Multi-dimensional review method for reservoir and dam safety evaluation report based on large language model

Abstract

The present invention relates to the technical field of large language models, and specifically to a multi-dimensional review method for reservoir dam safety evaluation reports based on large language models. By constructing a multi-dimensional insight auxiliary brain model, the reservoir dam safety evaluation report is processed; using risk pre-running, a risk warning text is output; applying scenario verification, refining parameters to drive external simulation, and outputting a comparison analysis report; using natural language processing technology, and constructing traceable data links, to generate a semantic link traceability set; the multi-dimensional insight auxiliary brain model conducts a comprehensive review of the semantic link traceability set, and records the review difficulties, and generates a multi-dimensional review report through a human-computer co-intelligence reporting engine. At present, the review method for reservoir dam safety evaluation reports has the problem of a single review dimension. To this end, the present invention proposes a multi-dimensional review method for reservoir dam safety evaluation reports based on a large language model.

Description

Technical Field

[0001] The present invention relates to the technical field of large language models, and in particular to a multi-dimensional review method for reservoir dam safety evaluation reports based on large language models. Background Art

[0002] Reservoir dam safety assessment reports are core technical documents for determining the operational status of dams and making management decisions. The accuracy of their textual content, the rigor of their analytical logic, and the reliability of their conclusions are directly related to project safety and the public interest, and therefore must be rigorously reviewed. The core function of this review is to utilize natural language understanding and text mining techniques to conduct a multi-dimensional examination and semantic verification of the report's textual narrative, data representation, computational representation, and logical reasoning. This approach aims to ensure the objectivity and scientific rigor of the assessment conclusions, efficiently extract key safety semantics and status knowledge, and proactively discover and accurately assess the various risk semantics revealed or implied in the report text. Ultimately, this provides intelligent decision-making support based on in-depth analysis for safety management, risk prevention, and scientific maintenance decisions throughout the dam's life cycle.

[0003] Currently, the review of reservoir and dam safety assessment reports is primarily based on manual review, supplemented by computer-assisted tools. During manual review, domain experts draw on their expertise, engineering experience, and relevant technical specifications to meticulously examine, analyze, and compare the vast amounts of text, data, and charts within the report, ultimately forming a comprehensive review judgment. Computer-assisted text search functions utilize keyword matching to quickly locate specific terms. Simultaneously, rule-based systems automatically identify and extract specific semantic patterns or structured data fragments within the text according to pre-set logic. Traditional natural language processing techniques are also being gradually incorporated into report analysis. Their applications can be seen in preliminary semantic categorization of text content through machine learning algorithms, or in the use of named entity recognition to extract key semantic engineering entities, technical parameters, and geographic designations within the text. Furthermore, some work utilizes established domain knowledge bases to systematically store engineering standards and experience, enabling reviewers to conduct knowledge-based text verification and obtain background knowledge. These technologies collectively form the technical foundation for text information processing and knowledge acquisition in current report review.

[0004] Currently, the review methods for reservoir and dam safety assessment reports still suffer from a single review dimension, which makes it difficult to identify deep-seated risks caused by multiple factors. To this end, this paper proposes a multi-dimensional review method for reservoir and dam safety assessment reports based on a large language model. Summary of the Invention

[0005] The purpose of the present invention is to provide a multi-dimensional review method for reservoir dam safety evaluation reports based on a large language model. By constructing a multi-dimensional insight auxiliary brain model, the reservoir dam safety evaluation report is processed; using AI risk pre-analysis, the risk path of the structured semantic text is deduced, and a risk warning text is output; applying scenario verification, the scenario description in the structured semantic text is parsed, and parameters are extracted to drive external simulation; the structured semantic text is compared with the simulation results, and a comparison analysis report is output; natural language processing technology is used, and traceable data links are constructed to generate a semantic link traceability set; the multi-dimensional insight auxiliary brain model first conducts a comprehensive review of the semantic link traceability set, and records the review difficulties, and then generates a multi-dimensional review report through the human-machine co-intelligence reporting engine.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A multi-dimensional review method for reservoir and dam safety assessment reports based on a large language model includes:

[0008] Construct a multi-dimensional insight-based brain-supporting model to process reservoir and dam safety assessment reports and generate multi-dimensional review reports;

[0009] The multi-dimensional insight-supporting brain model includes a layer-meaning parsing layer, a multi-dimensional text analysis layer, a natural language processing layer, and a comprehensive review layer;

[0010] The multi-dimensional text analysis layer obtains preliminary review data by analyzing the structured semantic text. The analysis step includes performing risk path deduction on the structured semantic text and outputting risk warning text; parsing the structured semantic text to obtain operating condition scenario descriptions, extracting simulation input parameter sets, and outputting multi-dimensional simulation data including confidence intervals; performing simulation scenario verification on the multi-dimensional simulation data and the scenario descriptions to generate a comparison analysis report;

[0011] The natural language processing layer performs semantic integration and cross-text association analysis on the preliminary review data to generate a semantic link traceability set;

[0012] The comprehensive review layer extracts features from the semantic link tracing set, obtains difficult points, and generates a multi-dimensional review report by combining the human-machine co-intelligence reporting model.

[0013] Furthermore, the layer meaning parsing layer includes:

[0014] Perform word segmentation, sentence splitting and grammatical annotation on the reservoir dam safety assessment report to generate a preliminary text structure;

[0015] Analyzing the preliminary text structure, identifying key semantic entities, and generating a semantic entity set; the key semantic entities include engineering terms, technical parameters, geographic locations, and time nodes;

[0016] The preliminary text structure and the semantic entity set are integrated based on a domain knowledge base to generate structured semantic text.

[0017] Furthermore, the multi-dimensional text analysis also includes: analyzing the structured semantic text against the regulations and standards in the dam field knowledge base, identifying non-compliant items, and generating compliance deviation marking text; extracting and analyzing key facts and original data statements in the structured semantic text to generate a set of conclusion summaries; the preliminary review data includes compliance deviation marking text, a set of conclusion summaries, the risk warning text and the comparison analysis report.

[0018] Furthermore, the specific acquisition process of the assertion summary set is as follows: through semantic dependency analysis of the structured semantic text, key facts are obtained, and the key facts include operating status description and abnormal data; and the key facts and the positions of the key facts in the structured semantic text are recorded to generate a key fact set; the original data declaration in the structured semantic text is extracted, and the consistency of the original data declaration with the context is verified by data verification, redundant data and contradictory data are eliminated, normal data declaration is obtained, and a data declaration set is generated; the original data declaration includes flow value and water level value; the data declaration set includes original data declaration, verification result and normal data declaration; combined with the dam field knowledge base, the key fact set and the data declaration set are conclusively described to form an assertion summary set.

[0019] Furthermore, the specific process of obtaining the risk warning text is as follows:

[0020] Through semantic analysis, the structured semantic text is interpreted to obtain potential risk points, including implicit failure paths and insufficiently demonstrated risk combinations; the potential risk points and their locations in the structured semantic text are recorded to generate a risk entity set; feature matching is performed on the dam domain knowledge base and the risk entity set, and risk features and historical related cases in the risk entity set are extracted to generate a risk feature set; the risk features include excessive seepage and / or abnormal water levels; and risk path deduction is performed on the risk entity set and the risk feature set in combination with the dam domain knowledge base to generate a risk warning text;

[0021] The specific steps of risk path deduction are as follows: construct risk points as nodes, and obtain risk propagation paths through temporal correlation, spatial proximity and causality; classify risk feature sets into three levels: trigger conditions, intermediate transmission and final consequences, calculate the inter-level state transition probability through the Markov chain model, and generate potential causal chains; based on historical accident data, integrate the three dimensions of risk probability, severity and urgency, and obtain the risk impact range through a weighted fusion algorithm; conduct multiple rounds of scenario simulations on critical paths that exceed the threshold, and output a risk evolution time series diagram containing confidence intervals; generate a risk warning text with risk propagation paths, potential causal chains and risk impact range.

