Dynamic collaborative arrangement system and method based on intelligent agent

Abstract

The invention discloses a dynamic collaborative arrangement system and method based on an intelligent agent, and relates to the technical field of artificial intelligence. By integrating core modules of an agent communication protocol, knowledge base management, agent arrangement, security authentication, resource scheduling and the like, not only is a standardized agent cooperation framework and a distributed knowledge sharing mechanism provided, but also dynamic task arrangement and secure and controllable resource scheduling capability among agents are realized. The problem of how to effectively realize standardized communication and dynamic cooperation of agents among enterprises and unified management and shared utilization of knowledge resources in the prior art is solved, and particularly, the problem of how to construct an efficient, safe and extensible agent cooperation network in a multi-enterprise cooperation environment is solved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and specifically to an agent-based dynamic collaboration orchestration system and method, and more particularly to technologies such as inter-enterprise agent collaboration, distributed knowledge base calling, and agent resource scheduling. Background Art

[0002] With the rapid development of artificial intelligence technology, various types of agents are playing an increasingly important role in the digital transformation of enterprises. Currently, many companies have emerged in the market to provide agent solutions, trying to improve the operational efficiency of enterprises through agent technology. For example, a company has launched agent application solutions for multiple fields such as automobiles, mobile terminals, and new retail, and has applied agent technology to specific business scenarios through cooperation with related companies.

[0003] However, the existing technical solutions have the following problems: First, the existing intelligent agent systems are often developed in a customized manner, and the intelligent agent systems in different scenarios are separated from each other. There is a lack of unified collaboration standards and mechanisms, and it is difficult to form a large-scale network effect. Secondly, in terms of knowledge base applications, the existing systems mostly adopt a closed architecture, which cannot effectively support the flexible access of multiple knowledge base centers and cross-enterprise knowledge sharing, limiting the scalability and applicability of the system. Third, the current intelligent agent capabilities often rely on specific models, and it is difficult for enterprises to select or combine different model capabilities according to their own needs. At the same time, there is a lack of effective autonomous collaboration mechanisms between intelligent agents. Finally, the deployment process of existing solutions is generally more complicated, requiring deep customization and development, and it is difficult to integrate with the company's existing systems, resulting in high deployment costs.

[0004] These problems have seriously restricted the widespread application of intelligent agent technology among enterprises. Especially today when cross-enterprise collaboration is becoming more and more frequent, how to build an open, flexible and efficient intelligent agent collaboration platform to achieve efficient collaboration of intelligent agents among enterprises has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] 1. Technical issues

[0006] The present invention aims to solve the following technical problems existing in the existing intelligent agent system in the process of inter-enterprise collaboration:

[0007] First, the existing intelligent agent system lacks a unified communication standard and collaboration mechanism, making it difficult for agents from different companies to achieve effective communication and collaboration. Each company uses independently developed communication protocols, resulting in poor interoperability between systems, high integration costs, and the inability to form a large-scale collaborative network. At the same time, the lack of standardized capability declaration and discovery mechanisms makes it difficult for agents to accurately identify and call the required collaborative capabilities.

[0008] Second, there are problems of decentralization and fragmentation in knowledge base management. The existing systems lack unified management and access support for multiple knowledge base centers, making it difficult for enterprises to conveniently call and integrate information from different knowledge bases. At the same time, the mechanisms for dynamic update and version management of knowledge are imperfect, resulting in poor timeliness of knowledge and affecting the decision-making quality of agents.

[0009] Third, the agent orchestration lacks the ability of dynamic optimization. The existing systems often adopt static task allocation methods and cannot adaptively adjust the collaboration strategies according to changes in the execution environment. At the same time, they lack the ability of semantic understanding and intelligent decomposition of tasks, making it difficult to efficiently handle complex collaboration tasks.

[0010] Fourth, in the process of inter-enterprise agent collaboration, there is a lack of a perfect security control mechanism. The existing systems are difficult to effectively manage access rights, data security, and behavior tracking among agents, and cannot achieve dynamic permission adjustment based on scenarios, increasing the security risks of inter-enterprise collaboration.

[0011] Fifth, the allocation of computing resources required for agent operation lacks a unified scheduling mechanism, resulting in low resource utilization efficiency. Especially in the scenario of multi-enterprise collaboration, it is impossible to achieve optimal configuration and dynamic adjustment of computing resources, affecting the overall performance of the system.

[0012] II. Technical Solutions

[0013] The embodiment of the present invention provides a dynamic collaboration orchestration system and method based on agents. The system includes an agent communication protocol module, a knowledge base management module, an agent orchestration module, a security authentication module, and a resource scheduling module.

[0014] The agent communication protocol module adopts a hierarchical architecture design and includes four core functional components: an intelligent collaboration engine, a capability registration and discovery mechanism, a status management mechanism, and a decision tracking mechanism. Among them, the intelligent collaboration engine is located in the core control layer and overall coordinates the operation of the entire system; the capability registration and discovery mechanism and the status management mechanism are located in the basic service layer and provide basic capability support; the decision tracking mechanism is located in the monitoring layer and is responsible for tracking the collaboration process throughout. The intelligent collaboration engine realizes the autonomous collaboration and task allocation of multiple agents through a collaboration mode recognition unit and an adaptive task allocation unit. The engine analyzes task characteristics and the execution environment, identifies and selects the optimal collaboration mode from three basic modes: parallel collaboration, serial collaboration, and hybrid collaboration, and at the same time automatically decomposes complex tasks into executable subtask units and performs dynamic allocation based on capability matching and load conditions.

[0015] Preferably, the capability registration and discovery mechanism adopts a semantic capability description framework to construct a standardized capability description system from the functional dimension, performance dimension, and constraint dimension. Among them, the functional dimension describes the basic capability types and business capabilities of the intelligent agent; the performance dimension includes quantifiable indicators such as response time, accuracy rate, and resource consumption; the constraint dimension defines the usage conditions, dependencies, and limiting factors of the capabilities. This framework supports the dynamic expansion of the capability model and ensures the standardization of capability invocation through a unified interface specification. Preferably, based on this framework, the capability registration and discovery mechanism implements an intelligent capability discovery algorithm. This algorithm uses a semantic similarity calculation method to achieve precise capability matching by analyzing the semantic association between task requirements and capability descriptions. At the same time, the algorithm supports the combined reasoning of capabilities and can intelligently combine multiple basic capabilities to meet complex task requirements. The system supports the real-time update and maintenance of capability information through the dynamic update mechanism of the capability catalog to ensure the timeliness of the capability library.

[0016] The state management mechanism adopts distributed state synchronization and predictive management methods. The distributed state synchronization adopts an innovative consistency protocol to ensure the consistency of states among multiple intelligent agents through an incremental synchronization strategy, while significantly improving the synchronization efficiency. The system also includes a conflict resolution mechanism that can effectively handle state conflicts caused by concurrent operations. Predictive state management analyzes historical execution data to predict the change trend of states and makes advance resource reservation and state preparation according to the prediction results.

[0017] The decision tracking mechanism uses a directed graph structure to record the decision chain of the intelligent agent, realizing the full-process tracking of the decision-making process and knowledge transfer. The decision chain record includes decision steps, dependencies, and knowledge reference information, and maintains the historical version of the decision-making process through a version control mechanism. The system implements a decision optimization mechanism based on the recorded decision data, evaluates the decision-making effect through multi-dimensional quality evaluation indicators, and mines the best practice patterns from historical decisions.

[0018] The knowledge base management module adopts a hierarchical architecture design, including an access layer, a processing layer, and a service layer, and implements a two-layer knowledge graph structure, a dynamic permission control mechanism, an adaptive multi-modal indexing mechanism, and an intelligent knowledge combiner.

[0019] The access layer implements a dynamic permission control mechanism based on knowledge characteristics, dynamically generates and adjusts access policies by real-time analyzing the sensitivity of knowledge, usage scenarios, and the credit rating of the access subject. The system automatically adjusts access permissions and control policies according to the comprehensive evaluation results in three dimensions: knowledge feature extraction, scenario recognition, and credit assessment.

[0020] The core innovation of the processing layer lies in the dual-layer knowledge graph structure. This structure includes a static ontology layer and a dynamic instance layer. By separating the knowledge model and specific instances, it solves the model conflict problem during the integration of multiple knowledge bases. The static ontology layer is responsible for maintaining a standardized knowledge model and automatically integrating concept definitions from different knowledge bases through semantic mapping. The dynamic instance layer is responsible for storing specific knowledge instances and supports real-time updates and dynamic expansion. The two layers are associated through a semantic linker to ensure the consistency and scalability of the knowledge system.

[0021] Preferably, the processing layer also implements an adaptive multi-modal indexing mechanism that integrates three indexing methods: full-text indexing, graph structure indexing, and semantic vector indexing, and can automatically select the optimal indexing strategy according to the type and access pattern of knowledge. The system dynamically adjusts the indexing weights and optimizes the indexing structure by analyzing the query pattern and access frequency, significantly improving the retrieval efficiency.

[0022] Preferably, the intelligent knowledge combiner in the service layer realizes the intelligent integration of multi-source knowledge through semantic similarity calculation and context correlation analysis. The system first calculates the semantic correlation between knowledge fragments to identify potential combination opportunities, then verifies the rationality of the combination through context analysis, and finally adopts a conflict detection and resolution mechanism to handle inconsistencies during the knowledge integration process. The combiner also includes a feedback learning module that continuously optimizes the combination strategy by analyzing the usage effect of the combination results.

[0023] The intelligent agent orchestration module adopts an autonomous learning task parsing mechanism, a dynamic orchestration decision-making mechanism, and a predictive execution control mechanism to achieve efficient collaborative organization among intelligent agents. The task parsing mechanism realizes continuous learning and optimization of task patterns by establishing a dynamically evolving task knowledge graph. The task semantic analyzer extracts typical patterns and execution paradigms of tasks based on incremental learning methods to form a reusable task template library. The requirement extractor uses knowledge distillation technology to transform complex task requirements into standardized ability requirement descriptions.

[0024] Preferably, the task parsing mechanism also includes a constraint analyzer that innovatively introduces a causal reasoning method. By analyzing the execution context and resource dependencies of tasks, it identifies implicit constraints and execution limitations between tasks to ensure the rationality of task decomposition. The dependency relationship reasoner uses a causal graph model to identify and maintain explicit and implicit dependency relationships between tasks through in-depth analysis of the task execution process, ensuring the sequentiality and parallel efficiency of task execution.

[0025] The dynamic orchestration decision-making mechanism generates the most suitable collaboration mode dynamically through the adaptive collaboration mode generator according to the current task characteristics and historical execution data. The system adopts the reinforcement learning method to continuously optimize the mode selection strategy and realize the dynamic evolution of the collaboration mode. The intelligent task decomposition engine adopts the hierarchical task planning method. By analyzing the complexity and dependency relationship of tasks, it automatically determines the optimal decomposition granularity to achieve accurate task allocation and dynamic adjustment.

