Method for automated construction of end-to-end agent based on graph structure semantic fusion

Abstract

The present application relates to the technical field of artificial intelligence, and in particular to an automatic construction method of an end-to-end intelligent agent based on graph structure semantic fusion. The method comprises the following steps: receiving user input business requirement data; analyzing the business requirement data for business objectives and requirement constraints to generate a core workflow framework of the intelligent agent; performing end-to-end execution path analysis on the core workflow framework of the intelligent agent to obtain an end-to-end workflow; constructing a dynamic evolution semantic graph; constructing an end-to-end call chain execution strategy based on the end-to-end workflow, and performing intelligent agent instance encapsulation and intelligent agent instance reinforcement learning enhancement processing according to the dynamic evolution semantic graph, thereby automatically constructing an end-to-end intelligent agent. The present application combines graph structure knowledge with the generation capability of a large language model to provide an efficient, accurate and scalable intelligent agent automatic construction scheme for various complex business scenarios.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to an automated method for constructing end-to-end intelligent agents based on graph structure semantic fusion. Background Technology

[0002] Traditional intelligent agent construction relies primarily on pre-defined rules and procedural logic, a method that exhibits significant limitations when dealing with complex and ever-changing business scenarios. On one hand, pre-defined rules struggle to cover all situations, resulting in a lack of flexibility for the agent when facing new contexts. On the other hand, the maintenance and update costs of the system increase exponentially with the addition of business rules. While machine learning-based intelligent agent technology has made significant progress in recent years, it still suffers from insufficient knowledge representation, limited contextual understanding, and weak cross-domain reasoning capabilities. The emergence of large language models has brought new possibilities to intelligent agent technology. These models, trained on massive amounts of text data, possess powerful natural language understanding and generation capabilities. However, intelligent agents built solely based on large language models still face problems such as poor knowledge timeliness, insufficient depth of professional domains, and limited reasoning accuracy. These problems are particularly pronounced in complex business scenarios requiring precise professional knowledge and rigorous logical reasoning. Large language models tend to generate fluent but imprecise answers, lacking a deep grasp and systematic understanding of professional knowledge. Graph-structured knowledge representation, as an important knowledge organization method, can clearly demonstrate the relationships between concepts and the hierarchical structure of knowledge. However, existing graph knowledge applications are mostly static structures, making it difficult to adapt to dynamically changing business environments. At the same time, there is a "disconnect" between traditional graph knowledge applications and large language models, which cannot effectively combine structured professional knowledge with natural language processing capabilities. Summary of the Invention

[0003] Based on this, the present invention provides an automated construction method for end-to-end intelligent agents based on graph structure semantic fusion to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, an automated method for constructing end-to-end intelligent agents based on graph structure semantic fusion includes the following steps:

[0005] Step S1: Receive business requirement data input by the user; analyze the business requirements data for business objectives and requirement constraints to obtain structured requirement parameters; process the technical blueprint based on the structured requirement parameters to obtain a preliminary end-to-end technical blueprint.

[0006] Step S2: Perform dynamic workflow processing on the initial end-to-end technology blueprint to generate the core workflow framework of the agent; perform multimodal context understanding based on structured requirement parameters to obtain multimodal context information; perform end-to-end execution path analysis on the core workflow framework of the agent based on the multimodal context information to obtain the end-to-end workflow;

[0007] Step S3: Obtain multimodal data based on structured requirement parameters and construct a dynamic evolutionary semantic graph; dynamically activate query nodes in the dynamic evolutionary semantic graph to generate highly activated candidate node data; dynamically select knowledge fragments from the dynamic evolutionary semantic graph based on the highly activated candidate node data to obtain the preferred knowledge fragments;

[0008] Step S4: Construct an end-to-end call chain execution strategy based on the end-to-end workflow; encapsulate agent instances through the end-to-end call chain execution strategy and dynamic evolution semantic graph, and perform agent instance reinforcement learning enhancement processing to automatically construct end-to-end agents.

[0009] This invention achieves high adaptability of the intelligent agent to complex and ever-changing business scenarios, significantly reducing system maintenance and update costs. Through structured requirement parameter analysis and technical blueprint processing, the intelligent agent can accurately understand the essence of business requirements and form a scientifically sound technical implementation path. The multimodal contextual understanding mechanism greatly enhances the intelligent agent's contextual awareness, enabling it to understand and process information from different channels, overcoming the limitations of limited contextual understanding in traditional methods. Dynamic evolutionary semantic graphs, as a core innovation, break through the limitations of traditional graph knowledge applications being mostly static structures, achieving real-time updates and optimization of the knowledge system. Through dynamic activation of query nodes and dynamic selection of knowledge fragments, the intelligent agent can accurately extract the most relevant professional knowledge for the current task, solving the problem of insufficient depth in professional domains for large language models. This organic integration of graph structure and language model successfully eliminates the "fragmentation" between the two, achieving seamless integration of structured professional knowledge and natural language processing capabilities. The end-to-end call chain execution strategy ensures the integrity and coherence of task execution, avoiding the task fragmentation problem common in traditional methods. The reinforcement learning-based enhancement mechanism for agent instances enables the system to self-optimize, continuously improving performance and accuracy through practical business applications. The automated nature of the entire construction process significantly reduces development barriers and labor costs, allowing even non-technical personnel to quickly build high-performance agents that meet specific business needs. Graph structure semantic fusion enables precise knowledge representation and flexible application, solving core technical challenges such as insufficient knowledge representation in traditional agents and limited accuracy in large language model reasoning. This provides an efficient, accurate, and scalable automated agent construction solution for various complex business scenarios. Therefore, this invention's end-to-end automated agent construction method based on graph structure semantic fusion, by integrating graph structure knowledge with the generation capabilities of large language models, achieves accurate knowledge retrieval, context-aware multi-turn dialogue, and cross-domain dynamic reasoning, providing efficient and accurate intelligent services for various complex business scenarios. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the steps of the automated construction method for end-to-end intelligent agents based on graph structure semantic fusion of the present invention.

[0011] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0012] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0013] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0014] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0015] To achieve the above objectives, please refer to Figure 1 This invention provides an automated method for constructing end-to-end intelligent agents based on graph structure semantic fusion, comprising the following steps:

[0016] Step S1: Receive business requirement data input by the user; analyze the business requirements data for business objectives and requirement constraints to obtain structured requirement parameters; process the technical blueprint based on the structured requirement parameters to obtain a preliminary end-to-end technical blueprint.

[0017] Step S2: Perform dynamic workflow processing on the initial end-to-end technology blueprint to generate the core workflow framework of the agent; perform multimodal context understanding based on structured requirement parameters to obtain multimodal context information; perform end-to-end execution path analysis on the core workflow framework of the agent based on the multimodal context information to obtain the end-to-end workflow;

[0018] Step S3: Obtain multimodal data based on structured requirement parameters and construct a dynamic evolutionary semantic graph; dynamically activate query nodes in the dynamic evolutionary semantic graph to generate highly activated candidate node data; dynamically select knowledge fragments from the dynamic evolutionary semantic graph based on the highly activated candidate node data to obtain the preferred knowledge fragments;

[0019] Step S4: Construct an end-to-end call chain execution strategy based on the end-to-end workflow; encapsulate agent instances through the end-to-end call chain execution strategy and dynamic evolution semantic graph, and perform agent instance reinforcement learning enhancement processing to automatically construct end-to-end agents.

[0020] In this embodiment of the invention, the automated construction method for end-to-end intelligent agents based on graph structure semantic fusion includes the following steps:

[0021] Step S1: Receive business requirement data input by the user; analyze the business requirements data for business objectives and requirement constraints to obtain structured requirement parameters; process the technical blueprint based on the structured requirement parameters to obtain a preliminary end-to-end technical blueprint.

[0022] In this embodiment of the invention, user business requirement data, including text descriptions, structured forms, and interactive question-and-answer content, is collected through multi-channel input interfaces. The collected business requirement data is processed by a tokenization preprocessor, which uses a byte-pair encoding algorithm to segment the text into sub-word sequences with a vocabulary size of 50,000. The preprocessed text is then processed by a 24-layer transformer architecture with a self-attention network, a hidden layer dimension of 4096, and 16 attention heads, capturing long-distance dependencies and deep semantics. The text representation data is processed through autoregression, applying a multi-head attention mechanism and a feedforward neural network to extract key entities and relationships, while simultaneously performing intent understanding to generate business intent label data. Based on the intent label data, business target description processing is performed, extracting core entities from the entity list as target subjects and objects, forming business target entity pairs. Subsequently, conditional reasoning is performed on the text representation data to extract business constraints, and the business target entity pairs and constraints are mapped to structured requirement parameters. Based on these parameters, the task is decomposed through the generation model specification generation layer, matching industry standard task templates, generating task flow sequences and API call chains, defining evaluation metrics, and finally constructing a preliminary end-to-end technical blueprint that includes an architecture view, component definitions, interface specifications, data flow diagrams, deployment plans, and evaluation mechanisms.

[0023] Step S2: Perform dynamic workflow processing on the initial end-to-end technology blueprint to generate the core workflow framework of the agent; perform multimodal context understanding based on structured requirement parameters to obtain multimodal context information; perform end-to-end execution path analysis on the core workflow framework of the agent based on the multimodal context information to obtain the end-to-end workflow;

[0024] In this embodiment of the invention, a workflow transformation engine is applied to the initial end-to-end technology blueprint. This engine uses graph structure analysis technology to extract key task nodes and dependencies. The workflow processing is implemented through a graph convolutional network, containing three graph convolutional layers with modified linear units as the activation function. It extracts the input and output parameter definitions and pre-dependent conditions for each node, constructing a directed acyclic graph to represent the node interface dependencies. Simultaneously, it integrates various formats of information, such as text, images, and structured data, based on structured requirement parameters, and performs cross-modal fusion through a cross-attention mechanism to generate a contextual representation. For text data, a 12-layer transformer encoder is used for processing, with a hidden layer dimension of 1024; image data features are extracted through a visual encoder, containing 8 residual blocks; features from each modality are fused through 4 layers of cross-attention to form a unified representation. Based on multimodal contextual information and node dependency data, an autoregressive inference engine is applied to perform execution inference for each task node. This engine is based on a 48-layer causal attention architecture with a hidden layer dimension of 4096, analyzing execution conditions and schemes. The inference results are converted into specific operation instructions by the action planner. The virtual environment simulator simulates the execution of these instructions, generating observation data. The "think-act-observe" loop is executed iteratively until all nodes are covered, forming a very long thought chain. The workflow synthesizer extracts key nodes and execution paths to generate the final end-to-end workflow.

[0025] Step S3: Obtain multimodal data based on structured requirement parameters and construct a dynamic evolutionary semantic graph; dynamically activate query nodes in the dynamic evolutionary semantic graph to generate highly activated candidate node data; dynamically select knowledge fragments from the dynamic evolutionary semantic graph based on the highly activated candidate node data to obtain the preferred knowledge fragments;

[0026] In this embodiment of the invention, a distributed data acquisition framework is deployed based on structured requirement parameters. This framework connects to multimodal data sources through dedicated connectors to perform information acquisition. For text data, the Latent Dirichlet Allocation algorithm is applied for topic modeling to extract core entities; for image data, deep convolutional networks are used for object detection and scene understanding to extract visual features. A dual-tower cross-modal fusion architecture is adopted for information integration, identifying entity relationships and event associations between different modalities, and constructing a cross-modal fusion structure. For each knowledge unit, a globally unique identifier is assigned and metadata is labeled, including knowledge source, credibility, and timeliness. Knowledge units are organized into a hierarchical structure of concept layer, relation layer, and instance layer according to abstraction level and domain relevance, forming a dynamically evolving semantic graph. When a query request is received, the query semantics are parsed and converted into a vector representation. Semantic similarity is analyzed in the graph, while considering the historical access frequency and activation time of nodes to evaluate the graph activation state. Activation values ​​are diffused through eight rounds of alternating horizontal and vertical propagation. Horizontal propagation proceeds along the same-level relationship, and vertical propagation proceeds along the hierarchical relationship until the activation values ​​converge. The activation threshold is dynamically calculated based on query complexity to filter highly activated nodes, and a graph clustering engine is applied to cluster knowledge nodes. Retrieval-enhanced generation techniques are used to integrate the clustered knowledge, identify conflicting knowledge points, and resolve conflicts through assessments of authority, timeliness, and relevance, ultimately generating optimal knowledge fragments.

