the multiple reachable paths corresponding to each personnel entity are aggregated to form a comprehensive matching score between the personnel entity and the special operation task.

[0160] The candidate special operations personnel corresponding to the candidate subgraphs are sorted according to the comprehensive matching score, and the candidate special operations personnel or combination of candidate special operations personnel with the highest score is selected as the recommended allocation result for the current task.

[0161] In this embodiment, after constructing a candidate subgraph centered on the current task, the system performs multi-hop association reasoning on the subgraph to comprehensively model the structured relationships between task semantic requirements, capability conditions, and personnel capabilities. Unlike methods based solely on explicit rules or simple path enumeration, this invention introduces a relation-aware graph neural network to learn the representation of the candidate subgraph, thereby adaptively modeling the contributions of different relational paths within a multi-hop scope.

[0162] The system represents candidate subgraphs as heterogeneous graph structures:

[0163] ;

[0164] Wherein, the node set V sub It includes task entities, semantic tag entities, capability tag entities, and personnel entities, with an edge set E. sub It includes various relation types such as has_semantic, requires_ability, has_ability, and performed. The system employs a relation-aware graph neural network to perform multi-layer message passing on the candidate subgraph. In the l-th layer, the representation vector of node v... The update is performed based on its neighboring nodes and relationship types. The update method can be represented as follows:

[0165] ;

[0166] in, Represents a set of relation types. Let r represent the set of neighboring nodes connected to node v through relation r. Let be the learnable parameter matrix corresponding to relation type r, and σ(∙) be the nonlinear activation function. Through multi-level iterative updates, the node representation can gradually fuse information from multi-hop neighborhoods, thereby achieving multi-hop semantic reasoning.

[0167] Building upon the aforementioned graph neural network, the system further explicitly models multi-hop semantic paths from task entities to personnel entities. For any personnel entity P, the system identifies multiple reachable paths between it and the current task entity T in the candidate subgraph, and represents each path as a sequence of nodes and relations:

[0168] ;

[0169] in, This represents the i-th relation in the path. Representing intermediate entity nodes, the system encodes paths based on the relationship types and node representations within the path, and calculates a path score for each path to measure its support for personnel matching decisions. The path score can be given by a combination function of the weights of each relationship and the node representation within the path:

[0170] ;

[0171] in, Representing relation type The importance weights in the reasoning process are predetermined by expert experience. For the same individual entity... The system typically generates multiple different inference paths. To synthesize the contributions of different paths to the person matching results, the system performs path aggregation on the set of path scores corresponding to that person, obtaining the person's comprehensive matching score:

[0172] ;

[0173] in, This represents the set of paths from task entity T to personnel entity P. The aggregate weight of path π is used to characterize the relative contribution of different paths in personnel matching decisions.

[0174] After completing multi-hop association reasoning and personnel matching score calculation on the candidate subgraph, the system generates special operation personnel allocation results based on the comprehensive matching score. Specifically, let the current task be T, and the candidate personnel set be... Based on the calculated personnel matching score The candidates are ranked, and the highest-scoring individual or combination is selected as the recommended assignment for the current task. The personnel assignment result can be formally represented as:

[0175] ;

[0176] in, This refers to the special operations personnel who best match the semantic requirements and capability conditions of the current task. This represents the j-th person ranked in descending order of matching scores. The personnel allocation reasoning process based on task semantics is as follows: Figure 3 As shown.

[0177] The technical solution described in this embodiment constructs a candidate subgraph centered on the current special operation task and performs multi-hop association reasoning, realizing a comprehensive modeling of task semantic requirements and personnel competence and qualifications, thus improving the accuracy and rationality of special operation personnel allocation. By representing the candidate subgraph as a heterogeneous graph and employing a relation-aware graph neural network for multi-layer message passing, the node representation vector can integrate multi-hop neighborhood information, capturing the structured association between tasks and personnel, overcoming the limitations of traditional explicit rules or simple path enumeration methods. By identifying and encoding multiple reachable paths between personnel entities and task entities, and calculating path scores, the degree of support for personnel matching of each path can be accurately measured. Aggregating the scores of multiple paths to obtain a comprehensive matching score can comprehensively consider the contribution of different reasoning paths and avoid matching bias caused by a single path. Finally, the optimal personnel or personnel combination is selected by sorting according to the comprehensive score, ensuring that the allocation result is highly consistent with the task semantic requirements and personnel competence and qualifications, improving the efficiency and scientific nature of personnel allocation, reducing human decision-making errors, providing reliable personnel allocation support for the safe and efficient conduct of special operation tasks, and improving the adaptability and robustness of the reasoning process.

