Agent tool dynamic discovery and adaptive calling arrangement method and device

Abstract

The application discloses an intelligent agent tool dynamic discovery and adaptive calling arrangement method and device. The method comprises the following steps: receiving a natural language analysis request and generating an analysis task; identifying a business capability and retrieving a candidate tool set; acquiring a dynamic tool image corresponding to the candidate tool, which is continuously updated according to historical tool calling execution feedback information; comprehensively scoring the candidate tools to determine a target execution tool; calling the target tool and collecting feedback information of this execution; updating the scores of each dimension of the dynamic tool image according to the feedback; adaptively arranging subsequent calling based on the updated image; and finally fusing tool results to generate a natural language analysis report. The application realizes real-time optimization of intelligent agent tool selection and calling strategy through an execution feedback closed loop, and improves the stability of multi-tool calling and the credibility of analysis results.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and data processing technology, and in particular to a method for dynamic discovery and adaptive invocation orchestration of intelligent agent tools. Background Technology

[0002] As enterprises deepen their digital transformation, they have gradually built multiple business platforms, such as sales, inventory, marketing, and supply chain systems. Each business system uses different data models and interface protocols, resulting in a large number of heterogeneous data sources. When conducting operational analysis or making business decisions, business personnel need to obtain data from multiple systems and perform comprehensive analysis. Traditional methods that rely on manual queries and cross-departmental communication are no longer sufficient to meet the needs of intelligent operations.

[0003] In recent years, the development of large language models and intelligent agent technologies has made natural language-driven data querying and business analysis possible. Existing intelligent agent systems typically use a predefined tool list approach, inputting the functional descriptions and invocation methods of multiple tools into the large language model, allowing the model to autonomously select the appropriate tool. However, as the number of available tools continues to increase, this approach has revealed significant limitations: First, a large number of tool descriptions occupy the model's context window, increasing inference costs and reducing the model's accuracy when selecting tools with similar functions; second, tools are directly coupled with the business system, resulting in high maintenance costs and poor scalability.

[0004] Therefore, existing technologies urgently need an agent tool scheduling method that can achieve dynamic tool discovery, reduce the dependence of tool selection on large language models, and support automatic analysis across business systems. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, the present invention provides a method and apparatus for dynamic discovery and adaptive invocation orchestration of intelligent agent tools. By introducing a dynamic tool profile that is continuously updated during operation on the basis of static tool discovery in the business capability registry, the system not only selects tools based on the capabilities declared when the tools are registered, but also dynamically adjusts the tool invocation strategy based on feedback information such as the success status, response time, result completeness, data timeliness, cross-source consistency and concurrency risk of the tools in the actual execution process.

[0006] One aspect of the present invention provides a method for dynamic discovery and adaptive invocation orchestration of intelligent agent tools, comprising the following steps: Receive a natural language analysis request input by a user, perform semantic understanding on the natural language analysis request, and generate one or more analysis tasks; Identify one or more business capabilities required to complete the current analysis task, and dynamically retrieve a set of candidate tools that meet the current analysis requirements through a business capability registry based on the identified business capabilities; wherein, the business capability registry maintains the mapping relationship between business capabilities and tool resources as well as the static metadata information corresponding to each tool; Obtain the dynamic tool profile corresponding to each candidate tool in the candidate tool set, and continuously update the dynamic tool profile based on the execution feedback information during the historical tool call process; Based on the static metadata information, the dynamic tool profile, and the task characteristics of the analysis task, the candidate tools are comprehensively scored, and the target execution tool is determined based on the scoring results. The target execution tool is invoked through a unified tool invocation interface to obtain the tool invocation result and collect the execution feedback information corresponding to this tool invocation. Update the dynamic tool profile corresponding to the target execution tool based on the execution feedback information; The subsequent tool calls are adaptively orchestrated based on the updated dynamic tool profile. The adaptive orchestration includes at least one of the following: candidate tool reordering, target execution tool switching, concurrency level adjustment, compensation call triggering, and return result management. The tool call results and subsequent tool call results generated by adaptive call orchestration are summarized and merged, and a natural language analysis report is generated according to the analysis objectives in the analysis task.

[0007] In a second aspect, the present invention also provides an intelligent agent tool dynamic discovery and adaptive invocation orchestration device, comprising: The analysis task generation module is used to receive a natural language analysis request input by the user, perform semantic understanding on the natural language analysis request, and generate one or more analysis tasks. The business capability identification and tool retrieval module is used to identify one or more business capabilities required to complete the current analysis task, and dynamically retrieve a set of candidate tools that meet the current analysis requirements through the business capability registry based on the identified business capabilities; wherein, the business capability registry maintains the mapping relationship between business capabilities and tool resources as well as the static metadata information corresponding to each tool; The dynamic profile acquisition module is used to acquire the dynamic tool profile corresponding to each candidate tool in the candidate tool set. The dynamic tool profile is continuously updated based on the execution feedback information during the historical tool call process. The tool filtering module is used to comprehensively score the candidate tools based on the static metadata information, the dynamic tool profile, and the task characteristics of the analysis task, and determine the target execution tool based on the scoring results. The tool invocation and feedback collection module is used to invoke the target execution tool through a unified tool invocation interface, obtain the tool invocation result, and collect the execution feedback information corresponding to this tool invocation. The profile update module is used to update the dynamic tool profile corresponding to the target execution tool based on the execution feedback information. The adaptive orchestration module is used to adaptively orchestrate subsequent tool calls based on the updated dynamic tool profile. The adaptive orchestration includes at least one of the following: candidate tool reordering, target execution tool switching, concurrency level adjustment, compensation call triggering, and return result management. The results fusion and report generation module is used to summarize and fuse the tool call results and subsequent tool call results generated by adaptive call orchestration, and generate a natural language analysis report according to the analysis objectives in the analysis task.

[0008] The method and apparatus for dynamic discovery and adaptive invocation orchestration of intelligent agent tools provided by this invention have the following beneficial effects: (1) By combining the static metadata information in the business capability registry with the dynamic tool profile that is continuously updated during operation, the problem of misselection caused by relying solely on the tool registration description is avoided, so that the tool selection can reflect the real-time availability and actual execution effect of the tool.

[0009] (2) By collecting execution feedback information such as call success status, exception type, response time, return result length, data timestamp, field completeness and cross-source consistency verification results, the tool profile is updated online so that the tool profile can be dynamically adjusted with changes in the business system status and tool running status.

[0010] (3) By using an adaptive call orchestration mechanism based on dynamic tool profiles, when tool stability decreases, results are incomplete, data expires, cross-source inconsistencies occur, or concurrency risks increase, candidate tool reordering, backup tool switching, compensation call triggering, or concurrency level adjustment are automatically executed to improve the stability of the multi-tool call process.