[0026] Preferably, the predictive execution control mechanism realizes the early prediction of execution anomalies by establishing a timing feature model of task execution. The system constructs a health assessment model based on historical execution data, which can timely detect and warn potential execution risks. The proactive anomaly processor continuously enriches the anomaly handling strategy through case learning to realize the rapid identification and automated processing of anomalies. The adaptive performance optimizer establishes a multi-dimensional performance evaluation system and dynamically adjusts the resource allocation strategy by real-time analyzing the task execution status.

[0027] The security authentication module implements three core mechanisms: dynamic trust assessment, scenario-adaptive permission control, and chained audit storage. The dynamic trust assessment engine adopts a multi-dimensional trust calculation model. Through the behavior model builder, it real-time analyzes the characteristics such as the collaboration behavior, resource call mode, and task completion quality of the intelligent agent to construct the behavior feature vector of the intelligent agent. The trust degree calculator dynamically calculates the trust score based on these feature vectors and combines the historical credit record by using the deep learning algorithm.

[0028] The scenario-adaptive permission controller combines the business scenario semantic understanding with the permission control to realize the dynamic adjustment of the permission policy. The scenario semantic analyzer extracts the key features and security requirements of business operations through the deep semantic understanding technology. The dynamic policy generator automatically constructs and optimizes the access control policy based on these features and combines the preset security rules, and adjusts the permission granularity and control intensity in real time according to the scenario changes.

[0029] Preferably, the chained audit system adopts a chained storage structure to record the behavior traces of the intelligent agent to ensure the integrity and immutability of the audit data. The chained memory adopts cryptographic technology to concatenate each operation record of the intelligent agent into an immutable evidence chain. The evidence processor extracts valuable security insights from the massive audit data through the intelligent analysis engine to support the ex post facto traceability and liability determination.

[0030] The resource scheduling module implements three core mechanisms: capability-based resource mapping, cross-enterprise resource collaboration, and intelligent scheduling of knowledge resources. The capability model manager analyzes the resource consumption patterns of different types of agents when executing various capabilities through deep learning methods, and constructs a dynamic resource profile. The resource consumption predictor establishes an association model between capabilities and resources based on historical execution data to achieve accurate prediction of resource requirements. The resource allocation matrix adopts a tensor calculation model to achieve dynamic allocation optimization of multi-dimensional resources.

[0031] Cross-enterprise resource collaboration adopts a resource negotiation strategy based on game theory to achieve dynamic coordination of resources between enterprises. The resource quota manager dynamically adjusts the resource quotas of each enterprise through credit assessment and utilization efficiency analysis. The resource access protocol introduces a distributed consistency algorithm to ensure the fairness and efficiency of cross-enterprise resource scheduling. The intelligent scheduling mechanism for knowledge resources constructs an optimal knowledge access path through a graph optimization algorithm, adopts a multi-level cache structure, and performs dynamic caching according to the usage frequency and importance of knowledge to achieve traffic control and priority management of knowledge access.

[0032] The collaborative work of the above modules forms a complete intelligent agent collaborative dynamic orchestration system. Standardized collaboration is achieved through the intelligent agent communication protocol, knowledge sharing is supported by relying on the knowledge base management, task scheduling is completed based on intelligent agent orchestration, collaboration security is ensured through security authentication, and system efficiency is improved by using resource scheduling, thus providing comprehensive technical support for intelligent agent collaboration between enterprises.

[0033] III. Beneficial Effects

[0034] By adopting the above technical solutions, the present invention has the following beneficial effects:

[0035] First, by providing a standardized intelligent agent communication protocol and a capability description framework, the present invention significantly reduces the technical threshold for enterprises to deploy and use intelligent agent systems. Enterprises do not need to conduct customized development for each collaboration scenario, and can achieve interoperability between intelligent agents by simply accessing the system according to the unified standard, greatly reducing the development and maintenance costs.

[0036] Second, the distributed knowledge base access framework of the present invention enables enterprises to flexibly call and integrate information resources from different knowledge base centers. Through a unified interface specification and permission management mechanism, enterprises can select and combine appropriate knowledge bases according to actual needs, significantly improving the knowledge acquisition ability and decision-making level of intelligent agents.

[0037] Third, the present invention innovatively realizes an intelligent agent dynamic orchestration mechanism, supporting efficient collaboration between intelligent agents of enterprises. The system can automatically decompose tasks, match capabilities, and execute scheduling according to task requirements, significantly improving the processing efficiency and accuracy of cross-enterprise business processes.

[0038] Fourth, the present invention has established a perfect security authentication system. Through dynamic trust assessment and multi-level security guarantee mechanisms, the security and controllability of agent collaboration among enterprises are ensured. The system supports fine-grained permission management and behavior auditing, effectively reducing the security risks of collaboration among enterprises.

[0039] Fifth, the resource scheduling mechanism provided by the present invention realizes the optimal allocation and dynamic adjustment of computing resources. Through unified resource management and intelligent scheduling strategies, the utilization efficiency of system resources is significantly improved, and the operation cost is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0041] Figure 1 is a schematic diagram of the hierarchical structure of the agent dynamic orchestration system proposed in the embodiment of the present invention;

[0042] Figure 2 is a flowchart of the interaction of core capability layer components proposed in the embodiment of the present invention;

[0043] Figure 3 is a diagram of the security authentication processing mechanism of the business application layer proposed in the embodiment of the present invention;

[0044] Figure 4 is a diagram of the hierarchical functional structure of the agent communication protocol module proposed in the embodiment of the present invention;

[0045] Figure 5 is a diagram of the multi-layer processing mechanism of the knowledge base management module proposed in the embodiment of the present invention;

[0046] Figure 6 is a flowchart of the task processing of the agent orchestration module proposed in the embodiment of the present invention;

[0047] Figure 7 is a diagram of the relationship between trust assessment and permission control of the security authentication module proposed in the embodiment of the present invention;

[0048] Figure 8 is a schematic diagram of multi-dimensional resource mapping of the resource scheduling module proposed in the embodiment of the present invention;

[0049] Figure 9 is a flowchart of the deployment and interaction of the cross-border e-commerce intelligent interaction system proposed in the embodiment of the present invention;

[0050] Figure 10This is a multi-level structure and access relationship diagram of the cross-institutional medical intelligent collaboration system proposed in the embodiments of the present invention. Detailed implementation manners

[0051] To more clearly illustrate the technical solutions of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners. It should be noted that for those of ordinary skill in the art, other implementation manners can also be obtained based on these drawings without creative efforts.

[0052] I. Overall system implementation solution

[0053] As Figure 1 shown, the present invention provides an intelligent agent dynamic orchestration system based on business scenarios. The system adopts a layered architecture design, including a basic service layer, a core capability layer, and a business application layer. Among them, the basic service layer provides the basic support capabilities of communication protocols and resource scheduling; the core capability layer realizes the core functions of intelligent agent orchestration and knowledge base management; the business application layer is responsible for security authentication and external service interfaces. Each layer is connected through standardized interfaces to form a complete intelligent agent collaboration link. The basic service layer solves the interoperability problem of enterprise intelligent agent systems through standardized interfaces and modular design. The communication protocol module provides a unified message format definition and routing mechanism to ensure that intelligent agents of different enterprises can communicate in a standardized manner. The resource scheduling module realizes the optimized allocation and efficient utilization of computing resources through dynamic load balancing and elastic scaling technologies. The system adopts a distributed architecture design and manages the service discovery and load balancing of each module through a unified service registry to ensure the high availability and scalability of the system.

[0054] As Figure 2 shown, the core capability layer realizes the core business functions of the system. The intelligent agent orchestration module realizes the automatic collaboration between intelligent agents through the coordinated work of three key components: a task parser, a capability matching engine, and an execution scheduler. Among them, the task parser is responsible for semantic understanding and decomposition of complex tasks, the capability matching engine matches intelligent agents based on semantic similarity, and the execution scheduler ensures the efficient execution of tasks. The knowledge base management module adopts an innovative double-layer knowledge graph structure and realizes the unified management and intelligent services of multi-source knowledge through the cooperation of three components: a knowledge base center, a knowledge graph, and a knowledge service. Data exchange is carried out between the two modules through standardized interfaces to form a knowledge-driven intelligent collaboration mechanism.

[0055] As Figure 3As shown in the figure, the business application layer ensures the secure and controllable operation of the system. The security authentication module first receives access requests through a unified interface and conducts permission verification through the access control component. After successful verification, the trust evaluation component and the permission management component work together to dynamically adjust access permissions, while the audit and trace component records all operation behaviors. The standardized interface provides services externally in the form of a development toolkit and interface documentation, supporting enterprises for rapid integration and secondary development. The entire security authentication process forms a closed loop to ensure the security and controllability of system operation.

[0056] During the system operation, when an enterprise needs to access the platform, it first completes identity authentication and permission configuration through the business application layer. The agent needs to complete identity authentication and declare its capability characteristics through semantic description. The system assigns a globally unique identifier to it, and the security authentication module establishes access permissions based on dynamic trust evaluation. When the agent initiates a task request, it submits a standardized task description to the platform through the communication protocol module. The task description includes information such as target requirements, capability requirements, and execution constraints. The task parser of the agent orchestration module conducts semantic understanding and decomposition of the task, identifying key elements and dependencies.

[0057] During the task execution stage, the capability matching engine conducts agent matching based on multi-dimensional evaluation. The matching process comprehensively considers factors such as capability similarity, historical performance, credit score, and current load to ensure the optimal collaboration combination. The execution scheduler formulates a task execution plan based on the matching result and manages task execution through a distributed coordination mechanism. During the task execution process, the agent can access the knowledge base management module based on permissions to obtain the required knowledge. The knowledge base management module provides intelligent knowledge combination services through a two-layer knowledge graph structure. At the same time, the resource scheduling module adjusts the resource allocation strategy in real time based on task priorities and resource status to ensure the efficient execution of tasks.

[0058] This hierarchical and collaborative system architecture design effectively solves problems existing in the agent collaboration system in the prior art, such as poor interoperability, limited knowledge sharing, and low task orchestration efficiency. Through unified interface specifications and modular design, the system supports enterprises for rapid deployment and flexible expansion, providing a reliable technical foundation for building an open and efficient agent collaboration platform.

