AI fool-proof algorithm and optimization method and system of digital model

Abstract

The application relates to the technical field of data processing, and provides an AI fool-proof algorithm and a digital model optimization method and system, which comprises the following steps: after collecting multi-modal process data, time alignment and event segmentation are performed to obtain a standard behavior sequence and an abnormal behavior sequence; a preset deep learning behavior prediction model is input according to the standard behavior sequence to obtain a behavior prediction result; structure matching is performed on the behavior prediction result and the abnormal behavior sequence to obtain an optimization instruction set and semantic prompt information; the preset deep learning behavior prediction model is optimized to obtain an optimized fool-proof intelligent prediction model. Through optimization of the original deep learning behavior prediction model, the self-adaptive recognition ability and the risk avoidance ability of the model to abnormal behaviors are improved, and the problem that in a complex actual application environment, high-frequency operation environment changes cannot be flexibly handled, resulting in that part of high-risk behaviors cannot be timely recognized or corrected, and the accuracy is low and the adaptability is poor is solved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to methods and systems for optimizing AI error-proof algorithms and digital models. Background Technology

[0002] More and more industries are leveraging AI algorithms and digital models to improve efficiency, optimize decision-making processes, and predict behavioral trends. However, in many industries, especially manufacturing, finance, and healthcare, abnormal behaviors in operational processes still occur frequently, posing risks to production efficiency and safety. These "mistake-proofing" problems often arise from human operator error or uncertainties in the external environment. Therefore, researching and adopting AI-based mistake-proofing algorithms, combined with efficient digital model optimization methods, is a key technology for improving automation and reducing human error.

[0003] Among the relevant technical means, the current mainstream error prevention algorithms use traditional supervised or unsupervised learning methods to label, cluster, and classify historical data. For the detection of abnormal behavior, existing technologies mostly use rule-based judgment methods or clustering algorithms, which can effectively identify abnormal behavior and issue alarms in most cases, thereby reducing human error.

[0004] Regarding the above-mentioned technical solutions, although existing technologies can effectively identify and warn of abnormal behavior in some standard scenarios through clustering, rule-based judgment, and other means, in complex real-world application environments, existing error-proofing algorithms are unable to cope with complex data with diverse data types, high dimensionality, and temporal sequence, and cannot flexibly handle high-frequency changes in the operating environment. This results in some high-risk behaviors not being identified or corrected in a timely manner, and there are problems of low accuracy and poor adaptability. Summary of the Invention

[0005] To address the issues of low accuracy and poor adaptability in complex real-world applications where high-frequency changes in the operating environment cannot be flexibly handled, resulting in the failure to promptly identify or correct some high-risk behaviors, this application provides an optimization method and system for AI error-proof algorithms and digital models.

[0006] This invention provides an optimization method for AI error prevention algorithms and digital models, comprising: collecting multimodal process data; performing time alignment and event segmentation on the multimodal process data to obtain standard behavior sequences and abnormal behavior sequences; inputting the standard behavior sequences into a preset deep learning behavior prediction model to obtain behavior prediction results; performing structural matching on the behavior prediction results and the abnormal behavior sequences to obtain fused feature data; performing high-dimensional clustering and bias correction on the fused feature data to obtain corrected clustering results and boundary residual information; generating an error prevention judgment rule set based on the corrected clustering results; using the error prevention judgment rule set to map and match the boundary residual information to obtain risk labels and optimization suggestions; mapping the risk labels to the corresponding behavior segments of the fused feature data to generate convergent optimization feature representations; using the convergent optimization feature representations to perform association annotation and behavior semantic mapping on the optimization suggestions to obtain an optimization instruction set and semantic prompt information; and using the optimization instruction set and the semantic prompt information to optimize the preset deep learning behavior prediction model to obtain an optimized error prevention intelligent prediction model.

[0007] As a preferred embodiment, the steps of collecting multimodal process data, performing time alignment and event segmentation on the multimodal process data, and obtaining standard behavior sequences and abnormal behavior sequences include: synchronously collecting action signals, environmental parameters, and user interaction logs during the operation process through a multi-channel sensing device to construct multimodal process data; performing time alignment and event segmentation on the multimodal process data to obtain event segment sequences and context window information; inputting the event segment sequences into a preset graph convolutional network model to extract spatial correlation features and temporal relationship features of the operation actions; performing abnormal pattern discrimination on the context window information to obtain an abnormal score matrix and behavior credibility vector; fusing the behavior credibility vector with the temporal relationship features to generate a standard behavior sequence; and jointly analyzing the abnormal score matrix with the spatial correlation features, and constructing an abnormal behavior sequence based on the analysis results.

[0008] As a preferred embodiment, the step of inputting the standard behavior sequence into a preset deep learning behavior prediction model to obtain behavior prediction results, and performing structural matching between the behavior prediction results and the abnormal behavior sequence to obtain fused feature data includes: performing sequence embedding encoding on the standard behavior sequence to obtain an encoded standard behavior sequence; inputting the encoded standard behavior sequence into a deep learning behavior prediction model based on a Transformer structure to output multi-dimensional behavior prediction results; performing hierarchical attention analysis on the behavior prediction results to extract key behavior nodes; constructing a structural difference vector using the key behavior nodes and the abnormal behavior sequence, and extracting trigger offset features based on the structural difference vector; performing residual feedback between the structural difference vector and the intermediate features of the deep learning behavior prediction model to obtain a feature compensation vector and an error inverse map; constructing a behavior error fusion feature tensor based on the trigger offset features and the feature compensation vector; and performing tensor-level fusion of the behavior error fusion feature tensor and the error inverse map to obtain fused feature data.

[0009] As a preferred embodiment, the step of performing hierarchical attention analysis on the behavior prediction results to extract key behavior nodes, and constructing a structural difference vector using the key behavior nodes and the abnormal behavior sequence includes: weighting the outputs of each layer of the behavior prediction results through a hierarchical attention mechanism; extracting key behavior nodes based on the weighting results; calculating the similarity between the key behavior nodes using a dynamic time warping algorithm; identifying the deviation portion in the abnormal behavior sequence; and constructing a structural difference vector using the similarity and the deviation portion.

[0010] As a preferred embodiment, the steps of performing high-dimensional clustering and bias correction on the fused feature data to obtain corrected clustering results and boundary residual information, generating a set of error-proofing judgment rules based on the corrected clustering results, and mapping and matching the boundary residual information using the set of error-proofing judgment rules to obtain risk labels and optimization suggestions include: performing dimensionality reduction preprocessing on the fused feature data according to principal component analysis to obtain dimensionality-reduced features; performing high-dimensional clustering on the dimensionality-reduced features to obtain clustering results and a behavior clustering mapping map; performing bias modeling on the clustering results and the behavior clustering mapping map to calculate behavior offset; dynamically adjusting the boundary conditions in the clustering results according to the behavior offset to correct the behavior category division range, obtaining corrected clustering results and boundary residual information; constructing a set of error-proofing judgment rules based on the corrected clustering results; mapping and matching the boundary residual information with the set of error-proofing judgment rules, and extracting rule conflict groups and risk level factors based on the matching results; comprehensively analyzing the historical frequency weights of the risk level factors and the rule conflict groups, and generating corresponding risk labels and optimization suggestions based on the analysis results.

[0011] As a preferred embodiment, the step of mapping the risk label to the corresponding behavioral segment of the fused feature data to generate a convergent optimization feature representation, and using the convergent optimization feature representation to perform association annotation and behavioral semantic mapping on the optimization suggestions to obtain an optimization instruction set and semantic prompt information, includes: mapping the risk label to the behavioral segment corresponding to the fused feature data to extract high-risk behavioral region features; using a convolutional coding network to perform contextual modeling and semantic normalization on the high-risk behavioral region features to generate a candidate optimization parameter set; and weighting the candidate optimization parameter set with the original fused features to construct a corrected input tensor. The corrected input tensor undergoes multiple rounds of residual regression and error inversion processing to generate a convergent optimization feature representation. The original fused features refer to a high-dimensional feature set resulting from the fusion of user interaction data, sensor data, and historical behavior data. The convergent optimization feature representation and the optimization suggestions are associated, labeled, and semantically mapped to construct an optimization instruction set. The task objectives in the optimization instruction set are transformed into prompt statement templates, which are then combined with the user behavior context to generate semantic prompt information. The user behavior context refers to the timestamp of the user's current operation, the operation sequence, the previous state, and environmental conditions.

