A standard text generation system based on artificial intelligence

Abstract

This invention relates to the fields of artificial intelligence and natural language processing technology, specifically to a standard text generation system based on artificial intelligence. It includes standard graph construction and parsing, multi-source heterogeneous data cleaning and mapping, hierarchical constraint generation, and real-time reverse verification and feedback correction iteration units. The system parses the standard to construct a constraint tree and uses semantic matching to attach the original material. Its core is to use a large language model to perform hierarchical generation and compare the original constraints to calculate compliance residual signals. Based on the residuals, targeted rewriting is triggered, and metadata constraints are optimized based on verification failure events. This invention eliminates the logical confusion caused by contextual limitations in large models by decomposing long texts into node-level micro-tasks, ensuring the logical rigor of the generation process.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and natural language processing technology, specifically to a standard text generation system based on artificial intelligence. Background Technology

[0002] In the field of standard text and professional technical document generation, business scenarios typically require the output of long documents that are logically rigorous and conform to specific industry standards, based on multi-source heterogeneous original materials. Existing solutions generally adopt a general large language model combined with an end-to-end streaming generation mode, or simply rely on prompt word engineering for loose guidance. However, this uncontrolled generation method faces significant challenges: on the one hand, when processing long texts, the model is limited by the context window and is easily affected by redundant information in the full text, resulting in logical drift and causal reversal; on the other hand, due to the lack of physical isolation and structured constraints on data boundaries, the model often exhibits data illusions, misattribution, and other phenomena, making it difficult to meet the rigid compliance requirements of professional documents for numerical consistency, clause citation, and content elements.

[0003] Therefore, overcoming the logical failure of general large models in long text generation, and achieving precise control over business logic and compliance while ensuring text fluency, is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a standard text generation system based on artificial intelligence. Specifically, the technical solution of this invention includes:

[0005] The standard graph construction and parsing unit is used to parse industry standard documents and construct a domain standard constraint tree; to configure metadata constraints for the nodes of the domain standard constraint tree; and to receive historical high-frequency defect statistics and optimize the metadata constraints based on the historical high-frequency defect statistics.

[0006] A multi-source heterogeneous data cleaning and mapping unit is used to receive the original materials of the project and the domain standard constraint tree; to perform noise reduction and standardization processing on the original materials; and to use a semantic matching algorithm to attach the processed data fragments to specific logical nodes of the domain standard constraint tree to generate an instantiated constraint tree.

[0007] The hierarchical constraint generation unit is used to receive the attached data and constraint conditions of a single node in the instantiated constraint tree; and to use a large language model to transform the attached data into candidate text fragments that meet the constraint conditions.

[0008] The instantaneous reverse verification unit is used to receive the candidate text fragment and the original metadata constraint corresponding to the node; and to perform a logical comparison between the candidate text fragment and the original metadata constraint to calculate the compliance residual signal.

[0009] The feedback correction iteration unit is used to receive the compliance residual signal; when the compliance residual signal indicates the existence of a defect, it is used to construct a correction prompt word and feed it back to the hierarchical constraint generation unit to trigger a targeted rewrite; and it is used to count verification failure events, generate the historical high-frequency defect statistics and feed them back to the standard map construction and parsing unit.

[0010] Preferably, the standard graph construction and parsing unit constructs a domain standard constraint tree, including:

[0011] The industry standard document is parsed into a business logic topology diagram;

[0012] The chapters of the industry standard document are converted into nodes of the business logic topology diagram;

[0013] The logical dependencies between the chapters are transformed into topological connections between the nodes.

[0014] Preferably, the standard map construction and parsing unit is configured with metadata constraints, including:

[0015] Configure a dependency input constraint for each node, the dependency input constraint defining that the node must reference data from the preceding node;

[0016] Configure content element constraints for each node, the content element constraints defining the formulas, clauses or terms that the node must contain;

[0017] Configure boundary constraints for each node, the boundary constraints defining the word count and tone of the node.

[0018] Preferably, the hierarchical constraint generation unit generates candidate text fragments, including:

[0019] A hierarchical task decomposition network strategy is adopted.

[0020] Perform a local optimal generation action, focusing only on the constraints of the current node without considering the contextual interference of the entire text;

[0021] The semantic capabilities of the large language model are used to perform text transformation.