[0059] II. Specific implementation of the agent communication protocol module

[0060] As Figure 4As shown in the figure, the intelligent agent communication protocol module provided by the present invention adopts a hierarchical architecture design, including four core functional components: an intelligent collaboration engine, a capability registration and discovery mechanism, a state management mechanism, and a decision tracking mechanism. Among them, the intelligent collaboration engine is located in the core control layer, overall coordinating the operation of the entire system; the capability registration and discovery mechanism and the state management mechanism are located in the basic service layer, providing basic capability support; the decision tracking mechanism is located in the monitoring layer, responsible for tracking the collaboration process throughout. This hierarchical architecture innovatively solves the problems of decoupling and coordination in the intelligent agent collaboration process.

[0061] The intelligent collaboration engine is used to achieve the autonomous collaboration and task allocation of multiple intelligent agents. This engine includes a collaboration mode recognition unit and an adaptive task allocation unit. The collaboration mode recognition unit identifies and selects the optimal collaboration mode from three basic modes: parallel collaboration, serial collaboration, and hybrid collaboration by analyzing task characteristics and execution environment. Among them, the parallel collaboration mode supports multiple intelligent agents to execute different subtasks simultaneously, the serial collaboration mode supports intelligent agents to execute tasks sequentially according to the dependency relationship, and the hybrid collaboration mode dynamically adjusts the collaboration method according to the task complexity. This unit innovatively realizes the automatic recognition of task characteristics and the dynamic selection of collaboration modes, significantly improving the adaptability and execution efficiency of the system.

[0062] The adaptive task allocation unit adopts a dynamic task allocation strategy, automatically decomposes complex tasks into executable subtask units, and performs dynamic allocation based on capability matching and load status. This unit constructs a task dependency graph through semantic understanding of tasks to achieve refined decomposition of tasks. In the task allocation process, the system comprehensively considers multiple factors such as the capability characteristics, historical performance, and current load of intelligent agents, and adopts an adaptive allocation strategy to ensure the balance and execution efficiency of task allocation. Through real-time monitoring of execution feedback, the system can dynamically adjust the allocation strategy to achieve continuous optimization of task execution.

[0063] The capability registration and discovery mechanism innovatively realizes the standardized description and intelligent discovery of intelligent agent capabilities. This mechanism first proposes a three-dimensional capability description framework, comprehensively depicting the intelligent agent capability characteristics from the functional dimension, performance dimension, and constraint dimension. Among them, the functional dimension describes the basic capability types and business capabilities of intelligent agents, using standardized semantic expressions to ensure the accuracy and comprehensibility of capability descriptions; the performance dimension defines a quantitative index system including response time, accuracy rate, resource consumption, etc., supporting the objective evaluation of capabilities; the constraint dimension clarifies the usage conditions, dependency relationships, and limiting factors of capabilities, ensuring the reliability of capability calls. The innovation of this framework lies in realizing the structuring and standardization of capability descriptions, laying a foundation for the precise matching of capabilities.

[0064] The core innovation of the present invention is to propose a dynamic semantic matching algorithm, which combines traditional semantic similarity calculation with context-aware factors to form a context-enhanced dynamic matching model:

[0065] S(R,C,E) = α·Sim(R,C) + β·Context(R,C,E) + γ·History(R,C)

[0066] Where:

[0067] ·S(R,C,E) is the comprehensive matching score of the agent's ability and task requirements

[0068] ·R represents the task requirement description vector

[0069] ·C represents the agent's ability description vector

[0070] ·E represents the current execution environment state

[0071] ·Sim(R,C) is the basic semantic similarity

[0072] ·Context(R,C,E) is the context relevance, which measures the applicability of the ability in the current execution environment

[0073] ·History(R,C) is the historical execution success rate

[0074] ·α, β, γ are dynamic weight coefficients, which are adaptively adjusted according to the task priority and execution environment. Based on the standardized ability description framework, the present invention realizes an intelligent ability discovery mechanism. This mechanism maps the ability requirements and ability descriptions to the same semantic space by establishing an ability feature space, realizing semantic-based ability matching. The system first performs feature analysis on the input task requirements, extracts key ability requirement elements; then retrieves the candidate ability set with the highest similarity in the ability feature space; finally, through multi-dimensional evaluation, considering the function matching degree, performance satisfaction degree and constraint conditions, selects the optimal ability combination. In particular, when a single ability cannot fully meet the requirements, the system can infer through ability combination to automatically construct an ability combination scheme that meets the requirements. This semantic-based ability discovery mechanism breaks through the limitations of traditional keyword-based matching and realizes more intelligent and accurate ability matching.

[0075] The state management mechanism innovatively adopts distributed state synchronization and predictive management methods. In terms of state synchronization, the system designs an incremental state synchronization protocol. Through semantic analysis of state changes, it accurately identifies the state information that needs to be synchronized. This protocol is based on the causal consistency model, which minimizes the synchronization overhead while ensuring state consistency. Specifically, when a state change is detected, the system first analyzes the semantic importance and impact scope of the change to determine the set of agents that need to be synchronized; then generates optimized synchronization instructions and uses incremental transmission to update the state; finally, ensures the correctness of the synchronization operation through consistency verification. This semantic-based incremental synchronization mechanism significantly improves the efficiency of state synchronization.

[0076] In terms of predictive management, the system implements a state prediction mechanism based on historical data analysis. By establishing a state change model, the system can predict the evolution trend of the state and make resource reservation and state preparation in advance. When an abnormal state is predicted, the system will initiate preventive adjustment measures to reduce the occurrence of abnormal situations. At the same time, the system also implements a state version management and rollback mechanism, which can quickly restore to the correct state in case of anomalies to ensure the stable operation of the system.

[0077] The decision tracking mechanism uses a directed graph structure to record the decision chain of agents, and innovatively realizes the full-process tracking of the decision-making process and knowledge flow. The decision chain record includes decision steps, dependency relationships, and knowledge reference information, and intuitively shows the logical relationship between decisions through the directed graph structure. The system records each key node in the decision-making process, including decision inputs, decision rules, decision results, etc., to construct a complete decision evidence chain. In particular, the system implements a knowledge traceability mechanism, which can trace the source of knowledge used in the decision-making process and maintain the historical version of the decision-making process through a version control mechanism.

[0078] Based on the complete decision tracking data, the system implements an innovative decision optimization mechanism. By establishing a multi-dimensional decision quality evaluation system, the system can objectively evaluate the effectiveness of decisions. The evaluation dimensions include decision accuracy, execution efficiency, resource utilization, etc. Through comprehensive analysis of these indicators, the system can identify the best decision-making mode. On this basis, the system continuously accumulates and optimizes decision-making experience, continuously improves decision-making strategies, and realizes the spiral improvement of decision quality.

[0079] In specific applications, when an agent initiates a collaboration request, the system first matches the collaboration object through the capability registration discovery mechanism, and then the intelligent collaboration engine selects the collaboration mode and assigns tasks. During the task execution process, the state management mechanism ensures state consistency, and the decision tracking mechanism records the decision-making process throughout. This multi-level collaborative working mechanism not only ensures the reliability and efficiency of the system operation but also provides data support for continuous optimization. Through the above innovative technical solutions, the present invention realizes efficient collaboration and reliable communication between agents, providing complete technical support for building an open and efficient agent collaboration platform.

[0080] III. Specific Implementation of the Knowledge Base Management Module

[0081] As Figure 5 shown, the present invention provides a knowledge base management module, which innovatively adopts a hierarchical architecture design, including an access layer, a processing layer, and a service layer. The module effectively solves technical problems such as decentralized knowledge management and limited knowledge sharing in the prior art by implementing a two-layer knowledge graph structure, a dynamic permission control mechanism, an adaptive multi-modal indexing mechanism, and an intelligent knowledge combiner. The access layer innovatively implements a dynamic permission control mechanism based on knowledge characteristics. This mechanism dynamically generates and adjusts access policies by real-time analyzing the sensitivity level of knowledge, the usage scenario, and the credit rating of the access subject. The system first uses a knowledge feature extractor to analyze the sensitivity level and usage restrictions of knowledge, and conducts a multi-dimensional security level assessment of the knowledge content; then it identifies the usage scenario and application environment of the knowledge through a scenario recognizer to ensure that the knowledge is reasonably used in an appropriate scenario; finally, based on a credit evaluator, it evaluates the historical behavior and credit level of the access subject to establish a dynamic trust rating mechanism. This multi-dimensional access control strategy innovatively realizes the dynamic balance between knowledge sharing and security.

[0082] The core innovation of the processing layer lies in the implementation of a two-layer knowledge graph structure. This structure creatively solves the model conflict problem during the integration of multiple knowledge bases by separating the knowledge model and specific instances. Among them, the static ontology layer is responsible for maintaining a standardized knowledge model, automatically integrating concept definitions from different knowledge bases through semantic mapping, and constructing a unified knowledge representation framework; the dynamic instance layer is responsible for storing specific knowledge instances, supporting real-time updates and dynamic expansion of knowledge. The two layers are associated through a semantic linker, which not only ensures the consistency of the knowledge system but also realizes the scalability of the knowledge model. This innovative two-layer structure design breaks through the limitations of traditional single-layer knowledge bases and provides an effective solution for large-scale knowledge integration.

[0083] The core innovation of the present invention is to propose a modeling method for a two-layer knowledge graph structure, and its formal representation is:

[0084] KG = (O, I, L)

[0085] Among them:

[0086] · O represents the static ontology layer, which is composed of a concept set C, a relationship set R, and an attribute set A, and O = (C, R, A)

[0087] · I represents the dynamic instance layer, which is composed of an instance set E, an instance relationship set RE, and an instance attribute value set V, and I = (E, RE, V)

[0088] · L represents the semantic link set, which defines the mapping relationship between the ontology layer and the instance layer, and L = {(c, e)|c ∈ C, e ∈ E}

[0089] The ontology mapping mechanism for the system to handle conflicts can be expressed as: Map(O1, O2) = {(c1, c2, s)|c1 ∈ O1.C, c2 ∈ O2.C, s = Sim(c1, c2)>θ}

[0090] Among them, Sim is the semantic similarity function, and θ is the mapping threshold

[0091] In terms of knowledge organization, the present invention designs an innovative knowledge structure: in the static ontology layer, a hierarchical concept model is adopted, and the internal relationships of domain knowledge are characterized through a concept relationship graph; in the dynamic instance layer, a graph structure is used to store specific knowledge instances, supporting the expression of complex association relationships between instances. The system maintains the mapping relationship between ontology concepts and knowledge instances through a semantic connection mechanism, realizing the unified management and flexible expansion of knowledge. This structured knowledge organization method lays a foundation for the efficient retrieval and intelligent application of knowledge.

[0092] The processing layer also innovatively implements an adaptive multi-modal indexing mechanism. This mechanism breakthroughly integrates three indexing methods: full-text indexing, graph structure indexing, and semantic vector indexing, and can automatically select the optimal indexing strategy according to the type and access mode of knowledge. The system establishes a query feature model through real-time analysis of the query pattern to predict the optimal indexing scheme. At the same time, the system realizes the adaptive optimization of indexing. By monitoring the indexing usage efficiency, the indexing structure and strategy are dynamically adjusted to ensure the continuous optimization of retrieval performance. This intelligent indexing mechanism significantly improves the efficiency and accuracy of knowledge retrieval.

