Heterogeneous AI engine interaction system and method based on cognitive firewall

Abstract

The invention discloses a heterogeneous AI engine interaction system and method based on a cognitive firewall, and relates to the technical field of artificial intelligence, and the system comprises a cognitive isolation channel which is used for building an isolation communication path between an RAG system and an Agent system, and generating standardized knowledge; the three-layer filtering architecture is used for performing grammar layer verification, semantic layer understanding and pragmatic layer evaluation on the standardized knowledge in sequence and outputting safety knowledge passing verification; the bidirectional value alignment module is used for mapping the safety knowledge to a multi-dimensional value space through a forward value alignment mechanism to calculate value conformity, and generating value alignment knowledge according to the value conformity; and the knowledge-instruction conversion engine is used for converting the value alignment knowledge into a structured instruction which can be executed by the Agent system. The method can actively identify, intercept and avoid potential dangerous, misleading, prejudice or ethical inconformity knowledge applications, and greatly reduce negative effects caused by wrong use of knowledge.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and more specifically to a heterogeneous AI engine interaction system and method based on a cognitive firewall. Background Technology

[0002] Currently, with the rapid development of artificial intelligence technology, Retrieval Augmentation (RAG) systems and intelligent agent systems have become two mainstream paradigms for building AI applications. Faced with increasingly complex application scenarios, the demand for combining RAG systems with agent systems has grown significantly. However, existing technologies typically integrate the two in a simple concatenation manner, where the knowledge content generated by the RAG is directly used as input to the agent to guide action decisions. This integration method exposes a series of serious problems in practical applications: significant semantic loss and distortion occur during the transformation of knowledge from content to action instructions; conflicts may exist between multimodal knowledge, leading to decisions based on contradictory information; AI systems struggle to ensure that action decisions accurately embody corporate values ​​and ethical guidelines; execution results cannot be effectively fed back to the knowledge base to drive system optimization; the system is vulnerable to semantic attacks, and the decision-making process is opaque, making effective auditing and traceability difficult.

[0003] Existing technological solutions, such as traditional content filtering systems, API gateways, semantic understanding components, rule engines, LLM security fine-tuning, and commercial AI development platforms, all have significant limitations in addressing these issues: traditional solutions can only perform surface-level filtering and cannot understand deep semantics and context; API gateways primarily focus on data format rather than semantic meaning; existing components lack the ability to build a complete closed loop for secure knowledge transformation; rule engines struggle to handle complex and dynamic semantic scenarios; security fine-tuning mainly focuses on the generation stage, paying insufficient attention to the critical transformation process from knowledge to instructions; and commercial platforms generally lack deep semantic security mechanisms specifically designed for RAG and Agent integration scenarios. Therefore, addressing the multiple key requirements in deep RAG and Agent integration—such as secure knowledge transformation, value consistency assurance, execution result feedback optimization, and transparent and auditable decision-making processes—is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a heterogeneous AI engine interaction system and method based on a cognitive firewall, which overcomes the above-mentioned defects.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A heterogeneous AI engine interaction system based on a cognitive firewall, deployed as a cognitive firewall between a retrieval enhancement generation system and an intelligent agent system, includes:

[0007] The cognitive isolation channel is used to establish an isolated communication path between the RAG system and the Agent system, receive knowledge content from the RAG system, perform preliminary structuring processing on the knowledge content, and generate standardized knowledge.

[0008] A three-layer filtering architecture, connected to a cognitive isolation channel, is used to sequentially perform syntactic layer verification, semantic layer understanding, and pragmatic layer evaluation on standardized knowledge, and output safe knowledge that has passed the verification.

[0009] The bidirectional value alignment module, connected to the three-layer filtering architecture, is used to map security knowledge to a multi-dimensional value space through a forward value alignment mechanism to calculate the value conformity, generate value alignment knowledge based on the value conformity, and optimize the value assessment system based on the execution results of the Agent system through a reverse value feedback mechanism.

[0010] The knowledge-instruction conversion engine, connected to the bidirectional value alignment module, is used to convert value-aligned knowledge into structured instructions that can be executed by the agent system.

[0011] The cognitive log module connects the cognitive isolation channel, the three-layer filtering architecture, the two-way value alignment module, and the knowledge-instruction conversion engine. It is used to record the complete path of knowledge processing and key decision points, and to build a traceable chain of evidence.

[0012] Optionally, the knowledge content includes semantic information and constraints.

[0013] Optionally, the three-layer filtering architecture includes a syntax layer filtering unit, a semantic layer understanding unit, and a pragmatic layer evaluation unit;

[0014] The syntax layer filtering unit is used to perform formal structure verification on standardized knowledge using a dynamic rule selection algorithm and to perform abnormal pattern recognition using a weighted feature function to obtain structurally compliant data.

[0015] The semantic layer understanding unit is used to perform deep semantic analysis on structurally compliant data using neural symbolic hybrid computation to filter out semantically safe data.

[0016] The pragmatic layer evaluation unit is used to simulate the potential consequences of knowledge application by leveraging multi-step influence on semantic security data, and output security knowledge.

[0017] Optionally, the two-way value alignment module includes:

[0018] The value function builder is used to construct a multi-dimensional value space and define the evaluation functions and weight vectors for each value dimension. It handles the contradictions and trade-offs between different value dimensions through a value conflict resolution mechanism.

[0019] The forward value aligner connects the value function builder and is used to map security knowledge to a multi-dimensional value space and calculate the value conformity degree through knowledge value assessment methods, and generate value-aligned knowledge based on the value conformity degree.

[0020] The reverse value feedback loop connects to the value function builder, which collects the execution results of the agent system and maps them to a multi-dimensional value space to obtain an evaluation vector. It then calculates the value realization gap and constructs a knowledge-execution-result association graph. The graph analysis algorithm is used to calculate the value contribution score of the knowledge. Based on the value realization gap and the value contribution score, the knowledge base index structure and retrieval algorithm parameters are dynamically optimized.

[0021] Optionally, in the reverse value feedback loop, the method for obtaining the value realization gap is as follows:

[0022] Collect multidimensional indicator data of the execution results of the Agent system and construct the result dataset;

[0023] The evaluation vector is obtained by mapping multidimensional indicator data to a multidimensional value space through an evaluation function.

[0024] The value realization gap is calculated based on the evaluation vector and the preset target vector.

[0025] Optionally, the knowledge-instruction conversion engine uses instruction compilation technology to compile value-aligned knowledge into initial instructions, and applies formal verification methods to perform multiple verifications to generate secure instructions.

[0026] Optionally, the knowledge-instruction conversion engine includes a formal verification module, which includes a syntax correctness verification unit, an execution feasibility verification unit, a permission compliance verification unit, and a side effect control verification unit.

[0027] Optionally, it also includes a closed-loop optimization mechanism to achieve a closed loop from knowledge generation to action execution and then to feedback learning, including:

[0028] The knowledge-action-feedback closed loop applies knowledge transfer packaging technology to build an encapsulation structure for knowledge entities, and applies the effect-knowledge mapping model and influence propagation function to establish a causal relationship between execution results and standardized knowledge.

[0029] The knowledge value assessment system applies a multi-dimensional utility function to comprehensively evaluate the utility of standardized knowledge and applies the Bayesian update principle to dynamically update the credibility of knowledge; based on the utility assessment and credibility update results, the knowledge retrieval weight is automatically adjusted.

[0030] A heterogeneous AI engine interaction method based on cognitive firewall, the specific steps of which are as follows:

[0031] An isolated communication path is established between the RAG system and the Agent system to receive knowledge content from the RAG system and perform preliminary structuring processing on the knowledge content to generate standardized knowledge.

[0032] Standardized knowledge is sequentially validated at the syntactic level, understood at the semantic level, and evaluated at the pragmatic level, and the validated safe knowledge is output.

[0033] The forward value alignment mechanism maps security knowledge to a multi-dimensional value space to calculate the value conformity, generates value alignment knowledge based on the value conformity, and optimizes the value assessment system based on the execution results of the Agent system through the reverse value feedback mechanism.

[0034] Transform value-aligned knowledge into structured instructions that can be executed by the agent system.