[0022] Preferably, the instantaneous reverse verification unit calculates the compliance residual signal, including:

[0023] Perform residual calculation and compliance assessment actions;

[0024] Determine whether the candidate text fragment completely contains all the elements required by the original metadata constraints;

[0025] If the candidate text fragment completely contains all the elements, the compliance residual signal is set to zero, instructing the system to merge the text;

[0026] If the candidate text fragments are missing or contradictory, the compliance residual signal is set to be greater than zero, and a defect diagnosis report is generated.

[0027] Preferably, the feedback correction iteration unit constructs correction prompts, including:

[0028] Extract the specific issues identified in the defect diagnosis report;

[0029] Construct the corrected prompt words for the specific problem;

[0030] The correction prompt is fed back to the hierarchical constraint generation unit, forcibly triggering a targeted rewrite for a specific node until the compliance residual signal returns to zero.

[0031] Preferably, the feedback correction iteration unit generates historical high-frequency defect statistics, including:

[0032] The structured record records each validation failure event;

[0033] Extract feature data containing node identifiers, defect types, and number of fixes, and store it in the historical defect database;

[0034] When the frequency of a specific type of defect at a specific node exceeds a preset threshold, the historical high-frequency defect statistics are generated, triggering the adaptive optimization process of the standard map construction and analysis unit.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. This system transforms industry standard documents into domain standard constraint trees containing business logic topology graphs through standard graph construction and parsing units, and executes local optimal generation strategies using hierarchical constraint generation units. This mechanism decomposes the long text generation task into micro-tasks targeting individual nodes, physically severing the attention connection between the current node and unrelated historical text in the full text. This eliminates the risk of logical confusion caused by context window limitations and redundant information interference in general large language models, ensuring that the generation process strictly follows the inherent causal chain of business logic.

[0037] 2. This system utilizes a multi-source heterogeneous data cleaning and mapping unit to precisely anchor discrete raw material data to specific logical nodes of the domain standard constraint tree through a semantic matching algorithm, generating an instantiated constraint tree. Combined with the dependency input constraints in the metadata constraints, the system establishes a strong binding relationship between data and nodes, physically blocking the model from accessing data beyond the boundaries of unrelated nodes. This design effectively prevents the model from fabricating data out of thin air, ensuring the consistency of cross-chapter data flow and the accuracy of numerical values ​​and entities in the generated content.

[0038] 3. This system constructs a dual-loop verification mechanism consisting of an immediate reverse verification unit and a feedback correction iteration unit. By calculating compliance residual signals, the system performs element checks on the generated candidate text fragments based on set theory. Once an element omission or logical contradiction is found, it immediately triggers targeted rewriting by constructing correction prompt words. This rigorous workflow of pre-generation constraint specification, local focus during generation, and immediate judgment after generation solves the problem that existing technologies that rely solely on prompt word engineering cannot meet the rigid requirements of professional documents for numerical consistency, clause citation, and content elements.

[0039] 4. This system generates historical high-frequency defect statistics by statistically analyzing failure events through feedback correction iteration units, and feeds them back to the standard map construction and parsing unit to drive the optimization of metadata constraints. The system can automatically identify logically weak nodes and dynamically increase the constraint strength of the nodes or adjust the parameters. This self-learning effect, which becomes stronger with use, enables the system to automatically adapt to the complex specifications and error-prone links in specific fields, thereby maintaining long-term technical vitality and high accuracy in different application scenarios. Attached Figure Description

[0040] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0041] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0043] Example 1:

[0044] Please see Figure 1 A standard text generation system based on artificial intelligence, comprising:

[0045] The standard graph construction and parsing unit is used to parse industry standard documents and build a domain standard constraint tree; it is used to configure metadata constraints for the nodes of the domain standard constraint tree; and it is used to receive historical high-frequency defect statistics and optimize metadata constraints based on the historical high-frequency defect statistics.

[0046] The multi-source heterogeneous data cleaning and mapping unit is used to receive the original materials and domain standard constraint tree of the project; to perform noise reduction and standardization processing on the original materials; and to use semantic matching algorithms to attach the processed data fragments to specific logical nodes of the domain standard constraint tree to generate instantiated constraint trees.

[0047] The hierarchical constraint generation unit is used to receive the attached data and constraint conditions of a single node in the instantiated constraint tree; it is used to transform the attached data into candidate text fragments that meet the constraint conditions using a large language model.

[0048] The instantaneous reverse verification unit is used to receive the original metadata constraints corresponding to the candidate text fragments and nodes; it is used to logically compare the candidate text fragments with the original metadata constraints and calculate the compliance residual signal.