[0027] Step S4: Construct an end-to-end call chain execution strategy based on the end-to-end workflow; encapsulate agent instances through the end-to-end call chain execution strategy and dynamic evolution semantic graph, and perform agent instance reinforcement learning enhancement processing to automatically construct end-to-end agents.

[0028] In this embodiment of the invention, a directed acyclic graph (DAG) of the task flow is constructed based on an end-to-end workflow. Nodes are extracted, edge relationships are built, and the graph structure is optimized through a three-stage process. A dual-policy network architecture is deployed on this basis, including a higher-order policy network and an execution policy network. Both share a graph attention network encoder but have independent decision heads. The higher-order policy network determines the necessity of retrieval, execution timing, and retrieval depth through a three-stage decision process; the execution policy network configures specific retrieval parameters and content generation strategies. A policy integration engine integrates the two-level decision results to generate a complete call chain execution policy. Atomic operations in the call chain are used as the agent's action space, and graph states and interaction contexts are used as the observation space to construct a training environment. The agent executes task processing based on the call chain policy, collecting decision sequences and feedback data. The call chain policy is optimized through a dual-channel learning mechanism: on the one hand, knowledge is transferred from the teacher network based on model distillation; on the other hand, reinforcement learning is performed using a proximal policy optimization algorithm. Expert demonstration data is introduced for supplementary training, and expert operations are imitated through behavior cloning. Finally, the optimized parameters are solidified into the agent instance through quantization compression, model graph optimization, knowledge embedding, interface standardization, and containerization encapsulation. When interacting with users, instances perform context awareness and intent understanding, execute multi-step queries to build evidence chains, provide personalized responses, and continuously learn from user feedback to complete the entire process of automating the construction of end-to-end intelligent agents.

[0029] Preferably, step S1, which involves analyzing the business requirements data for business objectives and constraints, includes:

[0030] The business requirement data is preprocessed with tokenization to obtain tokenized requirement text;

[0031] Based on a decoder-only architecture, multi-layer self-attention hierarchical association processing is performed on the tokenized requirement text to capture long-distance dependencies and deep semantic representations in the text, thereby obtaining the contextual representation data of the requirement text.

[0032] Autoregressive processing is performed on the contextual representation data of the demand text, and key entity and relation information is extracted through the multi-head attention mechanism and feedforward neural network in the decoder-only architecture to obtain a list of domain entity information.

[0033] Perform intent understanding on the contextual representation data of the demand text, identify the user's main business intent, and generate business intent tag data;

[0034] Based on the business intent tag data, the business objective description is processed, and at the same time, the core entities directly related to the business objective are extracted from the domain entity information list as the target subject or object, thus obtaining the preliminary business objective entity pair;

[0035] Perform contextual reasoning on the contextual representation data of the requirement text, process the constraints, and generate business constraints.

[0036] Based on the initial business objective entity pairs and the requirement constraints, structured parameter mapping is performed to obtain structured requirement parameters.

[0037] In this embodiment of the invention, when performing tokenization preprocessing on business requirement data, the input business requirement text is first processed using the BPE (Byte-Pair Encoding) word segmentation algorithm. The BPE algorithm establishes a vocabulary of 50,000 sub-word units by iteratively merging the most frequent character pairs. During the tokenization process, the business requirement text is segmented into a sequence of sub-words, and each sub-word is assigned a unique numerical identifier. For professional terms and domain-specific vocabulary, a pre-built domain dictionary is used for recognition. This dictionary contains 5,000 commonly used terms in the domain of graph structure intelligent agents. For Chinese text, character-level segmentation is performed first, and then the BPE algorithm is applied; for numbers and special symbols, they are marked as independent tokens. The tokenized text retains the original word order and adds a start token [BOS] and an end token [EOS] to form a complete tokenized requirement text sequence. When processing the tokenized requirement text based on a decoder-only architecture, a 24-layer transformer decoder structure is used, with each layer containing 16 attention heads and a hidden layer dimension of 4096. First, the tokenized text is converted into a 768-dimensional vector representation through an embedding layer. Then, location information is added to each token using a location encoder. The location encoding is generated using sine and cosine functions, supporting a maximum of 2048 locations. Next, the text is processed through 24 self-attention layers. In each self-attention mechanism, the query, key, and value matrices are obtained from the input vector through three independent linear transformations. Attention calculation uses a scaled dot product attention mechanism, with the attention temperature parameter set to 1.0. To capture long-distance dependencies, a sparse attention pattern is adopted, dividing the input sequence into blocks of fixed length 128. Global attention calculation is performed within each block, and local sliding window attention calculation is performed between blocks, with the window size set to 256. Each self-attention layer is followed by a LayerNorm normalization layer and a residual connection. Finally, it is processed by a location feedforward network to obtain the context representation vector of each token. The output dimension remains at 4096, constituting the context representation data of the required text. When performing autoregressive processing on the contextual representation data of the demand text, an entity recognition head network is introduced. This network consists of two fully connected layers: the first layer has a dimension of 4096×2048, and the second layer has a dimension of 2048×13 (corresponding to 13 predefined entity type labels). The contextual representation data is input sequentially from left to right in units of tokens. For each input token, a prediction is made based on all previously input tokens. By setting a lower triangular matrix in the attention mask matrix, it is ensured that each position can only access the information of its preceding tokens. For the representation vector of each token, a multi-head attention mechanism extracts features from eight different projection subspaces, each attention head having a dimension of 512, and then concatenates and fuses them to obtain the enhanced representation.The augmented representation is achieved through a feedforward neural network containing two linear transformation layers with a GELU activation function in between, mapping vectors to the entity label space. A Conditional Random Field (CRF) layer is applied to optimize the label sequence as a whole; the CRF transition matrix is ​​13×13, encoding the transition probabilities between entity types. Finally, consecutive segments with the same label are extracted as entities based on the annotation results, and the type, start position, end position, and corresponding original text of each entity are recorded, forming a structured domain entity information list containing entity name, type, and location information. A global pooling operation is used to extract the overall text representation; specifically, the context representation vectors of all tokens are averaged to obtain a 4096-dimensional global text representation vector. This vector is processed by an intent classifier, which consists of a three-layer fully connected network. The first layer has a dimension of 4096×1024 and uses the ReLU activation function; the second layer has a dimension of 1024×512 and uses the Tanh activation function; and the third layer has a dimension of 512×42, corresponding to 42 predefined business intent categories. The classifier output is converted into a probability distribution using a Softmax function. The three intents with the highest probabilities are selected as candidates, and their semantic compatibility scores are calculated. Semantic compatibility is determined using a pre-trained intent relationship matrix, which is 42×42 in size and represents the strength of co-occurrence relationships between different intent pairs. The final primary business intent is determined by a weighted sum of the intent graph probability value and the compatibility score. After selecting the intent graph, a business intent label data structure is generated, containing an intent graph category identifier, intent confidence, and a list of intent keyword triggers. A pre-built intent-target template library is loaded, which contains mapping relationships between 300 business intents and target descriptions. Each template defines the syntactic structure of the target description under a specific intent and the entity slots to be filled. Based on the identified primary intent label, the corresponding template is queried and matched. Then, core entities are selected from the domain entity information list based on entity relevance calculation. Relevance calculation employs a dual scoring mechanism based on location and semantics: location scoring considers the distance between the entity and the triggering intent keyword, with closer proximity resulting in higher scores; semantic scoring is calculated using a pre-trained entity-intent association tensor with dimensions of 13×42×5000, representing the importance distribution of words across different domains under different entity types and intents. Subject and object entities are selected based on relevance, forming preliminary business target entity pairs, including fields such as subject entity identifier, object entity identifier, relationship type, and entity attribute value list. The selected entities are then filled into template slots to generate standardized business target descriptions, which are output along with the entity pair information. A condition extraction network is deployed, based on an autoregressive model structure, containing an 8-layer transformer decoder with a hidden layer dimension of 2048.First, a conditional semantic mask is applied to the contextual representation data. This mask is trained to recognize language patterns representing conditional relationships, including 174 conditional trigger words such as "if" and "when" and their semantically equivalent expressions. The conditional semantic mask is applied to self-attention computation to enhance the attention weights on conditional expressions. The processed representation vectors undergo boundary detection through a conditional segmenter, which is composed of a bidirectional LSTM network. The output layer uses CRF for sequence labeling to identify the start and end positions of conditional statements. After extracting conditional segments, each segment undergoes semantic analysis to identify the condition type (precondition, postcondition, mutually exclusive condition, etc.), condition strength (necessary, sufficient, optional), and involved entities. The extracted conditional results are normalized and converted into triples (conditional subject, conditional predicate, conditional object). Finally, semantically similar conditions are merged to eliminate redundancy and form a structured list of business constraints. A parameter extractor is then enabled, consisting of an attention fusion network and a multi-task decoder. First, the representation vectors of business target entity pairs and business constraints are concatenated and input into the attention fusion network. The attention fusion network employs a cross-attention mechanism, calculating bidirectional attention weights between entity pairs and constraints separately. Weight calculation utilizes dot-product attention with a temperature coefficient of 0.7 for scaling. The fused representation is then processed through two layers of bidirectional GRU networks, resulting in a hidden state dimension of 1024, capturing long-range dependencies. The fused representation is input into 86 predefined parameter extractors. Each extractor is responsible for extracting a specific type of parameter, including metadata such as parameter name, type, value range, default value, and unit. For numerical parameters, regular expression matching and a validator are used to extract values ​​and units; for enumerated parameters, lookup tables are used to extract valid values; and for text parameters, semantic similarity calculations are used to extract standardized representations, generating a structured set of required parameters containing parameter names, values, types, constraints, and dependencies, output in JSON format.

[0038] Preferably, step S1, which involves processing the structured requirement parameters into a technical blueprint based on the GPT model, includes:

[0039] The structured requirement parameters are input into the specification generation layer of the GPT model, and the task decomposition sequence is processed according to the preset industry standard task template library and general task decomposition logic to obtain the preliminary task process sequence.

[0040] Based on the preliminary task flow sequence, API call interfaces are matched to obtain the expected API call chain;

[0041] Based on structured requirement parameters and preliminary task process sequences, task completion quality and efficiency evaluation indicators are defined, and task evaluation indicator data are obtained.

[0042] Based on the preliminary task flow sequence, expected API call chain, and task evaluation index data, a preliminary end-to-end technical blueprint is obtained through technical blueprint processing.