[0011] (4) By associating the quality of the tool's returned results with the subsequent tool call strategy, the system can handle situations where the tool call is successful but the results do not meet the requirements of the analysis task, thereby improving the completeness and credibility of the results of complex business data analysis.

[0012] (5) By combining a unified tool call interface with a standardized tool call object and call identifier backfilling mechanism, a closed-loop association between tool call results, execution feedback information and dynamic tool profiles is realized, providing a traceable, updatable and adaptive technical foundation for intelligent agent tool scheduling. Attached Figure Description

[0013] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the dynamic discovery and adaptive invocation orchestration method for intelligent agent tools provided in one embodiment of this application; Figure 2 This is a schematic diagram of the structure of an intelligent agent tool dynamic discovery and adaptive invocation orchestration device provided in one embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] In this invention, unless otherwise expressly stated, the following terms have the following meanings: Natural Language Analysis Request: Refers to a query or instruction entered by a user in natural language that contains the intent to analyze data.

[0016] Analysis task: refers to a structured task unit with clear analysis objectives and execution requirements generated by the system after semantic understanding of natural language analysis requests.

[0017] Business capabilities: refers to the abstract functional capabilities required to complete specific types of analysis tasks, such as sales analysis capabilities, inventory analysis capabilities, marketing analysis capabilities, supply chain analysis capabilities, etc.

[0018] Business Capability Registry: This refers to the registry center used to uniformly maintain the mapping relationship between business capabilities and tool resources. The business capability registry records one or more tools corresponding to each business capability and maintains the static metadata information corresponding to each tool.

[0019] Static metadata information refers to auxiliary information that describes the attributes and characteristics of tool resources, including but not limited to: capability description, supported indicators, supported analysis dimensions, business system to which it belongs, data source, interface type, calling method, historical call success rate, average response time, real-time performance level, and applicable scenarios.

[0020] Execution feedback information refers to the running status and result quality information collected during or after a tool call, including but not limited to call success status, exception type, response time, number of retries, length of returned result, result truncation identifier, data timestamp, field missing rate, indicator coverage, cross-data source consistency verification results, and concurrent execution status.

[0021] Dynamic tool profile: This refers to a stateful tool description obtained by continuously updating the tool's operational quality based on tool execution feedback information. Unlike the static metadata information at the time of tool registration, the dynamic tool profile is updated according to the tool's historical call performance and current operational feedback, and includes at least one of the following: stability score, timeliness score, result completeness score, consistency score, context cost score, and concurrency risk score.

[0022] Candidate tool set: refers to the collection of all available tools that meet the current analysis needs, dynamically retrieved through the business capability registry.

[0023] Target execution tools: These are tools selected from a set of candidate tools through comprehensive scoring and screening, and are used to actually perform analytical tasks.

[0024] Adaptive invocation orchestration: refers to the process by which the system automatically adjusts the subsequent tool invocation strategy based on the updated results of the dynamic tool profile. This includes at least one of the following: candidate tool reordering, target execution tool switching, compensation capability requirement generation, compensation tool invocation triggering, concurrency level adjustment, and return result governance.

[0025] Unified tool call interface: refers to the call interface layer that encapsulates the tool resources of heterogeneous business systems in a unified manner, enabling intelligent agents to schedule tools from different business systems in a standardized way.

[0026] The following describes in detail the method for dynamic discovery and invocation of intelligent agent tools provided by the present invention with reference to the accompanying drawings.

[0027] like Figure 1 As shown, the method provided by the present invention includes the following steps.

[0028] Step S101: Receive user analysis requests and generate structured analysis tasks.

[0029] This step is used to receive a natural language analysis request input by the user, perform semantic understanding on the natural language analysis request, and generate one or more analysis tasks.

[0030] Specifically, the intelligent agent system first receives an analysis request input by the user in natural language. This analysis request contains the data analysis objectives that the user wants to understand, which may involve indicator analysis or anomaly diagnosis under specific business objects, specific time ranges, and specific business dimensions.

[0031] The system utilizes a large language model combined with predefined data contracts and business semantic rules to perform semantic understanding on user-input natural language analysis requests. The semantic understanding process includes: identifying the intent of the natural language analysis request to determine the type of user's analysis intent; extracting entities from the natural language analysis request to identify key information such as the analysis object, analysis indicators, time range, and business dimensions; and semantically normalizing the extraction results, mapping the user's expression to standardized indicator and dimension names within the system.

[0032] After completing semantic understanding, the system decomposes the analysis request into one or more structured analysis tasks based on its complexity. Each analysis task contains the following structured fields: analysis target identifier, analysis object, list of target metrics, time range, business dimension constraints, and analysis type. When a user's natural language analysis request involves multiple analysis dimensions or multiple related issues, the system decomposes it into multiple independent analysis tasks and annotates the relationships between each task.

[0033] Step S102: Dynamically discover candidate tools based on the business capability registry.

[0034] This step is used to identify one or more business capabilities required to complete the current analysis task, and to dynamically retrieve a set of candidate tools that meet the current analysis requirements through the business capability registry based on the identified business capabilities.

[0035] Specifically, based on the structured analysis task generated in step S101, the system analyzes the indicator types, analysis dimensions, and analysis objectives involved in the analysis task, and identifies the business capabilities required to complete the analysis task from a predefined set of business capabilities.

[0036] Business capabilities are functional abstractions of underlying tool resources. The system categorizes an enterprise's internal data analysis capabilities according to business domains, forming a set of business capabilities. Each business capability has a unique capability identifier, capability name, capability description, and supported indicator and dimension ranges. When the system receives an analysis task, it analyzes the business domain and analysis type of the task's target indicators and retrieves one or more business capabilities from the set that match the task.

[0037] For comprehensive analysis tasks involving multiple business areas, the system identifies several business capabilities. For example, for a task involving analyzing the reasons for a decline in sales, the system needs to identify multiple business capabilities such as sales analysis, customer flow analysis, conversion analysis, and marketing campaign analysis. Each business capability will then be independently mapped to underlying tool resources.

[0038] The system pre-builds a business capability registry to uniformly maintain the mapping relationship between internal business capabilities and tool resources. The business capability registry stores the following information in a structured manner: (1) Mapping relationship between capabilities and tool resources Each business capability corresponds to one or more tool resources. The business capability registry records this mapping relationship, enabling the system to retrieve all associated tool resources through the business capability identifier.