[0035] Optionally, it also includes recording the complete path of knowledge content processing and key decision points to build a traceable chain of evidence.

[0036] As can be seen from the above technical solution, the present invention provides a heterogeneous AI engine interaction system and method based on a cognitive firewall, which has the following advantages compared with the prior art:

[0037] Effectively reduce knowledge misuse rate: Through cognitive firewall and three-stage processing pipeline, it can proactively identify, block and avoid potential dangerous, misleading, biased or unethical knowledge applications, and significantly reduce the adverse consequences caused by the misuse of knowledge;

[0038] Improve key constraint retention rate and value alignment: Through multi-dimensional value space constraint satisfaction solution and two-way value alignment mechanism, ensure that system decisions and behaviors strictly follow the preset key rules and core value objectives, and the output results are highly consistent with the expected value orientation;

[0039] Provides complete decision traceability capabilities: Based on a neural symbolic hybrid architecture and cognitive log module, it can clearly display the logical chain of decisions, the knowledge atoms on which they are based, the rule constraints applied, and the value alignment process, providing solid support for post-event auditing and problem diagnosis;

[0040] Establish continuous optimization and adaptability: Drive self-optimization through feedback from actual application effects, update the knowledge base, adjust constraint weights, and optimize the two-way value alignment model to achieve continuous improvement in system performance and security. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the system data processing flow provided by the present invention;

[0043] Figure 2 A schematic diagram of the three-layer filtering architecture provided by the present invention;

[0044] Figure 3 A schematic diagram of the two-way value alignment mechanism provided by the present invention;

[0045] Figure 4 A schematic diagram of a three-stage knowledge processing pipeline provided by the present invention;

[0046] Figure 5 This is a schematic diagram of the closed-loop optimization mechanism provided by the present invention;

[0047] Figure 6 A schematic diagram illustrating the implementation of the neural symbolic hybrid architecture provided by this invention;

[0048] Figure 7 This is a schematic diagram of the method flow provided by the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, 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.

[0050] One embodiment of this invention discloses a heterogeneous AI engine interaction system based on a cognitive firewall, including a cognitive firewall deployed between a retrieval enhancement generation system and an intelligent agent system, such as... Figure 1 As shown, it includes:

[0051] The cognitive isolation channel is used to establish an isolated communication path between the RAG system and the Agent system, receive knowledge content from the RAG system, perform preliminary structuring processing on the knowledge content, and generate standardized knowledge.

[0052] A three-layer filtering architecture, connected to a cognitive isolation channel, is used to sequentially perform syntactic layer verification, semantic layer understanding, and pragmatic layer evaluation on standardized knowledge, and output safe knowledge that has passed the verification.

[0053] The bidirectional value alignment module, connected to the three-layer filtering architecture, is used to map security knowledge to a multi-dimensional value space through a forward value alignment mechanism to calculate the value conformity, generate value alignment knowledge based on the value conformity, and optimize the value evaluation system based on the execution results of the Agent system through a reverse value feedback mechanism.

[0054] The knowledge-instruction conversion engine, connected to the bidirectional value alignment module, is used to convert value-aligned knowledge into structured instructions that can be executed by the agent system.

[0055] The cognitive log module connects the cognitive isolation channel, the three-layer filtering architecture, the two-way value alignment module, and the knowledge-instruction conversion engine. It is used to record the complete path of knowledge processing and key decision points, and to build a traceable chain of evidence.

[0056] Furthermore, the cognitive firewall is responsible for converting the knowledge content output by the RAG system into instructions that can be safely executed by the Agent system, achieving an organic integration of the accuracy of knowledge security conversion and the consistency of the value alignment mechanism. This solves the fundamental problem that traditional systems cannot simultaneously handle the semantic integrity of knowledge and the guarantee of value consistency. The overall architecture of the heterogeneous AI engine interaction system includes three core design dimensions: functional module layer, data flow path, and processing pipeline.

[0057] The core technical architecture of the cognitive firewall adopts a modular design, consisting of five functional modules: cognitive isolation channel, three-layer filtering architecture, two-way value alignment module, knowledge-instruction conversion engine, and cognitive log module. These modules work together to form a complete knowledge security conversion system.

[0058] The system comprises the following components: a cognitive isolation channel, used to establish a secure knowledge transmission path (i.e., an isolated communication path) between the Retrieval Enhancement Generation (RAG) system and the intelligent agent system, and to perform preliminary structuring processing on the received knowledge content; a three-layer filtering architecture, used to sequentially perform syntactic layer verification, semantic layer understanding, and pragmatic layer evaluation on the pre-structuring knowledge content (i.e., standardized knowledge), ensuring the security and integrity of the knowledge content; a bidirectional value alignment module, used to ensure that the knowledge conversion process conforms to preset value standards through a forward value alignment mechanism, and to optimize the value evaluation system based on the execution results through a reverse value feedback mechanism; a knowledge-instruction conversion engine, used to convert value-aligned knowledge content (i.e., value-aligned knowledge) into structured instructions executable by the agent system; and a cognitive log module, used to record the complete path and key decision points of knowledge processing, constructing a traceable evidence chain for decision-making.

[0059] The five functional modules of the cognitive firewall form a closely collaborative network. The data flow path is as follows: the cognitive isolation channel receives the knowledge content output by the RAG system, performs preliminary structuring processing to generate standardized knowledge, and then passes the standardized knowledge to the three-layer filtering architecture; the three-layer filtering architecture generates security knowledge after completing security checks and passes the security knowledge to the bidirectional value alignment module; the bidirectional value alignment module performs value assessment and adjustment to generate value-aligned knowledge and passes the value-aligned knowledge to the knowledge-instruction conversion engine; the knowledge-instruction conversion engine converts the value-aligned knowledge into structured instructions (i.e., security instructions) that can be executed by the agent; the cognitive log system runs through the entire processing flow, recording the key decision points and basis of each module, and building a complete decision traceability chain.

[0060] Among them, the feedback connection between the two-way value alignment module and the Agent system enables the heterogeneous AI engine interaction system to collect the execution results of the Agent system, evaluate the effect of knowledge application through the reverse value feedback mechanism, and optimize the knowledge value assessment and processing strategy accordingly. Through this closed-loop design, the heterogeneous AI engine interaction system can continuously learn and improve from practical application experience, forming a self-optimization mechanism.

[0061] The cognitive firewall's data processing follows a three-stage pipeline design, including a knowledge deconstruction stage, a value mapping stage, and an instruction compilation stage. After the RAG system generates knowledge content, it first enters the knowledge deconstruction stage. The heterogeneous AI engine interaction system uses a large language model-driven symbolic logic generator to decompose complex knowledge into a set of atomic symbolic propositions, achieving accurate understanding and representation of the knowledge. In the value mapping stage, a reinforcement learning-based dynamic value projection engine maps knowledge to a predefined multi-dimensional value space, solving for constraints to ensure the knowledge content meets value requirements. In the instruction compilation stage, a security instruction compiler using differentiable logic programmable converts the value-assessed knowledge content into formally verified security instructions for the Agent system to execute.

[0062] These three processing stages have cross-mapping relationships with the system's functional modules; the knowledge deconstruction stage is mainly supported by the cognitive isolation channel and the syntax and semantic layers of the three-layer filtering architecture; the value mapping stage is jointly completed by the pragmatic layer of the three-layer filtering architecture and the forward value alignment mechanism of the bidirectional value alignment module; the instruction compilation stage relies on the bidirectional value alignment module and the knowledge-instruction conversion engine; this cross-design ensures the coordinated operation of each part of the system, forming a seamless processing chain.

[0063] The overall architecture designed in this embodiment elevates traditional content filtering to the level of semantic understanding and value alignment. Through neural symbolic mixing technology and multidimensional value mapping, it achieves secure and accurate conversion of complex knowledge, providing a solid foundation for secure collaboration between the RAG system and the Agent system.

[0064] In one embodiment, the knowledge content is the professional knowledge output from the RAG system, which contains complex semantic information and constraints, and constitutes the core input data for processing by the heterogeneous AI engine interaction system.

