An LLM deployment and application system

Abstract

This invention relates to an LLM deployment and application system, comprising: a retrieval enhancement generation module, used to parse natural language queries into multi-hop graph query instructions and execute them to retrieve fact chains, and guide a large language model to generate answers; a robust agent execution module, used to monitor the tool call status of the executing agent; when a Type I error is returned, the executing agent is triggered to enter a thinking reflection mode; when a Type II error is returned, the dynamic fault tolerance mechanism of the executing agent is triggered; a multi-agent debate collaboration module, used to manage multiple decision-making agents to conduct multi-round debates to generate decision solutions; and a brain-computer interface implicit thought interaction module, used to translate user neural signals into text prompts through a decoding model and input them into the large language model to generate output results. The present invention significantly improves the accuracy of complex association retrieval, agent execution stability, multi-dimensional decision-making capabilities, and human-computer interaction dimensions of the large language model.

Description

Technical Field

[0001] This invention relates to large language model technology, and in particular to an LLM deployment and application system. Background Technology

[0002] In recent years, Large Language Models (LLMs) have made significant progress in the field of natural language processing, but their implementation in real-world enterprise applications and complex business scenarios still faces multiple technical bottlenecks.

[0003] First, existing retrieval enhancement techniques largely rely on vector similarity matching. However, when faced with queries requiring multi-hop logical reasoning or complex entity relationships (such as tracing multi-layered equity structures or cross-domain causal chain analysis), vector-based retrieval methods often lead to the omission of key information or the generation of illusions due to the lack of explicit logical path modeling, making it difficult to guarantee the accuracy and interpretability of the reasoning results.

[0004] Secondly, in terms of agent task execution, existing mainstream architectures (such as the ReAct pattern) have significant shortcomings in robustness. When external tools or application programming interfaces called by the agent fail due to abnormal environment configuration (such as key expiration or permission change) or network communication failure (such as request timeout or empty response), existing agents generally lack human-like "reflection" and "self-repair" mechanisms, resulting in the interruption of the task execution chain and a decrease in system availability.

[0005] Furthermore, for open and complex decision-making problems, single-agent systems are limited by fixed system prompts, resulting in a narrow perspective in perception and reasoning, making it difficult to generate comprehensive and objective decision solutions. Meanwhile, traditional human-computer interaction methods primarily rely on explicit text or voice input, failing to effectively capture the implicit cognition and thought processes of users during the conceptualization phase, thus limiting the depth of human-computer collaboration and the breadth of task exploration. Summary of the Invention

[0006] The purpose of this invention is to provide an LLM deployment and application system to solve the problems of insufficient accuracy in complex association retrieval of existing large language models, poor stability of agent execution, lack of multi-dimensional decision-making ability, and narrow human-computer interaction dimensions.

[0007] To address the above problems, this invention provides an LLM deployment and application system, the system comprising:

[0008] A task orchestrator and functional modules, wherein the task orchestrator is used to achieve coordinated control between the functional modules;

[0009] The functional modules include:

[0010] The knowledge graph-guided retrieval enhancement generation module is used to receive natural language queries, parse the queries into multi-hop graph query instructions on the knowledge graph, execute the instructions in the knowledge graph database to retrieve structured fact chains, and guide the large language model to generate answers based on the fact chains.

[0011] The robust agent execution module is used to monitor the tool call status of the execution agent; when the tool call returns a Type I error, it triggers the execution agent to enter a thinking reflection mode to repair the operating environment; when the tool call returns a Type II error, it triggers the dynamic fault tolerance mechanism of the execution agent to execute a backup plan.

[0012] The multi-agent debate collaboration module is used to initialize multiple decision agents with different preset roles for open problems, and to manage the multiple decision agents to conduct multiple rounds of debate through a debate coordinator to generate a decision scheme.

[0013] The brain-computer interface implicit thought interaction module is used to translate the collected user neural signals into text prompts through a decoding model, and input the text prompts into a large language model to generate output results.

