Multi-agent mixing cooperation method

Abstract

The invention provides a multi-agent mixed collaboration method, which comprises the following steps of: firstly, acquiring a task target of a target business scene and agent collaboration demand description, and generating a business task collaboration element topology through multi-dimensional semantic deconstruction and collaboration element association modeling; assigning tasks to the agent capability domain hierarchically based on the topology, pre-judging conflicts through an agent capability-task adaptation conflict pre-judging matrix, and determining a conflict-free pre-assigned task tensor; a cross-agent real-time cooperation-conflict resolution protocol cluster is constructed based on the tensor, and a cooperation interaction rule protocol cluster is generated; and finally, dynamic collaborative scheduling and execution deviation correction are carried out on multi-agent execution state real-time feedback data through the protocol cluster, a multi-agent collaborative execution dynamic tensor instruction graph is generated, and full-process closed-loop management and control of collaborative tasks are realized. And the fluency, reliability and task completion quality of multi-agent collaboration are improved.

Description

Technical Field

[0001] This application relates to the field of multi-agent cooperative technology, and more specifically, to a multi-agent hybrid cooperative method. Background Technology

[0002] In complex business scenarios such as intelligent manufacturing, intelligent transportation, and distributed AI decision-making, the application of multi-agent systems is becoming increasingly widespread. Their core requirement is to efficiently complete complex business tasks through the collaborative cooperation of multiple agents, thereby improving overall operational efficiency and quality. As the complexity of business scenarios increases, higher demands are placed on the real-time performance, compatibility, and stability of multi-agent collaboration.

[0003] Existing technologies include a multi-agent collaborative scheme. This scheme first breaks down business tasks into simple sub-tasks, then allocates sub-tasks based on the static functional parameters of the agents, achieves data interaction between agents through a preset fixed communication protocol, and uses a unified handling method to deal with conflicts. Its general process is as follows: after clarifying the business tasks, they are broken down into several sub-tasks according to functional modules; tasks are allocated based on static attributes such as the agent's computing power and task processing expertise; agents transmit data in a fixed format; and when resource contention or data interaction problems occur, a unified conflict resolution process is initiated.

[0004] However, the existing solution has obvious technical defects: it lacks dynamic evaluation and element association analysis of the collaborative capabilities of intelligent agents, and the task allocation is based only on static attributes. It does not fully consider the collaborative adaptability between intelligent agents and the potential conflicts in task execution, resulting in a high conflict rate and a lack of targeted conflict handling. Consequently, the collaborative efficiency is low and the task execution deviation is large, which cannot meet the high-quality requirements of multi-agent collaboration in complex business scenarios. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a multi-agent hybrid cooperative method to at least alleviate these problems.

[0006] The technical solutions provided in this application are as follows: A multi-agent hybrid collaboration method includes: acquiring the task objectives and agent collaboration requirements descriptions under the target business scenario, performing multi-dimensional semantic deconstruction and collaborative element association modeling to generate a business task collaboration element topology; based on the business task collaboration element topology, performing hierarchical task allocation for the capability domains of various types of agents to obtain a hierarchical agent task allocation scheme, and performing conflict prediction through an agent capability-task adaptation conflict prediction matrix to determine a conflict-free pre-allocated task tensor; based on the conflict-free pre-allocated task tensor, constructing a cross-agent real-time collaboration-conflict resolution protocol cluster to generate a collaborative interaction rule protocol cluster; and using the collaborative interaction rule protocol cluster, performing dynamic collaborative scheduling and execution deviation correction on the real-time feedback data of the multi-agent execution status to generate a multi-agent collaborative execution dynamic tensor instruction graph, and managing collaborative tasks under the target business scenario based on the multi-agent collaborative execution dynamic tensor instruction graph.

[0007] The advantages of the multi-agent hybrid cooperative method provided in this application are as follows: In the task and requirement analysis phase, existing solutions simply break down tasks without considering the relationship between core task metrics and collaborative elements, resulting in a lack of targeted task allocation. This solution breaks down task objectives from a business dimension and extracts core metrics, identifies and labels collaborative requirements as elements, and integrates these elements to generate a business task collaborative element topology. This makes the deconstruction of tasks and requirements more comprehensive and the relationships clearer. This multi-dimensional semantic deconstruction and topological modeling approach can accurately capture key nodes in task execution and core elements of collaborative requirements, providing a more realistic basis for subsequent task allocation and solving the problem of low matching between task allocation and requirements in existing solutions.

[0008] In the task allocation and conflict prediction stages, existing solutions allocate tasks based on the static attributes of the agent, lacking a conflict prediction mechanism, resulting in a high conflict rate. This solution first collects agent capability parameters and business adaptation records to construct an agent capability domain feature topology, achieving a comprehensive characterization of agent capabilities. Then, it combines the business task collaboration element topology to perform hierarchical task decomposition and matching, ensuring accurate adaptation between different levels of tasks and agent capabilities. By constructing a conflict prediction matrix including capabilities redundancy and task coupling, it identifies and resolves conflicting task items in advance, generating a conflict-free pre-allocated task tensor. This hierarchical allocation and advance prediction approach reduces the probability of conflicts during task execution, significantly improving the smoothness of collaboration compared to the "post-event processing" mode of existing solutions.

[0009] In the collaboration protocol and conflict resolution stages, existing solutions employ fixed communication protocols and uniform conflict handling methods, resulting in insufficient adaptability and specificity. This solution, based on the task association relationships of conflict-free pre-allocated task tensors, defines communication protocols and data interaction formats adapted to different task requirements, ensuring the standardization and efficiency of data interaction. It constructs a hierarchical strategy library containing four conflict types and corresponding dedicated resolution strategies, binding communication protocols and resolution strategies according to conflict type to form a cluster of collaborative interaction rule protocols, making conflict handling more targeted. This customized protocol design and distributed conflict resolution strategy, compared to the uniform processing of existing solutions, can more quickly and effectively address different types of collaborative conflicts, improving the efficiency and effectiveness of conflict resolution.

[0010] In the dynamic scheduling and closed-loop management stages, existing solutions lack real-time monitoring and dynamic adjustment of the agent's execution status, making them prone to execution deviations. This solution deploys status acquisition nodes to obtain real-time feedback data such as agent task progress and resource usage. After standardization, this data forms a feedback data pool. The feedback data is compared with the execution baseline in the collaborative interaction rule protocol suite to identify anomalies and match corresponding scheduling strategies, generating a dynamic tensor instruction graph for multi-agent collaborative execution. This achieves closed-loop management of the entire collaborative task process. This real-time monitoring and dynamic adjustment mechanism can promptly correct execution deviations, ensuring that tasks always progress along the preset goals. Compared to the fixed execution mode of existing solutions, this significantly improves the stability and accuracy of collaborative task execution.

[0011] In summary, the technical solution of this application, through the full-process design of "requirement deconstruction - precise allocation - protocol adaptation - dynamic control", specifically addresses the technical defects of existing solutions, such as low task matching degree, high conflict rate, inefficient conflict handling, and large execution deviation. It demonstrates better collaborative efficiency and stability in complex business scenarios, and provides reliable support for the efficient collaboration of multi-agent systems. Attached Figure Description

[0012] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0013] Figure 1 This is a flowchart of a multi-agent hybrid collaborative method according to an embodiment of the present invention. Detailed Implementation

[0014] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. Various aspects are provided by way of explanation and not limitation of the invention. Indeed, those skilled in the art will recognize that modifications and variations can be made to the invention without departing from its scope or spirit. For example, a feature represented or described as part of one embodiment may be used in another embodiment to produce yet another embodiment. Therefore, it is desirable that the invention encompass such modifications and variations falling within the scope of the appended claims and their equivalents.

[0015] In the description of this invention, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," and "bottom," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and do not require the invention to be constructed and operated in a specific orientation; therefore, they should not be construed as limitations on the invention. The terms "connected," "linked," and "set up" used in this invention should be interpreted broadly. For example, they can refer to a fixed connection or a detachable connection; they can refer to a direct connection or an indirect connection through intermediate components. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0016] like Figure 1 As shown, this application provides a multi-agent hybrid collaboration method, including: Step 1, obtaining the task objectives and agent collaboration requirements description under the target business scenario, performing multi-dimensional semantic deconstruction and collaborative element association modeling to generate a business task collaboration element topology; Step 2, based on the business task collaboration element topology, performing hierarchical task allocation for the capability domains of various types of agents to obtain a hierarchical agent task allocation scheme, and performing conflict prediction through an agent capability-task adaptation conflict prediction matrix to determine a conflict-free pre-allocated task tensor; Step 3, based on the conflict-free pre-allocated task tensor, constructing a cross-agent real-time collaboration-conflict resolution protocol cluster to generate a collaborative interaction rule protocol cluster; Step 4, through the collaborative interaction rule protocol cluster, performing dynamic collaborative scheduling and execution deviation correction on the real-time feedback data of the multi-agent execution status to generate a multi-agent collaborative execution dynamic tensor instruction graph, and managing collaborative tasks under the target business scenario based on the multi-agent collaborative execution dynamic tensor instruction graph.

[0017] Optionally, step 1 includes: step 11, decomposing the task objectives in the target business scenario into business dimensions and extracting core indicators to generate a task core indicator tensor; step 12, identifying collaborative elements and labeling relationships in the description of intelligent agent collaborative requirements to generate a collaborative requirement element construct; step 13, integrating the task core indicator tensor and the collaborative requirement element construct to perform topological modeling to generate a business task collaborative element topology. The nodes of the business task collaborative element topology are jointly composed of the indicator dimensions of the task core indicator tensor and the element dimensions of the collaborative requirement element construct, and the edge weights are determined by the correlation matching degree between the task core indicator tensor and the collaborative requirement element construct. Optionally, step 11 includes: step 111, decomposing the task objective into business process nodes and defining indicator dimensions to generate task process node features; step 112, selecting core indicators and formulating quantification rules for the task process node features to generate an initial indicator tensor; step 113, removing redundancy and logically integrating the initial indicator tensor to generate a task core indicator tensor, which maps each core indicator to a tensor dimension according to the business process node dimension.

[0018] Preferably, the specific implementation process of step 111 is as follows: The core requirements of the task objectives in the target business scenario are decomposed to generate a list of core task requirements. Specifically, in conjunction with multi-agent collaborative application scenarios (such as intelligent warehousing, intelligent manufacturing, and intelligent transportation), the core output requirements, constraints, and collaborative requirements in the task objectives are extracted. The core output requirements clarify the final result that the task needs to achieve; the constraints clarify the limiting factors for task execution (such as time, resources, and environment); and the collaborative requirements clarify the core direction of cooperation between multiple agents. For example, the core output requirement of the intelligent warehousing order sorting task is "to complete the sorting and warehousing of order goods on time," the constraints are "to complete 500 orders within 2 hours" and "the operating range of the northern area of ​​the warehouse," and the collaborative requirements are "the action connection between the handling robot and the sorting robot." The list of core task requirements includes a description of the core output requirements, constraint parameters, the direction of collaborative requirements, and scenario adaptation requirements. Preferably, in the specific technical implementation of step 111, based on the list of core requirements of the task, the task objectives are divided into business process modules to generate a task business process module set. Specifically, according to functional independence and collaborative relevance, the task objectives are divided into several business process modules, each business process module corresponding to a core functional block. The modules are connected through collaborative interfaces and data interaction. For example, the intelligent warehouse order sorting task is divided into an order parsing module, a goods positioning module, a pickup execution module, a transportation scheduling module, and a handover and warehousing module. The output of the order parsing module is goods information and order priority, which serve as the input of the goods positioning module. The task business process module set includes module identifiers, module function descriptions, input data types, output data types, and collaborative interface specifications. Preferably, in the specific technical implementation of step 111, based on the task business process module set, each business process module is split into business process nodes to generate a detailed list of business process nodes. Specifically, each business process module is split into several consecutive business process nodes according to the execution sequence and action decomposition logic. Each business process node corresponds to a specific execution action. The nodes form a logical closed loop through preconditions and postconditions. For example, the goods positioning module is split into four business process nodes: order information reception, goods storage location retrieval, storage location coordinate calibration, and positioning result output. The postcondition of the order information reception node is "complete goods code recognition". As a precondition of the storage location retrieval node, the detailed list of business process nodes includes the node identifier, node order, execution action description, preconditions, postconditions, and data interaction requirements under each module.Preferably, in the specific technical implementation of step 111, based on the details of the business process nodes, each business process node is processed to define the indicator dimensions to generate task process node features. Specifically, combined with the description of the execution actions and data interaction requirements of the business process nodes, four core indicator dimensions are defined: execution efficiency dimension, execution compliance dimension, resource adaptation dimension, and collaborative connection dimension. The execution efficiency dimension reflects the efficiency and effect of node execution, the execution compliance dimension reflects the degree to which the node execution meets the constraints, the resource adaptation dimension reflects the node's demand for intelligent agent hardware resources (such as computing power, load, and communication bandwidth), and the collaborative connection dimension reflects the node's collaborative adaptation capability with the preceding and following nodes. For example, the execution efficiency dimension of the warehouse location coordinate calibration node is the calibration time, the execution compliance dimension is the compliance rate of the allowable coordinate deviation value, the resource adaptation dimension is the calibration computing power requirement value, and the collaborative connection dimension is the calibration result transmission latency. The task process node features include the four indicator dimensions corresponding to each business process node, the dimension definition, the evaluation scope, and the mapping relationship with the intelligent agent's capability attributes, ensuring that the indicator dimensions can be directly associated with the intelligent agent's capability evaluation parameters.

