Text content generation method based on artificial intelligence

Abstract

The invention relates to the technical field of artificial intelligence and natural language processing, and particularly discloses a text content generation method based on artificial intelligence, and the method comprises the steps: obtaining natural language text input, and extracting a semantic recognition feature vector; acquiring context state data and encoding the context state data into a state recognition feature vector; generating a fusion feature vector containing a semantic and state association relationship through fusion analysis; constructing a causal discrimination model based on the fusion features, and outputting the matching confidence of semantics and states; dynamically adjusting a generation strategy according to the confidence coefficient, if the matching degree is high, generating a standard text, otherwise, triggering an error correction mechanism to output a corrected text; and finally, performing logic consistency verification on the generated text to ensure that physical constraints, technological procedures and safety standards in the industrial field are met. According to the method, by introducing multi-level feature fusion, causal reasoning, intelligent error correction and rule verification mechanisms, context perception and safety controllability in the text generation process are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence and natural language processing, and in particular to a text content generation method based on artificial intelligence. Background Art

[0002] With the rapid development of artificial intelligence technology, natural language processing is increasingly being used in industrial automation, intelligent manufacturing, equipment operation and maintenance, and other fields. Traditional text generation technology mainly relies on the semantic modeling capabilities of language models, and lacks the ability to perceive and integrate dynamic contextual states in actual application scenarios. In scenarios with high security requirements, such as industrial control, the generation of operating instructions requires not only semantic accuracy, but also strict compliance with equipment operating status, process procedures, and safety constraints. However, existing technologies often separate semantic understanding and context perception, resulting in logical conflicts, infeasible operations, and even safety hazards in the generated content in complex environments. This makes it difficult to meet the comprehensive requirements of industrial sites for text generation systems in terms of accuracy, consistency, and safety.

[0003] The existing technology has the following deficiencies: Existing text generation systems struggle to effectively integrate the deep causal relationships between semantic information and contextual states in dynamically changing industrial environments. This is particularly true in high-risk operational scenarios, where they lack a precise mechanism for assessing the degree of semantic-state matching. This can lead to generated content violating equipment physical limitations, process flows, or safety regulations, and the system itself is unable to promptly identify and correct such errors. This issue is often overlooked in traditional approaches, especially when faced with complex, ever-changing, and state-sensitive industrial instruction generation tasks. Existing technologies struggle to achieve an organic integration of semantic understanding, state perception, and causal reasoning, impacting the reliability and engineering practicality of generated text. Summary of the Invention

[0004] The purpose of the present invention is to provide a text content generation method based on artificial intelligence to solve the problems in the above background.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A method for generating text content based on artificial intelligence, comprising the following steps: Obtaining a natural language text input to be processed, extracting its semantic recognition feature vector, and simultaneously obtaining context state data associated with the natural language text and encoding it into a state recognition feature vector; Performing joint calculation and analysis on the semantic recognition feature vector and the state recognition feature vector to generate a fused feature vector, wherein the fused feature vector includes the association relationship between the semantic information and the context state information; Based on the fused feature vector, a causal discrimination model is constructed, wherein the causal discrimination model is used to identify the degree of match between the text semantics and the context state, and output the matching confidence; According to the matching confidence, the output strategy of the causal discrimination model is adjusted. If the confidence is higher than a set threshold, text content consistent with the context state is generated; otherwise, an error correction mechanism is triggered to output the corrected text; Perform logical consistency checks on the generated text to ensure that it complies with the preset domain constraint rules, and finally output the optimized text content.

[0006] As a further solution of the present invention: the extraction of semantic recognition feature vectors includes the following steps: A deep bidirectional semantic understanding network is used to perform multi-level parsing of natural language text, obtaining basic semantic representations through word-level encoders and capturing key semantic components through sentence-level attention mechanisms. The parsed semantic representation is input into the feature refinement module, which includes a domain term filter and a logical relationship extractor set in parallel to filter out irrelevant semantic components and strengthen the representation of core operational intent, respectively. The state-aware feature compression layer outputs a fixed-dimensional semantic recognition feature vector. The feature compression layer adopts a dynamic weight allocation mechanism to automatically adjust the contribution weight of each semantic component according to the type of contextual state data.

[0007] As a further solution of the present invention: the encoding state identification feature vector includes the following steps: Establish a multi-source state data fusion channel to perform time alignment and dimension normalization on equipment sensor data, process stage markers, and environmental monitoring parameters; The fused state data is processed through a spatiotemporal feature extraction network. The network consists of parallel temporal convolution branches and spatial attention branches, which respectively capture the dynamic evolution of state variables and the spatial correlation of key parameters. The state feature reorganization module is used to perform structured encoding on the extracted spatiotemporal features. The state feature reorganization module divides the features into three feature subspaces: equipment status, process constraints, and safety boundaries according to the preset industrial knowledge graph. The structured encoded state information is mapped into a state recognition feature vector that matches the semantic feature dimension through the feature projection layer.

[0008] As a further solution of the present invention: the joint calculation and analysis of the semantic recognition feature vector and the state recognition feature vector includes the following steps: A dual-channel feature interaction network is constructed. The first channel uses a gated fusion mechanism to dynamically adjust the complement strength of semantic features to state features, and the second channel calculates the correction weight of state features to semantic features through a reverse attention mechanism. A three-level feature reorganization operation is implemented in the feature interaction space. First, the element-by-element multiplication operation of feature dimension alignment is performed, then cross-channel feature splicing is performed, and finally the intermediate fusion feature is generated through a nonlinear transformation layer. A feature purification module based on industrial knowledge constraints is used to process intermediate fusion features. The feature purification module contains a logical consistency checker and a physical feasibility discriminator set in parallel, which are used to eliminate fusion results that violate domain rules and filter feature combinations that do not meet equipment operation constraints, respectively.

[0009] As a further solution of the present invention: the process of generating the fused feature vector specifically includes: Establish a semantic-state association matrix, and map the semantic recognition feature vector and state recognition feature vector into a unified association space through bidirectional feature projection; Implement feature cross-calculation in the association space, first perform outer product operations on feature vectors to generate interaction tensors, and then extract key interaction patterns through the compression excitation network; The interaction pattern is input into a multi-granularity feature synthesizer, which consists of a local feature focusing unit and a global feature integration unit connected in series to capture the fine-grained parameter association relationship and the macroscopic semantic and state matching rules respectively. Finally, the fused feature vector is output through the feature stability optimization layer. The optimization layer adopts an iterative refinement mechanism to gradually eliminate noise interference and redundant information in the feature fusion process.