[0065] In one embodiment, the three-layer filtering architecture includes a syntax layer filtering unit, a semantic layer understanding unit, and a pragmatic layer evaluation unit;

[0066] The syntax layer filtering unit is used to perform formal structure verification on standardized knowledge using a dynamic rule selection algorithm and to perform abnormal pattern recognition using a weighted feature function to obtain structurally compliant data.

[0067] The semantic layer understanding unit is used to perform deep semantic analysis on structurally compliant data using neural symbolic hybrid computation to filter out semantically safe data.

[0068] The pragmatic layer evaluation unit is used to simulate the potential consequences of knowledge application by leveraging multi-step influence on semantic security data, and output security knowledge.

[0069] Furthermore, the heterogeneous AI engine interaction system performs in-depth security checks and purification of knowledge content through a three-layer filtering architecture. For example... Figure 2 As shown, knowledge content is thoroughly examined and purified in three levels: syntax, semantics, and pragmatics, achieving comprehensive security filtering from surface structure to deep meaning and potential impact.

[0070] The three-layer filtering architecture receives standardized knowledge from the cognitive isolation channel. Through a series of three processing layers, it ensures that only knowledge that passes all security checks simultaneously can proceed to subsequent processing stages. Specifically: standardized knowledge first enters the syntactic layer for structural verification; content that passes structural verification enters the semantic layer for semantic checking; content that passes semantic checking enters the pragmatic layer for impact assessment; and finally, content that passes the impact assessment is passed as output safe knowledge to subsequent processing stages. Each layer has a rejection mechanism; content that fails the checks is marked as structurally non-compliant, semantically insecure, or impact-risk, respectively.

[0071] The semantic layer employs a syntactic layer filtering unit as the first line of defense in a three-layer filtering architecture, focusing on verifying the formal structure and basic specifications of standardized knowledge. It comprises two core components: an adaptive syntactic verification mechanism and a syntactic layer anomaly pattern recognition, which work together to form a complete formal safety inspection system. The adaptive syntactic verification mechanism achieves context-aware syntactic verification through a dynamic rule selection algorithm. The rule selection process can be represented as: RuleSel(d,c)=argmax_R{P(r|d,c)}, where RuleSel(d,c) is the output of the rule selection function; d is the data content; c is the context condition; R is the set of optional rules; and P(r|d,c) is the conditional probability of selecting rule r given the data and context conditions. The syntactic layer anomaly pattern recognition component uses a weighted feature function to perform multi-dimensional anomaly detection on standardized knowledge. Its anomaly detection process can be represented as: In the formula, AnomalyScore(o) is the anomaly score of the content fragment o of standardized knowledge; i is the index variable; w i For the corresponding weights; f i τ1 represents different feature detection functions; τ1 is an adjustable threshold; o is the content fragment to be evaluated. There is a dynamic collaborative relationship between the adaptive syntax verification mechanism and the syntax layer anomaly pattern recognition component. The output of the syntax layer anomaly pattern recognition component is fed back to the adaptive syntax verification mechanism to adjust the rule selection strategy, ensuring that only formally safe content can pass structural verification and enter the semantic layer.

[0072] The semantic layer employs a semantic understanding unit to perform deep semantic understanding and analysis on the received content (i.e., structurally compliant data) that has passed the grammatical layer verification. The semantic understanding unit is built upon a neural symbolic hybrid computation paradigm, comprising two closely cooperating components: an LLM-driven symbolic representation generation component and a differentiable logical reasoning mechanism. The symbolic representation generation component is responsible for converting the natural language expression into a formalized symbolic logical representation through a neural encoder and symbolic parser. The differentiable logical reasoning mechanism, based on the symbolic logical representation, implements neural logical reasoning functionality. Its reasoning process can be represented as: LogicInf(p,q) = σ(W·[p;q]+b), where LogicInf(p,q) is the output of the logical reasoning; p and q are the vector representations of the premise and conclusion, respectively; σ is the activation function; W is the learnable parameter matrix; [p;q] is vector concatenation; and b is the bias vector. The symbolic representation generation component and the differentiable logic reasoning mechanism work together to transform unstructured knowledge content into a reasonable formal representation, and perform security verification and knowledge enhancement to ensure that the core logical constraints of the knowledge content are fully preserved. Knowledge content that passes semantic inspection enters the pragmatic layer.

[0073] The pragmatic layer employs a pragmatic evaluation unit as the last line of defense in the cognitive firewall architecture. It receives knowledge content that has undergone grammatical verification and semantic analysis (i.e., semantic security data), extending the perspective to the actual scenarios and potential consequences of knowledge application. The pragmatic evaluation unit comprises two highly collaborative core components: multi-step impact simulation and dynamic calculation of security boundaries. The multi-step impact simulation component uses recursive prediction techniques to simulate the chain reactions that may be triggered after knowledge content is transformed into agent actions. Its prediction process can be represented as follows: Where EffectPred(a,t) is the predicted comprehensive impact assessment result; a is the initial action; t is the prediction step number; P(s|a) is the transition probability from the initial action a to state s; Utility(s,t) is the utility function of state s after t steps; and S is the set of possible outcome states. The dynamic security boundary calculation component dynamically determines the boundary conditions of knowledge security applications based on the current scenario and application environment. Its risk assessment process can be expressed as: RiskScore(a,c)=α·S inherent (a)+β·I interaction (a,c)+γ·C context (c), where RiskScore(a,c) is the overall risk score; S inherent (a) is the intrinsic risk score of the action; I interaction (a,c) represents the interaction risk between the action and the context; C context (c) represents the contextual risk factor, where α, β, and γ are weighting coefficients dynamically adjusted according to the application scenario. The multi-step impact simulation component and the dynamic calculation component for the security boundary form a complementary and collaborative relationship. The multi-step impact simulation component provides forward-looking risk analysis, while the dynamic calculation component for the security boundary sets specific security constraints based on the analysis results. The knowledge content of the impact assessment is ultimately passed on as output security knowledge to subsequent processing stages.

[0074] In this embodiment, the three-layer filtering architecture constructs a complete knowledge security filtering system through the organic collaboration of syntactic, semantic, and pragmatic levels. This architecture is not a simple layering of functions, but rather forms a tightly integrated information flow network. Each layer not only filters insecure content but also adds security markers and meta-information to the passed content, accumulating to form a complete security passport for knowledge. The processing results of higher layers can be fed back to lower layers to optimize filtering strategies. For example, high-risk patterns discovered by the pragmatic layer evaluation unit are fed back to the semantic layer understanding unit and the syntactic layer filtering unit, enhancing the early identification capability of similar patterns. The heterogeneous AI engine interaction system implements a tiered security strategy based on the sensitivity of the knowledge content and the importance of the application scenario. Stricter filtering standards are applied to high-risk knowledge content, while more lenient strategies are adopted for low-risk knowledge content, maintaining processing efficiency while ensuring security.

[0075] In one embodiment, the bidirectional value alignment module includes:

[0076] The value function builder is used to construct a multi-dimensional value space and define the evaluation function and weight vector for each value dimension. It handles the contradictions and trade-offs between different value dimensions through a value conflict resolution mechanism.

[0077] The forward value aligner connects to the value function builder and is used to map security knowledge to the value space and calculate the value conformity through knowledge value assessment methods, and generate value-aligned knowledge based on the value conformity.

[0078] The reverse value feedback loop connects to the value function builder, which collects the execution results of the agent system and maps them to the value space to obtain the evaluation vector. It then calculates the value realization gap and constructs a knowledge-execution-result association graph. The graph analysis algorithm is used to calculate the value contribution score of the knowledge. Based on the value realization gap and the value contribution score, the knowledge base index structure and retrieval algorithm parameters are dynamically optimized.

[0079] In one embodiment, the method for obtaining the value realization gap in the reverse value feedback system is as follows:

[0080] Collect multidimensional indicator data of the execution results of the Agent system and construct the result dataset;

[0081] The evaluation vector is obtained by mapping multidimensional indicator data to the value space through the evaluation function.

[0082] The value realization gap is calculated based on the evaluation vector and the preset target vector.