[0022] Furthermore, the specific process of obtaining the comparison analysis report is as follows:

[0023] Analyzing structured semantic text through semantic parsing to obtain an operating condition scenario description; extracting simulation parameters from the operating condition description using parameter extraction, wherein the simulation parameters include a calculated section, a permeability coefficient, and a permeability gradient; and converting the simulation parameters into a simulation input format to generate a simulation input parameter set; the simulation input parameter set includes simulation parameters, parameter values, and input formats;

[0024] Use external simulation tools to conduct simulation scenario verification, including:

[0025] Calling hydraulics and structural mechanics to calculate the simulation input parameter set and output multidimensional simulation data including confidence intervals;

[0026] The multidimensional simulation data is converted into a natural language description, and a semantic similarity algorithm is used to calculate the matching degree between the multidimensional simulation data and the scenario description to obtain the deviation; a numerical mapping relationship is established to quantify the deviation level and severity, and a comparison analysis report is generated.

[0027] Furthermore, the human-machine co-intelligence reporting model includes a verification task generation layer, an expert opinion analysis layer, an audit information interaction layer, and a report generation layer;

[0028] The verification task generation layer classifies the difficult points, obtains verification tasks, and assigns them to matching experts;

[0029] The expert opinion analysis layer analyzes the verification tasks and obtains expert task feedback;

[0030] The audit information interaction layer performs semantic analysis on the audit task and generates a primary audit result;

[0031] The report generation layer integrates expert task feedback and primary review results to form a multi-dimensional review report.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. In existing technology, the review of reservoir and dam safety assessment reports primarily relies on manual analysis or simple automated tools (such as keyword matching and fixed rule systems). Judgments based on known, fixed rules offer high accuracy, but their timeliness and comprehensiveness are limited. For example, traditional systems, based on hard-coded rules, are significantly inadequate for real-time updates and adaptation to newly enacted or revised regulations and standards, limiting the timeliness and comprehensiveness of their reviews. Furthermore, manual review is time-consuming, highly subjective, and difficult to ensure consistency. In contrast, this invention significantly enhances review capabilities by constructing a multidimensional insight-based brain model. Based on a universal large language model, this model integrates a dam domain knowledge base, including regulations and standards, historical accident data, and monitoring specifications, to transform the report into structured semantic text. Subsequently, compliance is checked against the regulatory standards library, deviations are noted, key facts and conclusive descriptions are extracted, and the preliminary review data is refined. This process ensures that the review covers the semantics, logic, and regulatory requirements of the text, far exceeding the single-dimensional analysis of traditional methods. Furthermore, through domain-adaptive training, the model is able to handle complex contextual relationships, such as identifying risks that are not explicitly mentioned but implicit in reports, significantly improving the ability to detect underlying risks. The Multidimensional Insight Supporting Brain Model, through deep semantic parsing and domain knowledge enhancement, overcomes the limitations of existing technologies in terms of insufficient review depth and inefficiency, providing a high-quality review foundation for dam safety management. This approach addresses the limitation of a single review dimension and proposes a multidimensional review method for reservoir and dam safety assessment reports based on a large language model.

[0034] 2. Existing technologies typically employ threshold-based rule-based detection or simple statistical analysis to identify risks in reservoir and dam safety assessment reports. These methods are limited to detecting known risks and struggle to capture complex causal relationships or predict undocumented potential risks. For example, traditional systems lack the ability to analyze multi-factor coupled risks, resulting in delayed early warnings or misjudgments. This present invention significantly improves the accuracy of risk identification through AI-based risk pre-analysis. This technology first uses semantic association analysis to identify risk-related entities in the report, such as "well-developed dam foundation joints and fissures," and integrates historical accident data for feature matching to generate a preliminary warning. The system then analyzes the causal relationships between entities, deducing risk pathways, such as "leakage from bedrock joints and fissures," and supplements potential risk points and evidence to form a comprehensive warning document. This dynamic pre-analysis mechanism simulates risk evolution, addressing the shortcomings of existing technologies in analyzing rare scenarios. By integrating historical data and causal reasoning, AI-based risk pre-analysis significantly enhances the ability to predict underlying risks, providing a scientific basis for early warnings in dam safety management and reducing the probability of unexpected accidents. It makes up for the defect of single review dimension and proposes a multi-dimensional review method for reservoir dam safety evaluation report based on large language model.