[0019] Preferably, the specific implementation process of step 112 is as follows: Based on the task flow node features and the agent capability attribute library, an indicator dimension-agent capability adaptation matrix is ​​constructed to generate an adaptation matrix data table; specifically, the agent capability attribute library contains core capability parameters such as the computing power limit, data processing rate, load capacity, communication latency, and action execution accuracy of each agent participating in the collaboration. The rows of the indicator dimension-agent capability adaptation matrix represent the indicator dimensions of the business flow nodes, the columns represent the capability attribute types of the agents, and the matrix elements are adaptation coefficients. The adaptation coefficients are quantified by the correlation strength between the indicator dimensions and the capability attributes, and the value range is from 0 to 1. The higher the value, the stronger the support of the capability attribute for the indicator dimension. For example, the adaptation coefficient between the pickup accuracy indicator dimension of the pickup execution node and the agent's visual recognition accuracy capability is 0.9, and the adaptation coefficient with the communication bandwidth capability is 0.2. The adaptation matrix data table records the adaptation coefficient between each indicator dimension and each agent capability attribute, as well as the correlation basis for the coefficient calculation. Preferably, in the specific technical implementation of step 112, based on the adaptation matrix data table and the core task requirements list, a designed scene perception index screening model is used to screen the core indicators for the index dimensions to generate a core indicator list. Specifically, the designed scene perception index screening model includes a requirement weight allocation layer, an adaptation intensity calculation layer, and a comprehensive priority ranking layer. The requirement weight allocation layer assigns requirement weights (ranging from 0.1 to 0.9) to the four types of index dimensions according to the scene adaptation requirements in the core task requirements list. For example, in the intelligent transportation autonomous driving collaborative task, safety and compliance are the core requirements, and the requirement weight for the compliance dimension is set to 0.8 (the weights for other dimensions are 0.6, 0.5, and 0.7, respectively). The adaptation strength calculation layer calculates the average adaptation strength (the average of the adaptation coefficients of all agent capability attributes) for each indicator dimension based on the adaptation matrix data table. The comprehensive priority ranking layer calculates the score for each indicator dimension by "comprehensive priority score = appeal weight × average adaptation strength × dimension discrimination". The dimension discrimination reflects the indicator dimension's ability to identify differences in the capabilities of different agents (the value range is 0.5 to 1.0). A comprehensive priority score threshold is set (the value range is 0.4 to 0.6, for example, 0.5). Indicator dimensions with scores higher than the threshold are selected as core indicators. The core indicator list includes the core indicator, comprehensive priority score, average adaptation strength, and dimension discrimination for each business process node.Preferably, in the specific technical implementation of step 112, a scenario-based quantization rule set is formulated for each core indicator in the core indicator list to generate an initial indicator tensor. Specifically, the scenario-based quantization rule set includes quantization calculation logic, data collection source identifier, data unit, accuracy requirements, anomaly judgment criteria, and data verification methods. The quantization calculation logic is designed according to the type of core indicator and scenario requirements to ensure that the calculation results can be directly used for agent capability matching and task allocation. The data collection source identifier clearly corresponds to the sensor module, local storage unit, or shared database of the intelligent agent. For example, the quantization calculation logic for the core indicator of pickup accuracy is "number of successful pickups / total number of pickups × 1". "00%", the data acquisition source is identified as the agent's visual sensor and order execution record module, the data unit is percentage, the precision requirement is to retain one decimal place, the anomaly judgment standard is a value lower than the preset percentage (e.g., 85%), the data verification method is to cross-compare the agent's execution record with the order data in the shared database, the initial indicator tensor is a four-dimensional tensor structure, the first dimension is the business process module, the second dimension is the business process node, the third dimension is the core indicator, and the fourth dimension is the scenario-based quantitative rule set. Each element in the tensor corresponds to a complete quantitative rule for a certain core indicator under a business process node, ensuring that the quantitative rules are highly adapted to the scenario requirements and the agent's data acquisition capabilities.

[0020] Preferably, the specific implementation process of step 113 is as follows: Redundancy detection processing is performed on the initial indicator tensor to generate a list of redundant indicators; specifically, the designed tensor redundancy detection algorithm is adopted. This algorithm determines redundancy by analyzing three core parameters: the similarity of the quantitative calculation logic of the core indicators, the overlap of data collection sources, and the correlation of the comprehensive priority score. The similarity of the quantitative calculation logic is calculated by weighting the semantic matching degree of the rule description with the overlap of the calculation factors. The overlap of data collection sources is the proportion of the number of collection sources shared by the core indicators to the total number of collection sources. The correlation of the comprehensive priority score is calculated using the Pearson correlation coefficient. When all three parameters are higher than the corresponding preset threshold (…), the redundancy is determined. When the similarity threshold is 0.85, the overlap threshold is 0.9, and the correlation threshold is 0.8, it is determined to be a redundant indicator group. The core indicator with the lower comprehensive priority score within the group is selected as the redundant indicator. For example, in the transportation scheduling module, the quantitative calculation logic similarity between "transportation time" and "transportation mileage time ratio" is 0.88, the data collection source overlap is 0.95, and the comprehensive priority score correlation is 0.83, all of which are higher than the threshold. Moreover, the comprehensive priority score of "transportation mileage time ratio" is lower, so it is marked as a redundant indicator. The redundant indicator marking list includes the redundant indicator identifier, the business process module and node to which it belongs, the three parameter values ​​for redundancy judgment, and the redundant group correlation indicators. Preferably, in the specific technical implementation of step 113, based on the redundant indicator labeling list, redundant dimensions are removed from the initial indicator tensor to generate a deredundant indicator tensor. Specifically, according to the redundant indicator identifiers in the redundant indicator labeling list, the corresponding core indicators and scenario-based quantitative rule sets are removed from the initial indicator tensor, while retaining the four-dimensional relationship of non-redundant core indicators. The structure of the deredundant indicator tensor is consistent with that of the initial indicator tensor, only deleting redundant core indicator dimensions to ensure that the core indicators under each business process node have no logical redundancy, data collection redundancy, or computational redundancy. For example, after removing "transportation mileage time ratio", the core indicators of the transportation scheduling module retain "transportation time", "transportation route compliance rate", and "transportation load stability", and their corresponding quantitative rule sets and dimensional relationship remain complete, and can directly support the task allocation and capability matching of the intelligent agent.Preferably, in the specific technical implementation of step 113, based on the redundancy-removing indicator tensor and the details of business process nodes, collaborative logical association modeling is performed on the core indicators to generate an indicator collaborative association graph. Specifically, the causal, dependency, and constraint relationships of core indicators between different business process nodes are analyzed. Causal relationship means that the numerical change of the core indicator of the preceding node directly affects the numerical value of the core indicator of the subsequent node. Dependency relationship means that the quantitative calculation of the core indicator of the subsequent node needs to reference the calculation result of the core indicator of the preceding node. Constraint relationship means that the values ​​of the two core indicators need to meet the logical constraints set by the scenario, such as the "picking" of the picking execution node. The core indicator "Goods Accuracy" is causally related to the core indicator "Successful Handover First" of the handover and warehousing node. The core indicator "Transportation Time" of the transportation scheduling node is dependently related to the core indicator "Handover Preparation Time" of the handover and warehousing node. The core indicator "Handing Load Value" of the picking execution node is constrainedly related to the core indicator "Transportation Load Upper Limit" of the transportation scheduling node. The indicator collaboration graph uses the core indicators as nodes and the association type as edges. The weight of the edge is the association strength (the value ranges from 0.1 to 1.0, determined based on historical collaboration data statistics). The node attributes include the core indicator identifier, the module and node to which it belongs, and the quantitative rule index. Preferably, in the specific technical implementation of step 113, tensor dimension mapping and integration processing is performed based on the deredundant indicator tensor and the indicator collaborative association graph to generate the task core indicator tensor. Specifically, the association type, association strength, and association node information in the indicator collaborative association graph are embedded into the deredundant indicator tensor, and a fifth dimension is added as collaborative association information, so that the task core indicator tensor forms a five-dimensional structure. The first dimension is the business process module, the second dimension is the business process node, the third dimension is the core indicator, the fourth dimension is the scenario-based quantitative rule set, and the fifth dimension is the collaborative association information. Each element in the tensor completely records the basic information, quantitative rules, and collaborative association relationships of the core indicator, and each dimension is arranged in order according to the execution order of the business process. The core indicators are arranged in descending order according to the comprehensive priority score, ensuring that when allocating collaborative tasks with multiple agents in the future, the core indicators, quantitative standards, and associated indicators of each business process node can be quickly located through the tensor, providing a correlated indicator basis for conflict prediction. At the same time, the tensor structure supports dynamic updates and can synchronously optimize the indicator dimensions and association relationships as the business scenario changes or the agent's capabilities are adjusted.

[0021] Optionally, step 12 includes: step 121, performing word segmentation and requirement intent recognition on the intelligent agent's collaborative requirement description to generate collaborative requirement intent tags; step 122, extracting four types of collaborative elements based on the collaborative requirement intent tags: collaborative participating entities, collaborative timeliness requirements, collaborative resource constraints, and task connection logic, to generate initial collaborative elements; step 123, labeling the initial collaborative elements with their relationships and prioritizing them to generate a collaborative requirement element construct, which constructs a spatial distribution construct of the four types of collaborative elements according to their relationships.

[0022] Preferably, the specific implementation process of step 121 is as follows: The description of the collaborative needs of intelligent agents is processed by scenario-based word segmentation to generate a scenario-adaptive word segmentation set; specifically, in conjunction with the application scenarios of multi-agent collaboration (such as intelligent warehousing, intelligent manufacturing, and intelligent transportation), a scenario-specific word segmentation dictionary is constructed. The dictionary includes scenario-specific professional terms, collaborative action vocabulary, resource type vocabulary, and timeliness description vocabulary. The designed bidirectional matching word segmentation algorithm is used to process the collaborative needs description. This algorithm first extracts long words through forward matching, then corrects the word segmentation deviation through reverse matching, while filtering out stop words without actual semantic meaning. For example, in a smart warehousing scenario, the collaborative requirement description of "handling robots and sorting robots need to complete the handover and warehousing of 500 orders within 2 hours, sharing storage location data and avoiding path conflicts" is segmented to obtain words such as "handling robot, sorting robot, within 2 hours, 500 orders, goods handover, warehousing, sharing storage location data, avoiding path conflicts". The scenario-adaptive word segmentation set includes word segmentation results, word types (agent type, time-sensitive, task-oriented, action-oriented, constraint-oriented) and scenario relevance. Preferably, in the specific technical implementation of step 121, based on the scenario-adaptive word segmentation set, the following is adopted: The designed collaborative demand intent recognition model is used to identify and process demand intents to generate collaborative demand intent labels. Specifically, the designed collaborative demand intent recognition model includes a scene feature fusion layer, an intent classification layer, and a label generation layer. The scene feature fusion layer fuses words from the scene-adapted word segmentation set with scene feature vectors to generate a word scene feature vector. The scene feature vector includes dimensions such as scene type, agent collaboration mode, and core task type. The intent classification layer adopts multi-label classification logic, inputting the fused feature vector into a preset intent classifier. The classifier includes collaborative target intent and time constraint. The system identifies five core intent categories: intent, resource sharing intent, conflict avoidance intent, and task coordination intent. The tag generation layer standardizes the classification results, generating collaborative requirement intent tags that include intent category, core keywords, and scenario adaptability. For example, the collaborative requirement intent tags for the aforementioned smart warehousing scenario include "Collaborative Goal Intent - Goods Handover and Warehousing", "Time Constraint Intent - Complete within 2 Hours", "Resource Sharing Intent - Share Storage Location Data", and "Conflict Avoidance Intent - Avoid Path Conflicts". The scenario adaptability value for each tag ranges from 0.6 to 1.0, calculated based on the correlation between words and scenarios.

