Dynamic tool integration method and system for intent-driven AI conversation system

Abstract

The application provides a dynamic tool integration method and system of an intent-driven AI conversation system, comprising the following steps: S5, according to a state persistence record, extracting resource occupation information from a transaction log, performing resource release on each heterogeneous component, cleaning up a temporary context, and obtaining a released resource list; S6, according to the released resource list, retrieving a breakpoint state snapshot and a transaction log from a distributed database, reconstructing a tool chain execution flow history state, and obtaining a fault context analysis result; and S8, according to a context recovery state and a semantic feature vector, updating a tool chain execution flow, re-distributing computing resources through a heterogeneous component coordination mechanism, and obtaining a task continuity confirmation result. The application significantly improves the recovery efficiency and resource utilization rate of the tool chain under an abnormal scenario, and guarantees the task continuity and system robustness under a high-concurrency environment.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a dynamic tool integration method and system for intent-driven AI dialogue systems. Background Technology

[0002] Intent-driven AI dialogue systems are crucial in the field of intelligent interaction. They dynamically organize toolchains by understanding user intent to meet complex task requirements, such as intelligent customer service and medical diagnostic assistance. Compared to traditional static dialogue systems, these systems can adapt more flexibly to changing scenarios, significantly improving user experience and task efficiency. However, their dynamic tool integration still faces many technical challenges, requiring breakthroughs to achieve more efficient system operation.

[0003] Existing methods have significant limitations in tool integration, primarily in their lack of robust handling of runtime exceptions and flexible response to interruption scenarios. Many systems struggle to effectively manage resource release or state recovery when faced with unexpected errors or user-initiated interruptions during toolchain execution. This not only leads to decreased system stability but can also cause data inconsistencies or context loss, affecting the continuity of subsequent tasks. For example, in a medical dialogue system, if the toolchain is interrupted due to network fluctuations, the system may be unable to properly preserve the current diagnostic state, thus impacting the doctor's ability to continue supporting the patient.

[0004] The core challenge lies in ensuring safe disconnection and state preservation during toolchain execution. Dynamic tool integration requires the system to terminate safely at any execution point, including releasing occupied resources, rolling back unfinished operations, and cleaning up temporary contexts. This requirement presents the first technical challenge: how to achieve efficient resource management and state synchronization in complex tool flows. Toolchains involve multiple heterogeneous components, and their respective resource usage and state updates are difficult to coordinate uniformly, especially in high-concurrency scenarios, significantly increasing the risk of resource leaks or state conflicts. This problem further evolves into the second technical challenge: how to retain sufficient critical execution information after disconnection to support fault analysis or task restart. For example, in an intelligent customer service scenario, if a user's conversation is interrupted due to network issues, the system needs to save the user's intent and the state of executed tools upon disconnection, so as to seamlessly resume after reconnection, rather than starting from scratch.

[0005] Therefore, designing an efficient disconnection mechanism in the dynamic tool integration of intent-driven AI dialogue systems to ensure the safe termination of toolchain execution and the preservation of critical states is a key issue in this research. Summary of the Invention

[0006] A first aspect of the present invention provides a dynamic tool integration method for an intent-driven AI dialogue system, the method comprising:

[0007] S1. Obtain the semantic feature vector of the current dialogue from the pre-established intent recognition model. For runtime exceptions or user interruptions, determine the current state of the toolchain execution flow, obtaining the semantic feature vector and toolchain state identifier. S2. Based on the semantic feature vector and toolchain state identifier, generate a heterogeneous component call sequence. In high-concurrency scenarios, use a distributed lock mechanism to coordinate the allocation of computing resources, obtaining the activated toolchain context. S3. For the activated toolchain context, detect runtime exceptions or user interruptions. If an exception occurs, trigger a safe disconnect mechanism, suspend the toolchain execution flow, and mark the current heterogeneous component execution node, obtaining an interruption point state snapshot. S4. Based on the interruption point state snapshot, serialize and store the toolchain context in a distributed database using the state preservation management module, utilizing transaction logs. S5. Based on the persistent state record, extract resource usage information from the transaction log, perform resource release for each heterogeneous component, clean up the temporary context, and obtain a list of released resources. S6. Based on the list of released resources, retrieve the interruption point state snapshot and transaction log from the distributed database, reconstruct the historical state of the toolchain execution flow, and obtain the fault context analysis result. S7. Based on the fault context analysis result or user reconnection request, restore the interruption point state snapshot from the distributed database, reload the toolchain execution flow, verify data integrity, and obtain the context recovery state. S8. Based on the context recovery state and semantic feature vector, update the toolchain execution flow, reallocate computing resources through the heterogeneous component coordination mechanism, and obtain the task continuity confirmation result.

[0008] Optionally, step S1 involves obtaining the semantic feature vector of the current dialogue from a pre-established intent recognition model, determining the current state of the toolchain execution flow for runtime exceptions or user interruption scenarios, and obtaining the semantic feature vector and toolchain state identifier, including:

[0009] Step S11: Obtain text data from the current dialogue content, extract semantic feature vectors, and obtain dialogue semantic feature vectors;

[0010] Step S12: If the dimension of the dialogue semantic feature vector meets the preset dimension threshold, then a standardized semantic vector is generated to obtain the standardized semantic vector.

[0011] Step S13: Based on the standardized semantic vector, classify the semantic vector to determine whether the dialogue involves runtime exceptions or user interruption scenarios, and obtain the scenario classification result.

[0012] Step S14: Obtain execution flow data from the toolchain execution log. If the scenario classification result indicates an anomaly or interruption, simulate the execution flow data to determine the current execution flow status of the toolchain and obtain the execution flow status identifier.

[0013] Step S15: By combining the execution flow state identifier with the standardized semantic vector, the toolchain state stability is predicted, and the toolchain state prediction result is obtained.

[0014] Step S16: Extract state features from the toolchain state prediction results. If the state features indicate instability, match the log pattern to obtain the abnormal cause features and obtain the abnormal cause feature vector.

[0015] Optionally, step S2, based on the semantic feature vector and toolchain state identifier, generates a heterogeneous component call sequence, and uses a distributed lock mechanism to coordinate the allocation of computing resources in high-concurrency scenarios to obtain the activated toolchain context, including:

[0016] Step S21: Obtain semantic feature vectors from the input data and determine the structured representation of the semantic feature vectors;

[0017] Step S22: Generate heterogeneous component call sequence based on the structured representation of semantic feature vector and toolchain state identifier to obtain the initial configuration of component call sequence;

[0018] Step S23: For the initial configuration of the component call sequence, coordinate the allocation of computing resources in high-concurrency scenarios, determine whether the resource allocation meets the requirements of concurrent task scheduling, and if so, generate the allocated resource pool.

[0019] Step S24: Obtain computing resources from the allocated resource pool, determine the execution order of concurrent tasks, and obtain a sorted concurrent task queue.

[0020] Step S25: Based on the sorted concurrent task queue, execute tasks through heterogeneous component calls to generate the intermediate state of the toolchain context;

[0021] Step S26: For the intermediate state of the toolchain context, determine whether the context meets the activation conditions by comparing the intermediate state threshold. If it meets the conditions, generate an activated toolchain context. If it does not meet the conditions, adjust the component call sequence, re-acquire computing resources from the allocated resource pool and execute the task to obtain an activated toolchain context.

[0022] Step S27: Obtain the final output from the activated toolchain context, integrate the context data, and obtain the business processing result.

[0023] Optionally, step S3 involves detecting runtime exceptions or user interruptions in the activated toolchain context. If such an exception occurs, a safe disconnect mechanism is triggered to pause the toolchain execution flow and mark the current heterogeneous component execution node, obtaining a snapshot of the interruption point state, including:

[0024] Step S31: Monitor the toolchain context, obtain runtime status data from it, detect whether there are runtime exceptions or user interruption signals, and obtain exception detection results;

[0025] Step S32: If the anomaly detection result indicates a runtime anomaly or user interruption signal, then the signal processing tool is triggered to pause the toolchain execution flow and generate an execution flow pause confirmation.