[0014] The retrieval enhancement generation module includes an entity linking unit, a graph query language generation unit, and a fact chain processing unit. The entity linking unit parses the natural language query input by the user to obtain query-related information, and then the graph query language generation unit converts it into a retrieval instruction executable by the graph database. After the graph database retrieves the structured fact chain, the fact chain processing unit processes it and guides the large language model to generate a calibration answer, thereby avoiding illusions in the large language model.

[0015] The process of obtaining query-related information includes: after receiving the natural language query, the entity linking unit uses a pre-trained large language model to perform intent recognition on the natural language query, determines whether it involves multi-hop relationship retrieval, and at the same time, through entity linking technology, maps the unstructured text in the query to a unique entity identifier in the knowledge graph to obtain query-related information. The query-related information includes: query intent, multi-hop relationship retrieval judgment result, unique entity identifier in the knowledge graph, and entity association tendency.

[0016] The process of obtaining the fact chain includes: the graph query language generation unit, based on the key entities and relational intents identified by the query-related information, converts the natural language query into a retrieval instruction that conforms to the graph database retrieval specification; the graph database outputs the structured fact chain after receiving the retrieval instruction; wherein the format of the retrieval instruction includes: Seifer Query Language or Resource Description Framework Query Language.

[0017] The process of obtaining the calibration answer includes: after receiving the structured fact chain, the fact chain processing unit serializes it into a natural language description, constructs a strong constraint context, and passes the strong constraint context and the natural language query to the large language model, guiding the large language model to generate a calibration answer based on the fact chain to avoid model illusion.

[0018] The robust agent execution module includes an error monitoring unit, an error classification unit, a reflection and repair unit, and a verification and retry unit. The error monitoring unit is used to intercept the interaction between the execution agent and the external environment in real time and parse the results returned by the tool call. The error classification unit is used to determine the first type of error in the environment configuration class and the second type of error in the network service class. The reflection and repair unit is used to trigger the reflection loop of the execution agent and call the correction tool to complete the environment configuration repair. The verification and retry unit is used to verify the repair results and re-execute the failed task.

[0019] The multi-agent debate collaboration module includes a multi-role agent instantiation unit, a debate coordination unit, and a summary and decision unit. The multi-role agent instantiation unit is used to instantiate agents with different personas according to complex problems. The debate coordination unit is used to control the multi-round debate process and realize anonymous distribution of proposals. The summary and decision unit is used to summarize the debate history and generate a comprehensive decision report.

[0020] Among them, the intelligent agents with different personas include a CEO intelligent agent that focuses on macro vision and growth, a CFO intelligent agent that focuses on cost control and risk, and a CTO intelligent agent that focuses on technological feasibility.

[0021] The implicit thought interaction module of the brain-computer interface includes an EEG signal acquisition unit, a signal preprocessing unit, a signal decoding unit, and a prompt word generation unit. The EEG signal acquisition unit acquires the user's EEG signals through a non-invasive brain-computer interface device. The signal preprocessing unit is used to filter and denoise the raw EEG signals. The signal decoding unit is used to decode the preprocessed EEG signals into the user's core thought concepts. The prompt word generation unit is used to assemble the core concepts into large language model prompt words.

[0022] The non-invasive brain-computer interface device includes a high-density electroencephalogram (EEG) headband.

[0023] The present invention has the following beneficial effects:

[0024] The knowledge graph-guided retrieval enhancement generation module replaces traditional shallow vector retrieval with entity linking, multi-hop graph querying, and fact chain calibration mechanisms. It accurately captures multi-hop relationships, guides the large language model to generate answers based on real facts, and ensures the reliability of the output.

[0025] The robust intelligent agent execution module enables autonomous repair and dynamic fault tolerance for errors related to environmental configuration and network fluctuations, reducing human intervention and supporting the stable deployment of large language models.

[0026] The multi-agent debate collaboration module generates a comprehensive and objective integrated decision report through multi-round debates among multiple agents, thereby enhancing the complex decision-making capabilities of large language models.

[0027] The brain-computer interface implicit thought interaction module enables the direct conversion of users' implicit thoughts into text prompts, breaking the limitations of traditional explicit input and expanding the application scope of large language models.

