A knowledge graph-based data labeling model iterative optimization method

Abstract

The present application relates to a kind of data annotation model iteration optimization method based on knowledge graph.For the tuning difficulty brought by the complexity of data modalities and the depth of label hierarchy in current multi-modal tasks, a solution based on knowledge graph and lightweight strategy distiller instance collaboration is proposed. The scheme generates a query vector through task signal analysis, intelligently retrieves high-confidence tuning paths in the knowledge graph, and uses semantic anchoring layers and behavior decoupling layers to realize the atomization of tuning behaviors and the autonomy of dependency relationships. It also dynamically adjusts the timing and intensity of the operation based on performance feedback during the training period. Finally, the model is locally optimized through a one-time stateless parameter adjustment script, improving the precision and adaptability of parameter adjustment. The technical effect lies in reducing the dependence on manual tuning, improving parameter adjustment efficiency and model generalization ability, and achieving efficient response to resource automatic release and task cycle.

Description

Technical Field

[0001] This invention relates to the field of knowledge graph-driven multimodal model parameter optimization technology, and in particular to an iterative optimization method for data annotation models based on knowledge graphs. Background Technology

[0002] In the current field of knowledge graph-driven data annotation and model parameter optimization, a technical system dominated by static tuning strategies has emerged for multimodal data modeling and parameter tuning. Existing technologies mostly rely on pre-set expert rule bases, fixed hyperparameter configuration templates, or pre-trained strategy models. These strategies possess certain capabilities in parameter space search and local optimization, meeting the model optimization needs of single tasks or homogeneous data scenarios. With the development of large-scale pre-training, multimodal integration, and online annotation systems, some solutions have begun to introduce methods such as knowledge graph-based semantic association reasoning, meta-learning-based hyperparameter transfer, and parameter reuse using historical task trajectories to improve the flexibility of parameter configuration and the model's adaptive capabilities in complex task scenarios. To a certain extent, these methods have promoted the intelligence of model parameter optimization, but they still fundamentally struggle to overcome the constraints of static overall tuning strategy settings and the high dependence of past task decisions on expert knowledge and historical function trajectories.

[0003] Currently, typical technical approaches mainly fall into three categories: The first category is parameter tuning methods for annotation tasks, which are based on expert rule bases or predefined parameter templates. These methods take the form of parameter adjustment suggestions or mapping rules for different tasks. This type of approach has a wide range of applications, but the parameter update cycle is slow, the generalization is limited, and it is not adaptable enough to new multimodal data distributions and changes in labeling systems.

[0004] The second category is a semi-dynamic optimization method that combines knowledge graphs. The system uses the domain features and task type information accumulated in the knowledge graph to generate optimization suggestions through reasoning retrieval. However, the relevant strategy models are usually a weighted mixture of historical task results and lack the ability to adjust in real time for the semantics of the latest task. Especially when faced with semantic evolution and frequent changes in the annotation process, the timeliness and accuracy of the adjustment cannot fully meet the needs of high-concurrency multi-task deployment.

[0005] The third type of approach involves meta-learning, historical hyperparameter transfer, federated gradient aggregation, etc., in order to achieve cross-task generalization and a certain degree of online updates. However, this type of strategy relies heavily on historical model integration and external large model interfaces, and cannot achieve completely task-specific, stateless, and low-overhead parameter adaptation.

[0006] However, with the evolution of multimodal data modeling and annotation tasks, and the increasing heterogeneity of data distribution, the existing parameter tuning system has revealed the following prominent problems: The optimization strategy capability exhibits highly static characteristics. Once the initial parameters or strategy model are set, subsequent adjustments can only be made to a few key hyperparameters. It is impossible to generate customized optimization solutions in real time based on rapid switching of task types, changes in data modality combinations, or adjustments to the depth of the labeling system.

[0007] The system heavily relies on expert experience, historical models, and external knowledge bases. Many systems must integrate expert knowledge bases or rely on historical task trajectories to generate parameter suggestions, making it difficult to cope with new tasks that are unprecedented or have blurred domain boundaries, and lacking the ability to self-evolve and adapt to complex multimodal scenarios.

[0008] The lack of online adaptive and zeroing features makes it difficult to achieve efficient tuning and resource recovery. In actual deployment, most tuning modules need to retain historical state data and long-term caching strategy models, which can easily lead to memory burden and computational latency. Furthermore, parameter updates are prone to failure due to accumulated noise or historical errors. Under conditions of rapid task granularity switching and concurrent deployment, efficient tuning and resource recovery cannot be achieved.

[0009] The utilization of semantic evolution in knowledge graphs is too superficial. Existing knowledge graph optimization methods mostly remain at the level of static path reasoning and causal relationship extraction, failing to fully integrate the lifecycle semantics of task change events and lacking a structured and quantitative expression of the dynamic characteristics of labeled tasks. As a result, it is difficult to support a task-specific, lightweight, and residue-free optimization process.

[0010] Therefore, addressing the technical shortcomings of current knowledge graph-driven multimodal annotation task parameter tuning processes, such as static policy models, limited generalization ability, inability to generate and recycle online, and high dependence on external knowledge, there is an urgent need for a parameter tuning strategy generation and execution mechanism that can fully utilize the internal semantic evolution of the knowledge graph, does not rely on external expert rules or historical parameter trajectories, and possesses fully online adaptive characteristics and low computational overhead. This mechanism can automatically quantize and dynamically respond based on multi-level meta-information such as annotation task type, data modality combination characteristics, and label structure depth. It can also generate tuning scripts specific to each task and destroyed immediately upon execution, providing highly scalable and practical system support for multimodal data annotation and large-scale batch processing scenarios. Summary of the Invention

[0011] This application provides an iterative optimization method for a knowledge graph-based data annotation model, which aims to solve one of the problems or issues of the existing technology mentioned in the background.

[0012] This application provides an iterative optimization method for a knowledge graph-based data annotation model, specifically including: S1: Obtain the change signal of the multimodal annotation task, extract the multidimensional structured description of the multimodal annotation task, and generate a task query vector; S2: Based on the task query vector, perform multi-hop traversal in the evolvable multi-granularity knowledge graph containing dynamic meta nodes to retrieve a set of high-confidence optimization behavior paths that match the current multimodal annotation task; S3: Map the key concepts in the set of high-confidence optimized behavior paths to low-dimensional policy anchors to construct a semantic anchoring layer; S4: Using the low-dimensional strategy anchor points output by the semantic anchoring layer, the original tuning action is decomposed into atomic operation units and a condition triggering dependency graph is established to construct a behavior decoupling layer instance. S5: Read the performance feedback snapshot data of the historical training process, combine it with the condition-triggered dependency graph to dynamically calibrate the execution timing and intensity of the atomic operation unit, and build a context cache layer; S6: Connect the semantic anchoring layer, behavior decoupling layer instance and context cache layer to form a policy distiller instance, and generate a one-time stateless parameter adjustment script for the current multimodal annotation task based on the lightweight policy distiller instance.

[0013] S7: Inject the one-time stateless parameter adjustment script into the training loop hook of the model optimization module to perform localized parameter adaptive adjustment operation.

[0014] S8: After completing this parameter adaptive adjustment operation, destroy the one-time stateless parameter adjustment script and strategy distiller instance to release computing resources in response to the next round of annotation task change signals.

[0015] This application provides an iterative optimization method for a knowledge graph-based data annotation model, which has the following beneficial effects: (1) By constructing an evolvable multi-granularity knowledge graph containing three types of dynamic meta nodes, namely “labeling task type”, “data modality combination features” and “quality assessment dimension”, and introducing event timestamps to mark the life cycle of triples, fine-grained modeling and dynamic tracking of the labeling task context environment are realized. This effectively overcomes the problems of high policy adaptation delay and weak generalization ability caused by static interest modeling and lagging context awareness in traditional recommendation or optimization systems. Combined with a multi-hop reasoning mechanism based on semantic path, the system performs accurate retrieval along the logical chain of “task type → associated data features → adaptation optimization behavior”, which significantly improves the relevance and pertinence of parameter optimization strategy generation. This enables the system to quickly identify the most matching behavior path when facing new or complex labeling tasks, greatly reducing the cold start response time to hundreds of milliseconds, which is significantly better than traditional methods that rely on historical parameter trajectory playback or expert rule base matching.

[0016] (2) A lightweight policy distiller instance is used to transform the retrieved semantic path into a one-time, stateless local execution script. Through a three-level structure of semantic anchoring layer, behavior decoupling layer instance and context cache layer, efficient mapping and dynamic calibration from high-level semantics to specific tuning actions are achieved, which breaks through the technical bottlenecks such as rigid policy reuse, coarse operation granularity and lack of feedback loop that are common in existing adaptive optimization frameworks. Among them, the condition dependency modeling of atomic operation units makes the tuning actions context-sensitive and time-series controllable. The design of retaining only the performance snapshots of the two most recent periods greatly reduces memory occupation and computational overhead while ensuring decision agility. It avoids dependence on large-scale policy models, external knowledge bases or reinforcement learning agents, and truly realizes online adaptive updates with low resource consumption. It is particularly suitable for continuous optimization needs in edge device deployment or real-time interactive scenarios.

[0017] (3) The entire optimization process is driven by the semantic evolution within the knowledge graph. All strategies are generated on demand, dedicated to the current multimodal annotation task, and destroyed after execution, forming a closed-loop, decentralized adaptive optimization paradigm. This not only eliminates the risk of negative migration that may be caused by cross-task parameter migration, but also enhances the system's security isolation and behavioral interpretability. At the same time, the architecture naturally supports multi-objective collaborative optimization—for example, suppressing validation set oscillations and balancing sample diversity and gradient stability while improving convergence speed—without the need for manually setting weight coefficients or performing cumbersome hyperparameter tuning, and has good scalability and engineering implementation potential. Especially in typical application scenarios such as campus football, this solution can support the efficient integration and accurate docking of heterogeneous resources inside and outside the school (such as video motion data, student physical fitness records, and competition tag systems), realize the synergistic enhancement of the relevance and diversity of recommended content, and effectively meet the technical requirements of personalized and refined service supply in a dynamically changing environment.

[0018] In summary, this solution constructs a semantically rich and dynamically evolving knowledge graph infrastructure, integrates multi-hop semantic reasoning and a lightweight policy distillation mechanism, and forms a highly flexible, low-latency, and dependency-free online parameter tuning system. Without introducing any external model interfaces or complex learning agents, it achieves instant generation, accurate matching, and secure execution of tuning strategies, significantly improving the system's adaptability, response sensitivity, and operational efficiency in dynamic task environments. It provides a reliable, interpretable, and easily deployable technical support path for intelligent training systems facing diverse annotation needs. Attached Figure Description

[0019] Figure 1 This is the main flowchart of an iterative optimization method for a knowledge graph-based data annotation model. Figure 2 This is a sub-flowchart of an iterative optimization method for a knowledge graph-based data annotation model; Figure 3 This is another sub-flowchart of an iterative optimization method for a knowledge graph-based data annotation model. Detailed Implementation

[0020] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0021] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0022] like Figure 1 As shown, this application provides an iterative optimization method for a knowledge graph-based data annotation model, specifically including: S1: Obtain the change signal of the multimodal annotation task, extract the multidimensional structured description of the multimodal annotation task, and generate a task query vector. The task query vector is generated based on a description of at least one of the following dimensions of the task: modal composition, semantic complexity, and tag hierarchy depth of the multimodal annotation task.

[0023] S2: Based on the task query vector, perform multi-hop traversal in the evolvable multi-granularity knowledge graph containing dynamic meta nodes to retrieve a set of high-confidence tuning behavior paths that match the current multimodal annotation task.

[0024] S3: Map the key concepts in the set of high-confidence optimized behavior paths to low-dimensional policy anchors, and construct a semantic anchoring layer with semantic anchoring function.