[0012] As a preferred embodiment, the step of optimizing the preset deep learning behavior prediction model using the optimized instruction set and the semantic prompt information to obtain the optimized error-proof intelligent prediction model includes: using the semantic prompt information to perform label reinforcement on the intermediate layer behavior representation of the preset deep learning behavior prediction model to obtain the reinforced deep learning behavior prediction model; performing modular decomposition on the optimized instruction set, constructing a corresponding parameter tuning path based on the decomposition result, and using the parameter tuning path to optimize the training hyperparameters, activation strategies, and inter-layer connection methods of the reinforced deep learning behavior prediction model to obtain the optimized error-proof intelligent prediction model.

[0013] This application also provides an optimization system for AI error prevention algorithms and digital models, comprising: a data acquisition module for acquiring multimodal process data, performing time alignment and event segmentation on the multimodal process data to obtain standard behavior sequences and abnormal behavior sequences; a matching module for inputting the standard behavior sequences into a preset deep learning behavior prediction model to obtain behavior prediction results, performing structural matching on the behavior prediction results and the abnormal behavior sequences to obtain fused feature data; and a correction module for performing high-dimensional clustering and bias correction on the fused feature data to obtain corrected clustering results and boundary residual information, based on the correction... Positive clustering results generate a set of error-prevention judgment rules. These rules are then used to map and match the boundary residual information to obtain risk labels and optimization suggestions. A mapping module maps the risk labels to corresponding behavioral segments of the fused feature data to generate convergent optimization feature representations. These representations are then used to associate and label the optimization suggestions with behavioral semantics, resulting in an optimization instruction set and semantic prompts. An optimization module uses these instruction sets and semantic prompts to optimize the preset deep learning behavior prediction model, resulting in an optimized error-prevention intelligent prediction model.

[0014] Compared with the prior art, this application has the following advantages: high accuracy and strong adaptability. By synchronously acquiring multimodal process data through multiple channels and performing time alignment and event segmentation, a comprehensive perception of user behavior and operational scenarios is ensured. Standard behavior sequences are input into a pre-defined deep learning behavior prediction model. Combined with hierarchical attention analysis, dynamic time warping algorithms, and structural matching of abnormal behavior sequences, structural difference vectors and trigger offset features are effectively identified and extracted. High-dimensional clustering and boundary residual information correction techniques are used to construct a set of error-proofing rules and output accurate risk labels and personalized optimization suggestions. Based on the mapping relationship between risk labels and fused feature data, high-risk behavior region features are extracted and convergent optimized feature representations are generated. An optimization instruction set and semantic prompts are generated through a semantic understanding mechanism, achieving a closed-loop connection between behavior control and model feedback. The original deep learning behavior prediction model is optimized by combining semantic prompts and the optimization instruction set, improving the model's adaptive recognition and risk avoidance capabilities for abnormal behaviors. This results in an optimized error-proofing intelligent prediction model, addressing the issues of low accuracy and poor adaptability in complex real-world application environments where the model cannot flexibly handle high-frequency changes in the operational environment, leading to the failure to promptly identify or correct some high-risk behaviors. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] The structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0017] Figure 1 This is a flowchart illustrating the optimization method of the AI ​​error prevention algorithm and digital model provided in an embodiment of the present invention;

[0018] Figure 2 This is a schematic block diagram of the structure of the AI ​​error prevention algorithm and digital model optimization system provided in the embodiments of the present invention.

[0019] Explanation of reference numerals in the attached figures:

[0020] 10. AI error prevention algorithm and digital model optimization system; 11. Data acquisition module; 12. Matching module; 13. Correction module; 14. Mapping module; 15. Optimization module. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0023] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0024] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0025] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0026] Example 1:

[0027] like Figure 1 As shown, the AI ​​error prevention algorithm and digital model optimization method and system provided in this application embodiment include steps S100 to S300.

[0028] Example 1:

[0029] This application provides an optimization method for AI error prevention algorithms and digital models, specifically including the following steps:

[0030] Step S100: Collect multimodal process data, perform time alignment and event segmentation on the multimodal process data, and obtain standard behavior sequences and abnormal behavior sequences.

[0031] In this step, multi-channel sensing devices, including visual cameras, motion capture sensors, voice recognition modules, and environmental monitoring sensors, are deployed in the operating environment to collect multimodal process data in real time. This data covers various aspects of information such as action signals, voice commands, environmental conditions (such as temperature and humidity), and equipment response status during the operation. Specifically, the multimodal process data is time-aligned, and the data is segmented into event segments using sliding window technology. Then, the event segments are further divided according to preset rules to extract standard behavior sequences and abnormal behavior sequences. The standard behavior sequences represent normal operating procedures, while the abnormal behavior sequences represent abnormal operations that do not conform to the standard behavior.

[0032] For example, on a production line, when operators perform tasks, their hand movements (via motion sensors), voice commands (via voice recognition), and environmental data (via temperature and humidity sensors) are collected. This information is synchronized using a time alignment algorithm and divided into several 10-second event segments. From these segments, normal operating procedures are identified to construct a standard behavior sequence, while abnormal operations that deviate from the standard procedure are identified to form an abnormal behavior sequence.

[0033] Step S200: Input the standard behavior sequence into the preset deep learning behavior prediction model to obtain the behavior prediction result. Perform structural matching between the behavior prediction result and the abnormal behavior sequence to obtain fused feature data.

[0034] In this step, a standard behavior sequence is input into a pre-defined deep learning-based behavior prediction model, specifically a deep learning behavior prediction model based on a Transformer structure, to obtain behavior prediction results. Specifically, the behavior prediction results include future behavior patterns predicted based on the standard behavior sequence. The model uses a hierarchical attention mechanism to weight key behavior nodes in the standard behavior sequence, then performs structural matching with abnormal behavior sequences to extract structural differences and behavior offset information. Finally, these features are fused into fused feature data.

[0035] For example, assuming the standard behavior sequence is "Operation A—Operation B—Operation C", the deep learning model will predict "Operation A'—Operation B'—Operation C'". If the abnormal behavior sequence is "Operation A—Operation X—Operation C", the inconsistency between "Operation B" and "Operation X" is detected through structure matching, and this information is fused with the behavior offset features to obtain fused feature data.

[0036] Step S300: Perform high-dimensional clustering and bias correction on the fused feature data to obtain corrected clustering results and boundary residual information. Generate a set of error prevention judgment rules based on the corrected clustering results. Use the set of error prevention judgment rules to map and match the boundary residual information to obtain risk labels and optimization suggestions.

[0037] In this step, principal component analysis (PCA) is used to reduce the dimensionality of the fused feature data, preserving the main anomalous feature subspaces to obtain the dimensionality-reduced feature data. Specifically, a density-based adaptive clustering algorithm (such as DBSCAN) is used to perform high-dimensional clustering on the dimensionality-reduced feature data, thereby obtaining preliminary clustering results and behavior clustering maps. Next, the boundary offset between behavior categories and clustering results is calculated through bias modeling, and the clustering results are dynamically adjusted based on the boundary residual information to generate corrected clustering results. Based on the corrected results, a set of error-proofing judgment rules is constructed, and this set of rules is used to map and match the boundary residual information to obtain risk labels and optimization suggestions.

[0038] For example, suppose that after dimensionality reduction, the behavioral features are represented as 10-dimensional data. After clustering using DBSCAN, multiple behavioral categories are obtained. During the clustering process, some overlapping areas are found between certain behavioral categories. By adjusting the boundaries through bias modeling, and generating mistake-proofing rules based on these adjustments, such as "operation sequence that does not comply with regulations should be marked as high risk," a corresponding risk label is generated, and an optimization suggestion of "adjusting the operation sequence" is given.

[0039] Step S400: Map the risk labels to the corresponding behavioral fragments of the fused feature data to generate convergent optimization feature representations. Use the convergent optimization feature representations to perform association annotation and behavioral semantic mapping on the optimization suggestions to obtain optimization instruction sets and semantic prompts.