[0028] By using a task orchestrator to coordinate the above four modules into a complete closed loop, the overall performance of the system is improved. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the functional modules of an embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of the retrieval enhancement generation module according to an embodiment of the present invention;

[0031] Figure 3 This is a schematic diagram of the robust intelligent agent execution module in an embodiment of the present invention;

[0032] Figure 4 This is a schematic diagram of the multi-agent debate collaboration module in an embodiment of the present invention;

[0033] Figure 5 This is a schematic diagram of the implicit thought interaction module of the brain-computer interface in an embodiment of the present invention. Detailed Implementation

[0034] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present invention can be combined with each other.

[0035] In an embodiment of the LLM deployment and application system provided by this invention, the system includes:

[0036] A task orchestrator and functional modules, wherein the task orchestrator is used to achieve coordinated control between the functional modules;

[0037] Figure 1 This is a schematic diagram of the functional modules of an embodiment of the present invention. The functional modules include:

[0038] The knowledge graph-guided retrieval enhancement generation module 101 is used to receive natural language queries, parse the queries into multi-hop graph query instructions on the knowledge graph, execute the instructions in the knowledge graph database to retrieve structured fact chains, and guide the large language model to generate answers based on the fact chains.

[0039] The robust agent execution module 102 is used to monitor the tool call status of the execution agent; when the tool call returns a first type of error, the execution agent is triggered to enter the thinking reflection mode to repair the operating environment; when the tool call returns a second type of error, the dynamic fault tolerance mechanism of the execution agent is triggered to execute the backup plan.

[0040] The multi-agent debate collaboration module 103 is used to initialize multiple decision agents with different preset roles for an open problem, and to manage the multiple decision agents to conduct multiple rounds of debate through a debate coordinator to generate a decision scheme.

[0041] The brain-computer interface implicit thought interaction module 104 is used to translate the collected user neural signals into text prompts through a decoding model, and input the text prompts into a large language model to generate output results.

[0042] The core responsibility of the task orchestrator is to achieve coordinated control among four functional modules: the implicit thought interaction module of the brain-computer interface, the retrieval enhancement generation module guided by the knowledge graph, the multi-agent debate and collaboration module, and the robust agent execution module. By organically integrating these modules to form a complete system closed loop, the overall performance can be effectively improved.

[0043] The task orchestrator consists of three core components within the system: an intent resolution and routing unit, a global state and context management unit, and a collaborative execution engine. The intent resolution unit receives instructions from the brain-computer interface or conventional input, parses the task requirements using classification models or rule-based algorithms, and generates a directed acyclic graph (DAG) of module calls. The global context management unit acts as a system-level "shared memory," maintaining a global object that records inputs, outputs, and states. The collaborative execution engine, as the central state machine, dynamically adjusts the execution path in an event-driven manner based on the topological sorting of the DAG and the status codes fed back by the modules, ensuring precise scheduling of each functional module.

[0044] In terms of specific data flow and collaborative control logic, the system relies on a task orchestrator to establish a rigorous multi-level state transition mechanism. This mechanism first captures the user's intent through the implicit thought interaction module of the brain-computer interface. Once it is determined that the task requires factual support, the orchestrator initiates a closed-loop "retrieval-decision-execution" chain: first, it schedules the knowledge graph-guided retrieval enhancement generation module to obtain structured facts and store them in the global context; then, it awakens the multi-agent debate collaboration module, which performs multi-dimensional deduction based on the aforementioned factual benchmarks to generate a decision report; finally, it extracts the action intent of the report, drives the robust agent execution module to complete external API interactions, and triggers a self-healing mechanism to repair the environment when encountering anomalies.

[0045] This invention uses the example of "formulating AI industry acquisition strategies for large enterprises" to fully illustrate the collaborative control mechanism of the task orchestrator. First, in the intent capture phase, corporate executives only need to wear a high-density EEG headband and conceive of "evaluating the acquisition plan of NLP startup B" in their minds. The implicit thought interaction module of the brain-computer interface can then collect EEG signals in real time, decode them into standard text prompts, and send them directly to the task orchestrator.