[0043] In this embodiment of the invention, the structured requirement parameters are converted into 1024-dimensional vectors via an input adapter, added to the positional encoding, and then input to the reduction generation layer. This generation layer is based on a 96-layer transformer architecture with a hidden layer dimension of 12288 and contains 96 attention heads. The model loads a pre-defined industry-standard task template library, covering 1500 templates derived from the abstraction and annotation of 3000 business scenarios across 50 industries. Task decomposition is achieved through a 10-layer causal self-attention module, each layer containing 32 attention heads, using a depth-first search algorithm to recursively decompose complex tasks. The system uses cosine similarity to match structured requirements and task templates, with a similarity threshold of 0.85. After task decomposition, the matched templates assign priorities of 1-10 to subtasks, constructing a directed acyclic graph (DAG) of task dependencies, outputting a list of subtasks, priorities, and dependencies, forming a preliminary task flow sequence. Subsequently, an interface knowledge base is loaded, containing 5000 structured interface descriptions, covering interface names, functional descriptions, parameter specifications, calling constraints, etc., extracted and maintained based on mainstream open platform documentation. The interface matching engine employs a 24-layer transformer encoder with a hidden layer dimension of 1024. It transforms each subtask into a 768-dimensional query vector, calculates the dot product similarity with the interface vector, and selects the top 5 candidate interfaces based on similarity. A three-layer graph neural network (GAT) is introduced, with each layer node having a feature dimension of 512, to globally optimize the interface candidates. This comprehensively considers interface compatibility, data flow, and call efficiency, generating an interface call chain, including call order, parameter passing relationships, and error handling strategies. The evaluation metric generator is based on a 12-layer encoder-decoder structure with a hidden layer dimension of 2048. It extracts key constraints from the requirement parameters, ranks them using an importance ranking algorithm (considering frequency, semantic strength, and business impact), and inputs them, along with the task flow, into a multilayer perceptron network (hidden layer dimensions of 1024, 512, and 256 respectively), mapping them to 18 detailed evaluation metrics covering task quality (accuracy, completeness, consistency), efficiency (response time, throughput, resource utilization), and stability (error rate, recovery time, fault tolerance). Each metric defines an evaluation method, threshold, weight, and period, outputting a structured metric system. The blueprint generation module employs a 32-layer transformer architecture (4096 hidden layer dimensions). It integrates data from various sources through multimodal fusion to construct a technical component graph, where nodes represent components and edges represent interactions. The graph is then converted into a 256-dimensional vector using an embedding algorithm and matched against 150 predefined architectural patterns (the pattern library is derived from 1000 successful cases), automatically selecting the optimal architectural combination to form a preliminary architectural skeleton. The final end-to-end blueprint output includes an architectural view, component definitions, interface specifications, data flow diagrams, deployment plans, and evaluation mechanisms, achieving intelligent mapping from structured requirements to system implementation.

[0044] Preferably, in step S2, the core workflow framework of the intelligent agent is analyzed end-to-end based on multimodal contextual information:

[0045] Based on the core workflow framework of the intelligent agent, extract the expected inputs, outputs and dependencies of each task node to generate node interface dependency data;

[0046] Node information is aggregated based on node interface dependency data and multimodal contextual information to obtain a node context information package;

[0047] Based on the thinking steps in the React mechanism, task event execution reasoning is performed on the node context information packet to obtain the node execution reasoning sequence;

[0048] Based on the action steps in the React mechanism, the task operation is determined by the node execution inference sequence to obtain node action instruction data;

[0049] Based on the observation steps in the React mechanism, the node action command data is simulated and executed to obtain the expected observation data of the node.

[0050] Iteratively execute thinking, action, and observation steps until all intermediate links in the initial end-to-end technical blueprint are covered, and the node execution reasoning sequence, node action instruction data, and node expected observation data are connected in an end-to-end execution path to form an ultra-long thinking chain;

[0051] By utilizing an extremely long thought chain, the nodes and paths of the workflow are determined, resulting in an end-to-end workflow.

[0052] In this embodiment of the invention, a workflow parser is used to perform structured analysis on the initial end-to-end technology blueprint. This parser comprises a three-layer graph convolutional network (kernel sizes of 32, 64, and 128) to process the directed graph structure after the blueprint transformation. Graph nodes represent tasks, and edges represent data flow directions. Each node extracts four types of information through a feature extractor: input parameters, output definition, preconditions, and triggering rules. The feature extractor is a bidirectional LSTM structure with a hidden layer dimension of 512 and a sequence length of 128. Node dependencies are determined by topological sorting to determine the execution order, and critical paths are identified through depth-first traversal. Interface dependency scores are calculated based on data type compatibility, semantic consistency, and transformation complexity. The results are output in DAG form, including node identifiers, interface specifications, dependency types, and strengths. After parsing, a cross-attention mechanism is introduced to fuse multimodal contextual information, including structured data (6-layer transformer encoder, 768-dimensional), text descriptions (12-layer encoder, 1024-dimensional), image examples (visual encoder with 8 residual blocks, 256 channels), and historical records (3-layer bidirectional GRU, 512-dimensional). The fusion process is completed through a 4-layer cross-attention network, with fusion weights automatically determined by meta-learning. The fusion result and interface-dependent data are tensor-concatenated and processed by a two-layer feedforward neural network to generate a 2048-dimensional node contextual information package, which is further compressed to 1024 dimensions through nonlinear dimensionality reduction. The node information and inference prompt templates (task objective, known conditions, resource list, constraint description, expected output) are input into the inference engine. The inference process adopts a recursive thought chain mechanism with a depth of no more than 7 layers, and each layer generates no more than 100 words of text, divided into five stages: problem decomposition, condition analysis, resource matching, solution construction, and risk identification. The confidence score is calculated for each inference step, and paths below 0.75 are pruned. The output includes the inference sequence, key decision points, decision basis, and expected results. The inference sequence is further input into the intent parsing module to extract the action intent, operation object, execution conditions, and expected effect. Parsing is achieved through a 5-layer graph attention network (1024 dimensions per hidden layer) to generate a structured intent representation. The structured intent is matched with an operation library containing 2000 basic operation templates, defining the operation name, parameters, preconditions / postconditions, and resource requirements. Matching uses cosine similarity calculation with semantic error correction. For complex operations, a hierarchical reinforcement learning algorithm is used for a maximum 5-layer decomposition, and the operation sequence is evaluated for feasibility, with metrics covering completion, resource consumption, execution time, and risk level. The optimization process uses beam search (width 16), outputting the operation sequence, parameter configuration, execution conditions, and error handling strategy. Next, the action execution engine receives the above instruction data and executes the operations sequentially. Before execution, graph pattern matching is used to verify whether the preconditions are met. Subsequently, the processing function is called to update the graph database environment state using a transaction mechanism to ensure atomicity. External interactions are handled by an interface simulator, with simulated responses based on 5,000 predefined response templates.During execution, environmental state changes, triggering events, resource consumption, and execution time are recorded. Based on this data, the observation generator constructs state differences, event logs, performance metrics, and anomaly records; complex observations are generated in detail by a 16-layer transformer decoder (hidden layer 2048). The output node observation data includes state changes, intermediate results, execution logs, and performance statistics. The entire operation adopts a "think-act-observe" loop mechanism, with the loop result serving as the input for the next step, forming a closed-loop control. An execution path graph is constructed for each node, and the nodes are arranged in a topological order to form a DAG structure. Automatic node decomposition is supported: if the complexity evaluation value is greater than 85, the node is split into several child nodes. The maximum number of iterations is set to 500, and convergence is considered when the state difference is less than 0.01 for three consecutive rounds. After all nodes are processed, the inference sequence, action instructions, and observation data are concatenated to form an ultra-long thought chain (the length can exceed 100,000 words). To improve processing efficiency, the thought chain is divided into segments of 4096 words each, with an overlap of 2048 words between segments. Each segment extracts key task nodes (using a 7-layer BiLSTM with 768 hidden layers) and their data flow / control flow relationships (implemented through a 6-layer graph attention network with 8 heads per layer) using a named entity recognition module. A cross-segment integration algorithm resolves inter-segment boundary conflicts and assembles all nodes and dependencies into a complete workflow graph. The workflow is further processed by a path optimization engine, with the optimization objective being to minimize the execution path length and maximize parallelism. The optimization model is based on a constraint satisfaction solution framework, with constraints including resource limitations, temporal dependencies, and business rules. Conditional branching modeling uses a decision tree with a depth of no more than 10 layers, while loop structures identify entry, body, and exit conditions and encapsulate them as modular components. The final output end-to-end workflow includes node definitions, execution paths, data flow graphs, control flow graphs, and resource allocation schemes, achieving full-chain automated execution support from high-level blueprints to refined operational processes.

[0053] Preferably, step S3, which involves acquiring multimodal data based on structured requirement parameters and constructing a dynamic evolutionary semantic graph, includes:

[0054] Access multimodal and multiparadigm data sources, and collect multimodal information based on structured requirement parameters to obtain multimodal data;

[0055] Topic modeling and keyword extraction are performed on text data from multimodal data to obtain core entity data of the text;

[0056] Based on the image data in the multimodal data, salient target detection and scene understanding are performed to obtain the core feature data of the image;

[0057] Cross-modal alignment and information integration are performed on core entity data of text and core feature data of images, and entity relationships and event associations between different modal information are identified to obtain cross-modal fusion structural information;

[0058] Knowledge unit identifiers are assigned to cross-modal fusion structural information, and meta-information is annotated to obtain annotated knowledge unit data. The meta-information annotation includes knowledge source, credibility, and timeliness.

[0059] The knowledge abstraction level and domain relevance of the labeled knowledge unit data are evaluated, and the data is organized into a hierarchical structure of concept layer, relation layer and instance layer to obtain multi-dimensional knowledge management graph structure data.

[0060] Domain knowledge base is populated using multi-dimensional knowledge management graph structure data, thereby constructing a dynamically evolving semantic graph.

[0061] In this embodiment of the invention, the system deploys a distributed data acquisition framework that supports access to five types of data sources: structured data, text, images, audio, and sensors. These data are collected and standardized by dedicated connectors to construct a multimodal dataset. Structured parameters are transformed into 128-dimensional query vectors via a parameter mapper and input into a dual-engine retrieval system of inverted index and vector. High-quality data is obtained under the constraint of 3000 domain identifiers. Relevance scoring covers matching degree, timeliness, and authority, with a screening threshold set to 0.75. Text data undergoes cleaning and processing through Chinese word segmentation, stop word filtering, and lemmatization. LDA modeling is used for topic modeling, automatically identifying 5 to 30 topics and outputting a topic distribution matrix and keyword set. Image data, after size normalization and enhancement, is input into a two-stage saliency detection network to extract target bounding boxes, category labels, and pixel-level masks. Simultaneously, an attention-enhanced fully convolutional network is used for scene understanding, outputting a semantic segmentation map and scene probability distribution, forming core image feature data. The system employs a dual-tower cross-modal fusion architecture, encoding text and image features into a 512-dimensional unified vector through a 12-layer text transformer and a 24-layer visual transformer, respectively. Modal alignment is established through contrastive learning, and temperature-scaled cosine similarity is used for similarity calculation, with the Hungarian algorithm achieving optimal entity matching. For entity pairs with a matching score higher than 0.8, feature concatenation and fusion are performed, followed by projection through a two-layer fully connected network. An 8-layer graph attention network further identifies 50 types of relationships between cross-modal entities, forming fused structural information. A knowledge identifier manager assigns a 64-bit hierarchical encoded identifier to each knowledge unit, ensuring uniqueness within the distributed system. Meta-information annotation is then performed, including data source (tracked by a source tracer), credibility (based on evidence strength), and timeliness (classified and marked with an expiration date by a time-sensitivity analyzer). Knowledge units, along with their identifiers and meta-information, are encapsulated to form a standardized labeled knowledge dataset. In the knowledge representation learning phase, the system embeds knowledge units into a 300-dimensional vector space and evaluates their abstraction level (levels 1 to 5) based on the cosine distance to a set of 1000 abstract concepts. Combining a domain ontology library (covering 50 core domains and 200 concepts / domains), a weighted graph traversal algorithm is used to calculate the correlation between knowledge units and the target domain. Based on this, knowledge is organized into a three-layer structure: a concept layer (abstraction level 4-5), a relation layer (abstraction level 2-3), and an instance layer (abstraction level 1), and a multi-dimensional knowledge management graph is constructed through an inter-layer pointer mechanism. The knowledge graph construction engine consists of a graph builder, a conflict resolver, and a knowledge inferencer, employing an incremental construction strategy. First, concept layer nodes are imported to establish a semantic skeleton; then, relation layer nodes are imported to create semantic connections; finally, instance layer nodes are imported to link specific samples. Conflict detection is based on three types of constraints: identifier uniqueness, logical consistency, and relation completeness, and is handled using 23 rules.The knowledge inference engine employs inductive logic programming to implement three types of reasoning logic: transitive, dual, and combinatorial. It also periodically updates the reasoning results in batches to improve the completeness of the knowledge graph. The final knowledge graph is stored based on an attribute graph model, featuring nodes, edges, attributes, and a snapshot mechanism. It supports dynamic evolution, including three types of evolutionary operations: update, expansion, and obsolescence. A consistency check ensures the stability and usability of the semantic graph.

