Reservoir dam safety evaluation report multi-dimensional examination method based on large language model

Abstract

The invention relates to the technical field of large language models, in particular to a reservoir dam safety evaluation report multi-dimensional examination method based on a large language model. A reservoir dam safety evaluation report is processed by constructing a multi-dimensional insight brain-assisting model; using the risk preview to output a risk early warning text; performing scene verification, extracting parameters to drive external simulation, and outputting a comparison analysis report; a natural language processing technology is adopted, a traceable data link is constructed, and a semantic link traceability set is generated; and the multi-dimensional insight auxiliary brain model performs comprehensive review on the semantic link traceability set, records review difficult points, and generates a multi-dimensional review report through a man-machine common intelligence report engine. At present, a review method of a reservoir dam safety evaluation report has the problem of single review dimension. Therefore, the invention provides a reservoir dam safety evaluation report multi-dimensional examination method based on a large language model.

Description

Technical Field

[0001] 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. Background Art

[0002] The reservoir dam safety evaluation report is a core technical document for determining the dam operation status and formulating management decisions. The accuracy of its text content, the rigor of the analysis logic, and the reliability of the evaluation conclusions are directly related to project safety and public interests, so strict review must be carried out. The core role of the review is to conduct multi-dimensional scrutiny and semantic verification on the text narrative, data representation, calculation expression, and logical argumentation of the report by using natural language understanding and text mining technologies. This is aimed at ensuring the objectivity, truth, scientific rigor of the evaluation conclusions, efficiently extracting key safety semantics and status knowledge from them, actively discovering and accurately evaluating various risk semantics manifested or implied in the report text, and ultimately providing an intelligent decision-making support basis after in-depth analysis for the safety management, risk pre-control, and scientific maintenance decision-making of the dam throughout its life cycle.

[0003] Currently, the review of reservoir dam safety evaluation reports mainly relies on manual review supplemented by computer-aided tools. During manual review, domain experts will use their professional knowledge, engineering experience, and relevant technical specifications to carefully study, analyze, and compare a large amount of text, data, and charts in the report to form a comprehensive review judgment. The text retrieval function in computer-aided technology assists in quickly locating specific terms by keyword matching; at the same time, rule-based systems automatically identify and extract specific semantic patterns or structured data fragments in the text according to preset logic. Traditional natural language processing technologies have also been gradually introduced into report analysis, and their applications can be seen in the preliminary semantic classification of text content through machine learning algorithms, or the extraction of engineering entities, technical parameters, and geographical references carrying key semantics in the text by using named entity recognition technology. In addition, some work also uses the constructed domain knowledge base to systematically store engineering standards and experience for reviewers to conduct knowledge-based text information verification or obtain background knowledge references. The above technologies together constitute the technical basis for text information processing and knowledge acquisition in the existing report review.

[0004] Currently, there is still a problem of single review dimension in the review method of reservoir dam safety evaluation reports, which makes it difficult to discover deep risks caused by multiple factors. Therefore, the present invention proposes a multi-dimensional review method for reservoir dam safety evaluation reports based on large language models. Summary of the Invention

[0005] The object of the present invention is to provide 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; AI risk preview analysis is used to deduce the risk path of the structured semantic text and output a risk warning text; scenario verification is applied to analyze the scenario description in the structured semantic text and refine parameter-driven external simulation; the structured semantic text is compared with the simulation results and a comparison analysis report is output; natural language processing technology is adopted and a traceable data link is constructed to generate a semantic link traceability set; the multi-dimensional insight auxiliary brain model first comprehensively reviews the semantic link traceability set, records the review difficulties, and then generates a multi-dimensional review report through a human-machine collaborative intelligence report engine.