[0026] Step S33: Based on the execution flow pause confirmation, locate the current component node and obtain the node information;

[0027] Step S34: Extract the interruption point status from the node information, generate status snapshot data, store it in the storage unit, and obtain snapshot storage confirmation;

[0028] Step S35: For snapshot storage confirmation, verify the integrity of the status snapshot data. If the integrity verification passes, generate a valid snapshot confirmation.

[0029] Step S36: After valid confirmation by snapshot, extract abnormal feature data from the interruption point status and generate an abnormal log report;

[0030] Step S37: Based on the exception log report, adjust the toolchain context parameters, restore the execution flow, and obtain the toolchain recovery status.

[0031] Optionally, step S6 involves retrieving the interruption point state snapshot and transaction log from the distributed database based on the list of released resources, reconstructing the historical state of the toolchain execution flow, and obtaining the fault context analysis results, including:

[0032] Step S61: Obtain the list of released resources and transaction logs from the distributed database, retrieve the breakpoint state snapshot, and determine the initial dataset;

[0033] Step S62, the initial dataset includes resource release timestamps, transaction log entries, and snapshot status parameters;

[0034] Step S63: Process transaction logs and state snapshots, reconstruct toolchain execution flow history, and generate execution sequences;

[0035] Step S64: parse the time series of the transaction log and the state changes in the snapshot, and output an ordered execution sequence;

[0036] Step S65: By performing sequence analysis to determine the relationship between the interruption point status and the released resources, the fault triggering conditions are determined, and the fault context is obtained.

[0037] Step S66: If the difference between the timestamp of the interruption point state and the timestamp of the resource release exceeds the preset timestamp difference threshold, then determine the fault triggering condition and output the fault context.

[0038] Step S67: If the fault context contains an abnormal execution mode, then classify the execution sequence to determine the abnormal category;

[0039] Step S68: Group the execution sequence according to its feature vectors and output the anomaly category labels;

[0040] Step S69: Extract key transaction log fragments based on the anomaly category and fault context to generate intermediate data for context analysis;

[0041] Step S610: Analyze intermediate data in context to predict the scope of the fault's impact and obtain the analysis results;

[0042] Step S611: Based on the characteristic attributes of the intermediate data, output the range of nodes affected by the fault and the list of transactions affected.

[0043] Optionally, step S69 involves extracting key transaction log fragments based on the anomaly category and fault context to generate intermediate context analysis data, including: the key transaction log fragments include log entries corresponding to the anomaly category and related resource release records.

[0044] Optionally, step S7, based on the fault context analysis results or user reconnection request, restores the interruption point state snapshot from the distributed database, reloads the toolchain execution flow, verifies data integrity, and obtains the context recovery state, including:

[0045] Step S71: Obtain a snapshot of the interruption point status from the distributed database;

[0046] Step S72: Parse the metadata in the interruption point state snapshot;

[0047] Step S73: Extract the timestamp and transaction identifier from the parsed metadata;

[0048] Step S74: Determine the interruption point status based on the extracted timestamp and transaction identifier;

[0049] Step S75: Query the fault context log based on the interruption point status;

[0050] Step S76: Match keywords in the fault context log to determine the reason for the transaction interruption;

[0051] Step S77: Based on the determined transaction interruption cause, classify the error type to obtain the first fault context;

[0052] Step S78: If the first fault context indicates a user reconnection request, then verify the user's identity and session validity.

[0053] Step S79: Obtain reconnection authorization based on the verified user identity and session validity;

[0054] Step S710: Load the tool integration module through reconnection authorization;

[0055] Step S711: Initialize the configuration for the loaded tool integration module;

[0056] Step S712: Based on the initialized configuration, match the toolchain version to obtain the toolchain execution flow;

[0057] Step S713: Use the toolchain execution flow to recover interrupted transactions segment by segment;

[0058] Step S714: Verify data integrity during the segmented recovery of interrupted transactions;

[0059] Step S715: If the verification is successful, the recovery transaction will be written to the distributed database.

[0060] Step S716: Generate context recovery state based on the written recovery transaction update state snapshot;

[0061] Step S717: Based on the context, the execution flow verification is completed by logging the recovery operation sequence of the recovery state synchronization toolchain.

[0062] Optionally, step S77, classifying error types based on the determined transaction interruption cause to obtain a first fault context, includes: classifying error types using a decision tree algorithm, wherein the input of the decision tree algorithm is a log feature vector and the output is an error category label.

[0063] Optionally, step S714, verifying data integrity when recovering interrupted transactions segment by segment, includes: performing a CRC check to verify data integrity, wherein the CRC check input is the recovered data block, and the output is the comparison result of the check value and a preset integrity threshold.

[0064] A second aspect of the present invention provides a dynamic tool integration system for an intent-driven AI dialogue system. The system integrates dynamic tools for the intent-driven AI dialogue system using the method described above. The system includes: an intent recognition and state monitoring module, used to obtain the semantic feature vector of the current dialogue from a pre-established intent recognition model, and determine the current state of the toolchain execution flow in the event of runtime anomalies or user interruptions, obtaining the semantic feature vector and toolchain state identifier; a dynamic tool integration module, used to generate a heterogeneous component call sequence based on the semantic feature vector and toolchain state identifier, and use a distributed lock mechanism to coordinate the allocation of computing resources in high-concurrency scenarios, obtaining the activated toolchain context; a safe disconnection control module, used to detect runtime anomalies or user interruptions in the activated toolchain context, and if such an event occurs, trigger a safe disconnection mechanism to pause the toolchain execution flow and mark the current heterogeneous component execution node, obtaining a snapshot of the interruption point state; and a state persistence module, used to persist the state based on the snapshot of the interruption point state. The invention employs a state preservation management module to serialize and store the toolchain context in a distributed database, and uses transaction logs to record resource usage information, resulting in a persistent state record. A resource release management module extracts resource usage information from the transaction logs based on the persistent state record, performs resource release for each heterogeneous component, cleans up temporary contexts, and obtains a list of released resources. A fault context reconstruction module retrieves the interruption point state snapshot and transaction logs from the distributed database based on the released resource list, reconstructs the historical state of the toolchain execution flow, and obtains the fault context analysis results. A context recovery module restores the interruption point state snapshot from the distributed database based on the fault context analysis results or user reconnection requests, reloads the toolchain execution flow, verifies data integrity, and obtains the context recovery state. A task continuity management module updates the toolchain execution flow based on the context recovery state and semantic feature vectors, reallocates computing resources through a heterogeneous component coordination mechanism, and obtains the task continuity confirmation result. The technical solution provided by this invention has the following beneficial effects:

[0065] This invention discloses a method and system for dynamic tool integration in an intent-driven AI dialogue system, which solves the business problem of task execution loss and resource waste caused by anomalies or interruptions in complex heterogeneous component environments.

[0066] This invention dynamically generates heterogeneous component call sequences through semantic feature vector extraction and toolchain state identification, and coordinates resource allocation using a distributed lock mechanism to ensure stable activation of the toolchain context under high concurrency. In response to runtime exceptions or user interruptions, this invention triggers a secure disconnection mechanism, saves a snapshot of the interruption point state to a distributed database, and records resource usage information through transaction logs to achieve state persistence. Subsequently, this invention uses log analysis algorithms to reconstruct the historical execution flow state, generates fault context analysis results, and restores the state snapshot from the database when a user reconnects or recovers, reloads the toolchain execution flow, verifies data integrity, and updates resource allocation, ultimately achieving task continuity.

[0067] This invention significantly improves the recovery efficiency and resource utilization of the toolchain in abnormal scenarios through dynamic tool integration and state synchronization mechanisms, ensuring task continuity and system robustness in high-concurrency environments. Attached Figure Description

[0068] Figure 1 This is a flowchart illustrating the dynamic tool integration method for the AI ​​dialogue system driven by the present invention.

[0069] Figure 2 This is a schematic diagram of the dynamic tool integration method for the AI ​​dialogue system driven by the present invention.

[0070] Figure 3 This is another schematic diagram of the dynamic tool integration method for driving the AI ​​dialogue system according to the present invention.