[0025] S4: Using the low-dimensional strategy anchor points output by the semantic anchoring layer, the original tuning action is decomposed into atomic operation units and a condition triggering dependency graph is established to construct a behavior decoupling layer instance with behavior decoupling function.

[0026] S5: Read the performance feedback snapshot data from the two most recent training cycles, and dynamically calibrate the execution timing and intensity of atomic operation units in conjunction with the condition-triggered dependency graph to construct a context cache layer with context caching functionality.

[0027] S6: Connect the semantic anchoring layer, behavior decoupling layer instance and context cache layer to form a lightweight policy distiller instance, and generate a one-time stateless parameter adjustment script for the current multimodal annotation task based on the lightweight policy distiller instance.

[0028] S7: Inject the one-time stateless parameter adjustment script into the training loop hook of the model optimization module, and perform localized parameter adaptive adjustment operation before each parameter update.

[0029] S8: After completing this parameter adaptive adjustment operation, destroy the one-time stateless parameter adjustment script and strategy distiller instance to release computing resources in response to the next round of annotation task change signals.

[0030] Step S1: Obtain the change signal of the multimodal annotation task, and extract the modal composition, semantic complexity, and tag hierarchy depth structured description of the multimodal annotation task to generate a task query vector. Specifically, this includes: S1.1: Perform protocol parsing processing on the received multimodal annotation task change signal to extract structured task description information containing data modality combination features, semantic complexity index and label level depth parameters.

[0031] The input data object is a multimodal labeled task change signal from the feedback iteration module. This signal contains the task update trigger event and multi-domain data features associated with the task, encoded in a preset communication protocol format. Upon receiving the change signal, a protocol header recognition operation is performed to locate the signal's encoding specification and field mapping rules by parsing the protocol type field. After completing the encoding specification recognition, the field decoding module is called to segment the binary or serialized structure of the signal body, restoring it to the original field set containing task metadata, data modality features, semantic complexity indicators, and tag structure descriptions. After obtaining the original field set, the field mapping table is used to map the protocol fields one-to-one with the task attribute names within the system, ensuring that task change signals from different sources can be uniformly converted into standardized attribute sets. For the data modality combination feature field, format verification and multimodal consistency detection are performed to remove task description entries with modality identifier conflicts or missing entries. For the semantic complexity indicator field, the numerical decoder is called to convert the encoded value into a floating-point quantity that can be directly used for subsequent calculations, and the value is divided and classified according to the system's preset complexity grading standard. For the label hierarchy depth parameter field, hierarchical topology parsing is performed to convert the label structure it represents into a tree-like hierarchical data object, facilitating subsequent spatial mapping operations. Through this chain-like parsing process, the original protocol signal is transformed into structured task description information containing data modality combination features, semantic complexity indicators, and label hierarchy depth parameters. This achieves a standardized conversion from multi-source change signals to unified task description data, providing consistent and complete input for subsequent feature encoding and task query vector generation.

[0032] S1.2: Perform feature encoding operation based on the modality combination features in the structured task description information to generate an initial modality feature vector that characterizes the data distribution characteristics of the current multimodal annotation task.

[0033] Based on the structured task description information containing data modality combination features output in step S1.1, the parameter values ​​of each modality combination feature are read and parsed to confirm the proportion, temporal relationship, and mutual information value of different modalities in the input data, forming a list of modality combination features to be encoded. Vectorization processing is performed on the modality combination feature list, mapping discrete modality category numbers to continuous real-number domain encoding. An embedding matrix is ​​used to establish a mapping relationship between modality categories and a high-dimensional feature space, ensuring that the encoding result preserves the similarity structure between modalities. Convolution or self-attention mechanisms are used to extract local patterns and model global relationships in the embedded modality features, capturing potential interaction features in modality combinations and forming a preliminary aggregated modality feature matrix. Multi-scale feature denoising and normalization processing is performed on the preliminary modality feature matrix. Noise components are removed using feature filtering logic based on a standard deviation threshold, while normalization mapping ensures that different modality features are comparable within a unified numerical domain. A weighting coefficient calculation mechanism is introduced. Based on the importance index of each modality in the task description information, the normalized modality feature matrix is ​​weighted and synthesized to obtain a single vector representation with global modality distribution representation capability. The initial modality feature vector is calculated using the following formula: in, The weight coefficients for the i-th mode are... For the i-th mode, the normalized feature components are... These serve as the initial modal features. Through the aforementioned feature encoding operations, the modal combination features in the structured task description information are transformed into initial modal feature vectors that characterize the data distribution characteristics of the current multimodal annotation task, thus achieving accurate input for subsequent semantic complexity fusion calculations.

[0034] S1.3: The initial modal feature vector is combined with the semantic complexity index for weighted fusion calculation to construct a composite semantic feature vector that reflects the inherent logical difficulty of the task.

[0035] The initial input conditions are the initial modal feature vector output from the previous sub-step S1.2 and the semantic complexity index obtained in S1.1. Both types of data have formatted numerical expressions and can be directly involved in the fusion calculation.

[0036] A reference table of weight coefficients is established for the initial modal feature vectors in the order of their components. The initial weight values ​​in the reference table are set according to the sensitivity of the modality to the model performance in historical tasks, thus forming the modal basis weight vector.

[0037] The semantic complexity index is normalized by scaling factor, which maps the complexity quantification parameters with different value ranges to a unified distribution interval and forms a complexity weight vector, so that it can be numerically matched with the modality basis weight vector in the fusion stage.

[0038] A weighted fusion calculation method is adopted. For each component in the initial modal feature vector, a multiplication operation is performed with the corresponding component of the complexity weight vector to obtain the component contribution value before fusion. Then, the summation and normalization processing of all component contribution values ​​is performed to generate the initial value of the composite semantic feature.

[0039] A local nonlinear modulation function is introduced to nonlinearly amplify or suppress the highly sensitive components in the initial value of the composite semantic features, thereby improving the discriminativeness in the task difficulty mapping. The modulation coefficient of this function depends on the complexity weight of the component.

[0040] The fusion value of each component of the composite semantic feature vector is calculated using the following formula: Where F represents the sum of the semantic feature vectors and the output mapping. This represents the i-th component value of the initial modal eigenvector. This represents the semantic complexity weight component corresponding to the i-th component value of the initial modality feature vector. The nonlinear modulation index is a real number and a core hyperparameter controlling the degree of nonlinearity. In actual calculations, the fused value F needs to be standardized again to ensure the comparability of the components.

[0041] By using the above processing method, the initial modal feature vector and semantic complexity index of the previous step are transformed into a composite semantic feature vector that reflects the inherent logical difficulty of the task, thereby optimizing the feature input parameters for subsequent multi-dimensional space mapping operations.

[0042] S1.4: Perform a multi-dimensional space mapping operation based on the composite semantic feature vector and the label level depth parameter to generate an original task query vector with globally unique identification capability.

[0043] S1.5: Normalize and standardize the original task query vector to output a standard task query vector that conforms to the knowledge graph retrieval interface specification as the final result.

[0044] Step S2: Based on the task query vector, perform a multi-hop traversal in the evolvable multi-granularity knowledge graph containing dynamic meta nodes to retrieve a set of high-confidence tuning behavior paths matching the current multimodal annotation task. Specifically, this includes: S2.1: Perform multi-dimensional feature alignment processing on the task query vector, and use the cosine similarity calculation algorithm to map the modality composition dimension, semantic complexity dimension and label level depth dimension of the vector to the dynamic meta node space in the evolvable multi-granularity knowledge graph, so as to generate a candidate node list containing an initial set of activated nodes, wherein the initial set of activated nodes consists of labeled task type nodes, data modality combination feature nodes and quality assessment dimension nodes that have a similarity to the task query vector exceeding a preset threshold.

[0045] The input is a standard task query vector that has been normalized and standardized, containing numerical feature components of modality composition dimension, semantic complexity dimension, and label level depth dimension.

[0046] Each dimension of the standard task query vector is extracted to form a set of modal feature components, a set of complexity feature components, and a set of label-level feature components.

[0047] The cosine similarity is calculated between the modal feature component set and the modal combination feature node vector in the dynamic meta node space of the knowledge graph. The same operation is performed between the complexity feature component set and the semantic complexity node vector in the dynamic meta node space to obtain the complexity dimension similarity value.

[0048] Perform a cosine similarity operation on the set of feature components at the label level and the node vector of the quality assessment dimension to obtain the label level depth dimension similarity value.

[0049] Based on the comparison results of the similarity values ​​of each dimension with the preset threshold, valid nodes that meet the similarity greater than the threshold in the modality composition dimension, semantic complexity dimension, and tag level depth dimension are selected, and these nodes are formed into an initial set of activated nodes.

[0050] The initial set of activated nodes is bound to their corresponding similarity values ​​to generate a list of candidate nodes, which serves as the input for the next traversal.

[0051] By applying the above processing method, the standard task query vector from the previous step is transformed into a candidate node list that includes the mapping effect of task semantic features, thus achieving accurate mapping from multi-dimensional features to the dynamic meta-node space.

[0052] S2.2: Perform a breadth-first search traversal with timestamp constraints based on the candidate node list, and use the event timestamp filtering mechanism to remove triplet connection edges whose lifecycles have expired, so as to construct a dynamic subgraph structure containing only valid semantic relationships. The node set of the dynamic subgraph structure comes from the initial set of activated nodes, and the edge set consists of entity relationship triplets that are within their valid lifecycles.

[0053] Based on the candidate node list formed by the initial set of activated nodes, the breadth-first search traversal interface with timestamp constraints is called, and the candidate nodes are input into the traversal engine as the starting point of the search.

[0054] During the traversal, the event timestamp parameter associated with each connection edge is read, and the difference between the timestamp and the current system time is calculated to determine the lifecycle state of the connection edge.

[0055] An event timestamp filtering mechanism is used to remove connection edges that are determined to be invalid in the lifecycle state. Invalid edges are marked as inaccessible and the corresponding edge indexes are removed from the traversal queue.

[0056] For the remaining connection edges within their valid lifecycle, expand the adjacent nodes according to the breadth-first search hierarchy, add the new nodes to the node set of the dynamic subgraph, and construct a topology containing all valid entity relationships within the current search range.

[0057] For the node set and edge set during the dynamic subgraph construction process, perform structural consistency checks to ensure that the node set is entirely derived from the initial active node set and the edge set consists only of valid entity relation triples.

[0058] By using the above processing method, the candidate node list generated in the previous step is transformed into a dynamic subgraph structure containing only valid semantic relationships, thereby automatically removing invalid connection edges in the knowledge graph and ensuring the accuracy of subsequent reasoning path mining.

[0059] For example, in a multimodal annotation task, the candidate node list contains 12 initially activated nodes, and each node's connecting edges are accompanied by an event timestamp. The current system time is 2024-06-10 12:00:00, and the lifecycle threshold is set to 72 hours. When traversing to a connecting edge with an event timestamp of 2024-06-07 08:30:00, the calculated difference is approximately 79.5 hours, exceeding the lifecycle threshold. Therefore, this connecting edge is removed and not added to the dynamic subgraph. For another connecting edge with an event timestamp of 2024-06-09 10:15:00, the difference is approximately 25.75 hours, making it a valid edge, and it is expanded to its neighboring nodes during breadth-first search. Ultimately, the filtered dynamic subgraph contains 9 nodes and 15 valid connecting edges. Structural consistency checks show that the node sources are completely consistent with the initial activated node set, and the edge sets are all valid lifecycle entity relationship triples, ensuring a significant improvement in the semantic association accuracy of the next step of multi-hop inference path mining.

[0060] S2.3: Perform multi-hop inference path mining processing on the dynamic subgraph structure. Use an attention weight-based path scoring algorithm to traverse the inference chain from task type to associated data features and then to adaptation and optimization behavior to extract several original semantic path sequences with high confidence scores. The starting point of the original semantic path sequence is the labeled task type node, the ending point is the adaptation and optimization behavior node, and the middle part passes through data modality combination feature nodes.