[0023] Preferably, the specific implementation process of step 122 is as follows: Based on the collaborative demand intent tags, extract the collaborative participating entity elements to generate a collaborative participating entity list; specifically, select keywords containing agent type and role description from the collaborative demand intent tags, combine them with agent type words in the scenario adaptation word segmentation set, identify the names, types, quantities and core functions of the collaborative participating agents, and label the participation mode of each agent (active execution, auxiliary cooperation, data provision). For example, in the intelligent warehousing scenario, the collaborative participating entity list includes "handling robots (executing entities, quantity several, core function: goods handling), sorting robots (executing entities, quantity several, core function: goods sorting), shared data..." The database (data provider, core function: storing location data) is defined as follows: A list of participating entities records the identifier, type, quantity, core function, and participation method of each participating entity. Preferably, in the specific technical implementation of step 122, based on the collaborative demand intent tags, collaborative timeliness requirement elements are extracted to generate a detailed collaborative timeliness requirement list. Specifically, keywords corresponding to timeliness constraint intents in the collaborative demand intent tags are screened, and the timeliness type (absolute timeliness, relative timeliness), timeliness value, and timeliness start and end conditions are identified. Absolute timeliness refers to a specific time node or time range, while relative timeliness refers to a time requirement triggered by a certain event. Simultaneously, the timeliness value is standardized to a unified time unit (second, minute, hour), as in the above scenario. The collaborative timeliness requirement details are as follows: "Absolute timeliness - completion time: 2 hours (7200 seconds), timeliness start and end conditions: from the completion of order receipt to the end of the last order's entry into the warehouse." The collaborative timeliness requirement details include timeliness type, timeliness value, time unit, start and end conditions, and timeliness priority reference (derived based on task importance). Preferably, in the specific technical implementation of step 122, collaborative resource constraint elements are extracted based on collaborative requirement intent tags to generate a collaborative resource constraint list. Specifically, keywords corresponding to resource sharing intent and constraint intent in the collaborative requirement intent tags are screened to identify resource types (data resources, hardware resources, space resources), resource identifiers, resource access rules, and resource restrictions. According to the resource, the data type and storage location that need to be shared or have restricted access are defined. Hardware resources refer to the equipment or components that need to be occupied during the collaboration process. Spatial resources refer to the physical or logical space in the scenario. For example, in the above scenario, the collaborative resource constraint list is "Data Resources - Storage Location Data (Shared, Access Rules: Real-time Read and Write, Restriction Conditions: Only robots participating in the collaboration can access it), Spatial Resources - Warehouse Operation Area (Restriction Conditions: Avoid Robot Path Overlap)". The collaborative resource constraint list includes resource type, resource identifier, access rules, restriction conditions and resource associated subject. Preferably, in the specific technical implementation of step 122, based on the collaborative requirement intent tag, the task connection logic elements are extracted to generate a task connection logic graph.Specifically, the keywords corresponding to the collaborative goal intent and task connection intent in the collaborative demand intent tags are filtered to identify the task type, task execution order, task input-output relationship, and connection triggering condition of each agent. The task execution order includes parallel execution, serial execution, and cross-execution. The connection triggering condition refers to the identifier or specific event of the completion of the previous task. For example, in the above scenario, the task connection logic graph includes "Sorting robot performs goods sorting task (input: order data, output: sorted goods and location information) → Handling robot performs goods handling task (input: sorted goods and location information, output: goods handled to the warehouse point) → Warehousing action execution (input: goods handled to the warehouse point, output: warehouse completion identifier)". The task connection logic graph uses tasks as nodes and execution order as edges. The attributes of the edges include connection triggering conditions and input / output data types.

[0024] Preferably, the specific implementation process of step 123 is as follows: Based on the initial collaborative elements (list of collaborative participants, details of collaborative timeliness requirements, list of collaborative resource constraints, and task connection logic diagram), an element association matrix is ​​constructed to generate an element association table; specifically, the rows and columns of the element association matrix represent specific element items in the initial collaborative elements, and the matrix elements represent the association strength between element items. The association strength ranges from 0 to 1, with higher values ​​indicating a closer association. The association strength is calculated based on the semantic relevance and functional dependency of the element items in the collaborative requirements. For example, the association strength between "handling robot" and "cargo handling task" is 0.95, and the association strength between "warehouse location data" and "cargo sorting task" is... 0.8, the element association table records the association strength and association type (semantic association, functional dependency, constraint association) of each element item with other element items; preferably, in the specific technical implementation of step 123, based on the element association table, the initial collaborative elements are labeled with association relationships to generate initial collaborative elements with association labels; specifically, each initial collaborative element item is added with an association element identifier, association strength, and association type attributes. For example, the association element identifier of "handling robot" in the collaborative participant list is "goods handling task, warehouse location data, sorting robot, completed within 2 hours", with association strengths of 0.95, 0.8, 0.9, and 0.7 respectively, and association types of functional dependency and semantic association respectively. Semantic association and constraint association are used to fully retain the information of the initial collaborative elements in the association-annotated type, while supplementing the association relationship attributes to visualize the association logic of each element item. Preferably, in the specific technical implementation of step 123, based on the association-annotated initial collaborative elements and collaborative demand intent tags, the designed element priority ranking model is used to prioritize the initial collaborative elements to generate an element priority sequence. Specifically, the designed element priority ranking model includes a demand contribution calculation layer, an association influence calculation layer, and a priority comprehensive calculation layer. The demand contribution calculation layer calculates the degree of contribution of each element item to the core intent of the collaborative demand, with the contribution value ranging from 0.1 to 1.0. The corresponding element items have a high contribution; the correlation impact calculation layer calculates the scope and intensity of the influence of each element item on other element items based on the element correlation table, and the impact degree = number of related element items × average correlation strength; the priority comprehensive calculation layer calculates the priority score of each element item by "priority score = demand contribution degree × 0.6 + correlation impact degree × 0.4", and generates an element priority sequence by arranging the scores in descending order. For example, in the smart warehousing scenario, the element priority sequence is "goods handover and warehousing task (score 0.92), completion within 2 hours (score 0.88), handling robot (score 0.85), sorting robot (score 0.83), storage location data (score 0.8), avoid path conflict (score 0.92)".78)”; Preferably, in the specific technical implementation of step 123, a spatial distribution configuration of collaborative demand elements is constructed based on the element priority sequence and the initial collaborative elements with association labels, so as to generate a collaborative demand element configuration; specifically, based on the element priority sequence as the hierarchical basis, the element item with the highest priority score is taken as the core node, and the remaining element items are distributed around the core node according to the association strength, forming a spatial hierarchical structure. The core node and the associated element items are connected by lines with association strength labels. The element items at the same level are clustered according to the association type. For example, with “goods handover and warehousing task” as the core node, the first layer is distributed as “complete within 2 hours”, “handling robot”, and “sorting robot” (association strength ≥ 0.8), and the second layer is distributed as “warehouse location data” and “avoid path conflict” (association strength ≥ 0.7). The collaborative demand element configuration intuitively presents the association relationship, priority and distribution logic of the four types of collaborative elements through the spatial hierarchical structure, and each element item contains complete attribute information and association information, which can be directly used for the construction of the collaborative element topology of subsequent business tasks.

[0025] Optionally, step 13 includes: Step 131, constructing task-collaboration element association mapping rules, performing feature matching between the task core indicator tensor and the collaborative requirement element construct to generate element association pairs, the matching basis of which is the business relevance between the indicator dimension of the task core indicator tensor and the element dimension of the collaborative requirement element construct; Step 132, defining topology nodes and assigning edge weights to the element association pairs to generate an initial collaborative topology; Step 133, performing integrity verification and structural optimization on the initial collaborative topology to generate a business task collaborative element topology, the business task collaborative element topology linking and binding the indicator nodes of the task core indicator tensor with the element nodes of the collaborative requirement element construct.

[0026] Preferably, the specific implementation process of step 131 is as follows: Based on the collaborative logic and task characteristics of the target business scenario, a task-collaborative element association mapping rule set is constructed; specifically, the association mapping rule set includes three core rules: scenario adaptation rules, semantic matching rules, and functional dependency rules. The scenario adaptation rules combine the application scenarios of multi-agent collaboration (such as intelligent warehousing and intelligent manufacturing) to clarify the adaptation priority of task indicators and collaborative elements under different scenarios. For example, in the intelligent warehousing scenario, the "picking accuracy" indicator is preferentially adapted to elements such as "collaborative participants" and "resource constraints". The semantic matching rules define the semantic similarity judgment criteria between indicator dimensions and element dimensions, and measure the degree of matching through keyword overlap and semantic association. The functional dependency rules clarify the dependency type (necessary dependency and auxiliary dependency) of indicator achievement on collaborative elements. Necessary dependency refers to the collaborative elements that must be available to achieve the indicator, and auxiliary dependency refers to the collaborative elements that can improve the quality of indicator achievement. The association mapping rule set also includes the rule effective conditions, priority, and conflict resolution mechanism (when multiple rules conflict, they are executed in the order of scenario adaptation rules first, functional dependency rules second, and semantic matching rules supplementing). Preferably, in the specific technical implementation of step 131, based on the task-collaboration element association mapping rule set, feature matching processing is performed on the task core indicator tensor and the collaboration requirement element construct to generate element association pairs; specifically, firstly, feature information of each core indicator dimension in the task core indicator tensor is extracted, including indicator name, quantification rule, business process node, and associated indicator identifier; then, feature information of each element item in the collaboration requirement element construct is extracted, including element type, element attribute, priority score, and associated element identifier; subsequently, matching is performed sequentially according to the association mapping rule set: the adapted element type is selected through scenario adaptation rules, and the semantic matching rules are used to calculate the semantic information of the feature information. Semantic similarity (ranging from 0 to 1, e.g., the semantic similarity between "picking accuracy" and "sorting robot" is 0.75) is used to determine the dependency type through functional dependency rules. When the semantic similarity is higher than a preset threshold (ranging from 0.6 to 0.8, e.g., 0.7) and there is a clear dependency type, an element association pair is formed. The element association pair includes the core indicator identifier of the task, the collaborative requirement element identifier, semantic similarity, dependency type, and rule matching result. For example, in the smart warehousing scenario, "picking accuracy (core indicator) - sorting robot (collaborative participating element)" and "transportation time (core indicator) - completion within 2 hours (collaborative timeliness requirement element)" are both valid element association pairs.

[0027] Preferably, the specific implementation process of step 132 is as follows: Based on the element association pairs, perform topology node definition processing to generate a topology node set; specifically, define the task core indicators and collaborative requirement elements in the element association pairs as two types of topology nodes respectively. The task core indicator nodes include attributes such as node identifier, indicator type, quantitative rule index, and the business process node to which they belong, while the collaborative requirement element nodes include attributes such as node identifier, element type, element attribute, and priority score; assign a unique node ID to each node, and mark the association status of the node (associated, not associated). Associated nodes refer to participating elements. Associating nodes are nodes that form pairs, while unassociating nodes are isolated nodes that do not form pairs. The topology node set contains complete attribute information, node IDs, and association status for both types of nodes. For example, in a smart warehousing scenario, the attributes of the core task indicator node "picking accuracy" include "node ID: Z101, indicator type: quality, quantitative rule index: L203, business process node: picking execution node". The attributes of the collaborative requirement element node "sorting robot" include "node ID: X201, element type: collaborative participant, element attribute: execution subject - sorting function, priority score: 0.83". Preferably, in the specific technical implementation of step 132, edge weight assignment is performed based on the topological node set and element association pairs to generate an initial collaborative topology. Specifically, the edge weights are quantified by comprehensive association strength, which is calculated by weighting three parameters: semantic similarity, dependency strength, and scene adaptability. Dependency strength is determined according to functional dependency rules (necessary dependencies range from 0.8 to 1.0, and auxiliary dependencies range from 0.4 to 0.7; for example, "picking accuracy - sorting robot" is a necessary dependency with a dependency strength of 0.9). Scene adaptability is taken from the scene adaptability of the element items in the collaborative requirement element construct. The comprehensive association strength ranges from 0 to 1. A higher value indicates a tighter association between nodes. For example, the semantic similarity of "picking accuracy - sorting robot" is 0.75, the dependency strength is 0.9, and the scene adaptability is 0.88. The overall association strength is calculated to be 0.85. To establish topological edges between corresponding nodes for element association, the overall association strength is assigned as the edge weight, and the association type of the edge is labeled (semantic association, functional dependency association). The initial collaborative topology includes a set of topological nodes, topological edges, edge weights, and association types, forming a "node-edge-node" association structure. For example, "picking accuracy node (Z101) - edge (weight 0.85, type: functional dependency association) - sorting robot node (X201)".