[0093] The core innovation of the service layer is the intelligent knowledge combiner. Through semantic similarity calculation and context - related analysis, this combiner realizes the intelligent fusion of multi - source knowledge. First, the system analyzes the content features of knowledge fragments through semantic understanding technology and calculates the semantic association degree between fragments; then, based on context analysis, it verifies the rationality of knowledge combination to ensure the accuracy and consistency of the combination results; finally, through the conflict detection and resolution mechanism, it handles the inconsistent problems in the knowledge integration process. In particular, the system has implemented a quality evaluation mechanism for knowledge combination, ensuring the usability of the combined knowledge through multi - dimensional evaluation indicators.

[0094] Another innovation point of the intelligent knowledge combiner lies in its feedback learning mechanism. The system collects the actual application effects of knowledge combination results, establishes an effect evaluation model, and continuously optimizes the combination strategy. This mechanism includes three key links: usage effect analysis, user feedback processing, and strategy optimization, forming a complete optimization closed - loop. Through continuous learning and optimization, the system can continuously improve the quality and efficiency of knowledge combination.

[0095] In terms of knowledge services, the system innovatively implements a context - aware knowledge push mechanism. By analyzing the knowledge demand characteristics and usage scenarios of users, the system can actively identify relevant knowledge content and achieve accurate knowledge push. This mechanism not only improves the timeliness of knowledge services but also significantly enhances the user's knowledge acquisition experience.

[0096] The collaborative working process of the above - mentioned layer mechanisms is as follows: when the system receives a knowledge access request, it first conducts access verification through the dynamic permission control mechanism; after passing the verification, it quickly locates relevant knowledge based on the double - layer knowledge graph structure and the adaptive multi - modal indexing mechanism; finally, it integrates multi - source knowledge through the intelligent knowledge combiner to generate a complete knowledge response. This innovative hierarchical collaboration mechanism realizes the safe, efficient, and intelligent management of knowledge, providing reliable technical support for knowledge sharing among enterprises. Through the above - mentioned technical solutions, the present invention has achieved multiple innovative breakthroughs in the field of knowledge base management, including core technologies such as dynamic permission control, double - layer knowledge graph, multi - modal indexing, and intelligent knowledge combination. These innovations not only solve the technical problems faced by traditional knowledge management systems but also provide a feasible technical path for building a new generation of intelligent knowledge service platforms.

[0097] IV. Specific implementation of the intelligent agent orchestration module

[0098] As Figure 6 shown, the present invention provides an intelligent agent orchestration module, which innovatively adopts an autonomous learning task parsing mechanism, a dynamic orchestration decision - making mechanism, and a predictive execution control mechanism to achieve efficient collaborative organization among intelligent agents. By establishing a deep understanding and intelligent scheduling system for tasks, this module has break - throughly solved the technical problems such as low intelligent agent collaboration efficiency and rigid task orchestration in the prior art.

[0099] The task parsing layer innovatively implements a task understanding mechanism for autonomous learning. This mechanism realizes continuous learning and optimization of task patterns by establishing a dynamically evolving task knowledge graph. Among them, the task semantic analyzer, based on the incremental learning method, analyzes the execution records of historical tasks, extracts typical patterns and execution paradigms of tasks, and forms a reusable task template library. This way of task understanding based on experience accumulation significantly improves the system's ability to understand new tasks and processing efficiency.

[0100] In terms of task requirement processing, the system innovatively designs an intelligent requirement extractor. This component realizes accurate understanding and structured expression of complex task requirements by establishing a multi-dimensional feature model of task requirements. The system first performs semantic analysis on the input task requirements, extracts key goals, constraints, and performance requirements; then maps these elements to a standardized description of ability requirements, and establishes an accurate mapping relationship between task requirements and agent capabilities. This standardized requirement processing mechanism lays the foundation for subsequent task decomposition and ability matching.

[0101] The constraint analyzer innovatively introduces a causal reasoning method to comprehensively understand task constraints by analyzing the execution context and resource dependencies of tasks. The system establishes a multi-layer constraint model including temporal constraints, resource constraints, and business constraints, and can accurately identify implicit constraints and execution limitations between tasks. In particular, the system can derive potential dependency relationships during the task execution process through context analysis to ensure the rationality and executability of task decomposition.

[0102] The core innovation of the present invention lies in proposing a task dependency reasoning algorithm based on a causal graph. This algorithm combines task semantic understanding and historical execution data to realize dynamic derivation of dependency relationships between tasks:

[0103] TaskDep(T) = ExplicitDep(T) ∪ InferredDep(T)

[0104] Where:

[0105] · TaskDep(T) represents the complete set of dependency relationships of task T

[0106] · ExplicitDep(T) represents explicitly defined dependency relationships

[0107] · InferredDep(T) represents implicitly derived dependency relationships through causal reasoning

[0108] The inference of implicit dependencies is based on the causal graph model: InferredDep(T) = {(Ti, Tj)|P(Tj|Ti) > λ, SimContext(Ti, Tj) > μ}

[0109] where:

[0110] · P(Tj|Ti) represents the conditional probability that Tj is executed after Ti is executed

[0111] · SimContext(Ti, Tj) represents the context similarity between task Ti and Tj

[0112] · λ and μ are the probability threshold and similarity threshold respectively

[0113] The core innovation of the orchestration decision-making layer lies in the implementation of a dynamic orchestration decision-making mechanism. The adaptive collaboration mode generator can dynamically generate the most suitable collaboration mode according to the current task characteristics and historical execution data. The system realizes the intelligent selection of collaboration modes by establishing an association model between task characteristics and collaboration modes. During the actual operation process, the system continuously accumulates and analyzes execution data, continuously optimizes the mode selection strategy, and realizes the dynamic evolution of collaboration modes.

[0114] The intelligent task decomposition engine adopts an innovative hierarchical task planning method. By analyzing the complexity and dependencies of tasks, it automatically determines the optimal decomposition granularity. The engine first constructs a task decomposition tree based on the semantic characteristics of the task, and identifies the set of subtasks that can be executed in parallel; then, according to the professional characteristics and current load status of the agents, it realizes the accurate allocation and dynamic adjustment of tasks. The system also realizes the adaptive optimization of task decomposition. By monitoring the task execution effect, it dynamically adjusts the decomposition strategy to ensure the rationality of the decomposition plan.

[0115] The dependency relationship reasoner innovatively adopts the causal graph model. Through in-depth analysis of the task execution process, it identifies and maintains the explicit and implicit dependency relationships between tasks. The system establishes a complete dependency relationship reasoning framework, including multiple dimensions such as pre-dependency, resource dependency, and data dependency. Through this comprehensive dependency analysis, the system can accurately grasp the execution order and parallel opportunities of tasks, significantly improving the efficiency of task execution.

[0116] The execution control layer implements a predictive execution control mechanism. The predictive status monitor realizes the early prediction of execution anomalies by establishing a timing feature model of task execution. The system constructs a health assessment model based on historical execution data. By real-time monitoring the change trends of key indicators, it can timely discover and warn of potential execution risks. This predictive monitoring mechanism greatly improves the system's fault prevention ability.

[0117] The active exception handler innovatively constructs an exception pattern knowledge base and continuously enriches the exception handling strategies through case learning. The system establishes a standardized processing flow for each exception situation, including steps such as exception recognition, impact assessment, and generation of handling solutions. In particular, the system can automatically select the most suitable handling strategy according to the specific characteristics of the exception, achieving rapid recognition and automated handling of exceptions.

[0118] The adaptive performance optimizer establishes a multi-dimensional performance evaluation system and dynamically adjusts the resource allocation strategy by analyzing the task execution status in real time. The system evaluates the task execution performance from multiple dimensions such as execution efficiency, resource utilization rate, and service quality, and adjusts the execution strategy in real time based on the evaluation results. Through this continuous performance optimization mechanism, the system can maximize the resource utilization efficiency while ensuring the execution quality.

[0119] Through the organic combination of the above innovative mechanisms, the present invention realizes the autonomous planning and dynamic optimization of agent collaboration. The system can adaptively adjust the collaboration strategy and execution plan according to the changes in task characteristics and execution environment, effectively improving the collaboration efficiency and execution reliability in complex task scenarios. This predictive execution control and adaptive decision-making optimization mechanism provides strong technical support for cross-enterprise agent collaboration. In particular, the system shows excellent adaptability and reliability in practical applications, can effectively handle various complex task orchestration scenarios, and significantly improves the efficiency and quality of agent collaboration between enterprises.

[0120] V. Specific Implementation of the Security Authentication Module

[0121] As Figure 7 shown, the present invention provides an agent security authentication module that innovatively realizes three core mechanisms: dynamic trust assessment, scenario-adaptive permission control, and chained audit storage, effectively solving the security authentication problem in cross-enterprise agent collaboration. The module breaks through the technical problems such as poor security and rigid permission management in agent collaboration in the prior art by establishing a multi-dimensional security guarantee system.

[0122] The dynamic trust assessment engine breaks through the limitations of traditional static trust models and creatively adopts a multi-dimensional trust calculation model. The engine includes two core components: a behavior model builder and a trust degree calculator. The behavior model builder constructs the behavior feature vector of the agent by analyzing in real time the characteristics such as the collaboration behavior, resource call pattern, and task completion quality of the agent. The system innovatively designs a behavior feature extraction mechanism to depict the behavior pattern of the agent from multiple dimensions such as interaction frequency, resource consumption, and task success rate, realizing an accurate portrait of the agent's behavior.

[0123] The core innovation of the present invention is to propose a dynamic trust assessment model, which combines historical behavior, real-time status, and context factors to generate trust scores adapted to different scenarios:

[0124] Trust(A) = α·HistoryScore(A) + β·RealtimeScore(A) + γ·ContextScore(A)

[0125] Where:

[0126] ·Trust(A) represents the comprehensive trust score of agent A

[0127] ·HistoryScore(A) represents the long-term credit score based on historical behavior

[0128] ·RealtimeScore(A) represents the real-time trust score based on the current status

[0129] ·ContextScore(A) represents the context trust score in the current scenario

[0130] ·α, β, γ are dynamic weight coefficients, automatically adjusted according to the operation risk level, and α + β + γ = 1

[0131] The trust calculator, based on the behavior feature vector and combined with the historical credit record, adopts a hierarchical trust calculation method to achieve dynamic update of the trust score. The system first standardizes the behavior features of different dimensions to establish a unified evaluation benchmark; then, by setting the weight coefficients of different evaluation dimensions, it realizes the scenario adaptability of the trust assessment; finally, based on the time series analysis model, it comprehensively evaluates the historical behavior and the current performance to obtain the final trust score. This dynamic trust assessment mechanism enables the system to accurately reflect the trustworthiness of the agent in different scenarios.