[0039] (2) Static metadata information corresponding to each tool Static metadata information includes, but is not limited to, the following fields: The capability description field describes the specific functions supported by the tool; Supports indicator fields, listing the set of business indicators that the tool can query or calculate; Supported analysis dimension fields: Lists the data analysis dimensions supported by this tool; The "Business System" field identifies the business system to which this tool is connected; The data source field describes the type of data source for this tool. The interface type field identifies the communication protocol and interface specification of this tool; The calling method field describes the format of the calling parameters and the format of the returned data for this tool; The historical call success rate field records the probability of the tool successfully calling itself in its historical runs; The average response time field records the average response time of this tool. The real-time rating field indicates the degree of real-time availability of the data provided by the tool. The "Applicable Scenarios" field describes the best application scenarios for this tool.

[0040] Once the system identifies the required business capability, it uses the business capability identifier as the search condition to query all tool resources corresponding to that business capability in the business capability registry. The system further filters the search results based on the specific needs of the current analysis task, including target indicators, analysis dimensions, time range, and other conditions, to select tool resources that meet the current analysis needs, forming a candidate tool set.

[0041] This step, by first identifying business capability requirements rather than directly selecting specific tools, decouples the tool selection problem from the reasoning process of the large language model, entrusting it to a dedicated registry retrieval and scoring mechanism. Simultaneously, the business capability registry, as a centralized metadata management service, enables dynamic discovery of tool resources, ensuring that the addition, modification, and decommissioning of tool resources are reflected in the registry in real time.

[0042] Step S103: Obtain the dynamic tool profile corresponding to the candidate tool.

[0043] This step is used to obtain the dynamic tool profile corresponding to each candidate tool in the candidate tool set. The dynamic tool profile is continuously updated based on the execution feedback information during the historical tool call process.

[0044] Specifically, for each candidate tool in the candidate tool set, the system reads its corresponding dynamic tool profile. The dynamic tool profile is a description of the tool's operational quality, continuously updated based on historical tool call feedback during system operation. When the underlying business system experiences interface instability, data latency, missing fields, or increased concurrency risks, these changes can be written into the dynamic tool profile through tool execution feedback and used in subsequent tool discovery and filtering processes, thereby avoiding misselection of tools due to inconsistencies between static registration information and the actual operational status.

[0045] The dynamic tool profile includes at least one of the following: stability score, timeliness score, result completeness score, consistency score, contextual cost score, and concurrency risk score. The specific meanings of each score are as follows: Stability scores are used to characterize the success rate of tool calls and the distribution of exception types, reflecting the stability of the tool in historical calls and the current running cycle.

[0046] The timeliness score is used to characterize the degree of match between the timestamp of the data returned by the tool and the time range of the analysis task, reflecting the timeliness of the data provided by the tool.

[0047] The result completeness score is used to characterize the coverage of required fields and indicators, reflecting the data quality of the results returned by the tool.

[0048] Consistency scores are used to characterize the degree of consistency between the results returned by the tool and those returned by other data sources, reflecting the reliability of the tool's data.

[0049] Context cost score is used to characterize the length or truncation ratio of the tool's returned results, reflecting the extent to which the tool's returned results occupy the agent's context window and subsequent inference costs.

[0050] Concurrency risk score is used to characterize the failure rate or timeout rate of a tool under concurrent call conditions, reflecting the tool's availability in high-concurrency scenarios.

[0051] Step S104: Perform dynamic scoring and filtering of tools based on static metadata and dynamic tool profiles.

[0052] This step is used to comprehensively score the candidate tools based on the static metadata information, the dynamic tool profile, and the task characteristics of the analysis task, and to determine the target execution tool based on the scoring results.

[0053] Specifically, when multiple available tools correspond to the same business capability in the candidate tool set retrieved in step S102, the system introduces a dynamic tool scoring mechanism to comprehensively evaluate each candidate tool and automatically select the tool with the highest score as the target execution tool.

[0054] The dynamic score for a tool is calculated based on both static and dynamic evaluation factors. Static evaluation factors originate from the tool's static metadata information in the business capability registry, while dynamic evaluation factors are derived from the dynamic tool profile. The definitions of each evaluation factor are as follows: (1) Data Range Matching Factor: This factor represents the degree of matching between the data range supported by the candidate tool and the data range required by the current analysis task. This factor is calculated based on the ratio of the intersection to the union of the set of analysis dimensions supported by the candidate tool and the set of dimensions required by the analysis task.

[0055] (2) Indicator Coverage Factor: This factor represents the degree of coverage between the set of business indicators supported by the candidate tool and the set of indicators required for the current analysis task. This factor is calculated based on the ratio of the intersection of the set of indicators supported by the candidate tool and the set of indicators required for the analysis task to the size of the set of indicators required for the analysis task.

[0056] (3) Historical call success rate factor: represents the probability of successful call of the candidate tool in historical operation, with a value range of [0,1]. The higher the value, the more stable the tool is.

[0057] (4) Response performance factor: This factor represents the contribution of the average response time of the candidate tool to the score. The shorter the response time, the higher the score of this factor.

[0058] (5) Real-time matching factor: indicates the degree of matching between the real-time level of the candidate tool and the real-time requirements of the current analysis task.

[0059] (6) System load factor: This indicates the current load status of the business system to which the candidate tool belongs. The lower the system load, the higher the score of this factor.

[0060] (7) Stability profile factor: This factor represents the success rate, anomaly type distribution, and retry status of candidate tools in historical calls and current running cycles. The higher the stability, the higher the score of this factor.

[0061] (8) Result Quality Profile Factor: This factor represents the overall performance of the candidate tool's returned results in terms of field completeness, indicator coverage, data freshness, and cross-source consistency. The higher the result quality, the higher the score of this factor.

[0062] (9) Context cost profile factor: This factor represents the extent to which the results returned by the candidate tool occupy the agent's context window and subsequent inference costs. The more concise the results are and the better they meet the task requirements, the higher the score of this factor.

[0063] The overall score of the candidate tools is calculated using a weighted summation method, and its mathematical expression is as follows: in: Candidate tool Overall score; This represents the total number of evaluation factors; Indicates the first The weight coefficients of each evaluation factor satisfy the following conditions: ,and ; Candidate tool In the The normalized scores on each evaluation factor range from [0,1].

[0064] The weight coefficients of each evaluation factor are pre-configured based on the business scenario and the type of analysis task. The system selects the corresponding weight configuration from the pre-configured weight scheme according to the characteristics of the current analysis task.

[0065] After calculating the comprehensive score, the system selects the candidate tool with the highest comprehensive score as the target execution tool corresponding to that business capability. When there are multiple candidate tools with the highest comprehensive score, the system further sorts them based on historical call success rates and selects the candidate tool with the highest historical call success rate as the target execution tool.