[0061] The input condition is the dynamic subgraph structure output by step S2.2. This structure only contains nodes within the effective lifecycle and their semantic relationships. The node types cover labeled task type nodes, data modality combination feature nodes, and adaptation and optimization behavior nodes.

[0062] The dynamic subgraph structure is loaded into the path mining operator, and a multi-hop inference chain initialization queue is established with the labeled task type node as the source node. The traversal direction is limited to the data modality combination feature node and then to the adaptation and optimization behavior node.

[0063] During the traversal, attention weights are calculated for each edge at each transition. The formula for calculating attention weights is: in, Here, is the attention weight, score(u,v) is the association strength between node u and node v, score(u,w) is the association strength between node u and node w, and N(u) is the set of neighbors of node u.

[0064] Cumulative weight aggregation is performed on all possible paths, and the overall score of each path is used as the path confidence score. The path score is calculated using the following formula: Where L is the path length.

[0065] Paths with confidence scores below a preset threshold are eliminated, while paths that meet the threshold conditions are retained as candidate original semantic path sequences.

[0066] Perform a duplicate pattern elimination operation on the candidate path sequence to remove duplicates from paths with completely identical semantic structures, ensuring the conceptual diversity of the output path set.

[0067] By using a path scoring algorithm based on attention weights and a direction-limited multi-hop traversal chain, the original semantic path sequence that is most relevant to the current multimodal annotation task and has high confidence is extracted from the dynamic subgraph structure, realizing a complete inference chain from task type nodes through data modality combination feature nodes to adaptation and optimization behavior nodes.

[0068] S2.4: Based on the original semantic path sequence, perform parameter-sensitive dimension parsing processing, and use path semantic mapping rules to transform the key concepts contained in each path into corresponding parameter-sensitive dimension descriptors and their function logic definitions, so as to generate a set of structured tuning behavior paths containing learning rate decay rhythm, gradient pruning threshold and batch sample diversity constraints, wherein each element in the set of structured tuning behavior paths corresponds to a quantitative expression of an original semantic path sequence.

[0069] Based on the original semantic path sequence output by the preceding sub-step S2.3, the attribute labels of the nodes and edges in each path are read as the parsing input objects to confirm the semantic content of the starting point node labeled with task type, the ending point node for adaptation and optimization behavior, and the intermediate data modality combination feature nodes.

[0070] The path semantic tagging scan operation is performed on the node and edge attribute labels to extract the domain feature words, optimization behavior action phrases and modality combination constraint information contained in the description of each node according to the predefined mapping rules, forming a key concept candidate set.

[0071] Using pre-defined path semantic mapping rules, domain feature words in the key concept candidate set are mapped to parameter-sensitive dimension categories, optimization behavior action phrases are mapped to specific parameter adjustment types, and modality combination constraint information is mapped to action logic conditions, generating a preliminary set of parameter-sensitive dimension descriptors.

[0072] Numerical matching processing is performed on the parameter adjustment types in the initial descriptor set. The parameter configuration base table is called to find the corresponding numerical pattern or adjustment rhythm, such as the numerical curve of the learning rate decay rhythm, the quantitative upper limit of the gradient pruning threshold, or the statistical indicator of the sample diversity constraint within the batch, and these numerical patterns are bound to the corresponding descriptors.

[0073] The execution logic definition generation operation is performed on the parameter-sensitive dimension descriptor set bound to the numerical pattern. The conditional execution operator and the timing trigger operator are combined to form an executable logic chain, ensuring that each descriptor not only contains the parameter type and numerical pattern, but also the complete execution conditions, triggering time and mutual exclusion relationship.

[0074] The parameter-sensitive dimension descriptor set containing numerical patterns and action logic chains is encapsulated according to the path sequence to generate a structured optimization behavior path set in sequence. This makes each set element a quantized expression of the original semantic path sequence, possessing a feature data format that can be directly used for subsequent policy distiller instance processing.

[0075] By using parameter-sensitive dimension parsing and numerical pattern binding, the original semantic path sequence from the previous step S2.3 is transformed into a set of structured optimization behavior paths, thereby achieving a quantitative expression of parameter-sensitive dimensions and a precise definition of their function logic, ensuring that the optimization strategy matches the task semantics and is executable.

[0076] For example, in a multimodal annotation task, the original semantic path sequence includes the nodes "cross-domain transfer annotation" → "video and text fusion features" → "learning rate decrement adjustment". The mapping rule maps "cross-domain transfer annotation" to the parameter-sensitive dimension category "learning rate", "learning rate decrement adjustment" to the parameter adjustment type "decay rhythm", and the video and text fusion features to the operational logic condition "weight gradient balance under modal fusion". The numerical matching process retrieves the decay rhythm pattern from the configuration base table as an exponential decay curve, expressed by the formula: Where η is the current learning rate, k is the decay coefficient, and t is the number of iterations. The logic definition sets a decay adjustment to be triggered when the variance of the modal weight distribution of the fused feature vector exceeds a threshold, to prevent a specific modality from dominating gradient updates. The encapsulated structured tuning behavior path element includes the category "learning rate," the adjustment type "exponential decay," the numerical mode parameter {k=0.05}, and the logic condition "triggered when gradient variance > 0.2 under modal fusion." In actual execution, this path element can significantly improve the model's convergence stability on cross-domain data and reduce the risk of overfitting.

[0077] like Figure 2 As shown, step S3: Map the key concepts in the high-confidence optimized behavior path set to low-dimensional policy anchors, and construct a semantic anchoring layer with semantic anchoring function. Specifically, this includes: S3.1: Perform structured extraction processing on key concept nodes in the high-confidence optimization behavior path set to obtain the original concept description vector sequence containing semantic information such as cross-domain transfer annotation and fine-grained attribute coupling, providing standardized input data objects for subsequent semantic mapping.

[0078] S3.2: Based on the original concept description vector sequence, a nonlinear dimensionality reduction mapping algorithm is performed to compress the high-dimensional sparse semantic features into dense low-dimensional latent semantic representation vectors, thereby eliminating redundant information between concepts and retaining the core semantic distinguishability.

[0079] The input condition is the original concept description vector sequence generated by S3.1. This sequence contains high-dimensional and sparse semantic feature components such as cross-domain transfer annotation and fine-grained attribute coupling, possessing a composite structure of multimodal concept attributes and task features, and serves as the input data for dimensionality reduction mapping. During execution, the original concept description vector sequence is first preprocessed by centering, subtracting the global mean from each feature component to eliminate the bias effect. Variance normalization is then performed on the centered vector sequence, scaling the standard deviation to unify the numerical scale of different feature components, thus eliminating the impact of scale differences on subsequent dimensionality reduction calculations. The covariance matrix is ​​calculated on the normalized feature matrix to characterize the correlation patterns between different semantic feature components. Eigenvalue decomposition is performed on the covariance matrix to extract the corresponding eigenvectors as candidate basis vectors for the dimensionality reduction mapping matrix, and these are sorted by eigenvalue size to retain basis vectors with high contribution rates. In selecting the number of basis vectors to retain, a cumulative contribution rate threshold is set, and the contribution rate is calculated according to the following formula: in Let represent the eigenvalue, k be the current basis vector index, and i be the eigenvalue index set. This ratio determines the number of low-dimensional basis vectors to retain to meet the information preservation target. The original high-dimensional sparse eigenvectors are projected onto the low-dimensional space formed by the retained basis vectors to generate dense low-dimensional latent semantic representation vectors. After generation, L2 normalization is performed to ensure the numerical stability of the low-dimensional vectors and the convergence properties of subsequent clustering calculations. Through this nonlinear dimensionality reduction mapping process, the high-dimensional sparse semantic features from the previous step are transformed into dense low-dimensional latent semantic representation data, achieving the expected effects of redundant information elimination and core semantic discriminative retention.

[0080] For example, in a cross-domain annotation task, the original concept description vector sequence has a dimension of 128, with each element ranging from -5 to 5. After centering, the global mean is 0, and the standard deviation after variance normalization is 1. The covariance matrix is ​​128×128, and its eigenvalue decomposition yields 128 eigenvalues. These eigenvalues ​​are arranged in descending order, and the basis vectors corresponding to the top 10 eigenvalues ​​with a cumulative contribution rate of 0.85 are used to construct a dimension reduction mapping matrix. The contribution rate ratio is calculated according to the formula, and the output is 0.085, which meets the preset contribution rate threshold requirement. Projecting the original 128-dimensional features onto this 10-dimensional space yields a dense low-dimensional latent semantic vector, with each element ranging from -0.8 to 0.9. After L2 normalization, the vector length is 1. In subsequent cluster center calculations, this dimension reduction vector significantly improves the clustering convergence speed, reduces the number of processing iterations, and achieves efficient semantic anchoring feature preparation.

[0081] S3.3: Utilize low-dimensional latent semantic representation vectors to perform cluster center calculation operations to generate a standardized set of policy anchor point coordinates representing prototypes of various optimization behavior patterns, thereby achieving a quantitative transformation from continuous semantic space to discrete policy control points.

[0082] Based on the low-dimensional latent semantic representation vector set output from the previous sub-steps, the feature components of each vector in the current set are read to establish a feature matrix for clustering operations. Euclidean distance metric operations are performed on the feature matrix to generate a similarity distance matrix between any two vectors, serving as the basis for calculating cluster centers. K-means clustering iterations are performed using the distance matrix combined with a preset optimized number of behavior pattern classifications. After initializing the coordinates of the cluster centers, the distance between the vector and the center point is calculated in each iteration using the following formula. The member set corresponding to each center point is updated based on the above distance values, and the new coordinates of each center point are calculated as the mean of the feature components of all its member vectors: Where n is the number of center point members, and x is the feature vector of a single data sample in the current set, representing the position of the sample in the multidimensional feature space.

[0083] The iteration process is repeated until the change in the center point coordinates falls below a preset convergence threshold, at which point the final set of cluster center coordinates is extracted. This set of cluster center coordinates is then normalized to form standardized policy anchor point coordinates within a unified numerical range, achieving a quantitative transformation from a continuous semantic space to discrete policy control points. Through cluster center calculation and normalization, the low-dimensional latent semantic representation vector from the previous step is transformed into a standardized set of policy anchor point coordinates for the prototype of the optimization behavior pattern, enabling the semantic anchoring layer to accurately map and control multi-task optimization behavior patterns.

[0084] For example, in the multimodal annotation task optimization scenario, the input low-dimensional latent semantic representation vector has a dimension of 12. Each vector corresponds to features such as cross-domain transfer annotation strength and fine-grained attribute coupling density, and the feature matrix contains 120 records. Euclidean distance is used to calculate the similarity between any two records, resulting in a 120×120 distance matrix. The number of optimization behavior pattern categories is set to 5, and K-means clustering is performed. The initial center point coordinates are randomly selected from 5 records, and the range of each feature component is [-1.0, 1.0]. The distance between any vector and the center point is calculated. After updating the member set by minimum distance allocation, the new coordinates of the center point are calculated. After 23 iterations, the change in the coordinates of each center point is less than 0.001, indicating clustering convergence. The center point coordinates are normalized to the range [0,1], resulting in 5 standardized policy anchor point coordinates. Each anchor point corresponds to an optimization behavior pattern prototype. Subsequent semantic anchoring layers can directly use this set for policy activation, significantly improving the convergence speed on the validation set and enhancing multi-task generalization ability.

[0085] S3.4: Construct a weight initialization matrix based on the standardized policy anchor point coordinate set to establish the neuron connection topology of the semantic anchoring layer, ensuring that the network can receive low-dimensional latent semantic representation vectors and output corresponding policy activation signals.

[0086] Using the standardized set of policy anchor point coordinates output from the preceding steps as input, the spatial positions of each policy anchor point are mapped and encoded during the matrix initialization phase according to the semantic anchoring layer design specifications, in order to determine the starting and ending indexes of neuron connections.