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

[0007] A multi-dimensional review method for reservoir dam safety evaluation reports based on large language models, including:

[0008] Construct a multi-dimensional insight auxiliary brain model to process the reservoir dam safety evaluation report and generate a multi-dimensional review report;

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

[0010] The multi-dimensional text analysis layer analyzes the structured semantic text to obtain preliminary review data; the analysis steps include deducing the risk path of the structured semantic text and outputting a risk warning text; analyzing the structured semantic text, obtaining the operating condition scenario description, and extracting the simulation input parameter set to output multi-dimensional simulation data including a confidence interval; performing simulation scenario verification on the multi-dimensional simulation data and the scenario description to generate a comparison analysis report;

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

[0012] The comprehensive review layer extracts features from the semantic link traceability set, obtains the difficulties, and combines with the human-machine collaborative intelligence report model to generate a multi-dimensional review report.

[0013] Further, the layer meaning analysis layer includes:

[0014] Segment the words, split the sentence patterns, and perform grammar annotation on the reservoir dam safety evaluation report to generate a preliminary text structure;

[0015] Analyze the preliminary text structure, identify key semantic entities, and generate a set of semantic entities; the key semantic entities include engineering terms, technical parameters, geographical locations, and time nodes;

[0016] Integrate the preliminary text structure and the set of semantic entities based on the domain knowledge base to generate a structured semantic text.

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

[0018] Furthermore, the specific process for obtaining the set of judgment summaries is as follows: obtain key facts through semantic dependency analysis of the structured semantic text, where the key facts include operation status descriptions and abnormal data; record the key facts and their positions in the structured semantic text to generate a set of key facts; extract the original data declarations in the structured semantic text, verify the consistency of the original data declarations with the context using data verification, eliminate redundant and contradictory data, and obtain normal data declarations to generate a set of data declarations; the original data declarations include flow values and water level values; the set of data declarations includes the original data declarations, verification results, and normal data declarations; combine the dam domain knowledge base to make conclusive descriptions of the set of key facts and the set of data declarations to form a set of judgment summaries.

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

[0020] Interpret the structured semantic text through semantic analysis to obtain potential risk points, where the potential risk points include implicit failure paths and insufficiently demonstrated risk combinations; record the potential risk points and their positions in the structured semantic text to generate a set of risk entities; perform feature matching on the dam domain knowledge base and the set of risk entities, extract the risk features and historical related cases in the set of risk entities to generate a set of risk features; the risk features include excessive seepage flow and / or abnormal water level; combine the dam domain knowledge base to perform risk path deduction on the set of risk entities and the set of risk features to form a risk warning text;

[0021] The specific steps of the risk path deduction are as follows: constructing risk points as nodes, and obtaining risk propagation paths through temporal relevance, spatial proximity, and causality; classifying the risk feature set into three levels of trigger conditions, intermediate conduction, and final consequences, calculating the state transition probability between levels through a Markov chain model, and generating potential causal chains; based on historical accident data, comprehensively considering the three dimensions of risk probability, severity, and urgency, obtaining the risk impact range through a weighted fusion algorithm; conducting multiple rounds of scenario simulations on critical paths exceeding the threshold, and outputting a risk evolution time series diagram including a confidence interval; generating a risk warning text with risk propagation paths, potential causal chains, and risk impact ranges.

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

[0023] Analyzing the structured semantic text through semantic parsing to obtain a description of the operating condition scenario; using parameter extraction to extract simulation parameters from the operating scenario description, where the simulation parameters include calculation sections, permeability coefficients, and hydraulic gradients; and converting the simulation parameters into a simulation input format to generate a set of simulation input parameters; the set of simulation input parameters includes simulation parameters, parameter values, and input formats.

[0024] Using an external simulation tool to verify the simulation scenario, which specifically includes:

[0025] Invoking hydraulics and structural mechanics to calculate the set of simulation input parameters, and outputting multi-dimensional simulation data including a confidence interval;

[0026] Converting the multi-dimensional simulation data into a natural language description, calculating the matching degree between the multi-dimensional simulation data and the scenario description using a semantic similarity algorithm to obtain a deviation; establishing a numerical mapping relationship to quantify the deviation level and severity, and generating a comparison and analysis report.