[0040] In this step, risk labels are mapped to corresponding behavioral segments in the fused feature data, identifying high-risk behavioral regions. Specifically, a convolutional coding network is used to perform contextual modeling on the high-risk behavioral regions, generating a set of candidate optimization parameters. Next, this set of optimization parameters is weighted and combined with the original fused feature data to construct a corrected input tensor. Multiple rounds of residual regression and error inversion are then performed to obtain a converged optimization feature representation. Finally, a semantic understanding module associates and annotates this optimization feature representation with optimization suggestions, performing behavioral semantic mapping to extract action targets, triggering conditions, and risk avoidance paths, resulting in an optimization instruction set and semantic prompts.

[0041] For example, when it detects that "Operation B" is executed too quickly, a risk label "Operation sequence too fast" is generated. The convolutional coding network analyzes the context of this segment to derive optimization parameters for "increasing operation delay," which are then processed by regression to obtain a convergent optimization feature representation. Through the semantic understanding module, an optimization instruction is generated: "Please increase the completion time of Operation A," and a semantic prompt message "Please check the task sequence" is displayed on the user interface.

[0042] Step S500: Optimize the preset deep learning behavior prediction model using the optimized instruction set and semantic prompt information to obtain the optimized foolproof intelligent prediction model.

[0043] In this step, semantic prompts are used to enhance the labels of intermediate layer features in the deep learning behavior prediction model, and the model's learning objective is adjusted according to the optimization instruction set. Specifically, the optimization instruction set is modularly decomposed to extract optimization factors such as training hyperparameters, activation strategies, and inter-layer connection methods, thus constructing an optimization path. Through this optimization path, the deep learning behavior prediction model is optimized, outputting an optimized error-proof intelligent prediction model.

[0044] For example, by optimizing the instruction set to adjust the weights of the "Transformer Layer 3 attention head" and reducing the training learning rate (e.g., from 0.001 to 0.0005), the model's sensitivity to anomalous behavior was further enhanced. After training, the optimized model improved the prediction accuracy of anomalous behavior in complex environments by 15%, successfully reducing false alarms.

[0045] In this embodiment, multimodal process data is synchronously collected using a multi-channel sensing device, covering action signals, environmental parameters, and user interaction logs during the operation. Next, the multimodal process data is time-aligned and segmented into events, extracting standard and abnormal behavior sequences. The standard behavior sequences are input into a preset deep learning behavior prediction model, which generates behavior prediction results. Then, the behavior prediction results are structurally matched with the abnormal behavior sequences to obtain fused feature data. Next, high-dimensional clustering and bias correction are performed on the fused feature data to obtain corrected clustering results and boundary residual information. A set of error-proofing rules is generated based on the corrected clustering results. The error-proofing rules are used to map and match the boundary residual information to obtain risk labels and optimization suggestions. Subsequently, the risk labels are mapped to the corresponding behavior segments in the fused feature data, generating a convergent optimized feature representation. Based on the convergent optimized feature representation, the optimization suggestions are associated with and semantically mapped to obtain an optimized instruction set and semantic prompts. Finally, the optimized instruction set and semantic prompts are used to optimize the preset deep learning behavior prediction model, resulting in an optimized error-proofing intelligent prediction model.

[0046] By efficiently collecting and analyzing multimodal process data, this invention achieves accurate identification and anomaly detection of complex behavioral patterns. Building upon existing error-proofing algorithms, it integrates behavioral prediction results with abnormal behavior sequences through deep learning and high-dimensional data processing techniques, effectively improving prediction accuracy. Through high-dimensional clustering and bias correction, this invention can accurately identify and correct abnormal behaviors and generate error-proofing judgment rule sets, greatly enhancing its ability to cope with changing environments. This addresses the issues of low accuracy and poor adaptability in complex real-world applications where the inability to flexibly handle frequent changes in the operating environment leads to the failure to identify or correct some high-risk behaviors in a timely manner. Furthermore, optimizing the generation method of instruction sets and semantic prompts not only provides timely feedback and correction of potential risks but also effectively improves the accuracy and safety of user operations, optimizing the overall performance of the error-proofing intelligent prediction model and ensuring a more stable and secure operating process.

[0047] Example 2:

[0048] Step S100 involves collecting multimodal process data, performing time alignment and event segmentation on the multimodal process data, and obtaining standard behavior sequences and abnormal behavior sequences. Specifically, this includes:

[0049] Multi-channel sensing devices are used to synchronously collect action signals, environmental parameters, and user interaction logs during the operation process to construct multi-modal process data. The multi-modal process data is then time-aligned and segmented into events to obtain event fragment sequences and context window information.

[0050] Multimodal process data is constructed by deploying multi-channel sensors in the operating environment to collect data from heterogeneous signal sources. Specifically, the multi-channel sensors include: an attitude capture device (for acquiring motion signals), an environmental monitoring sensor (for acquiring environmental parameters such as temperature, humidity, and illuminance), an operator console log interface (for recording user interaction behavior and device responses), and a voice recognition module (for capturing voice control commands). The multimodal process data consists of a joint behavioral data stream formed by arranging the above sensor data according to timestamps.

[0051] Time alignment uses a unified time base (such as millisecond-level operation timestamps) to synchronize all signals. A sliding window algorithm is used to divide continuous multimodal process data into a sequence of event segments of fixed duration (such as 10 seconds). In addition, combined with the context feature extraction module before and after the event, a time window (such as ±5 seconds) is introduced on both sides of each segment to extract context window information for causal modeling and state determination in subsequent behavior judgment.

[0052] For example, on an automotive assembly line, a worker issues a "lock" command via voice while simultaneously performing manual operations to secure the part. The voice recognition module captures the "lock" command, posture capture obtains the hand's grip and rotation angles, environmental sensors record the current temperature and illuminance, and the operation log records the equipment's response status. This information is synchronously integrated into a multimodal process data record and segmented into a sequence of event segments called "tightening bolts," with context window information such as "previous operation was part positioning," "current temperature is 28℃," and "lighting is normal."

[0053] The event fragment sequence is input into a pre-defined graph convolutional network model to extract spatial correlation features and temporal relationship features of the operation actions. Anomaly pattern discrimination is performed on the context window information, and comparative learning is carried out in combination with historical label samples to obtain anomaly score matrix and behavior credibility vector.

[0054] By constructing a graph-based behavioral node network, event fragment sequences are mapped to a graph data structure and input into a Graph Convolutional Network (GCN) model for spatial and temporal feature extraction. Specifically, each event fragment is modeled as a node in the graph, and the collaborative relationships and sequence of different operational events are established through edges. The graph convolutional network employs a multi-layer aggregation structure to perform aggregation calculations on each node and its neighbors, obtaining the spatial dependencies and temporal behavioral representations of each fragment.

[0055] Context window information is incorporated into node attributes as auxiliary features to enhance the ability to determine the current state of the behavior. Subsequently, the extracted features are compared with historically labeled normal and abnormal behavior sequences, using contrastive loss functions such as Triplet Loss or NT-Xent to train the feature discrimination ability. The output includes: an anomaly score matrix representing the degree of anomalousness of each segment (higher scores indicate greater anomalousness), and a behavior confidence vector composed of corresponding confidence scores (representing the confidence level that the current behavior is "standard behavior").

[0056] For example, in a certain process, the normal operating procedure is "grab—position—tighten". The graph model constructs a graph of these three operations into directed edges. After inputting into the GCN, node 1 (grab) and node 2 (position) are strongly connected. If the current data shows a "grab—tighten" skipping step, the GCN detects that this sequence violates the normal structure. Contextual information such as "missing equipment response log" is encoded into the node attributes to further enhance the recognition effect. This abnormal sequence is assigned an anomalous score of 0.87 (between 0 and 1), and the behavior confidence vector is [0.3, 0.1, 0.9], indicating that the confidence of the first two steps is low, and the confidence of the tightening operation is high.

[0057] By fusing behavioral confidence vectors with temporal relationship features, standard behavioral sequences are generated through confidence reconstruction.

[0058] By fusing temporal relationship features extracted from graph convolutional networks with behavioral confidence vectors, behavioral paths are reconstructed to generate more accurate standard behavioral sequences. Specifically, based on the confident operation nodes represented by the behavioral confidence vectors, high-confidence event segments are retained, while low-confidence segments are discarded or patched. The fused behavioral paths are then optimized through temporal alignment and sequence rearrangement to form coherent and representative standard paths.

[0059] This process is equivalent to extracting reliable subgraphs from the original graph structure and then outputting them as a sequence according to the order of behavior execution, forming a standard behavior sequence. This sequence is used for subsequent comparative analysis with abnormal behavior sequences, model training, and the construction of error prevention rules.