[0046] Subsequently, the task orchestrator first determines that the task relies on strong factual support and immediately invokes the knowledge graph-guided retrieval enhancement generation module. This module deeply mines the enterprise knowledge graph, accurately extracting multi-hop fact chains such as "profiles of core technical personnel," "historical financing rounds," and "competitor patent layouts," generating an objective background report that eliminates illusions and injects it into the global context.

[0047] Following this, once the global context confirms the facts are ready, the task orchestrator triggers the multi-agent debate and collaboration module to enter its working state. This module simulates a real-world game scenario involving a corporate executive team. The CEO agent advocates for a high-premium acquisition based on technological complementarity, the CFO agent proposes a risk control plan based on losses identified through a graph, and the CTO agent assesses the difficulty of technology integration. After multiple rounds of anonymous debate and cross-validation, the system outputs a comprehensive decision report containing the optimal bid range and integration path.

[0048] Finally, the task orchestrator extracts the action intent from the decision report and transfers execution authority to the robust agent execution module. When this module encountered a sudden exception—a token expiring—while calling the internal OA system API to send an email, its built-in dynamic fault-tolerance mechanism prevented the agent from interrupting the process. Instead, it autonomously triggered a reflection and invoked tools to refresh the token, successfully completing environment repair and resending the email. After receiving the successful execution callback signal, the task orchestrator ultimately reports the strategic report and execution notification to senior management.

[0049] Figure 2This is a schematic diagram of the retrieval enhancement generation module according to an embodiment of the present invention. The retrieval enhancement generation module includes an entity linking unit 201, a graph query language generation unit 202, and a fact chain processing unit 203. The entity linking unit parses the natural language query input by the user to obtain query-related information, and then the graph query language generation unit converts it into a retrieval instruction executable by the graph database. After obtaining a structured fact chain through the graph database retrieval, the fact chain processing unit processes and guides the large language model to generate a calibration answer, thereby avoiding illusions in the large language model.

[0050] The process of obtaining query-related information includes: after receiving the natural language query, the entity linking unit uses a pre-trained large language model to perform intent recognition on the natural language query, determines whether it involves multi-hop relationship retrieval, and at the same time, through entity linking technology, maps the unstructured text in the query to a unique entity identifier in the knowledge graph to obtain query-related information. The query-related information includes: query intent, multi-hop relationship retrieval judgment result, unique entity identifier in the knowledge graph, and entity association tendency.

[0051] The entity linking unit, serving as the system's semantic entry point, is responsible for receiving natural language queries input by users and leveraging the powerful semantic understanding capabilities of a pre-trained large language model to perform in-depth analysis of user questions (e.g., "Who are the notable alumni of the alma mater of the CEO of Company A?"). This unit not only performs basic named entity recognition but, more importantly, performs intent recognition and multi-hop determination. Specifically, it analyzes whether there are cross-entity logical reasoning chains in the query statement through model analysis. Simultaneously, it uses entity linking technology to accurately disambiguate unstructured text in the query (e.g., "Company A") and map it to unique entity identifiers in the knowledge graph (e.g., Company:A_Inc). Finally, it outputs query-related information including query intent category, multi-hop relationship retrieval results, unique entity identifiers in the knowledge graph, and potential association tendencies between entities, providing accurate metadata support for subsequent structured retrieval.

[0052] The process of obtaining the fact chain includes: the graph query language generation unit, based on the key entities and relational intents identified by the query-related information, converts the natural language query into a retrieval instruction that conforms to the graph database retrieval specification; the graph database outputs the structured fact chain after receiving the retrieval instruction; wherein the format of the retrieval instruction includes: Seifer Query Language or Resource Description Framework Query Language.

[0053] After obtaining the aforementioned query-related information, the graph query language generation unit then executes instruction compilation based on this structured metadata. This unit utilizes the code generation capabilities of the large language model, combined with the Few-Shot Prompting strategy, to fully learn the schema definition of the graph database, thereby transforming abstract natural language intents into executable retrieval instructions for the graph database. These instructions are specifically manifested as grammatically compliant Cypher query language or SPARQL resource description framework query language. For example, the system can generate a Cypher instruction that matches the path "Company → CEO → University → Alumni", ensuring the syntactic and logical correctness of the query statement. Subsequently, the graph database engine receives and executes these instructions, using its native graph traversal algorithm to perform efficient multi-hop path search between nodes and edges, directly returning complete path data composed of entity-relationship-entity triples, i.e., structured fact chains. This path-based retrieval result completely changes the limitations of traditional RAG relying on document fragment splicing, accurately restoring the logical topology of multi-hop reasoning.