[0010] As a further solution of the present invention: the construction of the causal discrimination model includes the following steps: A hierarchical causal inference architecture is established. A feature decoupling module is set up at the bottom layer to separate the fused feature vector into semantic-dominant and state-dominant components. A bidirectional causal attention mechanism is deployed at the middle layer to calculate the mutual influence relationship between components. Design a dynamic causal weight allocation strategy to automatically adjust the contribution ratio of semantic components and state components in the final judgment result according to the type of current processing task; A multi-scale matching evaluation network is constructed. Through the local matching analysis unit and the global consistency verification unit set in parallel, the parameter-level matching is evaluated from the micro level and the overall logical rationality is verified at the macro level.

[0011] As a further solution of the present invention: the output matching confidence of the causal discrimination model is achieved by: Adopting a confidence-grading calculation framework, the basic matching calculation module first generates a preliminary confidence score, and then the anomaly detection module identifies potential conflicting feature combinations; Deploy a feedback confidence calibration mechanism to input the error signal formed by comparing historical judgment results with actual execution results into the calibration network, and dynamically correct the current confidence calculation parameters; A confidence verification layer based on the industrial knowledge graph is established, and the discrimination results are secondary verified by querying the preset process rule library, and finally the matching confidence index after multi-level verification is output.

[0012] As a further solution of the present invention: the output strategy of adjusting and generating the causal discrimination model includes the following steps: Build a dynamic threshold adjustment module to automatically adjust the confidence threshold according to the criticality of the current process stage and set stricter judgment criteria for high-risk operation stages; Implement a multimodal output strategy selection mechanism. When the confidence is higher than a threshold, the standard text generation channel is activated. When it is lower than the threshold, the alternative solution retrieval channel and the safety constraint generation channel are started in parallel. Deploy the output result buffer verification layer to perform real-time feasibility simulation of the text content to be generated, and verify its execution effect under actual working conditions through the digital twin system.

[0013] As a further solution of the present invention: the triggering error correction mechanism includes the following processing flow: Establish a three-level error tracing system, first locating the characteristic dimensions of semantic-state mismatch, then identifying the key parameters that cause the mismatch, and finally analyzing the reliability of the underlying data collection; Activate the adaptive text reconstruction network, which consists of a series of error feature shielding layers, domain knowledge injection layers, and semantic repair layers to gradually correct the original generated content; A closed-loop feedback learning mechanism is implemented to store typical error patterns and their correction solutions found during the error correction process into the case library for optimizing subsequent causal discrimination model parameters.

[0014] As a further solution of the present invention: the logic consistency check includes the following steps: Build a multi-level rule verification engine. The first level verifies the conformity of text semantics with the physical constraints of the equipment. The second level verifies the matching degree between operating instructions and process procedures. The third level verifies the compatibility of generated content with safety standards. Deploy a knowledge graph-based anomaly detection network, which traverses a preset industrial relationship graph to identify logical conflict nodes in the text and locate specific semantic components that violate domain rules. A progressive text optimization algorithm is used to first replace obvious violations through rule mapping, then adjust the contextual coherence through a semantic repair network, and finally ensure that the output text conforms to industry standard expression through a style adapter; Implement a dynamic rule update mechanism to automatically add new logical conflict patterns discovered during the verification process to the domain constraint rule library, and continuously improve the coverage of the verification system.

[0015] Beneficial effects of the present invention: (1) By deeply integrating the semantic features of natural language text with contextual state information, the present invention constructs a discriminant model with causal reasoning capabilities, achieving refined modeling of the matching relationship between text semantics and industrial operating environments. Specifically, the system first uses a Transformer-based deep semantic understanding network to perform multi-level parsing on the input text, extracting semantic feature vectors containing key operation intentions and parameter information; at the same time, the multi-source state perception module collects contextual information such as equipment operation status, process stage, and environmental parameters, and encodes them into structured state recognition feature vectors. On this basis, the system introduces a dual-channel feature interaction network, combines the gated fusion mechanism with the reverse attention mechanism, realizes the dynamic alignment and deep fusion of semantic and state features, and generates a fused feature vector with semantic-state association expression capabilities. Based on the fused feature vector, the system constructs a hierarchical causal discriminant model. Through mechanisms such as feature decoupling, bidirectional causal attention, and multi-scale matching evaluation, the system identifies the matching degree between the operation instruction and the current industrial scenario from the bidirectional influence relationship between semantics and state, and outputs a structured matching confidence index. This metric not only reflects the semantic rationality of the generated text but also incorporates a comprehensive assessment of multiple factors, including equipment status, process constraints, and safety margins. Based on this confidence level, the system dynamically adjusts its text generation strategy, outputting precise operational instructions in high-confidence scenarios and triggering error correction mechanisms in low-confidence situations, generating revised text that complies with industry standards.

[0016] (2) In the present invention, the text generation process not only focuses on the fluency of language expression and the accuracy of semantics, but also deeply integrates the multi-dimensional constraints in industrial scenarios, and constructs a multi-level, structured logical consistency verification mechanism and adaptive error correction system. Before generating the final output of the text, the system introduces a multi-level rule verification engine composed of equipment physical constraints, process procedures, and safety standards. The system further deploys an anomaly detection network based on the industrial knowledge graph, which can identify problems such as logical conflicts, causal contradictions, or temporal violations hidden in the generated text, and classify and mark the abnormal content and trace its semantic origin. For the detected problems, the system activates the adaptive text reconstruction network, combines error feature shielding, domain knowledge injection, and semantic repair, etc., to achieve accurate correction of the text content, and ensure that the output results achieve the optimal balance between semantic integrity, logical consistency and domain compliance. The present invention also introduces a closed-loop feedback learning mechanism to automatically archive the key information (including error mode, correction plan, and execution effect) in each verification and error correction process to the case knowledge base, and continuously optimizes the parameter configuration of the causal discrimination model and the generation model through incremental learning strategy, so as to achieve continuous evolution and self-improvement of system capabilities. This mechanism not only improves the stability and fault tolerance of the system, but also enables it to adapt to the dynamic changes of the industrial environment and has the robustness and adaptability for long-term operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 It is a flowchart of a text content generation method based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] Example 1, please refer to Figure 1 As shown, the present invention is a method for generating text content based on artificial intelligence, comprising the following steps: Obtaining a natural language text input to be processed, extracting its semantic recognition feature vector, and simultaneously obtaining context state data associated with the natural language text and encoding it into a state recognition feature vector; Performing joint calculation and analysis on the semantic recognition feature vector and the state recognition feature vector to generate a fused feature vector, wherein the fused feature vector includes the association relationship between the semantic information and the context state information; Based on the fused feature vector, a causal discrimination model is constructed, wherein the causal discrimination model is used to identify the degree of match between the text semantics and the context state, and output the matching confidence; According to the matching confidence, the output strategy of the causal discrimination model is adjusted. If the confidence is higher than a set threshold, text content consistent with the context state is generated; otherwise, an error correction mechanism is triggered to output the corrected text; Perform logical consistency checks on the generated text to ensure that it complies with the preset domain constraint rules, and finally output the optimized text content.