[0060] For example, for the behavioral sequence "grab—locate—tighten—place", if the confidence level of "locate" is determined to be 0.2 and the rest are all above 0.8, then the "locate" step can be removed, and the reconstructed standard behavioral sequence can be formed by "grab—tighten—place". If the confidence level is low but the structure should be retained, representative samples obtained by matching historical frequencies can be inserted as replacements to ensure the integrity and rationality of the sequence.

[0061] By jointly analyzing the anomaly score matrix and spatial correlation features, fragments matching the anomaly pattern and their behavioral labels are selected, and anomaly behavior sequences are constructed based on the analysis results.

[0062] By jointly calculating the aforementioned anomaly score matrix with the spatial association features extracted by GCN, event segments that deviate from the normal path in spatial structure and have high scores are identified. Specifically, a threshold (such as anomaly score > 0.7) is set as the initial screening condition, and then the topological variations in the behavior graph are used to identify node structural anomalies, such as skipping steps, loops, and missing nodes.

[0063] Simultaneously, by combining historical tag samples from the abnormal behavior database, the current abnormal fragments are tag-matched and behavior tags are automatically assigned (such as "operation sequence error", "device not responding", "unauthorized operation", etc.). The selected fragments are arranged in the original time order to form an abnormal behavior sequence with semantic information.

[0064] For example, during robot operation, a sequence "pick up object—move—place object—pick up object" was detected. GCN spatial analysis revealed that "pick up object" occurred repeatedly without any intermediate task, thus classifying it as an anomaly. Based on the anomaly score matrix, this "repeated picking up object" behavior scored 0.91, exceeding the set threshold of 0.7. Matching it to the anomaly tag library, it was marked as "repeated execution," and this sequence was constructed as an anomalous behavior sequence for subsequent behavior model analysis and error-prevention strategy learning.

[0065] In step S200, the standard behavior sequence is input into a preset deep learning behavior prediction model to obtain the behavior prediction result. The behavior prediction result and the abnormal behavior sequence are then structurally matched to obtain fused feature data. This step specifically includes:

[0066] The standard behavior sequence is sequence embedded and encoded to obtain the encoded standard behavior sequence. The encoded standard behavior sequence is then input into a deep learning behavior prediction model based on the Transformer structure to output multi-dimensional behavior prediction results.

[0067] By performing sequence embedding encoding on standard behavior sequences to fully extract their spatial and temporal features, the embedding results are input into a pre-defined deep learning behavior prediction model based on the Transformer architecture. The model outputs multi-dimensional behavior prediction results. Specifically, an independent vector representation is generated for each behavior node in the standard behavior sequence. The attributes of the behavior nodes (such as duration, device response status, and environmental context) are compressed into a fixed-dimensional continuous vector through embedding. The embedding sequences corresponding to multiple nodes are then input into the embedding layer of the Transformer model. The Transformer model utilizes a multi-head self-attention mechanism to encode the node sequences, extracting node-level features (temporal dependencies and state associations of individual behavior nodes) and sequence-level features (the overall temporal evolution trend of the entire behavior sequence). The output layer of the model yields multi-dimensional behavior prediction results containing temporal, spatial, and behavioral feature dimensions.

[0068] For example, suppose the standard action sequence is "Operation A—Operation B—Operation C", where each operation node contains corresponding attributes such as "duration: 5 seconds", "device status: successful response", and "environment: temperature 35℃". The embedding encoding process generates a vector for each node, such as "Operation A: [0.2, 0.5, 0.3]", forming the sequence input Transformer. The model's multi-head attention mechanism detects that "Operation A and Operation C have a high correlation" and "abnormal device status occurs in Operation B", and outputs multi-dimensional prediction results, including the predicted next operation node, time span, and device response duration, providing a basis for further analysis.

[0069] Hierarchical attention analysis is performed on the behavior prediction results to extract key behavior nodes. A structural difference vector is constructed using key behavior nodes and abnormal behavior sequences. Trigger offset features are extracted based on the structural difference vector. By comparing and analyzing the changes in behavior patterns under different scenarios, key offset features that can trigger abnormal behavior are obtained and defined as trigger offset features.

[0070] By performing hierarchical attention analysis on the outputs of each layer of the behavior prediction results, the importance order of behavior nodes is calculated to extract key behavior nodes. Then, a structure matching algorithm is used to compare and analyze the extracted key behavior nodes with abnormal behavior sequences, and this structure matching is used to generate a structure difference vector. Specifically, a multi-layer attention weight reduction mechanism is set (such as doubling the encoding weights of the last two layers), and the temporal deviation difference between key behavior nodes and abnormal behavior nodes is quantified by the Dynamic Time Warping (DTW) algorithm to extract the temporal offset features of behavior. Then, by statistically analyzing and comparing the frequency of associated behavior sequences in different scenarios, the contribution of behavior offset to risk triggering is further analyzed to obtain key behavior variables defined as trigger offset features.

[0071] For example, given the predicted sequence "Operation A—Operation B—Operation C" and the abnormal behavior sequence "Operation A—Operation X—Operation C", the key behavior node corresponding to "Operation B" is identified. A hierarchical attention mechanism is used to calculate "Operation B weight = 0.9" and "Operation X weight = 0.2", determining that the deviation behavior of Operation X has a high risk of triggering. Combined with context frequency analysis, it is found that "Operation X often occurs in emergency stop environments". Therefore, "performing skipping actions before the action is completed" is defined as a trigger offset feature for subsequent error-proofing rule optimization.

[0072] By performing residual feedback between the structural difference vector and the intermediate features of the deep learning behavior prediction model, a feature compensation vector and an error inverse map are obtained.

[0073] By inputting the structural difference vector into the intermediate layer of the deep learning behavior prediction model and comparing it with the model's multi-head attention output through residual feedback, specifically, during the prediction of each behavior node, negative gradient correction is applied to the feature output of low-weight nodes (belonging to anomalous behaviors), making the dominant role of low-confidence anomalous behaviors in the model features more significant. Simultaneously, using the residual mechanism, an error inverse map is generated by calculating the magnitude of the difference vector and the partial derivative of the model's activation function. The purpose of the error inverse map is to apply "negative correlation weights" to low-impact nodes, using a compensation mechanism to retrain the model, making the behavior prediction more consistent with actual anomalous patterns, and generating feature compensation vectors for model optimization.

[0074] For example, if "Operation B" is determined to be a critical node but its predicted weight is too low at 0.4, the residual feedback mechanism calculates the feature deviation value of this node, and sets the function f(x) = αx + β to correct the weight (where α represents the model correction coefficient and β represents the compensation offset value), PB [compensated weight = 0.8]. The error backpropagation plot shows that the model has low sensitivity to the triggering mechanism of "Operation X", and the activation partial derivative value needs to be added as a correction path.

[0075] A behavior error fusion feature tensor is constructed based on the trigger offset feature and feature compensation vector. The behavior error fusion feature tensor is then fused with the error inverse map at the tensor level to obtain fused feature data.

[0076] By utilizing the obtained trigger offset features to perform weighted adjustments on the feature compensation vector at the feature layer, a behavior error fusion feature tensor is formed. This tensor is then progressively fused with the error inverse map at the tensor level. Specifically, the fusion process involves using matrix operations based on tensor multiplication to extract significant information points from the feature compensation vectors corresponding to multiple behavior categories using the trigger offset features. The weight deviations and abnormal positions are then redistributed to generate a stable global behavior feature tensor, i.e., the fused feature data. This process not only uniformly describes the differences between standard behavior sequences and abnormal behavior sequences but also effectively reflects the temporal offset and behavioral anomaly patterns in the entire graph structure feature space.

[0077] For example, for the standard behavior sequence "Operation A - Operation B - Operation C" and the abnormal behavior sequence "Operation A - Operation X - Operation C", the fused feature data will trigger the offset feature to focus on the "time offset value of Operation X" of 5 seconds. By using the behavior error fusion feature tensor operation, the "representative weight of Operation X in the abnormal environment" is enhanced, and its abnormal behavior features are visualized as a risk propagation path through tensor graphs for subsequent optimization module analysis.