[0087] The component values ​​of each strategy anchor point coordinate are transformed into initial weight values ​​based on their positional relationship in the low-dimensional latent semantic space. A Gaussian distribution sampling method is used to ensure that the initial weight values ​​match the variance and mean of the input semantic representation in terms of numerical range and distribution shape.

[0088] The generated initial weight matrix is ​​used to perform a normalization operation, and the matrix elements are standardized column-wise using the following formula: Where w is the original value of the matrix element, μ is the mean weight of the column, and σ is the standard deviation of the weight of the column.

[0089] The normalized weight matrix is ​​sparsified by setting a sparsity threshold to remove weights whose absolute values ​​are below the threshold, thereby enhancing the feature selectivity of the connection topology.

[0090] A neuron connection topology description table is constructed on the sparse matrix. The table records the connection index, weight value and activation function type of each input node and output node, ensuring that the network can accurately output the corresponding policy activation signal when receiving a low-dimensional latent semantic representation vector.

[0091] The above processing method transforms the cluster center coordinates from the previous step into a semantic anchoring layer neuron connection topology with an executable connection structure and initial weight configuration, enabling the network to respond directionally to low-dimensional semantic inputs.

[0092] For example, in a multimodal annotation task scenario, the input standardized policy anchor point coordinate set contains 6 anchor points, each with 3-dimensional coordinate components and a value range of [-0.5, 0.8]. During matrix initialization, a Gaussian distribution with a mean of 0 and a standard deviation of 0.1 is used to generate an initial weight matrix of size (3×6), corresponding to the input vector dimension and the number of anchor points. After standardization using a formula, the weight values ​​are all within the range of [-1.5, 1.5], with consistent variance. A sparsity threshold of 0.05 is set, and elements with absolute values ​​less than 0.05 are removed from the normalized matrix, achieving a sparsity rate of 40%. Effective connections are concentrated in dimensions highly relevant to the task. The final generated neuron connection topology description table contains 18 effective connection records, with ReLU activation function type. During forward propagation, the input low-dimensional latent semantic vector significantly improves the discriminative power of the policy activation signal. Verification results show that the response amplitude of this layer to cross-domain transfer annotation concepts is significantly greater than that to irrelevant concepts, effectively supporting the action decomposition process of subsequent behavior decoupling layer instances.

[0093] S3.5: Perform a forward propagation validation test on the neuron connection topology of the semantic anchoring layer to output a semantic anchoring layer instance with semantic anchoring function, confirming that the instance can accurately map any input key concept to a predefined low-dimensional policy anchor point.

[0094] For the low-dimensional latent semantic representation vector that has been standardized and input into the semantic anchoring layer neuron connection topology established according to step S3.4, the dimension of the input batch matrix for forward propagation is set to strictly correspond to the number of rows and columns of the weight initialization matrix of this layer.

[0095] The input batch matrix and the weight initialization matrix are multiplied together. The ReLU nonlinear activation function is used to perform element-level nonlinear mapping on the product result to ensure the sparsity and forward separability of the policy activation signal.

[0096] The activation signal vector generated by nonlinear mapping is subjected to standardized scaling adjustment. A batch normalization mechanism is used to eliminate the drift effect of different batch inputs on the neuron activation threshold. The activation signal is linearly reconstructed by statistically analyzing the mean and variance of the current batch.

[0097] The standardized activation signal is input to the output node mapping module, and the softmax(x) normalization function is used to generate the policy anchor probability distribution vector, ensuring a one-to-one correspondence between the low-dimensional latent semantic representation and the predefined policy anchor set. The output policy anchor probability distribution vector is then compared with the predefined low-dimensional policy anchor coordinate set using Euclidean distance.

[0098] Strategy anchor points with a matching distance lower than a preset tolerance threshold are marked as valid mapping results, and mapping accuracy metrics are recorded.

[0099] The multi-stage processing method described above, which verifies the forward propagation test, transforms the low-dimensional latent semantic representation vector from the previous step into mapping data that precisely corresponds to the predefined low-dimensional policy anchor points. This enables the output of semantic anchoring layer instances with semantic anchoring capabilities and confirms that the layers can handle any input key concepts and complete accurate mapping.

[0100] For example, when processing low-dimensional latent semantic representation vectors that include cross-domain transfer annotations and fine-grained attribute coupling, the input batch matrix dimension is set to 64×16, the weight initialization matrix uses He-normal initialization with 16×8 rows and columns, and the matrix product is activated by ReLU. The output activation signal ranges from 0 to 3.7. The batch normalization statistics show that the current batch mean is 0.45 and the variance is 0.12. The reconstructed output signal ranges from -0.85 to 1.95. The output nodes are processed by softmax(x) to generate probability distribution vectors for eight policy anchors. The Euclidean distance calculation result of the highest probability anchor is 0.031, which is lower than the set tolerance threshold of 0.05, verifying that the mapping accuracy is at a high level. This confirms that the semantic anchoring layer instance can stably and accurately map any input key concept to a compliant policy anchor, and the output is used by subsequent behavior decoupling layer instances.

[0101] like Figure 3 As shown, step S4: using the low-dimensional strategy anchor points output by the semantic anchoring layer, the original tuning action is decomposed into atomic operation units and a condition triggering dependency graph is established to construct a behavior decoupling layer instance with behavior decoupling function.

[0102] In this embodiment, the atomic operation unit refers to the smallest executable instruction unit that carries a single, explicit tuning intention, generated by vector space parsing and standardization of low-dimensional policy anchor points. Specifically, the system deconstructs and maps the anchor point coordinates representing behavioral patterns output by the semantic anchoring layer into two orthogonal physical feature components: cross-domain transfer annotation strength and fine-grained attribute coupling density. By normalizing and zero-meaning these two components respectively, the dimensions and biases are eliminated, forming standardized pairs of original policy feature components. Each such feature pair defines an atomic operation unit.

[0103] Specifically, it includes: S4.1: Perform vector space parsing on the low-dimensional policy anchor points output by the semantic anchoring layer to extract the original policy feature component set containing cross-domain transfer annotation strength and fine-grained attribute coupling density, which serves as the basic input data object for subsequent action decomposition.

[0104] The input object is a set of low-dimensional policy anchor points output by the semantic anchoring layer, which contains the coordinates of the optimized behavior pattern control points obtained through dimensionality reduction compression and cluster quantization.

[0105] A vector deconstruction operation is performed on the set of low-dimensional strategy anchors, decomposing each anchor from the dense latent semantic representation into independent components, which correspond to two physical feature dimensions: cross-domain transfer annotation strength and fine-grained attribute coupling density.

[0106] Normalization is performed on the cross-domain migration annotation intensity component. An interval scaling mechanism based on the maximum and minimum values ​​is used to map it to the standardized interval [0,1] to eliminate scale differences between different anchor points.

[0107] Zero-mean processing is performed on the fine-grained attribute coupled density components. This is done by subtracting the global mean to eliminate component bias and preserve the relative differences between anchor points.

[0108] A vector component pairing algorithm is used to combine the normalized cross-domain migration annotation intensity with the zero-mean fine-grained attribute coupling density in pairs to form original policy feature component pairs, which are then input into the subsequent discretization mapping module.

[0109] Through the above processing method, the low-dimensional policy anchor points of the previous step are transformed into a set of original policy feature components containing cross-domain transfer annotation strength and fine-grained attribute coupling density, realizing an accurate mapping from semantic space to physical feature space, and providing standardized basic input data objects for subsequent action decomposition.

[0110] For example, in a certain running instance, the set of low-dimensional policy anchor points output by the semantic anchoring layer contains 10 anchor points. Each anchor point is a two-dimensional vector, where the first dimension corresponds to the cross-domain transfer annotation strength, and the second dimension corresponds to the fine-grained attribute coupling density. Normalization is performed on the first dimension component. Assuming the original value range is [15, 60], the range scaling formula is used: Where x is the original component value, x min For 15, x max The value is 60, and the calculated intensity component is mapped to [0,1]. Zero-mean normalization is performed on the second-dimensional component, assuming the original mean is 0.35. The processing formula is as follows: Where y is the original coupling density component, μ is the original mean value, and the resulting component values ​​are symmetrically distributed around the zero mean. Two processed components are paired to form a feature component pair, for example (0.42, -0.05), and this pair is used to construct the original policy feature component set. This set is then transformed into corresponding atomic operation instruction prototypes in subsequent discretization mapping, realizing the technical transition from policy anchors to executable hyperparameter tuning actions. Ultimately, performance verification demonstrates a significant improvement in the accuracy of action selection and the agility of tuning response.

[0111] S4.2: Based on the original policy feature component set, a discretization mapping algorithm is executed to transform continuous semantic vectors into a standardized atomic operation instruction prototype library with clear physical meaning, where each instruction prototype corresponds to a specific hyperparameter adjustment action. The hyperparameter adjustment actions include, but are not limited to: learning rate decay and gradient clipping.

[0112] The input conditions are based on the original policy feature component set output from the preceding step S4.1. This set consists of cross-domain migration annotation intensity components and fine-grained attribute coupling density components obtained through vector space analytical processing, and is numerically quantifiable and can be used as high-precision mapping input. The multi-interval segmentation judgment module is called on this original policy feature component set to segment and group continuous values ​​according to a preset physical meaning threshold table, forming an interval index matrix corresponding to each hyperparameter action category. A discretization mapping algorithm is executed on the obtained interval index matrix. The core of the algorithm is a one-to-one mapping table from intervals to instruction categories, where each mapping rule encodes the physical meaning of a specific hyperparameter adjustment action. The mapping rule is applied to each row of the interval index matrix to generate a set of instruction prototype records containing action category numbers and action parameter templates. A rule base verification mechanism is used to check the legality of the instruction prototype record set, removing instruction prototype items that do not conform to the current optimization behavior pattern or physical constraints, ensuring the correctness and executability of the output results. The verified instruction prototype record set is stored as a standardized atomic operation instruction prototype library. Each record in this library, i.e., the instruction prototype, corresponds to a specific hyperparameter adjustment action, as well as the parameter adjustment range and constraints of that action. Through a discretization mapping algorithm, the result of the previous step is transformed into a standardized atomic operation instruction prototype library with clear physical meaning that can be directly used for subsequent logical rule matching, achieving a precise mapping effect from continuous semantic vectors to specific hyperparameter action categories.

[0113] For example, in a multimodal annotation task, the original policy feature component set includes a cross-domain transfer annotation intensity component value of 0.72 and a fine-grained attribute coupling density component value of 0.56. The interval segmentation judgment module sets the segmentation thresholds for cross-domain transfer annotation intensity to 0.3, 0.6, and 0.9, and the segmentation thresholds for fine-grained attribute coupling density to 0.4, 0.7, and 0.95. In the mapping rule table, the cross-domain transfer annotation intensity interval [0.6, 0.9) corresponds to the "learning rate decay action," and the fine-grained attribute coupling density interval [0.4, 0.7) corresponds to the "gradient clipping action." The input component values ​​correspond to the above intervals, and the generated instruction prototype records are "learning rate decay action - decay rate template 0.05" and "gradient clipping action - threshold template 0.8." When performing legality verification on the record set, the matching rule matrix is ​​used to remove action instructions that are incompatible with the current multimodal data characteristics, and the record set retains the above two items. The standardized atomic operation instruction prototype library consists of these two records. In this scenario, the learning rate decay rate r in the discretization mapping algorithm is calculated using the following formula: Where α is the cross-domain migration annotation intensity component value, β is the lower bound threshold of the mapping interval, and γ is the upper bound threshold of the mapping interval. Substituting α=0.72, β=0.6, and γ=0.9, we calculate r=0.05. The gradient clipping threshold g is calculated using the following formula: Where δ is the fine-grained attribute coupling density component value, ε is a preset coefficient of 0.85, and substituting δ=0.56, we get g=0.8. This atomic operation instruction prototype library can be combined with data modality combination features for logical rule matching in subsequent S4.3, outputting a list of effective atomic operation units adapted to the current multimodal annotation task, ensuring the targeting and semantic consistency of tuning actions, and significantly improving the accuracy and executability of model parameter tuning.