[0035] 3. In the existing technology, the integration of review results of reservoir and dam safety evaluation reports mainly relies on manual summary, the efficiency of expert collaboration is low, and there is a lack of a systematic dispute resolution mechanism. For example, in the traditional process, expert opinions are scattered, it is difficult to reach a consensus quickly, and the traceability of the review results is weak, which affects the reliability of decision support. The present invention significantly optimizes the efficiency of collaborative review through a human-machine co-intelligence reporting model. The human-machine co-intelligence reporting model reduces manual coordination costs through automated task allocation and semantic analysis; guiding questions effectively resolve disputes and improve the consistency of conclusions. The human-machine co-intelligence reporting model overcomes the limitations of low efficiency and insufficient credibility of existing technologies through intelligent collaboration and traceability design, significantly improves the efficiency of report generation, makes up for the defect of a single review dimension, and proposes a multi-dimensional review method for reservoir and dam safety evaluation reports based on a large language model. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of the structure of a multi-dimensional review method for reservoir and dam safety assessment reports based on a large language model provided by an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of the structure of the multi-dimensional insight-assisted brain model provided by an embodiment of the present invention;

[0038] Figure 3 A schematic diagram of the human-machine co-intelligence reporting model structure provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] 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 creative efforts are within the scope of protection of the present invention.

[0040] Example 1

[0041] In order to better conduct multi-dimensional review of the safety evaluation report of a large reservoir dam, a multi-dimensional review method and system for reservoir dam safety evaluation report based on large language model is proposed. Figure 1 、 Figure 2 and Figure 3 As shown:

[0042] Construct a multi-dimensional insight-based brain-supporting model to process reservoir and dam safety assessment reports and generate multi-dimensional review reports;

[0043] The multi-dimensional insight-supporting brain model includes a layer-meaning parsing layer, a multi-dimensional text analysis layer, a natural language processing layer, and a comprehensive review layer;

[0044] The layer semantic parsing layer analyzes the reservoir dam safety evaluation report to obtain structured semantic text;

[0045] Furthermore, the specific steps are:

[0046] The multi-dimensional insight auxiliary brain model received a reservoir safety assessment report.

[0047] The layered semantic parsing layer first performs word segmentation, sentence splitting, and grammatical annotation on the reservoir dam safety assessment report to generate a preliminary text structure. It then analyzes this preliminary text structure to identify key semantic entities and generate a semantic entity set. These key semantic entities include engineering terms, technical parameters, geographic locations, and time nodes. Finally, the preliminary text structure and the semantic entity set are integrated based on the domain knowledge base to generate structured semantic text. The layered semantic parsing layer converts the report into structured semantic text through word segmentation, syntactic analysis, and semantic entity extraction, accurately preserving engineering terms and parameters and providing a reliable data foundation for scenario analysis. However, its ability to extract latent semantic entities is limited. To address this, an improved approach is proposed: integrating a latent semantic mining module, using a generative adversarial network to mine latent semantic associations, and combining it with real-time monitoring data (such as water level changes) to dynamically extract scenario semantic entities, thereby optimizing the depth and accuracy of the structured semantic text.

[0048] The multidimensional text analysis layer analyzes structured semantic text to obtain preliminary review data; this preliminary review data includes compliance deviation mark text, assertion summary collections, risk warning text, and comparative analysis reports; the multidimensional text analysis includes qualification review, assertion extraction, risk rehearsal, and scenario verification; the multidimensional text analysis layer generates preliminary review data such as compliance deviation and risk warning through compliance review and assertion extraction, comprehensively covering scenario reporting issues, improving the systematicness and depth of the review, and assisting in engineering safety assessments. However, the analysis relies on a preset knowledge base and has limited coverage. To this end, an improvement method is proposed: a new dynamic knowledge base update module is added, which uses machine learning to incrementally learn from real-time cases and regulations, expands analysis coverage, and generates more accurate compliance deviation marks and risk warning text.