[0078] The steps involved in performing hierarchical attention analysis on the behavior prediction results to extract key behavior nodes, and then constructing a structural difference vector using these key behavior nodes and anomalous behavior sequences, specifically include:

[0079] The hierarchical attention mechanism is used to weight the output of each layer of the behavior prediction result. Based on the weighted result, the behavior nodes with high attention are extracted to obtain key behavior nodes. Key behavior nodes are the nodes that best represent specific operation modes and behavior types in the prediction model. The dynamic time warping algorithm is applied to calculate the similarity between key behavior nodes.

[0080] By utilizing a hierarchical attention mechanism to perform saliency analysis on the outputs of each layer of a deep learning behavior prediction model, and dynamically weighting the outputs based on attention weights, highly influential behavior nodes are extracted as key behavior nodes. Specifically, the hierarchical attention mechanism divides the multi-layer outputs of the behavior prediction model into different layers, such as a feature encoding layer, an attention mechanism extraction layer, and a behavior prediction output layer. The output weights of each layer are based on a preset attention evaluation function (such as activation strength or convergence information during model training). Behavior nodes with high weights (e.g., nodes assigned weights > 0.7) are defined as "key behavior nodes," and are considered the nodes in the model that best represent the current operating mode and behavior type.

[0081] Subsequently, Dynamic Time Warping (DTW) is employed to calculate the temporal similarity between key behavioral nodes. This aims to measure the temporal variation of standard and anomalous behavioral sequences and the patterns of behavioral continuity. DTW calculates the time offset of each behavioral node point-by-point and establishes the optimal matching path, thereby obtaining the temporal similarity distribution between behavioral nodes.

[0082] For example, when predicting the standard behavioral sequence "Operation A—Operation B—Operation C", the hierarchical attention mechanism found that the attention weight of "Operation B" was 0.85, thus defining Operation B as a key behavioral node. Subsequently, compared with the anomalous sequence "Operation A—Operation X—Operation C", DTW calculation showed that the matching score between "Operation X" and the key node "Operation B" was 0.68, indicating that "Operation X" had a large time offset and weak sequential correlation, thus it was regarded as a potential anomalous operation and requires further analysis.

[0083] Identify the deviation components in abnormal behavior sequences and construct a structural difference vector using similarity and deviation components.

[0084] By using hierarchical attention extension analysis based on key behavior nodes and combining the temporal similarity calculation results of the dynamic time warping algorithm, abnormal operations in abnormal behavior sequences are screened and accurately identified, and deviation features are extracted to construct a structural difference vector. Specifically, for behavior nodes in abnormal behavior sequences that have significant time offsets, disordered sequences, or semantic inconsistencies with key behavior nodes, the offset value (such as delays, advances, and skips between operations) is first calculated through the time offset detection module, and then the association distance between nodes (such as the disorder of the order between two behaviors) is determined through the sequence matching module. The deviation behavior nodes are feature quantified to generate a structural difference vector containing multi-dimensional features such as time offset, order difference, and semantic deviation.

[0085] The structural difference vector is used to describe the deviations between standard behavior sequences and abnormal behavior sequences in terms of spatial location, temporal connection, and behavioral logic chain, providing information for subsequent trigger feature extraction and error prevention judgment rule generation.

[0086] For example, suppose the standard behavior sequence is "Operation A—Operation B—Operation C", while the abnormal behavior sequence is "Operation A—Operation X—Operation C". The key behavior node "Operation B" is replaced by "Operation X" in the abnormal sequence. The time offset detection module calculates that "Operation X" is delayed by 3 seconds compared to "Operation B", and the sequence analysis module determines that the correlation between "Operation X" and "Operation A" is only 0.45. Combining this deviation information, a structural difference vector is generated with a time offset of 3 seconds, a sequence matching degree of 0.45, and a semantic difference degree of 0.7, representing the degree of temporal and logical deviation between the abnormal behavior and the standard behavior.

[0087] In step S300, the fused feature data undergoes high-dimensional clustering and bias correction to obtain corrected clustering results and boundary residual information. A set of error-proofing rules is generated based on the corrected clustering results. This set of rules is then used to map and match the boundary residual information to obtain risk labels and optimization suggestions. Specifically, this includes:

[0088] Principal component analysis is used to perform dimensionality reduction preprocessing on the fused feature data, retaining the key abnormal feature subspace to obtain dimensionality-reduced features. A density-based adaptive clustering algorithm is then used to perform high-dimensional clustering on the dimensionality-reduced features to obtain the clustering results and behavioral clustering mapping.

[0089] Principal Component Analysis (PCA), a statistical analysis technique, was used to reduce the dimensionality of the fused feature data to preserve key anomalous feature subspaces. Specifically, the variance of each dimension in the fused feature data was first calculated, and the top N principal components with the highest variance contribution rates (N is determined based on a set threshold, such as a cumulative variance contribution rate of over 95%) were selected to generate the dimensionality-reduced feature representation. The dimensionality-reduced features not only reduce computational complexity but also focus on the salient features of anomalous behavior for subsequent cluster analysis.

[0090] Subsequently, a density-based adaptive clustering algorithm (DBSCAN) performs high-dimensional clustering on the dimensionality-reduced feature data, automatically clustering behavior categories by defining a "density threshold" and a "radius threshold". The clustering results can divide multiple behavior categories, and the features of each category are labeled and mapped to a behavior clustering map, forming a visualization of the behavior distribution in a high-dimensional feature space.

[0091] For example, assuming the fused feature data is a high-dimensional matrix containing 100 features, PCA analysis extracts the top 10 principal components, whose cumulative variance accounts for over 97%. The 10-dimensional data after dimensionality reduction is then input into the DBSCAN algorithm, which automatically clusters the data with a radius threshold of 1.5 and a density threshold of 5. The results show three behavioral categories: "Normal Action A," "Normal Action B," and "Abnormal Action C." Furthermore, the spatial distribution relationship of each category is mapped onto a visualization map, forming a behavioral clustering mapping map, which supports subsequent differential analysis.

[0092] The clustering results are modeled with the behavior clustering mapping to calculate the behavior offset. The boundary conditions in the clustering results are dynamically adjusted based on the behavior offset to correct the range of behavior category division, thus obtaining the corrected clustering results and boundary residual information.

[0093] By designing a deviation modeling mechanism, we analyze boundary samples within behavioral categories and calculate behavioral offsets at category boundaries. Specifically, we perform statistical analysis on samples within each category in the clustering results, calculating the distribution of abnormal feature values ​​(such as feature mean and feature variance) at category boundary points. Simultaneously, we screen out boundary samples in category intersection areas. By calculating the feature offset (behavioral offset) between boundary samples and center samples, we dynamically adjust the category boundary conditions. Adjusting the category boundaries allows overlapping region samples to be reclassified into their corresponding categories, generating more accurate corrected clustering results. We also output boundary residual information to annotate the sample characteristics adjusted by the boundary.

[0094] For example, suppose the original clustering results divide the behavior into "normal action A" and "abnormal action C". In the boundary region, some samples are found to be close to both categories A and C. By calculating the feature offset of the samples in this region—for example, if a sample is 0.7 units away from the center of category A and 0.3 units away from the center of category C—it is determined that it is closer to category A, and thus dynamically reclassified to "normal action A", forming a corrected clustering result. Simultaneously, boundary residual information is generated, recording the adjusted sample feature values ​​and deviations, supporting subsequent rule generation and analysis.

[0095] Based on the modified clustering results, a set of error prevention judgment rules is constructed. The rule content includes behavioral pattern thresholds, temporal conditions, and action logic chains. Boundary residual information is mapped and matched with the set of error prevention judgment rules, and rule conflict groups and risk level factors are extracted based on the matching results.

[0096] By analyzing the corrected clustering results and boundary residual information, a set of error-proofing judgment rules is constructed. Specifically, the error-proofing judgment rule set is based on the boundary conditions of sample categories, feature value ranges, and behavioral temporal characteristics. For example, threshold restrictions on behavioral patterns are set, such as the execution time of an action not exceeding a specified range, or a sequential logical chain is specified (e.g., action A must be executed after action B). Boundary residual information is used to perform item-by-item mapping and matching with the rules to identify whether sample features violate the preset conditions of the rules, extract rule conflict groups (i.e., behavioral items that violate multiple rules), and further define risk level factors (e.g., a weighted score of the frequency of behavior occurrence and the intensity of conflict).