[0114] S4.3: Utilize the standardized atomic operation instruction prototype library in conjunction with the data modality combination features of the current multimodal annotation task to perform logical rule matching operations, thereby filtering out a list of effective atomic operation units that are suitable for the current scenario and eliminating redundant adjustment actions that are incompatible with the current data distribution characteristics.

[0115] The standardized atomic operation instruction prototype library and the combined data modality features of the current multimodal annotation task are correlated and filtered before operation execution to ensure that the input of the matching logic completely covers all key parameters of the two types of data objects.

[0116] The data modality combination features refer to a set of structured feature vectors parsed in real time from the current multimodal annotation task to be processed, used to quantitatively describe the data composition and intermodal relationships. Specifically, these data modality combination features characterize key attributes such as the existence state, data ratio, alignment quality, and interaction complexity of different modalities (e.g., images, text, audio, video) in the multimodal annotation task. As the core input condition for logical rule matching, the system dynamically selects the most suitable effective atomic operation units for the current task's modal characteristics by matching them with trigger conditions in a standardized atomic operation instruction prototype library. This achieves precise adaptation from general strategies to specific task contexts.

[0117] Modal feature matching calculation is performed on each instruction prototype in the standardized atomic operation instruction prototype library. A modal adaptation matrix based on Boolean rules is used to compare the data modal combination required by the instruction with the actual modal composition of the current multimodal annotation task one by one to generate a modal adaptation label vector.

[0118] Perform a consistency check on the modality adaptation tag vector, combine it with the predefined tuning action dependency conditions in the instruction prototype, remove instruction entries whose trigger conditions cannot be met under the current data distribution, and output a validity tag matrix.

[0119] The validity marker matrix is ​​subjected to conflict detection processing. The conditional mutual exclusion judgment algorithm is used to identify instruction combinations that have execution conflicts in the same training iteration stage. Conflicting instructions are removed from the candidate set to eliminate interference from timing and numerical values.

[0120] After conflict detection, the candidate set is sorted and filtered according to the action intensity adaptation coefficient. The instruction units whose action intensity and influence range meet the preset thresholds in the current data modality combination are retained first, forming the final list of effective atomic operation units.

[0121] Through the above matching filtering, condition checking and conflict elimination methods, the standardized atomic operation instruction prototype library of the previous step is transformed into a list of effective atomic operation units that accurately match the data distribution characteristics of the current multimodal annotation task, thereby achieving the initial adaptation and optimization of the tuning action in the behavior decoupling layer instance.

[0122] For example, in a multimodal annotation task, the standardized atomic operation instruction prototype library contains 15 instruction prototypes, including learning rate decay, gradient clipping, and batch sample diversity control. The current multimodal annotation task's data modality combination features include both text and visual modalities. The modality adaptation matrix is ​​a 2×15 Boolean matrix, and the adaptation rule is set so that a certain instruction must be enabled when the visual modality is present. The adaptation calculation result for the learning rate decay instruction is 1 (match), the gradient clipping instruction result is 1 (match), and the batch sample diversity control instruction result is 0 (mismatch) and is therefore discarded. The validity marker matrix, in the dependency condition test, found that learning rate decay requires a visual modality feature density greater than 0.8 to execute; the current multimodal annotation task corresponds to a feature value of 0.85, passing the test. Gradient clipping requires a text modality feature length exceeding 500 characters; the current multimodal annotation task corresponds to 450 characters, failing the test and being discarded. The conflict detection algorithm identified a conflict between the learning rate decay instruction and another "warm-up step adjustment" instruction's trigger condition in the 10th iteration. After removing the conflicting instruction, only the learning rate decay instruction remained in the candidate set. A threshold of 0.6 was set based on the strength fit coefficient; the current coefficient for the learning rate decay instruction was 0.75, meeting the retention criteria. The final output list of valid atomic operation units contained only the learning rate decay instruction, which served as the core input in subsequent behavior decoupling layer instances, ensuring that action execution was completely consistent with the task modality characteristics and significantly improving the adaptability of the tuning strategy.

[0123] S4.4: Perform causal dependency deduction processing on each element in the list of effective atomic operation units. By analyzing the temporal mutual exclusion and numerical superposition effect between different adjustment actions, construct a directed acyclic graph structure, i.e., a condition triggering dependency graph, that describes the execution order and triggering conditions of each unit.

[0124] The aforementioned causal dependency analysis refers to the dynamic interaction logic deduction and analysis of each candidate operation instruction in the list of effective atomic operation units to clarify the potential timing constraints and numerical interferences during actual execution. This deduction unfolds from two dimensions: first, temporal mutual exclusion, analyzing the sequential constraints of different operation units on the timeline, such as unit A must be executed after unit B, or non-concurrent execution relationships caused by resource conflicts; second, numerical superposition effects, quantifying and predicting whether the adjustments to model parameters by multiple operation units, when triggered in parallel or sequentially, result in simple linear superposition, nonlinear enhancement, or mutual cancellation. Based on these deduction results, the system can construct a conditional triggering dependency graph with operation units as nodes and temporal and numerical dependencies as directed edges and edge weights, thus laying the logical foundation for subsequent topology sorting and precise scheduling.

[0125] The list of valid atomic operation units obtained through logical rule matching and filtering is subjected to causal dependency inference processing. The input objects are the execution parameter configuration and data modality feature information of each atomic operation unit in the current multimodal annotation task scenario.

[0126] Based on the execution time window parameters and scope of each atomic operation unit, a temporal mutual exclusion determination matrix is ​​constructed. By comparing the overlapping of trigger step size intervals of different units in the training iteration sequence, unit pairs with time conflicts are identified and mutual exclusion constraint markers are established.

[0127] Based on the numerical change records of model gradient and weight updates by each atomic operation unit, the numerical superposition effect of the unit execution is calculated using the effect coefficient calculation formula: Among them, w i With w j Let w represent the parameter update amounts for any two atomic operation units. conbined Δ represents the net update amount generated on the model parameters after the combined execution of two atomic operation units i and j. Δ is the superposition effect coefficient, which is used to determine whether the combined execution of the units results in a linear or nonlinear enhancement relationship.

[0128] By combining the mutual exclusion constraint label and the superposition effect coefficient, a causal association weight matrix is ​​generated. Associations with positive matrix element values ​​are considered as reinforcing relationships, and associations with negative matrix element values ​​are considered as inhibiting relationships. The matrix is ​​then converted into weighted edges of a graph structure.

[0129] During the construction of the graph structure, a unique node identifier is established for each atomic operation unit, and the direction of the directed edge is set according to the association type to ensure that all paths point from the first triggered unit to the next triggered unit.

[0130] By using a topological consistency check algorithm to eliminate potential loops and ensure that the graph structure satisfies the acyclic property, while retaining the set of directed edges with causal triggering significance, a directed acyclic graph, i.e., a conditional triggering dependency graph, is obtained that describes the execution order and triggering conditions of each atomic operation unit.

[0131] By using causal dependency inference and graph structure construction, the list of effective atomic operation units from the previous step is transformed into quantifiable and verifiable execution order and condition constraint data, thereby achieving multi-action logic coordination and conflict avoidance within the behavior decoupling layer instance.

[0132] S4.5: Based on the condition-triggered dependency graph, perform topological sorting and state encapsulation operations on the list of effective atomic operation units to generate a complete behavior decoupling layer instance with behavior decoupling function. This instance can output a parameter adjustment instruction sequence with a clear execution logic chain independently of the specific model architecture.

[0133] Step S5: Read performance feedback snapshot data from the two most recent training cycles, and dynamically calibrate the execution timing and intensity of atomic operation units based on the conditional triggering dependency graph to construct a context cache layer with context caching functionality. Specifically, this includes: S5.1: Obtain the raw performance log stream from the two most recent training cycles, and use the time window sliding truncation algorithm to segment the raw performance log stream to extract the initial performance feedback sequence containing the convergence slope mutation point, the validation set index oscillation amplitude, and the inflection point of the decrease in label consistency.

[0134] After the conditional trigger dependency graph with behavior decoupling function is constructed, the training loop execution records pointed to by the dependency graph are used as the data source input. The original performance log stream covering the two most recent training cycles is read, and the start and end time markers are clearly defined during the data input process to ensure accurate time sequence boundaries. The time window sliding truncation algorithm is executed on the original performance log stream, and the global log is equidistantly slicing according to a fixed-length window. The window length is determined based on the number of iteration steps and the frequency of performance fluctuations within the training cycle, and the slice overlap ratio is automatically adjusted by minimizing the missed detection rate of important events. The log content of each time window slice is parsed by the event detection mechanism to identify the iteration records containing the convergence slope abrupt change point. This abrupt change point is obtained by calculating the gradient change value of the validation set loss function, and the gradient change value is expressed as follows: in To verify the instantaneous gradient change of the loss function at iteration step t, For the iteration time step, This is the loss function value calculated on the validation set at the end of the t-th iteration step of the model.

[0135] When extracting oscillation events, the oscillation amplitude is quantified by calculating the extreme value difference of key indicators in the validation set. The oscillation amplitude of the indicators is expressed by the following formula: in This is a sequence of validation set performance metrics within the same time window. The maximum value in sequence M. This represents the minimum value in sequence M. The results of identifying the abrupt change in convergence slope and oscillation amplitude are correlated with the label consistency decline event. The inflection point of the decline in label consistency is obtained by analyzing the matching rate sequence of the label output and detecting its maximum negative gradient position in a short time interval. The three types of event data are combined into an initial performance feedback sequence according to the timestamp sequence, and event type labels and numerical quantization features are added during the sequence assembly process to achieve event data unification.

[0136] By using event detection and time window sliding interception processing, the original log stream associated with the condition triggering dependency graph in the previous step is transformed into an initial performance feedback sequence containing three core events: convergence mutation, indicator oscillation, and consistency decline. This enables accurate extraction of performance events and preparation of input data for the subsequent context caching layer.

[0137] S5.2: Based on the initial performance feedback sequence, a multi-dimensional feature alignment mechanism is used to standardize and map the heterogeneous data in the initial performance feedback sequence to generate performance feedback snapshot data with a unified time-series benchmark.

[0138] Based on the initial performance feedback sequence output by S5.1, a unified set of execution objects is constructed for the multi-source heterogeneous performance monitoring data, including convergence slope mutation points, validation set index oscillation amplitudes, and inflection points of label consistency decline. Heterogeneous data type identification is performed on this set, labeling it with three data categories: numerical time series, categorized state markers, and signal-type mutation events, providing a refined data category basis for subsequent feature alignment. Linear interpolation and timestamp resampling operations are applied to the numerical time series to map data with different sampling frequencies to a unified time series reference point set; the time mapping formula is: Where v(t) is the performance value corresponding to time t after resampling.

[0139] The categorized state labels are processed using one-hot encoding and expanded based on a unified time reference, ensuring that each time reference point has a corresponding state feature vector. Signal-type abrupt event data is processed using high-pass filtering and event peak detection to extract event amplitude and occurrence time, which are then mapped to event impact feature components at the unified time reference point. Z-score normalization is performed on the three types of features processed above. The normalized numerical features, state features, and event features are then concatenated in time reference point order to form a structured performance feedback snapshot matrix. A multi-dimensional feature alignment mechanism is used to aggregate different feature components at each time reference point into a unified-dimensional performance snapshot vector, achieving dual alignment in time and feature space. Through this processing method, the heterogeneous performance data from the previous step is transformed into performance feedback snapshot data with a unified time reference and a unified feature format, providing consistent data input for subsequent condition-triggered dependency graph matching and dynamic calibration.