[0178] One embodiment of the present invention constructs an interpretable mechanism based on multiple reachable paths formed during the reasoning process, and outputs the key correlation path between the requirements of the special operation task and the personnel capabilities, including:

[0179] Retrieve the personnel entity corresponding to any recommended special operations personnel from the recommended allocation results, and retrieve its corresponding set of high-contribution inference paths from the candidate subgraph. The method for determining high-contribution inference paths is as follows: all reachable paths corresponding to the personnel entity are sorted in descending order of path score, and paths with a score higher than a preset threshold are selected as high-contribution inference paths. The preset threshold is set by the dispatcher according to actual needs.

[0180] The reachable paths are sorted according to the path score of each reachable path included in the set of high contribution inference paths, and the top-ranked paths are selected as explanatory paths, that is, the key correlation paths between the requirements of the special operation task and the personnel's capabilities.

[0181] In the above technical solution of this embodiment, after generating the allocation results of special operation personnel, the system further interpretively models the allocation decision-making process and displays the basis for personnel recommendations to the dispatcher in a visual and interactive manner, thereby supporting the dispatcher's understanding, review and necessary intervention of the allocation results, which is extremely important for high-risk special operations.

[0182] The system uses the obtained multi-hop association reasoning path as the core explanation basis for the allocation result. For any recommended person entity... The system extracts the set of high-contribution inference paths corresponding to each candidate subgraph. The paths are then sorted according to their path scores, and several paths that contribute the most to the matching score are selected as explanatory paths. The explanatory paths characterize the logical connection between task semantic requirements and personnel capability conditions, and their structural form can be represented as follows:

[0183] ;

[0184] Where T represents the task entity, A represents the task semantic label entity, and C represents the capability label entity. This refers to the entity of the person being recommended.

[0185] The system displays key semantic tags for the task, corresponding capability requirement tags, and personnel capability tags sequentially according to the path. It also highlights key semantic elements that trigger the path, allowing dispatchers to intuitively understand "why this personnel is recommended for this task." In cases where multiple interpretation paths exist for the same personnel, the system displays them in descending order of path score to reflect the relative importance of different capability matching criteria in decision-making.

[0186] To support dispatchers' proactive intervention and decision-making control, the system provides an interactive intervention mechanism while displaying recommendation results and explanation paths. In one embodiment, dispatchers can confirm, adjust, or reject recommendation results based on explanation paths. When a dispatcher does not approve of a certain capability tag or semantic association, the system allows them to temporarily block the corresponding path or reduce its weight through the interactive interface, thereby triggering the system to recalculate the personnel matching score and update the personnel ranking results in real time.

[0187] This embodiment addresses the challenge of interpretability in personnel allocation decisions for special operations by constructing an interpretable mechanism for the reasoning process. This enhances the transparency and credibility of decisions, providing strong support for the scheduling and control of high-risk special operations. By retrieving the set of high-contribution reasoning paths corresponding to recommended personnel and selecting key related paths based on path scores, the logical relationship between special operation task requirements and personnel capabilities is clearly depicted, clarifying the core basis for personnel recommendations. By displaying key semantic tags for tasks, capability requirement tags, and personnel capability tags, and highlighting key semantic elements, dispatchers can intuitively understand the internal logic of allocation decisions, facilitating rapid review and judgment of allocation results. The interactive intervention mechanism allows dispatchers to confirm, adjust, or reject recommendations based on actual needs, block unreasonable paths or adjust path weights, trigger recalculation of matching scores and updates to personnel ranking, enhancing the dispatcher's decision-making initiative and intervention flexibility. This mechanism not only ensures the rationality and traceability of personnel allocation decisions and reduces decision disputes but also improves scheduling efficiency, strengthens the safety control capabilities of high-risk special operations, and ensures that allocation decisions better align with actual operational needs.