[0028] Preferably, the specific implementation process of step 133 is as follows: Based on the initial collaborative topology and the task core indicator tensor and the collaborative requirement element configuration, integrity verification is performed to generate a topology integrity verification report; specifically, the integrity verification includes three types of verification items: node coverage verification, association integrity verification, and logical consistency verification. Node coverage verification checks whether all core indicator nodes in the task core indicator tensor and all element nodes in the collaborative requirement element configuration have been included in the initial collaborative topology, and nodes not included are marked as missing nodes; association integrity verification checks whether each core indicator node is associated with at least one collaborative requirement element node, and each collaborative requirement element must be associated with at least one collaborative requirement element node. The system checks whether each element node is associated with at least one core indicator node. Nodes that are not associated are marked as isolated nodes. The logical consistency check verifies whether the association type of the topological edges, the dependency type of the edge weights and element association pairs, and the semantic similarity are consistent. Inconsistent associations are marked as logical conflicts. The topological integrity check report includes the check results, a list of missing nodes, a list of isolated nodes, details of logical conflicts, and an analysis of the reasons for the conflicts. For example, in a smart warehousing scenario, if the "warehouse location data sharing" element node is not associated with any core indicator node, it is marked as an isolated node. If the association type of a topological edge is marked as "semantic association" but is actually a necessary functional dependency, it is marked as a logical conflict. Preferably, in the specific technical implementation of step 133, based on the topology integrity verification report, the initial collaborative topology is structurally optimized to generate a business task collaborative element topology. Specifically, the structural optimization includes four operations: node supplementation, association completion, conflict correction, and weight optimization. Node supplementation incorporates missing nodes into the topology structure, assigns node IDs to missing nodes, and improves attribute information. Association completion targets isolated nodes, re-matches features based on the task-collaborative element association mapping rule set, generates new element association pairs, and establishes topological edges. For example, it establishes associations between the "warehouse location data sharing" element node and core indicator nodes such as "picking accuracy rate" and "transportation route compliance rate," and calculates... The system comprehensively assesses the strength of associations and assigns edge weights; conflict correction addresses logical conflicts by re-evaluating association types and edge weights according to the association mapping rule set, correcting contradictory information; weight optimization dynamically adjusts edge weights based on historical collaboration data. For example, if historical data shows that the actual improvement in the association indicator of "picking accuracy - warehouse location data sharing" is higher than expected, the edge weight is adjusted from 0.78 to 0.83; the optimized business task collaboration element topology enables full linkage and binding of indicator nodes of core task indicators with element nodes of collaboration requirements. The topology clearly presents the association strength and type between nodes, directly supporting the formulation of subsequent intelligent agent capability domain matching and task allocation logic.

[0029] Optionally, step 2 includes: step 21, collecting capability parameters and business adaptation records of various types of intelligent agents, and constructing an intelligent agent capability domain feature topology; step 22, based on the business task collaboration element topology and the intelligent agent capability domain feature topology, disassembling tasks hierarchically and matching them to corresponding intelligent agents to generate a hierarchical intelligent agent task allocation scheme; step 23, constructing an intelligent agent capability-task adaptation conflict prediction matrix, performing conflict detection and resolution on the hierarchical intelligent agent task allocation scheme to determine a conflict-free pre-allocated task tensor, and reconstructing the tasks in the hierarchical intelligent agent task allocation scheme according to the intelligent agent dimension using the conflict-free pre-allocated task tensor. Optionally, step 21 includes: step 211, collecting and standardizing capability parameters such as computing power, communication bandwidth, and task processing expertise of each agent in real time to generate basic capability characteristics of the agent; step 212, retrieving historical business collaboration completion rate and adaptation type records of each agent to generate business adaptation characteristics of the agent; step 213, integrating the basic capability characteristics and business adaptation characteristics of the agent to construct a capability domain feature topology of the agent, which constructs a hierarchical topology structure of the capability characteristics of each agent according to the business adaptation type.

[0030] Preferably, the specific implementation process of step 211 is as follows: Based on the collaborative requirements of the target business scenario, determine the collection dimensions and collection strategies of the basic capability parameters of the intelligent agent to generate parameter collection specifications; specifically, the collection dimensions cover hardware performance parameters and functional adaptation parameters. Hardware performance parameters include peak computing power, data processing rate, communication bandwidth, load limit, response latency, and collaborative interface type. Functional adaptation parameters include task processing expertise type, data interaction format support, and environmental adaptability parameters (such as temperature tolerance range and obstacle recognition distance). The collection strategy is formulated according to the parameter type. Hardware performance parameters are collected periodically (with a period of 5-10 minutes), and functional adaptation parameters are collected event-triggered (such as when the intelligent agent accesses the system or when the task type changes). The parameter collection specifications clearly define the collection source (sensor, local control module, network interface), data unit, accuracy requirements, and outlier judgment criteria for each dimension (such as judging an outlier as a value exceeding the normal range by 30%). Preferably, in the specific technical implementation of step 211, the basic capability parameters of each intelligent agent are collected and standardized in real time according to the parameter acquisition specifications to generate the basic capability characteristics of the intelligent agent. Specifically, the real-time acquisition stage uses the sensors and communication modules built into the intelligent agent to obtain the raw data of each parameter and synchronously record the acquisition timestamp and data validity identifier. The standardization process adopts the designed parameter normalization algorithm to map parameters of different units and magnitudes to a unified value range (0-1). For example, the peak computing power is normalized according to the maximum computing power of the intelligent agent in the scenario, and the communication bandwidth is normalized according to the scenario requirements. Calculate threshold normalization; use interpolation to complete abnormal data and generate reasonable values ​​based on valid data from previous and subsequent collection periods; store the basic capabilities of the agent in the form of feature vectors, each feature vector containing the standardized values ​​of each collection dimension, data confidence (calculated based on the reliability of the collection device and data consistency) and the most recent update timestamp. For example, the basic capability feature vector of the handling robot A in the intelligent warehousing scenario is [computing power 0.85, bandwidth 0.78, load limit 0.92, response latency 0.65, JSON format support 1.0, obstacle recognition distance 0.8].

[0031] Preferably, the specific implementation process of step 212 is as follows: retrieve historical business data of each agent from the collaborative knowledge base, and filter core adaptation-related records to generate the original dataset of agent historical adaptation; specifically, the core adaptation-related records include the types and identifiers of historically executed tasks, task completion quality scores (calculated based on the achievement of core task indicators), business collaboration completion rate (number of successfully completed tasks / total number of assigned tasks), adaptation type tags (such as "heavy object handling adaptation" and "high-precision sorting adaptation"), number of collaborative conflicts and their handling results, and collaborative interaction records with other agents. The retrieval time range of historical business data is the most recent 3-6 months to ensure the timeliness and reference value of the data; the original dataset of agent historical adaptation is classified according to the unique identifier of the agent, and each record is associated with the corresponding task scenario features (such as "high-concurrency orders" and "narrow channel operations"). Preferably, in the specific technical implementation of step 212, feature extraction and quantization processing are performed on the original dataset of the agent's historical adaptation to generate agent business adaptation features; specifically, the feature extraction step extracts three types of core features: adaptation preference features (based on the frequency statistics of historical task types, to determine the task types and weights that the agent is good at), collaborative performance features (business collaboration completion rate, conflict handling success rate, and collaborative compatibility score), and scenario adaptation features (based on the task completion quality in different scenarios, to determine the scenario type and adaptation degree of the agent); quantization processing converts various features into values ​​in the range of 0-1. For example, the weight of "heavy object handling" in the adaptation preference features is normalized according to the proportion of this type of task, and the collaborative compatibility score is calculated based on the weighted calculation of historical data interaction efficiency and conflict handling ability; the agent business adaptation features include feature name, quantization value, feature confidence level, and data source identifier. For example, the business adaptation features of sorting robot B are [high-precision sorting preference 0.91, collaborative completion rate 0.88, conflict handling success rate 0.83, narrow channel scenario adaptation degree 0.87].

[0032] Preferably, the specific implementation process of step 213 is as follows: Construct a feature fusion weight matrix to perform weighted fusion of the agent's basic capability features and the agent's business adaptation features to generate agent fusion capability features; specifically, the rows of the feature fusion weight matrix represent the dimensions of the basic capability features and the business adaptation features, the columns represent the task types of the target business scenario, and the matrix elements are the influence weights of each feature dimension on different task types. The weight values ​​are calculated based on scenario requirements and historical collaborative data (range 0-1). For example, in the heavy handling task of intelligent warehousing, the weight of the basic capability feature "load limit" is 0.8, and the weight of the business adaptation feature "heavy handling adaptation" is 0.9; during weighted fusion, the corresponding weight vector is extracted according to the current task type, and multiplied by the quantized values ​​of the two types of features to obtain the fused feature vector; the agent fusion capability features include the fusion values ​​of each dimension, the ranking of feature importance, and the fusion confidence, ensuring that the core features are highlighted. Preferably, in the specific technical implementation of step 213, an intelligent agent capability domain feature topology is constructed based on the intelligent agent fusion capability characteristics. Specifically, the topology structure adopts a four-level hierarchical design, with the root node being the unique identifier of the intelligent agent, the first-level nodes being feature categories (basic capability category, business adaptation category, fusion category), the second-level nodes being specific feature dimensions (such as "computing power" and "bandwidth" under the basic capability category), and the third-level nodes being feature quantization values, confidence levels, and associated feature identifiers. The weight of the topology edges is the correlation strength between features (feature correlation calculated based on historical data), for example, the correlation strength between "computing power" and "high-precision task adaptation" is 0.75. The capability domain feature topologies of the same intelligent agent type are connected by edges to form a cluster topology, indicating the complementary relationship of capabilities between intelligent agents (such as the "load capacity" of the handling robot and the "sorting accuracy" of the sorting robot being complementary). The intelligent agent capability domain feature topology supports dynamic updates, adjusting node values ​​and edge weights in real time as basic capability parameters are collected and historical adaptation data is accumulated. For example, after an intelligent agent completes a high-precision task, the quantization value and correlation strength of the "high-precision task adaptation" feature are updated synchronously.

[0033] Optionally, step 22 includes: step 221, based on the hierarchical structure of the business task collaboration element topology, the overall task is divided into a core task layer, an auxiliary task layer, and a collaboration connection layer; step 222, according to the agent capability domain feature topology, different levels of tasks are matched to agents with corresponding capabilities, and the core task layer is matched to agents whose core capability dimensions meet the criteria in the agent capability domain feature topology; step 223, the agent matching results of each level of tasks are integrated to generate a hierarchical agent task allocation scheme, which binds each level of task to the capability domain feature topology node of the corresponding agent.

[0034] Preferably, the specific implementation process of step 221 is as follows: Analyze the hierarchical structure and relationship of the business task collaboration element topology to determine the task layering basis and decomposition rules; specifically, the hierarchical structure of the business task collaboration element topology corresponds to the core level and execution logic of the business process, and the hierarchical mapping from the root node to the leaf node is the task importance gradient; the layering basis includes the contribution weight of the task to the business objective (core contribution weight higher than 0.7 is a core task), the intensity of resource demand (high computing power / high bandwidth demand is a core task), and the degree of collaboration dependency (multiple intelligent agents need to work together to form a collaborative connection task); the decomposition rules clarify the boundary definition, input and output standards, and core indicator association requirements of each level of task. For example, the core task layer needs to be bound to the core business indicators, and the collaborative connection layer needs to clarify the cross-task data interaction format. The decomposition rules also include the connection conditions and timing constraints of tasks between levels. Preferably, in the specific technical implementation of step 221, based on the task layering criteria and decomposition rules, the overall task is divided into a core task layer, an auxiliary task layer, and a collaborative connection layer to generate a hierarchical task set. Specifically, the core task layer includes key tasks that directly determine the achievement of business objectives, such as the tasks of "goods pickup," "precise transportation," and "delivery to warehouse" in a smart warehousing scenario. Each core task has clearly defined corresponding core indicators (such as pickup accuracy and transportation timeliness). The auxiliary task layer includes supporting tasks that support the execution of core tasks, such as "path planning," "data verification," and "status monitoring," and their outputs serve as input parameters for core tasks. The collaborative connection layer includes transitional tasks that coordinate the linkage of tasks at each level, such as "task status synchronization," "data interaction adaptation," and "conflict warning transmission," and is responsible for connecting the collaborative links between core tasks and auxiliary tasks. The hierarchical task set records the hierarchical affiliation, task identifier, core requirements, associated element nodes, and connecting task identifiers for each task. For example, the "goods pickup" task is associated with the collaborative element nodes "sorting robot" and "warehouse location data," and the connecting task is "path planning."