[0054] The process of obtaining the calibration answer includes: after receiving the structured fact chain, the fact chain processing unit serializes it into a natural language description, constructs a strong constraint context, and passes the strong constraint context and the natural language query to the large language model, guiding the large language model to generate a calibration answer based on the fact chain to avoid model illusion.

[0055] The fact chain processing unit is responsible for post-processing and answer synthesis of the retrieved structured fact chain. This unit first serializes the machine-readable path data into human-understandable natural language descriptions, constructing a highly constrained contextual environment. The system then injects this highly constrained context along with the original user query into the large language model, guiding the model to discard potentially inaccurate or outdated parameterized knowledge and strictly perform logical deduction and language organization based on the provided structured fact chain. This allows for accurate answers to complex questions such as "The CEO of Company A is Person_A, who graduated from University_X, and another prominent alumnus of that university is Person_B." Through this "fact chain-guided" generation mechanism, the large language model is forced to answer within verified knowledge boundaries, generating calibrated answers that are highly consistent with the facts in the knowledge graph and possess a complete reasoning process. This fundamentally avoids model illusions and significantly improves the system's accuracy, robustness, and interpretability in complex relational query scenarios.

[0056] Figure 3This is a schematic diagram of a robust intelligent agent execution module in an embodiment of the present invention. The robust intelligent agent execution module includes: an error monitoring unit 301, an error classification unit 302, a reflection and repair unit 303, and a verification and retry unit 304. The error monitoring unit is used to intercept the interaction between the execution intelligent agent and the external environment in real time and parse the return results of the tool call. The error classification unit is used to determine the first type of error of environment configuration and the second type of error of network service. The reflection and repair unit is used to trigger the reflection loop of the execution intelligent agent and call the correction tool to complete the environment configuration repair. The verification and retry unit is used to verify the repair results and re-execute the failed task.

[0057] The robust intelligent agent execution module proposed in this embodiment specifically includes an error monitoring unit, an error classification unit, a reflection and repair unit, and a verification and retry unit. Each unit works together to effectively solve the vulnerability problem of the intelligent agent crashing when encountering errors due to environmental configuration issues during task execution.

[0058] First, the error monitoring unit intercepts the interaction process between the execution agent and the external environment in real time. When the agent sends a tool call request and receives a return result, the error monitoring unit parses the returned HTTP status code and response body to capture the execution status of each tool call, providing basic data for subsequent error identification.

[0059] After receiving the return result of the tool call, the error classification unit immediately judges the result. It primarily distinguishes between Category I errors related to environment configuration (such as expired API keys, insufficient permissions, etc.) and Category II errors related to network services. When the return result contains specific keywords such as "401 Unauthorized," "API Key Expired," or "Permission Denied," it can be accurately classified as a Category I error. These errors typically reflect configuration defects in the agent's operating environment, rather than errors in the task logic itself, thus providing a basis for subsequent targeted repairs. Once a Category I error is determined, the reflection and repair unit is triggered. This unit pauses the current task flow and forces the agent into a reflection loop. By constructing prompts containing error logs, the current environment state, and a list of available tools, it inputs these prompts into the large language model kernel, guiding the agent to generate a thought chain encompassing observation, reasoning, and planning. For example, after observing a 401 authentication error, it infers the need to update credentials and plans to call a tool to update the API key. Subsequently, based on the reflection results, it autonomously generates and executes corrective tool call instructions, completing the environment configuration repair without manual intervention. This transforms the agent from a simple task executor into a manager with environment maintenance capabilities.