[0188] In one embodiment of the present invention, after the completion of the current special operation task, execution feedback information is collected, and the task personnel knowledge graph is dynamically updated. The state and / or weights of relevant entities and relationships in the task personnel knowledge graph are adjusted according to the task execution result and personnel performance corresponding to the current special operation task, including:

[0189] Real-time collection of execution data information during the execution of special operations tasks; wherein, the execution data information includes, but is not limited to, whether the task is completed as planned, the actual execution time, abnormal or risk events during the execution process, personnel cooperation, and the dispatcher's subjective evaluation of the execution effect;

[0190] The execution data information is associated with the corresponding task entities and personnel entities in a structured form;

[0191] The personnel behavior statistics entity of special operation personnel is dynamically adjusted based on the execution data information of special operation tasks during the execution process and the preset update rules.

[0192] Based on the actual performance of special operations personnel in special operations tasks, the weights of the relationship edges in the personnel knowledge graph are adjusted; that is: if special operations personnel have the corresponding ability tags and successfully complete the task, the association weights between the task and the ability tags, and between the ability tags and the personnel are enhanced; if special operations personnel perform poorly in special operations tasks or risk events occur, the association weights between the task and the ability tags, and between the ability tags and the personnel are reduced.

[0193] When existing task semantic tags or capability tags cannot fully describe the actual needs during the execution of special operations tasks, the situation is marked as a "semantic insufficiency" or "capability deficiency" event and submitted to the manual review process.

[0194] In this embodiment, the technical solution described above achieves digital perception and quantitative evaluation of the entire task execution status by collecting execution data in real time after the completion of special operations tasks, providing a reliable data source for the dynamic iteration of the knowledge graph. By establishing structured associations between execution data and task and personnel entities, the information in the knowledge graph is no longer limited to static qualifications and historical records, but is closely linked to actual operational results, improving the accuracy and timeliness of data-entity association. Dynamically adjusting personnel behavior statistics entities based on execution data and update rules allows for real-time updates to core metrics such as task completion rate and response efficiency of special operations personnel, forming a dynamic correction of the personnel's comprehensive capability profile and ensuring the accuracy and timeliness of personnel capability assessment. Simultaneously, adaptive adjustment of the relation edge weights in the knowledge graph based on task execution results achieves closed-loop optimization and feedback iteration of the matching relationship between task requirements and personnel capabilities. Successful task execution strengthens the association weights, while poor performance weakens them, making the system's matching logic more closely aligned with the characteristics and patterns of actual operational scenarios. Furthermore, by marking "semantic deficiencies" and "capability gaps" and triggering a manual review process, the limitations of the existing tagging system are identified, promoting the continuous improvement of the task semantic tagging and capability tagging systems. This enhances the coverage and representation capabilities of the knowledge graph for complex special operation scenarios from the source. Overall, this mechanism achieves lifecycle management and continuous evolution of the task personnel knowledge graph, enhancing the system's adaptability, robustness, and decision-making guidance value in long-term operation, and providing more accurate, dynamic, and realistic model support for subsequent task allocation.

[0195] Corresponding to the methods provided in any of the foregoing embodiments, this embodiment of the invention also provides a job task allocation system based on task semantics and capability constraints, the job task allocation system comprising:

[0196] The data acquisition and preprocessing module is used to collect raw data assigned to special operation personnel, clean and preprocess the raw data to generate processed raw data, and combine the raw data with manual annotation and expert knowledge embedding to construct a special operation task corpus.

[0197] The competency and qualification constraint acquisition module is used to automatically perform semantic parsing on the input special operation task based on the constructed special operation task corpus and using the few-shot learning mechanism of the large language model to obtain the competency and qualification constraints required to complete the feature operation task.

[0198] The knowledge graph construction module is used to combine the ability and qualification constraints with the personnel and their semantic elements related to the input special operation tasks to construct a task personnel knowledge graph in the special operation scenario;

[0199] The task allocation result acquisition module is used to construct a semantically driven candidate subgraph centered on the currently input special operation task and perform multi-hop association reasoning. In the candidate subgraph, the task semantic requirements and personnel ability and qualification conditions are comprehensively considered to generate the task allocation result of special operation personnel.