[0083] Furthermore, the two-way value alignment module of the heterogeneous AI engine interaction system achieves value unification between knowledge generation and action execution through a two-way value alignment mechanism. For example... Figure 3 As shown, the two-way value alignment mechanism forms a complete value closed loop through three core components: a value function builder, a forward value aligner, and a reverse value feedback mechanism, achieving value collaboration between the RAG system and the Agent system. The forward value aligner receives security knowledge, performs value assessment and constraint processing, and generates instructions that meet value requirements (i.e., value-aligned knowledge) which are then transmitted to the Agent system. Simultaneously, it collects value assessment data from the Agent system's execution results, and uses this data to reverse-optimize the RAG system's knowledge base and value function, forming a complete value cycle.

[0084] Among them, the value function builder, as the foundational component of the two-way value alignment mechanism, provides a mathematical framework for value assessment and decision-making, encompassing two core functions: multi-dimensional value space design and value conflict resolution. The multi-dimensional value space design constructs an N-dimensional value vector space, the construction process of which can be represented as: ValSpace={v1,v2,...,v...}i}, where v i Let be the i-th value dimension; ValSpace is the constructed multi-dimensional value space. Each value dimension is associated with a specific evaluation function: eval. i (g')→[0,1], used to calculate the score of knowledge or action g' on this dimension. The value space also contains a weight vector: Weights={w1,w2,...,w i This reflects the relative importance of each value dimension. For example, in a medical setting, the weight of patient safety is significantly higher than that of efficiency (e.g., w). safety =0.6,w efficiency =0.1), w safety Weights for the security dimension; w efficiency The weight is set for efficiency; however, in everyday queries, the weight for efficiency can be appropriately increased (e.g., w). safety =0.3,w efficiency =0.3). The value conflict resolution function is based on a multi-dimensional value space, handling the contradictions and trade-offs between different value dimensions. This function uses optimization algorithms to find the optimal solution within the feasible solution space, aiming to maximize the weighted comprehensive score of each value dimension while satisfying all hard constraints. Hard constraints ensure that the evaluation scores of certain key value dimensions must reach a preset minimum threshold. The heterogeneous AI engine interaction system adopts a weighted Pareto optimal method to maximize overall value while ensuring the core value baseline.

[0085] The forward value aligner is the first direction of the bidirectional alignment mechanism, ensuring that knowledge content conforms to predetermined value standards when transformed into agent instructions. It includes two key functions: knowledge value assessment and value-constrained instruction generation. Knowledge value assessment maps knowledge (i.e., security knowledge) to a value space through a mapping function. This mapping process can be represented as: Where KnowToVal(k) is the vector representation of the knowledge content in the value space; k is the knowledge content to be evaluated, and eval i (k) is the scoring function for knowledge content k on the i-th value dimension, e i Let be the unit vector for the i-th value dimension. The value alignment of knowledge computed in a heterogeneous AI engine interaction system: When the compliance rate falls below a preset threshold, a knowledge adjustment process is triggered. Value-constrained instruction generation directly embeds value constraints into the instruction generation process. The system first generates basic instructions based on knowledge content and execution context, then performs a value check on the generated instructions. If the instruction passes the value check, it is output directly; if it fails, the system modifies the instruction according to preset value constraint requirements to ensure that the final output instruction meets the value standard. For example, in medical scenarios, heterogeneous AI engine interaction systems automatically embed necessary safety check steps into treatment instructions; in financial scenarios, the system adds a risk assessment process to transaction instructions.

[0086] The reverse value feedback mechanism is the second direction of the two-way alignment mechanism. It optimizes the knowledge base and value function by analyzing the execution results of the agent system, forming a closed-loop learning system. It includes two key functions: execution result value assessment and knowledge base value optimization. Execution result value assessment collects multi-dimensional indicator data of the execution results. The data collection process can be represented as: ResultData = {metric1, metric2, ..., metric...} m}, where metric m Let m be the m-th evaluation metric, and ResultData be the collected results dataset. The heterogeneous AI engine interaction system maps the results to the value space through an evaluation function: Where ResEval(ResultData) is the evaluation vector of the result in the value space; g j Let j be the evaluation metric. j Value mapping function; e j Let be the weight vector of the j-th evaluation indicator. The heterogeneous AI engine interaction system calculates the value realization gap: ValGap = |ResEval(ResultData) - ExpectedVal|, as the basis for value optimization, where ResEval(ResultData) is the value of the result dataset; ExpectedVal is the preset target vector. Knowledge base value optimization establishes a knowledge-execution-result association graph, whose construction process can be represented as: KERGraph = (KNodes, ENodes, RNodes, Edges), where KERGraph is the knowledge-execution-result association graph; KNodes are the knowledge node set; ENodes are the execution node set; RNodes are the result node set; and Edges are the causal relationship edges between nodes. The value contribution score of each piece of knowledge is calculated using a graph analysis algorithm: Where ImpactVal(k) is the value contribution score of knowledge content k; ImpactRel(k,r) represents the degree of influence of knowledge content k on result r; ValOfRel(r) is the value score of result r; based on the calculation results, the heterogeneous AI engine interaction system dynamically adjusts the knowledge base index structure and retrieval algorithm parameters to improve the retrieval weight of high-value knowledge.

[0087] In this embodiment, the bidirectional value alignment mechanism achieves a complete value loop from knowledge generation to action execution and feedback optimization through value mapping and calibration in both forward and reverse directions. The forward value aligner ensures that instruction generation meets value requirements, while the reverse value feedback mechanism ensures that knowledge base optimization follows value orientation; together, they guarantee the consistency between system behavior and corporate values. This mechanism transforms abstract value concepts into calculable and executable technical constraints, enabling corporate values ​​and ethical principles to genuinely influence every decision in the heterogeneous AI engine interaction system. Through the reverse value feedback mechanism, the heterogeneous AI engine interaction system can continuously adjust and optimize its value function and knowledge base based on actual application results, forming a dynamic optimization process of value understanding.

[0088] In one embodiment, the knowledge-instruction conversion engine uses instruction compilation technology to compile value-aligned knowledge into initial instructions, and applies formal verification methods to perform multiple verifications to generate secure instructions.

[0089] In one embodiment, the knowledge-instruction conversion engine includes a formal verification module, which includes a syntax correctness verification unit, an execution feasibility verification unit, a permission compliance verification unit, and a side effect control verification unit.

[0090] Furthermore, the knowledge-instruction conversion engine transforms value-aligned knowledge content into structured instructions executable by the agent system through a three-stage knowledge processing pipeline, such as... Figure 4 As shown, the knowledge content output by RAG is converted into security instructions that can be executed by the Agent system, realizing a complete processing chain from knowledge understanding to value assessment to instruction generation.

[0091] The three-stage knowledge processing pipeline receives knowledge content from the RAG system, such as professional knowledge content like medical guidelines and financial advice. Through three consecutive processing stages—knowledge atomization decomposition, multi-dimensional value space constraint solving, and security instruction formal verification—it ultimately generates executable, structured instructions for the Agent system. This entire pipeline forms a complete knowledge transformation chain, ensuring semantic integrity, value consistency, and execution security of knowledge during the transformation process.

[0092] The knowledge atomization decomposition stage, as the first step in the three-stage knowledge processing pipeline, receives the knowledge content output by the RAG system, decomposes the knowledge content into the smallest semantic units, and generates atomic knowledge representations. This stage comprises two core components: semantic unit recognition and proposition extraction methods. These components work sequentially and collaboratively to achieve accurate knowledge decomposition. The semantic unit recognition component decomposes the input knowledge content into a self-contained set of semantic units using a semantic segmentation function. This decomposition process can be represented as: SemDec(T)={s1,s2,...,s...} h}, where SemDec(T) is the output of the semantic segmentation function; T is the input knowledge content, s h Let h be the h-th self-contained semantic unit. For example, a heterogeneous AI engine interaction system can break down a composite medical guideline such as "When a patient has hypertension, the dosage should be reduced; when a patient has renal insufficiency, it should be avoided" into two independent clinical recommendation units, avoiding safety risks caused by confusion between different conditions. The proposition extraction method, based on semantic unit recognition, converts each semantic unit into a formalized proposition representation. Its extraction process can be expressed as: PropEx(s) h )={l,r',C}, where, PropEx(s h Let $\{\r\}$ represent the proposition extraction result, $\l$ represent the logical subject, $\r\}$ represent the relation type, and $C$ represent the condition set. For example, this method represents "This drug is contraindicated in patients with renal insufficiency" as a prohibition relation (FORBID, drug, {patient condition: renal insufficiency}). The system supports multiple relation types such as recommendation (RECOMMEND), prohibition (FORBID), requirement (REQUIRE), and condition (IF-THEN). After the knowledge atomization decomposition stage is completed, complex knowledge content is transformed into a structured set of propositions, providing a precise semantic foundation for subsequent value assessment.