[0049] The feedback correction iteration unit is used to receive compliance residual signals; when the compliance residual signal indicates a defect, it is used to construct correction prompts and feed them back to the hierarchical constraint generation unit to trigger targeted rewriting; it is used to statistically analyze verification failure events, generate historical high-frequency defect statistics and feed them back to the standard map construction and parsing unit.

[0050] This embodiment describes an AI-based standard text generation system. This system addresses the logical drift and compliance failure issues inherent in general large models during long text generation by constructing a graph-driven, hierarchical generation, and dual-loop verification technical architecture. The standard graph construction and parsing unit acts as the logic controller in this system. Its operating mechanism transforms unstructured industry standard documents into machine-parsable domain standard constraint trees. These domain standard constraint trees are not simple directory structures but rather directed acyclic graphs containing node and edge attributes. Nodes represent specific business chapters, and edges represent strong logical dependencies between chapters. This unit is also equipped with an adaptive evolution mechanism. Upon receiving historical high-frequency defect statistics from the feedback correction iteration unit, it utilizes reverse index mapping logic based on unique node identifiers to accurately trace back and lock the received discrete defect data to the specific weak node in the graph that generated the defect, and automatically increases the metadata constraint strength of that node, thereby achieving dynamic iteration of the system's knowledge base.

[0051] The multi-source heterogeneous data cleaning and mapping unit is configured to perform structured data placement operations. Faced with messy original project materials, after denoising and format standardization, this unit does not directly input the data into the large model context window. Instead, it uses a high-dimensional vector semantic matching algorithm to accurately anchor discrete data fragments to specific logical nodes of the domain standard constraint tree, thereby generating an instantiated constraint tree. This process establishes a strong data-node binding relationship, physically blocking the model from accessing unrelated nodes in the subsequent generation process, effectively eliminating the illusion of misattributed data.

[0052] The hierarchical constraint generation unit performs text output based on a local optimum strategy. Unlike traditional end-to-end streaming generation, this unit loads only the information of a single node in the instantiated constraint tree each time. In a controlled environment that isolates the interference of the entire long context, it uses the semantic reconstruction capability of the large language model to transform the structured data attached to the node into candidate text fragments that meet the specific constraints of the node. This mechanism uses the divide-and-conquer approach to ensure that the model's attention is highly focused, thereby ensuring the accuracy of the content at the micro level.

[0053] The instant reverse verification unit builds the first line of quality defense in the system. This unit performs element verification by executing a deterministic logical comparison procedure, comparing the generated candidate text fragments with the original metadata constraints preset by the node. During this process, a compliance residual signal is calculated, which is a logical vector used to quantify the deviation between the generated content and the standard requirements. The text will only be accepted by the system when the signal indicates that the deviation is zero; otherwise, the system will block the process and output the defect characteristics.

[0054] The feedback correction iteration unit constitutes the second line of defense for the system. Once the compliance residual signal indicates a defect, the unit immediately analyzes the defect type, dynamically constructs correction prompts containing specific rectification instructions, and forces the hierarchical constraint generation unit to perform targeted rewriting for the specific node until the logical deviation is eliminated. At the same time, the unit performs long-term statistics on verification failure events and generates feedback data to drive the structural optimization of the front-end graph.

[0055] Through the coordinated operation of the above-mentioned units, this invention establishes a rigorous workflow of pre-generation constraint specification, local focus during generation, and immediate judgment after generation, which is significantly different from the loose generation mode in the prior art that only relies on prompt word engineering. Thus, while ensuring the fluency of the text, it achieves rigid control of industry standard compliance.

[0056] Example 2:

[0057] Standard graph construction and parsing unit construction of domain standard constraint trees, including:

[0058] Parse industry standard documents into business logic topology diagrams;

[0059] Transform the chapters of industry standard documents into nodes of a business logic topology diagram;

[0060] Transform the logical dependencies between chapters into topological connections between nodes.

[0061] In this embodiment, the standard graph construction and parsing unit executes a deep parsing process based on natural language understanding, aiming to reconstruct linear text into a topological network. During the construction of the domain standard constraint tree, this unit utilizes paragraph recognition and semantic segmentation techniques to break down the input industry standard document into its smallest semantic units, mapping these independent chapters to specific nodes in the business logic topology graph. To establish the logical order between nodes, this unit further performs dependency parsing to identify implicit logical connectives in the document and instantiates the logical dependencies between these chapters into directed topological connections linking each node. After completing the above topology construction, the business logic topology graph is established as the basic structural architecture of the domain standard constraint tree.