[0200] The explanation mechanism construction module is used to construct an explainable mechanism based on multiple reachable paths formed during the reasoning process, and output the key correlation path between the requirements of the special operation task and the personnel's capabilities.

[0201] The information feedback and graph adjustment module is used to collect execution feedback information after the completion of the current special operation task, and to dynamically update the task personnel knowledge graph. Based on the task execution results and personnel performance corresponding to the current special operation task, the module adjusts the status and / or weight of relevant entities and relationships in the task personnel knowledge graph.

[0202] The technical solution described in this embodiment constructs a task-personnel knowledge graph for special operation scenarios by structurally modeling multi-source heterogeneous elements such as tasks, personnel, competency qualifications, and working environment. Based on task semantic-driven subgraph construction and multi-hop association reasoning, it achieves coordinated matching of task semantic requirements and personnel qualification conditions. Furthermore, through the visualization of reasoning paths and a dynamic graph update mechanism, it addresses the problems of insufficient task semantic expression, difficulty in reasoning about heterogeneous element relationships, and lack of interpretability and dynamic adaptability in existing personnel allocation methods. Moreover, by combining the aforementioned structured modeling with the introduction of a task semantic-driven knowledge graph reasoning mechanism, it can more accurately express the complex semantic requirements implicit in special operation tasks, achieving multi-hop association reasoning between task semantics and personnel competency conditions, thereby improving the matching accuracy and applicability of special operation personnel allocation results in complex scenarios.

[0203] Meanwhile, the task allocation method and system based on task semantics and capability constraints provided in this embodiment clearly demonstrate the basis for personnel allocation to the scheduler through an interpretable mechanism based on reasoning paths. It also supports interactive review and intervention by the scheduler during the allocation process, and dynamically updates the knowledge graph based on task execution feedback, enabling the personnel allocation system to continuously evolve. Compared to existing static allocation methods, this invention improves the interpretability, credibility, and dynamic adaptability of personnel allocation decisions while ensuring allocation efficiency, making it more suitable for the actual needs of special operation scenarios.

[0204] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A job task allocation method based on task semantics and capability constraints, characterized in that, include: Raw data for special operations personnel allocation is collected, and the raw data is cleaned and preprocessed to generate processed raw data. The raw data is then combined with manual annotation and expert knowledge embedding to construct a special operations task corpus. Based on the constructed special operation task corpus, the few-shot learning mechanism of the large language model is used to automatically perform semantic parsing on the input special operation task to obtain the ability and qualification constraints required to complete the feature operation task. The aforementioned competency and qualification constraints are combined with the personnel and their semantic elements related to the input special operation tasks to construct a knowledge graph of task personnel in special operation scenarios; A semantically driven candidate subgraph is constructed centered on the currently input special operation task, and multi-hop association reasoning is performed. The task semantic requirements and personnel ability and qualification conditions are comprehensively considered in the candidate subgraph to generate the task allocation result for special operation personnel. An interpretable mechanism is constructed based on multiple reachable paths formed during the reasoning process, and the key correlation path between the requirements of the special operation task and the personnel's capabilities is output. After the completion of the special operation task, execution feedback information is collected, and the knowledge graph of the task personnel is dynamically updated. The status and / or weight of relevant entities and relationships in the knowledge graph of the task personnel are adjusted according to the task execution results and personnel performance corresponding to the special operation task.

2. The task allocation method according to claim 1, characterized in that, Raw data for special operations personnel allocation is collected, and the raw data is cleaned and preprocessed to generate processed raw data. This raw data is then combined with manually labeled data and expert knowledge embedding to construct a special operations task corpus, including: Collect raw data for the allocation of special operations personnel, wherein the raw data includes task data and personnel data; The task data is uniformly represented as a text-tag correspondence pattern and stored using a structured tag format; The task data is cleaned and preprocessed to form a standardized representation of the task text data. The task text data is then manually annotated to generate a set of task semantic tags with task semantic tags. By combining the aforementioned task semantic tag set with an expert knowledge embedding mechanism, empirical knowledge in the field of special operation scheduling is transformed into structured constraint relationships; The structured constraint relationship corresponding to the transformation of the empirical knowledge in the special operation scheduling field is the special operation task corpus contained in the special operation task corpus.