[0071] Figure 4 This is a schematic diagram of the structure of the dynamic tool integration system that drives the AI ​​dialogue system of the present invention. Detailed Implementation

[0072] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0073] like Figures 1-3 As shown, in a first aspect, the present invention provides a dynamic tool integration method for an intent-driven AI dialogue system, the method specifically including:

[0074] S1: Obtain the semantic feature vector of the current dialogue from the pre-established intent recognition model. For runtime exceptions or user interruption scenarios, determine the current state of the toolchain execution flow and obtain the semantic feature vector and toolchain state identifier.

[0075] Optionally, this step also includes:

[0076] Step S11: Obtain text data from the current dialogue content, extract semantic feature vectors through the Bert model, and obtain the dialogue semantic feature vector.

[0077] Step S12: If the dimension of the dialogue semantic feature vector meets the preset dimension threshold, then a standardized semantic vector is generated through principal component analysis to obtain the standardized semantic vector.

[0078] Step S13: Based on the standardized semantic vector, the K-means clustering algorithm is used to classify the semantic vector to determine whether the dialogue involves runtime exceptions or user interruption scenarios, and the scenario classification result is obtained.

[0079] Step S14: Obtain execution flow data from the toolchain execution log. If the scenario classification result indicates an error or interruption, simulate the execution flow data using a finite state machine to determine the current execution flow state of the toolchain and obtain the execution flow state identifier.

[0080] Step S15: By combining the execution flow state identifier with the standardized semantic vector, the decision tree algorithm is used to predict the toolchain state stability, and the toolchain state prediction result is obtained.

[0081] Step S16: Extract state features from the toolchain state prediction results. If the state features indicate instability, obtain the abnormal cause features by matching the log pattern using regular expressions, and obtain the abnormal cause feature vector.

[0082] For example, semantic feature extraction based on dialogue content first processes the text data using the BERT model. BERT uses a pre-trained Transformer structure to transform dialogue text into high-dimensional semantic vectors. For instance, a user feedback message like "Toolchain operation interrupted, logs show insufficient memory" would be encoded by BERT into a 768-dimensional vector, capturing semantic information such as "interrupted" and "insufficient memory." This vector generation effectively preserves the contextual semantics of the dialogue, providing a foundation for subsequent analysis.

[0083] In one possible implementation, if the semantic vector dimension meets a preset dimensionality threshold (e.g., 768 dimensions), then principal component analysis (PCA) is used to reduce the dimensionality and generate a standardized semantic vector. PCA compresses the high-dimensional vector to 50 dimensions, retaining the main semantic features while reducing computational complexity.

[0084] For example, the dimensionality-reduced vectors highlight the correlation between "insufficient memory" and "interruption," while removing redundant noise. This standardization process improves the efficiency of subsequent clustering algorithms.

[0085] Specifically, the K-means clustering algorithm is used to classify standardized semantic vectors to determine whether the dialogue involves runtime anomalies or user interruptions. Assuming K=3, the clustering results may classify the dialogue into three categories: "normal," "abnormal," and "interrupted." If the dialogue vector is classified as "abnormal," it indicates a potential toolchain failure. The advantage of clustering lies in its ability to quickly identify dialogue scenarios, providing clues for anomaly localization.

[0086] For example, execution flow data is obtained from the toolchain execution log, which might record "memory allocation failed, process terminated". If the scenario classification indicates an anomaly, the execution flow is simulated using a finite state machine (FSM). The FSM defines states such as "initialization", "running", and "termination". Based on the event sequence in the log, the current state is determined to be "termination". This state identification clearly reflects the toolchain's execution trajectory.

[0087] In one embodiment, a decision tree algorithm is used to predict toolchain state stability by combining execution flow state identifiers with standardized semantic vectors. The decision tree determines stability based on features such as "termination state" and "out-of-memory semantics".

[0088] For example, if the state is "Terminated" and the semantic vector indicates "insufficient memory," the prediction result is "unstable." The transparency of decision trees makes it easy to trace the basis of predictions.

[0089] Preferably, if the prediction results indicate instability, the abnormal cause features are extracted by matching the log pattern using regular expressions.

[0090] For example, the regular expression ".*memory.*failure.*" matches "memory allocation failed" in the log, generating a feature vector that highlights the association between "memory" and "failure". This feature extraction accurately locates the cause of the anomaly, providing a basis for repair.

[0091] Understandably, the combination of the above methods enables a comprehensive analysis from dialogue semantics to toolchain state. Semantic vector extraction preserves the details of user feedback, PCA and K-means improve processing efficiency, FSM and decision trees clearly depict execution flow and stability, and regular expressions directly pinpoint the root cause of the problem. These technologies work together to significantly improve the accuracy and efficiency of anomaly detection and fault localization.

[0092] S2, based on semantic feature vectors and toolchain state identifiers, generates heterogeneous component call sequences through the dynamic tool integration module, and uses a distributed lock mechanism to coordinate the allocation of computing resources in high-concurrency scenarios to obtain the activated toolchain context.

[0093] Optionally, this step also includes:

[0094] Step S21: Obtain semantic feature vectors from the input data and use the BERT model to determine the structured representation of the semantic feature vectors.

[0095] Step S22: Generate heterogeneous component call sequence based on the structured representation of semantic feature vector and toolchain state identifier to obtain the initial configuration of component call sequence.

[0096] Step S23: For the initial configuration of the component call sequence, in high-concurrency scenarios, Redis is used to coordinate the allocation of computing resources to determine whether the resource allocation meets the requirements of concurrent task scheduling. If it does, an allocated resource pool is generated.

[0097] Step S24: Obtain computing resources from the allocated resource pool, use the Round-Robin algorithm to determine the execution order of concurrent tasks, and obtain a sorted concurrent task queue.

[0098] Step S25: Based on the sorted concurrent task queue, execute tasks through heterogeneous component calls to generate the intermediate state of the toolchain context.

[0099] Step S26: For the intermediate state of the toolchain context, determine whether the context meets the activation conditions by comparing the intermediate state threshold. If it does, generate an activated toolchain context. If it does not, adjust the component call sequence, re-acquire computing resources from the allocated resource pool and execute the task to obtain an activated toolchain context.

[0100] Step S27: Obtain the final output from the activated toolchain context, integrate the context data using the Pandas library, and obtain the business processing result.

[0101] For example, in dialogue-based toolchain management, the BERT model is used to extract semantic feature vectors, which can effectively capture semantic information in dialogue.

[0102] Specifically, suppose a user inputs the dialogue: "The system suddenly stopped responding while processing an order." The BERT model will transform this sentence into a high-dimensional semantic vector, possibly with 768 dimensions, containing contextual and intent information from the dialogue. This vector representation can reflect the potential problems described by the user, such as system anomalies.

[0103] It should be noted that BERT's pre-training feature enables it to adapt to various language scenarios, ensuring the robustness of semantic extraction.

[0104] In one possible implementation, the structured representation of semantic feature vectors is combined with toolchain state identifiers to generate heterogeneous component call sequences.

[0105] For example, a toolchain status identifier might indicate that the current system is in the "order processing" stage. Combined with semantic vectors, the system infers that it needs to call the database query component and the log analysis component. The initial configuration might include the call order: first query the order data, then analyze the logs. This sequence design ensures logical continuity between components.

[0106] Specifically, in high-concurrency scenarios, Redis is used to coordinate the allocation of computing resources.

[0107] For example, suppose a system needs to handle 1000 concurrent order requests. Redis manages a resource pool through a key-value store, recording the number of available CPU cores and memory capacity, such as 4 CPU cores and 16GB of memory. The system determines whether the resources meet the demand. If so, it generates a resource pool and allocates 2 CPU cores and 8GB of memory to the order processing tasks. This allocation method improves resource utilization.

[0108] For example, when using the Round-Robin algorithm to determine the execution order of concurrent tasks, the system distributes 1000 order requests to four processing threads in a round-robin fashion according to their arrival time, ensuring that each thread has a balanced load. The sorted task queue can avoid single-thread overload and improve processing efficiency.