[0062] Specifically, if clause A in the standard document stipulates that analysis must be based on the results of test data B, the parsing unit will establish a data flow-oriented topology connection between the test data B node and the analysis node, defining a unidirectional data flow path. Through this technical means of transforming static documents into dynamic graphs, this invention constructs a navigation system with strict temporal constraints for text generation tasks, ensuring that the generation process strictly follows the inherent causal chain of business logic, effectively avoiding the risks of chapter logic inversion or chaotic causal relationships commonly found in traditional linear generation.

[0063] Example 3:

[0064] Standard graph construction and parsing unit configuration metadata constraints, including:

[0065] Configure dependency input constraints for each node. Dependency input constraints define that a node must reference data from its predecessor node.

[0066] Configure content element constraints for each node. Content element constraints define the formulas, clauses, or terms that a node must contain.

[0067] Configure boundary constraints for each node, which define the number of words and tone of the node.

[0068] In this embodiment, the standard graph construction and parsing unit achieves fine-grained control over the generation behavior by implanting multi-dimensional rigid rules, i.e., metadata constraints, into each node of the domain standard constraint tree; these constraints constitute the boundary fence of model generation, specifically including the following three dimensions:

[0069] Dependency Input Constraint: This constraint defines the data ingestion path for the current node. For example, for the economic benefit analysis node, this constraint explicitly specifies that it must and can only read the total cost value output by the preceding cost accounting node and the estimated sales value output by the market forecasting node. To address the potential interruption of data flow in real-world scenarios, this constraint mechanism also includes a null value circuit breaker strategy: if the preceding node fails to produce valid output, the system will trigger an abnormal interruption, prompting the user to manually supplement the data or call a preset industry average benchmark value as a substitute input. This mechanism enforces cross-chapter data consistency, eliminating the possibility of the model fabricating data out of thin air.

[0070] Content element constraints: This constraint defines the list of required content for the current node; the list is stored in structured data format and includes the specific legal clause numbers that must be cited in this chapter, the calculation formulas that must be displayed, and the industry-specific terms that must appear; this constraint provides a clear checklist for subsequent verification units;

[0071] Boundary constraints: This constraint defines the stylistic features and size limits of the generated text; it quantifies the word count range of nodes, specifies specific tone parameters, and defines negative constraints on the generation.

[0072] By combining the above-mentioned input constraints, content element constraints, and boundary constraints, this invention restricts the originally divergent generation capabilities of large language models to a predetermined logical framework. This mechanism of "dancing in shackles" ensures that the generated content not only possesses the language polishing advantages of large models but also fully complies with rigorous industry standards and specifications, thus solving the compliance impedance matching problem in professional document generation.

[0073] Example 4:

[0074] The hierarchical constraint generation unit generates candidate text fragments, including:

[0075] A hierarchical task decomposition network strategy is adopted.

[0076] Perform local optimal generation actions, focusing only on the constraints of the current node without considering the contextual interference of the entire text;

[0077] Leveraging the semantic capabilities of large language models, text transformation is performed.

[0078] In this embodiment, the hierarchical constraint generation unit changes the computational paradigm of long text generation by implementing a hierarchical task decomposition network strategy. Addressing the issue of long text forgetting caused by context window limitations in traditional models, this unit adopts a divide-and-conquer approach. During the generation process, this unit performs locally optimal generation actions: the system physically severs the attention connection between the current node and irrelevant historical texts in the full text, but retains the directional data channel. This unit is configured with a dynamic context injection interface. When generating a specific chapter, the system, based on the dependency input constraints defined in Embodiment 3, directionally retrieves key data generated by the preceding node from the system cache and injects it as temporary prompts into the current context window. This allows the model to completely lock computational resources onto the specific data slice and specific constraints attached to the current node without considering the interference of long contextual information throughout the full text.

[0079] Within this controlled local context, the unit invokes the advanced semantic processing module of the large language model to perform text conversion from structured data to natural language. For example, it transforms a set of discrete data containing experimental conditions of 25°C, 60% humidity, and acceptable results into a coherent statement that meets engineering report specifications, indicating that the test results are acceptable under experimental conditions of 25°C and 60% relative humidity. This generation strategy significantly improves the model's ability to follow detailed instructions by reducing the contextual complexity of the task, ensuring that each local segment achieves extremely high accuracy and compliance, thereby achieving high-quality global output through the accumulation of high-quality local data.