[0035] Preferably, the specific implementation process of step 222 is as follows: Analyze the core capability dimensions and hierarchical relationships of the agent's capability domain feature topology to formulate hierarchical task-agent matching criteria; specifically, the core capability dimensions are extracted from the agent's capability domain feature topology, including basic core capabilities (computing power, load limit, etc.), business core capabilities (task expertise adaptability), and collaborative core capabilities (collaborative compatibility score); the matching criteria are formulated according to the task level: the core task layer requires the agent's basic core capability compliance rate to be higher than a preset proportion (e.g., 80%), business core capability adaptability to be higher than a threshold (e.g., 0.8), and collaborative core capability score to be higher than a threshold (e.g., 0.82); the auxiliary task layer requires the basic core capability compliance rate to be higher than a medium proportion (e.g., 60%), and business core capability adaptability to be higher than a medium threshold (e.g., 0.6); the collaborative connection layer requires the collaborative core capability score to be higher than a medium threshold (e.g., 0.75), and data interaction adaptability to be met; the criteria also include agent load balancing constraints to avoid a single agent being assigned too high a proportion of tasks (e.g., not exceeding 30% of the total task volume). Preferably, in the specific technical implementation of step 222, based on the hierarchical task-agent matching criterion, tasks in the hierarchical task set are matched to corresponding agents to generate preliminary task-agent matching results; specifically, for the core task layer, the topology of agent capability domain features is traversed, agents that meet the core capability dimensions are selected, and the task-agent adaptation score is calculated (weighted by the basic capability compliance rate, business adaptability, and collaborative compatibility score). Tasks are assigned in descending order of the score. For example, in the intelligent warehousing scenario, the "goods pickup" task is matched with sorting robot B (adaptation score 0.85). For the auxiliary task layer, agents with satisfactory core business capabilities are matched and prioritized for allocation to backup nodes or low-load agents of core task layer agents. For example, the "path planning" task is matched to the auxiliary computing node of robot B. For the collaborative connection layer, agents with outstanding collaborative core capabilities are matched and priority is given to those with high collaborative compatibility scores with core task layer agents. For example, the "state synchronization" task is matched to transport robot A (with a compatibility score of 0.82 with B). The preliminary task-agent matching results record the assigned object, adaptation score, core capability matching items, and load percentage for each task.

[0036] Preferably, the specific implementation process of step 223 is as follows: The initial task-agent matching results are checked and adjusted to generate adjusted matching results. Specifically, the conflict check includes capability conflicts (the agent's core capabilities do not meet the dynamic requirements of the task), load conflicts (the agent's task proportion exceeds the standard), and coordination conflicts (insufficient coordination compatibility between agents in different tasks). For capability conflicts, a backup agent in the topology (with similar core capability dimensions) is used. For load conflicts, some tasks are migrated to low-load, compliant agents. For coordination conflicts, an agent with a higher coordination compatibility score is used. For example, if robot A's task proportion reaches 35%, its "state synchronization" task is migrated to robot C (with a compatibility score of 0.78 with B). The adjusted matching results ensure no conflicts and balanced load, and the reasons for adjustment, the allocation objects before and after adjustment, and the changes in adaptation scores are recorded. Preferably, in the specific technical implementation of step 223, the integrated and adjusted matching results are bound to the capability domain feature topology nodes of the corresponding intelligent agents to generate a hierarchical intelligent agent task allocation scheme. Specifically, the binding process associates the core requirements of the task with the secondary nodes (specific feature items) of the capability domain feature topology of the intelligent agent, such as binding the "goods pickup" task with the "high-precision sorting feature" and "JSON format interaction feature" of robot B. The scheme clarifies the task execution sequence (such as the core task starting first and the auxiliary task preparing in advance), data interaction interface (determined based on the intelligent agent collaboration interface feature), and task priority (the core task layer is the highest, followed by the collaboration connection layer, and the auxiliary task layer is the lowest). The hierarchical intelligent agent task allocation scheme is presented in a structured form, including intelligent agent identifier, task level, task identifier, bound feature node, execution sequence, interaction interface, priority, and adaptation score. For example, the allocation scheme of robot B is "core task layer - goods pickup - bound high-precision sorting feature - priority execution - JSON interface - adaptation score 0.85".

[0037] Optionally, step 23 includes: step 231, constructing an agent capability-task adaptation conflict prediction matrix, the matrix dimensions including four conflict judgment dimensions: capability redundancy, task coupling, timeliness matching, and cross-agent linkage complexity; step 232, inputting the hierarchical agent task allocation scheme into the matrix to calculate conflict scores and identify conflicting task items, the judgment criterion for conflicting task items being that the conflict scores of all four dimensions exceed a preset threshold; step 233, executing a multi-agent distributed conflict resolution consensus mechanism for conflicting task items, performing task redistribution or task splitting through capability redundancy nodes of the agent capability domain feature topology to determine a conflict-free pre-allocated task tensor, the conflict-free pre-allocated task tensor integrating the resolved tasks according to the hierarchical structure of the agent capability domain feature topology tensor dimension.

[0038] Preferably, the specific implementation process of step 231 is as follows: Based on the collaborative characteristics and conflict types of the target business scenario, define the quantitative standards and calculation logic for four conflict judgment dimensions to generate conflict dimension quantitative specifications; specifically, the capability redundancy dimension quantifies the remaining capability of the agent after executing the task, and the calculation logic is (maximum capability value of the agent - capability value required by the task) / maximum capability value of the agent, with a value range of 0-1, and the lower the value, the higher the risk of conflict; the task coupling dimension quantifies the intensity of resource competition between different tasks on the same agent, calculated based on the overlap of task resource requirements and the intersection of execution time, with a value range of 0-1, and the higher the value, the higher the risk of conflict. The higher the risk, the better. The timeliness matching dimension quantifies the fit between the agent's execution speed and the task time limit. The calculation logic is the task's required execution rate / the agent's actual execution rate, with a value range of 0-1.5. Values ​​below the preset ratio (e.g., 0.8) or above 1.2 are considered to be of high conflict risk. The cross-agent linkage complexity dimension quantifies the interaction difficulty when multiple agents collaborate. It is calculated based on the compatibility of the collaborative interface, the frequency of data interaction, and the historical linkage conflict rate, with a value range of 0-1. The higher the value, the higher the conflict risk. The conflict dimension quantification specification clarifies the calculation factor, data source (agent capability domain feature topology, task allocation scheme), and validity threshold for each dimension.

[0039] Preferably, in the specific technical implementation of step 231, a conflict prediction matrix for agent capability-task adaptation is constructed based on the conflict dimension quantification specification to establish a conflict identification framework. Specifically, the rows of the matrix represent each task item (including task identifier and assigning agent identifier) ​​in the hierarchical agent task allocation scheme, and the columns represent four conflict judgment dimensions (capability redundancy, task coupling, timeliness matching, and cross-agent linkage complexity). The matrix elements are the conflict scores of the corresponding task item in that dimension. At the same time, a dynamic preset threshold is set for each dimension. The threshold is adjusted based on scenario requirements and historical conflict data. For example, in the intelligent warehousing scenario, the capability redundancy threshold is set to 0.3 (below which is considered a conflict), the task coupling threshold is set to 0.6 (above which is considered a conflict), the timeliness matching threshold range is set to 0.8-1.2 (exceeding which is considered a conflict), and the cross-agent linkage complexity threshold is set to 0.7 (above which is considered a conflict). The matrix also includes auxiliary columns such as task level and associated task identifier, which are used for conflict tracing and resolution decisions.

[0040] Preferably, the specific implementation process of step 232 is as follows: Input the task information of the hierarchical intelligent agent task allocation scheme and the intelligent agent capability domain feature topology data into the conflict prediction matrix, and calculate the conflict score to generate a task conflict score matrix; specifically, extract the resource requirements, execution time limit, associated intelligent agents and other information of each task item, and combine the basic capability features and collaborative performance features in the intelligent agent capability domain feature topology to calculate the conflict score of each dimension according to the conflict dimension quantification specification; for example, in the intelligent warehousing scenario, the "transportation sub-task" and "handover sub-task" assigned to the handling robot A have a capability redundancy score of 0.25 (below the threshold of 0.3), a task coupling score of 0.65 (above the threshold of 0.6), a timeliness matching score of 0.75 (below the threshold of 0.8), and a cross-intelligent agent linkage complexity score of 0.72 (above the threshold of 0.7). The task conflict score matrix completely records the four dimensions of each task item, the threshold judgment result and the data calculation basis.

[0041] Preferably, in the specific technical implementation of step 232, conflicting task items are identified and a conflict tracing report is generated based on the task conflict score matrix and a preset threshold. Specifically, the criteria for determining conflicting task items are that the conflict scores in all four dimensions exceed the corresponding preset thresholds, i.e., simultaneously satisfying the conditions of insufficient capability redundancy, excessive task coupling, mismatched timeliness, and excessive linkage complexity. The conflict tracing report includes the conflicting task item identifier, the assigning agent identifier, the conflict scores in the four dimensions, the threshold comparison results, the conflict type classification (such as resource competition type, capability mismatch type), and the scope of influence of related tasks. For example, the above-mentioned "transportation sub-task" and "handover sub-task" are determined to be conflicting task items, the conflict type is classified as "resource competition + capability mismatch type", and the scope of influence of related tasks includes "warehousing sub-task".

[0042] Preferably, the specific implementation process of step 233 is as follows: Based on the conflict source tracing report and the topology of the agent's capability domain, a multi-agent distributed conflict resolution consensus mechanism is constructed to formulate conflict resolution strategies. Specifically, the mechanism includes a conflict assessment layer, a strategy generation layer, and a consensus verification layer. The conflict assessment layer combines the conflict type and the topology of the agent's capability domain to analyze the core reasons for the conflict (such as insufficient agent capabilities or overlapping task resources). The strategy generation layer provides two types of core resolution strategies: task redistribution strategy (migrating conflicting task items to backup agents corresponding to capability redundancy nodes) and task splitting strategy (splitting conflicting task items into multiple sub-tasks and assigning them to different capability nodes or multiple agents of the same agent). The consensus verification layer verifies the feasibility of the resolution strategy through inter-agent collaborative communication (such as whether the backup agent's capabilities meet the standards and whether the timing requirements are met after task splitting), ensuring that no new conflicts are generated by the strategy. For example, for the above-mentioned conflicting task items, a resolution strategy of "redistributing the handover sub-task to the handling robot C (capability redundancy score 0.45)" is generated.

[0043] Preferably, in the specific technical implementation of step 233, a conflict resolution strategy is executed and the task allocation scheme is reconstructed. Tensor dimensions are integrated according to the hierarchical structure of the agent's capability domain feature topology to determine the conflict-free pre-allocated task tensor. Specifically, the task redistribution strategy prioritizes backup agents with a collaborative compatibility score higher than a threshold (e.g., 0.8) to ensure smooth collaboration. The task splitting strategy splits tasks according to the secondary capability nodes (e.g., computing power nodes, bandwidth nodes) of the agent's capability domain feature topology to ensure that the split sub-tasks are accurately matched with the capability nodes. The reconstructed task allocation scheme clarifies the task items, execution sequence, resource allocation ratio, and collaborative interface of each agent. The dimension of the conflict-free pre-allocated task tensor is "agent identifier - capability domain level - task item - execution parameters". For example, the tensor element of robot C is "transport robot C - basic capability layer - handover sub-task - execution time limit 180 seconds, bandwidth allocation 0.3". The tensor completely records the task allocation information after resolution, supporting accurate scheduling of subsequent multi-agent collaborative execution.