[0140] For example, during model training for a multimodal annotation task, the raw performance feedback logs output from the two most recent training epochs contain 600 numerical convergence slope sequence samples, sampled once per iteration step and once every two iteration steps, respectively; 100 class state label sequences, marking the model's generalization evaluation level at the corresponding iteration step; and 50 event data records of sudden peak changes in validation set metrics. For the numerical convergence slope sequences, the aforementioned linear interpolation formula is used for interpolation, ensuring that each sequence corresponds to a performance value at a unified set of 100 time reference points, preserving the slope abrupt change characteristics during interpolation. One-hot encoding is performed on the state label sequences, expanding them to 100 time reference points, each with a state feature vector. High-pass filtering is applied to the event data to remove low-frequency fluctuations, detecting peak amplitudes and occurrence times, and mapping them to the corresponding reference point event feature components. Z-score standardization is performed on the three types of features to ensure they are directly comparable in the feature space. The final output structured performance feedback snapshot matrix is ​​100×15 dimensional, with significantly improved temporal alignment and feature format consistency, providing a reliable input basis for the next step of dependency graph matching.

[0141] S5.3: Read the performance feedback snapshot data and the conditional triggering dependency graph generated in the previous steps, and use a graph traversal retrieval algorithm to locate the set of candidate atomic operation units in the conditional triggering dependency graph that match the abrupt change point of the convergence slope in the performance feedback snapshot data.

[0142] The performance feedback snapshot data is interpreted using a key-value index, converting performance metric entries containing abrupt changes in convergence slope into event label vectors that can be used for graph structure matching. A node attribute mapping operation is performed on the conditional trigger dependency graph generated in the preceding steps, attaching task execution feature labels to each atomic operation unit node in the dependency graph for accurate comparison with the event label vectors in the retrieval algorithm. A graph traversal retrieval algorithm based on depth-first search is used, starting from the initial node set of the dependency graph and proceeding along the topological order, performing similarity calculations between node feature labels and event label vectors, where the similarity calculation formula is: Where S is the average similarity score. Let N be the feature similarity of the i-th matching node and N be the number of candidate nodes. A threshold-based decision logic is used to filter out the set of matching nodes whose similarity scores exceed a preset threshold, and the atomic operation unit identifiers corresponding to these matching nodes are extracted as a candidate set. Duplicate detection and deduplication are performed on the candidate set to ensure that each atomic operation unit retains only a unique identifier and a unique trigger path. Through the above chain-like derivation process, the performance feedback snapshot data from the previous step is matched and located with the conditional trigger dependency graph, outputting the candidate atomic operation unit set corresponding to the convergence slope abrupt change point, thus achieving precise association between performance events and parameter adjustment instructions.

[0143] For example, in a multimodal text-image annotation task, the performance feedback snapshot data contains a convergence slope mutation point value of -0.004 within the last two training epochs. The validation set metric oscillation amplitude and the inflection point of annotation consistency decline are not considered in this sub-step. The conditional trigger dependency graph nodes include learning rate adjustment units, gradient clipping units, and batch diversity adjustment units. Each node's feature label includes a numerical threshold range and an execution time sequence marker. The convergence slope mutation point value is encoded as an event label vector [-0.004, t=12], where t=12 indicates that the mutation point occurred in the 12th training iteration. When traversing the dependency graph, the cosine similarity between the event label vector and the feature labels of each node is calculated. Nodes with a similarity score greater than 0.92 are added to the candidate set. After deduplication, only the two identifiers—learning rate adjustment unit and gradient clipping unit—are retained as the matching results. This processing achieves a directional association of convergence slope mutation points, significantly improving the matching accuracy between parameter adjustments and performance changes, and ensuring the effectiveness of subsequent strength and timing calibration.

[0144] S5.4: Based on the oscillation amplitude of the validation set index in the candidate atomic operation unit set and the performance feedback snapshot data, execute dynamic weight calculation logic to quantize and correct the execution intensity coefficient of each unit in the candidate atomic operation unit set, so as to output a list of atomic operation units to be calibrated with intensity labels.

[0145] After receiving the candidate atomic operation unit set and the validation set index oscillation amplitude from the performance feedback snapshot data, the validation set index oscillation amplitude is loaded as an input variable into the dynamic weight calculation module to form a basic metric sequence for performing intensity coefficient correction.

[0146] The basic metric sequence is normalized to map the oscillation amplitudes of different scales to a unified range, thereby eliminating the impact of differences in metric units on subsequent calculations.

[0147] Based on the normalized oscillation amplitude value, the execution intensity coefficient of each candidate atomic operation unit is calculated using the weight update formula, and the preliminary correction factor is obtained by the ratio of the amplitude to the preset sensitivity threshold. in, This is the corrected execution strength coefficient. The original strength coefficient, To normalize the oscillation amplitude, This is the preset sensitivity threshold.

[0148] The final strength coefficient of the fused causal influence is obtained by multiplying the above preliminary correction factor with the causal weight of the atomic operation unit in the conditional triggering dependency graph: in, This refers to the causal weights in the conditional trigger dependency graph.

[0149] The corrected final intensity coefficients are written back to the parameter description objects of each unit in the candidate atomic operation unit set, and an execution intensity flag field is added to each unit to form a list of atomic operation units to be calibrated with intensity flags.

[0150] By combining dynamic weight calculation with causal weight fusion, the candidate operation set from the previous step is transformed into a calibration list with quantitative execution intensity information, thereby achieving a precise response to performance oscillations.

[0151] S5.5: Using the list of atomic operation units to be calibrated and the inflection point of the decrease in annotation consistency in the performance feedback snapshot data, the timing offset inference algorithm is executed to dynamically replan the execution trigger time of each unit in the list of atomic operation units to be calibrated, so as to construct a context cache layer that completes the dual calibration of timing and intensity.

[0152] By utilizing the inflection point of decreased consistency between the list of atomic operation units to be calibrated and the annotations in the performance feedback snapshot data, the time index vector corresponding to the inflection point is read as the baseline input for timing adjustment calculation.

[0153] The difference between the default execution trigger time and the inflection point time index vector of each atomic operation unit to be calibrated is calculated, and the trigger offset vector is obtained by using the timing offset derivation algorithm. The sign of the offset depends on the direction of the slope of the performance curve before and after the inflection point.

[0154] The offset vector is correlated with the dynamic execution strength coefficient to form an offset-strength coupling matrix, which is used to evaluate the feedback sensitivity of different timing adjustments to execution strength.

[0155] Constraint solving is performed based on the offset-strength coupling matrix to ensure that the causal order and mutual exclusion conditions in the conditional triggering dependency graph are satisfied at the adjusted triggering time, and to output a compliant replanning triggering sequence table.

[0156] By binding the replanning trigger timing table with the dynamic intensity correction data, a dual-calibrated execution scheduling configuration file is formed and injected into the internal cache unit of the context cache layer, thereby achieving synchronous optimization of task-level timing and intensity.

[0157] By using the above processing method, the output of the previous step is transformed into a context cache layer data configuration that simultaneously possesses time-series compliance and intensity-adaptive characteristics, thereby enabling precise timing control and optimized execution of parameter adjustment instructions after the performance inflection point during the training process.

[0158] For example, in the model training of a multimodal annotation task, the inflection point of annotation consistency decline occurs at the 1200th iteration, corresponding to the time index vector [1200, 1850]. The original trigger time for the learning rate decay operation in the list of atomic operation units to be calibrated is iteration step 1500, and the original trigger time for the gradient clipping threshold adjustment operation is iteration step 1900. The difference calculation yields offsets of -300 and +50, respectively. The formula for calculating the offset is: in, The original trigger time, This is the inflection point time index. The offset vector and the intensity coefficients (learning rate decayed to 0.8, gradient clipping to 1.2) form the offset-intensity coupling matrix: Solving the constraints ensures that the learning rate decay adjustment is performed before gradient clipping and that the trigger time after the offset is not less than iteration step 1000. Therefore, the replanning trigger times are iteration steps 1200 and 1950. After binding the correction intensity coefficient, an execution scheduling configuration file is generated and injected into the context cache layer. This ultimately enables the early or delayed triggering of key parameter adjustments when the model training shows a performance decline trend, while ensuring that the logical order and optimization magnitude between adjustment actions are significantly improved.

[0159] Step S6: Concatenate the semantic anchoring layer, behavior decoupling layer instances, and context caching layer to form a lightweight policy distiller instance. Based on the lightweight policy distiller instance, generate a one-time stateless parameter adjustment script for the current multimodal annotation task. Specifically, this includes: S6.1: Based on the set of low-dimensional policy anchors generated in the previous steps, the condition triggering dependency graph, and the performance feedback snapshot data in the last two training cycles, the three-layer micro-network architecture is dynamically instantiated to build a lightweight policy distiller instance with a complete data flow path.

[0160] Based on the set of low-dimensional policy anchors generated in the previous steps, the conditional triggering dependency graph, and the performance feedback snapshot data in the last two training cycles, the predefined template parameters of the three-layer micro-network architecture are loaded to establish an initial structural framework including the input interface, feature mapping layer, execution logic layer, and feedback calibration layer.

[0161] The set of low-dimensional policy anchor points is injected into the semantic anchoring unit through the interface layer. Vector dimension matching operation and node attribute binding operation are performed to make the neuron connection weight matrix inside the semantic anchoring layer correspond one-to-one with the policy control points of the current multimodal annotation task, and the anchor point mapping path is locked in the topology.

[0162] The condition-triggered dependency graph is read, and the dependency relationship is mapped to the logical rule node of the behavior decoupling layer instance using a directed acyclic graph parser. The node priority allocation and trigger condition preloading are performed to enable the behavior decoupling layer instance to obtain complete action sequence and mutual exclusion constraint information.

[0163] The performance feedback snapshot data from the two most recent training cycles is sent to the context cache layer. A feedback trigger signal mapping table is constructed using a time series index and multi-dimensional feature alignment mechanism. Each performance change event is mapped to the execution intensity and timing adjustment domain of the atomic operation unit.

[0164] After initializing the parameters for the three layers, the network path connection operation is performed. Through matrix concatenation and cross-layer data channel registration, the policy feature vector of the semantic anchoring layer can be directly transmitted to the behavior decoupling layer instance. Then, the strength and timing calibration are completed through the context caching layer, forming a lightweight policy distiller instance with a complete and continuous data flow path.

[0165] By using dynamic instantiation, the results of the previous step are transformed into an executable integrated strategy generation unit, enabling the construction of online adaptive tuning scripts for the current multimodal annotation task.

[0166] For example, in a multimodal text-image annotation task, the set of low-dimensional policy anchors contains 12 dense vectors, each with a dimension of 16, derived from the cross-domain transfer and fine-grained attribute coupling concept mapping results of the semantic anchoring layer. The conditional trigger dependency graph contains 15 nodes and 22 directed edges, with nodes covering action types such as learning rate decay, gradient clipping, and batch diversity constraints. The performance feedback snapshot data includes 4 convergence slope abrupt change points in the last two training epochs, a mean oscillation amplitude of 0.35 for the validation set metrics, and 2 inflection points of annotation consistency decline. After injecting the set of policy anchors into the semantic anchoring layer, a 16-dimensional vector matching operation is performed and bound to the weight matrix. The behavior decoupling layer instance loads the dependency graph and completes priority allocation, and the context caching layer constructs an event-action mapping table. A cross-layer data channel is constructed by using matrix concatenation operations. The resulting strategy distiller instance outputs a parameter adjustment logic script for the task in a single run. The execution results show that the convergence slope stability is significantly improved during the training period, the oscillation amplitude of the validation set index is significantly reduced compared to the initial state, and the label consistency remains at a high level, thus achieving the goal of the dynamic optimization mechanism.