[0021] In Example 2, during the natural language text input phase, the system first receives a natural language instruction from an operator, such as "Reduce burner power." This text input is captured through the human-computer interface at the industrial site and transmitted to the central processing unit for preprocessing, including basic processing steps such as text normalization, word segmentation, and part-of-speech tagging. The system has established a specialized domain dictionary specifically tailored to the specific terminology of the industrial field to ensure accurate recognition of specialized terminology.

[0022] The system employs a dedicated parsing network based on the Transformer architecture to perform multi-level parsing of natural language text using a deep bidirectional semantic understanding network. This network first extracts word-by-word features from the input text using a word-level encoder to generate a basic semantic representation. This encoding process specifically considers abbreviations, technical terms, and the unique expressions of operating instructions commonly found in industrial scenarios. The system then captures key semantic components within the text using a sentence-level attention mechanism, which automatically identifies and enhances semantic elements such as core verbs, key parameters, and important modifiers within operating instructions.

[0023] When the parsed semantic representation is input into the feature refinement module, the system first activates a parallel domain term filter. This filter, based on a pre-built industry domain knowledge base, removes semantic components irrelevant to the current operational scenario, such as redundant expressions in everyday language. Simultaneously, the logical relationship extractor analyzes the operational logic within the text. The outputs of these two modules are weighted and fused to form a refined semantic representation.

[0024] When the state-aware feature compression layer outputs a fixed-dimensional semantic recognition feature vector, the system dynamically adjusts the contribution weight of each semantic component based on the currently acquired contextual state data type. For example, when the state data contains high temperature warning information, the weight of the temperature-related semantic component is automatically increased. The feature compression layer uses adaptive dimensionality reduction technology to convert the variable-length semantic representation into a fixed-length semantic recognition feature vector, ensuring compatibility with subsequent processing.

[0025] During the state identification feature vector encoding stage, the system first establishes a multi-source state data fusion channel. This channel connects to various industrial sensors and control systems, collecting real-time equipment sensor data (such as temperature, pressure, and flow), process stage indicators (such as heating, cooling, and insulation), and environmental monitoring parameters (such as humidity and dust concentration). The collected multi-source data undergoes time alignment to ensure consistent timestamps for each parameter, and dimensional normalization eliminates the impact of different physical units.

[0026] The system utilizes a unique network architecture to process the fused state data through the spatiotemporal feature extraction network. The temporal convolution branch uses multiple layers of one-dimensional convolution kernels, sliding along the time axis to extract the dynamic evolution of state variables, effectively capturing parameter mutation points and trend characteristics. The spatial attention branch analyzes the spatial correlations between different parameters, such as the interaction between temperature and pressure. The feature outputs of the two branches are concatenated along specific dimensions to form a complete spatiotemporal feature representation.

[0027] When using the state feature recombination module to perform structured encoding on the extracted spatiotemporal features, the system intelligently divides the features into three feature subspaces based on a pre-defined industrial knowledge graph. The equipment state subspace primarily represents the real-time operating parameters of the equipment; the process constraint subspace records the permitted operating ranges for the current process stage; and the safety boundary subspace stores various safety thresholds and alarm limits. This structured encoding approach imparts clear semantic meaning to the state information, facilitating subsequent feature fusion and decision-making.

[0028] Finally, the feature projection layer maps the structured encoded state information into a state identification feature vector. This projection layer uses a learnable linear transformation matrix to ensure that the output state identification feature vector and the semantic feature vector have the same dimensions, laying the foundation for subsequent joint computation and analysis. The projection process specifically preserves key information in each feature subspace, avoiding loss or confusion of important features.

[0029] The entire implementation process strictly adheres to the real-time requirements of industrial control systems, with processing times for each step kept to milliseconds. The system utilizes a distributed computing architecture, with semantic processing and condition monitoring modules running in parallel and exchanging information via a high-speed data bus. All feature extraction and encoding operations are performed on dedicated hardware accelerators, ensuring processing efficiency that meets the requirements of industrial sites.

[0030] In Example 3, during the joint calculation and analysis phase of the semantic recognition feature vector and the state recognition feature vector, the system first constructs a dual-channel feature interaction network. This network adopts a unique parallel architecture design, in which the first channel deploys a gated fusion mechanism, which dynamically evaluates the complementary value of semantic features to state features through a learnable gating function, and adjusts the complementary strength accordingly. The parameters of the gating function are automatically adjusted according to the current process stage to ensure that the optimal fusion strategy is adopted under different working conditions. The second channel implements a reverse attention mechanism, which calculates the correction weight of state features to semantic features through a multi-layer perception network, paying special attention to key state parameters that may affect operational safety.

[0031] When implementing the three-level feature recombination operation within the feature interaction space, the system first performs an element-by-element multiplication operation to align the feature dimensions. This operation not only achieves basic feature fusion but also enhances the relevance of semantic and state features through element-level interaction. Next, a cross-channel feature concatenation operation is performed, concatenating the gated fusion and inverse attention-corrected features along specific dimensions to form an extended feature representation. Finally, the concatenated features are processed through a nonlinear transformation layer, which utilizes a deep neural network structure with residual connections to ensure smooth information flow and output intermediate fused features with rich representational capabilities.

[0032] When processing intermediate fusion features using a feature purification module based on industrial knowledge constraints, the system activates a parallel logic consistency checker and a physical feasibility discriminator. The logic consistency checker, which incorporates a built-in rule base of industry experts, detects potential logical inconsistencies in the fused features through pattern matching and rule reasoning. The physical feasibility discriminator, connected to a database of device physical models, verifies that the feature combination complies with the fundamental physical laws of device operation. The outputs of these two checkers are used through a voting mechanism to determine whether to retain or exclude specific feature combinations, ensuring the rationality and security of the fusion results.

[0033] To generate the fused feature vector, the system first establishes a semantic-state association matrix. This matrix is ​​generated through bidirectional feature projection, using a parameter-sharing mapping network to ensure that the semantic and state features are transformed into a unified association space. The dimensions of this association space are carefully designed to preserve the key information of the original features while providing sufficient operational space for subsequent interactive calculations.