3. The task allocation method according to claim 1, characterized in that, Based on the constructed special task corpus, the few-shot learning mechanism of a large language model is used to automatically perform semantic parsing on the input special task to obtain the ability and qualification constraints required to complete the special task, including: When a special task with new input is obtained, a few-shot prompt based on a large language model is constructed for automatic semantic parsing of the task. By using the structure corresponding to the few-sample prompts, the large language model performs semantic understanding on the newly input special operation task under the given distribution of labeled task examples and generates candidate labels aligned with the controlled vocabulary; wherein the labeled task examples are from the special operation task corpus. The task semantic label set corresponding to the candidate label results is mapped to the corresponding personnel ability label set according to the expert knowledge mapping rules, thereby obtaining the ability and qualification constraints that need to be met to complete the feature task.

4. The task allocation method according to claim 3, characterized in that, The few-sample prompts include at least the following: The task semantic parsing instruction is used to constrain the output of a large language model to a controlled set of labels rather than free text; Several labeled task examples are presented, each in the form of "task description - tag set"; The normalized text representation of the special operation task corresponding to the current input to be parsed.

5. The task allocation method according to claim 1, characterized in that, The aforementioned competency constraints are combined with the personnel and their semantic elements related to the input special operation task to construct a knowledge graph of task personnel in the special operation scenario, including: For a feature task that has completed the acquisition of competency and qualification constraints, a task entity corresponding to it in the task personnel knowledge graph is constructed, and the semantic tag corresponding to the feature task is introduced as a task semantic tag entity. The task entity and the task semantic tag entity are connected through semantic association. Each special operations personnel is constructed as a personnel entity, and the ability attributes and historical behavioral characteristics formed in historical tasks of each special operations personnel are introduced into the task personnel knowledge graph in the construction process along with the personnel entity in the form of attribute entities; The capability and qualification constraints required for the featured task are retrieved and introduced into the task personnel knowledge graph during the construction process to characterize the relationship between the task and personnel capabilities.

6. The task allocation method according to claim 5, characterized in that, Each special operations worker is constructed as a personnel entity, and the ability attributes and historical behavioral characteristics formed in historical tasks of each special operations worker are introduced into the task personnel knowledge graph during the construction process as attribute entities, along with the personnel entity, including: Retrieve the ability attributes of each special operations personnel; The ability attributes possessed by each special operations personnel are constructed into a corresponding personnel ability tag entity; Retrieve the performance records of each special operations personnel in historical missions; The performance of each special operations personnel in historical tasks is constructed into a personnel behavior statistical entity. The personnel capability tag entity and personnel behavior statistics entity are combined with the personnel entity and introduced into the task personnel knowledge graph during the construction process.

7. The task allocation method according to claim 5, characterized in that, The aforementioned competency constraints are incorporated into the task personnel knowledge graph during the construction process to characterize the relationship between task requirements and personnel capabilities, including: When the expert knowledge mapping rule determines that the combination of task semantic tags has a corresponding personnel ability tag entity, a requirement relationship is established between the task entity and the corresponding personnel ability tag entity. The historical task execution records of special operations personnel are introduced into the task personnel knowledge graph structure. When a special operations personnel has performed a task that is the same as or similar to the input special operations task in the historical record, a historical execution relationship is established between the personnel entity corresponding to the special operations personnel and the task entity. By adding basic attribute information to task entities, personnel entities, personnel ability tag entities, and personnel behavior statistics entities and their corresponding relationships, a task personnel knowledge graph is constructed.