[0167] S6.2: Utilize the semantic anchoring layer in the lightweight policy distiller instance to perform feature weighted fusion processing on the set of input low-dimensional policy anchor points to generate an enhanced policy feature vector carrying the semantic constraints of the current multimodal annotation task.

[0168] The set of low-dimensional policy anchor points is input to the semantic anchoring layer in the dynamically instantiated lightweight policy distiller instance. Feature vector preprocessing is performed to remove noise components that are not related to the semantic constraints of the current multimodal annotation task, forming a subset of anchor points with priority given to task relevance.

[0169] For each low-dimensional latent semantic representation vector in the anchor point subset, element-wise multiplication is performed based on the preset task semantic weight coefficient matrix to fuse the task semantic constraint parameters with the anchor point vector component by component, generating the first-stage weighted feature matrix.

[0170] A normalized weight aggregation method is adopted to perform weight normalization and summation operations on the first-stage weighted feature matrix column by column, so as to eliminate the influence of uneven weight distribution among anchor points and improve global semantic consistency.

[0171] By using a multi-kernel similarity mapping algorithm, the normalized weighted feature matrix is ​​projected onto the high-dimensional embedding space of the policy features to obtain a second-stage enhanced feature representation that reflects the relative positional relationship between different tuning modes.

[0172] By combining the current multimodal annotation task data modality combination features with the performance snapshot fed back from the context cache layer, feature phase alignment correction processing is performed. Phase offset compensation is applied to different temporal components in the second-stage enhanced feature representation to ensure that the final feature vector is consistent with the optimization execution timing.

[0173] Through the aforementioned feature weighted fusion and multi-level enhancement processing, the set of low-dimensional policy anchor points from the previous step is transformed into an enhanced policy feature vector carrying the semantic constraints of the current multimodal annotation task, thereby achieving precise semantic mapping and policy control point enhancement for task-specific optimization logic.

[0174] S6.3: Based on the enhanced policy feature vector and condition triggering dependency graph, perform logic gating mapping operation in the behavior decoupling layer instance to output a standardized atomic operation instruction sequence with dynamic execution timing markers.

[0175] Based on the enhanced policy feature vector and the conditional triggering dependency graph, the enhanced policy feature vector is parsed in the behavior decoupling layer instance. Each component is matched with the node attributes in the dependency graph to form a mapping table as the logic gating input parameter.

[0176] Based on the logic-gated input parameters, a weight threshold calculation is performed. The activation value of the atomic operation unit is generated by summing the products between the vector component strengths and the trigger condition weights of the dependency graph. The calculation formula is as follows: Where A is the activation value of the atomic operation unit. Let be the value of the i-th component of the enhanced policy eigenvector at time t. Let t be the weight corresponding to the i-th triggering condition of the atomic operation unit in the dependency graph at time t, where t is the temporal index of the current multimodal annotation task.

[0177] The activation values ​​are compared with a preset dynamic threshold, and a conditional branch function is used to filter out the set of target atomic operation units that meet the activation conditions. The conditional branch function takes the following form: Where G is the logic gate output flag, and I is the indicator function, which outputs 1 when A is greater than or equal to the dynamic threshold θ, and outputs 0 otherwise.

[0178] The selected set of target atomic operation units is time-series labeled, and each operation unit is given a dynamic execution timing mark according to the time constraint relationship in the dependency graph, thus binding the operation unit with its corresponding execution time.

[0179] The target atomic operation unit with the execution timing mark is formatted and transformed into a standardized atomic operation instruction sequence to ensure that the instruction can be correctly parsed by the subsequent context cache layer at the output interface of the behavior decoupling layer instance.

[0180] Through the above logic gating mapping operation processing method, the enhanced policy feature vector of the previous step is transformed into a standardized atomic operation instruction sequence with dynamic execution timing mark, so as to realize the precise control and timing coordination of the behavior decoupling layer instance on the current multimodal annotation task optimization strategy.

[0181] For example, the components of the enhanced policy feature vector are set to [0.85, 0.65, 0.40], corresponding to the intensity values ​​of the three types of parameter sensitivity dimensions. The weights of the same type of nodes in the conditional triggering dependency graph are [0.90, 0.70, 0.50]. Each component is multiplied by its weight and summed to obtain the activation value A. The calculation formula yields A = 1.34. The dynamic threshold θ is set to 1.00, and the indicator function outputs 1, indicating that all atomic operation units in this group are activated. The execution times set by the time constraints in the dependency graph are the 5th, 8th, and 12th training iterations. The system appends corresponding execution timing markers to the instruction sequence and formats it into a standardized atomic operation instruction sequence for output to subsequent layers. In this scenario, the behavior decoupling layer instance can significantly improve the temporal matching degree of policy instructions, ensuring a high degree of fit between parameter adjustment actions and model performance change nodes, thereby improving the convergence efficiency and generalization performance of the parameter tuning process.

[0182] S6.4: Combining the standardized atomic operation instruction sequence with the convergence slope mutation points and validation set index oscillation amplitudes in the performance feedback snapshot data, an intensity calibration algorithm is executed in the context cache layer to generate a fine-tuned parameter adjustment instruction set with adaptive execution intensity.

[0183] The context cache layer receives the standardized atomic operation instruction sequence generated by the behavior decoupling layer instance, the convergence slope mutation point in the performance feedback snapshot data, and the oscillation amplitude of the validation set index as input conditions.

[0184] The standardized atomic operation instruction sequence is parsed by an instruction mapping process. Each instruction is associated with a corresponding performance index in the performance feedback snapshot to form a list of matching pairs that can be called by the intensity calibration operation.

[0185] For entries in the matching pair list that involve abrupt changes in the convergence slope, perform gradient rate of change extraction operation to calculate the instantaneous gradient rate of change of parameters during the training period, which serves as the first benchmark quantity for intensity calibration.

[0186] A dynamic weighted calculation method based on the oscillation amplitude of validation set indicators is adopted to generate a weight coefficient vector reflecting performance stability. The execution strength of each atomic operation unit is initially corrected using the following formula: in, This represents the original execution intensity vector of the atomic operation unit to be corrected. The performance stability weights are obtained by mapping the oscillation amplitude. Execute the intensity vector for the modified atomic operation unit. This is the element-wise multiplication operator.

[0187] The initially modified execution intensity coefficient vector is normalized according to the frequency of convergence slope abrupt change points to ensure the balance of intensity adjustment of different atomic operation units on a global scale.

[0188] The normalized execution strength coefficient is applied to the standardized atomic operation instruction sequence to generate a fine-tuned parameter adjustment instruction set with adaptive execution strength. The consistency of the instruction set is then checked to ensure that the adjustment range matches the semantic and data distribution constraints of the current multimodal annotation task.

[0189] By using the above-mentioned intensity calibration algorithm, the dynamic execution timing markers and performance feedback data from the previous step are transformed into a set of fine-grained parameter adjustment instructions that can directly drive the model optimization module, thereby achieving an adaptive adjustment effect of instructions in the dimension of execution intensity.

[0190] S6.5: The fine-grained parameter adjustment instruction set with adaptive execution strength is serialized, encapsulated, and stripped of its state to generate a one-time stateless parameter adjustment script that contains only the logic specific to the current multimodal annotation task and is destroyed after execution.

[0191] Step S7: Inject the one-time stateless parameter adjustment script into the training loop hook of the model optimization module, and perform localized parameter adaptive adjustment before each parameter update. Specifically, this includes: S7.1: Perform syntax parsing and instruction decoding on the one-time stateless parameter adjustment script injected into the training loop hook to extract a set of structured atomic operation instructions containing learning rate decay rhythm, gradient clipping threshold and batch sample diversity constraints, providing standardized control logic input for subsequent parameter space mapping.

[0192] S7.2: Based on the parameter-sensitive dimension descriptor in the structured atomic operation instruction set, the multidimensional tensor index mapping algorithm is used to slice and locate the current weight parameter space of the model optimization module to generate a set of local parameter subspace coordinates that strictly correspond to the hyperparameters to be adjusted, and to establish the physical domain of adaptive adjustment.

[0193] Based on the parameter-sensitive dimension descriptors in the structured atomic operation instruction set, the current weight parameter space matrix of the model optimization module is loaded, and a corresponding tensor index mapping table is established. Each sensitive dimension descriptor is associated one-to-one with the physical location index of the weight matrix. Using a multidimensional tensor index mapping algorithm, dimensional decomposition processing is performed on the weight parameter space, splitting the global weight tensor into a set of local sub-tensors organized by hierarchy, channel, and parameter category, facilitating precise positioning of the target hyperparameter. For each sensitive dimension descriptor, the index mapping table is called to calculate coordinates, mapping the hyperparameter position specified by the descriptor to three-dimensional coordinate vectors such as row, column, and depth of the local sub-tensor. Index entries not within the current training scope are filtered using a constraint matrix. The obtained set of local sub-tensor coordinate vectors is deduplicated and sorted to ensure the uniqueness of the coordinate set and the stability of the execution order, thus forming a set of local parameter subspace coordinates that strictly correspond to the hyperparameters to be adjusted. Through the above positioning process, the logical goal of the structured instructions is transformed into precise tensor physical location markers, achieving the establishment of the physical scope for adaptive adjustment.

[0194] S7.3: Using the set of local parameter subspace coordinates combined with the dynamic execution timing markers in the structured atomic operation instruction set, execute the context-aware trigger condition verification logic to determine whether the current training iteration step meets the activation threshold of the atomic operation unit, thereby outputting a queue of pending parameter adjustment requests with time-compliant execution.

[0195] The set of local parameter subspace coordinates input and the dynamic execution timing markers in the structured atomic operation instruction set together serve as the initial data object for triggering the condition verification logic.

[0196] Perform dimensional deconstruction operations on the local parameter subspace coordinate set to map each coordinate component to the precise physical location of the weight tensor inside the model optimization module, so as to ensure that the parameter indexes referenced in the trigger condition judgment process are correct.

[0197] The execution timing markers of the dynamic execution timing in the structured atomic operation instruction set are normalized and mapped to a unified temporal scale of the current training loop iteration step, thereby eliminating the time reference differences between different source data.

[0198] Based on the normalized execution timing marker and the current iteration step, a timing difference calculation formula is constructed to quantify the distance between the iteration step and the activation threshold of each atomic operation unit: Where Δ is the step difference, t is the current iteration step, and τ is the activation threshold step of the atomization operation unit.

[0199] The step difference Δ is compared with the preset tolerance range parameter, and the condition satisfaction judgment operation is performed to select the set of atomic operation units that meet the activation threshold condition in terms of iteration steps.

[0200] The set of atomic operation units that meet the conditions is encapsulated in a queue and arranged in order according to the priority weight of the atomic operations to generate a queue of pending parameter adjustment requests with time-order compliance.

[0201] By using the above processing method, the local parameter subspace positioning results of the previous step are transformed into parameter adjustment request data under strict temporal constraints, so as to achieve precise triggering of local parameter adaptive adjustment in the training loop.

[0202] S7.4: Apply intensity calibration coefficients to each element in the parameter adjustment request queue to be executed, and use an adaptive gradient scaling mechanism to perform nonlinear correction calculations on the original gradient update amount in the local parameter subspace to generate a refined parameter increment matrix that incorporates task-specific semantic constraints, thereby achieving targeted optimization for the current data distribution characteristics.

[0203] For each element in the pending parameter adjustment request queue that meets the timing compliance requirements, the corresponding intensity calibration coefficient is read to form a parameter adjustment intensity mapping table.

[0204] The values ​​in the intensity mapping table are associated with the set of coordinates in the local parameter subspace to establish a mapping matrix between each hyperparameter to be adjusted and its target correction intensity, which is used for subsequent adaptive gradient scaling calculations.

[0205] Based on the mapping matrix, the original gradient update amount extracted from the local parameter subspace is subjected to nonlinear scaling. An adaptive gradient scaling function is used to dynamically adjust the gradient values ​​at different locations to avoid the degradation of generalization performance caused by scaling at a uniform ratio.