[0062] Preferably, the dynamic activation of query nodes in step S3 of the dynamically evolving semantic graph includes:

[0063] Receive the user's current query behavior and perform behavior semantic parsing to obtain the current user's query semantics;

[0064] Perform semantic representation processing on the current user's query semantics to obtain the query semantic vector;

[0065] Extract the historical access frequency and the most recent activation time of nodes based on the dynamic evolution semantic graph;

[0066] Using the query semantic vector as input, semantic similarity analysis is performed in the dynamically evolving semantic graph to obtain node semantic similarity data;

[0067] The graph activation status is evaluated based on node semantic similarity data, node historical access frequency, and node recent activation time to obtain graph activation status data.

[0068] The dynamic evolution semantic graph is processed by alternating horizontal and vertical propagation using graph activation state data to generate a post-diffusion node activation value graph.

[0069] The query complexity is evaluated based on the current user query semantics, and the activation threshold for this query is dynamically calculated to obtain the dynamic activation filtering threshold.

[0070] Based on the dynamic activation screening threshold, all nodes whose activation values ​​exceed the dynamic activation threshold are selected from the post-diffusion node activation value map to generate high activation candidate node data.

[0071] In this embodiment of the invention, a multi-channel input receiver supports three input methods: text, voice, and image. Text is directly acquired; voice is identified and 40-dimensional Mel-Cepstral features are extracted using a convolutional-recurrent neural network; and images are identified by a visual question-answering converter (containing a 16-layer visual encoder and a 12-layer text decoder) to determine the query intent. The parsing module employs a three-stage pipeline for query processing: dependency parsing (using a 10-layer bidirectional GRU to construct a syntax tree), intent recognition (using a conditional random field to divide the domain, intent, and slot), and referential resolution (based on a dynamic memory network with an 8-layer context window). The parsing result outputs structured query semantics. The query semantics are encoded by a 24-layer transformer semantic encoder with a hidden layer dimension of 1024 and 16 attention heads. The text is first segmented, part-of-speech tagging, and entity linking, with an embedding dimension of 512. A 768-dimensional semantic vector is generated through attention pooling and normalized. A graph access statistician records node access history and activation time. Access counting uses an exponential decay model, and timestamps use a circular buffer (recording a maximum of 50 times). The statistical data outputs node identifiers, historical access frequencies, and most recent activation times. Semantic graph matching employs a dual-path similarity engine, comprising a semantic matching network (nodes and queries are encoded into 768-dimensional vectors through independent 24-layer transformers, and cosine similarity is calculated) and a structural matching network (a 5-layer graph attention network with a feature dimension of 256, considering node position and neighborhood structure). The similarity scores of the two networks are fused at a 7:3 ratio to output a matching score. Graph activation state evaluation is performed using a three-stage multi-factor model. The first stage normalizes semantic similarity, access frequency, and activation time; the second stage inputs the data into a multilayer perceptron (64-32-16 layers, ReLU activation) to output an activation score; the third stage applies a sigmoid function to convert the score into a binary activation flag. Based on activated nodes, an activation diffusion engine is initiated, employing a message-passing neural network for eight rounds of alternating horizontal and vertical propagation. Horizontal propagation is based on relation type and propagation factor (0.85), while vertical propagation is based on hierarchical relationship and hierarchical factor (0.8). After each round, a soft maximum function with a temperature parameter of 0.5 is used to normalize the node activation value. To adapt to query complexity, a complexity evaluation engine is deployed, comprising three parallel modules: semantic complexity (based on entity density and relational complexity, score 0-10), domain span (calculated by the cosine similarity between the query vector and 50 domain vectors, score range 1-50), and contextual dependency (scored by pronoun density and slot missing rate, range 0-5). The three components are weighted and summed in a 5:3:2 ratio, dynamically adjusting the activation filtering threshold: threshold = base value 0.65 × (1 - complexity adjustment factor 0.5 × standardized score). An adaptive filter selects highly activated nodes from the output nodes of the diffusion graph.The first step is to statistically analyze the distribution of activation values ​​(maximum, minimum, mean, median, quartiles, and standard deviation). The second step is to compare the percentile position of the dynamic threshold and adjust the threshold using linear interpolation if necessary to ensure reasonable results (the number of nodes should be no less than 50 and no more than 10%). The third step is to filter nodes that meet the condition that the activation value is greater than or equal to the threshold, sort them by activation value and weighted importance score, and output the node identifier, activation value, importance score, and core attributes.

[0072] Of particular importance is the process of performing alternating horizontal and vertical propagation of the dynamically evolving semantic graph using graph activation state data, specifically as follows:

[0073] The activation state data of the graph is used as the initial node activation value for iterative processing, and the horizontal activation diffusion calculation is performed on the nodes in the dynamic evolution semantic graph to obtain the node activation value after horizontal diffusion processing.

[0074] Based on the node activation values ​​after lateral diffusion processing and the vertical hierarchical relationships and propagation rules defined in the dynamic evolution semantic graph, the node activation values ​​after a single round of alternating propagation are calculated.

[0075] Based on the node activation values ​​after a single round of alternating propagation, iterative horizontal-vertical alternating propagation is performed until the node activation values ​​meet the preset convergence conditions or reach the preset maximum number of iterations, thus obtaining the final stable activation values ​​of each node.

[0076] Based on the final stable activation values ​​of each node, the activation state of each node in the dynamic evolution semantic graph is assigned, generating a node activation value graph after diffusion.

[0077] In this embodiment of the invention, when using the graph activation state data as the initial node activation value for iterative processing, an activation value vector is first constructed. The vector dimension is equal to the total number of graph nodes. Nodes marked as active in the graph activation state data are assigned an initial activation value of 1.0, while inactive nodes are assigned a value of 0. Next, a lateral activation diffusion calculator is started. This calculator implements activation propagation between nodes based on a graph convolutional network architecture. Lateral diffusion only occurs between nodes at the same level. A lateral relationship matrix is ​​extracted from the dynamically evolving semantic graph. The matrix size is N×N (N is the total number of nodes), and the matrix elements represent the connection strength between nodes, with values ​​ranging from [0, 1]. The diffusion calculation formula is: New activation value = Current activation value + ∑(Neighbor node activation value × Connection strength × Distance decay coefficient), where the distance decay coefficient D = 0.85^L, and L is the propagation step size. An activation truncation threshold of 0.01 is set; activation propagation below this value will be ignored to improve computational efficiency. The computation process employs sparse matrix multiplication, dividing the large-scale graph into multiple subgraphs for parallel processing using graph partitioning techniques. Each subgraph contains no more than 10,000 nodes. After lateral diffusion, the output is a vector of node activation values ​​containing the updated activation values ​​of all nodes. When performing vertical activation diffusion calculations based on the node activation values ​​after lateral diffusion, a hierarchical propagation engine is deployed. This engine specifically handles activation transfer between the three layers (concept layer, relation layer, and instance layer) in the dynamically evolving semantic graph. First, a vertical hierarchical relation matrix is ​​extracted from the graph. This matrix consists of three sub-matrices: the concept-to-relation layer mapping matrix C2R, the relation-to-instance layer mapping matrix R2I, and their transposes. Vertical diffusion follows a directed propagation rule: upward propagation (from concrete to abstract) and downward propagation (from abstract to concrete) use different propagation weights, with the upward propagation weight ωup set to 0.6 and the downward propagation weight ωdown set to 0.8. The propagation calculation formula is: Target layer activation value = Target layer current activation value + Source layer activation value × Propagation weight × Layer correction factor, where the layer correction factor is adaptively adjusted according to layer differences, with 1.0 for adjacent layers and 0.5 for cross-layer layers. Calculation is performed for each layer node according to topological sorting to ensure correct handling of dependencies. After all layers of propagation are completed, the sigmoid normalization function is applied to compress the node activation values ​​to the [0, 1] interval, and the node activation value vector after a single round of alternating propagation is output. When performing iterative horizontal-vertical alternating propagation based on the node activation values ​​after a single round of alternating propagation, an iterative controller is deployed to manage the execution flow of the entire propagation process. The maximum number of iterations is set to 20 rounds, with each round containing one horizontal diffusion and one vertical diffusion. The iterative controller calculates the activation value change rate after each round of iteration. The change rate calculation formula is: Change rate = ∑|Current round activation value - Previous round activation value| / ∑Current round activation value, where ∑ represents the summation over all nodes. The convergence threshold is set to 0.01. When the rate of change is lower than this threshold, it is considered to have converged and the iteration stops.To prevent activation value divergence, global normalization is applied after each iteration by dividing all node activation values ​​by the maximum activation value, ensuring that the activation values ​​remain within the range [0, 1]. Simultaneously, an activation history matrix is ​​maintained to store the activation values ​​of each node in each iteration, used for analyzing convergence trends and detecting oscillations. For detected oscillating nodes, a damping factor of 0.7 is applied to slow down the rate of change in their activation values. The iteration process is executed in batch mode, processing 10,000 nodes per batch, improving efficiency through multi-threaded parallel computation. After iteration, the final stable activation value vector of each node that meets the convergence condition is output. When assigning activation states to each node in the dynamic evolution semantic graph based on the final stable activation values, an activation mapping engine is deployed, which converts continuous activation values ​​into a graph representation. First, stable activation values ​​undergo post-processing, including denoising and enhancement steps: activation values ​​are smoothed using a Gaussian filter with a standard deviation of 0.5; the difference in activation values ​​is enhanced using a contrast enhancement function, with the enhancement formula: Enhanced activation value = (Original activation value - Mean) × Gain coefficient + Mean, and the gain coefficient is set to 1.5. Next, the processed activation values ​​are combined with node metadata to construct a post-diffusion node activation value graph. This graph is represented using an attribute graph model, with each node containing five key attributes: basic identification attributes (node ​​ID, type, name), semantic attributes (definition, description, keywords), relational attributes (in-degree, out-degree, relation type statistics), activation attributes (original activation value, regularized activation value, activation ranking), and metadata attributes (creation time, modification time, source). Atomic operations are used to update the node activation attributes in the graph database to ensure data consistency. After the update, an activation value index is created to support fast queries based on activation value ranges. The final generated post-diffusion node activation value graph is serialized in JSON format while maintaining the graph structure representation in memory, supporting subsequent efficient query and analysis operations.

[0078] Preferably, step S3, which involves dynamically selecting knowledge fragments from the dynamically evolving semantic graph based on highly activated candidate node data, includes:

[0079] Based on highly activated candidate node data, knowledge node clustering operations are performed on the dynamic evolution semantic graph to obtain semantic cluster knowledge clusters;

[0080] RAG technology is used to integrate knowledge fragments from semantic clustering knowledge clusters to obtain preliminary integrated knowledge fragments;

[0081] Based on the initial integration of knowledge fragments, conflicting knowledge points and their sources are identified, resulting in a list of potential knowledge conflicts.

[0082] When the list of potential knowledge conflicts is not empty, the authority, timeliness of information and relevance to the current query of the knowledge sources associated with the conflicting knowledge points are comprehensively evaluated and weighted to obtain the conflicting knowledge source weight evaluation data.

[0083] Based on the conflict knowledge source weight evaluation data, conflict knowledge points are resolved in the initially integrated knowledge fragments to obtain the optimal knowledge fragments.