[0097] For example, suppose the rule set for error prevention includes: "(1) Step time ≤ 5 seconds; (2) Action A and Action B must be executed consecutively; (3) Deviation of abnormal features ≤ 20%". The sample characteristics in the corrected clustering results are matched with the rules one by one. It is found that a certain sample does not meet the requirements of both rule 1 and rule 3, forming a rule conflict group. The risk level factor of this sample is calculated as 80 (where 100 is the maximum risk) based on the weight of the number of execution conflicts. This sample is output to the high-risk behavior list for error prevention analysis and optimization suggestion generation.

[0098] The historical frequency weights of risk level factors and rule conflict groups are comprehensively analyzed, and corresponding risk labels and optimization suggestions are generated based on the analysis results.

[0099] By combining the historical records of rule conflict groups and sample risk level factors to comprehensively analyze behavioral characteristics, refined risk labels and optimization suggestions are generated. Specifically, based on the historical frequency weight of rule conflict groups in the database (such as the number of times a certain behavior item violates a certain rule) and the sample risk level factors, risk labels for behaviors (such as "high-risk operation" or "low-risk behavior") are generated, and the content of optimization suggestions (such as increasing time buffer, adjusting the operation sequence, or rectifying the range of abnormal characteristic values) is analyzed.

[0100] For example, for a sample containing the rule conflict groups "step time mismatch" and "logical sequence error," it was found that this behavior item had a frequency of 50 times in the historical records. When assessing the risk weight, combined with the risk level factor of 80, a risk label of "high-risk operation" was generated. In addition, it is recommended to optimize the content by "adjusting the operation time limit to ≤3 seconds and prohibiting operations from crossing logical chain links," so as to prompt users to rectify or adjust their operation methods.

[0101] In step S400, the risk labels are mapped to the corresponding behavioral fragments of the fused feature data to generate convergent optimization feature representations. These convergent optimization feature representations are then used to perform association annotation and behavioral semantic mapping on the optimization suggestions to obtain the optimization instruction set and semantic prompt information. Specifically, this includes:

[0102] Risk labels are mapped to the corresponding behavioral segments of the fused feature data to extract high-risk behavioral region features. Convolutional coding networks are then used to perform contextual modeling and semantic normalization of the high-risk behavioral region features to generate a set of candidate optimization parameters.

[0103] By leveraging the mapping relationship between risk labels and fused feature data, the feature regions of high-risk behavior segments are located, and a contextual semantic model is constructed to understand the causal relationships before and after the risky behavior. Specifically, risk labels are used as indexes, and a graph structure matching method is used to compare the segment sequences in the fused feature data point by point to extract the relevant temporal, spatial, and logical features of the risky behavior segments. A convolutional coding network (CNN) is used to model the identified high-risk behavior regions, extracting the contextual features of the segment behavior, including the time window information before and after the behavior, the associated logic, and the device response status. Subsequently, a semantic normalization module is used to normalize the contextual features, generating a set of candidate optimization parameters for subsequent optimization.

[0104] For example, suppose the detected behavior segment corresponding to the risk label "Operation Timeout" is "Operation A - Operation B," and its device response time is 15 seconds (exceeding the threshold of 10 seconds). CNN network modeling reveals a delay in the voice command before "Operation A," while there is no significant response between "Operation B" and the device status. The associated contextual information is extracted, such as "Operation A voice response time is 5 seconds" and "Operation B device inactivity." After normalization, a set of candidate optimization parameters is generated, such as "adjust the voice response time to 3 seconds" and "set the minimum device activity time to 1 second," for further optimization analysis.

[0105] The candidate optimization parameter set is weighted and combined with the original fusion features to construct a corrected input tensor. The corrected input tensor is then subjected to multiple rounds of residual regression and error inversion to generate a convergent optimization feature representation. The original fusion features refer to the high-dimensional feature set after fusing user interaction data, sensor data, and historical behavior data, which covers information such as behavior patterns, temporal relationships, and environmental context.

[0106] A corrected input tensor is constructed by weighting and combining the normalized candidate optimization parameter set with the original fusion features. Residual regression and error inversion mechanisms are then used to iteratively optimize the corrected input tensor, generating a convergent optimized feature representation. Specifically, the original fusion features include user interaction data (such as mouse clicks and voice commands), sensor data (such as temperature and pressure signals), and historical behavioral data (such as operation paths and time series). These data are aligned by time and embedded in context to form a feature set. The weighted combination process amplifies or reduces the weight of each feature dimension based on the weight values ​​of the optimization parameter set (depending on the priority and historical frequency of risk labels), for example, increasing the weight of risk feature data and decreasing the weight of redundant feature data. Residual regression techniques are used to dynamically adjust the weights from error inversion, ensuring that the convergent optimized feature representation meets the fitting requirements of the behavioral model.

[0107] For example, consider the original fused features including "Duration Data=[5,7,15]", "Device Response Time=[3,6,12]", and the adjusted values ​​of the optimized parameter set: "Duration weight reduction = 0.2" and "Device weight increase = 0.3". After weighted combination, the modified input tensor is constructed as 6.4, 8.1, 12.2. Through error inversion, the weight of device response time is further adjusted to 4 times to obtain feature representations of 6.8, 9.0, 14.8, ensuring that the optimized feature representation can converge quickly and be used for model feedback.

[0108] The convergent optimization feature representation is input into the semantic understanding module, and associated with the optimization suggestions through annotation and behavioral semantic mapping. The action target, triggering condition, and risk avoidance path in the behavioral semantic mapping are extracted to construct an optimization instruction set.

[0109] By leveraging the label enhancement and behavior pattern matching mechanisms of the semantic understanding module, semantic information is extracted from the convergent optimization feature representation. This information is then associated and labeled with optimization suggestions to generate actionable behavioral semantic mappings. Specifically, the semantic understanding module, based on the feature parser, decomposes the convergent optimization feature representation into action goals (such as the behavior's end state), triggering conditions (such as the behavior's start time), and risk avoidance paths (such as alternatives to avoid high-risk nodes). By querying a pre-defined semantic rule database, each goal, condition, and path is annotated with sentences and its logical chain reconstructed to form a set of optimization instructions, ensuring that user-operable behavioral instructions are clearly expressed in real-world scenarios.

[0110] For example, if the convergence optimization feature representation includes the objective "device status recovery", the condition "operation interval time adjusted to 5 seconds", and the path "skip abnormal device", then by combining the semantic rule database, an optimization instruction set can be generated, such as "ensure the device recovers normally before proceeding to the next step", "adjust the operation interval time to more than 5 seconds", and "bypass the abnormal device status node to complete the task", and these instruction sets can be provided to the user interface for operation.

[0111] The task objectives in the optimized instruction set are transformed into prompt statement templates. These prompt statement templates are then combined with the user behavior context to generate semantic prompt information. The user behavior context refers to the timestamp of the user's current operation, the operation sequence, the previous state, and information about environmental conditions, which serve as the contextual basis for model inference.

[0112] By combining user behavior context information with templated processing of the optimization instruction set, task objectives are transformed into concise and readable semantic prompts. Specifically, the user behavior context extraction module collects the timestamps of operations, operation sequences, previous states, and current environmental data, and embeds this information into template sentences of optimization instructions (such as "Action Objective: Complete the current task" and "Current State: Device is idle"), thereby generating semantic prompts with contextual explanation. The prompts not only provide users with the exact content of the optimization instructions but also intuitively present the optimization action path through semantic expression.

[0113] For example, in a certain process, the user's operation timestamp is "12:00:15", the device status is "idle", and the previous status was "data upload completed". Based on the optimization instruction set, prompts are generated such as "Please pause your operation for 5 seconds after 12:00:15 to complete the upload" and "The current device is idle, allowing continued operation; it is recommended to start the next process." These semantic prompts not only guide the user's operation but also provide detailed explanations of the optimization suggestions through context.

[0114] Step S500, which involves optimizing a preset deep learning behavior prediction model using an optimized instruction set and semantic prompts to obtain an optimized error-proof intelligent prediction model, includes:

[0115] The intermediate layer behavior representation of the pre-defined deep learning behavior prediction model is labeled and enhanced using semantic prompts, and the learning target is semantically aligned to obtain the enhanced deep learning behavior prediction model.