[0109] In one possible implementation, the intermediate state of the toolchain context is generated after the heterogeneous component invokes and executes the task.

[0110] For example, the database query component returns order data, the log analysis component extracts exception logs, and intermediate states record this information. An intermediate state threshold comparison determines whether the context meets the activation conditions, such as complete order data and no serious errors in the logs. If satisfied, an activated toolchain context is generated; otherwise, the call sequence is adjusted, resources are reallocated, and execution is resumed.

[0111] Specifically, the final output integrates contextual data using the Pandas library.

[0112] For example, Pandas merges order data and log analysis results into a structured table containing fields such as order ID, status, and reason for the exception. This integration allows business personnel to quickly view the processing results and improves data readability.

[0113] It should be noted that the above solution ensures the efficient operation of the toolchain in complex dialogue scenarios through semantic analysis, resource coordination, and task scheduling. The implementation methods of each technical topic support each other, jointly improving the stability and responsiveness of the system.

[0114] S3 detects runtime exceptions or user interruptions for the activated toolchain context. If an interruption occurs, it triggers a safe disconnect mechanism, suspends the toolchain execution flow, marks the current heterogeneous component execution node, and obtains a snapshot of the interruption point state.

[0115] Optionally, this step also includes:

[0116] Step S31: Monitor the toolchain context through Prometheus to obtain runtime status data, and use Grafana to detect whether there are runtime exceptions or user interruption signals to obtain exception detection results.

[0117] Step S32: If the anomaly detection result indicates a runtime anomaly or user interruption signal, the signal processing tool is triggered to pause the toolchain execution flow and generate an execution flow pause confirmation.

[0118] Step S33: Based on the execution flow pause confirmation, locate the current component node and obtain the node information.

[0119] Step S34: Extract the breakpoint status from the node information, generate status snapshot data, store it in the Redis storage unit, and obtain snapshot storage confirmation.

[0120] Step S35: For snapshot storage confirmation, use MD5 checksum to verify the integrity of the status snapshot data. If the integrity verification passes, a valid snapshot confirmation is generated.

[0121] Step S36: After valid confirmation through snapshot, use ELKStack to extract abnormal feature data from the interruption point status and generate an abnormal log report.

[0122] Step S37: Based on the exception log report, use Ansible to adjust the toolchain context parameters, restore the execution flow, and obtain the toolchain recovery status.

[0123] Specifically, in the realm of runtime management within the toolchain context, the combined use of Prometheus and Grafana is a common approach for monitoring and anomaly detection.

[0124] For example, Prometheus generates time-series data by collecting runtime status data from the toolchain context, such as CPU usage, memory consumption, and task execution latency. Taking an e-commerce platform's order processing toolchain as an example, Prometheus collects the response time of order processing nodes every second. If the response time of a node suddenly increases from the normal value of 0.2 seconds to 2 seconds, it indicates a potential anomaly. Grafana displays this data through a visual dashboard and configures alert rules, such as triggering anomaly detection results when the response time exceeds 1 second or when the user actively sends an interrupt signal. Alert rules can be set such that five consecutive data collections exceed the alert threshold to avoid false alarms.

[0125] Specifically, after an anomaly detection result triggers the signal processing tool, the execution flow is paused to prevent further errors.

[0126] For example, if an anomaly is detected in the payment module within the order processing toolchain, the signal processing tool will pause the processing of subsequent payment requests and generate an execution flow pause confirmation. This confirmation records the context state at the time of the pause, such as the currently processed order ID and timestamp. After the pause confirmation is generated, the system locates the component node of the payment module, extracts node information such as module version and runtime parameters, and generates state snapshot data. The snapshot data includes the order ID, payment status, timestamp, etc., and is stored in a Redis unit. Redis's high-speed read and write capabilities ensure rapid snapshot storage; for example, storing 1000 snapshot data entries takes only 0.1 seconds.

[0127] In one possible implementation, the MD5 checksum is used to verify the integrity of the snapshot data.

[0128] For example, the system generates an MD5 value for the snapshot data and compares it with the MD5 value at the time of storage to ensure that the data has not been tampered with. If the verification passes, a valid snapshot confirmation is generated, indicating that the snapshot can be used for subsequent analysis. ELKStack then extracts abnormal feature data from the snapshot, such as error codes or timeout records from the payment module, and generates an anomaly log report.

[0129] For example, the report showed that the payment module timed out due to network jitter, specifically manifested as 10 consecutive failed requests. Ansible adjusted the toolchain context parameters based on the report, such as extending the payment module's timeout from 3 to 5 seconds, or switching to a backup payment gateway to restore execution flow.

[0130] For example, the restored toolchain context can continue processing orders, and system monitoring shows that the response time has recovered to 0.3 seconds, improving order processing efficiency. This approach ensures the stability and continuous operation of the toolchain under abnormal scenarios through rapid pausing, precise positioning, and dynamic adjustment.

[0131] S4, based on the snapshot of the interruption point state, serializes and stores the toolchain context to the distributed database through the state preservation management module, and uses the transaction log to record resource usage information to obtain persistent state records.

[0132] Optionally, this step also includes:

[0133] Step S41: Capture the state snapshot through the interrupt point, serialize the toolchain context to the distributed database, record the resource usage information in the transaction log, and generate persistent records.

[0134] Step S42: If the state snapshot is complete and no data is lost, extract the toolchain context from the state snapshot, and use Java's ObjectOutputStream tool to serialize the toolchain context to obtain serialized data.

[0135] Step S43: Extract key fields from serialized data, perform write operations in the database using a two-phase commit protocol, and generate a transaction log.

[0136] Step S44: Analyze resource usage information based on transaction logs, allocate storage nodes using the Jenkinshash function, and obtain resource distribution records.

[0137] Step S45: If the resource distribution record shows that the node load is balanced, then use the Gzip tool to compress the transaction log and generate an optimized log sequence.

[0138] Step S46: Extract operation timestamps from the optimized log sequence, update the distributed database using an optimistic concurrency control mechanism, and obtain a consistent state.

[0139] Step S47: Verify the integrity of the persistent record through the consistency state, use the MD5 algorithm to confirm that the data has not been tampered with, and generate the final state persistent record.

[0140] For example, in a scenario where an interruption point is captured and a state snapshot is generated during toolchain execution, suppose a distributed task scheduling system is processing a batch job, and a node triggers an interruption due to insufficient resources. When the interruption point is captured, the system records the current execution state of the task, such as the amount of data processed and the memory usage of the current computing node, forming a state snapshot. The snapshot includes the task ID, timestamp, and resource usage information, such as 2GB of memory usage and 70% CPU usage. This data is serialized into the toolchain context to ensure that the execution environment can be accurately restored during subsequent recovery.

[0141] Specifically, when serializing the toolchain context, Java's ObjectOutputStream utility is used to convert the snapshot object into a byte stream. Serialized data includes task configuration parameters, runtime variables, and breakpoint locations. Key fields such as task priority and dependencies are extracted for transactional writes to the distributed database.

[0142] For example, data with a high task priority and dependent task ID T123 will be written first. Using a two-phase commit protocol, the system first pre-commits data on the master node, and then formally commits it after confirming that all slave nodes are ready, ensuring data consistency. The transaction log records resource usage information for each write, such as disk I / O rate of 500MB / s and the number of database connections of 20.

[0143] In one possible implementation, based on resource usage information from transaction logs, the system uses the Jenkinshash function to allocate storage nodes. Assuming there are three storage nodes, log data is distributed to the least loaded node (node ​​1) through hash calculation. Load balancing records show that node 1 has a storage utilization rate of 60%, while nodes 2 and 3 have 65% and 70%, respectively. After load balancing, the transaction logs are compressed using Gzip to reduce storage space usage, e.g., 100MB of logs compressed to 30MB. The compressed log sequence is then used to extract operation timestamps, such as 2025-08-01 01:14:00, for optimistic concurrency control. The system compares the timestamps; if there are no conflicts, the database is updated to ensure consistency.