[0034] When computing feature interactions in the context space, the system first performs an outer product operation on the feature vectors. This operation generates a high-dimensional interaction tensor that comprehensively captures the underlying correlation patterns between semantic and state features. This interaction tensor is then processed by a compression-excitation network, which employs a channel-wise attention mechanism to automatically identify and enhance the most important interaction patterns for the task at hand while suppressing irrelevant or redundant interactions.

[0035] When interaction patterns are fed into the multi-granularity feature synthesizer, the system first processes the input data through a local feature focusing unit. This unit employs dilated convolution techniques to analyze fine-grained correlations between features at multiple scales, focusing specifically on microscopic interactions between key parameters. The global feature integration unit then receives the local features and, through a self-attention mechanism, exploits macroscopic semantic and state matching patterns to form a comprehensive understanding of the overall operational scenario. These two units are jointly trained in an end-to-end manner to ensure the coordination and integration of local and global features.

[0036] When the fused feature vector is finally output through the feature stability optimization layer, the system employs an iterative refinement mechanism. This mechanism gradually eliminates noise and redundant information during the fusion process through multiple forward computations and feature adjustments. Each iteration includes two sub-steps: feature importance assessment and selective filtering. This ensures that the final fused feature vector output contains sufficient semantic and state association information while maintaining a high degree of stability and reliability. The optimization process uses an adaptive termination strategy, automatically terminating iterations when the feature change falls below a preset threshold, balancing computational efficiency and feature quality.

[0037] The entire feature fusion process is executed on a dedicated computing unit, utilizing a pipelined parallel architecture for accelerated processing. The system pre-sets differentiated fusion parameters for different industrial operation scenarios and automatically selects the optimal configuration based on the current task. All intermediate results undergo rigorous numerical stability checks to prevent error accumulation during the calculation process. The resulting fused feature vector retains the core features of the original semantic and state information while also reflecting the deep-seated correlation between them, providing high-quality input features for subsequent causal identification.

[0038] Example 4. In the stage of building a causal discrimination model, the system first establishes a hierarchical causal reasoning architecture. The underlying feature decoupling module uses a learnable projection matrix to intelligently separate the input fused feature vector into a semantically dominant component and a state-dominant component. The semantically dominant component mainly contains feature elements related to natural language instructions, while the state-dominant component focuses on the feature expression of the device's operating status. The decoupling process ensures the independence of the two types of components through orthogonal constraints to avoid feature confusion. The bidirectional causal attention mechanism deployed in the middle layer adopts a symmetric network structure to calculate the influence weight of the semantic component on the state component, as well as the constraint relationship between the state component and the semantic component, forming a complete causal influence map.

[0039] When designing a dynamic causal weight allocation strategy, the system maintains a task type classifier to identify the attribute characteristics of the task being processed in real time. For tasks primarily focused on state monitoring, the contribution of the state component to the judgment result is automatically increased; for tasks primarily focused on semantic analysis, the decision weight of the semantic component is appropriately increased. The weight allocation process uses a soft adjustment strategy to avoid drastic fluctuations in decision results through smooth transitions. The system also sets weight boundary constraints to ensure that the contribution of any component type is never completely ignored.

[0040] When constructing a multi-scale matching evaluation network, the local matching analysis unit employs a densely connected network structure to compare key parameters in the semantic and state components one by one. This unit specifically focuses on core parameters in industrial scenarios, such as temperature setpoints and pressure thresholds, performing detailed difference analysis. The global consistency verification unit, using graph neural network modeling, abstracts various parameters and their relationships into graph nodes and edges, verifying the rationality of the overall logic at a system level. The outputs of these two units are weighted and fused to form a comprehensive matching evaluation result.

[0041] When implementing the confidence grading calculation framework, the basic matching calculation module first normalizes the input features to eliminate the effects of dimensionality differences. This module utilizes a multilayer perceptron architecture to map high-dimensional features into preliminary confidence scores through nonlinear transformations. The scores are normalized to a range between zero and one to facilitate subsequent processing. The anomaly detection module uses the isolation forest algorithm to identify outliers in the feature space, which often correspond to potentially conflicting feature combinations. Detected anomalous features trigger specialized processing to prevent interference with the confidence calculation.

[0042] When deploying the feedback-based confidence calibration mechanism, the system establishes a closed-loop learning framework. The actual performance of each judgment result is recorded and quantitatively evaluated, generating an error signal. These signals are filtered and input into the calibration network. This network uses time series modeling techniques to capture the changing patterns of confidence errors and dynamically adjusts the confidence calculation parameters accordingly. The calibration process adheres to the principle of incremental optimization, keeping each adjustment within a reasonable range to ensure system stability. The system also incorporates an abnormal error fuse mechanism, automatically switching to safe mode when a sudden large error is detected.

[0043] When establishing a confidence verification layer based on the industrial knowledge graph, the system first maps the key features of the identification results to the corresponding nodes in the knowledge graph. The verification process utilizes a bidirectional propagation algorithm, performing both forward inference along the knowledge graph relationship edges from the identification results and reverse verification from the relevant process rule nodes. The verification results are converted into confidence correction factors to fine-tune the initial confidence score. The knowledge graph is regularly updated to ensure the timeliness and accuracy of the verification rules. The final output matching confidence indicator undergoes the aforementioned multi-level verification and is highly reliable and interpretable.

[0044] The entire causal discrimination model construction and confidence calculation process is executed on a dedicated inference engine. The system adopts a modular design, and each functional unit can be independently updated and maintained. The inference process implements strict numerical stability checks, and all intermediate results are recorded in an audit log, supporting post-analysis and model optimization. The system has pre-configured expert modes for different industrial scenarios, allowing users to select the most appropriate discrimination strategy based on actual needs. The causal discrimination model connects to pre- and post-processing modules through standardized interfaces, ensuring overall system interoperability and scalability.

[0045] Example 5. In the stage of adjusting the output strategy of the causal discrimination model, the system first constructs a dynamic threshold adjustment module. This module is connected to the real-time process monitoring system to continuously obtain the safety level assessment data of the current process stage. For high-risk operation stages such as high-temperature and high-pressure reaction periods, the system automatically raises the confidence threshold to the preset highest safety level standard; for routine operation stages, the baseline threshold is used. The threshold adjustment process uses a fuzzy control algorithm to achieve a smooth transition between different safety levels. The system also sets a threshold change rate limit to avoid frequent fluctuations in the threshold due to rapid switching of process states. The criticality parameters of each process stage are pre-defined by domain experts and stored in a configurable knowledge base.