[0116] By inputting semantic prompts into the intermediate layers of a pre-defined deep learning behavior prediction model, the labels of behavior representations are strengthened, and semantic alignment is performed during training. Specifically, firstly, intermediate layer feature representations are extracted from the deep learning behavior prediction model. These intermediate layer features contain temporal features and semantic patterns of the operational behavior. Then, semantic prompts are used to label and strengthen these feature representations. By adding labels for high-risk and low-confidence behaviors, the model can pay more attention to and correct these behaviors. In this way, the model not only optimizes standard behaviors but also improves its sensitivity to abnormal or boundary behaviors, thereby enhancing the model's predictive ability.

[0117] During this process, semantic alignment will also be performed on the model's learning objectives, that is, matching the objectives, conditions and semantic elements in the optimization instructions with the model's training objectives, to ensure that the model's output behavior sequence can be better aligned with the semantic hierarchy, thereby improving the model's understanding and execution of task semantics.

[0118] For example, if the semantic prompts in the optimized instruction set include "Please execute operation B immediately after operation A is completed", then semantic alignment can be used to transform this objective into the training objective of the model. This allows the model to pay more attention to the temporal relationship between operation A and operation B during the prediction process, thereby improving the accuracy and timeliness of task completion.

[0119] The optimized instruction set is modularly decomposed, and the corresponding parameter tuning path is constructed based on the decomposition results. The training hyperparameters, activation strategies and inter-layer connection methods of the enhanced deep learning behavior prediction model are optimized using the parameter tuning path to obtain the optimized foolproof intelligent prediction model.

[0120] By modularly decomposing the optimization instruction set, complex task objectives are transformed into easily optimizable small modules, and corresponding parameter tuning paths are constructed based on the functional requirements of each module. Specifically, each task objective in the optimization instruction set (such as "operation order adjustment" and "time delay requirement") is first broken down, and its core parameters (such as time threshold, action order, delay, etc.) are extracted. An optimization path is then constructed for each parameter. The generation of the optimization path is based on the adjustment requirements of hyperparameters (such as learning rate, regularization factor, etc.), activation function selection (such as ReLU, Sigmoid, etc.), and inter-layer connection methods (such as fully connected layers, convolutional layers, etc.) during model training, resulting in an optimized error-proof intelligent prediction model.

[0121] These tuning paths allow for dynamic adjustments to the model's training process, ensuring that each module effectively enhances the model's predictive ability for error-proofing tasks while maintaining the model's structure to cope with changing operating environments.

[0122] For example, suppose one task in the optimization instruction set is "Operation B must be completed within 2 seconds after Operation A". This objective can be broken down into a time delay requirement ("complete within 2 seconds after Operation A") and a task sequence requirement ("Operation B follows Operation A"). Based on this, the model tuning path will focus on adjusting the model's time series processing capabilities, including increasing the weight of the attention mechanism for operation sequence, while optimizing the learning rate and activation function to ensure that the model can accurately predict the relationship between Operation A and Operation B.

[0123] In this embodiment, a closed-loop process from multimodal data acquisition to deep model optimization is achieved through a fully designed AI-based error-proofing algorithm and digital model optimization method, solving the technical challenges of behavior prediction and anomaly handling in complex operating environments. Specifically, firstly, multimodal process data, including user behavior, device response, and environmental attributes, are simultaneously collected using multi-channel sensing devices. The data is then time-aligned and segmented to extract standard and abnormal behavior sequences. Combining graph convolutional networks and contrastive learning algorithms, the spatial correlation features and temporal relationships in the behavior sequences are further analyzed, calculating the anomaly score matrix and behavior confidence vector. Based on the confidence level, a standard behavior sequence is reconstructed, while simultaneously constructing anomaly behavior sequences to locate high-risk operations. In the behavior prediction stage, the standard behavior sequence is embedded using a deep learning model, and key behavior nodes are extracted using a hierarchical attention mechanism. These nodes are then structurally matched with the abnormal behavior sequence to generate a structural difference vector. Subsequently, combining a feature compensation mechanism and an error inverse graph, the behavior error fusion feature tensor is generated, and fused feature data is further constructed to provide data support for high-dimensional clustering and bias correction. In the high-dimensional clustering stage, principal component analysis (PCA) is used for dimensionality reduction and density-adaptive clustering algorithms to classify behavior categories. Bias modeling is used to dynamically adjust cluster boundaries to generate corrected clustering results and boundary residual information. Based on this information, a set of error-proofing rules is constructed, mapping rule conflicts and risk level factors to generate targeted risk labels and optimization suggestions. In the optimization instruction generation stage, convergent optimization feature representation is used to perform contextual modeling and semantic mapping on high-risk areas, forming an optimization instruction set and semantic prompts. Label reinforcement and parameter tuning path optimization using a deep learning model enhance predictive capabilities and improve anomaly handling response capabilities, outputting an optimized error-proofing intelligent prediction model.

[0124] The solution in this embodiment has achieved significant results in terms of accuracy, timeliness, and model adaptability in both standard behavior prediction and abnormal behavior handling, further ensuring the safety, stability, and efficiency of operation processes in complex scenarios.

[0125] Example 3:

[0126] like Figure 2 As shown, this application also provides an AI error-proofing algorithm and digital model optimization system 10, which includes the following modules:

[0127] The acquisition module 11 is used to acquire multimodal process data, perform time alignment and event segmentation on the multimodal process data, and obtain standard behavior sequences and abnormal behavior sequences.

[0128] The acquisition module 11 uses multi-channel sensing devices and a unified timestamp to perform time alignment and event segmentation of the data, analyze user behavior, device status and environmental parameters, and generate standard behavior sequences and abnormal behavior sequences to provide basic data support for subsequent analysis.

[0129] The matching module 12 is used to input the standard behavior sequence into the preset deep learning behavior prediction model to obtain the behavior prediction result, and to perform structural matching between the behavior prediction result and the abnormal behavior sequence to obtain fused feature data.

[0130] The matching module 12 inputs the standard behavior sequence into the deep learning behavior prediction model, combines the behavior prediction results with the abnormal behavior sequence for structural matching, further extracts the behavior pattern deviation, and generates fused feature data.

[0131] The correction module 13 is used to perform high-dimensional clustering and bias correction on the fused feature data to obtain the corrected clustering results and boundary residual information. Based on the corrected clustering results, a set of error prevention judgment rules is generated. The error prevention judgment rule set is used to map and match the boundary residual information to obtain risk labels and optimization suggestions.

[0132] The correction module 13 performs dimensionality reduction on the high-dimensional fused feature data based on principal component analysis, preserving key feature subspaces. It then utilizes a density-adaptive clustering algorithm to perform high-dimensional clustering analysis on behavior categories. Simultaneously, it calculates the bias using boundary residual information and dynamically adjusts the clustering range to achieve accurate behavior category segmentation and generate corrected clustering results. The correction module 13 also generates a set of error-proofing rules, extracts rule conflicts through a mapping matching mechanism, and outputs risk labels and optimization suggestions.

[0133] The mapping module 14 is used to map risk labels to behavioral fragments corresponding to fused feature data to generate convergent optimization feature representations. The convergent optimization feature representations are then used to perform association annotation and behavioral semantic mapping on optimization suggestions to obtain optimization instruction sets and semantic prompts.

[0134] The mapping module 14 further maps the risk labels to the high-risk segments of the fused feature data to construct a convergent optimization feature representation, and realizes the semantic normalization and contextual association of the optimization suggestions through convolutional network modeling, thereby generating an optimization instruction set and semantic prompt information.

[0135] The optimization module 15 is used to optimize the preset deep learning behavior prediction model by using the optimization instruction set and semantic prompt information to obtain the optimized foolproof intelligent prediction model.

[0136] Based on this, the optimization module 15 comprehensively optimizes the training objectives, training parameters, and inter-layer connection strategies of the deep learning behavior prediction model. The optimized foolproof intelligent prediction model has higher prediction accuracy and adaptability to abnormal behavior.

[0137] In this embodiment, the systematic design consisting of acquisition module 11, matching module 12, correction module 13, mapping module 14 and optimization module 15 realizes a full-chain error prevention algorithm and digital model optimization from multimodal process data acquisition to deep learning model optimization. This significantly improves the system's recognition accuracy, risk avoidance ability and error prevention effect for complex behavior patterns in actual operation, and effectively supports intelligent operation management in various scenarios.

[0138] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and each module described above can be referred to the corresponding process in the aforementioned Embodiment 1, and will not be repeated here.