[0144] For example, when verifying the integrity of persistent records, the system calculates an MD5 checksum on the final state record. Assuming the record size is 50MB, if the checksum matches the pre-stored value, it confirms that the data has not been tampered with. Persistent records are stored in a distributed database, containing task status, resource distribution, and log sequences, providing a reliable basis for subsequent fault recovery. This approach ensures that the toolchain can recover quickly after an outage, while optimizing resource allocation and storage efficiency, supporting the system's high availability and stability.

[0145] S5 extracts resource usage information from the transaction log based on the persistent state records, performs resource release for each heterogeneous component, cleans up the temporary context using the state synchronization mechanism, and obtains a list of released resources.

[0146] Optionally, this step also includes:

[0147] Step S51: Obtain resource usage records from the transaction log, and extract resource allocation information of each heterogeneous component using the awk tool to obtain a resource usage dataset.

[0148] Step S52: Based on the resource occupancy dataset, perform resource release operations for each heterogeneous component, use a preset occupancy threshold to determine whether the resource can be released, and obtain a set of released resources.

[0149] Step S53: Update the status information of each heterogeneous component through the set of released resources to generate a status synchronization record.

[0150] Step S54: Based on the state synchronization record, clean up the temporary context of each heterogeneous component, use the grep tool to identify useless context, and obtain the cleaned context set.

[0151] Step S55: Generate a list of released resources using the cleaned context set, and format the output using the sed tool to obtain a structured resource list.

[0152] Step S56: Update the resource status in the transaction log using the structured resource list to generate updated log records.

[0153] For example, in resource management scenarios of distributed systems, transaction logs record the resource usage during system runtime, such as CPU, memory, and storage allocation data. Transaction logs are typically stored in structured formats, such as JSON or XML, containing fields such as timestamps, component IDs, and resource usage. When using the awk tool to extract resource allocation information, resource usage data for specific heterogeneous components can be filtered based on component IDs and timestamps. Assuming the system contains compute node A and storage node B, the awk tool uses regular expressions to extract that A's CPU usage is 60%, memory usage is 4GB, and B's storage usage is 2TB, generating a resource usage dataset. The dataset is stored in a table format, with columns including component ID, resource type, and usage.

[0154] Specifically, when performing resource release operations on a resource-occupied dataset, a preset occupancy threshold is used to determine whether a resource can be released.

[0155] For example, resources with CPU utilization below 30% or memory usage below 1GB are considered releaseable. In one implementation, if compute node A's CPU utilization is 20%, below the utilization threshold, a release operation is triggered, returning excess CPU resources to the resource pool and generating a set of released resources. This set records the type and quantity of released resources, such as "Node A releases 0.5 CPU cores." This process ensures efficient resource utilization and avoids idle waste.

[0156] In one embodiment, when updating the state information of heterogeneous components through the set of released resources, the system will synchronously update the running status of each component.

[0157] For example, after node A releases its CPU, its state changes from "high load" to "normal," generating a state synchronization record. This record is stored as a log, containing the update time and details of the state change. Next, the grep tool is used to clean up the temporary context, identifying useless temporary files or caches.

[0158] For example, grep can find a 1GB temporary cache file for node A by matching the prefix "temp_", clean it up, and generate a cleaned context set. This set retains only the necessary context, improving system performance.

[0159] For example, when generating a list of released resources based on the cleaned context set, the `sed` tool is used to format the output. `sed` converts the set data into JSON format, generating a structured resource list, such as `{"Node A":{"CPU":"0.5 cores","Memory":"1GB"}}`. This list clearly displays the released resources, facilitating subsequent management. When updating the transaction log, the system modifies the resource status in the log according to the resource list, for example, updating the CPU status of node A from "occupied" to "idle," generating an updated log record. This record provides an accurate basis for subsequent resource scheduling.

[0160] It should be noted that the entire process ensures the transparency and consistency of resource management through the linkage of transaction logs, resource datasets, and status records.

[0161] For example, log-recorded resource status updates can be used for auditing and tracking resource usage history. This approach is particularly important in distributed systems, as it effectively manages the dynamic allocation of resources to heterogeneous components.

[0162] S6. Based on the list of released resources, retrieve the interruption point state snapshot and transaction log from the distributed database, use log analysis algorithms to reconstruct the historical state of the toolchain execution flow, and obtain the fault context analysis results.

[0163] Optionally, this step also includes:

[0164] Step S61: Obtain the list of released resources and transaction logs from the distributed database, retrieve the snapshot of the interruption point state, and determine the initial dataset.

[0165] Step S62: The initial dataset includes resource release timestamps, transaction log entries, and snapshot status parameters.

[0166] Step S63: Use ApacheLog4j to process transaction logs and state snapshots, reconstruct the toolchain execution flow history, and generate execution sequences.

[0167] In step S64, Apache Log4j parses the time series and state changes in the transaction log snapshots and outputs an ordered execution sequence.

[0168] Step S65: By performing sequence analysis to determine the relationship between the interrupt point status and the released resources, the fault triggering conditions are determined, and the fault context is obtained.

[0169] Step S66: If the difference between the timestamp of the interruption point state and the timestamp of the resource release exceeds the preset timestamp difference threshold, then the fault triggering condition is determined and the fault context is output.

[0170] Step S67: If the fault context contains an abnormal execution mode, then the K-means clustering algorithm is used to classify the execution sequence and determine the abnormal category.

[0171] Step S68: The K-means clustering algorithm groups the execution sequence according to its feature vectors and outputs anomaly category labels.

[0172] Step S69: Extract key transaction log fragments based on the anomaly category and fault context to generate intermediate data for context analysis. Preferably, the key transaction log fragments include log entries corresponding to the anomaly category and related resource release records.

[0173] Step S610: Analyze intermediate data through context and use a decision tree algorithm to predict the scope of the fault's impact to obtain the analysis results.

[0174] Step S611: The decision tree algorithm outputs the range of nodes affected by the fault and the list of transactions affected based on the feature attributes of the intermediate data.

[0175] Specifically, the process of obtaining the list of released resources and transaction logs from the distributed database, retrieving the snapshot of the interruption point state, and determining the initial dataset can be understood as extracting key information from multi-node storage.

[0176] For example, in a distributed database like Cassandra, a list of released resources might record 100MB of memory released by a compute node at 01:00:00 on 2025-08-01. The transaction log contains metadata about this release operation, such as the transaction ID and timestamp. An interrupt point state snapshot records the system's running state at that point in time, such as CPU utilization at 50%. By integrating this data, an initial dataset is formed, containing timestamps, transaction entries, and state parameters, facilitating subsequent analysis. Apache Log4j's log parsing capabilities are crucial when using it to process transaction logs and state snapshots, reconstruct the toolchain execution flow history, and generate execution sequences.

[0177] For example, a log entry might show that a node performed a resource release operation at 01:00:05, while a state snapshot records the memory changes before and after the release. Apache Log4j uses time series analysis to organize these events into an ordered execution sequence, such as "01:00:00 allocate memory → 01:00:05 release memory," providing a clear timeline for subsequent fault analysis. Analyzing the relationship between the interruption point state and the released resources through execution sequence analysis to determine the fault triggering conditions and obtain the fault context requires careful attention to timestamp differences.

[0178] For example, if the resource release timestamp is 01:00:05, but the interruption point state snapshot shows a system crash at 01:00:10, the difference of 5 seconds exceeds the preset timestamp difference threshold of 3 seconds, thus indicating a fault trigger. The fault context may include anomalies caused by "failure to update the state in a timely manner after memory release." This analysis helps to quickly locate the root cause of the problem. If the fault context contains abnormal execution patterns, the K-means clustering algorithm is used to classify the execution sequences and determine the anomaly category.

[0179] For example, an execution sequence might contain multiple log entries, and the feature vector includes operation type, duration, and resource usage. K-means clustering categorizes the sequence into normal and abnormal classes, with the abnormal category label potentially pointing to a "high-frequency resource release" pattern. This classification method effectively identifies potential abnormal behavior patterns. Based on the abnormal category and fault context, the steps of extracting key transaction log fragments and generating intermediate data for contextual analysis focus on filtering relevant information.