[0093] The multidimensional value space constraint solving stage receives the proposition set output from the knowledge atomization decomposition stage, ensuring that the knowledge content conforms to predefined value standards and constraints, thus generating value-compliant knowledge. This stage includes two key components: value space construction and dynamic value projection. These components work together to complete value assessment and constraint satisfaction. The value space construction component first constructs a multidimensional value space, defining a value framework for evaluating knowledge. Its construction process can be represented as: ValSpace={v1,v2,...,v...} i}, where ValSpace is the constructed multi-dimensional value space, and vi represents the i-th value dimension; the heterogeneous AI engine interaction system defines the relative weights of each dimension according to enterprise policies and application scenarios: Weights={w1,w2,...,w iFor example, in a medical setting, the weight of "patient safety" is significantly higher than that of "processing efficiency," while in a typical query scenario, the weight of "response speed" may be relatively higher. The dynamic value projection component, based on the construction of the value space, maps knowledge propositions onto the value space; its projection process can be represented as: Where ValProj(u) is the projection result of the knowledge proposition in the value space; u is the knowledge proposition to be evaluated; eval i (u) is the scoring function for proposition u on the i-th value dimension; e i Let be the unit vector of the i-th dimension. The heterogeneous AI engine interaction system calculates the value conformity of knowledge: ValComp(u) = cos(ValProj(u),TargetVal); where ValProj(u) is the value conformity of knowledge proposition u; and TargetVal is the target value vector. When the value conformity is lower than a preset threshold or violates hard constraints, knowledge adjustment or rejection is triggered. After the multi-dimensional value space constraint solving stage is completed, the knowledge content is confirmed to conform to the predetermined value standard, forming value-constrained knowledge.

[0094] The formal verification phase of security instructions receives the knowledge after value constraints output from the multi-dimensional value space constraint solving phase and transforms it into structured instructions that can be securely executed by the Agent system. This phase includes two core components: instruction compilation technology and formal verification methods. These components work together sequentially to generate the final security instructions. The instruction compilation technology combines template filling and parameterized instruction generation. Its compilation process can be represented as: InstComp(u,c) = Template(TypeOf(u),ArgsOf(u,c)), where InstComp(u,c) is the initial instruction generated by compilation; u is the knowledge proposition to be evaluated; c is the execution context; the TypeOf function determines the instruction type; the ArgsOf function extracts parameters; and the Template function generates specific instructions based on the type and parameters. The heterogeneous AI engine interaction system maintains a rich set of instruction templates, covering various instruction types such as query, analysis, decision-making, and operation. For example, for medication recommendations in the medical field, a comprehensive template including dosage calculation, contraindication checks, and monitoring reminders is selected; for simple information queries, a concise query template is selected. Formal verification methods rigorously validate the generated instructions. The validation process can be represented as: Verify(e,s') → {true,false,FixInst(e)}, where Verify(e,s') is the return result of the verification function; e is the instruction to be verified; s' is the execution status; true indicates successful verification; false indicates failed verification; and FixInst(e) is the automatic fix function. Verification covers four key dimensions: syntactic correctness, execution feasibility, permission compliance, and side effect control. For instructions that fail verification, the system attempts to automatically fix common problems; serious problems that cannot be fixed will trigger execution rejection. Instructions that pass verification undergo further execution optimization, including step merging, resource usage optimization, and enhanced exception handling, ultimately generating secure, efficient, and robust execution instructions.

[0095] In this embodiment, the three stages of the knowledge processing pipeline are closely interconnected, forming a coherent processing chain. The knowledge atomization decomposition stage provides a precise foundation for knowledge understanding, the multi-dimensional value space constraint solving stage ensures value consistency, and the security instruction formal verification stage guarantees execution security. An information enhancement loop is formed between the stages; the analysis results of the previous stage provide rich contextual information for subsequent stages, and the conditional relationships identified in the knowledge atomization decomposition stage directly affect the weight allocation of value assessment. The processing results of subsequent stages are fed back to the preceding stages to optimize processing strategies, and security issues discovered during instruction verification are fed back to the value assessment stage to adjust the weights of relevant value dimensions. In actual implementation, the three stages adopt a pipelined parallel processing mode to improve system throughput, while a synchronous checkpoint mechanism is used at key decision points to ensure consistency between stages.

[0096] In one embodiment, a closed-loop optimization mechanism is also included to achieve a closed loop from knowledge generation to action execution and then to feedback learning, including:

[0097] The knowledge-action-feedback closed loop applies knowledge transfer packaging technology to build an encapsulation structure for knowledge entities, and applies the effect-knowledge mapping model and influence propagation function to establish a causal relationship between execution results and standardized knowledge.

[0098] The knowledge value assessment system applies a multi-dimensional utility function to comprehensively evaluate the utility of standardized knowledge and applies the Bayesian update principle to dynamically update the credibility of knowledge; based on the utility assessment and credibility update results, the knowledge retrieval weight is automatically adjusted.

[0099] Furthermore, such as Figure 5 As shown, the heterogeneous AI engine interaction system achieves a complete closed loop from knowledge generation to action execution and then to feedback learning through a closed-loop optimization mechanism. This mechanism enables the system to continuously learn and optimize itself from the results of practical applications, constantly improving the quality of knowledge and decision-making level, and forming a virtuous cycle of evolution.

[0100] Furthermore, the closed-loop optimization mechanism consists of two main parts: a knowledge-action-feedback closed loop and a knowledge value assessment system, forming a complete optimization cycle. After knowledge content is generated by the RAG system, it is packaged and passed to the Agent system for execution using knowledge transfer packaging technology. The execution results are attributed to effects through an effect-knowledge mapping model, and then the knowledge value assessment system performs utility calculations and credibility updates. Finally, the optimization information is fed back to the RAG system, completing the full optimization cycle.

[0101] The knowledge-action-feedback closed loop is the core architecture of the entire optimization mechanism, realizing the cyclical flow and value accumulation of information among various system components. This closed loop includes two key technical components: knowledge transfer packaging technology and effect-knowledge mapping model. Knowledge transfer packaging technology ensures the integrity and traceability of knowledge during the transfer process, constructing an encapsulation structure for each knowledge entity: KnowPack = {content, metadata, constraints, trace_id}, where content is the actual content of the knowledge; metadata includes metadata such as source, confidence level, and update time; constraints record usage restrictions and applicable conditions; and trace_id is a globally unique tracking identifier. For example, the applicable population restrictions and contraindications information in medical recommendations are explicitly retained in the constraints field: constraints = {"applicable_to":["adult patients","weight>50kg"],"contraindications":["pregnancy","liver dysfunction","allergy to ingredients"]}. This technology combines automated semantic extraction with rule injection. The system can automatically identify and extract key constraints based on knowledge type, effectively solving the "semantic decay" problem in the knowledge transfer process.