[0044] Optionally, step 3 includes: step 31, defining the communication protocol and data interaction format for cross-agent collaborative interaction based on the task association relationship of the conflict-free pre-allocated task tensor; step 32, constructing a multi-agent hierarchical conflict tracing-spinning and resolution strategy library, and formulating response mechanisms for different types of conflicts; step 33, integrating the collaborative interaction communication protocol and the multi-agent hierarchical conflict tracing-spinning and resolution strategy library to construct a cross-agent real-time collaborative-conflict resolution protocol cluster, so as to generate a collaborative interaction rule protocol cluster, which binds the communication protocol and resolution strategy according to the conflict type. Optionally, step 31 includes: step 311, determining the real-time communication link and data transmission interface for cross-agent collaboration, and defining the field specifications and verification rules for the interface data; step 312, formulating synchronous / asynchronous interaction communication protocols for the collaboration requirements of different types of tasks, with the synchronous interaction protocol adapting to the high-timeliness collaboration requirements of the core task layer; step 313, integrating the communication link configuration and interaction protocol to generate a collaborative interaction communication protocol module, which embeds the communication link parameters and interaction protocol rules into the task nodes of the conflict-free pre-allocated task tensor.

[0045] Preferably, the specific implementation process of step 311 is as follows: Based on the task node attributes of the conflict-free pre-allocated task tensor and the collaborative interface type of the agent capability domain feature topology, design the topology structure and transmission parameters of the cross-agent real-time communication link to generate a link configuration scheme; specifically, the topology structure adopts a dual-link architecture of "main link + backup link". The main link is used for real-time transmission of core data, and the backup link is used for fault switching and redundancy backup. For example, in the intelligent warehousing scenario, the main link of the handling robot and the sorting robot adopts Wi-Fi 6 communication technology, and the backup link adopts millimeter wave communication technology; the transmission parameters are dynamically configured according to the timeliness requirements and data volume of the task nodes. The link bandwidth of the core task layer is set to a high value (e.g., 50-100Mbps), and the transmission delay threshold is set to a low value (e.g., ≤50 milliseconds). The bandwidth of the auxiliary task layer and the collaborative connection layer is set to a medium value (e.g., 20-50Mbps), and the delay threshold is set to a high value (e.g., ≤100 milliseconds); the link configuration scheme clarifies the link identifier, agent association relationship, transmission technology type, bandwidth allocation ratio, delay threshold, and fault switching trigger condition (automatic switch to backup link when the main link interruption duration is ≥10 milliseconds). Preferably, in the specific technical implementation of step 311, the field specifications and verification rules of the data transmission interface are defined to generate the interface communication specification. Specifically, the field specifications are divided according to the data type of the task node, including basic information fields (smart agent identifier, task identifier, timestamp), data content fields (business data, status data, control instructions), and verification fields (checksum, data length). For example, in the smart warehousing scenario, the interface fields for goods handover data are [robot ID, order ID, handover timestamp, goods weight, location coordinates, CRC32 checksum, data length]. The field type is clearly defined as integer, floating point, string, etc., and the field length is set according to the data precision requirements (e.g., the location coordinate field length is 32 bits). The verification rules adopt a "dual verification mechanism". The first layer is format verification (checking whether the field type and length conform to the specification), and the second layer is content verification (verifying data integrity through checksum and excluding abnormal values ​​through data range verification). When the verification fails, the data retransmission mechanism is triggered. The number of retransmissions does not exceed a preset threshold (e.g., 3 times). If it still fails, it switches to the backup link for transmission.

[0046] Preferably, the specific implementation process of step 312 is as follows: Based on the task-level attributes and collaborative requirements of the business task collaboration element topology, a synchronous / asynchronous interaction communication protocol architecture is designed to generate a protocol architecture specification; specifically, the synchronous interaction protocol architecture includes a request module, a response module, and a timeout processing module. The request module specifies the data sending format and triggering conditions, the response module defines the structure of feedback data and the return timing, and the timeout processing module sets a timeout threshold (≤30 milliseconds for the core task layer and ≤100 milliseconds for other layers). After the timeout, a re-request or fault alarm is automatically triggered; the asynchronous interaction protocol architecture includes a sending cache module, a receiving confirmation module, and a message sorting module. The sending cache module temporarily stores the data to be sent, the receiving confirmation module provides feedback on the receiving status through an acknowledgment character (ACK), and the message sorting module reassembles the received data in an orderly manner according to the timestamp; the protocol architecture specification clarifies the structure, field meaning, and interaction sequence of the protocol data unit (PDU) to ensure that the protocol can adapt to the collaborative requirements of different task layers. Preferably, in the specific technical implementation of step 312, for the collaborative needs of different types of tasks, the details of synchronous / asynchronous interaction communication protocols are formulated to generate a scenario-based communication protocol set. Specifically, the core task layer (such as picking and transportation tasks in smart warehousing) adopts a synchronous interaction protocol, which supports instant interaction of request and response to ensure the consistency of task execution timing. For example, after robot A sends a goods handover request to robot B, it needs to wait for B's confirmation response before it can perform the next action. The protocol specifies the request sending interval, response waiting time, and conflict handling logic. The auxiliary task layer (such as path planning and data verification) and the collaborative connection layer (such as state synchronization) adopt an asynchronous interaction protocol, which allows independent data transmission and subsequent integration, reducing interaction blocking. For example, when a robot reports non-core status data to the central server, it does not need to wait for the server's instant response. The protocol specifies the data sending cycle, confirmation character feedback mechanism, and message loss handling scheme. The scenario-based communication protocol set also includes protocol adaptation rules, which can dynamically switch between synchronous / asynchronous modes according to the task execution status and link quality. For example, when the link delay exceeds the threshold during the execution of a core task, it temporarily switches to asynchronous mode to ensure the continuity of data transmission.

[0047] Preferably, the specific implementation process of step 313 is as follows: Integrate the link configuration scheme, interface communication specifications, and scenario-based communication protocol set to generate the core components and associated logic of the collaborative interactive communication protocol module, so as to form a protocol module architecture; specifically, the core components include a link management component, an interface adaptation component, a protocol parsing component, and a dynamic adjustment component. The link management component is responsible for the switching and status monitoring of the primary and backup links. The interface adaptation component realizes the automatic matching of data format and interface field specifications. The protocol parsing component performs protocol decoding and content extraction on the received data. The dynamic adjustment component adjusts the transmission parameters and protocol mode according to the task node status and link quality. The associated logic clarifies the interaction process of each component. For example, when data is sent, it is first formatted by the interface adaptation component, then encapsulated into protocol data units by the protocol parsing component, and finally transmitted through the link management component by selecting a suitable link. Preferably, in the specific technical implementation of step 313, the core parameters and rules of the collaborative interactive communication protocol module are embedded into the task nodes of the conflict-free pre-allocated task tensor to complete the binding between the protocol module and the task nodes. Specifically, each task node is associated with the corresponding link identifier, interface field specification, communication protocol type, transmission parameter threshold, and fault handling rules. For example, the parameters bound to the "goods handover" task node in smart warehousing are [main link ID: L001, backup link ID: L002, interface field specification: F003, communication protocol: synchronization protocol, bandwidth threshold: 80Mbps, latency threshold: 40 milliseconds]. After binding, the conflict-free pre-allocated task tensor contains both task allocation information and communication configuration requirements. When the intelligent agent executes a task, it can directly read the communication rules from the task node without additional configuration, ensuring seamless connection between communication and task execution.

[0048] Optionally, step 32 includes: step 321, identifying four types of conflicts that may occur during the collaboration process: resource preemption, task timeout, capability insufficiency, and cross-agent linkage failure; step 322, formulating corresponding resolution strategies for each type of conflict: resource preemption conflicts are resolved using a cross-agent capability redundancy and complementarity strategy; task timeout conflicts are resolved using a multi-agent task segmentation and parallel resolution strategy; capability insufficiency conflicts are resolved using a node migration strategy based on the capability domain feature topology; and cross-agent linkage failure conflicts are resolved using a backup node switching strategy for the collaborative link; step 323, establishing conflict triggering thresholds and strategy matching rules to form a multi-agent hierarchical conflict tracing-diversion resolution strategy library, which binds the conflict tracing results and resolution strategies according to the agent linkage hierarchy.

[0049] Preferably, the specific implementation process of step 321 is as follows: Based on the application scenarios of multi-agent collaboration (such as intelligent warehousing and intelligent manufacturing) and historical collaborative conflict data, the core characteristics and triggering conditions of four types of conflicts are extracted to generate conflict type definition specifications; specifically, the core characteristic of resource preemption type conflict is that multiple agents simultaneously request the same limited resource (such as shared sensors, communication bandwidth, physical space), and the triggering condition is that the number of concurrent resource requests exceeds the resource's carrying capacity limit, or the resource occupation time exceeds a preset threshold. For example, in intelligent warehousing, multiple handling robots simultaneously request to enter the same narrow channel (physical space resource), or simultaneously request to read shared storage location data (data resource); the core characteristic of task timeout type conflict is that the actual time spent by the agent in executing the sub-task exceeds the collaborative timing baseline, and the triggering condition is that the ratio of the remaining task quantity to the remaining time exceeds the efficiency threshold, or the timeout time of a single task node reaches the total time. The core characteristics of conflict types include: 1) Preset proportions for sequential tasks, such as the actual time taken by a sorting robot to perform a picking task exceeding the baseline time by 50%, causing subsequent handover tasks to fail to start on time; 2) Insufficient capability type conflicts are characterized by the agent's actual capability failing to meet the sub-task requirements. The triggering conditions are that the core capability parameters of the agent's capability domain feature topology are lower than the task requirement threshold, or the capability attenuation during task execution exceeds a preset proportion, such as the load capacity of a handling robot being lower than the weight requirement of the goods, or the computing power attenuation leading to insufficient data processing rate; 3) Cross-agent linkage failure type conflicts are characterized by interrupted data interaction between agents, failed state synchronization, or abnormal coordination of actions. The triggering conditions are that the communication link interruption duration exceeds the threshold, the interaction response time exceeds the baseline, or the coordination deviation exceeds the allowable range, such as the data interaction response time between robots A and B continuously exceeding 50 milliseconds, or the state synchronization frequency being lower than 10 times / second.

[0050] Preferably, in the specific technical implementation of step 321, a conflict feature identification matrix is ​​constructed to match and identify abnormal data in the collaboration process, so as to accurately classify four types of conflicts. Specifically, the rows of the conflict feature identification matrix represent conflict types (resource preemption, task timeout, capability deficiency, and cross-agent linkage failure), and the columns represent feature dimensions (trigger source, core performance, data features, and scope of influence). The matrix elements are the quantitative standards of each conflict type in the corresponding feature dimension. For example, the trigger source feature of resource preemption conflict is "multiple agents - same resource", the data feature is "concurrent resource request ≥ 2", and the scope of influence feature is "local task node". The core performance feature of cross-agent linkage failure conflict is "data interaction interruption / synchronization failure", the data feature is "response time > baseline threshold", and the scope of influence feature is "related collaboration link". By comparing the abnormal data collected in the collaboration process (such as resource request records, task time data, capability parameter data, and interaction logs) with the matrix features, the accurate classification of conflict types is achieved.

[0051] Preferably, the specific implementation process of step 322 is as follows: For resource preemption-type conflicts, a cross-agent capability redundancy complementarity resolution strategy is designed to achieve dynamic resource allocation and conflict resolution. Specifically, this strategy includes three core links: resource occupancy assessment, redundant agent identification, and dynamic resource allocation. The resource occupancy assessment link calculates the current occupancy rate, remaining available amount, and task priority of each requesting agent in real time. The redundant agent identification link traverses the feature topology of the agent capability domain and filters redundant agents with conflict resource replacement capabilities and in a low-load state. The redundancy judgment criterion is that the redundancy of the corresponding capability of the agent is ≥1.2 (redundancy = (maximum capability value - current occupancy value) / conflict resource demand value). The dynamic resource allocation link formulates a resource allocation scheme based on task priority and agent collaboration compatibility score. The agent corresponding to the high-priority task gets the resource first, or the load of the conflict resource is shared by the redundant agent. For example, when multiple robots compete for narrow channel resources in a smart warehouse, priority is given to the robot that performs the core task and has a collaboration compatibility score higher than 0.8. The remaining robots are replaced by redundant robots or their paths are adjusted to avoid conflict.