8. The task allocation method according to claim 1, characterized in that, A semantically driven candidate subgraph is constructed centered on the currently input special operation task, and multi-hop association reasoning is performed. The task semantic requirements and personnel competency qualifications are comprehensively considered within the candidate subgraph to generate task allocation results for special operation personnel, including: The candidate subgraph is represented as a heterogeneous graph structure, and a relation-aware graph neural network is used to perform multi-level message passing on the candidate subgraph. In the process of multi-level message passing, the representation vector of each node in each layer is updated according to its neighboring nodes and relation types. For each person entity, identify multiple reachable paths between it and the input special operation task in the candidate subgraph, and represent each reachable path as a sequence of nodes and relations; The paths are encoded using the relation types and node representations contained in each reachable path, and a path score is calculated for each reachable path. The path scores of each reachable path included in the multiple reachable paths corresponding to each personnel entity are aggregated to form a comprehensive matching score between the personnel entity and the special operation task. The candidate special operations personnel corresponding to the candidate subgraphs are sorted according to the comprehensive matching score, and the candidate special operations personnel or combination of candidate special operations personnel with the highest score is selected as the recommended allocation result for the current task.

9. The task allocation method according to claim 1, characterized in that, Based on the multiple reachable paths formed during the reasoning process, an interpretable mechanism is constructed to output the key correlation paths between the requirements of the special operation task and the personnel capabilities, including: Retrieve the personnel entity corresponding to any recommended special operation personnel in the recommendation allocation result, and retrieve all reachable paths between the personnel and the current special operation task and their corresponding path scores from the candidate subgraph. Determine the reachable paths with path scores higher than a preset threshold as high contribution reasoning paths to form a set of high contribution reasoning paths. The reachable paths are sorted according to the path score of each reachable path included in the set of high contribution inference paths, and the top-ranked paths are selected as explanatory paths, that is, the key correlation paths between the requirements of the special operation task and the personnel's capabilities. and / or Upon completion of the current special operation task, execution feedback information is collected, and the task personnel knowledge graph is dynamically updated. Based on the task execution results and personnel performance corresponding to the current special operation task, the state and / or weights of relevant entities and relationships in the task personnel knowledge graph are adjusted, including: Real-time collection of execution data information during the execution of special operations tasks; wherein, the execution data information includes whether the task is completed as planned, the actual execution time, abnormal or risk events during the execution process, personnel cooperation status, and the dispatcher's subjective evaluation of the execution effect; The execution data information is associated with the corresponding task entities and personnel entities in a structured form; The personnel behavior statistics entity of special operation personnel is dynamically adjusted based on the execution data information of special operation tasks during the execution process and the preset update rules. Based on the actual performance of special operations personnel in special operations tasks, the weights of the relationship edges in the personnel knowledge graph are adjusted. When existing task semantic tags or capability tags cannot fully describe the actual needs during the execution of special operations tasks, the situation is marked as a semantic deficiency or capability deficiency event and submitted to the manual review process.

10. A job task allocation system based on task semantics and capability constraints, characterized in that, include: The data acquisition and preprocessing module is used to collect raw data assigned to special operation personnel, clean and preprocess the raw data to generate processed raw data, and combine the raw data with manual annotation and expert knowledge embedding to construct a special operation task corpus. The competency and qualification constraint acquisition module is used to automatically perform semantic parsing on the input special operation task based on the constructed special operation task corpus and using the few-shot learning mechanism of the large language model to obtain the competency and qualification constraints required to complete the feature operation task. The knowledge graph construction module is used to combine the ability and qualification constraints with the personnel and their semantic elements related to the input special operation tasks to construct a task personnel knowledge graph in the special operation scenario; The task allocation result acquisition module is used to construct a semantically driven candidate subgraph centered on the currently input special operation task and perform multi-hop association reasoning. In the candidate subgraph, the task semantic requirements and personnel ability and qualification conditions are comprehensively considered to generate the task allocation result of special operation personnel. The explanation mechanism construction module is used to construct an explainable mechanism based on multiple reachable paths formed during the reasoning process, and output the key correlation path between the requirements of the special operation task and the personnel's capabilities. The information feedback and graph adjustment module is used to collect execution feedback information after the completion of the current special operation task, and to dynamically update the task personnel knowledge graph. Based on the task execution results and personnel performance corresponding to the current special operation task, the module adjusts the status and / or weight of relevant entities and relationships in the task personnel knowledge graph.

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