[0066] Step S105: Call the tool through the unified tool call interface and collect execution feedback information.

[0067] This step is used to invoke the target execution tool through the unified tool invocation interface, obtain the tool invocation result, and collect the execution feedback information corresponding to this tool invocation.

[0068] Specifically, the system invokes the target execution tool through a unified tool invocation interface. This unified tool invocation interface encapsulates tool resources from heterogeneous business systems, providing standardized invocation parameter formats and return data formats. When the system initiates a tool invocation through the unified tool invocation interface, it converts the required execution parameters according to the target tool's interface specification and parses the data returned by the tool according to a unified format.

[0069] Before executing a tool call, the system standardizes the tool call request into a standardized tool call object. The standardized tool call object includes a call identifier, a tool name, and call parameters. Based on the call identifier, the system fills the corresponding standardized tool call object with the tool call result and execution feedback information, thus establishing a correlation between the call request and the call result.

[0070] When multiple standardized tool invocation objects are generated in the same round of intelligent agent inference, the system divides these objects into one or more execution batches based on the read-only attribute, concurrency safety attribute, side effect type, risk level, and concurrency risk score of the corresponding tool. Tool invocations that meet the concurrency safety conditions and have no dependencies are assigned to the same concurrent execution batch, while tool invocations that do not meet the concurrency safety conditions are assigned to separate execution batches.

[0071] The system collects execution feedback information after each tool call. The execution feedback information includes at least one of the following: call success status, exception type, response time, number of retries, return result length, result truncation identifier, data timestamp, field missing rate, indicator coverage, cross-data source consistency verification result, and concurrent execution status.

[0072] The system provides unified monitoring of exceptions, timeouts, and failures during tool invocation. When an exception occurs during a tool invocation, the system records the exception information and determines the exception type. For retryable exception types, including transient exceptions such as network timeouts and temporary service unavailability, the system executes retry processing according to the preset retry policy.

[0073] The retry policy parameters are configured as follows: maximum number of retries. This indicates the maximum number of retries for the same tool call; the retry interval. , representing the waiting time between two consecutive retries; backoff factor This indicates the multiplier by which the waiting time increases with each retry. The formula for calculating the waiting time for each retry is: in: Indicates the first Waiting time for each retry; Indicates the initial retry interval; Indicates the avoidance factor. ; Indicates the current number of retries. .

[0074] If a tool call fails within the maximum number of retries, the system marks the execution node as failed and handles it according to a preset degradation strategy. The degradation strategy includes: re-executing the current tool using an alternative tool, skipping the current execution node and recording the exception information, terminating the current analysis process and returning an error message.

[0075] Step S106: Update the dynamic tool profile.

[0076] This step is used to update the dynamic tool profile corresponding to the target execution tool based on the execution feedback information.

[0077] Specifically, based on the execution feedback information collected in step S105, the system updates each scoring dimension in the dynamic tool profile of the corresponding tool: The stability score is updated based on the success status of the call and the type of exception. Specifically, the stability score increases when the call succeeds and decreases when an exception or failure occurs, with different types of exceptions corresponding to different degrees of decrease.

[0078] The timeliness score is updated based on the deviation between the returned data timestamp and the time range required for the analysis task. Specifically, the closer the returned data timestamp is to the time range required by the analysis task, the higher the timeliness score; the greater the deviation, the lower the timeliness score.

[0079] The result completeness score is updated based on the field missing rate and the indicator coverage. Specifically, the lower the field missing rate and the higher the indicator coverage, the higher the result completeness score; conversely, the higher the field missing rate and the higher the indicator coverage, the lower the score.

[0080] The consistency score is updated based on the cross-data source consistency verification results. Specifically, the consistency score increases when the consistency verification between the tool's returned results and those returned by other data sources passes; and decreases when the verification fails or a conflict occurs.

[0081] The context cost score is updated based on the length of the returned result and the result truncation flag. Specifically, the more concise the returned result and the less truncation occurs, the higher the context cost score; the more long the returned result or the more truncation occurs, the lower the context cost score.

[0082] The concurrency risk score is updated based on the response time, failure status, or timeout status during concurrent execution. Specifically, under concurrent call conditions, the shorter the response time and the lower the failure rate, the lower the concurrency risk score; the longer the response time and the higher the failure rate or timeout rate, the higher the concurrency risk score.

[0083] Step S107: Adaptive invocation orchestration based on dynamic tool profiles.

[0084] This step is used to adaptively orchestrate subsequent tool calls based on the updated dynamic tool profile. The adaptive orchestration includes at least one of the following: candidate tool reordering, target execution tool switching, concurrency level adjustment, compensation call triggering, and return result governance.

[0085] Specifically, the system dynamically adjusts the subsequent tool invocation strategy based on the updated dynamic tool profile in step S106: When the stability score of the target execution tool falls below the first threshold, it indicates that the tool's reliability is insufficient in the current operating environment. In this case, the system will reorder candidate tools corresponding to similar business capabilities, prioritizing tools with higher stability, or directly switch to a backup tool for subsequent calls.

[0086] When the timeliness score is below the second threshold, it indicates that the timestamps of the data returned by the tool deviate significantly from the time range required for the analysis task, indicating insufficient data freshness. In this case, the system triggers a compensation tool that provides updated data to obtain data that better meets the time requirements.

[0087] When the completeness score of the results is below the third threshold, it indicates that the tool's returned results have missing fields or insufficient indicator coverage. In this case, the system generates compensation capability requirements based on the missing fields or missing indicators and re-searches for candidate tools to supplement the missing data content.

[0088] When the consistency score falls below the fourth threshold, it indicates that there is inconsistency between the results returned by the tool and those returned by other data sources. At this point, the system triggers a cross-source verification tool call to perform consistency checks and conflict resolution on the data from different data sources.

[0089] When the concurrency risk score exceeds the fifth threshold, it indicates that the tool has a high failure rate or timeout rate under high-concurrency call conditions. In this case, the system reduces the concurrency level of the target execution tool in subsequent calls, or adjusts parallel calls to serial calls, to reduce concurrency pressure.

[0090] The first, second, third, fourth, and fifth thresholds mentioned above are all preset scoring thresholds of the system, which can be configured according to specific business scenarios and tool types.

[0091] Step S108: Analysis results fusion and intelligent report generation.

[0092] This step is used to summarize and merge the tool call results and subsequent tool call results generated by adaptive call orchestration, and generate a natural language analysis report based on the analysis objectives in the analysis task.