[0046] When implementing a multimodal output strategy selection mechanism, the system maintains multiple parallel output channels. The standard text generation channel uses a language model optimized for industrial scenarios to directly generate operational instruction text that meets process requirements based on high-confidence judgment results. The alternative solution retrieval channel queries the historical case library, searching for successful operational cases similar to the current scenario as alternative suggestions. The safety constraint generation channel connects to the process rule database to generate conservative operational text that meets minimum safety requirements. Channel selection decisions are made by a dedicated arbitration module, which comprehensively considers factors such as confidence level, process risk level, and operational urgency to ensure the optimality of the output strategy.

[0047] When deploying the output result buffer verification layer, the system converts the generated text content into a control instruction format recognizable by the digital twin system. This verification process is performed using a high-fidelity process simulation model, simulating the execution of each generated operation instruction and predicting its impact on equipment status parameters. Simulation results are compared with safety thresholds in real time, triggering immediate alerts when potential risks are identified. The system employs a multi-level verification strategy, providing rapid verification for routine operations and in-depth, full-parameter simulation for critical operations. All verification results are recorded in an audit log, supporting post-event analysis and accountability.

[0048] In the process of triggering the error correction mechanism, the system first establishes a three-level error traceability system. The first level of traceability uses a feature importance analysis algorithm to locate the most significant feature dimensions of semantic-state mismatch. The second level of traceability focuses on identifying the specific parameters that cause the mismatch, using decision tree analysis to track the propagation path of the abnormal parameters. The third level of traceability conducts in-depth analysis of the acquisition quality of the underlying sensor data, including indicators such as signal stability, timeliness, and accuracy. The traceability process adopts a top-down analysis strategy, gradually locating the mismatch phenomenon from the macro level to the micro data anomalies, forming a complete error analysis report.

[0049] When the adaptive text reconstruction network is activated, the error feature masking layer first identifies and filters semantic components related to error features in the generated content. This layer uses attention masking technology to accurately mask problematic fragments without disrupting the overall structure of the text. The domain knowledge injection layer then retrieves supplementary information from the process knowledge base to fill the semantic gaps caused by the masking operation. The semantic repair layer finally optimizes the text's fluency and logic, ensuring that the corrected content meets technical requirements while maintaining natural language readability. These three layers are jointly trained end-to-end to ensure the coherence and consistency of the reconstruction process.

[0050] When implementing a closed-loop feedback learning mechanism, the system establishes a dedicated case knowledge base to store key information from the error correction process. Each case contains complete data, including the original error text, error analysis report, correction plan, and implementation effect evaluation. Cases undergo a quality review before being stored to ensure the representativeness and accuracy of the data. The system regularly scans the case base and extracts common error patterns as optimization targets for the causal discriminant model. Model parameter updates utilize an incremental learning strategy, gradually incorporating new experience while maintaining existing knowledge. The system also incorporates a learning effect evaluation process to verify model improvements after each update, forming a complete quality closed loop.

[0051] The entire output strategy adjustment and error correction mechanism runs on an industrial-grade computing platform, meeting real-time requirements. The system utilizes a redundant design with backups of key components to ensure high reliability. Detailed logs are generated for all decision-making processes, supporting operational status monitoring and problem diagnosis. To meet the specific needs of different industrial scenarios, the system provides a flexible configuration interface, allowing users to customize key parameters such as threshold parameters, verification rules, and learning strategies. The system also features self-diagnostic capabilities, regularly checking the operational status of each module to promptly identify and correct potential issues and ensure long-term stable operation.

[0052] Example 6. In the logical consistency verification stage, the system first builds a multi-level rule verification engine. The first level of verification is aimed at checking the physical constraints of the equipment. The system parses the generated text content into structured operating parameters and compares them one by one with the limit values ​​in the equipment technical specification. For example, for the instruction "raise the temperature of the reactor to 400°C", the system will check the rated operating temperature range of the reactor of this model, and immediately mark the violation if it is found to exceed the threshold. The verification process uses multi-threaded parallel processing technology to ensure efficient response during large-scale parameter checks. The system's built-in physical constraint database supports online updates and can synchronize the latest equipment technical parameters at any time.

[0053] The second level of verification focuses on the matching analysis of process procedures. The system accesses the process knowledge base and extracts the standard operating procedures for the current production stage. The verification algorithm uses sequence alignment-based technology to calculate the similarity between the operating steps in the generated text and the standard process. For key process links, the system implements more stringent item-by-item inspections to ensure that the parameter settings, execution sequence and time control of each operation node comply with the process specifications. The verification results generate a detailed compliance report, marking all deviation items and their severity ratings. The system also supports version management of process procedures to ensure that the verification standards are consistent with the currently executed process version.

[0054] The third-level audit checks compliance with safety standards. The system integrates multiple industry safety regulation databases to construct a multi-dimensional safety inspection matrix. The audit process utilizes a combination of forward reasoning and reverse verification, deriving potential safety impacts from generated text and verifying compliance with safety regulations. For work instructions involving significant hazards, the system performs an additional HAZOP analysis to identify potential hazards and operability risks. The output of the safety audit module includes a risk level assessment and specific improvement recommendations, providing clear guidance for subsequent document optimization.

[0055] When deploying a knowledge graph-based anomaly detection network, the system first maps the semantic elements of the generated text to the corresponding nodes of the industrial relationship graph. The detection algorithm uses a bidirectional propagation strategy. On the one hand, it expands along the relationship edges from the text nodes to construct a local reasoning subgraph; on the other hand, it verifies the semantic compliance of the text starting from the constraint rule nodes in the graph. Logical conflicts discovered during the detection process are classified and labeled, including parameter out-of-bounds conflicts, timing violation conflicts, and causal contradiction conflicts. The system creates a feature fingerprint for each type of conflict, supporting rapid conflict identification and location. The knowledge graph is updated and maintained using an incremental learning mechanism to ensure that newly acquired domain knowledge can be promptly integrated into the detection network.

[0056] When using a progressive text optimization algorithm, the system first applies a rule mapping mechanism to process content that clearly violates regulations. This mechanism accesses a predefined rule replacement table and automatically converts illegal expressions into compliant ones. For example, "immediately stop the cooling system" might be replaced with "gradually reduce the cooling system load according to procedure X." The semantic repair network then analyzes the contextual logic of the text and adjusts the connection between sentences to ensure that the revised content maintains semantic coherence. The repair process uses an attention mechanism to capture long-distance dependencies and avoid global semantic distortion caused by local optimization. Finally, the style adapter processes the text to industry language standards, unifies terminology, and adjusts sentence structure to ensure that the output text meets the document style requirements of the enterprise standard.