[0132] The scene adaptive permission controller innovatively combines business scene semantic understanding and permission control to achieve dynamic adjustment of permission policies. The scene semantic analyzer realizes in-depth understanding of business operations by establishing a scene feature model. The system first extracts scene features from the operation request, including elements such as operation type, business objective, and execution environment; then, based on the preset security rules, it classifies the scenes to identify the security sensitivity of the operation; finally, combined with the trust level of the agent, it dynamically generates a permission policy adapted to the current scene.

[0133] The dynamic policy generator innovatively implements adaptive adjustment of permissions based on scenario analysis results. The generator adopts a hybrid decision-making method based on rules and history to maximize collaboration efficiency while ensuring security. The system has established a complete permission policy generation framework, including three levels: basic permission definition, scenario permission mapping, and dynamic permission adjustment. Through this multi-level permission control mechanism, the system can adjust permission granularity and control intensity in real time according to scenario changes.

[0134] The chain audit system innovatively uses a chain storage structure to record the behavior trajectory of the intelligent agent, ensuring the integrity and non-tamperability of the audit data. The chain storage adopts an innovative data structure design to connect each operation record of the intelligent agent into an unalterable evidence chain. For each operation record, the system not only stores the specific content of the operation, but also contains key information such as timestamp, operator identity, and execution environment. Through cryptographic technology, the system ensures the authenticity and integrity of the audit records.

[0135] The evidence processor innovatively implements intelligent analysis of massive audit data through an intelligent analysis engine. The system first extracts features from the audit data to identify abnormal operation patterns and security risks; then, through correlation analysis, it discovers potential problems in the operation sequence; and finally, based on the analysis results, it generates a detailed security analysis report. This intelligent audit analysis mechanism not only supports post-event tracing and responsibility identification, but also proactively discovers security risks.

[0136] In terms of audit tracking, the system implements an innovative multi-dimensional tracking mechanism. By establishing an operation association diagram, the system can clearly display the complete link of the agent's operation, including operation sequence, resource access, data flow and other information. This comprehensive tracking mechanism provides strong support for the analysis and processing of security incidents. In particular, the system supports flexible audit query functions, which can quickly locate and trace back related operation records.

[0137] The above three core mechanisms work together to form a complete intelligent agent security authentication system. When an intelligent agent initiates an operation request, the system first determines its credibility through dynamic trust evaluation; then dynamically generates permission policies based on scenario analysis; and finally records the entire process through a chain audit system. This multi-level security mechanism not only ensures the security of intelligent agent collaboration, but also provides complete security management and traceability capabilities.

[0138] Through innovative technical solutions, the present invention has achieved multiple breakthroughs in the field of intelligent agent security authentication, including core technologies such as dynamic trust assessment, scenario-based permission control, and full-process security auditing. These innovations not only solve the limitations of traditional security authentication solutions but also provide complete technical support for building a secure and reliable intelligent agent collaboration environment. The system demonstrates excellent security protection capabilities in practical applications, can effectively respond to various security threats, and ensure the security and controllability of intelligent agent collaboration.

[0139] VI. Specific Implementation of the Resource Scheduling Module

[0140] As Figure 8 shown, the present invention provides a resource scheduling module for intelligent agent collaboration, which innovatively implements three core mechanisms: ability-based resource mapping, cross-enterprise resource collaboration, and intelligent scheduling of knowledge resources. By establishing a deep mapping relationship between resources and capabilities, this module has breakthroughly solved technical problems such as low resource scheduling efficiency and limited cross-enterprise collaboration in the prior art. The ability-based resource mapping mechanism innovatively establishes a dynamic association model between intelligent agent capabilities and computing resources. The ability model manager constructs a dynamic resource profile by deeply analyzing the resource consumption patterns of different types of intelligent agents when executing various capabilities. The system first classifies the ability characteristics of intelligent agents, identifies the demand characteristics of different capabilities for computing resources, storage resources, and network resources; then through continuous monitoring and analysis, establishes a resource consumption model during the execution of capabilities; finally, based on the feedback of execution effects, continuously optimizes the resource allocation strategy. This resource mapping mechanism based on ability characteristics realizes the precision and intelligence of resource allocation.

[0141] Based on historical execution data, the resource consumption predictor innovatively realizes the construction of an ability-resource association model. The system analyzes the execution records of historical tasks, extracts the association rules between ability execution and resource consumption, and establishes a prediction model. This model not only considers static resource requirements but also includes predictions of dynamic load changes. Through this predictive resource management mechanism, the system can plan resource allocation schemes in advance to avoid resource competition and conflicts.

[0142] The core innovation of the present invention is to propose a multi-dimensional resource allocation model based on tensor calculation, which maps different types of resource requirements and ability requirements to a unified tensor space for optimization:

[0143]

[0144] Where:

[0145] ·R represents the resource allocation tensor, which describes the association relationship between capabilities, computing resources, and performance parameters

[0146] ·A represents the agent ability tensor, which contains the ability characteristics of different agents

[0147] ·C represents the computing resource tensor, which contains resource dimensions such as CPU, memory, storage, and bandwidth

[0148] ·P represents the performance parameter tensor, which contains performance metrics such as response time, throughput, and accuracy

[0149] · represents the tensor product operation

[0150] Through tensor decomposition and optimization algorithms, the system can efficiently calculate the optimal resource allocation scheme

[0151] The resource allocation matrix adopts the above tensor calculation model to realize the dynamic allocation optimization of multi-dimensional resources. The system maps the resource requirements of different types such as computing resources, storage resources, and network resources to a unified resource tensor space, and calculates the optimal resource allocation scheme through multi-dimensional optimization algorithms. This tensor-based resource modeling method effectively solves the complex problem of collaborative allocation of multi-type resources

[0152] In terms of cross-enterprise resource collaboration, the present invention innovatively solves the problem of resource sharing during the collaboration of multi-enterprise agents. The resource negotiation engine adopts a resource negotiation strategy based on game theory to realize the dynamic coordination of resources among enterprises. The system first establishes a resource sharing model among enterprises, clarifies the resource contributions and usage rights of all parties; then through fairness calculation, it ensures the balance of resource allocation; finally, through a dynamic adjustment mechanism, it realizes the flexible adjustment of resource quotas

[0153] The resource quota manager realizes the dynamic management of enterprise resource quotas through innovative credit assessment and usage efficiency analysis. The system comprehensively evaluates the resource usage behaviors of enterprises, including multiple dimensions such as resource contribution degree, usage efficiency, and credit records. Based on the evaluation results, the system dynamically adjusts the resource quotas of each enterprise to encourage efficient and trustworthy resource sharing behaviors. This credit-based resource management mechanism effectively promotes resource sharing and collaboration among enterprises

[0154] The resource access protocol innovatively introduces a distributed consistency algorithm to ensure the reliability and fairness of cross-enterprise resource scheduling. The system establishes a unified resource access specification, which stipulates the standard processes for resource request, allocation, usage, and release. In particular, the system realizes the atomicity guarantee of resource access, ensuring data consistency in concurrent access scenarios. Through this standardized access control, the system effectively avoids resource access conflicts and deadlock problems

[0155] In terms of knowledge resource scheduling, the present invention has realized an innovative intelligent knowledge resource scheduling mechanism. The knowledge access optimizer constructs an optimal knowledge access path through graph optimization algorithms. The system first analyzes the distribution of knowledge resources and establishes an access cost model between knowledge nodes; then, through path optimization algorithms, it calculates the optimal access path; finally, through a dynamic adjustment mechanism, it realizes the real-time optimization of the access path.

[0156] The knowledge caching strategy adopts a multi-level caching structure and performs dynamic caching according to the usage frequency and importance of knowledge. The system innovatively designs a cache replacement algorithm, and through analyzing the access patterns and importance of knowledge, it realizes the intelligent update of cache content. In particular, the system supports predictive caching, which can pre-load knowledge resources that may be needed in advance according to the usage scenario, significantly improving the access efficiency.

[0157] The knowledge resource controller realizes the traffic control and priority management of knowledge access. The system establishes a multi-level access control mechanism to ensure the access quality of key knowledge resources. At the same time, the system realizes an adaptive traffic control strategy, which can dynamically adjust the access limit according to the system load condition, effectively preventing the overload of knowledge resources.

[0158] The collaborative work of these mechanisms enables the system to accurately understand the resource requirements of the agents, efficiently coordinate cross-enterprise resources, and realize the intelligent scheduling of knowledge resources. Especially when dealing with complex agent collaboration tasks, the system can predict resource usage according to the ability requirements, improve the overall efficiency through resource collaboration among enterprises, and ensure the task execution quality through the intelligent scheduling of knowledge resources. Through the innovative technical solution, the present invention significantly improves the resource utilization efficiency of the agent collaboration platform, providing strong support for building an efficient and reliable agent collaboration environment. VII. Specific Embodiments

[0160] The technical solution of the present invention is further illustrated by the following embodiments, but the protection scope of the present invention is not limited thereto.

[0161] Embodiment 1: Cross-border E-commerce Intelligent Interaction System

[0162] As Figure 9 shown, this embodiment provides a cross-border e-commerce intelligent interaction system based on the present invention. Through the deployment of an agent collaboration platform, this system realizes the automated interaction of buyer and seller enterprise agents, effectively solving problems such as slow information acquisition and low communication efficiency in cross-border trade.

[0163] In this embodiment, the system adopts a three-layer deployment architecture. The infrastructure layer implements a unified intelligent agent communication standard and resource scheduling system. Through standardized message formats and communication interfaces, it ensures reliable information exchange between intelligent agents of different enterprises. At the same time, through a dynamic load balancing mechanism, it realizes efficient scheduling of computing resources. The core capability layer provides functions such as knowledge base construction, intelligent agent configuration, and collaboration rule management, supporting enterprises to quickly build and deploy intelligent business capabilities. The business application layer is responsible for access control and call monitoring. Through fine-grained permission management and full-process behavior auditing, it ensures the safe and controllable operation of the system. The system as a whole adopts a microservices architecture, and flexible interconnection is achieved between each functional module through standardized interfaces.

[0164] In the system configuration stage, the specific implementation steps are as follows:

[0165] 1. Buyer enterprise knowledge base construction process

[0166] The buyer enterprise first imports business documents such as procurement standards and product requirement descriptions into the system through the document upload interface of the system. The document processing unit of the knowledge base management module deeply analyzes the uploaded documents and extracts the key information and semantic features in the documents. The system adopts the innovative double-layer knowledge graph structure of the present invention and organizes the parsed content according to the standardized conceptual model to form a structured knowledge representation. In terms of knowledge permission management, the system realizes hierarchical management of knowledge content through a dynamic permission control mechanism. Specifically, the system first analyzes the sensitivity of the knowledge content, and then sets corresponding access permissions for different levels of knowledge according to the preset security policies to ensure the effective protection of the enterprise's core information.