[0102] The effect-knowledge mapping model, based on knowledge packaging, establishes a causal relationship between execution results and source knowledge. Its mapping model can be represented as: EffectKnowGraph = (KnowNodes, ResultNodes, Edges), where KnowNodes is the set of knowledge nodes, ResultNodes is the set of result nodes, Edges is the set of edges, and EffectKnowGraph is the constructed effect-knowledge mapping graph. The system calculates the attribution strength of the results to knowledge through the influence propagation function. Where ImpactProp(r,k) is the calculated attribution strength; DirectImp(r,k) represents the direct impact of knowledge content k on result r, and IndirectImp(r,k) represents the direct impact of knowledge content k on result r. i ) represents knowledge content k i The direct impact on the result r, DepProb(k) i (k) represents the knowledge content k i The probability of dependence on knowledge content k. This sophisticated attribution mechanism enables the system to accurately identify which knowledge has a positive or negative impact on a specific outcome, providing precise guidance for knowledge optimization. For example, in a medical context, the system can recognize that the effectiveness of a treatment plan may stem from the combined effect of multiple medical recommendations, rather than just the contribution of a single piece of knowledge.

[0103] The knowledge value assessment system is the core engine of closed-loop optimization, providing quantitative basis for knowledge optimization and decision adjustment. This system comprises two key technical components: a multi-dimensional utility function and knowledge credibility calculation. The multi-dimensional utility function comprehensively evaluates the actual value of knowledge, and its evaluation process can be expressed as follows: Where UtilFunc(k) is the comprehensive utility value of knowledge content k; evalDim i (k) is the evaluation function for the i-th dimension, weight i Let be the weight coefficient for the i-th dimension. Evaluation dimensions include accuracy, timeliness, applicability, frequency of use, and success rate. A multi-dimensional evaluation system can comprehensively measure the actual utility of knowledge, avoiding biased optimization caused by a single indicator. For example, some knowledge may be highly accurate but have a narrow scope of application, while other knowledge may be universally applicable but have poor timeliness. Multi-dimensional evaluation can balance these factors according to the needs of the application scenario. This component adopts an adaptive weighting mechanism, dynamically adjusting the importance of each dimension according to different domains and application scenarios.

[0104] Knowledge credibility calculation dynamically adjusts the credibility score of knowledge based on the Bayesian update principle. The update process can be represented as: T new =T prior ×T ratio Among them, T new For the updated credibility score, T prior T represents the prior confidence level of knowledge content k; ratio This represents the likelihood ratio of evidence relative to the knowledge content. Whenever knowledge is applied and produces a new execution result, its credibility score is automatically updated, reflecting changes in the reliability of the knowledge in actual use. For example, if a medical suggestion produces the expected therapeutic effect after multiple applications, its credibility score will increase accordingly; conversely, if unexpected complications occur or the effect is poor, the credibility will decrease. This component employs a hierarchical Bayesian network structure, capable of processing different types of evidence and outcome data. Through continuously accumulating application evidence, the system forms a dynamically evolving credibility distribution for each piece of knowledge.

[0105] In this embodiment, the closed-loop optimization mechanism achieves the system's self-evolution capability through a knowledge-action-feedback closed loop and a knowledge value assessment system. Execution results are transmitted back to the knowledge source via an effect-knowledge mapping. The knowledge value assessment system calculates the utility and credibility of the knowledge based on the feedback, thereby influencing subsequent knowledge retrieval, ranking, and application strategies. Based on the utility and credibility assessment results, the system automatically adjusts the knowledge retrieval weights: WeightAdjust(k) = OrigWeight(k)·(1+AdjFactor·(UtilFunc(k)-AvgUtil)), where WeightAdjust(k) is the adjusted knowledge weight, OrigWeight(k) is the original weight of knowledge content k, AdjFactor is the adjustment coefficient, and AvgUtil is the average utilization. For knowledge with a credibility below a preset threshold, the system will take corresponding security measures, including adding a usage warning, triggering manual review, or directly discarding the knowledge. Compared to the static knowledge base and one-way processing flow of traditional systems, the closed-loop optimization mechanism enables the system to exhibit the characteristics of "living knowledge". The knowledge base can continuously optimize itself based on the usage effect, which is reflected in three aspects: improved knowledge quality, enhanced adaptability, and domain-specific capabilities.

[0106] Furthermore, the heterogeneous AI engine interaction system achieves the integration and coordination of the overall technical framework based on a neural-symbolic hybrid architecture; such as Figure 6 As shown, the neuro-symbolic hybrid architecture combines the pattern recognition and learning capabilities of neural networks with the precise reasoning and interpretability of symbolic systems, overcoming the limitations of single technical approaches and providing a robust technical foundation for cognitive firewalls. This hybrid architecture is particularly suitable for handling the complex semantic understanding, security verification, and precise conversion needs in the knowledge-to-action transformation process.

[0107] The neural-symbolic hybrid architecture employs a dual-fusion design, comprising two core components: a neural-symbolic fusion architecture and hybrid reasoning innovation. It also achieves organic coordination of different technological paradigms based on a modular hierarchical structure. The system receives natural language knowledge input, represents it as semantic vectors through a neural perception layer, converts it into formal logical expressions through a symbolic logic generation system, performs logical reasoning calculations through a differentiable logic programming engine, and finally outputs securely verified instructions through neural-guided symbolic search and symbolic constraint neural generation. The entire process demonstrates the deep integration and complementary enhancement of neural and symbolic methods.

[0108] The neural symbolic fusion architecture, as the foundational technical framework of the system, breaks through the traditional isolation between neural networks and symbolic systems, achieving an organic combination of the two technological paradigms. This architecture comprises two core components: a symbolic logic generation system and a differentiable logic programming engine. The symbolic logic generation system employs a two-stage transformation method, converting knowledge expressed in natural language into formalized symbolic logic representations. The first stage involves semantic mapping through a neural encoder: NeuralEncode(x (1) = Transformer(x) (1) ), where NeuralEncode(x (1) ) represents the output of the neural encoding, x (1) For input content, Transformer(x) (1) The encoder is specially trained. The second stage performs logical transformation through a symbol parser: SymbParse(f) = ParseToLogic(f), where SymbParse(f) is the output of the symbol parsing, f is the semantic vector representation, and ParseToLogic(f) is the symbol parser. This system constructs specific ontology mapping layers for different professional domains, such as medical, financial, and legal ontology, ensuring that the generated symbolic representations are consistent with domain-specific knowledge.

[0109] The differentiable logic programming engine handles the fuzziness and uncertainty of the real world, transforming logical reasoning rules into differentiable mathematical operations. The engine defines continuous-value implementations for core logical operations: the logical AND operation LogicAnd(a',b') = a'×b', and the logical OR operation LogicOr(a',b') = a'+b'-a'×b', where LogicAnd(a',b') is the result of the logical AND operation, LogicOr(a',b') is the result of the logical OR operation, and a' and b' are the input logical values. The system also defines the gradients of the core logical operations, enabling end-to-end differentiability and optimization of the entire reasoning process. in, This represents the partial derivative of the LogicAnd function with respect to the logical value a'; the engine is based on a t-norm fuzzy logic framework and supports multiple families of t-norms, including the product t-norm. t-norm sum The t-norm allows for the selection of the most suitable logical semantics based on the application scenario. This embodiment unifies logical reasoning and deep learning within the same computational framework, enabling the system to learn logical rules from data while simultaneously applying known rules to guide the learning process.

[0110] Hybrid reasoning innovation achieves complementary enhancements between neural and symbolic methods, encompassing two core technologies: neural-guided symbolic search and neural generation of symbolic constraints. Neural-guided symbolic search uses neural networks to provide search guidance for symbolic reasoning, significantly improving reasoning efficiency. Its heuristic function can be expressed as: HeurFunc(s1) = GNN_params(s1), where HeurFunc(s1) is the heuristic evaluation result, s1 is the current search state, and GNN_params is the heuristic function based on a graph neural network; params are the neural network parameters optimized through reinforcement learning. This technology combines the pattern learning ability of neural networks with the accuracy of symbolic reasoning, employing a method that combines Monte Carlo tree search with neural network evaluation. For example, in medical rule verification, the heuristic function can quickly identify the subset of clinical rules most relevant to the current patient state, avoiding an exhaustive search of the entire medical knowledge base and reducing the search space by more than 95%.