[0057] When implementing a dynamic rule update mechanism, the system establishes an automatic mining process for new conflict patterns. After each verification cycle, the system analyzes all flagged logical conflict cases and extracts common abnormal pattern characteristics. Newly discovered conflict patterns are evaluated by the expert system, formalized into computable constraint rules, and added to the domain constraint rule library. The rule conversion process uses semantic template technology to convert constraints described in natural language into machine-executable verification logic. The system also maintains rule effectiveness evaluation indicators and regularly eliminates outdated or inefficient constraint rules to keep the rule library streamlined and efficient. Rule update operations use hot deployment technology to ensure the continuous availability of verification services.

[0058] The entire logical consistency verification process is executed on a distributed computing platform, supporting horizontal scalability to meet large-scale text verification needs. The system utilizes a microservices architecture, allowing each verification component to be independently deployed and upgraded. Verification results generate standardized assessment reports containing detailed compliance analysis, risk warnings, and optimization recommendations. The system provides multiple output interfaces, enabling seamless integration with various industrial application systems. All verification operations are recorded in an audit log, meeting industrial quality traceability requirements. The system also possesses self-learning capabilities, continuously analyzing verification cases to continuously optimize algorithm parameters at each stage and improve overall verification performance.

[0059] Example 7, clear definitions and implementation methods of key terms, specifically including: In the present invention, matching confidence refers to the probability value output by the causal discrimination model to quantify the degree of match between the semantics of natural language text and contextual state data, and its value range is [0,1]. Specifically, this confidence is generated through a three-level computing framework: first, the basic matching degree calculation module performs a nonlinear transformation on the fused feature vector and outputs a preliminary score; second, the anomaly detection module uses the isolation forest algorithm to identify conflicting combinations in the feature space and downgrade the abnormal matching results; finally, the confidence verification layer performs a secondary verification of the discrimination results by querying the process rule library in the industrial knowledge graph. For example, when the matching confidence is greater than or equal to 0.9, the system determines that the semantics and state are highly matched and directly generates standard text; if it is less than or equal to 0.6, the error correction mechanism is triggered. The confidence calculation process is deployed in a dedicated inference engine to ensure real-time and repeatability.

[0060] The dynamic threshold adjustment module is a functional unit used to dynamically adjust the confidence threshold based on real-time operating conditions. Its core logic consists of three components: input parameters, adjustment rules, and hardware implementation. Input parameters include the safety level of the current process stage (e.g., high-risk, normal, or low-risk), the real-time anomaly index of equipment sensor data (e.g., temperature fluctuation), and historical operation success rate statistics. The adjustment rules utilize a fuzzy control algorithm. For example, in high-risk stages, the threshold is raised to 1.5 times the baseline value (e.g., from 0.7 to 1.05). In normal stages, the threshold is restored to the default value, with a smooth transition to avoid step changes. This module is deployed on an FPGA chip, with a response time of less than 10ms to meet the real-time requirements of industrial scenarios.

[0061] The fused feature vector is a high-dimensional vector of fixed dimension (e.g., 512) generated through a two-channel feature interaction network. It encodes the relationship between semantic information and contextual state data. The generation process includes the following steps: the first channel uses a gated fusion mechanism to dynamically adjust the complementarity of semantic features to state features; the second channel uses an inverse attention mechanism to calculate the correction weight of state features to semantic features. Subsequently, the feature purification module uses the industrial knowledge graph to filter out feature combinations that violate physical rules or process constraints, ultimately outputting an optimized fused feature vector. This vector serves as input to the causal discrimination model and directly determines the accuracy and security of the generated text.

[0062] The causal discrimination model is the core architecture for evaluating semantic and state matching in this paper. It employs a hierarchical reasoning design: At the bottom layer, a feature decoupling module (using an orthogonal constraint loss function) separates the fused feature vector into semantically dominant and state-dominant components. A bidirectional causal attention mechanism (based on the Query-Key-Value model) is deployed in the middle layer to calculate the mutual influence between the two components. The output layer consists of a parallel local matching analysis unit (evaluating parameter-level differences) and a global consistency check unit (verifying macro-level logic using a graph neural network). The model is trained using historical operational data from industrial scenarios and corresponding actual execution results as labels to ensure that the discrimination results are consistent with actual operating conditions.

[0063] The logical consistency checker is a functional module used to ensure that generated text complies with domain rules. Its workflow is divided into three levels: the first level verifies the consistency of text semantics with the physical constraints of the equipment (e.g., whether temperature instructions exceed the equipment's thermal limits); the second level verifies the compatibility of operating steps with process specifications (e.g., whether the process sequence conforms to standard procedures); and the third level verifies the compatibility of content with safety standards (e.g., whether hazardous operations are accompanied by protective warnings). During the verification process, an anomaly detection network traverses the industrial knowledge graph, locates semantic nodes that violate the rules, and generates compliant text using progressive optimization algorithms (e.g., rule mapping and replacement, semantic repair networks). The verification results are synchronously fed back to the causal discriminant model, forming a closed-loop optimization loop.

[0064] Example 8: To verify the feasibility and effectiveness of the present invention in the text content generation method based on artificial intelligence, the following implementation parameters and test procedures are selected for exemplary verification from the perspective of semantic processing: Semantic modeling architecture: This model employs a hierarchical semantic encoding structure consisting of four processing modules: a bottom-level word embedding layer (based on the BERT pre-trained model, with a dimension of 768 and a vocabulary size of 50,000); a mid-level syntactic-semantic fusion network (using the Graph Attention Network (GAT), with 8 heads and 512 hidden layers); a high-level context-aware module (a bidirectional LSTM with 3 layers and 256 hidden units); and an output-level semantic decoder (a Transformer decoder with 6 layers and 8 attention heads). The model supports long-text context modeling, with a maximum context window of 4096 tokens.

[0065] Semantic Embedding Channel Design: Based on the Semantic Dependency Graph, 12 pre-defined semantic role channels (such as agent, patient, time, place, and manner) are unevenly distributed throughout the sentence structure. The specific mapping relationships are dynamically generated based on the results of dependency syntax analysis (see the attached figure for details). Each channel independently encodes semantic features, enhancing local semantic focus.

[0066] Semantic sensitivity parameters: The word embedding sensitivity is set to a cosine similarity threshold of 0.85 to identify semantically similar words; the sentence vector change detection sensitivity is set to 1.2×10⁻³ Euclidean distance / word to capture subtle semantic shifts; the context coherence score is evaluated using the BLEURT-2.0 model, with a target score ≥ 0.78.