[0084] In this embodiment of the invention, when performing knowledge clustering operations on a dynamically evolving semantic graph based on high-activation candidate node data, a graph clustering engine is deployed, employing a multi-perspective clustering strategy. First, four types of feature vectors are extracted from the high-activation nodes: semantic vectors (768-dimensional embedding representation), structural vectors (containing 10 graph topological indices such as degree centrality and betweenness centrality), activation vectors (node ​​activation values ​​and their first and second derivatives), and attribute vectors (vectors of discrete attributes such as node type, level, and timeliness encoded using one-hot encoding). After constructing the feature vectors, a spectral clustering algorithm is used for clustering: first, a similarity matrix is ​​calculated based on cosine similarity; then, the first K feature vectors of the graph Laplacian matrix are extracted for low-dimensional representation; finally, clustering is performed using the K-means algorithm, with the number of clusters K automatically determined by the silhouette coefficient. To improve processing efficiency, the Nyström approximation method with a 20% sampling rate is used, and the central node of each cluster is identified through density peak detection, calculating the cluster density. The output is a semantic clustering knowledge cluster containing cluster IDs, member nodes, central nodes, and density. When integrating knowledge fragments from semantically clustered knowledge clusters, a two-stage retrieval generator is deployed, comprising a dense retrieval module and a knowledge integration module. The dense retrieval module constructs a 1536-dimensional query vector (concatenated from the user query vector and the cluster center node vector), and uses maximum inner product search to retrieve relevant knowledge units from the knowledge graph index. The index structure is a hierarchical vector index containing 100 first-level clusters and 10,000 second-level buckets, with a recall limit of 200 units. The recall results are reordered by a 24-layer cross-attention encoder, and the top 50 knowledge units are selected as integration material. The knowledge integration module, based on a 32-layer transformer, uses beam search decoding (bundle width 5), combined with fixed templates (task definition, integration requirements, conflict handling, etc.) to generate preliminary knowledge fragments. Finally, format standardization and citation annotation are performed, outputting a coherent knowledge description and relationship explanation. When identifying conflicting knowledge points in the preliminary integrated fragments, a knowledge consistency analyzer is deployed to execute a three-layer conflict detection process. First, the knowledge fragment is decomposed into atomic triples, and the extraction method combines graph convolutional dependency parsing and named entity recognition. Subsequently, a conflict detection rule base is matched, covering 150 domain conflict patterns and 30 logical rules, supporting direct conflicts (such as "A is B" or "A is not B"), numerical conflicts (judged by overlapping numerical ranges), and implicit conflicts (discovered through indirect contradictions via reasoning chains). Each conflict is scored for severity based on its type, source credibility, and content importance. Through a source tracing mechanism, combined with citation tags and knowledge graph metadata, the source of the conflict is identified, forming a conflict list containing conflict type, conflict description, severity, related entities, and sources. When conflicting knowledge points exist, their associated knowledge sources are evaluated using a multi-dimensional weighted assessment, deploying a three-dimensional evaluation engine to quantify the quality of knowledge sources from the perspectives of authority, timeliness, and relevance.The authority rating considers the document type level, publishing institution rating, citation frequency, and certification status, with a weighting of 3:3:2:2; the timeliness rating is based on publication time (calculated using exponential decay), update frequency, and version number, with a weighting of 5:3:2; relevance is assessed through the semantic similarity between the query and content, and the degree of topic matching, with a weighting of 7:3. Finally, the three scores are linearly weighted and aggregated to output evaluation data with knowledge source identifiers and a three-dimensional score. Based on the conflicting knowledge source weight data, a conflict resolver is deployed to resolve conflicting knowledge points. A weight comparison strategy is prioritized; if a source has a significantly higher weight (difference exceeding 0.3), it is directly adopted. When weights are close, a majority voting strategy is used, weighted according to the number of supporting sources. If a decision cannot be made, a knowledge fusion strategy is applied, such as predicate harmonization or conditional constraints to generate compatible expressions. For deep conflicts that cannot be resolved, an uncertainty labeling strategy is used to explicitly mark the disputed content. The entire conflict resolution process is implemented using a decision tree structure, with nodes determined based on conflict type, weight differences, and knowledge importance. The final output of the preferred knowledge fragment includes: consistent knowledge content after resolution, uncertainty markers, and knowledge source information, forming a structured knowledge representation.

[0085] Preferably, the end-to-end call chain execution strategy built based on the end-to-end workflow in step S4 includes:

[0086] Construct a directed acyclic graph of the task flow based on the end-to-end workflow;

[0087] A dual-policy network architecture is deployed based on a directed acyclic graph of the task flow; the dual-policy network architecture deployment includes a higher-order policy network and an execution policy network;

[0088] By using a high-order policy network to determine the necessity of retrieval operations on the directed acyclic graph of the task flow, select the execution timing, and set the retrieval depth, a high-order decision network instance is obtained.

[0089] An execution strategy network instance is obtained by setting the specific retrieval parameters to be used for the retrieval nodes and the specific content generation strategy to be used for each generation node in the directed acyclic graph of the task flow through the execution strategy network.

[0090] Construct an end-to-end call chain execution strategy based on high-order decision network instances and execution strategy network instances.

[0091] In this embodiment of the invention, when constructing a directed acyclic graph of the task flow based on the end-to-end workflow, a workflow graph structure converter is deployed. This converter adopts a three-stage processing flow. The first stage involves node extraction, identifying four types of key nodes in the end-to-end workflow: retrieval nodes (responsible for knowledge acquisition), processing nodes (responsible for information transformation), generation nodes (responsible for content creation), and decision nodes (responsible for conditional branches). Each node is assigned a globally unique identifier using 32-bit encoding, with the first 8 bits representing the node type and the last 24 bits representing the sequence number. The second stage involves edge relationship construction, determining directed connections between nodes based on dependencies in the workflow. Dependencies are divided into two types: strong dependencies (preceding nodes must complete) and weak dependencies (preceding nodes complete first), marked by edge attributes. Edge weights are set according to dependency strength, ranging from 1 to 10, with strong dependencies defaulting to 10 and weak dependencies defaulting to 5. The third stage involves graph optimization, detecting and removing redundant edges using a transitive closure algorithm, identifying execution bottlenecks using critical path analysis, and calculating the earliest start time and latest finish time for each node. The resulting directed acyclic graph (DAG) is stored using an adjacency list structure, containing a node attribute table and an edge relationship table, supporting efficient traversal and parallel execution planning. When deploying a dual-policy network architecture based on a task-flow DAG, the basic architecture of the policy network is first constructed. This architecture contains two core components: a higher-order policy network and an execution policy network, both sharing the task graph encoder but having independent decision heads. The task graph encoder adopts a graph attention network structure, containing 5 graph convolutional layers, each with 16 attention heads and a feature dimension of 256. The encoder represents the task graph as a combination of node feature matrices and adjacency matrices. Node features include node type, resource requirements, estimated execution time, and historical performance metrics. The graph encoding process consists of two steps: local feature extraction captures structural information around nodes through a neighborhood aggregation function; global feature extraction generates an overall graph representation through a readout function. The higher-order policy network is built based on a transformer decoder, containing 12 attention blocks with a hidden layer dimension of 1024, used for macro-level decision-making; the execution policy network is built based on a multi-head attention mechanism, containing 8 attention heads, each focusing on different execution parameter dimensions, used for micro-level operations. Two networks share knowledge through a policy distillation mechanism, with the decision output of the higher-order network serving as the conditional input to the execution network. The policy network is trained using reinforcement learning and employs a proximal policy optimization algorithm. Its reward function is designed based on three dimensions: task completion quality, resource efficiency, and execution time. When the higher-order policy network performs retrieval operations on the directed acyclic graph of the task flow, a three-stage decision-making process is adopted. The first stage determines necessity. The higher-order policy network receives graph encoding features, the current execution state vector, and historical retrieval records as input and outputs a retrieval necessity score. The necessity score is calculated based on three indicators: information uncertainty (quantified by knowledge graph coverage), task complexity (evaluated by the number of dependencies between nodes), and the ambiguity of the user query (calculated by entity slot fill rate).When the necessity score exceeds a preset threshold of 0.75, the retrieval is deemed necessary. The second stage involves selecting the execution timing. The network calculates the optimal execution time, considering three factors: the completion status of dependent nodes, resource utilization, and expected retrieval latency. The timing score is modeled using a continuous-time Markov decision process, and the state transition probability is learned from historical execution data. The third stage sets the retrieval depth. The network determines the retrieval depth level (1-5) based on task importance, time constraints, and expected information gain, with each level corresponding to a different retrieval resource allocation strategy. Depth calculation uses a Monte Carlo tree search method to simulate the retrieval results at different depth levels and their impact on subsequent nodes. The final output is a high-order decision network instance containing the necessity judgment result, execution timing parameters, and retrieval depth settings. By setting retrieval parameters and content generation strategies for the directed acyclic graph of the task flow through the execution strategy network, two parallel decision-making mechanisms are implemented. For retrieval nodes, the execution strategy network configures five types of retrieval parameters for each node: keyword generation strategy (based on context entity extraction or query expansion), retrieval scope limitation (knowledge base scope, time window, source type filtering), ranking method (relevance priority, timeliness priority, or authority priority), recall quantity (range 10-100, step size 10), and deep fusion method (shallow splicing or deep semantic fusion). The parameter determination process adopts a context-aware multi-armed slot machine algorithm, dynamically adjusting the balance between exploration and utilization based on historical retrieval results. For generation nodes, the execution strategy network sets three types of generation strategies: prompt template selection (selected from a pre-set library of 37 domain-specific prompt templates), decoding strategy (bundle search, kernel sampling, or greedy decoding), and generation parameter configuration (temperature coefficient, output length limit, and diversity control factor). Strategy selection is based on node features, upstream node output quality, and task objective requirements, and is implemented through a deep Q-network, which contains three fully connected layers with hidden layer dimensions of 512, 256, and 128. Each generating node is assigned an independent policy configuration, forming a fine-grained control scheme at the node level, and outputting an execution policy network instance containing a complete parameter set. When constructing the end-to-end call chain execution policy based on the higher-order decision network instance and the execution policy network instance, a policy integration engine is deployed. This engine generates the final execution policy through a four-stage processing flow. The first stage performs policy alignment verification, checking the consistency between the higher-order decision and the execution policy, and using a conflict detection algorithm to identify conflict points, such as situations where the higher-order network determines that retrieval is unnecessary, but the execution network still configures retrieval parameters. Conflict resolution prioritizes the adoption of the higher-order decision, while recording the reasons for the adjustment. The second stage generates the execution plan, converting the task flowchart into a specific call sequence, including API call specifications, parameter passing schemes, and error handling strategies. The conversion process uses topology sorting to determine the execution order, and for parallelizable nodes, calculates key resource dependencies to determine the degree of parallelism.The third phase optimizes resource allocation by allocating computing resources based on node importance and resource requirements. A linear programming algorithm is used to maximize throughput, with constraints including total memory limits, CPU core count, and network bandwidth. The fourth phase implants execution monitoring points, setting up 16 types of monitoring checkpoints at key nodes, including intermediate result verification, timeout detection, and quality assessment. Each checkpoint is configured with trigger conditions and response strategies, such as retry logic, degradation schemes, and failure recovery mechanisms. The output end-to-end call chain execution strategy includes a complete execution graph, resource configuration table, monitoring rule set, and exception handling process.

[0092] Preferably, step S4, which involves encapsulating agent instances using an end-to-end call chain execution strategy and a dynamic evolutionary semantic graph, includes:

[0093] The atomic operations in the end-to-end call chain execution strategy are used as the agent's optional action space, and the current state of the dynamically evolving semantic graph and the user interaction context are used as the agent's state observation space, thus obtaining the agent's interaction training environment.

[0094] Based on the intelligent agent interactive training environment, the selected knowledge fragments are used as the retrieval enhancement source of RAG technology. The scenario task processing is performed based on the end-to-end call chain execution strategy, and the decision sequence, resource consumption and preliminary output results of the intelligent agent during the task execution process are collected to obtain scenario task execution feedback data.

[0095] A dual-channel learning mechanism is used to iteratively optimize the end-to-end call chain execution strategy. At the same time, a near-end strategy optimization algorithm is used for reinforcement learning based on the scenario task execution feedback data. A pruning update strategy is applied to adjust the network parameters to maximize the cumulative reward, resulting in a scenario-optimized call chain strategy.

[0096] Based on the scenario-optimized call chain strategy, expert demonstrations were used for supplementary training, and agent reinforcement learning was performed to obtain the optimized agent strategy network parameters.

[0097] The network parameters of the optimized agent policy are embedded into the agent, and agent instances are encapsulated based on the dynamically evolving semantic graph, thereby automatically constructing end-to-end agent instances.