[0206] In the adaptive gradient scaling process, the following calculation formula is performed: Where Δp is the parameter increment, g is the original gradient value, α is the base scaling factor, and β is the gradient strength adjustment factor.

[0207] By performing the aforementioned nonlinear scaling operation on the positions of each local parameter, a refined parameter increment matrix with semantic constraints specific to the fusion task is generated, ensuring that the gradient adjustment is highly matched with the current multimodal data distribution characteristics.

[0208] By performing adaptive gradient scaling and nonlinear correction in parallel, the result of the previous step is transformed into a parameter increment matrix for targeted optimization, thereby achieving localized performance improvement for the target task.

[0209] S7.5: Based on the refined parameter increment matrix, perform an in-situ cumulative update operation on the global weight parameters of the model optimization module to complete the localized parameter adaptive adjustment process in this training cycle, and output the model parameter instance carrying the latest optimization state as the initial input for the next round of iteration calculation.

[0210] Using the refined parameter increment matrix as input, the global weight tensor storage structure of the model optimization module is called to execute the in-situ cumulative update instruction to achieve synchronous correction of the weight values.

[0211] For each parameter slice of the global weight tensor, an incremental matrix component matching mechanism with task semantic constraints is adopted to align the value to be updated with the target position at the element level and establish a mapping path from the local parameter subspace to the global weight index.

[0212] Based on the alignment, a gradient update correction operator is introduced to perform arithmetic addition on the original weight values ​​and corresponding increment values ​​at the matching positions, ensuring the accuracy and numerical stability of the accumulation process.

[0213] The internal consistency check logic is triggered by the cumulatively updated weight tensor. By checking the constraints of the global tensor in terms of numerical range, sparsity distribution and gradient direction, potential outliers or update segments that disrupt training stability are eliminated.

[0214] The global weight tensors that pass the consistency check are cached and marked to generate model parameter instances that indicate the latest optimization state. These instances are then used as the initial input for the next training iteration to continue the optimization results of the current round.

[0215] By using an in-situ cumulative update method for global weight parameters, the refined parameter increment matrix from the previous step is transformed into a model parameter instance of the latest optimized state, thereby achieving targeted optimization for the data distribution characteristics of the current multimodal task.

[0216] Step S8: After completing this parameter adaptive adjustment operation, destroy the one-time stateless parameter adjustment script and strategy distiller instance to release computing resources in response to the next round of annotation task change signals. Specifically, this includes: S8.1: Obtain the status flag of the one-time stateless parameter adjustment script that has been executed in the current training loop hook, and unbind the handle of the instruction cache occupied by the one-time stateless parameter adjustment script based on the memory management interface to generate a script memory block identifier to be released.

[0217] S8.2: Using the identifier of the script memory block to be released as the input object, serialize and archive the semantic anchoring layer weight matrix, behavior decoupling layer instance condition triggering dependency graph, and context cache layer performance feedback snapshot data contained in the lightweight policy distiller instance to generate a temporary policy data archive package.

[0218] The input conditions include the identifier of the script memory block to be released output by step S8.1, as well as three types of core data objects inside the lightweight policy distiller instance: the semantic anchoring layer weight matrix, the behavior decoupling layer instance condition triggering dependency graph, and the context cache layer performance feedback snapshot data.

[0219] The instance memory location operation is performed based on the identifier of the script memory block to be released. The corresponding semantic anchoring layer weight matrix memory segment and behavior decoupling layer instance condition triggering dependency graph storage segment are retrieved in the address mapping table of the runtime container. At the same time, the data buffer address where the context cache layer performance feedback snapshot data is located is obtained.

[0220] The located semantic anchoring layer weight matrix is ​​subjected to row and column scanning and formatting processing to convert the original sparse matrix data into dense matrix encoding blocks that are serialized in row priority, ensuring that the consistency of row and column indexes is not compromised during cross-platform archiving.

[0221] By leveraging the behavior decoupling layer instance condition triggering of the directed acyclic graph structure of the dependency graph, performing node topology sorting, and combining the sorting results with edge weight information into an adjacency list serialization representation, a recoverability marker for the logical execution chain is established in the archive file.

[0222] Multidimensional compression encoding is performed on the performance feedback snapshot data of the context cache layer. The data is divided into blocks by timestamp, and the convergence slope mutation point, the oscillation amplitude of the validation set index, and the inflection point of the decrease in label consistency are mapped to the compressed fragments in the form of key-value pairs. Metadata indexes are generated for subsequent fast retrieval.

[0223] The three types of standardized coded archived data are written into the binary buffer in a preset module order, and a unified checksum field and version number field are added to form a temporary policy data archive package with complete metadata.

[0224] By serializing the archive, the results of the previous step are transformed into binary archive files with a recoverable structure and cross-platform compatibility, enabling subsequent data integrity verification and policy state retention before resource reclamation.

[0225] S8.3: Based on the integrity verification result of the temporary policy data archive, send a termination signal to the runtime container process where the lightweight policy distiller instance is located, so as to perform an explicit destruction operation of the lightweight policy distiller instance and generate a resource reclamation confirmation signal.

[0226] S8.4: Receive the resource reclamation confirmation signal, call the system's underlying garbage collection mechanism to forcibly clear the memory space pointed to by the memory block identifier of the script to be released and the storage sector occupied by the temporary policy data archive package, so as to generate a released computing resource pool state.

[0227] S8.5: Based on the released computing resource pool status, reset the task listening port status flag of the model optimization module to complete the system ready state switch and trigger the waiting response mechanism for the next round of labeling task change signals.

[0228] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

[0229] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the elements or objects preceding “comprising” or “including” encompass the elements or objects listed following “comprising” or “including” and their equivalents, and do not exclude other elements or objects. The “multiple” mentioned in the embodiments of this application refers to two or more. A and / or B indicate three possibilities: A; B; and A and B.

[0230] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An iterative optimization method for a knowledge graph-based data annotation model, characterized in that, Specifically, it includes: S1: Obtain the change signal of the multimodal annotation task, extract the multidimensional structured description of the multimodal annotation task, and generate a task query vector; S2: Based on the task query vector, perform multi-hop traversal in the evolvable multi-granularity knowledge graph containing dynamic meta nodes to retrieve a set of high-confidence optimization behavior paths that match the current multimodal annotation task; S3: Map the key concepts in the set of high-confidence optimized behavior paths to low-dimensional policy anchors to construct a semantic anchoring layer; S4: Using the low-dimensional strategy anchor points output by the semantic anchoring layer, the original tuning action is decomposed into atomic operation units and a condition triggering dependency graph is established to construct a behavior decoupling layer instance. S5: Read the performance feedback snapshot data of the historical training process, combine it with the condition-triggered dependency graph to dynamically calibrate the execution timing and intensity of the atomic operation unit, and build a context cache layer; S6: Connect the semantic anchoring layer, behavior decoupling layer instance and context cache layer to form a policy distiller instance, and generate a one-time stateless parameter adjustment script for the current multimodal annotation task based on the lightweight policy distiller instance; S7: Inject the one-time stateless parameter adjustment script into the training loop hook of the model optimization module to perform localized parameter adaptive adjustment operation.

2. The iterative optimization method for a knowledge graph-based data annotation model according to claim 1, characterized in that, Step S4 specifically includes: The low-dimensional policy anchor points output by the semantic anchoring layer are subjected to vector space parsing processing to extract the original policy feature component set; Based on the original strategy feature component set, a discretization mapping algorithm is performed to obtain a standardized atomic operation instruction prototype library; By combining the standardized atomic operation instruction prototype library with the data modality combination features of the current multimodal annotation task, a logical rule matching operation is performed to obtain a list of valid atomic operation units; A causal dependency deduction process is performed on each element in the list of effective atomic operation units. By analyzing the temporal mutual exclusion and numerical superposition effect between different adjustment actions, a condition triggering dependency graph is constructed. Based on the condition-triggered dependency graph, the list of effective atomic operation units is topologically sorted and state-encapsulated to generate the behavior decoupling layer instance.

3. The iterative optimization method for a knowledge graph-based data annotation model according to claim 2, characterized in that, The original strategy feature component set includes: cross-domain migration annotation strength and fine-grained attribute coupling density.

4. The iterative optimization method for a knowledge graph-based data annotation model according to claim 2, characterized in that, Each instruction prototype in the standardized atomic operation instruction prototype library defines a specific hyperparameter adjustment action, as well as the parameter adjustment range and constraints of that action.

5. The iterative optimization method for a knowledge graph-based data annotation model according to claim 4, characterized in that, The hyperparameter adjustment actions include: learning rate decay and gradient clipping.

6. The iterative optimization method for a knowledge graph-based data annotation model according to claim 1, characterized in that, Step S5 specifically includes: The raw performance log stream of the historical training process is obtained, and the raw performance log stream is segmented using a time window sliding truncation algorithm to extract the initial performance feedback sequence containing the convergence slope mutation point, the oscillation amplitude of the validation set index, and the inflection point of the decrease in label consistency. Based on the initial performance feedback sequence, a multi-dimensional feature alignment mechanism is used to standardize and map the heterogeneous data in the initial performance feedback sequence to generate performance feedback snapshot data. Read the performance feedback snapshot data and the conditional triggering dependency graph, and use a graph traversal retrieval algorithm to locate the set of candidate atomic operation units in the conditional triggering dependency graph that match the abrupt change point of the convergence slope in the performance feedback snapshot data; Based on the oscillation amplitude of the validation set indicators in the candidate atomic operation unit set and the performance feedback snapshot data, the dynamic weight calculation logic is executed to quantify and correct the execution intensity coefficient of each unit in the candidate atomic operation unit set, and outputs a list of atomic operation units to be calibrated. Using the list of atomic operation units to be calibrated and the inflection point of the decrease in annotation consistency in the performance feedback snapshot data, the timing offset inference algorithm is executed to dynamically replan the execution trigger time of each unit in the list of atomic operation units to be calibrated, and the context cache layer is constructed.

7. The iterative optimization method for a knowledge graph-based data annotation model according to claim 6, characterized in that, Step S6 specifically includes: Based on the set of low-dimensional policy anchors, the condition triggering dependency graph, and the performance feedback snapshot data, the three-layer micro-network architecture is dynamically instantiated to construct a policy distiller instance. Using the semantic anchoring layer in the policy distiller instance, the set of input low-dimensional policy anchors is subjected to feature weighted fusion processing to generate an enhanced policy feature vector carrying the semantic constraints of the current multimodal annotation task; Based on the enhanced policy feature vector and condition triggering dependency graph, a logic gating mapping operation is performed in the behavior decoupling layer instance to output a standardized atomic operation instruction sequence with dynamic execution timing markers; By combining the standardized atomic operation instruction sequence with the convergence slope mutation points and validation set index oscillation amplitudes in the performance feedback snapshot data, an intensity calibration algorithm is executed in the context cache layer to generate a parameter adjustment instruction set. The parameter adjustment instruction set is serialized, encapsulated, and stripped of its state to generate a one-time stateless parameter adjustment script that contains only the logic specific to the current multimodal annotation task and is destroyed after execution.

8. The iterative optimization method for a knowledge graph-based data annotation model according to claim 1, characterized in that, After step S7, the following is included: S8: After completing this parameter adaptive adjustment operation, destroy the one-time stateless parameter adjustment script and the strategy distiller instance to release computing resources.

9. The iterative optimization method for a knowledge graph-based data annotation model according to claim 1, characterized in that, The task query vector is generated based on a description of at least one of the following dimensions of the task: the modal composition, semantic complexity, and tag hierarchy depth of the multimodal annotation task.

10. The iterative optimization method for a knowledge graph-based data annotation model according to claim 1, characterized in that, The process of reading performance feedback snapshot data from the historical training process includes reading performance feedback snapshot data from the two most recent training cycles.

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