[0167] 2. Buyer intelligent agent configuration process

[0168] After the knowledge base construction is completed, the system starts to configure the behavior mode of the procurement Agent based on the intelligent agent orchestration module. First, the system provides a standardized prompt word template library for enterprises to select and customize. These templates cover common procurement scenarios such as inquiry, negotiation, and confirmation. Through the task parsing mechanism, the system converts these business scenarios into structured task templates, defining the input requirements, processing processes, and output specifications for each scenario. In particular, the system uses a dynamic orchestration decision-making mechanism to support the intelligent agent to flexibly adjust strategies according to the actual situation during the execution process. For example, during the price negotiation process, the intelligent agent can dynamically adjust the negotiation strategy according to the other party's quotation and market conditions to ensure the effectiveness of the negotiation.

[0169] 3. Seller enterprise knowledge base construction process

[0170] The knowledge base construction of the seller enterprise adopts the same technical framework, but has its particularity in content processing. The system first conducts intelligent processing on information such as product catalogs, specification descriptions, and price strategies uploaded by the seller. Through the adaptive multi-modal indexing mechanism, the system can process product information in multiple formats such as text, pictures, and tables simultaneously and establish an efficient retrieval system. This indexing mechanism supports multi-dimensional retrieval of product information, including specification parameters, price ranges, supply capabilities, etc. At the same time, the system uses an intelligent knowledge combiner to achieve intelligent association between product information. For example, it automatically associates similar products, alternative products, or recommends appropriate product combinations according to customer demand characteristics. In addition, the system also supports dynamic updates of product information. When product parameters, prices, and other information change, the relevant knowledge nodes will be automatically updated to ensure the timeliness of information.

[0171] 4. Seller Agent Configuration Process

[0172] The configuration of the seller agent focuses on the implementation of sales strategies and negotiation rules. The system first helps the enterprise build a standardized response strategy, including the processing logic for scenarios such as product introduction, price quotation, and delivery date confirmation. Through the predictive execution control mechanism, the system can predict potential interaction paths and prepare corresponding response strategies in advance. For example, based on historical transaction data, the system predicts the product features or price-sensitive points that customers may be interested in and prepares the corresponding explanatory materials or price plans in advance. In terms of resource scheduling, the system uses an intelligent load balancing mechanism to ensure stable response performance in the case of concurrent queries from multiple customers. In particular, the system dynamically adjusts resource allocation according to the priority and complexity of the queries to ensure that important queries can be processed in a timely manner.

[0173] During the actual operation process, the system supports two core interaction scenarios: commodity information consultation and price negotiation, and realizes automated collaboration through a standardized interaction process:

[0174] 1. Specific Processing Process of Commodity Information Consultation Scenario

[0175] When the buyer Agent receives a commodity consultation request, the system first parses and matches the request through the capability registration discovery mechanism. The specific steps are as follows:

[0176] (1) Local Knowledge Retrieval Process

[0177] The system first activates the adaptive multi-modal indexing mechanism and selects the most suitable indexing strategy according to the characteristics of the consultation content. When retrieving relevant information from the local knowledge base, the system adopts a multi-dimensional matching algorithm, which not only considers keyword matching but also analyzes semantic relevance to ensure the accuracy of the retrieval results. For the retrieved results, the system will evaluate their integrity and applicability. If it is found that the local knowledge cannot fully meet the query requirements, the cross-enterprise query process will be triggered.

[0178] (2) Cross-enterprise query process

[0179] When initiating a cross-enterprise query, the system first constructs a standardized query request through the communication protocol module. Before sending the request, the dynamic trust assessment mechanism will evaluate the credibility of the target enterprise to ensure the security of information exchange. Based on the scenario-adaptive permission control mechanism, the system will dynamically determine the scope of information that can be obtained according to the current interaction scenario and trust level, effectively preventing the leakage of sensitive information.

[0180] (3) Intelligent response generation process

[0181] After receiving the query results, the knowledge combiner will intelligently integrate multi-source information. The system will not only merge information from different sources but also handle possible information conflicts to ensure the consistency of the response. The decision tracking mechanism will record the key decision points in the entire query process, including retrieval strategy selection, information filtering rules, etc. At the same time, the chain audit system will completely record all interaction operations, providing a basis for subsequent optimization and problem tracing.

[0182] 2. Specific processing flow of the price negotiation and consultation scenario

[0183] In the price negotiation scenario, the system realizes automated negotiation through a series of intelligent mechanisms, and the specific implementation is as follows:

[0184] (1) Strategy evaluation process

[0185] The system first conducts in-depth analysis of historical transaction data and extracts effective negotiation patterns and price rules through data mining techniques. When evaluating the current market situation, the system will comprehensively consider multiple dimensions of market indicators, including supply and demand relationships, seasonal fluctuations, competitor prices, and other factors. Based on these analysis results, the system generates targeted negotiation strategy suggestions, including key decision-making elements such as the initial offer, concession space, and optimal transaction price.

[0186] (2) Automatic negotiation process

[0187] In the actual negotiation process, the intelligent agent first formulates an initial offer based on preset negotiation rules. These rules cover considerations in multiple dimensions such as price range, gross profit margin requirements, quantity discounts, etc. During the negotiation process, the system dynamically adjusts its strategy according to the other party's feedback. For example, it adjusts its own offer strategy according to the concession margin of the other party. At the same time, the system evaluates the progress of the negotiation in real time, including indicators such as the concession trends of both parties and the likelihood of reaching a deal. When necessary, it triggers a manual intervention mechanism.

[0188] Through practical application verification, this embodiment shows significant application effects in multiple dimensions:

[0189] 1. Specific performance in terms of efficiency improvement

[0190] The system has achieved significant optimization in terms of information response time, reducing the response time for product inquiries that originally required manual processing from an average of 30 minutes to within 9 minutes, and the overall response time has been reduced by 70%. In terms of the transaction completion cycle, through intelligent demand matching and automatic negotiation mechanisms, the traditional cross-border transaction process has been shortened from an average of 7 days to 3.5 days, and the overall cycle has been shortened by 50%. In terms of manual processing costs, the system has reduced the workload of human customer service by 60% through automated processing of a large number of regular inquiry and bargaining requests, significantly improving the operation efficiency.

[0191] 2. Specific performance in terms of accuracy improvement

[0192] In terms of product information matching, the system has increased the product matching accuracy rate to 95% through semantic understanding and intelligent recommendation mechanisms, significantly reducing communication costs and customer churn. In terms of price negotiation, through intelligent pricing and negotiation strategies, the deal closing rate has been increased from the original 35% to 49%, and the overall negotiation success rate has been increased by 40%. In terms of transaction quality, the system has reduced the incidence of problems such as specification errors and price deviations in transactions from 5% to 1% through strict parameter matching and automated verification, and the overall transaction error rate has been reduced by 80%.

[0193] This embodiment fully verifies the practical value of the various innovative mechanisms of the present invention through a detailed implementation process and specific application effects. Especially when dealing with high-concurrency queries and complex negotiation scenarios, the system demonstrates excellent performance and reliability. Through standardized interface design and flexible extension mechanisms, this implementation plan can adapt to the personalized needs of enterprises of different scales, providing a replicable technical solution for building an efficient cross-border trade intelligent interaction platform.

[0194] Embodiment Two: Cross-institutional Medical Intelligent Collaboration System

[0195] As Figure 10As shown in the figure, this embodiment provides a cross-institutional medical intelligent collaboration system based on the present invention. By deploying an intelligent agent collaboration platform, the system realizes the collaborative diagnosis of professional intelligent agents among multiple medical institutions, effectively solving problems such as difficult expert resource scheduling, difficult diagnosis process tracking, and difficult medical record data sharing in traditional remote consultations.

[0196] In this embodiment, the system adopts a hierarchical deployment architecture, and each layer has a clear functional positioning and technical implementation. The infrastructure layer realizes the standardized communication protocol of medical intelligent agents and the intelligent scheduling strategy of diagnostic resources, ensuring reliable information exchange and resource sharing among intelligent agents of different medical institutions. The core capability layer provides basic support for medical collaboration through three core modules: medical knowledge base management, intelligent agent diagnosis configuration, and collaboration rule definition. The business application layer is responsible for data access control and diagnosis process monitoring, ensuring the security of medical data and the traceability of the diagnosis process. The overall system adopts a distributed microservice architecture, and through mechanisms such as service governance, load balancing, and fault transfer, ensures stable operation in large-scale concurrent diagnosis scenarios.

[0197] The specific implementation process of the system configuration stage is subdivided into the following steps:

[0198] 1. The construction process of the medical institution knowledge base

[0199] In the knowledge base construction stage, the system first systematically organizes and standardizes the clinical guidelines, diagnosis and treatment specifications, and medical record data provided by each medical institution. The knowledge base management module is organized using the innovative double-layer knowledge graph structure of the present invention, and a standardized medical concept system including disease classification, diagnosis criteria, treatment plans, etc. is constructed in the static ontology layer. This concept system is designed based on the International Classification of Diseases (ICD) and the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT), ensuring the standardization and integrity of concept definitions.

[0200] At the dynamic instance level, the system stores a large number of actual cases and diagnosis and treatment records. Through semantic standardization processing, these records map medical terms and diagnosis and treatment records from different institutions into a unified conceptual system. The system adopts an innovative semantic mapping algorithm that can accurately identify the corresponding relationships of terms between different institutions, effectively solving the problem of semantic interoperability of medical data. For example, for different expressions of the same disease, the system can automatically identify their equivalence to ensure the accuracy of data retrieval and analysis. In terms of data privacy protection, the system has implemented a multi-level security protection mechanism. First, through a dynamic permission control mechanism, patient information is managed at different levels, and users at different levels can only access the data within their permissions. Second, the system uses advanced data desensitization technology to intelligently process patients' personal identification information, retaining the analysis value of the data while protecting privacy. Finally, the system dynamically generates data views with different granularities for different levels of access permissions, achieving a balance between medical data sharing and privacy protection.

[0201] 2. Configuration Process of Diagnostic Agent

[0202] In the diagnostic agent configuration section, the system adopts a modular design scheme to build specialized diagnostic capabilities for different specialties. First, the system configures a standardized diagnostic rule library and clinical pathways for each specialty. These rules cover the complete diagnosis and treatment process from initial consultation to treatment plan formulation. Each rule contains clear trigger conditions, execution steps, and expected results to ensure the standardization and repeatability of the diagnostic process.