[0067] Training control strategy: During the model fine-tuning phase, an adaptive learning rate optimizer (AdamW) is used with an initial learning rate of 2e⁻ 5 , batch size 32, gradient clipping threshold 1.0. A semantic coherence loss (SCL) function is introduced with a weight coefficient λ=0.6 to prevent semantic drift. The attention mask is dynamically adjusted during training to ensure that key semantic nodes converge first.

[0068] Training environment parameters: NVIDIA A100 GPU cluster (8 cards in parallel), FP16 mixed precision training, 100,000 total training steps, 5,000 warm-up steps, and a training period of 72 hours.

[0069] Generation quality indicators: generated text semantic coherence score ≥ 0.81 (BLEURT), semantic fidelity ≥ 95% (compared with ROUGE-L through manual annotation), keyword retention rate ≥ 98%, and logical contradiction rate < 2%.

[0070] Test Input: We selected 10 typical text generation tasks as the test set, including technical report summary generation, news rewriting, dialogue continuation, legal document drafting, and popular science content creation. Each task contained 100 examples, for a total test set of 1,000 items. The input text ranged from 256 to 2,048 tokens in length, covering a variety of domains and genres.

[0071] Semantic collection and analysis: During the generation process, the sampling frequency is once per token, and the attention weight and hidden state vector of each semantic channel are collected. A semantic checkpoint is set at every 200 tokens to extract three indicators: semantic coherence, thematic consistency, and emotional stability. A two-dimensional semantic heat matrix is ​​constructed for monitoring the generation process.

[0072] Semantic Anomaly Detection: Through multi-scale semantic fluctuation analysis and contextual cross-validation, we identify three typical semantic anomaly patterns: Topic Drift, Logical Gap, and Anaphora Confusion. The localization error is ≤ 1 sentence unit (approximately 30 tokens).

[0073] Local semantic refinement model: For the identified semantic anomaly areas, a high-density semantic sub-model is constructed, and a fine-grained pointer network is used for local regeneration. The context window is expanded to 3 sentences before and after, and the number of model parameters is increased to 3 times that of the original model, with the number of nodes ranging from 1.3k to 1.6k.

[0074] Data fusion and iterative optimization: Real-time generated feedback (such as user corrections and semantic scoring) is used as boundary input; a historical high-quality generated corpus (containing 100,000 high-quality texts from five typical scenarios) is introduced; a multi-round iterative optimization algorithm based on semantic residuals is used. After 50 iterations, the semantic residual converges to a Euclidean distance of <0.005, significantly improving the semantic integrity of the generated text.

[0075] Performance evaluation results: After large-scale testing, the system successfully generated 1.2×10 6 The total amount of text that reached the user satisfaction threshold (≥4.5 / 5.0) was 1.24×10 6 The prediction accuracy error is <5%.

[0076] Risk Level Response Mechanism: When semantic deviation exceeds 70% of the threshold, a "semantic warning" (manual review) is triggered; when it exceeds 90%, a "semantic alarm" (automatic pause of generation) is triggered; when remaining semantic consistency falls below 5%, a "forced termination of generation" is triggered. Timeline Accuracy: Warning response delay is <0.5 seconds, and system upload delay is <1 second.

[0077] Communication and integration technology: Adopting a redundant WebSocket+MQTT dual-channel communication architecture, the KPI upload success rate is ≥99.0%, supporting real-time linkage with external knowledge bases (such as Wikidata and professional terminology libraries), and a trigger rate of 100%.

[0078] Comprehensive performance: During online generation, semantic consistency fluctuation is <0.03, and noise interference is <0.01 semantic unit; positioning accuracy: semantic anomaly detection error is ≤1 sentence, and accuracy is >96%; generation reliability: semantic fidelity prediction error is <5%, which is better than the average error of traditional models (13%); response timeliness: early warning response is <1 second, and remote intervention linkage success rate is 100%.

[0079] Through systematic verification of this example, an AI-based text content generation method has achieved a complete closed-loop chain, from semantic structure modeling, precise semantic acquisition, multi-channel data fusion, dynamic model evolution, generation quality prediction, to semantic risk control. This method not only significantly improves the semantic coherence and content fidelity of text generation, but also enables interpretable monitoring and real-time intervention of the generation process. It possesses high stability and cross-domain transferability, fully demonstrating the innovation and practical value of this invention in the field of intelligent content generation.

[0080] The present invention operates by acquiring natural language text input to be processed and extracting its semantic recognition feature vector. Simultaneously, it acquires contextual state data associated with the text and encodes it into a state recognition feature vector. The system then jointly calculates and analyzes the semantic recognition feature vector and the state recognition feature vector to generate a fused feature vector that incorporates the relationship between semantic information and contextual state information. Based on this fused feature vector, the system constructs a causal discrimination model to identify the degree of match between the text semantics and the contextual state and outputs a match confidence score. Based on this confidence score, the system dynamically adjusts the generation strategy: if the confidence score exceeds a set threshold, text content consistent with the contextual state is generated; otherwise, an error correction mechanism is triggered, outputting a corrected text. Finally, the system performs a logical consistency check on the generated text to ensure that it complies with pre-set domain constraints and outputs the optimized text content. This technical solution combines multi-dimensional processing, including semantic understanding, context awareness, causal reasoning, confidence assessment, error correction, and logical verification, to improve the accuracy, security, and adaptability of text generation to industrial scenarios. It is particularly suitable for industrial control environments with high reliability requirements.

[0081] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for generating text content based on artificial intelligence, characterized in that: The following steps are involved: Obtaining a natural language text input to be processed, extracting its semantic recognition feature vector, and simultaneously obtaining context state data associated with the natural language text and encoding it into a state recognition feature vector; Performing joint calculation and analysis on the semantic recognition feature vector and the state recognition feature vector to generate a fused feature vector, wherein the fused feature vector includes the association relationship between the semantic information and the context state information; Based on the fused feature vector, a causal discrimination model is constructed, wherein the causal discrimination model is used to identify the degree of match between the text semantics and the context state, and output the matching confidence; According to the matching confidence, the output strategy of the causal discrimination model is adjusted. If the confidence is higher than a set threshold, text content consistent with the context state is generated; otherwise, an error correction mechanism is triggered to output the corrected text; Perform logical consistency checks on the generated text to ensure that it complies with the preset domain constraint rules, and finally output the optimized text content.