[0093] Specifically, it includes the following sub-steps.

[0094] Sub-step S1081: Obtain the data result returned by the tool call.

[0095] The system collects data results returned by various tool calls. These data results may come from different business systems and have different data formats, field names, and data structures.

[0096] Sub-step S1082: Format unification and field mapping.

[0097] The system standardizes the format and maps fields for data results from different business systems. It maintains a field mapping table, recording the correspondence between field names in each business system and their corresponding internal standard field names. For each tool's returned data results, the system converts the original field names to standard field names based on the field mapping table and standardizes the data format to the system's internal standard format.

[0098] Sub-step S1083: Cross-validation and consistency check.

[0099] The system performs cross-validation and consistency checks on related metrics from different data sources based on the relationships between business metrics. The purpose of cross-validation is to ensure that related data from different business systems are logically consistent.

[0100] The formula for calculating consistency verification is: in: This indicates the same indicator value from two data sources. and The relative deviation between them; It is a very small positive number, used to prevent the denominator from being zero; when Less than or equal to the preset consistency threshold At that time, the system determines that the indicator values ​​from the two data sources pass the consistency check. For indicators that fail the consistency check, the system marks them as abnormal and records the deviation information, which will be explained in the subsequent report generation process.

[0101] Sub-step S1084: Fusion calculation.

[0102] The system integrates and calculates the validated data results according to the calculation logic defined in the analysis task, generating complete data analysis results. The integrated calculation includes: aggregating and calculating basic data from multiple business systems to generate composite indicators; performing trend calculations on time series data to generate change rates and year-on-year and month-on-month data; and performing cross-analysis on multi-dimensional data to generate multi-dimensional analysis results.

[0103] Sub-step S1085: Natural language analysis report generation.

[0104] The system integrates the complete data analysis results obtained from the calculations, combines them with the analysis objectives and business rules in the analysis task, and inputs them into a large language model to generate a natural language analysis report. The large language model comprehensively interprets the analysis results and generates an analysis report containing the following: analysis conclusions, summarizing the current status and changes of the analysis target indicators; cause identification, based on the inference results of influencing factors and indicator calculation results, identifying the main reasons for the abnormal changes in the indicators; risk warnings, identifying potential business risks and issues that need attention; and optimization suggestions, providing targeted business improvement suggestions based on the analysis conclusions.

[0105] The method provided by the present invention will be described in detail below through specific embodiments.

[0106] This embodiment takes the applicant's retail data analysis intelligent agent product as the implementation object.

[0107] (1) Initial state The intelligent agent system is deployed within the data analytics platform of a large retail enterprise. This enterprise has already built multiple business platforms, including sales, inventory, marketing, supply chain, and membership systems. Each business system operates independently, employing different data models and interface protocols.

[0108] The system has pre-built a business capability registry. The business capabilities registered in the registry include: sales analysis capability, inventory analysis capability, customer flow analysis capability, conversion analysis capability, marketing campaign analysis capability, replenishment analysis capability, supply chain analysis capability, etc.

[0109] Each business capability is mapped to one or more corresponding tool resources. For example, the sales analysis capability is mapped to a batch sales volume query interface from the data warehouse and a real-time sales volume query interface from the online transaction system; the customer flow analysis capability is mapped to a customer flow data interface from the store customer flow statistics system.

[0110] (2) Receive user analysis requests The user inputs a natural language analysis request through the intelligent agent interaction interface: "Please analyze the reasons for the decline in sales of goods at the Shanghai Pudong store this week."

[0111] The agent receives the natural language analysis request and invokes a large language model for semantic understanding. The large language model identifies the following: the analysis object is "Shanghai Pudong Store," the analysis metric is "sales revenue," the time frame is "this week," and the analysis goal is "analysis of the reasons for the decline." Based on the semantic understanding results, the agent generates a structured analysis task, which includes fields such as the analysis object "Shanghai Pudong Store," the analysis metric "sales revenue," the time frame "this week," and the analysis goal "analysis of the reasons for the decline."

[0112] (3) Identify business capabilities Based on the analysis indicators "product sales" and the analysis objective "reasons for decline" in the structured analysis task, the system identifies one or more business capabilities required to complete the analysis task.

[0113] The system determines that the following business capabilities are required for this analysis task: sales analysis capability, used to obtain product sales data and changes; customer flow analysis capability, used to obtain customer flow data and changes; conversion analysis capability, used to obtain conversion rate data and changes; and marketing campaign analysis capability, used to obtain marketing campaign execution status.

[0114] (4) Dynamic search candidate tools The system dynamically retrieves the four identified business capabilities through the business capability registry.

[0115] For "sales analysis capabilities", the business capability registry maps to two candidate tools: Tool A is a data warehouse sales volume batch query interface, which supports batch query of historical data, covering daily granular data of the past three years, with a historical call success rate of 99.2% and an average response time of 800 milliseconds; Tool B is an online transaction system real-time sales volume query interface, which supports query of real-time data, covering minute granular data of the past 30 days, with a historical call success rate of 98.5% and an average response time of 200 milliseconds.

[0116] For "customer flow analysis capability", a candidate tool is mapped in the business capability registry: Tool C is the interface of the store customer flow statistics system, which supports querying customer flow data on a daily basis.

[0117] For "conversion analysis capability", the business capability registry maps to a candidate tool: Tool D is a conversion rate calculation service that calculates conversion rates based on sales data and customer traffic data.

[0118] For "Marketing Campaign Analysis Capability", the business capability registry maps to a candidate tool: Tool E is the activity data query interface of the Marketing Campaign Management System.

[0119] (5) Obtain dynamic tool profiles and conduct comprehensive scoring. The system retrieves the dynamic tool profiles for each candidate tool. Taking two candidate tools under the sales analysis capability as an example, the system reads the dynamic tool profiles for tool A and tool B.

[0120] If tool A experiences missing fields or increased batch interface latency recently, its result completeness score or timeliness score will decrease accordingly; if tool B has a shorter historical coverage but higher recent real-time and stability scores, its dynamic profile will reflect this status.

[0121] For the two candidate tools under "Sales Analysis Capability", the system combines static metadata information and dynamic tool profiles to make a comprehensive score.

[0122] The current analysis task requires analyzing the reasons for the decline in merchandise sales this week, and comparing this week's data with last week's or the same period last year. Therefore, tools capable of querying historical data are needed. At the same time, the analysis task does not have high real-time requirements, but it does require a wide historical coverage of the data.