[0080] Example 5:

[0081] The real-time reverse verification unit calculates the compliance residual signal, including:

[0082] Perform residual calculation and compliance assessment actions;

[0083] Determine whether the candidate text fragment fully contains all the elements required by the original metadata constraints;

[0084] If the candidate text fragment completely contains all elements, the compliance residual signal is set to zero, instructing the system to merge the text;

[0085] If there are omissions or contradictions in the candidate text fragments, the compliance residual signal is set to be greater than zero, and a defect diagnosis report is generated.

[0086] In this embodiment, the real-time reverse verification unit introduces a logical verification mechanism based on set theory. The residual calculation and compliance judgment actions performed by this unit are not simple numerical calculations, but comparison operations based on semantic sets. Specifically, the system calls a preset set of constraint elements A. At the same time, the unit has a built-in entity relationship extraction model for a specific domain, which is used to scan the generated candidate text fragments in real time, extract the key indicators, clause references and values, and instantiate them into a set of generated content elements B. This unit determines the compliance status by calculating the difference set.

[0087] During the judgment process, if the difference set is empty, meaning the candidate text fragment completely covers all the key information points required by the constraints, the unit sets the compliance residual signal to a logic zero level. This signal acts as a gating trigger, directly instructing the system to merge the current fragment into the main document stream. Conversely, if the difference set is not empty, it indicates that there is a missing element; or if a numerical conflict is detected between the data in set B and the mounted data, it indicates a logical contradiction. At this time, the unit sets the compliance residual signal to a high-level alarm state greater than zero. Accompanying this alarm signal, the unit simultaneously generates a defect diagnosis report, which details the specific missing or erroneous items. This mechanism builds a quality valve with mandatory veto power on the text generation pipeline, ensuring that any flawed content that does not meet industry standards cannot flow into the final deliverable.

[0088] Example 6:

[0089] The feedback correction iteration unit constructs correction prompts, including:

[0090] Extract the specific issues identified in the defect diagnosis report;

[0091] Develop corrective prompts for specific problems;

[0092] The correction prompt is fed back to the hierarchical constraint generation unit, forcibly triggering a targeted rewrite for a specific node until the compliance residual signal is zero.

[0093] In this embodiment, the feedback correction iteration unit achieves automated error correction by constructing an adaptive prompt word engineering closed loop. When a high-level residual signal is received, the unit immediately starts the parsing program to accurately extract the specific problem features that caused the verification failure from the defect diagnosis report. Based on these features, the unit uses a preset instruction template to dynamically construct a high-priority correction prompt word. This prompt word typically includes three parts: the original generation task instruction, the specific defect description, and the mandatory correction requirements.

[0094] This unit sends the correction prompt back to the hierarchical constraint generation unit, forcibly triggering a targeted rewrite process for the erroneous node. This generation-verification-correction loop will continue to iterate until the compliance residual signal is zero, that is, by multiple approximations, it ensures that the generated content is completely fitted to the standard constraints. This mechanism transforms the repeated modification process commonly found in manual proofreading into automatic iteration within the machine, greatly improving the one-time usability of the generated results. In particular, for legal and engineering documents with extremely high requirements for logical rigor, this technical solution demonstrates significant advantages.

[0095] Example 7:

[0096] The feedback correction iteration unit generates historical high-frequency defect statistics, including:

[0097] The structured record records each validation failure event;

[0098] Extract feature data containing node identifiers, defect types, and number of fixes, and store it in the historical defect database;

[0099] When the frequency of a specific type of defect at a specific node exceeds a preset threshold, historical high-frequency defect statistics are generated, triggering the adaptive optimization process of the standard map construction and analysis unit.

[0100] In this embodiment, the feedback correction iteration unit is not only a real-time error corrector, but also a data miner for system evolution. During the system's operating cycle, this unit will structurally record each verification failure event that triggers the correction process. The system cleans and extracts key multi-dimensional feature data from the logs, including the identifier of the faulty node, the defect type, and the number of corrections required to resolve the problem, and persists this data in the historical defect database.