[0180] For example, log segments categorized as "high-frequency resource release" might include multiple records of memory releases within short periods, such as releasing 100MB at 01:00:05 and 50MB at 01:00:06. These segments, combined with resource release records, form intermediate data, providing a basis for subsequent predictions. The process of analyzing intermediate data through context and using decision tree algorithms to predict the scope of fault impact to obtain the analysis results relies on the analysis of feature attributes.

[0181] For example, decision trees can predict the range of nodes affected by a fault based on features of intermediate data, such as "release frequency" and "timestamp difference," potentially resulting in "affecting 3 compute nodes" and a list of transactions affected, such as "transaction ID 1234 interrupted." This predictive approach helps to quickly assess the scope of a fault, facilitating targeted measures by system maintenance personnel.

[0182] In one possible implementation, the above process can be extended to a multi-cluster environment.

[0183] For example, in a multi-cluster distributed system, the initial dataset may contain resource release records across clusters. The execution sequences parsed by Apache Log4j can cover multi-node operations, while K-means clustering and decision tree algorithms can further analyze cross-cluster failure modes. This extension approach improves system robustness and ensures efficient fault diagnosis in complex environments.

[0184] S7, based on the fault context analysis results or user reconnection requests, restores the interruption point state snapshot from the distributed database, reloads the toolchain execution flow through the dynamic tool integration module, verifies data integrity, and obtains the context recovery state.

[0185] Optionally, this step also includes:

[0186] Step S71: Obtain a snapshot of the interruption point status from the distributed database.

[0187] Step S72: Use Apache Kafka to parse the metadata in the breakpoint state snapshot.

[0188] Step S73: Extract the timestamp and transaction identifier from the parsed metadata.

[0189] Step S74: Determine the interruption point status based on the extracted timestamp and transaction identifier.

[0190] Step S75: Query the fault context log based on the interruption point status.

[0191] Step S76: Use the Splunk tool to match keywords in the fault context log to determine the cause of the transaction interruption.

[0192] Step S77: Based on the determined cause of transaction interruption, the error type is classified using a decision tree algorithm, where the input of the decision tree algorithm is the log feature vector and the output is the error category label, thus obtaining the first fault context.

[0193] Step S78: If the first fault context indicates a user reconnection request, verify the user's identity and session validity.

[0194] Step S79: Obtain reconnection authorization based on the verified user identity and session validity.

[0195] Step S710: Load the tool integration module by reconnecting and authorizing. Step S711: Initialize the configuration for the loaded tool integration module.

[0196] Step S712: Based on the initialized configuration, match the toolchain version to obtain the toolchain execution flow.

[0197] Step S713: Use the toolchain execution flow to recover interrupted transactions segment by segment.

[0198] Step S714: When recovering interrupted transactions segment by segment, perform CRC check to verify data integrity. The input of CRC check is the recovered data block, and the output is the comparison result of the check value and the preset integrity threshold.

[0199] Step S715: If the CRC check passes, the recovery transaction is written to the distributed database.

[0200] Step S716: Generate context recovery state based on the written recovery transaction update state snapshot.

[0201] Step S717: Based on the context, the execution flow verification is completed by logging the recovery operation sequence of the recovery state synchronization toolchain.

[0202] For example, when retrieving a snapshot of the breakpoint state from a distributed database, a distributed database management system like Cassandra can be used to query the breakpoint data for a specific node. Suppose a financial trading system experiences an interruption under high concurrency. Cassandra uses the partition key to locate the snapshot table containing the breakpoint state and extracts the snapshot record containing the timestamp 2025-08-01 01:14:23 and the transaction identifier TXN_123. This ensures rapid breakpoint location and avoids performance bottlenecks caused by global scanning.

[0203] Specifically, when parsing snapshot metadata using Apache Kafka, Kafka's streaming capabilities can be leveraged to extract timestamps and transaction identifiers from the snapshot.

[0204] For example, a Kafka consumer subscribes to a snapshot data topic, parses out the timestamp and TXN_123, and generates a structured event stream. This approach facilitates real-time processing of high-throughput data and ensures accurate extraction of metadata.

[0205] In one embodiment, when determining the interruption point status based on timestamps and transaction identifiers, it can be determined whether the interruption was caused by network timeout by comparing the timestamp with the most recent record in the transaction log.

[0206] For example, the timestamp shows that the transaction was interrupted at 01:14:23, while the log shows a normal commit record at 01:14:20, suggesting that a 3-second delay may have triggered a timeout interruption. This method clearly identifies the interruption point and provides a basis for subsequent analysis.

[0207] For example, when querying fault context logs, the Splunk tool can be used to perform keyword matching. Suppose the log contains the keyword "connection_lost," Splunk can use regular expressions to confirm that the interruption was caused by a client connection failure. This precise matching reduces manual troubleshooting costs and improves fault location efficiency.

[0208] Specifically, when using the decision tree algorithm to classify error types, classification can be based on log feature vectors such as "interruption frequency" and "transaction type".

[0209] For example, if the input feature vector contains "high-frequency interruptions" and "transfer transactions", the decision tree outputs the error category as "network instability". This classification method is intuitive and efficient, facilitating the rapid identification of failure modes.

[0210] In one embodiment, when verifying a user's reconnection request, the user's identity and session validity can be checked via the OAuth2 protocol.

[0211] For example, verifying the token of user ID USER_001 confirms that their session is valid for 30 minutes and generates a reconnection authorization. This method ensures security and prevents unauthorized access.

[0212] For example, when initializing the configuration after loading the tool integration module, toolchain parameters can be set according to the configuration file.

[0213] For example, specifying API version 2.3 in the configuration will automatically load compatible toolchain modules. This approach ensures the compatibility between the toolchain and the system.

[0214] Specifically, when resuming an interrupted transaction, data integrity is verified segment by segment using CRC check.

[0215] For example, if a recovery block contains 100 transaction records, the CRC value is calculated and compared with a preset integrity threshold to ensure no data loss. This verification method guarantees the reliability of transaction recovery.

[0216] In one embodiment, when updating the state snapshot and synchronizing the toolchain execution log, the timestamp of the recovery transaction, 2025-08-01 01:15:00, can be written to the snapshot, along with the recovery operation sequence. This synchronization mechanism ensures complete traceability of the execution flow, facilitating subsequent auditing and verification.

[0217] S8 restores the context state and semantic feature vector, updates the toolchain execution flow, reallocates computing resources through the heterogeneous component coordination mechanism, and obtains the task continuity confirmation result.

[0218] Optionally, this step also includes:

[0219] Step S81: Obtain state feature data through context analysis, extract feature vectors using the PCA function of Scikit-learn, and obtain the initial state feature set.

[0220] Step S82: For the initial state feature set, if the proportion of non-empty dimensions of the feature vector is higher than the preset dimension proportion threshold, then obtain the current computing resource status from the heterogeneous components, use the Kubernetes resource scheduler to allocate resources, and determine the dynamic allocation scheme.

[0221] Step S83: Adjust the resource occupancy ratio of heterogeneous components through a dynamic allocation scheme to obtain the resource allocation result.

[0222] Step S84: If the task execution continuity meets the preset conditions for the resource allocation result, the task continuity result is confirmed and continuity confirmation data is obtained.

[0223] Step S85: Based on the continuity confirmation data, a recurrent neural network algorithm is used to predict potential interruption risks and determine the task continuity assurance strategy.

[0224] Step S86: Update the initial state feature set through the task continuity guarantee strategy to obtain the final task execution state.

[0225] Specifically, the core of obtaining state feature data through context analysis lies in extracting key state information from the distributed system environment.

[0226] For example, in a distributed task scheduling system, context analysis can extract status data such as task execution time, resource utilization, and error frequency from task logs.

[0227] Specifically, suppose a task scheduling platform processes 1000 tasks per day, and its logs record the CPU usage, memory consumption, and execution time of each task. By analyzing these logs, a state feature dataset containing task execution efficiency and resource consumption can be generated. This approach provides a reliable data foundation for subsequent feature extraction. The process of using Scikit-learn's PCA function to extract feature vectors aims to reduce the dimensionality of the state feature dataset while retaining key information.