[0049] Furthermore, the specific steps are:

[0050] Compliance review: The system compares the regulatory standards in the dam field knowledge base, analyzes the structured semantic text, and finds that the crack width is not recorded, which violates the relevant regulations. It generates compliance deviation mark text, records the deviation location and regulatory basis.

[0051] Assertion Extraction: Through semantic dependency analysis, the system extracts key facts, such as "heavy rain caused a surge in flow" and "crack expansion," and records their locations. The system further extracts raw data assertions, verifies their consistency with the context through data validation, and generates a data assertion set. Integrating this with the dam domain knowledge base, the system summarizes the facts and data, concluding that "strengthening structural monitoring is necessary," forming an assertion summary set. Assertion Extraction uses semantic dependency analysis to extract key facts and data assertions, verifies their consistency, and integrates this knowledge base to generate an assertion summary set, providing clear conclusions for flood scenarios and reducing misjudgments.

[0052] Risk Preview Analysis: Through semantic analysis using a large language model and combining historical dam failure cases (such as cracks leading to seepage safety hazards and structural safety hazards), the system identifies potential risk points, such as "crack expansion may trigger concentrated seepage" and "local collapse or landslide." The system performs feature matching between historical cases and risk points, extracting features such as "crack width exceeds the standard," and associating them with relevant cases. Through causal path analysis, the system derives the path of "crack expansion → concentrated seepage → dam failure risk," generates risk warning text, and enhances the accuracy of risk prevention and control. However, dynamic risk prediction capabilities are limited. To this end, an improved method is proposed: a new dynamic risk modeling module is added, which uses time series prediction and reinforcement learning, combined with real-time hydrological data (such as flow changes), to adaptively generate risk paths and improve the accuracy of warning text.

[0053] Scenario verification: Analyze structured semantic text through semantic parsing to obtain operating condition scenario description; use parameter extraction to extract simulation parameters from the operating scenario description, and convert the simulation parameters into simulation input format to generate a simulation input parameter set; use external simulation tools to perform simulation scenario verification, specifically including: calling hydraulics and structural mechanics to calculate the simulation input parameter set, and output multidimensional simulation data containing confidence intervals; convert the multidimensional simulation data into natural language description, use semantic similarity algorithm to calculate the matching degree between the multidimensional simulation data and the scenario description, and obtain deviation; establish a numerical mapping relationship to quantify the deviation level and severity, and generate a comparison analysis report.

[0054] The natural language processing layer performs semantic integration and cross-text association analysis on the preliminary review data to generate a semantic link traceability set;

[0055] Furthermore, the specific steps are:

[0056] Initial review data, including compliance deviation marker text, conclusion summary sets, risk warning text, and comparative analysis reports, is input into the natural language processing layer. This layer uses semantic association mapping to identify the correspondence between non-compliant items and the original report text. Cross-text citation analysis establishes a mapping relationship between the review data and structured semantic text. This integration of association points and mappings extracts problem points (e.g., "leakage discovered in the dam foundation") and correction points (e.g., "development of a reinforcement plan for curtain grouting"). This generates a semantic link traceability set, documenting the issues, correction suggestions, and regulatory basis.

[0057] The comprehensive review layer extracts features from the semantic link tracing set, obtains difficult points, and generates a multi-dimensional review report by combining the human-machine co-intelligence reporting model.

[0058] Furthermore, the specific steps are:

[0059] The comprehensive review layer extracts features from the semantically linked traceability set, identifying key issues, such as "missing reinforcement plan," and classifying them as structural safety issues. Verification tasks are generated and assigned to domain experts. The experts analyze the tasks and provide feedback, such as "a structural reinforcement plan is needed and long-term crack risk assessment is required." The system generates preliminary review results through semantic analysis, identifying key issues. Finally, the system integrates expert feedback and review results to generate a multi-dimensional review report.

[0060] The human-machine collaborative reporting model generates multidimensional audit reports through verification task generation, expert feedback, semantic analysis, and report fusion. This model integrates flood scenario compliance, risks, and simulation results, guides corrective actions, and enhances the practicality of audits. However, expert feedback is inefficient. To address this, we propose an improved approach: a new intelligent suggestion generation module leverages knowledge graphs and generative models to pre-generate suggestions for difficult flood scenario issues, prioritize high-risk tasks, and improve report generation efficiency.