[0101] The system has a statistical judgment logic: when the cumulative frequency of a specific type of defect at a specific node exceeds a preset threshold based on statistical significance, the unit determines that node as a logical weak point in the system and generates historical high-frequency defect statistics accordingly. This data signal is immediately sent to the standard map construction and analysis unit as a negative feedback signal to trigger the system's adaptive optimization process. The specific execution logic is as follows: the system has a preset set of mapping rule tables. If the historical high-frequency defect statistics indicate that the frequency of content element omission defects at a specific node exceeds a preset 5% threshold, the system will automatically add a set of high-weight essential keywords to the content element constraints of that node and add the edges... The minimum word count in the boundary constraints is increased by 10%; if the frequency of data consistency defects exceeds 3%, the system will automatically upgrade the mandatory level of the dependent input constraints of that node from suggested to rigidly locked, and add a negative constraint instruction prohibiting the use of external knowledge to the prompt message; through the above rule-based parameter adjustment, this process may include automatically tightening the constraints of the node, adding additional prompt messages, or adjusting the data mapping rules; through this mechanism, the present invention achieves a self-learning effect that gets stronger with use, enabling the system to automatically adapt to the complex specifications and error-prone links of specific domains as business data accumulates, thereby maintaining the long-term vitality and robustness of the technical solution in different application scenarios.

[0102] It should be noted that the above 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A standard text generation system based on artificial intelligence, characterized in that, include: The standard graph construction and parsing unit is used to parse industry standard documents and construct domain standard constraint trees. Used to configure metadata constraints for nodes in the domain standard constraint tree; Used to receive historical high-frequency defect statistics and optimize the metadata constraints based on the historical high-frequency defect statistics; A multi-source heterogeneous data cleaning and mapping unit is used to receive the original materials of the project and the domain standard constraint tree; and to perform noise reduction and standardization processing on the original materials. This is used to attach the processed data fragments to specific logical nodes of the domain standard constraint tree using a semantic matching algorithm, thereby generating an instantiated constraint tree; A hierarchical constraint generation unit is used to receive the mounted data and constraint conditions of a single node in the instantiated constraint tree; This is used to utilize a large language model to transform the mounted data into candidate text fragments that meet the constraints. An instantaneous reverse verification unit is used to receive the original metadata constraints corresponding to the candidate text fragment and the node. Used to logically compare the candidate text fragments with the original metadata constraints and calculate the compliance residual signal; A feedback correction iteration unit is used to receive the compliance residual signal; This is used to construct a correction prompt word and feed it back to the hierarchical constraint generation unit to trigger a targeted rewrite when the compliance residual signal indicates a defect. Used to statistically analyze verification failure events, generate the historical high-frequency defect statistics, and feed them back to the standard map construction and parsing unit; The instantaneous reverse verification unit calculates the compliance residual signal, including: Perform residual calculation and compliance assessment actions; Determine whether the candidate text fragment completely contains all the elements required by the original metadata constraints; If the candidate text fragment completely contains all the elements, the compliance residual signal is set to zero, instructing the system to merge the text; If the candidate text fragments are missing or contradictory, the compliance residual signal is set to be greater than zero, and a defect diagnosis report is generated. The feedback correction iteration unit constructs correction prompts, including: Extract the specific issues identified in the defect diagnosis report; Construct the corrected prompt words for the specific problem; The correction prompt is fed back to the hierarchical constraint generation unit, forcibly triggering a targeted rewrite for a specific node until the compliance residual signal returns to zero. The feedback correction iterative unit generates historical high-frequency defect statistics, including: The structured record records each validation failure event; Extract feature data containing node identifiers, defect types, and number of fixes, and store it in the historical defect database; When the frequency of a specific type of defect at a specific node exceeds a preset threshold, the historical high-frequency defect statistics are generated, triggering the adaptive optimization process of the standard map construction and analysis unit.

2. The standard text generation system based on artificial intelligence according to claim 1, characterized in that, The standard graph construction and parsing unit constructs a domain standard constraint tree, including: The industry standard document is parsed into a business logic topology diagram; The chapters of the industry standard document are converted into nodes of the business logic topology diagram; The logical dependencies between the chapters are transformed into topological connections between the nodes.

3. The standard text generation system based on artificial intelligence according to claim 1, characterized in that, The standard map construction and parsing unit configuration metadata constraints include: Configure a dependency input constraint for each node, the dependency input constraint defining that the node must reference data from the preceding node; Configure content element constraints for each node, the content element constraints defining the formulas, clauses or terms that the node must contain; Configure boundary constraints for each node, the boundary constraints defining the word count and tone of the node.

4. The standard text generation system based on artificial intelligence according to claim 1, characterized in that, The hierarchical constraint generation unit generates candidate text fragments, including: A hierarchical task decomposition network strategy is adopted. Perform a local optimal generation action, focusing only on the constraints of the current node without considering the contextual interference of the entire text; The semantic capabilities of the large language model are used to perform text transformation.

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