[0052] Preferably, in the specific technical implementation of step 322, a multi-agent task sharding parallel resolution strategy is formulated to address task timeout conflicts in order to improve task execution efficiency. Specifically, this strategy first shards the timeout subtasks, dividing them into several independent sub-shards according to the task's functional modules and data correlation, with the complexity and time consumption of each sub-shard controlled within a preset range. Then, based on the agent's capability domain feature topology and conflict-free pre-allocated task tensor, the sub-shards are allocated to idle agents or low-load agents with corresponding capabilities, ensuring that the execution of the sub-shards does not generate new conflicts with other tasks during allocation. Finally, a sub-shard execution synchronization mechanism is established, using a collaborative interactive communication protocol to synchronize the execution status of each agent and integrate the results. For example, after a sorting task times out, the remaining orders are split into multiple sub-shards and allocated to idle sorting robots for parallel processing, with the sorting results being summarized synchronously.

[0053] Preferably, in the specific technical implementation of step 322, a node migration and resolution strategy based on the characteristic topology of the agent's capability domain is adopted to address capability-deficient conflicts and compensate for the agent's capability shortcomings. Specifically, this strategy first locates the core nodes with insufficient capabilities (such as computing power nodes, load nodes, and data processing nodes), and then selects target agents with the ability to replace the core nodes from the characteristic topology of the agent's capability domain. The target agents must meet the requirements of a collaborative compatibility score higher than a threshold (such as 0.75) and a capability redundancy ≥ 1.0. Finally, the task modules corresponding to the insufficient capabilities are migrated to the target agents, and seamless data handover is achieved through a collaborative interface. For example, when the load capacity of the handling robot is insufficient, the handling task module for overweight goods is migrated to a backup robot with a stronger load capacity, and key data such as the location and weight of the goods are transmitted synchronously.

[0054] Preferably, in the specific technical implementation of step 322, a strategy for switching and resolving backup nodes of the collaborative link is designed to restore collaborative linkage in response to cross-agent linkage failure conflicts. Specifically, this strategy first locates the core cause of linkage failure (such as communication link failure, collaborative node failure, protocol incompatibility) through a conflict feature identification matrix. If it is a communication link failure, it automatically switches to a backup communication link (such as switching from Wi-Fi 6 to millimeter-wave communication). If it is a collaborative node failure, a backup node with a similar collaborative compatibility score is selected from the agent capability domain feature topology to replace the original linkage node. If it is a protocol incompatibility, it automatically switches to an appropriate collaborative interaction protocol. For example, when the linkage between robots A and B fails due to communication link congestion, it switches to a backup link and increases the priority of linkage data transmission to ensure that state synchronization and data interaction are restored to normal.

[0055] Preferably, the specific implementation process of step 323 is as follows: Establish a conflict triggering threshold system and strategy matching rules, and bind the conflict tracing results and resolution strategies according to the agent linkage hierarchy to form a multi-agent hierarchical conflict tracing-diversion resolution strategy library; specifically, the conflict triggering threshold system sets dynamic thresholds for four types of conflicts. For example, the triggering threshold for resource preemption conflicts is "the number of concurrent resource requests ≥ 2 and the duration ≥ 10 milliseconds," and the triggering threshold for task timeout conflicts is "the timeout duration ≥ 30% of the baseline duration." The thresholds can be automatically adjusted according to the collaborative scenario and task execution status. The strategy is adapted and adjusted; the strategy matching rules clearly define the optimal resolution strategy corresponding to different conflict types, trigger thresholds, and linkage levels. For example, resource preemption conflicts at the core level are resolved by prioritizing cross-agent capability redundancy and complementarity, while task timeout conflicts at the auxiliary level are resolved by prioritizing multi-agent task sharding and parallel resolution. The strategy library stores strategies according to the agent linkage level (core linkage layer, auxiliary linkage layer, and connecting linkage layer). Each strategy includes trigger conditions, execution steps, parameter configuration, applicable scenarios, and success case indexes, which can be directly called by the collaborative conflict dynamic resolution operation unit.

[0056] Optionally, step 4 includes: Step 41, deploying multi-agent execution status acquisition nodes to obtain real-time feedback data such as task progress, resource consumption, and abnormal alarms of each agent; Step 42, inputting the feedback data into the collaborative interaction rule protocol suite to identify execution deviations and generate collaborative scheduling instructions. The generation of collaborative scheduling instructions requires calling the resolution strategy module in the collaborative interaction rule protocol suite; Step 43, integrating the scheduling instructions with the task execution baseline to generate a multi-agent collaborative execution dynamic tensor instruction graph. Based on the multi-agent collaborative execution dynamic tensor instruction graph, the collaborative task is managed in a closed-loop manner throughout the entire process. The multi-agent collaborative execution dynamic tensor instruction graph dynamically updates the scheduling instructions according to the agent dimension and the task dimension.

[0057] Optionally, step 41 includes: step 411, embedding a state acquisition interface into the task execution module of each agent, and setting the data acquisition cycle and reporting rules; step 412, performing format normalization and anomaly filtering on the acquired raw state data to generate standardized feedback data, which needs to match the data dimension of the conflict-free pre-allocated task tensor; step 413, constructing a temporal storage and association index for the feedback data to form a real-time feedback data pool for the execution state of multiple agents, wherein the index dimension of the data pool is consistent with the node dimension of the agent's capability domain feature topology.

[0058] Preferably, the specific implementation process of step 411 is as follows: Based on the data dimension of the conflict-free pre-allocated task tensor and the node dimension of the agent's capability domain feature topology, the functional architecture and data acquisition items of the embedded state acquisition interface are designed to generate the interface design specification; specifically, the functional architecture includes a data capture module, a timing marking module, a local cache module, and a communication transmission module. The data capture module is responsible for extracting state data from the core components of the agent's task execution module (such as the action execution unit, resource scheduling unit, and data interaction unit). The timing marking module adds a high-precision timestamp (accuracy to the millisecond level) to each data item. The local cache module temporarily stores the unuploaded acquisition data to prevent loss. The communication transmission module supports low-latency data reporting. The data acquisition items cover four core categories: task execution progress, resource occupancy status, data interaction status, and abnormal alarm information. For example, in the intelligent warehousing scenario, the acquisition items for the handling robot include the proportion of goods handling completed, the current computing power occupancy rate, the communication bandwidth usage, the data interaction success rate, and the robotic arm fault alarm. The interface design specification clarifies the data type, acquisition accuracy, extraction source, and transmission protocol (such as TCP / IP protocol) of each acquisition item.

[0059] Preferably, in the specific technical implementation of step 411, a status acquisition interface is embedded in the task execution module of each intelligent agent, and the data acquisition cycle and reporting rules are set according to the interface design specifications to achieve real-time capture and uploading of status data. Specifically, the data acquisition cycle adopts a dynamic adaptive mechanism, with the acquisition cycle of the core task layer set to a shorter duration (e.g., 100-500 milliseconds), and the acquisition cycle of the auxiliary task layer and the collaborative connection layer set to a longer duration (e.g., 1-5 seconds), which can be automatically adjusted according to the task execution progress and data fluctuations. The reporting rules include two modes: immediate reporting and batch reporting. Abnormal alarm information adopts the immediate reporting mode to ensure rapid response to problems, while regular status data adopts the batch reporting mode (triggered every time a certain amount of data is accumulated or a preset reporting cycle is reached) to reduce communication overhead. The reporting rules also specify a data verification mechanism, verifying data integrity through a verification code. Data that fails verification triggers re-acquisition. For example, the core task of robot A, "cargo transportation," is acquired at a 300-millisecond cycle, and status data is reported in batches of 5. Robotic arm fault information is reported immediately with fault codes and the location of occurrence.

[0060] Preferably, the specific implementation process of step 412 is as follows: The raw state data reported by each agent through the state acquisition interface is received, and format verification and data cleaning are performed according to the interface design specifications to generate preprocessed state data. Specifically, the format verification checks whether the data type, field length, and value range of the raw state data conform to the interface design specifications, and removes data with missing fields or incorrect formats. Data cleaning uses a designed noise filtering algorithm to remove random noise data caused by sensor fluctuations and transmission interference, and smooths data fluctuations based on the sliding window mean method. For example, values ​​in the computing power utilization rate data that exceed the normal fluctuation range by 20% are judged as noise and replaced with the window mean. The preprocessed state data retains the timestamp, agent identifier, task identifier, and data source identifier of the original data to ensure data traceability.

[0061] Preferably, in the specific technical implementation of step 412, the preprocessed state data is subjected to format normalization and anomaly filtering to generate standardized feedback data. Specifically, format normalization is based on the data dimension of the conflict-free pre-allocated task tensor, mapping the preprocessed state data of different agents and different collection items to a unified data format and value range (0-1). For example, the resource occupancy rate (percentage, absolute value) of different units is uniformly normalized, and the task progress is normalized according to the proportion of the total task volume. Anomaly filtering adopts the designed anomaly detection model, which includes a static threshold detection layer and a dynamic trend detection layer. The static threshold detection layer sets the normal value range of each collection item (such as computing power occupancy). The normal range for the rate is 10%-90%. Data outside this range is marked as abnormal. The dynamic trend detection layer analyzes the time-series change trend of the data. When the rate of change of the data exceeds the preset threshold (such as bandwidth usage increasing by 50% within 1 second), it is judged as abnormal. The marked abnormal data is isolated and stored and is not included in the standardized feedback data. The dimensions of the standardized feedback data are completely matched with the conflict-free pre-allocated task tensor. Each data includes a normalized value, data confidence, timestamp and associated task node identifier. For example, the standardized feedback data of robot A is [task progress 0.75, computing power utilization 0.68, bandwidth usage 0.52, interaction success rate 0.93, confidence 0.95].

[0062] Preferably, the specific implementation process of step 413 is as follows: Based on the node dimension of the intelligent agent's capability domain feature topology, a time-series storage structure and associated index rules for feedback data are designed to generate a data storage specification; specifically, the time-series storage structure adopts a three-level storage architecture of "intelligent agent identifier - time dimension - data type". The first level is partitioned and stored according to the unique identifier of the intelligent agent, the second level is divided into storage units according to time slices (e.g., 1 minute), and the third level is classified and stored according to data type (task progress, resource status, etc.), supporting fast query by time range; the associated index rules define the index dimensions, including intelligent agent identifier index, task identifier index, timestamp index, and feature topology node index. The feature topology node index associates the feedback data with the secondary nodes (e.g., computing power nodes, bandwidth nodes) of the intelligent agent's capability domain feature topology. For example, the computing power utilization rate data is associated with the "basic capability - computing power" node; the data storage specification specifies the storage medium (e.g., distributed database), data retention period (e.g., 3 months), and backup strategy (real-time incremental backup).

[0063] Preferably, in the specific technical implementation of step 413, a time-series storage and association index for feedback data is constructed according to the data storage specification, and standardized feedback data from all agents is integrated to form a real-time feedback data pool for the execution status of multiple agents. Specifically, in the time-series storage stage, standardized feedback data is written into a distributed database according to a three-level storage architecture, and the storage path and index identifier of the data are recorded synchronously. In the association index stage, a multi-dimensional index table is generated based on index rules. Through the index table, feedback data for a specific agent, a specific task, and a specific time range, as well as all data associated with a certain feature topology node, can be quickly located. The real-time feedback data pool for the execution status of multiple agents supports high-concurrency read and write, and the data query response time is controlled within a preset duration (e.g., 50 milliseconds). The index dimension is consistent with the node dimension of the agent's capability domain feature topology, ensuring that the agent's capability features and execution status can be quickly associated through the data pool, providing data support for the generation of collaborative scheduling instructions.

[0064] Optionally, step 42 includes: step 421, comparing the standardized feedback data with the execution baseline in the collaborative interaction rule protocol suite to identify three types of anomalies: task progress deviation, resource consumption exceeding the limit, and cross-agent linkage delay; step 422, matching the corresponding collaborative scheduling strategy according to the anomaly type, and calling the cross-agent capability redundancy complementarity resolution strategy in the multi-agent hierarchical conflict tracing-diversion resolution strategy library for resource consumption exceeding the limit to generate agent task adjustment or resource allocation instructions; step 423, pre-evaluating the execution effect of the scheduling instructions, the pre-evaluation basis being the capability redundancy of the agent capability domain feature topology, so that the instructions conform to the overall collaborative goal.