[0061] Example 2

[0062] In order to better and quickly review the safety evaluation report of a certain small and medium-sized reservoir dam, a multi-dimensional review method and system for reservoir dam safety evaluation report based on a large language model is proposed, including:

[0063] Construct a multi-dimensional insight-based brain-supporting model to process reservoir and dam safety assessment reports and generate multi-dimensional review reports;

[0064] The multi-dimensional insight-supporting brain model includes a layer-meaning parsing layer, a multi-dimensional text analysis layer, a natural language processing layer, and a comprehensive review layer;

[0065] The layer semantic parsing layer analyzes the reservoir dam safety evaluation report to obtain structured semantic text;

[0066] Furthermore, the specific steps are:

[0067] The multi-dimensional insight auxiliary brain model received a reservoir safety assessment report.

[0068] The layered semantic parsing layer first performs word segmentation, sentence splitting, and grammatical annotation on the reservoir dam safety assessment report to generate a preliminary text structure. It then analyzes this preliminary text structure to identify key semantic entities and generate a semantic entity set. These key semantic entities include engineering terms, technical parameters, geographic locations, and time nodes. Finally, the preliminary text structure and the semantic entity set are integrated with the domain knowledge base to generate structured semantic text. Through word segmentation, syntactic analysis, and semantic entity extraction, the layered semantic parsing layer converts the report into structured semantic text, accurately preserving engineering terms (such as "seepage gradient") and parameters, providing a reliable data foundation for review and analysis. However, its ability to mine latent semantic associations is limited. To address this, an improved approach is proposed: integrating a semantic association mining module, using a generative adversarial network to mine latent semantic associations, and combining it with real-time hydrological data to dynamically generate structured semantic text, improving parsing depth and accuracy.

[0069] The multi-dimensional text analysis layer obtains preliminary review data by analyzing structured semantic text; the preliminary review data includes compliance deviation mark text, conclusion summary set, risk warning text and comparative analysis report; the multi-dimensional text analysis includes compliance review, conclusion extraction, risk pre-play and scenario verification;

[0070] The multi-dimensional text analysis layer generates preliminary review data, including compliance deviations and risk warnings, through steps such as compliance review and assertion extraction. This improves the systematicity and depth of review and facilitates engineering safety assessments. However, this analysis relies on a pre-set knowledge base, which limits its coverage. To address this, we propose an improvement: a new dynamic knowledge base update module leverages machine learning to incrementally learn from real-time cases and regulations, expanding analysis coverage and generating more accurate compliance deviation markers and risk warning text.

[0071] Furthermore, the specific steps are:

[0072] Compliance review: The system compares the regulatory standards in the dam field knowledge base, analyzes the structured semantic text, and finds that the abnormal seepage is not accompanied by an emergency plan, which violates the relevant regulations. It generates compliance deviation mark text and records the deviation location and regulatory basis.

[0073] Assertion Extraction: Through semantic dependency analysis, the system extracts key facts, such as "leakage within the fault has developed into piping" and "seepage is occurring in the diversion tunnel." The system further extracts raw data assertions, verifies their consistency with the context through data validation, and generates a data assertion set. Integrating this with the dam domain knowledge base, the system summarizes the facts and data, concluding that "serious seepage safety hazards exist," forming an assertion summary set. Assertion Extraction uses semantic dependency analysis to extract key facts and data assertions, verifies their consistency, and integrates this knowledge base to generate an assertion summary set, providing clear conclusions for flood scenarios and reducing misjudgments.