[0060] To address "Type II errors" (such as request timeouts, 503 Service Unavailable, and corrupted return formats) caused by network fluctuations or service instability, this embodiment proposes a dynamic fault-tolerance mechanism. First, the system captures and categorizes errors. When a tool call returns a timeout, null value, or non-standard JSON format, it is explicitly classified as a Type II error, thus distinguishing it from configuration errors. In the event of service unavailability, the agent triggers "Plan B" logic. By retrieving metadata from the tool library, it automatically finds a backup tool with the same functional tags. For example, if the main tool `stock_api_v1` fails, it immediately identifies and calls `stock_api_v2` or `web_search_tool` as a replacement, thereby achieving dynamic tool switching and ensuring the task flow is not paralyzed by a single point of failure. For scenarios where data is returned but the format is corrupted (such as missing JSON syntax), the system initiates a "data repair" subroutine. The corrupted text fragment is input into the LLM, which instructs it to repair the syntax without altering the data content, outputting a standard JSON format to maintain process continuity.

[0061] In summary, this embodiment forms a complete robust closed loop for the two types of errors: once the first type of error (environment configuration error) is confirmed, the system will immediately pause the current task flow and force the Agent to enter the "Reflection Loop". At this time, the system will construct a Prompt input containing error logs, the current environment state, and a list of available tools to the LLM kernel, guiding it to generate a thought chain containing observation, reasoning, and planning. For example, it first observes the 401 authentication failure, then infers that this is an environment error that requires updating credentials, and finally plans to call the update_api_key function. Based on this reflection result, the Agent will autonomously generate the calling instructions for the correction tool (such as executing update_api_key(service='weather_api', source='vault')), thereby transforming from a simple "executor" into a "manager" with environment maintenance capabilities, completing the environment repair without human intervention.

[0062] After the correction tool completes its execution, the verification and retry unit will verify the repair results. When the correction tool returns a success status (such as Status 200), the system will automatically push the previously failed original task back into the execution queue for retry. Since the environment configuration problem has been resolved, the original task is likely to be executed successfully, thus forming a robust closed loop from error monitoring, classification, repair to retry, effectively improving the stability and fault tolerance of the agent in complex environments.

[0063] Figure 4This is a schematic diagram of a multi-agent debate collaboration module in an embodiment of the present invention. The multi-agent debate collaboration module includes a multi-role agent instantiation unit 401, a debate coordination unit 402, and a summary decision unit 403. The multi-role agent instantiation unit is used to instantiate agents with different personas according to complex problems. The debate coordination unit is used to control the multi-round debate process and realize anonymous distribution of proposals. The summary decision unit is used to summarize the debate history and generate a comprehensive decision report.

[0064] Among them, the intelligent agents with different personas include a CEO intelligent agent that focuses on macro vision and growth, a CFO intelligent agent that focuses on cost control and risk, and a CTO intelligent agent that focuses on technological feasibility.

[0065] First, the multi-role intelligent agent instantiation unit responds to complex questions raised by users (such as "formulate the company's annual strategy") and instantiates multiple intelligent agents with differentiated System Prompts (personas) based on task characteristics. These intelligent agents with different personas specifically include a CEO intelligent agent that focuses on macro vision and growth, a CFO intelligent agent that focuses on cost control and risk, and a CTO intelligent agent that focuses on technical feasibility. Each intelligent agent makes an initial understanding and plan for the problem based on its unique persona perspective, providing a diversified decision-making starting point for subsequent debates.

[0066] After the agents are instantiated, the debate coordination unit intervenes and takes the lead in controlling the multi-round debate process. Its core responsibility is to achieve anonymous distribution of proposals and scheduling of the debate pace. Specifically, the coordination unit first initiates the first round of proposals, prompting each agent to independently output a solution based on its own perspective. Then, in the second round of critique, the coordinator anonymously distributes all solutions to other agents, allowing agents such as the CFO to criticize the CEO agent's solution as "too expensive" without being named, thus avoiding interpersonal bias and focusing on the clash of viewpoints. Next, in the third round of revision, each agent must respond to the critique feedback and modify its own solution. For example, the CEO agent may revise its solution to reduce the budget. Through this anonymous interaction and iterative revision mechanism, collective wisdom is effectively stimulated and the optimal solution is approached.