[0111] Symbolically constrained neural generation embeds symbolic logic constraints during the neural network generation process to ensure that the output conforms to predetermined rules. The constraint satisfaction process can be represented as: In this framework, ConstSatLayer(x',C') is the final output of the constraint-satisfying layer; x' is the original output of the neural network; C' is the set of symbolic constraints; PenaltyFunc is the penalty function for violating constraints; λ is the trade-off parameter; y' is the optimized output for satisfying constraints; and i is the index of the constraint condition. This technique uses the Lagrange multiplier method to transform hard constraints into soft constraints, making the constraint-satisfying process differentiable and optimizable. For example, when generating drug usage guidelines, the system can ensure that the generated content follows the basic medical principle of "do not use if contraindicated," even if the neural model has not explicitly learned this rule.

[0112] The modular hierarchical structure employs a three-layer design to achieve coordinated integration of different technological paradigms. The neural perception layer is responsible for initial pattern recognition and feature extraction. It utilizes a Transformer architecture combined with a large-scale language model that has undergone pre-training and domain fine-tuning, capable of processing natural language text, semi-structured content, and multimodal information, converting them into a unified vector representation. This layer also integrates a professional vocabulary enhancement mechanism, enabling the system to accurately understand medical, financial, and other specialized terms. The symbolic knowledge layer maintains a structured knowledge and rule base, using a knowledge graph-like organization to store domain ontology, causal relationships, constraint rules, and reasoning templates. This layer supports multiple knowledge representation forms, including first-order logic, descriptive logic, and probabilistic logic. The knowledge base adopts a hierarchical structure, containing core domain knowledge, organization-specific knowledge, and user context knowledge. The hybrid reasoning layer coordinates the interaction and information fusion between the neural perception layer and the symbolic knowledge layer, implementing an intelligent routing mechanism that dynamically selects the most suitable processing mode based on task characteristics: neural methods are prioritized for pattern recognition and semantic understanding tasks, symbolic methods are prioritized for precise reasoning and verification tasks, and a combination of both methods is used for complex tasks.

[0113] The layers communicate with each other through an interface matrix, enabling a bidirectional mapping between neural representations and symbolic representations. The cross-layer information exchange process can be represented as follows: Here, InterfaceMap(n's') is the result of cross-level mapping, where n' is the neural representation, s' is the symbolic representation, NeuToSym is the neural-to-symmetric transformation function, and SymToNeu is the symbolic-to-neural transformation function. This represents semantic connection operations. The interface matrix achieves seamless conversion between neural and symbolic representations through a shared embedding space, enabling mutual understanding and utilization of information from two different paradigms. The system employs an attention mechanism to achieve dynamic mapping between representations, automatically adjusting conversion parameters based on the current task context, and continuously optimizing the conversion function through ongoing learning during long-term operation.

[0114] In this embodiment, the neural-symbolic hybrid architecture provides robust technical support for the cognitive firewall. This architecture offers deep semantic understanding capabilities for the three-layer filtering architecture, a precise conversion mechanism for the three-stage knowledge processing pipeline, a value implementation method for symbolic constraints for bidirectional value alignment, and a knowledge representation and learning framework for closed-loop optimization. Through this comprehensive technical support, the cognitive firewall achieves secure, accurate, and reliable knowledge-to-action conversion.

[0115] In some embodiments, this application also provides an intelligent medical decision support system based on a cognitive firewall, applied to the formulation of personalized treatment plans for patients with complex diabetic complications. This intelligent medical decision support system, through its cognitive firewall architecture, simultaneously achieves strict adherence to medical guidelines and flexible adaptation to personalized treatment, addressing key technical challenges of medical AI systems in complex, multi-complication scenarios.

[0116] In this intelligent medical decision support system, a cognitive firewall is deployed between the RAG medical knowledge retrieval system and the Agent diagnosis and treatment execution system, forming a secure and reliable knowledge conversion middleware. It is primarily used in hospital endocrinology departments to specifically handle the diagnosis and treatment decisions for complex diabetic patients with multiple complications.

[0117] The core application of cognitive firewalls in intelligent medical decision support systems is manifested in a complete knowledge security transformation process. When the RAG system retrieves medical knowledge about diabetes drug treatment, a three-layer filtering architecture first performs a security check. The syntax layer applies an adaptive syntax validation mechanism, automatically selecting appropriate validation rules for medical literature through a dynamic rule selection algorithm RuleSel(d,c)=argmax_R{P(r|d,c)}. For example, when processing drug dosage information, numerical format validation rules are prioritized, and medical terminology standardization rules are selected when processing contraindication descriptions. The anomaly pattern recognition component, through… The system detects formatting anomalies in medical literature, such as inconsistent dosage units or misspelled drug names. The semantic layer converts natural language drug usage guidelines into a formal logical representation; the differentiable logical reasoning component LogicInf(p,q)=σ(W·[p;q]+b) verifies the consistency of the logical relationship between drug contraindications and patient status. The pragmatic layer uses multi-step influence simulations to assess the long-term effects of treatment plans, for example through… Predict the 3-5 step chain effects of a certain hypoglycemic drug in patients with renal insufficiency, including the risks of drug accumulation, toxic reactions, and organ function deterioration.

[0118] The three-stage knowledge processing pipeline performs in-depth transformation of security-checked medical knowledge. The knowledge atomization and decomposition stage breaks down complex diabetes treatment guidelines into independent clinical decision-making elements, for example, through SemDec(T)={s1,s2,...,s...}. h The statement "Guidelines for the use of medication in diabetic patients: The starting dose for adult patients is 500 mg daily; the dose should be reduced in cases of renal insufficiency; and the medication is contraindicated in cases of severe hepatic insufficiency" is decomposed into three independent semantic units. The proposition extraction method PropEx(s) hThe semantic unit {l, r', C} is transformed into a structured proposition. For example, renal function limitation is transformed into the propositional form (REDUCE_DOSE, hypoglycemic drug, {patient condition: renal insufficiency}). The multidimensional value space constraints satisfy the construction of a medical value space containing dimensions such as patient safety and treatment effectiveness during the solution phase. Contraindication-related propositions are mapped to a value space, with the safety dimension receiving the highest weight score. The system calculates the value compliance degree ValComp(u) = cos(ValProj(u),TargetVal) to ensure that safety-related medical decision propositions are prioritized. In the formal verification phase of safety instructions, a medical instruction template is selected using InstComp(u,c) = Template(TypeOf(u),ArgsOf(u,c)). For example, a comprehensive template including dosage calculation, contraindication checks, and monitoring reminders is selected for medication recommendations. The medical safety of the generated instructions is verified using Verify(e,s') → {true,false,FixInst(e)}.

[0119] The two-way value alignment module plays a crucial role in healthcare scenarios. The forward value aligner, through... This process maps drug use knowledge to a medical value space. For example, contraindication knowledge can be scored as 0.95 on the safety dimension and 0.20 on the efficiency dimension, reflecting the value orientation of prioritizing safety in medical decision-making. When the value compliance of a drug's knowledge falls below the medical safety threshold, the value constraint instruction generation ValConstInst(k,c) automatically embeds additional safety check steps into the treatment instruction, such as requiring renal function assessment or liver function monitoring before medication. A reverse value feedback system collects patient treatment results through... Clinical indicators such as treatment efficacy and adverse reaction rates are mapped to a value space. The Knowledge-Execution-Outcome Graph (KERGraph = (KNodes, ENodes, RNodes, Edges)) establishes a causal relationship between drug knowledge and clinical outcomes. Calculate the value contribution of each piece of medical knowledge to the treatment outcome, such as how identifying a contraindication helps avoid adverse reactions.

[0120] A closed-loop optimization mechanism enables continuous optimization of medical knowledge. Knowledge delivery packaging technology constructs a KnowPack = {content, metadata, constraints, trace_id} encapsulation structure for each piece of medical knowledge. For example, the constraints field explicitly records key constraints such as "Applicable population: adult diabetic patients" and "Contraindications: severe renal insufficiency, pregnancy." Effect - Knowledge mapping is achieved through... Precise attribution of clinical outcomes, such as identifying that the success of a treatment regimen may result from a combination of knowledge about drug selection, dosage adjustment, and monitoring protocols. Multidimensional utility functions. The system comprehensively evaluates the value of medical knowledge. For example, knowledge about a new drug might score 0.9 in accuracy but only 0.6 in timeliness. The system balances these factors based on the needs of clinical scenarios. Knowledge credibility calculation T new =T prior ×T ratio Knowledge credibility is dynamically adjusted based on clinical application results. For example, if a drug recommendation produces the expected effect in multiple clinical applications, its credibility score will increase accordingly.