[0065] Preferably, the specific implementation process of step 421 is as follows: Based on the collaborative interaction rule protocol cluster and the conflict-free pre-allocated task tensor, a three-dimensional execution baseline system is constructed to generate a scenario-based execution baseline set; specifically, the three-dimensional execution baseline system includes a task progress baseline, a resource consumption baseline, and a cross-agent linkage baseline. The task progress baseline sets the expected completion ratio for each time node according to the time sequence of business process nodes and task weights. For example, after the "picking sub-task" in smart warehousing is started, it is expected to complete 30% in 10 minutes, 60% in 20 minutes, and 100% in 30 minutes; the resource consumption baseline is based on the capabilities of the agent. The basic capability parameters and task requirements of the domain feature topology are defined, and the normal occupancy range of resources such as computing power and bandwidth is set. For example, the baseline computing power occupancy of the handling robot is 20%-70% and the bandwidth is 10%-50%. The cross-agent linkage baseline is based on the cooperative communication protocol and cooperative timing requirements, and the thresholds for data interaction response time and state synchronization frequency are set. For example, the response time baseline of robots A and B is ≤50 milliseconds and the synchronization frequency is ≥10 times / second. The scenario-based execution baseline set also includes dynamic adjustment rules, which can be adaptively optimized according to the task stage and agent state. The resource baseline range can be tightened in the middle of the core task. Preferably, in the specific technical implementation of step 421, standardized feedback data is extracted from the real-time feedback data pool of multi-agent execution status, and compared with the scenario-based execution baseline set through a dimension-by-dimensional deviation quantification to generate an anomaly list. Specifically, the designed deviation quantification algorithm is used to calculate the deviation rate = (actual value - baseline standard value) / baseline standard value. An absolute value of the task progress deviation rate exceeding ±20% is considered an anomaly. For example, if the actual completion rate of the pickup sub-task is 20% (baseline 30%), a deviation rate of -33.3% is considered a progress lag. Resource usage continuously exceeding the baseline limit for 30 seconds or momentarily exceeding 50% is considered an anomaly. For example, if the robot's computing power continuously reaches 85% (baseline 70%) for 40 seconds, it is considered an over-standard. Cross-agent linkage response time exceeding the baseline by 50% or synchronization frequency being 30% lower is considered an anomaly. For example, an interaction response time of 80 milliseconds (baseline 50 milliseconds) is considered a delay. The anomaly list records the agent identifier, task identifier, deviation details, anomaly level, and scope of impact.

[0066] Preferably, the specific implementation process of step 422 is as follows: Based on the anomaly type and root cause of the anomaly item list, an anomaly-policy matching matrix is ​​constructed to generate a targeted scheduling policy set; specifically, the rows of the anomaly-policy matching matrix represent the combination of anomaly type and root cause (such as "excessive resource consumption - excessive task allocation"), the columns represent the scheduling policy type, and the matrix elements are the matching priorities; the scheduling policy set includes three categories: task adjustment, resource allocation, and collaborative optimization. For excessive resource consumption, priority is given to matching cross-agent capability redundancy complementarity mitigation strategies; for task progress deviation, matching task reassignment or timing adjustment strategies; for cross-agent linkage delay, matching communication protocol switching or node replacement strategies; the policy set specifies the execution conditions and constraints, such as requiring a backup agent collaborative compatibility score ≥ 0.8 for task reassignment. Preferably, in the specific technical implementation of step 422, for the abnormal item of excessive resource consumption, the multi-agent hierarchical conflict tracing-diversion and resolution strategy library is called to generate agent resource allocation instructions; specifically, firstly, the resource consumption structure of the target agent is analyzed to identify the core consumption tasks; then, the topology of the agent capability domain features is traversed to select backup agents with collaborative compatibility ≥ 0.8 and resource redundancy ≥ 1.2; finally, a resource sharing scheme is formulated to clarify the sharing ratio, transmission protocol and timing, such as robot A's computing power exceeds the standard by 20%, and robot C (redundancy 1.5) shares 30% of the idle computing power, using TCP / IP protocol to transmit within 5-15 seconds.

[0067] Preferably, the specific implementation process of step 423 is as follows: Based on the capability redundancy data of the agent's capability domain feature topology, a scheduling instruction pre-evaluation model is constructed to generate pre-evaluation results; specifically, the pre-evaluation model includes a capability redundancy verification layer, a collaborative impact analysis layer, and a target adaptation verification layer. The capability redundancy verification layer calculates the remaining capability redundancy of the agent after executing the scheduling instruction, which must satisfy ≥0.3 (to ensure the execution space of subsequent tasks); the collaborative impact analysis layer evaluates the impact of the instruction on the collaborative compatibility of associated agents to avoid generating new conflicts; the target adaptation verification layer verifies whether the instruction fits the overall collaborative goal (such as the total task duration and total resource utilization); the pre-evaluation results include a feasibility indicator, risk level, and optimization suggestions. The risk level is divided into three levels: low, medium, and high. High-risk instructions need to be returned to the policy set for re-matching. Preferably, in the specific technical implementation of step 423, the scheduling instructions are optimized based on the pre-evaluation results to ensure that the instructions conform to the overall collaborative goals. Specifically, low-risk instructions are directly output for execution, medium-risk instructions have their parameters adjusted according to optimization suggestions (such as adjusting the resource sharing ratio), and high-risk instructions are re-matched with the scheduling strategy. The optimized scheduling instructions clearly define the executing subject, operation content, execution sequence, and feedback requirements. For example, the optimized resource allocation instruction is "Robot C shares 25% of its idle computing power with Robot A, using the TCP / IP protocol, with an execution time window of 5-12 seconds. After execution, the computing power redundancy of Robot A and Robot C is not less than 0.35 and 0.4, respectively." The instructions embed the corresponding task nodes of the conflict-free pre-allocated task tensor for the intelligent agent to read and execute in real time.

[0068] The above are merely preferred embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-agent hybrid cooperative method, characterized in that, include: Step 1: Obtain the task objectives and agent collaboration requirements description in the target business scenario, and perform multi-dimensional semantic deconstruction and collaboration element association modeling to generate the business task collaboration element topology. Step 2: Based on the topology of business task collaboration elements, hierarchical task allocation is performed on the capability domains of each type of intelligent agent to obtain a hierarchical intelligent agent task allocation scheme. Conflict prediction is performed through the intelligent agent capability-task adaptation conflict prediction matrix to determine the conflict-free pre-allocated task tensor. Step 3: Based on the conflict-free pre-assigned task tensor, construct a cross-agent real-time collaboration-conflict resolution protocol cluster to generate a collaborative interaction rule protocol cluster; Step 4: Through the collaborative interaction rule protocol suite, perform dynamic collaborative scheduling and execution deviation correction on the real-time feedback data of the multi-agent execution status to generate a multi-agent collaborative execution dynamic tensor instruction graph, and manage collaborative tasks in the target business scenario based on the multi-agent collaborative execution dynamic tensor instruction graph.

2. The multi-agent hybrid cooperative method according to claim 1, characterized in that, Step 1 includes: Step 11: Decompose the task objectives in the target business scenario into business dimensions and extract core indicators to generate a task core indicator tensor. Step 12: Identify collaborative elements and label relationships in the description of agent collaborative needs to generate a configuration of collaborative needs elements; Step 13: Integrate the task core indicator tensor and the collaborative requirement element construct to perform topological modeling, so as to generate the business task collaborative element topology. The nodes of the business task collaborative element topology are jointly composed of the indicator dimension of the task core indicator tensor and the element dimension of the collaborative requirement element construct. The edge weight is determined by the correlation matching degree between the task core indicator tensor and the collaborative requirement element construct.

3. The multi-agent hybrid cooperative method according to claim 2, characterized in that, Step 11 includes: Step 111: Decompose the task objectives into business process nodes and define indicator dimensions to generate task process node characteristics; Step 112: Select core indicators and formulate quantification rules for the characteristics of task flow nodes to generate an initial indicator tensor; Step 113: Redundancy removal and logical association integration are performed on the initial indicator tensor to generate the task core indicator tensor. The task core indicator tensor maps each core indicator to the tensor dimension according to the business process node dimension.

4. The multi-agent hybrid cooperative method according to claim 2, characterized in that, Step 13 includes: Step 131: Construct task-collaboration element association mapping rules, perform feature matching between the task core indicator tensor and the collaboration requirement element construct to generate element association pairs. The matching basis for the element association pairs is the business relevance between the indicator dimension of the task core indicator tensor and the element dimension of the collaboration requirement element construct. Step 132: Define topological nodes and assign edge weights to the element association pairs to generate an initial collaborative topology; Step 133: Perform integrity verification and structural optimization on the initial collaborative topology to generate a business task collaborative element topology. The business task collaborative element topology links and binds the indicator nodes of the task core indicator tensor with the element nodes of the collaborative requirement element construct.

5. The multi-agent hybrid cooperative method according to claim 1, characterized in that, Step 2 includes: Step 21: Collect capability parameters and business adaptation records for each type of intelligent agent, and construct the capability domain feature topology of the intelligent agent; Step 22: Based on the business task collaboration element topology and the agent capability domain feature topology, the task is hierarchically decomposed and matched to the corresponding agent to generate a hierarchical agent task allocation scheme. Step 23: Construct an agent capability-task adaptation conflict prediction matrix, perform conflict detection and resolution on the hierarchical agent task allocation scheme to determine the conflict-free pre-allocated task tensor, and reconstruct the tasks in the hierarchical agent task allocation scheme according to the agent dimension.

6. The multi-agent hybrid cooperative method according to claim 5, characterized in that, Step 21 includes: Step 211: Collect and standardize the capability parameters of each agent in real time, such as computing power, communication bandwidth, and task processing expertise, to generate basic capability characteristics of the agent. Step 212: Retrieve historical business collaboration completion rates and adaptation type records for each intelligent agent to generate intelligent agent business adaptation features; Step 213: Integrate the basic capability features and business adaptation features of the intelligent agent to construct the capability domain feature topology of the intelligent agent. The capability domain feature topology of the intelligent agent constructs a hierarchical topology structure of the capability features of each intelligent agent according to the business adaptation type.

7. The multi-agent hybrid cooperative method according to claim 1, characterized in that, Step 3 includes: Step 31: Based on the task association relationship of the conflict-free pre-allocated task tensor, define the communication protocol and data interaction format for cross-agent collaborative interaction; Step 32: Construct a multi-agent hierarchical conflict tracing and diversion resolution strategy library, and formulate response mechanisms for different types of conflicts; Step 33: Integrate the collaborative interaction communication protocol with the multi-agent hierarchical conflict tracing-diversion and resolution strategy library to construct a cross-agent real-time collaborative-conflict resolution protocol cluster, so as to generate a collaborative interaction rule protocol cluster. The collaborative interaction rule protocol cluster binds the communication protocol and resolution strategy according to the conflict type.

8. The multi-agent hybrid cooperative method according to claim 7, characterized in that, Step 31 includes: Step 311: Determine the real-time communication link and data transmission interface for cross-agent collaboration, and define the field specifications and verification rules for the interface data; Step 312: For the collaborative needs of different types of tasks, formulate synchronous / asynchronous interaction communication protocols. The synchronous interaction protocol is adapted to the high-time-efficiency collaborative needs of the core task layer. Step 313: Integrate communication link configuration and interaction protocol to generate a collaborative interaction communication protocol module. The collaborative interaction communication protocol module embeds communication link parameters and interaction protocol rules into the task nodes of the conflict-free pre-allocated task tensor.

9. The multi-agent hybrid cooperative method according to claim 1, characterized in that, Step 4 includes: Step 41: Deploy multi-agent execution status acquisition nodes to obtain real-time feedback data such as task progress, resource consumption, and abnormal alarms of each agent; Step 42: Input the feedback data into the collaborative interaction rule protocol suite to identify execution deviations and generate collaborative scheduling instructions. The generation of collaborative scheduling instructions requires calling the resolution strategy module in the collaborative interaction rule protocol suite. Step 43: Integrate scheduling instructions with task execution baseline to generate a multi-agent collaborative execution dynamic tensor instruction graph. Based on the multi-agent collaborative execution dynamic tensor instruction graph, perform full-process closed-loop management of collaborative tasks. The multi-agent collaborative execution dynamic tensor instruction graph dynamically updates scheduling instructions according to agent and task dimensions.

10. The multi-agent hybrid cooperative method according to claim 9, characterized in that, Step 41 includes: Step 411: Embed the status acquisition interface in the task execution module of each intelligent agent, and set the data acquisition cycle and reporting rules; Step 412: Normalize the format and filter anomalies of the collected raw state data to generate standardized feedback data. The standardized feedback data must match the data dimensions of the conflict-free pre-allocated task tensor. Step 413: Construct a time-series storage and association index for feedback data to form a real-time feedback data pool for the execution status of multiple agents. The index dimension of this data pool is consistent with the node dimension of the agent's capability domain feature topology.

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