[0098] In this embodiment of the invention, when constructing the intelligent agent interaction training environment, the atomic operation set is first extracted from the end-to-end call chain execution strategy. Each atomic operation is then standardized into a five-tuple representation (operation type, operation object, input parameters, output format, and resource constraints) using an action parser. Fifty-six basic operation types are defined, covering four major functional categories: knowledge retrieval, information processing, content generation, and decision control. Next, a state observer is deployed, which collects state information from two sources: a dynamic evolutionary semantic graph provides the knowledge state, including the currently active node set, node activation values, and relationships between nodes; the user interaction context provides the interaction state, including historical dialogue records, user query intent, and response evaluation. The state information is processed by a feature extractor and converted into a 1024-dimensional state vector. A Markov decision process is used to model the training environment, comprising four components: state space S, action space A, transition function T, and reward function R. The transition function is implemented using a simulator that simulates action execution results based on a rule engine. The reward function comprises five scoring dimensions: task completion (0-10 points), response quality (0-8 points), efficiency metrics (0-5 points), resource consumption (-5-0 points), and user satisfaction (0-7 points). A three-stage execution process is employed. The first stage involves knowledge enhancement preprocessing, indexing selected knowledge fragments, and establishing a dual retrieval mechanism using a dense vector index (employing a hierarchical quantized index structure containing 12,800 primary cluster centers) and an inverted index (based on the BM25 algorithm). The retrieval process employs a hybrid retrieval strategy, first recalling the candidate set through vector nearest neighbor search, and then performing fine-grained ranking through keyword matching. The second stage executes the call chain task, traversing the execution graph according to topological sorting. Before each node is executed, dependencies are checked, and after execution, four types of metrics are recorded: functional metrics (correctness, completeness), performance metrics (execution time, resource usage), contextual metrics (state changes, intermediate results), and anomaly metrics (error type, failure reason). Checkpoints are set for key nodes, and the quality of intermediate results is evaluated through an output validator. The third stage involves data collection and processing, recording the complete decision sequence, including the state, selected action, execution result, and reward signal for each decision point. A dual-channel policy optimizer is deployed, comprising a model distillation channel and a reinforcement learning channel. The model distillation channel first constructs a teacher network based on a 64-layer transformer architecture with 4096 hidden layers, obtained through large-scale pre-training. The teacher network scores the collected task trajectories and identifies the optimal operation sequence. A student network with the same architecture but 75% fewer parameters is constructed, achieving knowledge transfer by minimizing the cross-entropy loss between the student and teacher network outputs. The reinforcement learning channel optimizes policies based on scenario task execution feedback data, using a proximal policy optimization algorithm for training. Specifically, a policy network π and a value network V are maintained, both sharing a 16-layer transformer encoder backbone network. The proximal policy optimization algorithm sets the pruning parameter ε to 0.2 to limit the range of variation in the ratio of new to old policies.The training process employs gradient updates based on the dominance function, calculated using generalized dominance estimation. The discount factor γ is set to 0.99, and the λ parameter is set to 0.95. An adaptive learning rate adjustment mechanism is used, with an initial learning rate of 3e-5, dynamically adjusted according to the policy update magnitude. To prevent overfitting, entropy regularization is applied, with an entropy coefficient set to 0.01. After training through 5 million environment steps, the optimized scenario-optimized call chain strategy is output. A human-machine collaborative training platform is deployed, comprising an expert demonstration acquisition module and a demonstration learning module. The expert demonstration acquisition module first constructs a set of 200 high-difficulty scenario tasks. Scenario complexity is quantified by three metrics: number of task steps (15-30 steps), dependency chain length (5-12 layers), and number of decision branches (8-20). Fifteen domain experts are invited to perform tasks in these scenarios. Expert actions are recorded through behavior capture, collecting three types of data: operation sequences (including operation type, parameter selection, and execution time), decision explanations (textual descriptions of the expert's thought process), and attention distribution (the information areas the expert focuses on). The demonstration learning module uses a behavior cloning method to process expert demonstration data and constructs an imitation learning objective function. The objective function includes three loss terms: action matching loss (negative log-likelihood between expert action and predicted action), state prediction loss (mean squared error between predicted and actual state), and policy consistency loss (KL divergence between learned and base policies). The training process employs gradient accumulation, updating model parameters every 32 demonstration batches. To address the distributional differences between demonstration and automatically generated data, a domain-adaptive approach is applied, introducing an adversarial loss term. After 5000 batches of iterative training, the output includes optimized agent policy network parameters that enhance decision-making capabilities. An agent packer is deployed, employing a five-stage process. The first stage performs parameter quantization and compression, applying weight quantization techniques to the optimized network parameters, quantizing 32-bit floating-point parameters into 8-bit integer representations. The quantization method uses channel-level non-uniform quantization, with each channel independently determining its quantization scaling factor. The second stage optimizes the model graph, using operator fusion, constant folding, and redundancy elimination techniques to optimize the computation graph, reducing inference overhead by 30%. The third stage involves knowledge embedding, extracting the core knowledge structure from the dynamically evolving semantic graph. This structure includes 2000 highest-weight nodes and their relationships, which are then converted into a structured retrieval knowledge base embedded within the agent. The knowledge base employs a three-level caching architecture: hot topics are loaded into memory (500MB), frequently used knowledge is stored in a local index (5GB), and complete knowledge is accessed through an interface. The fourth stage standardizes the interfaces, defining a unified agent interface protocol, comprising 25 methods across four main categories: initialization, state update, action execution, and resource management. The fifth stage involves containerization, packaging optimization parameters, the runtime environment, the knowledge base, and the interfaces into independent containers. The container image size is kept under 3GB, supporting horizontally scalable deployment.The final output agent instance includes complete decision-making logic, knowledge base, execution engine and monitoring components, realizing end-to-end automated construction.

[0099] Preferably, the agent instance reinforcement learning enhancement processing in step S4 includes:

[0100] Upon receiving a new user interaction request, the end-to-end agent instance is used to perform multi-turn dialogue context awareness and generate context-aware data.

[0101] Interaction intent is understood based on context-aware data, and reasoning and cross-domain knowledge intent analysis are performed to obtain reasoning-based interaction intent;

[0102] Based on reasoning-based interactive intents, end-to-end agent instances are used to perform multi-step queries and evidence chain construction, generating multi-step graph reasoning evidence chains;

[0103] Based on the multi-step graph reasoning evidence chain, semantic-level personalized knowledge retrieval is performed to obtain personalized response knowledge core data.

[0104] Based on personalized response knowledge core data and multi-step graph reasoning evidence chain, natural language generation processing is performed using a pre-set large language model to obtain intelligent response text data.

[0105] During the process of end-to-end intelligent agent instances continuously interacting with users and generating intelligent response text data, user feedback data is collected in real time, including explicit ratings, text correction suggestions, and implicit behavioral signals, to obtain the raw user feedback data stream.

[0106] The raw user feedback data stream is cleaned, labeled, and quantified to transform it into a reward signal in the reinforcement learning environment, thus obtaining a quantified reward signal.

[0107] By quantifying reward signals and performing reinforcement learning on end-to-end agent instances, end-to-end agents can be automatically constructed.

[0108] In this embodiment of the invention, upon receiving a new user interaction request, a context processor is activated. This processor first extracts historical interaction records from the session cache, containing the content of the most recent 20 rounds of dialogue. The historical records are processed by a temporal encoder, which employs an 8-layer bidirectional gated recurrent unit network with a hidden layer dimension of 512, converting the dialogue history into a sequence of context vectors. A hierarchical attention mechanism is applied to the vector sequence, containing a two-level attention network: word-level attention to identify keywords and sentence-level attention to capture key rounds. Attention calculation uses a scaled dot product method, with the attention temperature parameter set to 0.1. The current user input is processed by a 32-layer transformer encoder, which contains 16 attention heads with a hidden layer dimension of 1024, generating a representation of the current query. The relevance between the query and historical content is calculated using a cross-attention mechanism, with the relevance matrix size being the current query length multiplied by the length of the historical content. Historical content with high relevance is weighted by importance, with the weights calculated using standardized relevance scores. Furthermore, the evolution trajectory of the dialogue topic is extracted using a dynamic topic tracking algorithm based on a hidden Dirichlet assignment model, with the number of topics set to 30. When understanding interactive intent based on context-aware data, a three-stage intent analysis engine is deployed. The first stage performs basic intent recognition, inputting context-aware data into an intent classifier. This classifier employs a fully connected network structure with three hidden layers containing 1024, 512, and 256 nodes respectively, using a modified linear unit (MRU) activation function. The classifier outputs probability distributions for 120 predefined intent types. A threshold filter is used to retain the top three candidate intents with a probability exceeding 0.3. The second stage extracts inferential intent by analyzing implicit assumptions, causal relationships, and logical inferences in user queries using an inference-enhanced network. This network employs a natural language inference architecture, containing eight layers of cross-attention blocks, each with eight attention heads. It identifies five inference patterns: deductive reasoning, inductive reasoning, abductive reasoning, analogical reasoning, and counterfactual reasoning, assigning a reliability score to each type. The third stage performs cross-domain knowledge intent analysis, mapping intents to a predefined knowledge graph ontology containing 50 core domains, each defining 200 concepts. Cross-domain relationships are identified through a domain bridging mechanism implemented using a graph neural network, comprising 5 graph convolutional layers with a feature dimension of 256. An initial query statement is constructed based on inferential interaction intent, consisting of core entities, relational constraints, and attribute conditions. Entity linking is performed for each core entity using a dual-encoder architecture, mapping entity names to standard entity identifiers in the knowledge graph. The second step involves multi-step query planning, breaking down complex queries into 5-12 subqueries using a query decomposer based on semantic dependency tree analysis. Subqueries are used to construct a directed acyclic execution graph based on dependencies, with the execution order determined by topological sorting. The third step executes the subqueries and collects intermediate evidence, with each subquery retrieving matching nodes and relationships from a dynamically evolving semantic graph using a graph query engine.The query engine employs a bidirectional graph traversal algorithm with a maximum traversal depth of 6, returning a set of paths that meet the conditions. The fourth step involves a credibility assessment of the collected evidence. The assessment algorithm considers three dimensions: the reliability of the evidence source, the freshness of the evidence, and the consistency of the evidence, with a comprehensive score ranging from 0 to 10. Based on the semantic relationships and logical dependencies between the evidence, a multi-layered evidence graph is constructed. Each layer of evidence is connected through reasoning relationships, forming a complete multi-step graph reasoning evidence chain. A personalized retrieval engine is deployed, comprising a user profiling module and a semantic matching module. The user profiling module first extracts user interest features from historical interaction data, including short-term interest vectors (based on the most recent 50 queries) and long-term interest vectors (based on all historical data). Interest vectors are calculated using a multi-head self-attention network, which contains 6 layers of attention blocks, each with 12 attention heads and a hidden layer dimension of 768. Similarity calculations are performed between the user interest vectors and key nodes in the evidence chain to determine the focus of personalized retrieval. The semantic matching module adopts a dual-tower retrieval architecture: the query tower processes evidence chain information, and the document tower indexes knowledge base content. The two towers share a 12-layer transformer encoder architecture, trained using a contrastive learning method. The retrieval process consists of two stages: The first stage retrieves 1000 candidate items from the index using an approximate nearest neighbor algorithm, employing locality-sensitive hashing (LSH) to accelerate the retrieval; the second stage reorders the candidate items using a cross-encoder containing eight cross-attention layers, calculating the precise relevance score between the query and the document. Further, by combining the reasoning logic within the evidence chain, core knowledge points are identified and relevant context is extracted, including knowledge point definitions, attribute descriptions, and relationship descriptions, forming structured, personalized core knowledge data for responses. A hierarchical generation engine is deployed, comprising two core components: a content planner and an expression generator. The content planner first performs structured analysis on the input data, transforming the core knowledge data and evidence chain into a semantic framework containing a three-layer structure of argument-evidence-conclusion. The planner uses 36 preset argument templates, selecting the most suitable template based on reasoning complexity and argument strength. Content organization is guided by rhetorical structure theory, constructing a chapter structure tree containing 15 rhetorical relationships. The expression generator is implemented based on a preset large language model, which includes a 96-layer transformer decoder with 17.5 billion parameters and a vocabulary size of 250,000 words. The generation process employs a two-stage strategy: the first stage generates a detailed outline, including paragraph themes, key arguments, and turning points; the second stage generates text content paragraph by paragraph based on the outline. Decoding uses a kernel sampling method, with a temperature parameter set to 0.7, a sample size of 20, and a selection factor of 0.8. Post-processing optimizations are applied to the generated text, including factual consistency checks (comparing the generated content with knowledge data), language fluency enhancement (correcting incoherent expressions), and stylistic adjustment (adjusting formality and professionalism according to user preferences). The final output intelligent response text data includes the main content, citation tags, and confidence annotations. A multi-channel feedback collector is deployed to capture three types of feedback signals in real time.Explicit ratings are collected through a 5-point satisfaction rating component embedded in the interactive interface. Rating data is transmitted to the feedback processing center via a real-time transmission protocol, with latency controlled within 100 milliseconds. Text correction suggestions are captured by a text difference analysis engine, which uses a minimum edit distance algorithm to compare the generated text with the user-modified text, extracting the modification operations (addition, deletion, replacement, rearrangement) and their location information. Implicit behavioral signals are collected through a behavior tracking module, which records 12 key user behavior indicators: dwell time, scrolling pattern, click behavior, copying behavior, quoting behavior, questioning behavior, interruption count, repeated questions, session persistence, query rewriting pattern, subsequent question depth, and sharing behavior. Behavioral data collection is based on an event-triggered mechanism, executed via a lightweight client script. Data is transmitted in batches in JSON format, triggered every 10 seconds or after 20 accumulated events. Timestamps, session identifiers, and context markers are added to all feedback data to construct a structured raw user feedback data stream. A feedback processing pipeline is deployed, consisting of three sequential processing stages. The data cleaning phase first detects and filters outliers. Anomaly detection is based on statistical methods, identifying numerical indicators exceeding three standard deviations and behavioral sequences that violate preset rules. For time series data, mean filtering with a window size of 5 is applied to eliminate short-term fluctuations. Missing values ​​are handled using a combination of forward imputation and multiple imputation, with the imputation model based on conditional random fields, considering contextual relevance. The data annotation phase employs a semi-supervised learning method, processing text feedback through a pre-trained sentiment analysis model based on a 12-layer bidirectional transformer, achieving an accuracy of 85%. Implicit behavioral signals are processed using a behavioral pattern recognition algorithm, mapping the original behavioral sequences to nine predefined user attitude categories: satisfaction, confusion, disappointment, surprise, doubt, focus, distraction, impatience, and neutrality. The data quantification phase converts multidimensional feedback into a unified reward signal using a weighted fusion method: explicit rating weight is 0.5, text correction signal weight is 0.3, and implicit behavioral signal weight is 0.2. The reward calculation formula is: [Formula omitted for brevity]. Where s is the standardized score, e is the edit distance, l is the text length, and b is the behavioral positivity index. A time decay factor is further applied to mitigate the influence of historical feedback; the decay formula is: Where γ is the decay coefficient, set to 0.95, and t is the time step. The final output is a quantized reward signal with a value range of [-10, 10]. When performing reinforcement learning enhancement processing on end-to-end agent instances using the quantized reward signal, a distributed reinforcement learning framework is deployed, which adopts an actor-commentator architecture. The actor network is based on the agent policy network and contains a 32-layer transformer structure with a hidden layer dimension of 2048; the commentator network adopts the same architecture but with independent parameters and is responsible for value estimation. The reinforcement learning process uses the proximal policy optimization algorithm, which sets the pruning parameter to 0.2 to limit the range of change in the ratio of new to old policies. The agent interaction environment is distributed across 128 computing nodes, with each node simulating 8 user sessions in parallel to accumulate experience data. The training iteration comprises four phases: First, interaction trajectories are collected based on the current policy, each containing a state sequence, action sequence, and reward sequence. Second, the generalized advantage estimate is calculated, with a discount factor set to 0.99 and a λ parameter set to 0.95. Then, the policy network and value network are updated in batches based on the collected data, using the Adam optimizer with a learning rate of 3e-5 and a batch size of 1024. Finally, key performance indicators, including cumulative reward, policy entropy, and value loss, are calculated to monitor training progress. To prevent catastrophic forgetting, an experience replay mechanism is implemented, retaining 20% ​​of historical high-reward trajectories. A course-based learning strategy is employed, gradually increasing task difficulty, starting with simple interaction scenarios and gradually transitioning to complex inference tasks. After reinforcement learning converges, the optimized parameters are embedded into agent instances, completing the automated end-to-end agent construction process.