[0074] Risk Prediction Analysis: Using semantic analysis using a large language model and combining it with historical dam accident cases (e.g., seepage leading to dam instability), the system identifies potential risk points, such as "abnormal seepage may cause dam instability" and "inadequate downstream flood control facilities," and records their locations. The system performs feature matching between historical cases and risk points, extracting features such as "seepage volume exceeds the standard" and linking them to relevant cases. Through causal path analysis, the system derives the risk path from "serious seepage safety hazard → dam failure → causing severe downstream flooding losses," generating a risk warning. Risk Prediction Analysis uses semantic analysis to identify potential risk points (e.g., "seepage volume exceeds the standard"), matches historical cases, and derives the flood scenario risk path (e.g., "serious seepage safety hazard → dam failure") to generate warning text, enhancing the accuracy of risk prevention and control. However, dynamic risk prediction capabilities are limited. To address this, an improved approach is proposed: a new dynamic risk modeling module is added. This module leverages time series forecasting and reinforcement learning, combined with real-time flood data (e.g., flow rate fluctuations), to adaptively generate risk paths and improve the accuracy of warning text.

[0075] Scenario verification: Through semantic parsing, the system extracts flood scenario descriptions and records their locations. Simulation parameters are extracted from the scenario descriptions and converted into simulation input format. The system uses external simulation tools to verify the flood scenario and generate results. Comparing the simulation results with the report content, the system found that the report did not mention the overflow height, recorded this difference, and generated a comparison analysis report. Scenario verification extracts flood scenario parameters, drives simulation, generates a comparison analysis report, reveals omissions in the report, and improves the depth of flood scenario review. However, the simulation parameters are not adaptable enough. To this end, an improvement method is proposed: integrating an adaptive parameter optimization module, using Bayesian optimization to dynamically adjust flood scenario simulation parameters, and combining real-time data (such as rainfall) to generate high-precision comparison analysis reports.

[0076] The natural language processing layer performs semantic integration and cross-text association analysis on the preliminary review data to generate a semantic link traceability set;

[0077] Furthermore, the specific steps are:

[0078] Initial review data, including compliance deviation markers, conclusion summaries, risk warnings, and comparative analysis reports, is fed into the natural language processing layer. This layer uses semantic association mapping to identify the correspondence between non-compliant items and the original report text. Cross-text citation analysis establishes a mapping relationship between the review data and structured semantic text. This integration of associations and mappings extracts problem areas, such as "missing emergency response plan," and correction points, such as "developing an emergency response plan for leakage." This generates a semantic link traceability set, documenting issues, correction recommendations, and regulatory basis.

[0079] The comprehensive review layer extracts features from the semantic link traceability set, obtains difficult points, and generates a multi-dimensional review report by combining the human-machine co-intelligence reporting model;

[0080] Furthermore, the specific steps are:

[0081] The comprehensive review layer extracts features from the semantic link traceability set, identifying key issues, such as "missing emergency response plan," and classifies them as compliance issues. Verification tasks are generated and assigned to domain experts. The experts analyze the tasks and provide feedback, such as "an emergency response plan needs to be developed and downstream evacuation plans improved." The system generates preliminary review results through semantic analysis, identifying key issues. Finally, the system integrates expert feedback and review results to generate a multi-dimensional review report.

[0082] The human-machine collaborative reporting model generates multidimensional audit reports through verification task generation, expert feedback, semantic analysis, and report fusion. This model integrates flood scenario compliance, risks, and simulation results, guides corrective actions, and enhances the practicality of audits. However, expert feedback is inefficient. To address this, we propose an improved approach: a new intelligent suggestion generation module leverages knowledge graphs and generative models to pre-generate suggestions for difficult flood scenario issues, prioritize high-risk tasks, and improve report generation efficiency.