[0123] The system obtains each evaluation factor: For Tool A, the data range matching score is 0.95 (covering three years of historical data), the indicator coverage score is 1.0 (fully supporting sales-related indicators), the response performance score is 0.7 (average response time of 800 milliseconds), the historical call success rate score is 0.99, the stability profile score is 0.85 (a few timeouts have occurred recently), and the timeliness score is 0.80 (data delay of approximately 2 hours).

[0124] For tool B, the data range matching score is 0.6 (covering only 30 days of historical data), the indicator coverage score is 1.0, the response performance score is 0.95 (average response time 200 milliseconds), the historical call success rate score is 0.985, the stability profile score is 0.95 (recently running stably), and the timeliness score is 0.95 (data delay of about 1 minute).

[0125] The system determines the weights based on the characteristics of the analysis task: the weight of data range matching degree is 0.35, the weight of indicator coverage capability is 0.15, the weight of response performance is 0.10, the weight of historical call success rate is 0.15, the weight of stability profile is 0.15, and the weight of timeliness score is 0.10.

[0126] Calculate the overall score: The overall score for Tool A = 0.35 × 0.95 + 0.15 × 1.0 + 0.10 × 0.7 + 0.15 × 0.99 + 0.15 × 0.85 + 0.10 × 0.80 = 0.3325 + 0.15 + 0.07 + 0.1485 + 0.1275 + 0.08 = 0.9085 The overall score for Tool B = 0.35 × 0.6 + 0.15 × 1.0 + 0.10 × 0.95 + 0.15 × 0.985 + 0.15 × 0.95 + 0.10 × 0.95 = 0.21 + 0.15 + 0.095 + 0.14775 + 0.1425 + 0.095 = 0.84025 Tool A's overall score (0.9085) was higher than Tool B's (0.84025), so the system selected Tool A as the target execution tool for "sales analysis capability".

[0127] For other business capabilities, since there is only one candidate tool for each, the system directly selects the corresponding tool as the target execution tool.

[0128] (6) Tool call and execution feedback collection Based on the analysis objectives, the system invokes the execution tools for each objective through a unified tool call interface.

[0129] For the task "Query this week's sales revenue", the system calls tool A (data warehouse sales revenue batch query interface) to query this week's sales revenue data for the Shanghai Pudong store. For the task "Query last week's sales revenue", the system also calls tool A to query last week's data.

[0130] During the tool invocation process, the system monitors the invocation status in real time and collects execution feedback information. Suppose that in this invocation, tool A returns batch data with a timestamp from the previous day (data delay of approximately 24 hours), triggering a decrease in the timeliness score. The system records this execution feedback information.

[0131] For the task "Query this week's customer traffic", the system calls tool C (store customer traffic statistics system interface) to query this week's customer traffic data. For the task "Query this week's marketing campaign exposure", the system calls tool E (marketing campaign management system interface) to query this week's marketing campaign exposure data.

[0132] (7) Dynamic tool profile update The system updates the dynamic tool profiles for each tool based on the collected execution feedback information.

[0133] For tool A, because the timestamp of the data returned in this call was from the previous day's batch data, while the analysis task required data from "this week," the timeliness discrepancy was significant, and the system lowered tool A's timeliness score. Meanwhile, the call completed successfully without any anomalies, and the stability score remained unchanged or slightly increased.

[0134] Both tools C and E were successfully invoked, and the system updated their stability scores.

[0135] (8) Adaptive call orchestration The system determines whether subsequent tool calls need adaptive orchestration based on the updated dynamic tool profile.

[0136] Because Tool A's timeliness score is below the preset second threshold (insufficient data timeliness), the system determines that the data returned by the current tool cannot meet the time requirements for "this week's data" analysis. The system triggers a call to a compensation tool that can provide updated data, namely Tool B (the online transaction system's real-time sales query interface), to obtain more recent sales data as a supplement.

[0137] The system merges the historical batch data returned by tool A with the recent data returned by tool B to ensure the timeliness of data analysis.

[0138] (9) Data results aggregation and report generation After the system obtains the data results returned by each tool call, it performs summary and fusion processing.

[0139] The system unifies the format of data results from different business systems: it converts the date format (YYYY-MM-DD) returned by the data warehouse and the date format (YYYYMMDD) returned by the store customer flow statistics system into a standard date format; and it maps the codes representing the Shanghai Pudong store in different systems to the same store identifier.

[0140] The system performs cross-validation and consistency checks based on the relationships between business metrics: it verifies whether the sales data of the sales system and the revenue data of the financial system are consistent in trend, and the verification passes.

[0141] The system integrates and calculates the verified data results according to its analytical logic: it calculates the change rate of merchandise sales from last week to this week as -8.5%, the change rate of customer traffic as -3.2%, the change rate of conversion rate as -2.1%, and the change rate of average order value as -3.4%. Based on the contribution of each factor, the decrease in customer traffic contributed approximately 38% to the decrease in merchandise sales, the decrease in conversion rate contributed approximately 25%, and the decrease in average order value contributed approximately 37%.

[0142] The system inputs the data analysis results into the large language model and generates a natural language analysis report based on the user's analysis objectives. The report includes the following: Sales at the Shanghai Pudong store this week decreased by 8.5% compared to last week; the main reasons were decreased customer traffic (38%), decreased average transaction value (37%), and decreased conversion rate (25%). Recommendations include strengthening marketing and promotional activities around the store to increase customer traffic, optimizing product mix and pricing strategies to increase average transaction value, and improving in-store product display to improve conversion rate.

[0143] The agent returns a natural language analysis report to the user, completing the analysis task.

[0144] As can be seen from this embodiment, the method of the present invention can automatically complete the entire closed-loop process from understanding user natural language requests, identifying business capabilities, dynamically discovering tools, intelligently filtering tools, calling tools and collecting feedback, updating dynamic profiles, adaptive orchestration to data fusion and report generation, and realize intelligent adaptive tool scheduling based on feedback from real execution results.

[0145] See Figure 2 Another embodiment of the present invention provides an intelligent agent tool dynamic discovery and adaptive invocation orchestration device 200, including an analysis task generation module 201, a business capability identification and tool retrieval module 202, a dynamic profile acquisition module 203, a tool filtering module 204, a tool invocation and feedback collection module 205, a profile update module 206, an adaptive orchestration module 207, and a result fusion and report generation module 208. This intelligent agent tool dynamic discovery and adaptive invocation orchestration device 200 is capable of executing the intelligent agent tool dynamic discovery and adaptive invocation orchestration method in the method embodiment.