[0067] When the debate reaches the preset number of rounds (such as 3 rounds) or the amplitude of the plan change converges, the summary decision-making unit will take over the process. By calling a dedicated summary agent, this unit systematically reads the entire debate history, comprehensively weighs the viewpoints and arguments of all parties such as the CEO agent, CFO agent, and CTO agent, and finally generates a comprehensive decision-making report that is both macroscopically innovative and technically feasible, while also taking into account cost control and risk prevention. Through the three-level linkage of multi-role instantiation, structured debate coordination, and comprehensive summary decision-making in the above manner, this embodiment significantly improves the robustness and comprehensiveness of complex open-ended problem decision-making.

[0068] Figure 5 It is a schematic diagram of the brain-computer interface implicit thinking interaction module in an embodiment of the present invention. The brain-computer interface implicit thinking interaction module includes an electroencephalogram signal acquisition unit 501, a signal preprocessing unit 502, a signal decoding unit 503, and a prompt word generation unit 504. The electroencephalogram signal acquisition unit acquires the user's electroencephalogram signal through a non-invasive brain-computer interface device. The signal preprocessing unit is used to filter and denoise the original electroencephalogram signal. The signal decoding unit is used to decode the preprocessed electroencephalogram signal into the user's core thinking concept. The prompt word generation unit is used to assemble the core concept into a large language model prompt word.

[0069] Among them, the non-invasive brain-computer interface device includes a high-density electroencephalogram headband.

[0070] First, the electroencephalogram signal acquisition unit uses a non-invasive brain-computer interface device such as a high-density electroencephalogram (EEG) headband to continuously acquire the electroencephalogram signal generated by the user when in the "conception" state. This process does not require the user to make any verbal expression or physical movement, thus capturing the most primitive thinking intention at the source. The acquired original electroencephalogram signal is then transmitted to the signal preprocessing unit, which is responsible for a series of refined processing such as filtering and denoising the signal to remove environmental interference and physiological noise, and extracting pure and high signal-to-noise ratio neural features, laying a solid data foundation for subsequent decoding work. Immediately afterwards, the signal decoding unit intervenes. It uses a deep learning model (such as a spatio-temporal feature extraction network based on the Transformer architecture) pre-trained on a large amount of <EEG signal, text / image description> paired data to map the preprocessed neural signal features to the semantic space and accurately decode the core thinking concept in the user's mind.

[0071] Finally, the prompt word generation unit receives these decoded core concepts, assembles them into coherent and semantically rich large language model prompt words, and inputs them into the generative model to generate corresponding images, code, or text content. Through the above process, the system achieves a seamless connection and direct mapping from "implicit thinking" to "digital content," realizing a new human-computer interaction paradigm of "what you think is what you get."

[0072] The present invention has the following beneficial effects:

[0073] The knowledge graph-guided retrieval enhancement generation module replaces traditional shallow vector retrieval with entity linking, multi-hop graph querying, and fact chain calibration mechanisms. It accurately captures multi-hop relationships, guides the large language model to generate answers based on real facts, and ensures the reliability of the output.

[0074] The robust intelligent agent execution module enables autonomous repair and dynamic fault tolerance for errors related to environmental configuration and network fluctuations, reducing human intervention and supporting the stable deployment of large language models.

[0075] The multi-agent debate collaboration module generates a comprehensive and objective integrated decision report through multi-round debates among multiple agents, thereby enhancing the complex decision-making capabilities of large language models.

[0076] The brain-computer interface implicit thought interaction module enables the direct conversion of users' implicit thoughts into text prompts, breaking the limitations of traditional explicit input and expanding the application scope of large language models.

[0077] By using a task orchestrator to coordinate the above four modules into a complete closed loop, the overall performance of the system is improved.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An LLM deployment and application system, characterized in that, The system includes: A task orchestrator and functional modules, wherein the task orchestrator is used to achieve coordinated control between the functional modules; The functional modules include: The knowledge graph-guided retrieval enhancement generation module is used to receive natural language queries, parse the queries into multi-hop graph query instructions on the knowledge graph, execute the instructions in the knowledge graph database to retrieve structured fact chains, and guide the large language model to generate answers based on the fact chains. The robust agent execution module is used to monitor the tool call status of the execution agent; when the tool call returns a Type I error, the execution agent is triggered to enter a thinking reflection mode to repair the operating environment; when the tool call returns a Type II error, the dynamic fault tolerance mechanism of the execution agent is triggered to execute a backup plan. The multi-agent debate collaboration module is used to initialize multiple decision agents with different preset roles for open problems, and to manage the multiple decision agents to conduct multiple rounds of debate through a debate coordinator to generate a decision scheme. The brain-computer interface implicit thought interaction module is used to translate the collected user neural signals into text prompts through a decoding model, and input the text prompts into a large language model to generate output results.