[0083] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-dimensional review method for reservoir and dam safety evaluation reports based on a large language model, characterized by: include: Construct a multi-dimensional insight-based brain-supporting model to process reservoir and dam safety assessment reports and generate multi-dimensional review reports; The multi-dimensional insight-supporting brain model includes a layer-meaning parsing layer, a multi-dimensional text analysis layer, a natural language processing layer, and a comprehensive review layer; The layer semantic parsing layer includes word segmentation, sentence splitting and grammatical annotation of the reservoir dam safety assessment report to generate a preliminary text structure; analyzing the preliminary text structure, identifying key semantic entities, and generating a semantic entity set; the key semantic entities include engineering terms, technical parameters, geographical locations and time nodes; Integrating the preliminary text structure and the semantic entity set based on a domain knowledge base to generate a structured semantic text; The multi-dimensional text analysis layer obtains preliminary review data by analyzing structured semantic text. The analysis step includes risk path deduction of the structured semantic text, constructing risk points into nodes, and obtaining risk propagation paths through temporal correlation, spatial proximity, and causality. The risk feature set is classified into three levels: trigger conditions, intermediate transmission, and final consequences. The state transition probability between levels is calculated using a Markov chain model to generate a potential causal chain. Based on historical accident data, the risk impact range is obtained by integrating the three dimensions of risk probability, severity, and urgency through a weighted fusion algorithm; multiple rounds of scenario simulation are performed on critical paths that exceed the threshold, and a risk evolution time series diagram with confidence intervals is output; a risk warning text is output with the risk propagation path, potential causal chain, and risk impact range; the structured semantic text is parsed to obtain the operating condition scenario description, and the simulation input parameter set is extracted to output multidimensional simulation data with confidence intervals; Conduct simulation scenario verification on multi-dimensional simulation data and scenario descriptions, and generate a comparison analysis report; The natural language processing layer performs semantic integration and cross-text association analysis on the preliminary review data to generate a semantic link traceability set; The comprehensive review layer extracts features from the semantic link tracing set, obtains difficult points, and generates a multi-dimensional review report by combining the human-machine co-intelligence reporting model.

2. The multi-dimensional review method for reservoir dam safety evaluation report based on large language model according to claim 1 is characterized in that: The multi-dimensional text analysis also includes: Analyze structured semantic text against regulatory standards in the dam domain knowledge base, identify non-compliant items, and generate compliance deviation markup text; Extract and analyze key facts and raw data statements from structured semantic text to generate a set of assertion summaries; The preliminary review data includes the compliance deviation marking text, the conclusion summary set, the risk warning text and the comparison analysis report.

3. The multi-dimensional review method for reservoir dam safety evaluation report based on large language model according to claim 2 is characterized in that: The specific process of obtaining the summary set of conclusions is as follows: Through semantic dependency analysis of structured semantic text, key facts are obtained, and the key facts include operating status description and abnormal data; the key facts and the positions of the key facts in the structured semantic text are recorded to generate a key fact set; the original data declaration in the structured semantic text is extracted, and the consistency of the original data declaration with the context is verified by data verification, and redundant data and contradictory data are eliminated to obtain normal data declarations and generate a data declaration set; the original data declaration includes a project overview and basic parameters; the data declaration set includes the original data declaration, verification results and normal data declarations; combined with the dam field knowledge base, the key fact set and the data declaration set are conclusively described to form a conclusion summary set.

4. The multi-dimensional review method for reservoir dam safety evaluation report based on large language model according to claim 2 is characterized in that: The specific process of obtaining the comparison analysis report: Analyzing the structured semantic text through semantic parsing to obtain an operating condition scenario description; extracting simulation parameters from the operating condition scenario description through parameter extraction, and converting the simulation parameters into a simulation input format to generate a simulation input parameter set; Utilizing external simulation tools to perform simulation scenario verification, specifically including: invoking hydraulics and structural mechanics to calculate the simulation input parameter set and outputting multi-dimensional simulation data including confidence intervals; The multidimensional simulation data is converted into a natural language description, and a semantic similarity algorithm is used to calculate the matching degree between the multidimensional simulation data and the scenario description to obtain the deviation; a numerical mapping relationship is established to quantify the deviation level and severity, and a comparison analysis report is generated.

5. The multi-dimensional review method for reservoir dam safety evaluation report based on large language model according to claim 1 is characterized in that: The human-machine co-intelligence reporting model includes a verification task generation layer, an expert opinion analysis layer, an audit information interaction layer, and a report generation layer; The verification task generation layer classifies the difficult points, obtains verification tasks, and assigns them to matching experts; The expert opinion analysis layer analyzes the verification tasks and obtains expert task feedback; The audit information interaction layer performs semantic analysis on the audit task and generates a primary audit result; The report generation layer integrates expert task feedback and primary review results to form a multi-dimensional review report.

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