[0121] In specific clinical applications, when dealing with patients with complex diabetic complications, the system can accurately identify key decision-making factors such as indications and limitations of multiple drugs, organ function-related contraindications, and dosage regimens requiring individualized adjustments through the synergistic application of the aforementioned algorithms. It generates comprehensive treatment instructions that include personalized medication plans, detailed monitoring plans, and multidisciplinary collaborative recommendations, effectively solving the problem that traditional systems are prone to overlooking drug interactions and contraindications when dealing with patients with complex complications, and providing clinicians with safe and reliable decision support.

[0122] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the intelligent medical decision support system based on the cognitive firewall described above can be referred to the corresponding process in the aforementioned heterogeneous AI engine interaction system embodiment, and will not be repeated here.

[0123] This embodiment also discloses a heterogeneous AI engine interaction method based on a cognitive firewall, such as... Figure 7 As shown, the specific steps are as follows:

[0124] An isolated communication path is established between the RAG system and the Agent system to receive knowledge content from the RAG system and perform preliminary structuring processing on the knowledge content to generate standardized knowledge.

[0125] Standardized knowledge is sequentially validated at the syntactic level, understood at the semantic level, and evaluated at the pragmatic level, and the validated safe knowledge is output.

[0126] The forward value alignment mechanism maps security knowledge to a multi-dimensional value space to calculate the value conformity. Value alignment knowledge is generated based on the value conformity. The reverse value feedback mechanism optimizes the value evaluation system based on the execution results of the Agent system.

[0127] Transform value-aligned knowledge into structured instructions that can be executed by the agent system.

[0128] In one embodiment, the complete path and key decision points of knowledge content processing are recorded to construct a traceable chain of evidence.

[0129] Furthermore, a cognitive isolation channel is established between the RAG system and the Agent system to perform preliminary structuring processing of knowledge content. This cognitive isolation channel receives various forms of knowledge content from the RAG system, establishes a unified knowledge input interface, and achieves secure isolation and standardized processing of knowledge content through knowledge reception, structured preprocessing, secure encapsulation, and transmission management, providing a standardized input foundation for subsequent security filtering.

[0130] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0131] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A heterogeneous AI engine interaction system based on a cognitive firewall, deployed as a cognitive firewall between a retrieval enhancement generation system and an intelligent agent system, characterized in that, include: The cognitive isolation channel is used to establish an isolated communication path between the RAG system and the Agent system, receive knowledge content from the RAG system, perform preliminary structuring processing on the knowledge content, and generate standardized knowledge. A three-layer filtering architecture, connected to a cognitive isolation channel, is used to sequentially perform syntactic layer verification, semantic layer understanding, and pragmatic layer evaluation on standardized knowledge, and output safe knowledge that has passed the verification. The bidirectional value alignment module, connected to the three-layer filtering architecture, is used to map security knowledge to a multi-dimensional value space through a forward value alignment mechanism to calculate the value conformity, generate value alignment knowledge based on the value conformity, and optimize the value assessment system based on the execution results of the Agent system through a reverse value feedback mechanism. The knowledge-instruction conversion engine, connected to the bidirectional value alignment module, is used to convert value-aligned knowledge into structured instructions that can be executed by the agent system. The cognitive log module connects the cognitive isolation channel, the three-layer filtering architecture, the two-way value alignment module, and the knowledge-instruction conversion engine. It is used to record the complete path of knowledge processing and key decision points, and to build a traceable chain of evidence.

2. The heterogeneous AI engine interaction system based on a cognitive firewall according to claim 1, characterized in that, The knowledge content includes semantic information and constraints.

3. The heterogeneous AI engine interaction system based on a cognitive firewall according to claim 1, characterized in that, The three-layer filtering architecture includes a syntax layer filtering unit, a semantic layer understanding unit, and a pragmatic layer evaluation unit. The syntax layer filtering unit is used to perform formal structure verification on standardized knowledge using a dynamic rule selection algorithm and to perform abnormal pattern recognition using a weighted feature function to obtain structurally compliant data. The semantic layer understanding unit is used to perform deep semantic analysis on structurally compliant data using neural symbolic hybrid computation to filter out semantically safe data. The pragmatic layer evaluation unit is used to simulate the potential consequences of knowledge application by leveraging multi-step influence on semantic security data, and output security knowledge.

4. The heterogeneous AI engine interaction system based on a cognitive firewall according to claim 1, characterized in that, The two-way value alignment module includes: The value function builder is used to construct a multi-dimensional value space and define the evaluation functions and weight vectors for each value dimension. It handles the contradictions and trade-offs between different value dimensions through a value conflict resolution mechanism. The forward value aligner connects the value function builder and is used to map security knowledge to a multi-dimensional value space and calculate the value conformity degree through knowledge value assessment methods, and generate value-aligned knowledge based on the value conformity degree. The reverse value feedback loop connects to the value function builder, which collects the execution results of the agent system and maps them to a multi-dimensional value space to obtain an evaluation vector, thereby calculating the value realization gap. It also constructs a knowledge-execution-result association graph and calculates the value contribution score of knowledge through graph analysis algorithms. Based on the value realization gap and value contribution score, it dynamically optimizes the knowledge base index structure and retrieval algorithm parameters.

5. The heterogeneous AI engine interaction system based on a cognitive firewall according to claim 4, characterized in that, In a reverse value feedback loop, the method for obtaining the value realization gap is as follows: Collect multidimensional indicator data of the execution results of the Agent system and construct the result dataset; The evaluation vector is obtained by mapping multidimensional indicator data to a multidimensional value space through an evaluation function. The value realization gap is calculated based on the evaluation vector and the preset target vector.

6. The heterogeneous AI engine interaction system based on a cognitive firewall according to claim 1, characterized in that, The knowledge-instruction conversion engine uses instruction compilation technology to compile value-aligned knowledge into initial instructions, and applies formal verification methods to perform multiple verifications to generate secure instructions.

7. A heterogeneous AI engine interaction system based on a cognitive firewall according to claim 6, characterized in that, The knowledge-instruction conversion engine includes a formal verification module, which comprises a syntax correctness verification unit, an execution feasibility verification unit, a permission compliance verification unit, and a side effect control verification unit.

8. The heterogeneous AI engine interaction system based on a cognitive firewall according to claim 1, characterized in that, It also includes a closed-loop optimization mechanism to achieve a closed loop from knowledge generation to action execution and then to feedback learning, including: The knowledge-action-feedback closed loop applies knowledge transfer packaging technology to build an encapsulation structure for knowledge entities, and applies the effect-knowledge mapping model and influence propagation function to establish a causal relationship between execution results and standardized knowledge. The knowledge value assessment system applies a multi-dimensional utility function to comprehensively evaluate the utility of standardized knowledge and applies the Bayesian update principle to dynamically update the credibility of knowledge; based on the utility assessment and credibility update results, the knowledge retrieval weight is automatically adjusted.

9. A heterogeneous AI engine interaction method based on a cognitive firewall, characterized in that, The specific steps are as follows: An isolated communication path is established between the RAG system and the Agent system to receive knowledge content from the RAG system and perform preliminary structuring processing on the knowledge content to generate standardized knowledge. Standardized knowledge is sequentially validated at the syntactic level, understood at the semantic level, and evaluated at the pragmatic level, and the validated safe knowledge is output. The forward value alignment mechanism maps security knowledge to a multi-dimensional value space to calculate the value conformity, generates value alignment knowledge based on the value conformity, and optimizes the value assessment system based on the execution results of the Agent system through the reverse value feedback mechanism. Transform value-aligned knowledge into structured instructions that can be executed by the agent system.

10. The heterogeneous AI engine interaction method based on a cognitive firewall according to claim 9, characterized in that, It also includes recording the complete path of knowledge content processing and key decision points to build a traceable chain of evidence.

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