[0203] The system decomposes the complex diagnostic process into a series of executable decision-making steps through a task parsing mechanism. For example, in internal medicine diagnosis, the system decomposes the diagnostic process into specific steps such as medical history collection, physical examination, auxiliary examination suggestions, preliminary diagnosis, and treatment plan formulation. Each step is configured with corresponding decision rules and execution logic to support the intelligent agent to make accurate medical decisions based on the actual situation.

[0204] It is particularly worth mentioning that the system supports a dynamic optimization mechanism for diagnostic rules. By collecting and analyzing feedback data on the actual diagnosis and treatment effects, the system can identify the deficiencies in the existing rules and optimize and adjust the rules according to the statistical analysis results. This adaptive learning mechanism ensures that the diagnostic system can continuously improve its accuracy and practicality.

[0205] 3. Setting Process of Diagnostic Collaboration Rules

[0206] In terms of setting collaboration rules, the system has implemented a flexible multi-specialty collaboration mechanism through the agent orchestration module. First, the system has established a set of standardized consultation process templates, which specify in detail the processing norms for each link from the initiation of the consultation to the determination of the plan. These templates cover common consultation scenarios, such as two-department consultations, multi-disciplinary joint consultations, emergency consultations, etc., and each scenario has a corresponding workflow definition.

[0207] The system's collaboration mode recognition unit can automatically select the most suitable collaboration mode according to the complexity and urgency of the condition. In the case of relatively simple conditions, the system adopts a serial consultation mode and solicits opinions from each specialty in a preset order. For complex cases, the system will enable a parallel consultation mode, where multiple specialties analyze and diagnose simultaneously to improve the diagnostic efficiency. In special cases, the system also supports a hybrid consultation mode, flexibly combining serial and parallel processing to meet different diagnostic needs.

[0208] 4. Configuration process of resource scheduling strategy

[0209] In the link of configuring the resource scheduling strategy, the system has implemented a refined management mechanism for expert resources and diagnostic equipment. By establishing a comprehensive portrait of expert resources, the system details information such as each expert's professional expertise, consultation time, historical performance, etc. This information is used to build an expert ability evaluation model to support the system in making the optimal choice during scheduling.

[0210] The system adopts an innovative intelligent scheduling algorithm, considering multiple factors for resource allocation. These factors include the professional matching degree of experts, current workload, historical diagnostic accuracy, etc. Through dynamic weight adjustment, the algorithm can optimize scheduling decisions according to the actual situation. For example, when dealing with difficult cases, the system will give priority to experts with rich experience; when dealing with routine consultations, it will focus on the load balance of expert resources.

[0211] In the actual application process, the specific implementation of the core diagnostic scenarios supported by the system is as follows:

[0212] 1. Implementation process of the multi-disciplinary consultation process

[0213] When the system receives a multi-disciplinary consultation request, it first starts a comprehensive medical record information collection and preprocessing process. The knowledge base management module quickly retrieves and integrates the complete medical record information of the patient through an adaptive multi-modal indexing mechanism. This information includes various types of medical data such as historical medical records, examination reports, and medical imaging materials. The system uses an intelligent knowledge combiner to perform correlation analysis on these scattered information and establish a complete medical record view of the patient. In this process, the system not only integrates the data but also evaluates the data quality to ensure the integrity and accuracy of the information.

[0214] In the expert resource scheduling phase, the system first conducts a preliminary analysis of the condition to determine the required specialty type and expert level. Based on the preset scheduling strategy, the system screens the most suitable expert combination from the expert resource pool. This screening process comprehensively considers multiple factors, including the matching degree of the expert's professional direction, the level of clinical experience, and the historical diagnostic effectiveness. Through the dynamic scoring mechanism, the system can quickly determine the optimal expert team combination.

[0215] In the collaborative diagnosis stage, the system coordinates the diagnostic activities of multiple experts through the dynamic orchestration decision-making mechanism. Each specialty agent conducts independent analysis based on its professional knowledge base and generates preliminary diagnostic opinions. The system records each diagnostic step through the decision tracking mechanism, including key decision-making points such as the analysis and judgment of symptoms, the proposal of examination suggestions, and the formulation of treatment plans. This full-process recording mechanism not only ensures the traceability of the diagnostic process but also provides a data basis for subsequent experience summary and optimization.

[0216] In the final stage of summarizing the diagnostic plan, the system comprehensively analyzes the diagnostic opinions of each expert through the intelligent knowledge combiner. This process includes steps such as opinion similarity analysis, conflict detection, and plan integration. When differences in expert opinions are found, the system will activate a special conflict handling mechanism, and through medical knowledge reasoning and statistical analysis, assist the expert team to reach a consensus and finally form the optimal treatment plan recommendation.

[0217] 2. Implementation process of the emergency rapid consultation process

[0218] In the emergency scenario, the system adopts a special rapid response mechanism to ensure diagnostic efficiency. First, in terms of resource scheduling, the system has implemented a predictive expert resource reservation mechanism. By analyzing historical emergency data, the system can predict the emergency demand at different times and arrange expert resources in advance. When receiving an emergency consultation request, the system can complete the expert scheduling in the shortest time, significantly shortening the response time.

[0219] During the diagnostic process, the system realizes parallel processing of multiple diagnostic links through intelligent task decomposition. For example, while waiting for the test report, the system will synchronously conduct image analysis and expert appointment. This parallel processing mechanism greatly improves the overall efficiency of emergency diagnosis. At the same time, the system also sets up a dynamic priority adjustment mechanism, which can adjust the processing order in real time according to the urgency of the condition to ensure that the most urgent cases receive the fastest response.

[0220] Through practical application verification, this embodiment has achieved remarkable application effects in many aspects:

[0221] 1. Performance improvement in diagnostic efficiency and quality

[0222] Through intelligent process optimization and resource scheduling, the system has shortened the consultation response time from 4 hours to within 1 hour, a 75% improvement; the multi-disciplinary consultation process has been shortened from 3 days to 1 day, a 67% improvement; and the emergency consultation time has been reduced from 60 minutes to 20 minutes, a 67% improvement. In terms of diagnostic quality, through continuous optimization of the knowledge base and dynamic adjustment of diagnostic rules, the system has increased the diagnostic accuracy rate from 85% to 93%, reduced the missed diagnosis rate from 8% to 3%, and increased patient satisfaction from 75% to 90%.

[0223] 2. Effect of medical resource utilization

[0224] In terms of the utilization efficiency of medical resources, the system has also achieved an overall improvement. Through an intelligent scheduling mechanism and appointment management, the utilization efficiency of expert resources has increased by 40%, and the equipment utilization efficiency has increased by 35%; through precise resource allocation and process optimization, the resource waste situation has been reduced by 45%, effectively improving the overall utilization efficiency of medical resources.

[0225] This embodiment fully verifies the application value of the present invention in the professional medical field. Through predictive resource scheduling and intelligent diagnostic support, the system has significantly improved the efficiency and quality of cross-institutional consultations. Especially when dealing with emergency and complex cases, the system has demonstrated excellent performance and reliability. Through standardized interface design and flexible expansion mechanisms, it not only meets the personalized needs of medical institutions of different scales but also provides a reliable technical foundation for building an intelligent medical collaboration system.

[0226] The third embodiment of the present invention provides a computer-readable storage medium. The storage medium stores computer program instructions, which, when executed by a computer, cause the computer to implement the various functions of the cross-border e-commerce intelligent interaction system as described in the first embodiment and the various functions of the cross-institutional medical intelligent collaboration system as described in the second embodiment. The specifically implemented functions include but are not limited to: the functions of the intelligent agent communication protocol module, the knowledge base management module, the intelligent agent orchestration module, the security authentication module, and the resource scheduling module.

[0227] Among them, the computer-readable storage medium may include but is not limited to: various media that can store program codes such as ROM, RAM, magnetic disks, optical disks, solid-state drives, flash memories, and USB flash drives. The computer may be but is not limited to: various devices with information processing capabilities such as general-purpose computers, dedicated servers, distributed computer clusters, cloud computing platforms, mobile terminals, and embedded devices.

[0228] The fourth embodiment of the present invention provides a computer program product. The computer program product contains computer program instructions, which, when executed by a processor, cause the processor to perform the following steps:

[0229] 1. Execute the functions of the intelligent agent communication protocol module to achieve standardized communication between intelligent agents;

[0230] 2. Execute the functions of the knowledge base management module to achieve unified management of knowledge and intelligent services;

[0231] 3. Execute the functions of the intelligent agent orchestration module to achieve autonomous collaboration and task allocation of intelligent agents;

[0232] 4. Execute the functions of the security authentication module to achieve secure and controllable access management;

[0233] 5. Execute the functions of the resource scheduling module to achieve optimal allocation of computing resources.

[0234] This computer program product can exist in the form of an independent software product, can also be integrated into a hardware system, and can also be provided in the form of a service through a network. No matter which form is adopted, as long as it realizes the above functions, it falls within the protection scope of the present invention.

[0235] It should be noted that the description of the above embodiments is only illustrative and not a limitation to the present invention. Those skilled in the art should understand that various improvements and modifications can be made to the devices or methods in the embodiments. For example:

[0236] 1. Each functional module can be implemented centrally or distributedly deployed;

[0237] 2. Each processing step can be executed in the described order, or in other suitable orders or in parallel;

[0238] 3. The system architecture can be adjusted and expanded according to actual needs.

[0239] Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A dynamic collaborative orchestration method based on intelligent agents, characterized in that: include: Constructing an agent communication protocol, which adopts a layered architecture design, including an intelligent collaboration engine, a capability registration and discovery mechanism, a state management mechanism, and a decision tracking mechanism, to achieve standardized communication and collaboration between agents; Deploy a knowledge base management module, which adopts a layered architecture design, including an access layer, a processing layer, and a service layer, and implements a two-layer knowledge graph structure, a dynamic authority control mechanism, an adaptive multimodal indexing mechanism, and an intelligent knowledge combiner to provide knowledge sharing and security management; Configure an agent orchestration module, which implements the autonomous learning task parsing mechanism, dynamic orchestration decision mechanism and predictive execution control mechanism to achieve efficient collaborative organization and task allocation among agents; establish a security authentication module, which implements three core mechanisms: dynamic trust evaluation, scenario adaptive authority control and chained audit storage to ensure the security and controllability of agent collaboration; Implement a resource scheduling module that implements three core mechanisms: capability-based resource mapping, cross-enterprise resource collaboration, and knowledge resource intelligent scheduling, to improve system resource utilization efficiency; Execute the system workflow, which includes: in the enterprise access stage, identity authentication and permission configuration are completed through the business application layer; in the agent registration stage, the agent declares capability characteristics through semantic description and obtains a globally unique identifier; in the task processing stage, the task request is parsed through the agent communication protocol module, the agent orchestration module performs task decomposition and capability matching, the knowledge base management module provides knowledge support, and the resource scheduling module ensures efficient allocation of resources; the security authentication module ensures the safety, controllability and traceability of the operation throughout the process.