[0228] For example, in the task scheduling system mentioned above, the initial state feature set may include multiple dimensions such as task execution time, CPU utilization, and memory usage. Through PCA analysis, assuming the original dataset has 10 dimensions, PCA can reduce the dimensionality of the data to 3 principal components, retaining 90% of the information variance. This dimensionality reduction operation helps reduce the complexity of subsequent calculations while highlighting key features. For the initial state feature set, if the proportion of non-empty dimensions in the feature vectors exceeds a preset dimensionality threshold (e.g., 80%), it is necessary to obtain the current computing resource status from heterogeneous components.

[0229] For example, in a heterogeneous computing cluster consisting of CPUs, GPUs, and FPGAs, assuming that 80% of the feature vector dimensions are valid, the system will query the real-time resource status of each component, such as CPU utilization at 60% and GPU utilization at 70%. Through Kubernetes' resource scheduler, resources can be dynamically allocated according to task requirements.

[0230] For example, tasks with high computational demands can be prioritized for allocation to GPU nodes, forming a dynamic allocation scheme. This approach ensures efficient and targeted resource allocation. The resource allocation result is obtained by adjusting the resource utilization ratio of heterogeneous components through the dynamic allocation scheme.

[0231] For example, in a task scheduling system, if a task requires 20% GPU resources and 10% CPU resources, the scheduler will adjust the allocation ratio according to the current cluster status to ensure that the task runs efficiently.

[0232] It should be noted that this dynamic adjustment can avoid resource bottlenecks and improve the overall system throughput. If the task execution continuity meets the preset conditions (such as more than 99% of tasks being uninterrupted), the task continuity result is confirmed.

[0233] For example, if the system detects that 99.5% of tasks have been executed continuously without interruption over the past 24 hours, it generates continuity confirmation data. This data reflects the system's stability and reliability. When using recurrent neural network algorithms to predict potential outage risks, modeling can be based on continuity confirmation data.

[0234] For example, an RNN model takes task execution data from the past 7 days as input and predicts the risk of interruption within the next 24 hours. Suppose the model predicts a 10% probability of interruption for a task, the system will generate a contingency plan, such as adding redundant resources or adjusting task priority. This approach allows for proactive intervention in potential problems. After updating the initial state feature set using the task continuity guarantee strategy, the final task execution state is obtained.

[0235] For example, the system allocates an additional 5% of CPU resources to high-risk tasks based on prediction results, and the updated state feature set reflects the optimized task execution efficiency and stability. This update ensures the continuous optimization of the task scheduling system.

[0236] like Figure 4As shown, in a second aspect, the present invention provides a dynamic tool integration system for an intent-driven AI dialogue system. This system integrates dynamic tools for the intent-driven AI dialogue system using the method described above. The system mainly includes: an intent recognition and state monitoring module, used to obtain the semantic feature vector of the current dialogue from a pre-established intent recognition model, and to determine the current state of the toolchain execution flow in the event of runtime anomalies or user interruptions, obtaining the semantic feature vector and toolchain state identifier; a dynamic tool integration module, used to generate a heterogeneous component call sequence based on the semantic feature vector and toolchain state identifier, and to coordinate the allocation of computing resources using a distributed lock mechanism in high-concurrency scenarios, obtaining the activated toolchain context; a safe disconnection control module, used to detect runtime anomalies or user interruptions in the activated toolchain context, and if such an event occurs, to trigger a safe disconnection mechanism, suspend the toolchain execution flow, and mark the current heterogeneous component execution node, obtaining a snapshot of the interruption point state; and a state persistence module, used to persist the state based on the interruption point state snapshot, and to maintain the state through state persistence. The storage management module serializes and stores the toolchain context in a distributed database, records resource usage information using transaction logs, and obtains a persistent state record. The resource release management module extracts resource usage information from the transaction logs based on the persistent state record, performs resource release for each heterogeneous component, and cleans up temporary contexts using a state synchronization mechanism to obtain a list of released resources. The fault context reconstruction module retrieves the interruption point state snapshot and transaction logs from the distributed database based on the list of released resources, reconstructs the historical state of the toolchain execution flow using log analysis algorithms, and obtains the fault context analysis results. The context recovery module restores the interruption point state snapshot from the distributed database based on the fault context analysis results or user reconnection requests, reloads the toolchain execution flow through the dynamic tool integration module, verifies data integrity, and obtains the context recovery state. The task continuity management module updates the toolchain execution flow based on the context recovery state and semantic feature vectors, reallocates computing resources through a heterogeneous component coordination mechanism, and obtains the task continuity confirmation result. The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A dynamic tool integration method for an intent-driven AI dialogue system, characterized in that, The method includes: S1. Obtain the semantic feature vector of the current dialogue from the pre-established intent recognition model. For runtime exceptions or user interruptions, determine the current state of the toolchain execution flow, obtaining the semantic feature vector and toolchain state identifier. S2. Based on the semantic feature vector and toolchain state identifier, generate a heterogeneous component call sequence. In high-concurrency scenarios, use a distributed lock mechanism to coordinate the allocation of computing resources, obtaining the activated toolchain context. S3. For the activated toolchain context, detect runtime exceptions or user interruptions. If an exception occurs, trigger a safe disconnect mechanism, suspend the toolchain execution flow, and mark the current heterogeneous component execution node, obtaining an interruption point state snapshot. S4. Based on the interruption point state snapshot, serialize and store the toolchain context in a distributed database using the state preservation management module, utilizing transaction logs. S5. Based on the persistent state record, extract resource usage information from the transaction log, perform resource release for each heterogeneous component, clean up the temporary context, and obtain a list of released resources. S6. Based on the list of released resources, retrieve the interruption point state snapshot and transaction log from the distributed database, reconstruct the historical state of the toolchain execution flow, and obtain the fault context analysis result. S7. Based on the fault context analysis result or user reconnection request, restore the interruption point state snapshot from the distributed database, reload the toolchain execution flow, verify data integrity, and obtain the context recovery state. S8. Based on the context recovery state and semantic feature vector, update the toolchain execution flow, reallocate computing resources through the heterogeneous component coordination mechanism, and obtain the task continuity confirmation result.

2. The dynamic tool integration method for an intent-driven AI dialogue system according to claim 1, characterized in that, Step S1 involves obtaining the semantic feature vector of the current dialogue from a pre-established intent recognition model, and determining the current state of the toolchain execution flow for runtime exceptions or user interruption scenarios, thereby obtaining the semantic feature vector and toolchain state identifier, including: Step S11: Obtain text data from the current dialogue content, extract semantic feature vectors, and obtain dialogue semantic feature vectors; Step S12: If the dimension of the dialogue semantic feature vector meets the preset dimension threshold, then a standardized semantic vector is generated to obtain the standardized semantic vector. Step S13: Based on the standardized semantic vector, classify the semantic vector to determine whether the dialogue involves runtime exceptions or user interruption scenarios, and obtain the scenario classification result. Step S14: Obtain execution flow data from the toolchain execution log. If the scenario classification result indicates an anomaly or interruption, simulate the execution flow data to determine the current execution flow status of the toolchain and obtain the execution flow status identifier. Step S15: By combining the execution flow state identifier with the standardized semantic vector, the toolchain state stability is predicted, and the toolchain state prediction result is obtained. Step S16: Extract state features from the toolchain state prediction results. If the state features indicate instability, match the log pattern to obtain the abnormal cause features and obtain the abnormal cause feature vector.