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

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

[0029] The expert opinion analysis layer analyzes the verification tasks to obtain expert task feedback;

[0030] The review information interaction layer conducts semantic analysis on the verification tasks to generate a primary review result;

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

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] 1. In the prior art, the review of reservoir dam safety evaluation reports mainly relies on manual analysis or simple automated tools (such as keyword matching, fixed rule systems, etc.). The judgment accuracy under known and fixed rules is high, but the timeliness and comprehensiveness are limited. For example, when faced with newly promulgated or revised regulations and standards, the traditional system's real-time update and adaptation capabilities based on hard-coded rules are significantly insufficient, resulting in limited timeliness and comprehensiveness of its review. In addition, manual review is time-consuming, highly subjective, and difficult to ensure consistency. In contrast, the present invention significantly improves the review capability by constructing a multi-dimensional insight auxiliary brain model. The model is based on a general large language model, integrating a dam field knowledge base, including regulatory standards, historical accident data, and monitoring specifications, and converting the report into a structured semantic text. Subsequently, compliance is checked against the regulatory standard library, deviations are annotated, key facts and conclusive descriptions are extracted, and the preliminary review data is improved. 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 adaptability training, the model can handle complex contextual associations, such as identifying risks that are not explicitly mentioned but implicit in the report, significantly improving the ability to discover deep risks. The multi-dimensional insight auxiliary brain model overcomes the limitations of insufficient review depth and low efficiency of existing technologies through deep semantic analysis and domain knowledge enhancement, and provides a high-quality review basis for dam safety management. It makes up for the defect of a single review dimension and proposes a multi-dimensional review method for reservoir dam safety evaluation reports based on a large language model.

[0034] 2. In the risk identification of reservoir dam safety evaluation reports, the existing technology usually adopts threshold-based rule detection or simple statistical analysis. These methods are limited to the detection of known risks, and it is difficult to capture complex causal relationships or predict unrecorded potential risks. For example, the traditional system has insufficient analysis capabilities for multi-factor coupling risks, and early warnings are often delayed or misjudged. The present invention significantly improves the accuracy of risk identification through AI risk preview analysis. The technology first identifies risk-related entities in the report through semantic association analysis, such as "the dam foundation joints and fissures are relatively developed", and integrates historical accident data for feature matching to generate a preliminary warning. Subsequently, the system analyzes the causal relationship between entities, deduces risk paths, such as "leakage on the bedrock joint fissure surface", and supplements potential risk points and basis to form a complete warning text. In contrast, this dynamic deduction mechanism can simulate risk evolution and make up for the shortcomings of existing technologies in analyzing rare scenarios. AI risk preview analysis significantly improves the prediction ability of deep risks through historical data fusion and causal deduction, provides a scientific early warning basis for dam safety management, and reduces the probability of sudden 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 prior art, the integration of the review results of reservoir dam safety evaluation reports mainly relies on manual summarization, resulting in low efficiency of expert collaboration and a lack of a systematic dispute resolution mechanism. For example, in the traditional process, expert opinions are scattered, making it difficult to quickly reach an agreement, and the traceability of review results is weak, affecting the reliability of decision-making support. The present invention significantly optimizes the collaborative review efficiency through a human-machine co-intelligence report model. The human-machine co-intelligence report model reduces the manual coordination cost through automated task allocation and semantic analysis; guiding questions effectively resolve disputes and improve the consistency of conclusions. The human-machine co-intelligence report model overcomes the limitations of low efficiency and insufficient credibility in the prior art through intelligent collaboration and traceability design, significantly improves the report generation efficiency, makes up for the defect of a single review dimension, and proposes a multi-dimensional review method for reservoir dam safety evaluation reports based on large language models. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic structural diagram of a multi-dimensional review method for a reservoir dam safety evaluation report provided by an embodiment of the present invention;

[0037] Figure 2 It is a schematic structural diagram of a multi-dimensional insight auxiliary brain model provided by an embodiment of the present invention;

[0038] Figure 3 It is a schematic structural diagram of a human-machine co-intelligence report model provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] Embodiment 1

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

[0042] Construct a multi-dimensional insight auxiliary brain model to process the safety evaluation report of the reservoir dam and generate a multi-dimensional review report;