[0109] Of particular importance is that the multi-step query and evidence chain construction based on reasoning-based interactive intent using end-to-end agent instances specifically involves:

[0110] Based on reasoning-based interactive intents, end-to-end agent instances are used to decompose and plan the initial query target, resulting in the first graph query instruction.

[0111] Based on the first graph query command, an end-to-end intelligent agent instance is used to perform the first knowledge node and relationship retrieval on the dynamic evolution semantic graph, and obtain the preliminary query result set and initial evidence node data.

[0112] Based on the preliminary query result set, initial evidence node data, and reasoning interaction intent, the end-to-end intelligent agent instance is used to determine the next query direction and generate query instructions, thus obtaining subsequent graph query instructions.

[0113] Using end-to-end intelligent agent instances, iterative knowledge-deepening retrieval is performed on the dynamically evolving semantic graph based on subsequent graph query commands. The relevance and importance of the new knowledge nodes and relationships obtained in each retrieval are evaluated to obtain incremental candidate evidence data.

[0114] Logical connections and extended verifications are performed based on incremental candidate evidence data, and verified evidence nodes and relationships are integrated into the current evidence chain to obtain a gradually expanding graph reasoning path.

[0115] The process iteratively executes subsequent graph query command generation, iterative knowledge deepening retrieval, incremental acquisition of candidate evidence data, logical connection and expansion verification steps until the reasoning interaction intent is fully parsed or the preset reasoning depth is reached. All relevant evidence nodes and reasoning relationships in the progressively expanding graph reasoning path are orderly and structuredly integrated to generate a multi-step graph reasoning evidence chain.

[0116] In this embodiment of the invention, a query planner is deployed for the initial query target of reasoning-based interactive intents, employing a hierarchical target decomposition algorithm. A semantic parser performs dependency parsing on complex intents, extracting the core reasoning target and decomposing it into atomic query units, following an "entity-relationship-attribute" pattern with an accuracy of 91%. The decomposed query units are sorted in three dimensions according to causal priority, entity importance, and information entropy (weight ratio 4:3:3), constructing a directed acyclic query graph. A greedy algorithm optimizes the execution order, and graph pruning removes redundancy. The first query unit is converted into a standard graph query language expression, including query type, retrieval conditions, and return format. In the initial query, a graph retrieval engine is deployed, employing a three-layer architecture: the query parser constructs an abstract syntax tree, the index access layer calls a hybrid index structure (B+ tree attribute index, inverted full-text index, adjacency list structure index), and the graph traversal engine performs a bidirectional breadth-first search (maximum depth 3). Multi-condition filtering (activation value > 0.6, relation strength > 0.4, update cycle < 180 days) is applied to the result nodes to extract small subgraphs containing core entities, first-order relations, and directly related nodes. Each node is accompanied by five types of metadata (identifier, semantic type, confidence level, data source, update time), and preliminary query results and evidence nodes are output. Subsequent queries are driven by an inference navigator, with three stages determining the next direction: The first stage performs information gap analysis, using a knowledge graph path algorithm to measure semantic distance. Path lengths exceeding 3 are considered gaps, and gap types include missing entities / relationships / attributes, temporal discontinuities, and logical contradictions. The second stage evaluates the expected information gain of potential directions (=direction relevance × success probability × information value), using Monte Carlo tree search (depth 4, simulations 1000) to select the optimal direction. The third stage generates structured query instructions, including the query subject, conditions, and return format, and optimizes parameters to control depth and breadth. When performing graph iterative retrieval, an adaptive depth searcher is deployed to achieve knowledge deepening in four steps: 1) Determine the starting point of the retrieval and link entities using a dual encoder algorithm (accuracy 96%); 2) Adopt an alternating graph traversal strategy (breadth-first search in the first layer, depth-first search in subsequent layers) and dynamically adjust the expansion and deepening factors; 3) Evaluate the results of each round of retrieval, scoring them based on semantic relevance, evidentiary value, novelty, and credibility (weight ratio 4:3:2:1), and retain only nodes with a score >0.7; 4) Organize them into structured data packets as incremental candidate evidence data.To ensure the validity of newly added evidence, an evidence verifier is deployed, completing verification and integration in five steps: 1) Logical consistency check: using a formal knowledge base and the Dropgen inference engine to verify logical compatibility, supporting 12 basic inference patterns; 2) Evidence association analysis: constructing an evidence graph, measuring edge strength by referencing overlap, with values ​​below 0.3 indicating low association; 3) Source reliability assessment: retaining only those with scores >0.65; 4) Calculating information entropy gain: ensuring that the increment brings positive information gain; 5) Conflict resolution: comparing evidence strength (three factors: source reliability, content certainty, and timeliness) to decide whether to retain or replace it. Verified evidence is incorporated into the existing evidence chain, expanding the graph inference path. The evidence chain integrator controls the iterative execution of the inference process. Three termination conditions are set: inference completeness reaches 90%, the maximum number of iterations is 12, and convergence is defined as evidence gain being below 0.05 for two consecutive iterations. After each round, evidence is rearranged, and a comprehensive centrality score (degree centrality, betweenness centrality, and eigenvector centrality) is used. A causal structure graph is constructed based on a Bayesian network, where nodes represent facts and edges represent causal relationships and their strengths. The evidence chain is organized into a three-layer structure: the top layer is the conclusion, the middle layer is the supporting evidence, and the lower middle layers are the original evidence. The confidence of the entire reasoning path is calculated by the geometric mean of the node confidences. The output multi-step graph reasoning evidence chain includes four parts: a structured evidence graph, a linear reasoning path, evidence source annotations, and a comprehensive confidence assessment, achieving automated, multi-round, and logically complete knowledge retrieval and reasoning support for reasoning tasks.

[0117] This invention first receives multimodal information input by the user, including various data formats such as text and images. Then, it uses feature extraction technology to transform the information from different modalities into a unified semantic vector representation, forming an initial set of semantic nodes. Based on this unified vector representation, a dynamic semantic graph is constructed. This graph organizes knowledge through three levels: concept layer, relation layer, and instance layer. Each node has an independent semantic embedding representation, and the connections between nodes are weighted according to the strength of semantic association. After the semantic graph is constructed, a hierarchical semantic activation network is activated. When a user submits a query, the query is first encoded into a semantic vector, and initial activation is triggered on the graph based on this vector. The activation value is determined by the semantic similarity between the node and the query, the node's historical access frequency, and the time of the most recent activation. Next, a unique horizontal-vertical alternating propagation algorithm diffuses the activation value throughout the graph, first horizontally between nodes at the same level, and then vertically between different levels. After diffusion, an activation threshold is dynamically calculated based on the complexity of the current query and the user's interaction history, and a set of nodes with activation values ​​exceeding this threshold is selected as candidate knowledge fragments. For these candidate fragments, semantic clustering and merging algorithms are further applied to integrate semantically related nodes into coherent knowledge fragments. When knowledge conflicts occur, a multi-source evidence weighting mechanism is used to resolve them, comprehensively considering the authority, timeliness, and relevance of the knowledge sources. After the knowledge fragments are ready, a multi-level call chain optimization framework is constructed, representing the agent's task processing flow as a directed acyclic graph. Nodes represent atomic operations such as retrieval, reasoning, and generation, while edges represent dependencies between operations. A dual-policy network architecture is deployed, including a high-order policy network responsible for high-level decision-making and an execution policy network responsible for specific execution. The former determines the necessity, timing, and depth of retrieval, while the latter determines the specific retrieval parameters and generation strategies. After the dual-network architecture is established, the training phase begins. For scenarios with clear feedback, a proximal policy optimization algorithm is used for reinforcement learning; for scenarios lacking clear feedback but with expert demonstrations, behavioral cloning technology is used to learn the optimal strategy, forming a complete dual-channel learning mechanism. After training, the system enters the actual execution phase. By monitoring the output quality of each operation node, it identifies poorly performing steps and dynamically adjusts the call chain through node replacement, path reconstruction, or node insertion / deletion. This achieves adaptive optimization of the call chain. By optimizing the pruning function through a near-end policy, performance is improved while limiting the update magnitude when updating the policy network. Ultimately, this enables the automated construction of an end-to-end intelligent agent with semantic understanding capabilities. This agent integrates graph structure knowledge with the generative capabilities of large language models, achieving accurate knowledge retrieval, context-aware multi-turn dialogue, and cross-domain dynamic reasoning, providing efficient and accurate intelligent services for various complex business scenarios.