[0146] Specifically, the intelligent agent tool dynamic discovery and adaptive invocation orchestration device 200 includes: The analysis task generation module 201 is used to receive a natural language analysis request input by a user, perform semantic understanding on the natural language analysis request, and generate one or more analysis tasks. The business capability identification and tool retrieval module 202 is used to identify one or more business capabilities required to complete the current analysis task, and dynamically retrieve a set of candidate tools that meet the current analysis requirements through the business capability registry based on the identified business capabilities; wherein, the business capability registry maintains the mapping relationship between business capabilities and tool resources as well as the static metadata information corresponding to each tool; The dynamic profile acquisition module 203 is used to acquire the dynamic tool profile corresponding to each candidate tool in the candidate tool set. The dynamic tool profile is continuously updated based on the execution feedback information during the historical tool call process. The tool filtering module 204 is used to comprehensively score the candidate tools based on the static metadata information, the dynamic tool profile, and the task characteristics of the analysis task, and determine the target execution tool based on the scoring results. The tool invocation and feedback collection module 205 is used to invoke the target execution tool through a unified tool invocation interface, obtain the tool invocation result, and collect the execution feedback information corresponding to this tool invocation. The profile update module 206 is used to update the dynamic tool profile corresponding to the target execution tool based on the execution feedback information. The adaptive orchestration module 207 is used to adaptively orchestrate subsequent tool calls based on the updated dynamic tool profile. The adaptive orchestration includes at least one of candidate tool reordering, target execution tool switching, concurrency level adjustment, compensation call triggering, and return result management. The results fusion and report generation module 208 is used to summarize and fuse the tool call results and the subsequent tool call results generated by adaptive call orchestration, and generate a natural language analysis report according to the analysis objectives in the analysis task.

[0147] Furthermore, the execution feedback information includes at least one of the following: call success status, exception type, response time, number of retries, return result length, result truncation identifier, data timestamp, field missing rate, indicator coverage, cross-data source consistency verification result, and concurrent execution status. The profile update module 206 is further configured to: update the stability score based on the success status and exception type of the call; update the timeliness score based on the deviation between the timestamp of the returned data and the time range of the analysis task; update the result completeness score based on the field missing rate and indicator coverage; update the consistency score based on the cross-data source consistency verification result; update the context cost score based on the length of the returned result and the result truncation identifier; and update the concurrency risk score based on the response time, failure status, or timeout status under concurrent execution status.

[0148] Furthermore, the dynamic tool profile includes at least one of the following: stability score, timeliness score, result completeness score, consistency score, context cost score, and concurrency risk score; wherein, the stability score is used to characterize the tool's call success rate and anomaly type distribution; the timeliness score is used to characterize the degree of matching between the timestamp of the data returned by the tool and the time range of the analysis task; the result completeness score is used to characterize the coverage of required fields and required indicators; the consistency score is used to characterize the degree of consistency between the tool's returned results and the results returned by other data sources; the context cost score is used to characterize the length or truncation ratio of the tool's returned results; and the concurrency risk score is used to characterize the tool's failure rate or timeout rate under concurrent call conditions. The adaptive orchestration module 207 is further configured to: when the stability score of the target execution tool is lower than a first threshold, reorder or switch the candidate tools corresponding to the same business capabilities to backup tools; when the timeliness score is lower than a second threshold, trigger the call of a compensation tool that can provide updated data; when the result completeness score is lower than a third threshold, generate compensation capability requirements based on missing fields or missing indicators and re-search for candidate tools; when the consistency score is lower than a fourth threshold, trigger the call of a cross-source verification tool; and when the concurrency risk score is higher than a fifth threshold, reduce the concurrency level of the target execution tool in subsequent calls or adjust it to a serial call.

[0149] It should be noted that the technical solutions for the dynamic discovery and adaptive invocation orchestration device 200 of the intelligent agent tool provided in this embodiment, which can be used to execute various method embodiments, have similar implementation principles and technical effects to the methods, and will not be repeated here.

[0150] Figure 3 This is a schematic diagram of an electronic device 300 provided in another embodiment of the present invention. The electronic device 300 is used to implement the intelligent agent tool dynamic discovery and adaptive invocation orchestration method in the method embodiment. The electronic device 300 in the embodiments of the present invention may include, but is not limited to, smartphones, tablet computers, PCs, laptops, etc. Figure 3 The electronic device 300 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0151] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes to implement the methods of the embodiments described herein, based on a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing device 301, ROM 302, and RAM 303 are interconnected via a bus 305. An input / output (I / O) interface 304 is also connected to the bus 305.

[0152] Typically, the following devices can be connected to I / O interface 304: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0153] The above description is merely a preferred embodiment of the present invention. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to the specific combination of the above-described technical features, but should also cover other technical solutions formed by any combination of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A method for dynamic discovery and adaptive invocation orchestration of intelligent agent tools, characterized in that, Includes the following steps: Receive a natural language analysis request input by a user, perform semantic understanding on the natural language analysis request, and generate one or more analysis tasks; Identify one or more business capabilities required to complete the current analysis task, and dynamically retrieve a set of candidate tools that meet the current analysis requirements through a business capability registry based on the identified business capabilities; wherein, the business capability registry maintains the mapping relationship between business capabilities and tool resources as well as the static metadata information corresponding to each tool; Obtain the dynamic tool profile corresponding to each candidate tool in the candidate tool set, and continuously update the dynamic tool profile based on the execution feedback information during the historical tool call process; Based on the static metadata information, the dynamic tool profile, and the task characteristics of the analysis task, the candidate tools are comprehensively scored, and the target execution tool is determined based on the scoring results. The target execution tool is invoked through a unified tool invocation interface to obtain the tool invocation result and collect the execution feedback information corresponding to this tool invocation. Update the dynamic tool profile corresponding to the target execution tool based on the execution feedback information; The subsequent tool calls are adaptively orchestrated based on the updated dynamic tool profile. The adaptive orchestration includes at least one of the following: candidate tool reordering, target execution tool switching, concurrency level adjustment, compensation call triggering, and return result management. The tool call results and subsequent tool call results generated by adaptive call orchestration are summarized and merged, and a natural language analysis report is generated according to the analysis objectives in the analysis task.

2. The method for dynamic discovery and adaptive invocation orchestration of intelligent agent tools according to claim 1, characterized in that: The dynamic tool profile includes at least one of the following: stability score, timeliness score, result completeness score, consistency score, context cost score, and concurrency risk score. The stability score is used to characterize the success rate of tool calls and the distribution of anomaly types; the timeliness score is used to characterize the degree of matching between the timestamp of the data returned by the tool and the time range of the analysis task; the result completeness score is used to characterize the coverage of required fields and required indicators; the consistency score is used to characterize the degree of consistency between the results returned by the tool and the results returned by other data sources; the context cost score is used to characterize the length or truncation ratio of the results returned by the tool; and the concurrency risk score is used to characterize the failure rate or timeout rate of the tool under concurrent call conditions.