2. The method for generating text content based on artificial intelligence according to claim 1, wherein: The extraction of semantic recognition feature vectors comprises the following steps: A deep bidirectional semantic understanding network is used to perform multi-level parsing of natural language text, obtaining basic semantic representations through word-level encoders and capturing key semantic components through sentence-level attention mechanisms. The parsed semantic representation is input into the feature refinement module, which includes a domain term filter and a logical relationship extractor set in parallel to filter out irrelevant semantic components and strengthen the representation of core operational intent, respectively. The state-aware feature compression layer outputs a fixed-dimensional semantic recognition feature vector. The feature compression layer adopts a dynamic weight allocation mechanism to automatically adjust the contribution weight of each semantic component according to the type of contextual state data.

3. The method for generating text content based on artificial intelligence according to claim 1, wherein: The encoding state identification feature vector comprises the following steps: Establish a multi-source state data fusion channel to perform time alignment and dimension normalization on equipment sensor data, process stage markers, and environmental monitoring parameters; The fused state data is processed through a spatiotemporal feature extraction network. The network consists of parallel temporal convolution branches and spatial attention branches, which respectively capture the dynamic evolution of state variables and the spatial correlation of key parameters. The state feature reorganization module is used to perform structured encoding on the extracted spatiotemporal features. The state feature reorganization module divides the features into three feature subspaces: equipment status, process constraints, and safety boundaries according to the preset industrial knowledge graph. The structured encoded state information is mapped into a state recognition feature vector that matches the semantic feature dimension through the feature projection layer.

4. The method for generating text content based on artificial intelligence according to claim 1, wherein: The combined calculation and analysis of the semantic recognition feature vector and the state recognition feature vector comprises the following steps: A dual-channel feature interaction network is constructed. The first channel uses a gated fusion mechanism to dynamically adjust the complement strength of semantic features to state features, and the second channel calculates the correction weight of state features to semantic features through a reverse attention mechanism. A three-level feature reorganization operation is implemented in the feature interaction space. First, the element-by-element multiplication operation of feature dimension alignment is performed, then cross-channel feature splicing is performed, and finally the intermediate fusion feature is generated through a nonlinear transformation layer. A feature purification module based on industrial knowledge constraints is used to process intermediate fusion features. The feature purification module contains a logical consistency checker and a physical feasibility discriminator set in parallel, which are used to eliminate fusion results that violate domain rules and filter feature combinations that do not meet equipment operation constraints, respectively.

5. The method for generating text content based on artificial intelligence according to claim 1, wherein: The process of generating the fused feature vector specifically includes: Establish a semantic-state association matrix, and map the semantic recognition feature vector and state recognition feature vector into a unified association space through bidirectional feature projection; Implement feature cross-calculation in the association space, first perform outer product operations on feature vectors to generate interaction tensors, and then extract key interaction patterns through the compression excitation network; The interaction pattern is input into a multi-granularity feature synthesizer, which consists of a local feature focusing unit and a global feature integration unit connected in series to capture the fine-grained parameter association relationship and the macroscopic semantic and state matching rules respectively. Finally, the fused feature vector is output through the feature stability optimization layer. The optimization layer adopts an iterative refinement mechanism to gradually eliminate noise interference and redundant information in the feature fusion process.

6. The method for generating text content based on artificial intelligence according to claim 1, characterized in that: The construction of the causal discrimination model includes the following steps: A hierarchical causal inference architecture is established. A feature decoupling module is set up at the bottom layer to separate the fused feature vector into semantic-dominant and state-dominant components. A bidirectional causal attention mechanism is deployed at the middle layer to calculate the mutual influence relationship between components. Design a dynamic causal weight allocation strategy to automatically adjust the contribution ratio of semantic components and state components in the final judgment result according to the type of current processing task; A multi-scale matching evaluation network is constructed. Through the local matching analysis unit and the global consistency verification unit set in parallel, the parameter-level matching is evaluated from the micro level and the overall logical rationality is verified at the macro level.

7. The method for generating text content based on artificial intelligence according to claim 1, wherein: The output matching confidence of the causal discriminant model is achieved in the following way: Adopting a confidence-grading calculation framework, the basic matching calculation module first generates a preliminary confidence score, and then the anomaly detection module identifies potential conflicting feature combinations; Deploy a feedback confidence calibration mechanism to input the error signal formed by comparing historical judgment results with actual execution results into the calibration network, and dynamically correct the current confidence calculation parameters; A confidence verification layer based on the industrial knowledge graph is established, and the discrimination results are secondary verified by querying the preset process rule library, and finally the matching confidence index after multi-level verification is output.

8. The method for generating text content based on artificial intelligence according to claim 1, wherein: The output strategy of the adjustment generation causal discrimination model includes the following steps: Build a dynamic threshold adjustment module to automatically adjust the confidence threshold according to the criticality of the current process stage and set stricter judgment criteria for high-risk operation stages; Implement a multimodal output strategy selection mechanism. When the confidence is higher than a threshold, the standard text generation channel is activated. When it is lower than the threshold, the alternative solution retrieval channel and the safety constraint generation channel are started in parallel. Deploy the output result buffer verification layer to perform real-time feasibility simulation of the text content to be generated, and verify its execution effect under actual working conditions through the digital twin system.

9. The method for generating text content based on artificial intelligence according to claim 1, wherein: The triggering error correction mechanism includes the following processing flow: Establish a three-level error tracing system, first locating the characteristic dimensions of semantic-state mismatch, then identifying the key parameters that cause the mismatch, and finally analyzing the reliability of the underlying data collection; Activate the adaptive text reconstruction network, which consists of a series of error feature shielding layers, domain knowledge injection layers, and semantic repair layers to gradually correct the original generated content; A closed-loop feedback learning mechanism is implemented to store typical error patterns and their correction solutions found during the error correction process into the case library for optimizing subsequent causal discrimination model parameters.

10. The method for generating text content based on artificial intelligence according to claim 1, characterized in that: The logic consistency check includes the following steps: Build a multi-level rule verification engine. The first level verifies the conformity of text semantics with the physical constraints of the equipment. The second level verifies the matching degree between operating instructions and process procedures. The third level verifies the compatibility of generated content with safety standards. Deploy a knowledge graph-based anomaly detection network, which traverses a preset industrial relationship graph to identify logical conflict nodes in the text and locate specific semantic components that violate domain rules. A progressive text optimization algorithm is used to first replace obvious violations through rule mapping, then adjust the contextual coherence through a semantic repair network, and finally ensure that the output text conforms to industry standard expression through a style adapter; Implement a dynamic rule update mechanism to automatically add new logical conflict patterns discovered during the verification process to the domain constraint rule library, and continuously improve the coverage of the verification system.

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