2. The system according to claim 1, characterized in that, The retrieval enhancement generation module includes an entity linking unit, a graph query language generation unit, and a fact chain processing unit. The entity linking unit parses the natural language query input by the user to obtain query-related information, and then the graph query language generation unit converts it into a retrieval instruction executable by the graph database. After the structured fact chain is obtained by the graph database retrieval, the fact chain processing unit processes it and guides the large language model to generate a calibration answer, thereby avoiding the large language model from exhibiting illusions.

3. The system according to claim 2, characterized in that, The process of obtaining query-related information includes: after receiving the natural language query, the entity linking unit uses a pre-trained large language model to perform intent recognition on the natural language query, determines whether it involves multi-hop relationship retrieval, and at the same time, through entity linking technology, maps the unstructured text in the query to a unique entity identifier in the knowledge graph to obtain query-related information. The query-related information includes: query intent, multi-hop relationship retrieval judgment result, unique entity identifier in the knowledge graph, and entity association tendency.

4. The system according to claim 2, characterized in that, The process of obtaining the fact chain includes: the graph query language generation unit, based on the key entities and relational intents identified by the query-related information, converts the natural language query into a retrieval instruction that conforms to the graph database retrieval specification; the graph database outputs the structured fact chain after receiving the retrieval instruction; wherein the format of the retrieval instruction includes: Seifer Query Language or Resource Description Framework Query Language.

5. The system according to claim 2, characterized in that, The process of obtaining the calibration answer includes: after receiving the structured fact chain, the fact chain processing unit serializes it into a natural language description, constructs a strong constraint context, and passes the strong constraint context and the natural language query to the large language model, guiding the large language model to generate a calibration answer based on the fact chain to avoid model illusion.

6. The system according to claim 1, characterized in that, The robust agent execution module includes an error monitoring unit, an error classification unit, a reflection and repair unit, and a verification and retry unit. The error monitoring unit is used to intercept the interaction between the execution agent and the external environment in real time and parse the results returned by the tool call. The error classification unit is used to determine the first type of error in the environment configuration class and the second type of error in the network service class. The reflection and repair unit is used to trigger the reflection loop of the execution agent and call the correction tool to complete the environment configuration repair. The verification and retry unit is used to verify the repair results and re-execute the failed task.

7. The system according to claim 1, characterized in that, The multi-agent debate collaboration module includes a multi-role agent instantiation unit, a debate coordination unit, and a summary and decision unit. The multi-role agent instantiation unit is used to instantiate agents with different personas according to complex problems. The debate coordination unit is used to control the multi-round debate process and realize anonymous distribution of proposals. The summary and decision unit is used to summarize the debate history and generate a comprehensive decision report.

8. The system according to claim 7, characterized in that, The different AI agents include a CEO AI agent that focuses on macro vision and growth, a CFO AI agent that focuses on cost control and risk, and a CTO AI agent that focuses on technological feasibility.

9. The system according to claim 1, characterized in that, The implicit thought interaction module of the brain-computer interface includes an EEG signal acquisition unit, a signal preprocessing unit, a signal decoding unit, and a prompt word generation unit. The EEG signal acquisition unit acquires the user's EEG signals through a non-invasive brain-computer interface device. The signal preprocessing unit is used to filter and denoise the raw EEG signals. The signal decoding unit is used to decode the preprocessed EEG signals into the user's core thought concepts. The prompt word generation unit is used to assemble the core concepts into large language model prompt words.

10. The system according to claim 9, characterized in that, The non-invasive brain-computer interface device includes a high-density electroencephalogram (EEG) headband.

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