3. The dynamic tool integration method for an intent-driven AI dialogue system according to claim 1, characterized in that, Step S2 generates a heterogeneous component call sequence based on the semantic feature vector and toolchain state identifier. In high-concurrency scenarios, a distributed lock mechanism is used to coordinate the allocation of computing resources, resulting in an activated toolchain context, including: Step S21: Obtain semantic feature vectors from the input data and determine the structured representation of the semantic feature vectors; Step S22: Generate heterogeneous component call sequence based on the structured representation of semantic feature vector and toolchain state identifier to obtain the initial configuration of component call sequence; Step S23: For the initial configuration of the component call sequence, coordinate the allocation of computing resources in high-concurrency scenarios, determine whether the resource allocation meets the requirements of concurrent task scheduling, and if so, generate the allocated resource pool. Step S24: Obtain computing resources from the allocated resource pool, determine the execution order of concurrent tasks, and obtain a sorted concurrent task queue. Step S25: Based on the sorted concurrent task queue, execute tasks through heterogeneous component calls to generate the intermediate state of the toolchain context; Step S26: For the intermediate state of the toolchain context, determine whether the context meets the activation conditions by comparing the intermediate state threshold. If it meets the conditions, generate an activated toolchain context. If it does not meet the conditions, adjust the component call sequence, re-acquire computing resources from the allocated resource pool and execute the task to obtain an activated toolchain context. Step S27: Obtain the final output from the activated toolchain context, integrate the context data, and obtain the business processing result.

4. The dynamic tool integration method for an intent-driven AI dialogue system according to claim 1, characterized in that, Step S3 involves detecting runtime exceptions or user interruptions in the activated toolchain context. If such an event occurs, a safe disconnect mechanism is triggered, pausing the toolchain execution flow and marking the current heterogeneous component execution node. A snapshot of the interruption point state is then obtained, including: Step S31: Monitor the toolchain context, obtain runtime status data from it, detect whether there are runtime exceptions or user interruption signals, and obtain exception detection results; Step S32: If the anomaly detection result indicates a runtime anomaly or user interruption signal, then the signal processing tool is triggered to pause the toolchain execution flow and generate an execution flow pause confirmation. Step S33: Based on the execution flow pause confirmation, locate the current component node and obtain the node information; Step S34: Extract the interruption point status from the node information, generate status snapshot data, store it in the storage unit, and obtain snapshot storage confirmation; Step S35: For snapshot storage confirmation, verify the integrity of the status snapshot data. If the integrity verification passes, generate a valid snapshot confirmation. Step S36: After valid confirmation by snapshot, extract abnormal feature data from the interruption point status and generate an abnormal log report; Step S37: Based on the exception log report, adjust the toolchain context parameters, restore the execution flow, and obtain the toolchain recovery status.

5. The dynamic tool integration method for an intent-driven AI dialogue system according to claim 1, characterized in that, Step S6 involves retrieving the interruption point state snapshot and transaction log from the distributed database based on the list of released resources, reconstructing the historical state of the toolchain execution flow, and obtaining the fault context analysis results, including: Step S61: Obtain the list of released resources and transaction logs from the distributed database, retrieve the breakpoint state snapshot, and determine the initial dataset; Step S62, the initial dataset includes resource release timestamps, transaction log entries, and snapshot status parameters; Step S63: Process transaction logs and state snapshots, reconstruct toolchain execution flow history, and generate execution sequences; Step S64: parse the time series of the transaction log and the state changes in the snapshot, and output an ordered execution sequence; Step S65: By performing sequence analysis to determine the relationship between the interruption point status and the released resources, the fault triggering conditions are determined, and the fault context is obtained. Step S66: If the difference between the timestamp of the interruption point state and the timestamp of the resource release exceeds the preset timestamp difference threshold, then determine the fault triggering condition and output the fault context. Step S67: If the fault context contains an abnormal execution mode, then classify the execution sequence to determine the abnormal category; Step S68: Group the execution sequence according to its feature vectors and output the anomaly category labels; Step S69: Extract key transaction log fragments based on the anomaly category and fault context to generate intermediate data for context analysis; Step S610: Analyze intermediate data in context to predict the scope of the fault's impact and obtain the analysis results; Step S611: Based on the characteristic attributes of the intermediate data, output the range of nodes affected by the fault and the list of transactions affected.

6. The dynamic tool integration method for an intent-driven AI dialogue system according to claim 5, characterized in that, Step S69 involves extracting key transaction log fragments based on the anomaly category and fault context to generate intermediate context analysis data, including: the key transaction log fragments include log entries corresponding to the anomaly category and related resource release records.

7. The dynamic tool integration method for an intent-driven AI dialogue system according to claim 1, characterized in that, Step S7, based on the fault context analysis results or user reconnection request, restores the interruption point state snapshot from the distributed database, reloads the toolchain execution flow, verifies data integrity, and obtains the context recovery state, including: Step S71: Obtain a snapshot of the interruption point status from the distributed database; Step S72: Parse the metadata in the interruption point state snapshot; Step S73: Extract the timestamp and transaction identifier from the parsed metadata; Step S74: Determine the interruption point status based on the extracted timestamp and transaction identifier; Step S75: Query the fault context log based on the interruption point status; Step S76: Match keywords in the fault context log to determine the reason for the transaction interruption; Step S77: Based on the determined transaction interruption cause, classify the error type to obtain the first fault context; Step S78: If the first fault context indicates a user reconnection request, then verify the user's identity and session validity. Step S79: Obtain reconnection authorization based on the verified user identity and session validity; Step S710: Load the tool integration module through reconnection authorization; Step S711: Initialize the configuration for the loaded tool integration module; Step S712: Based on the initialized configuration, match the toolchain version to obtain the toolchain execution flow; Step S713: Use the toolchain execution flow to recover interrupted transactions segment by segment; Step S714: Verify data integrity during the segmented recovery of interrupted transactions; Step S715: If the verification is successful, the recovery transaction will be written to the distributed database. Step S716: Generate context recovery state based on the written recovery transaction update state snapshot; Step S717: Based on the context, the execution flow verification is completed by logging the recovery operation sequence of the recovery state synchronization toolchain.

8. The dynamic tool integration method for an intent-driven AI dialogue system according to claim 7, characterized in that, Step S77, classifying error types based on the determined transaction interruption cause to obtain the first fault context, includes: classifying error types using a decision tree algorithm, wherein the input of the decision tree algorithm is a log feature vector and the output is an error category label.

9. The dynamic tool integration method for an intent-driven AI dialogue system according to claim 8, characterized in that, Step S714, verifying data integrity during the segmented recovery of interrupted transactions, includes: performing a CRC check to verify data integrity, wherein the input of the CRC check is the recovered data block, and the output is the comparison result of the check value and the preset integrity threshold.

10. A dynamic tool integration system for intent-driven AI dialogue systems, characterized in that, The method described in any one of claims 1-9 is used to integrate dynamic tools of an intent-driven AI dialogue system. The system comprises: an intent recognition and state monitoring module, used to obtain the semantic feature vector of the current dialogue from a pre-established intent recognition model, and determine the current state of the toolchain execution flow in the event of runtime exceptions or user interruptions, obtaining the semantic feature vector and toolchain state identifier; a dynamic tool integration module, used to generate a heterogeneous component call sequence based on the semantic feature vector and toolchain state identifier, and use a distributed lock mechanism to coordinate the allocation of computing resources in high-concurrency scenarios, obtaining an activated toolchain context; a safe disconnection control module, used to detect runtime exceptions or user interruptions in the activated toolchain context, and if such an event occurs, trigger a safe disconnection mechanism to pause the toolchain execution flow and mark the current heterogeneous component execution node, obtaining an interruption point state snapshot; and a state persistence module, used to persist the toolchain execution flow based on the interruption point state snapshot through a state saving management module. The toolchain execution flow is serialized and stored in a distributed database. Transaction logs are used to record resource usage information, resulting in a persistent state record. A resource release management module extracts resource usage information from the transaction logs based on the persistent state record, performs resource release for each heterogeneous component, cleans up temporary contexts, and obtains a list of released resources. A fault context reconstruction module retrieves the interruption point state snapshot and transaction logs from the distributed database based on the released resource list, reconstructs the historical state of the toolchain execution flow, and obtains the fault context analysis results. A context recovery module restores the interruption point state snapshot from the distributed database based on the fault context analysis results or user reconnection requests, reloads the toolchain execution flow, verifies data integrity, and obtains the context recovery status. A task continuity management module updates the toolchain execution flow based on the context recovery status and semantic feature vectors, reallocates computing resources through a heterogeneous component coordination mechanism, and obtains the task continuity confirmation result.

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