2. The method according to claim 1, characterized in that Constructing an intelligent agent communication protocol includes: configuring an intelligent collaboration engine, which is located in the core control layer, realizing autonomous collaboration and task allocation of multiple intelligent agents through collaboration mode recognition units and adaptive task allocation units, and identifying and selecting the optimal collaboration mode from three basic modes: parallel collaboration, serial collaboration and hybrid collaboration; realizing a capability registration and discovery mechanism, which is located in the basic service layer, adopts a semantic capability description framework, builds a standardized capability description system from the functional dimension, performance dimension and constraint dimension, and realizes capability registration and discovery; establishing a state management mechanism, which is located in the basic service layer, adopts distributed state synchronization and predictive management methods, and ensures the consistency of states among multiple intelligent agents through incremental synchronization strategies; constructing a decision tracking mechanism, which is located in the monitoring layer, adopts a directed graph structure to record the decision chain of the intelligent agent, and realizes full tracking of the decision process and knowledge flow.

3. The method according to claim 2, characterized in that Implementing a capability registration and discovery mechanism includes: building a semantic capability description framework, which describes the basic capability types and business capabilities of the intelligent agent from a functional dimension, and the performance dimension includes quantifiable indicators such as response time, accuracy, and resource consumption, and the constraint dimension defines the usage conditions, dependencies, and limiting factors of the capability; implementing a capability registration and discovery algorithm, which uses a semantic similarity calculation method to achieve accurate capability matching by analyzing the semantic association between task requirements and capability descriptions; supporting combined reasoning of capabilities to intelligently combine multiple basic capabilities to meet complex task requirements; establishing a dynamic update mechanism for the capability catalog, which supports real-time updating and maintenance of capability information to ensure the timeliness of the capability library; ensuring the standardization of capability calls through unified interface specifications; and adopting dynamic semantic matching technology, which combines semantic similarity calculation with context-aware factors to form a context-enhanced dynamic matching model by comprehensively considering basic semantic similarity, context relevance in the current execution environment, and historical execution success rate.

4. The method according to claim 1, characterized in that Deploy the knowledge base management module, including: constructing an access layer, the access layer implements a dynamic permission control mechanism based on knowledge characteristics, dynamically generates and adjusts access policies by real-time analysis of the sensitivity of knowledge, usage scenarios and credit ratings of access subjects; deploying a processing layer, the processing layer implements a two-layer knowledge graph structure, including a static ontology layer and a dynamic instance layer, and solves the model conflict problem when integrating multiple knowledge bases by separating knowledge models and specific instances; the processing layer also implements an adaptive multimodal indexing mechanism, integrating three indexing methods: full-text indexing, graph structure indexing and semantic vector indexing; configuring a service layer, the intelligent knowledge combiner of the service layer realizes intelligent fusion of multi-source knowledge through semantic similarity calculation and context association analysis, and the combiner also includes a feedback learning module, which continuously optimizes the combination strategy by analyzing the use effect of the combination results.

5. The method according to claim 4, characterized in that The two-layer knowledge graph structure includes a static ontology layer and a dynamic instance layer, as well as a set of semantic links connecting the two layers; wherein the static ontology layer contains a concept set, a relationship set and an attribute set, which are used to maintain a standardized knowledge model; the dynamic instance layer contains an instance set, an instance relationship set and an instance attribute value set, which are used to store specific knowledge instances; the semantic link set defines the mapping relationship between the ontology layer and the instance layer; the system also includes an ontology mapping mechanism, which processes concept conflicts between different knowledge bases through semantic similarity calculation, establishes a mapping relationship when the similarity exceeds a preset threshold, and realizes the effective integration of multiple knowledge bases.

6. The method according to claim 1, characterized in that The configuration of the intelligent agent orchestration module includes: establishing a task parsing mechanism, which realizes continuous learning and optimization of task patterns by establishing a dynamically evolving task knowledge graph; the task semantic analyzer extracts typical patterns and execution paradigms of tasks based on an incremental learning method; the task parsing mechanism also includes a constraint analyzer, which introduces a causal reasoning method to identify implicit constraints and execution restrictions between tasks by analyzing the execution context and resource dependencies of tasks; realizing a dynamic orchestration decision mechanism, which dynamically generates the most suitable collaboration mode according to current task characteristics and historical execution data through an adaptive collaboration mode generator; the intelligent task decomposition engine adopts a hierarchical task planning method to automatically determine the optimal decomposition granularity; constructing a predictive execution control mechanism, which realizes early prediction of execution anomalies by establishing a temporal feature model of task execution; the active exception handler continuously enriches the exception handling strategy through case learning; The adaptive performance optimizer establishes a multi-dimensional performance evaluation system and dynamically adjusts the resource allocation strategy.

7. The method according to claim 6, characterized in that The task dependency reasoner adopts the task dependency analysis technology based on causal graph. By combining task semantic understanding and historical execution data, it identifies explicitly defined dependencies and implicit dependencies obtained through causal reasoning, and constructs a complete dependency set for the task. The implicit dependency analysis calculates the conditional execution probability and context similarity, and confirms the existence of the dependency when both exceed the preset threshold at the same time, thereby ensuring the rationality of task decomposition and scheduling.

8. The method according to claim 1, characterized in that Establishing a security authentication module includes: configuring a dynamic trust evaluation engine, which adopts a multi-dimensional trust calculation model, and uses a behavior model builder to perform real-time analysis on the collaborative behavior, resource call mode, task completion quality and other characteristics of the intelligent body to construct the behavior feature vector of the intelligent body; the trust calculator dynamically calculates the trust score based on these feature vectors and combines historical credit records; implements a scenario-adaptive permission controller, which combines business scenario semantic understanding with permission control to achieve dynamic adjustment of permission policies; the scenario semantic analyzer extracts key features and security requirements of business operations through deep semantic understanding technology; the dynamic policy generator automatically builds and optimizes access control policies based on these features and combined with preset security rules; constructs a chain audit system, which adopts a chain storage structure to record the behavior trajectory of the intelligent body to ensure the integrity and non-tamperability of the audit data; the chain storage adopts cryptographic technology to connect each operation record of the intelligent body into a non-tamperable evidence chain; the evidence processor extracts valuable security insights from massive audit data through an intelligent analysis engine.

9. The method according to claim 8, characterized in that The dynamic trust assessment engine adopts a multi-factor weighted assessment method to comprehensively analyze the historical behavior, current status and scenario factors of the intelligent body to generate trust scores that are suitable for different scenarios; among them, historical behavior analysis focuses on long-term credit performance, current status assessment reflects real-time behavioral characteristics, and scenario factors consider the specific needs of the current business environment; the system sets a dynamic weight coefficient and automatically adjusts the weight of each factor according to the operational risk level to achieve accurate adaptation to different scenarios and ensure the safety and controllability of the collaboration process.

10. The method according to claim 1, characterized in that Implementing the resource scheduling module includes: establishing a capability-based resource mapping mechanism, which uses a deep learning method through a capability model manager to analyze the resource consumption patterns of different types of intelligent agents when executing various capabilities and build a dynamic resource portrait; a resource consumption predictor establishes a capability-resource association model based on historical execution data; a resource allocation matrix uses a tensor computing model to map capability requirements, computing resources and performance parameters to a unified tensor space for optimization; realizing a cross-enterprise resource coordination mechanism, which uses a resource negotiation strategy based on game theory to achieve dynamic coordination of resources between enterprises; a resource quota manager dynamically adjusts the resource quota of each enterprise through credit evaluation and usage efficiency analysis; a resource access protocol introduces a distributed consistency algorithm to ensure the fairness and efficiency of cross-enterprise resource scheduling; and configuring a knowledge resource intelligent scheduling mechanism, which uses a graph optimization algorithm to build an optimal knowledge access path, uses a multi-level cache structure, and dynamically caches knowledge according to its frequency of use and importance, to achieve flow control and priority management of knowledge access.

11. The method according to claim 10, characterized in that The tensor computing model used in the resource allocation matrix maps capacity requirements, computing resources and performance parameters to a unified tensor space for optimization, and efficiently calculates the optimal resource allocation plan through tensor decomposition and optimization algorithms. At the same time, the system supports real-time monitoring and dynamic adjustment of resource utilization efficiency to ensure the optimal configuration of resources.

12. A dynamic collaborative orchestration system based on intelligent agents, characterized in that: include: The intelligent agent communication protocol module is used to adopt a layered architecture design, including an intelligent collaboration engine, a capability registration and discovery mechanism, a state management mechanism, and a decision tracking mechanism to achieve standardized communication and collaboration between intelligent agents; the knowledge base management module is used to adopt a layered architecture design, including an access layer, a processing layer, and a service layer to achieve a two-layer knowledge graph structure, a dynamic permission control mechanism, an adaptive multimodal indexing mechanism, and an intelligent knowledge combiner to provide knowledge sharing and security management; The agent orchestration module is used to implement the autonomous learning task parsing mechanism, dynamic orchestration decision-making mechanism and predictive execution control mechanism, so as to achieve efficient collaborative organization and task allocation among agents; The security authentication module is used to implement three core mechanisms: dynamic trust evaluation, scenario-adaptive permission control, and chained audit storage, to ensure the security and controllability of intelligent collaboration; Resource scheduling module, which is used to implement three core mechanisms: capability-based resource mapping, cross-enterprise resource collaboration, and intelligent scheduling of knowledge resources, to improve the efficiency of system resource utilization; The system workflow includes: in the enterprise access stage, identity authentication and permission configuration are completed through the business application layer; in the agent registration stage, the agent declares capability characteristics through semantic description and obtains a globally unique identifier; in the task processing stage, the task request is parsed through the agent communication protocol module, the agent orchestration module performs task decomposition and capability matching, the knowledge base management module provides knowledge support, and the resource scheduling module ensures efficient allocation of resources; the security authentication module ensures the safety, controllability and traceability of the operation throughout the process.

13. A computer-readable storage medium, characterized in that: The storage medium stores computer program instructions, and when the computer program instructions are executed by the computer, the computer executes the agent-based dynamic collaborative orchestration method as described in any one of claims 1 to 11; wherein the computer-readable storage medium may include but is not limited to: ROM, RAM, magnetic disk, optical disk, solid-state hard disk, flash memory, USB flash drive and other media that can store program codes; the computer may be but is not limited to: general-purpose computers, dedicated servers, distributed computer clusters, cloud computing platforms, mobile terminals, embedded devices and other devices with information processing capabilities.

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