[0139] The structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0140] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimization method for an AI error-proofing algorithm and a digital model, characterized in that, include: Multimodal process data is collected, and the multimodal process data is time-aligned and segmented into events to obtain standard behavior sequences and abnormal behavior sequences; wherein, the multimodal process data includes action signals, voice commands, and environmental conditions that occur during the operation; The standard behavior sequence is sequence embedded and encoded to obtain an encoded standard behavior sequence. This encoded standard behavior sequence is then input into a pre-defined deep learning behavior prediction model based on a Transformer structure, outputting multi-dimensional behavior prediction results. Hierarchical attention analysis is performed on the behavior prediction results to extract key behavior nodes. A structural difference vector is constructed using the key behavior nodes and the abnormal behavior sequence, and trigger offset features are extracted based on the structural difference vector. Residual feedback is performed between the structural difference vector and the intermediate features of the deep learning behavior prediction model to obtain a feature compensation vector and an error inverse graph. A behavior error fusion feature tensor is constructed based on the trigger offset features and the feature compensation vector. This behavior error fusion feature tensor is then fused with the error inverse graph at the tensor level to obtain fused feature data. The fused feature data is subjected to high-dimensional clustering and bias correction to obtain corrected clustering results and boundary residual information. A set of error prevention judgment rules is generated based on the corrected clustering results. The set of error prevention judgment rules is used to map and match the boundary residual information to obtain risk labels and optimization suggestions. The risk labels are mapped to the corresponding behavioral fragments of the fused feature data to generate convergent optimization feature representations. The convergent optimization feature representations are then used to perform association annotation and behavioral semantic mapping on the optimization suggestions to obtain optimization instruction sets and semantic prompts. The preset deep learning behavior prediction model is optimized using the optimized instruction set and the semantic prompt information to obtain the optimized foolproof intelligent prediction model.

2. The optimization method for the AI ​​error-proofing algorithm and digital model according to claim 1, characterized in that, The steps of collecting multimodal process data, performing time alignment and event segmentation on the multimodal process data, and obtaining standard behavior sequences and abnormal behavior sequences include: Multi-channel sensing devices are used to synchronously collect action signals, environmental parameters and user interaction logs during the operation process to construct multi-modal process data. The multi-modal process data is then time-aligned and segmented into events to obtain event fragment sequences and context window information. The event fragment sequence is input into a preset graph convolutional network model to extract the spatial correlation features and temporal relationship features of the operation actions, and to perform anomaly pattern discrimination on the context window information to obtain an anomaly score matrix and behavior credibility vector. The behavior credibility vector is fused with the temporal relationship features to generate a standard behavior sequence; The anomaly score matrix and the spatial correlation features are jointly analyzed, and anomaly behavior sequences are constructed based on the analysis results.

3. The optimization method for the AI ​​error-proof algorithm and digital model according to claim 1, characterized in that, The step of performing hierarchical attention analysis on the behavior prediction results to extract key behavior nodes, and constructing a structural difference vector using the key behavior nodes and the abnormal behavior sequence, includes: The outputs of each layer of the behavior prediction result are weighted by a hierarchical attention mechanism, key behavior nodes are extracted based on the weighted results, and the similarity between the key behavior nodes is calculated by applying a dynamic time warping algorithm. Identify the deviation portion in the abnormal behavior sequence, and construct a structural difference vector using the similarity and the deviation portion.

4. The optimization method for the AI ​​error-proofing algorithm and digital model according to claim 1, characterized in that, The steps of performing high-dimensional clustering and bias correction on the fused feature data to obtain corrected clustering results and boundary residual information, generating a set of error prevention judgment rules based on the corrected clustering results, and mapping and matching the boundary residual information using the set of error prevention judgment rules to obtain risk labels and optimization suggestions include: The fused feature data is preprocessed by dimensionality reduction based on principal component analysis to obtain dimensionality-reduced features. High-dimensional clustering is then performed on the dimensionality-reduced features to obtain clustering results and a behavior clustering mapping map. The clustering results are modeled with the behavior clustering map to calculate the behavior offset. The boundary conditions in the clustering results are dynamically adjusted according to the behavior offset to correct the behavior category division range and obtain the corrected clustering results and boundary residual information. Based on the corrected clustering results, a set of error prevention judgment rules is constructed. The boundary residual information is mapped and matched with the set of error prevention judgment rules, and the rule conflict groups and risk level factors are extracted based on the matching results. The risk level factor and the historical frequency weight of the rule conflict group are comprehensively analyzed, and corresponding risk labels and optimization suggestions are generated based on the analysis results.

5. The optimization method for the AI ​​error-proofing algorithm and digital model according to claim 1, characterized in that, The step of mapping the risk label to the corresponding behavioral fragment of the fused feature data to generate a convergent optimization feature representation, and using the convergent optimization feature representation to perform association annotation and behavioral semantic mapping on the optimization suggestions to obtain an optimization instruction set and semantic prompt information includes: The risk labels are mapped to the behavioral segments corresponding to the fused feature data to extract high-risk behavioral region features. Convolutional coding networks are used to perform contextual modeling and semantic normalization on the high-risk behavioral region features to generate a set of candidate optimization parameters. The candidate optimization parameter set is weighted and combined with the original fusion features to construct a modified input tensor. The modified input tensor is then subjected to multiple rounds of residual regression and error inversion processing to generate a convergent optimization feature representation. The original fusion features refer to the high-dimensional feature set after fusing user interaction data, sensor data, and historical behavior data. The convergent optimization feature representation and the optimization proposal are associated, labeled, and mapped with behavioral semantics to construct an optimization instruction set; The task target items in the optimized instruction set are converted into prompt statement templates, and the prompt statement templates are combined with the user behavior context to generate semantic prompt information; wherein, the user behavior context refers to the timestamp of the user's current operation, the operation sequence, the previous state, and environmental conditions.

6. The optimization method for the AI ​​error-proof algorithm and digital model according to claim 1, characterized in that, The step of optimizing the preset deep learning behavior prediction model using the optimized instruction set and the semantic prompt information to obtain the optimized error-proof intelligent prediction model includes: The semantic prompt information is used to enhance the labels of the intermediate layer behavior representation of the preset deep learning behavior prediction model, resulting in an enhanced deep learning behavior prediction model. The optimized instruction set is modularly decomposed, and a corresponding parameter tuning path is constructed based on the decomposition results. The training hyperparameters, activation strategies, and inter-layer connection methods of the enhanced deep learning behavior prediction model are optimized using the parameter tuning path to obtain the optimized foolproof intelligent prediction model.

7. An optimization system for an AI-based error-proofing algorithm and digital model, characterized in that, include: The acquisition module is used to acquire multimodal process data, perform time alignment and event segmentation on the multimodal process data, and obtain standard behavior sequences and abnormal behavior sequences; wherein, the multimodal process data includes action signals, voice commands, and environmental conditions that occur during the operation; A matching module is used to perform sequence embedding encoding on the standard behavior sequence to obtain an encoded standard behavior sequence. This encoded standard behavior sequence is then input into a preset deep learning behavior prediction model based on a Transformer structure, outputting multi-dimensional behavior prediction results. Hierarchical attention analysis is performed on the behavior prediction results to extract key behavior nodes. A structural difference vector is constructed using the key behavior nodes and the abnormal behavior sequence, and trigger offset features are extracted based on the structural difference vector. Residual feedback is performed between the structural difference vector and the intermediate features of the deep learning behavior prediction model to obtain a feature compensation vector and an error inverse graph. A behavior error fusion feature tensor is constructed based on the trigger offset features and the feature compensation vector. The behavior error fusion feature tensor is then fused with the error inverse graph at the tensor level to obtain fused feature data. The correction module is used to perform high-dimensional clustering and bias correction on the fused feature data to obtain corrected clustering results and boundary residual information. Based on the corrected clustering results, a set of error prevention judgment rules is generated. The set of error prevention judgment rules is used to map and match the boundary residual information to obtain risk labels and optimization suggestions. The mapping module is used to map the risk label to the behavioral fragment corresponding to the fused feature data to generate a convergent optimization feature representation. The convergent optimization feature representation is used to perform association annotation and behavioral semantic mapping on the optimization suggestions to obtain an optimization instruction set and semantic prompt information. The optimization module is used to optimize the preset deep learning behavior prediction model using the optimization instruction set and the semantic prompt information to obtain the optimized error-proof intelligent prediction model.

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