[0118] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0119] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement 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 present 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 of the invention herein.

Claims

1. An automated construction method for end-to-end intelligent agents based on graph structure semantic fusion, characterized in that, Includes the following steps: Step S1: Receive business requirement data input by the user; analyze the business requirement data for business objectives and requirement constraints to obtain structured requirement parameters; Based on the structured requirement parameters, a preliminary end-to-end technology blueprint is obtained; this includes analyzing the business requirements data for business objectives and requirement constraints, including: The business requirement data is preprocessed with tokenization to obtain tokenized requirement text; Based on the decoder-only architecture, multi-layer self-attention hierarchical association processing is performed on the tokenized requirement text to capture long-distance dependencies and deep semantic representations in the text, thereby obtaining the contextual representation data of the requirement text. Autoregressive processing is performed on the contextual representation data of the demand text, and key entity and relation information is extracted through the multi-head attention mechanism and feedforward neural network in the decoder-only architecture to obtain a list of domain entity information. Perform intent understanding on the contextual representation data of the demand text, identify the user's main business intent, and generate business intent tag data; Based on the business intent tag data, the business objective description is processed, and core entities directly related to the business objective are extracted from the domain entity information list as the target subject or object, thus obtaining preliminary business objective entity pairs. Perform contextual reasoning on the contextual representation data of the requirement text, process the constraints, and generate business constraints. Based on the initial business objective entity pairs and the requirement constraints, structured parameter mapping is performed to obtain structured requirement parameters; Step S2: Perform dynamic workflow processing on the initial end-to-end technology blueprint to generate the core workflow framework of the agent; perform multimodal context understanding based on structured requirement parameters to obtain multimodal contextual information; perform end-to-end execution path analysis on the core workflow framework of the agent based on the multimodal contextual information to obtain the end-to-end workflow; wherein, the end-to-end execution path analysis on the core workflow framework of the agent based on the multimodal contextual information is as follows: Based on the core workflow framework of the intelligent agent, extract the expected inputs, outputs and dependencies of each task node to generate node interface dependency data; Node information is aggregated based on node interface dependency data and multimodal contextual information to obtain a node context information package; Based on the thinking steps in the React mechanism, task event execution reasoning is performed on the node context information packet to obtain the node execution reasoning sequence; Based on the action steps in the React mechanism, the task operation is determined by the node execution inference sequence to obtain node action instruction data; Based on the observation steps in the React mechanism, the node action command data is simulated and executed to obtain the expected observation data of the node. Iteratively execute thinking, action, and observation steps until all intermediate links in the initial end-to-end technical blueprint are covered, and the node execution reasoning sequence, node action instruction data, and node expected observation data are connected in an end-to-end execution path to form an ultra-long thinking chain; By utilizing extremely long thought chains, the nodes and paths of the workflow are determined, resulting in an end-to-end workflow; Step S3: Obtain multimodal data based on structured requirement parameters and construct a dynamic evolutionary semantic graph; dynamically activate query nodes in the dynamic evolutionary semantic graph to generate highly activated candidate node data; dynamically select knowledge fragments from the dynamic evolutionary semantic graph based on the highly activated candidate node data to obtain the preferred knowledge fragments; Step S4: Construct an end-to-end call chain execution strategy based on the end-to-end workflow; encapsulate agent instances through the end-to-end call chain execution strategy and dynamic evolution semantic graph, and perform agent instance reinforcement learning enhancement processing to automatically construct end-to-end agents.

2. The automated construction method for end-to-end intelligent agents based on graph structure semantic fusion according to claim 1, characterized in that, Step S1, which involves processing the technical blueprint based on structured requirements parameters, includes: The structured requirement parameters are input into the specification generation layer of the GPT model, and the task decomposition sequence is processed according to the preset industry standard task template library and general task decomposition logic to obtain the preliminary task process sequence. Based on the preliminary task flow sequence, API call interfaces are matched to obtain the expected API call chain; Based on structured requirement parameters and preliminary task process sequences, task completion quality and efficiency evaluation indicators are defined, and task evaluation indicator data are obtained. Based on the preliminary task flow sequence, expected API call chain, and task evaluation index data, a preliminary end-to-end technical blueprint is obtained through technical blueprint processing.

3. The automated construction method for end-to-end intelligent agents based on graph structure semantic fusion according to claim 1, characterized in that, Step S3 involves acquiring multimodal data based on structured requirement parameters and constructing a dynamic evolutionary semantic graph, including: Access multimodal and multiparadigm data sources, and collect multimodal information based on structured requirement parameters to obtain multimodal data; Topic modeling and keyword extraction are performed on text data from multimodal data to obtain core entity data of the text; Based on the image data in the multimodal data, salient target detection and scene understanding are performed to obtain the core feature data of the image; Cross-modal alignment and information integration are performed on core entity data of text and core feature data of images, and entity relationships and event associations between different modal information are identified to obtain cross-modal fusion structural information; Knowledge unit identifiers are assigned to cross-modal fusion structural information, and meta-information is annotated to obtain annotated knowledge unit data. The meta-information annotation includes knowledge source, credibility, and timeliness. The knowledge abstraction level and domain relevance of the labeled knowledge unit data are evaluated, and the data is organized into a hierarchical structure of concept layer, relation layer and instance layer to obtain multi-dimensional knowledge management graph structure data. Domain knowledge base is populated using multi-dimensional knowledge management graph structure data, thereby constructing a dynamically evolving semantic graph.

4. The automated construction method for end-to-end intelligent agents based on graph structure semantic fusion according to claim 1, characterized in that, Step S3 involves dynamically activating query nodes in the dynamically evolving semantic graph, including: Receive the user's current query behavior and perform behavior semantic parsing to obtain the current user's query semantics; Perform semantic representation processing on the current user's query semantics to obtain the query semantic vector; Extract the historical access frequency and the most recent activation time of nodes based on the dynamic evolution semantic graph; Using the query semantic vector as input, semantic similarity analysis is performed in the dynamically evolving semantic graph to obtain node semantic similarity data; The graph activation status is evaluated based on node semantic similarity data, node historical access frequency, and node recent activation time to obtain graph activation status data. The dynamic evolution semantic graph is processed by alternating horizontal and vertical propagation using graph activation state data to generate a post-diffusion node activation value graph. The query complexity is evaluated based on the current user query semantics, and the activation threshold for this query is dynamically calculated to obtain the dynamic activation filtering threshold. Based on the dynamic activation screening threshold, all nodes whose activation values ​​exceed the dynamic activation threshold are selected from the post-diffusion node activation value map to generate high activation candidate node data.

5. The automated construction method for end-to-end intelligent agents based on graph structure semantic fusion according to claim 1, characterized in that, Step S3, which involves dynamically selecting knowledge fragments from the dynamically evolving semantic graph based on highly activated candidate node data, includes: Based on highly activated candidate node data, knowledge node clustering operations are performed on the dynamic evolution semantic graph to obtain semantic cluster knowledge clusters; RAG technology is used to integrate knowledge fragments from semantic clustering knowledge clusters to obtain preliminary integrated knowledge fragments; Based on the initial integration of knowledge fragments, conflicting knowledge points and their sources are identified, resulting in a list of potential knowledge conflicts. When the list of potential knowledge conflicts is not empty, the authority, timeliness of information and relevance to the current query of the knowledge sources associated with the conflicting knowledge points are comprehensively evaluated and weighted to obtain the conflicting knowledge source weight evaluation data. Based on the conflict knowledge source weight evaluation data, conflict knowledge points are resolved in the initially integrated knowledge fragments to obtain the optimal knowledge fragments.

6. The automated construction method for end-to-end intelligent agents based on graph structure semantic fusion according to claim 1, characterized in that, Step S4, which involves constructing an end-to-end call chain execution strategy based on the end-to-end workflow, includes: Construct a directed acyclic graph of the task flow based on the end-to-end workflow; A dual-policy network architecture is deployed based on a directed acyclic graph of the task flow; the dual-policy network architecture deployment includes a higher-order policy network and an execution policy network; By using a high-order policy network to determine the necessity of retrieval operations on the directed acyclic graph of the task flow, select the execution timing, and set the retrieval depth, a high-order decision network instance is obtained. An execution strategy network instance is obtained by setting the specific retrieval parameters to be used for each retrieval node and the specific content generation strategy to be used for each generation node in the directed acyclic graph of the task flow through the execution strategy network. Construct an end-to-end call chain execution strategy based on high-order decision network instances and execution strategy network instances.

7. The automated construction method for end-to-end intelligent agents based on graph structure semantic fusion according to claim 1, characterized in that, Step S4 involves encapsulating agent instances using an end-to-end call chain execution strategy and a dynamically evolving semantic graph, including: The atomic operations in the end-to-end call chain execution strategy are used as the agent's optional action space, and the current state of the dynamically evolving semantic graph and the user interaction context are used as the agent's state observation space, thus obtaining the agent's interaction training environment. Based on the intelligent agent interactive training environment, the selected knowledge fragments are used as the retrieval enhancement source of RAG technology. The scenario task processing is performed based on the end-to-end call chain execution strategy. The decision sequence, resource consumption and preliminary output results of the intelligent agent during the task execution process are collected to obtain scenario task execution feedback data. A dual-channel learning mechanism is used to iteratively optimize the end-to-end call chain execution strategy. At the same time, a near-end strategy optimization algorithm is used for reinforcement learning based on the scenario task execution feedback data. A pruning update strategy is applied to adjust the network parameters to maximize the cumulative reward, resulting in a scenario-optimized call chain strategy. Based on the scenario-optimized call chain strategy, expert demonstrations were used for supplementary training, and agent reinforcement learning was performed to obtain the optimized agent strategy network parameters. The network parameters of the optimized agent policy are embedded into the agent, and agent instances are encapsulated based on the dynamically evolving semantic graph, thereby automatically constructing end-to-end agent instances.

8. The automated construction method for end-to-end intelligent agents based on graph structure semantic fusion according to claim 1, characterized in that, Step S4 includes the following reinforcement learning enhancement processing for agent instances: Upon receiving a new user interaction request, the end-to-end agent instance is used to perform multi-turn dialogue context awareness and generate context-aware data. Interaction intent is understood based on context-aware data, and reasoning and cross-domain knowledge intent analysis are performed to obtain reasoning-based interaction intent; Based on reasoning-based interactive intents, end-to-end agent instances are used to perform multi-step queries and evidence chain construction, generating multi-step graph reasoning evidence chains; Based on the multi-step graph reasoning evidence chain, semantic-level personalized knowledge retrieval is performed to obtain personalized response knowledge core data. Based on personalized response knowledge core data and multi-step graph reasoning evidence chain, natural language generation processing is performed using a pre-set large language model to obtain intelligent response text data. During the process of end-to-end intelligent agent instances continuously interacting with users and generating intelligent response text data, user feedback data is collected in real time, including explicit ratings, text correction suggestions, and implicit behavioral signals, to obtain the raw user feedback data stream. The raw user feedback data stream is cleaned, labeled, and quantified to transform it into a reward signal in the reinforcement learning environment, thus obtaining a quantified reward signal. By quantifying reward signals and performing reinforcement learning on end-to-end agent instances, end-to-end agents can be automatically constructed.

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