3. The method for dynamic discovery and adaptive invocation orchestration of intelligent agent tools according to claim 1, characterized in that: The execution feedback information includes at least one of the following: call success status, exception type, response time, number of retries, return result length, result truncation identifier, data timestamp, field missing rate, indicator coverage, cross-data source consistency verification result, and concurrent execution status.

4. The method for dynamic discovery and adaptive invocation orchestration of intelligent agent tools according to claim 3, characterized in that, The step of updating the dynamic tool profile corresponding to the target execution tool based on the execution feedback information includes: Update the stability score based on the success status and exception type of the call. The timeliness score is updated based on the deviation between the returned data timestamp and the analysis task time range; The completeness score of the results is updated based on the field missing rate and indicator coverage. Update the consistency score based on the cross-data source consistency verification results; Update the context cost score based on the length of the returned result and the result truncation flag; In addition, the concurrency risk score is updated based on the response time, failure status, or timeout status during concurrent execution.

5. The method for dynamic discovery and adaptive invocation orchestration of intelligent agent tools according to claim 2, characterized in that, The adaptive invocation orchestration of subsequent tool calls based on the updated dynamic tool profile includes: When the stability score of the target execution tool is lower than the first threshold, the candidate tools corresponding to the same type of business capability will be reordered or switched to backup tools. When the timeliness score falls below the second threshold, a compensation tool that can provide updated data is invoked. When the completeness score of the result is lower than the third threshold, compensation capability requirements are generated based on the missing fields or missing indicators, and candidate tools are retrieved again. When the consistency score falls below the fourth threshold, the cross-source verification tool is invoked. When the concurrency risk score is higher than the fifth threshold, the concurrency level of the target execution tool in subsequent calls is reduced or it is adjusted to a serial call.

6. The method for dynamic discovery and adaptive invocation orchestration of intelligent agent tools according to claim 1, characterized in that, Before invoking the target execution tool through the unified tool invocation interface, the following is also included: The tool call request to be executed is standardized into a standardized tool call object, which includes a call identifier, a tool name, and call parameters; The tool call result and execution feedback information are filled back into the corresponding standardized tool call object according to the call identifier.

7. The method for dynamic discovery and adaptive invocation orchestration of intelligent agent tools according to claim 6, characterized in that: When multiple standardized tool invocation objects are generated in the same round of intelligent agent inference, the multiple standardized tool invocation objects are divided into one or more execution batches according to the read-only attribute, concurrency safety attribute, side effect type, risk level and concurrency risk score of the corresponding tool of each standardized tool invocation object; Tool calls that meet the concurrency safety conditions and have no dependencies are grouped into the same concurrent execution batch, while tool calls that do not meet the concurrency safety conditions are grouped into separate execution batches.

8. A device for dynamic discovery and adaptive invocation orchestration of intelligent agent tools, characterized in that, include: The analysis task generation module is used to receive a natural language analysis request input by the user, perform semantic understanding on the natural language analysis request, and generate one or more analysis tasks. The business capability identification and tool retrieval module is used to identify one or more business capabilities required to complete the current analysis task, and dynamically retrieve a set of candidate tools that meet the current analysis requirements through the business capability registry based on the identified business capabilities; wherein, the business capability registry maintains the mapping relationship between business capabilities and tool resources as well as the static metadata information corresponding to each tool; The dynamic profile acquisition module is used to acquire the dynamic tool profile corresponding to each candidate tool in the candidate tool set. The dynamic tool profile is continuously updated based on the execution feedback information during the historical tool call process. The tool filtering module is used to comprehensively score the candidate tools based on the static metadata information, the dynamic tool profile, and the task characteristics of the analysis task, and determine the target execution tool based on the scoring results. The tool invocation and feedback collection module is used to invoke the target execution tool through a unified tool invocation interface, obtain the tool invocation result, and collect the execution feedback information corresponding to this tool invocation. The profile update module is used to update the dynamic tool profile corresponding to the target execution tool based on the execution feedback information. The adaptive orchestration module is used to adaptively orchestrate subsequent tool calls based on the updated dynamic tool profile. The adaptive orchestration includes at least one of the following: candidate tool reordering, target execution tool switching, concurrency level adjustment, compensation call triggering, and return result management. The results fusion and report generation module is used to summarize and fuse the tool call results and subsequent tool call results generated by adaptive call orchestration, and generate a natural language analysis report according to the analysis objectives in the analysis task.

9. The intelligent agent tool dynamic discovery and adaptive invocation orchestration device according to claim 8, characterized in that: The execution feedback information includes at least one of the following: call success status, exception type, response time, number of retries, return result length, result truncation identifier, data timestamp, field missing rate, indicator coverage, cross-data source consistency verification result, and concurrent execution status. The profile update module is further used to: update the stability score based on the success status and exception type of the call; and update the timeliness score based on the deviation between the timestamp of the returned data and the time range of the analysis task. Update the result completeness score based on field missing rate and indicator coverage; update the consistency score based on cross-data source consistency verification results; update the context cost score based on the returned result length and result truncation flag. In addition, the concurrency risk score is updated based on the response time, failure status, or timeout status during concurrent execution.

10. The intelligent agent tool dynamic discovery and adaptive invocation orchestration device according to claim 8, characterized in that: The dynamic tool profile includes at least one of the following: stability score, timeliness score, result completeness score, consistency score, context cost score, and concurrency risk score. Specifically, the stability score characterizes the tool's success rate and anomaly type distribution; the timeliness score characterizes the degree of match between the timestamp of the data returned by the tool and the time range of the analysis task; the result completeness score characterizes the coverage of required fields and indicators; the consistency score characterizes the consistency between the tool's returned results and those returned by other data sources; the context cost score characterizes the length or truncation ratio of the tool's returned results; and the concurrency risk score characterizes the tool's failure rate or timeout rate under concurrent call conditions. The adaptive orchestration module is further configured to: when the stability score of the target execution tool is lower than a first threshold, reorder or switch candidate tools corresponding to similar business capabilities to backup tools; when the timeliness score is lower than a second threshold, trigger a compensation tool call that can provide updated data; when the result completeness score is lower than a third threshold, generate compensation capability requirements based on missing fields or missing indicators and re-search for candidate tools; when the consistency score is lower than a fourth threshold, trigger a cross-source verification tool call; and when the concurrency risk score is higher than a fifth threshold, reduce the concurrency level of the target execution tool in subsequent calls or adjust it to a serial call.

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