[0043] The multi-dimensional insight auxiliary brain model includes a layer meaning analysis 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 assessment report to obtain structured semantic text;

[0045] Furthermore, the specific steps are:

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

[0047] The layer 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; then the preliminary text structure is analyzed to identify key semantic entities and generate a semantic entity set; the 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 a structured semantic text. The layer semantic parsing layer converts the report into a structured semantic text through word segmentation, syntactic analysis and semantic entity extraction, accurately retains engineering terms and parameters, and provides a reliable data basis for scenario analysis. However, the ability to extract implicit semantic entities is limited. To this end, an improved method is proposed: integrating the implicit semantic mining module, using the generative adversarial network to mine implicit semantic associations, combining real-time monitoring data (such as water level changes), dynamically extracting scene semantic entities, and optimizing the depth and accuracy of structured semantic text.

[0048] The multi-dimensional text analysis layer obtains preliminary review data by analyzing structured semantic text; the preliminary review data includes compliance deviation mark text, judgment summary set, risk warning text and comparison analysis report; the multi-dimensional text analysis includes qualification review, judgment extraction, risk rehearsal and scenario verification; the multi-dimensional text analysis layer generates preliminary review data such as compliance deviation and risk warning through compliance review, judgment extraction and other steps, comprehensively covers scenario report issues, improves the systematicness and depth of review, and assists engineering safety assessment. 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, machine learning is used to incrementally learn from real-time cases and regulations, expand the analysis coverage, and generate more accurate compliance deviation marks and risk warning texts.

[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 specifications. It generates a compliance deviation mark text to record the deviation location and regulatory basis.

[0051] Argument extraction: Through semantic dependency analysis, the system extracts key facts, such as "heavy rains cause a surge in flow" and "crack expansion", and records their locations. The system further extracts the original data statement, confirms its consistency with the context through data verification, and generates a data statement set. Combined with the dam field knowledge base, the system summarizes the facts and data and concludes that "structural monitoring needs to be strengthened" to form an argument summary set. Argument extraction extracts key facts and data statements through semantic dependency analysis, verifies consistency, and generates an argument summary set in combination with the knowledge base, providing clear conclusions for flood scenarios and reducing misjudgments.

[0052] Risk rehearsal analysis: Through semantic analysis of large language models, combined with 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 cause concentrated seepage" and "local collapse or landslide" and other potential risks. The system performs feature matching on historical cases and risk points, extracts features such as "crack width exceeds the standard", and associates related cases. Through causal path analysis, the system derives the path of "crack expansion → concentrated seepage → dam break risk", generates risk warning text, and enhances the accuracy of risk prevention and control. However, the dynamic risk prediction capability is limited. To this end, an improvement 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), adaptively generates risk paths, and improves the accuracy of warning texts.

[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: call 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 the 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] The preliminary review data, including compliance deviation marked text, summary set of judgments, risk warning text, and comparison analysis report, are input into the natural language processing layer. Through semantic association mapping, the natural language processing layer identifies the correspondence between non-compliance items and the original report text. Through cross-text reference analysis, the mapping relationship between review data and structured semantic text is established. By integrating association points and mappings, problem points such as "seepage found in the dam foundation" and correction points such as "formulate a reinforcement plan for setting up curtain grouting" are extracted to generate a semantic link traceability set, recording problems, correction suggestions, and regulatory bases.

[0057] The comprehensive review layer extracts features from the semantic link traceability set to obtain difficult points, and combines with the human-machine co-intelligence report model to generate a multi-dimensional review report.

[0058] Furthermore, the specific steps are as follows:

[0059] The comprehensive review layer extracts features from the semantic link traceability set to obtain difficult points such as "lack of reinforcement plan", classifies them as structural safety problems, generates verification tasks and assigns them to domain experts. The experts analyze the tasks and put forward feedback such as "it is necessary to formulate a structural reinforcement plan and evaluate the long-term crack risk". The system generates a preliminary review result through semantic analysis to confirm the problem points. Finally, the system integrates expert feedback and review results to generate a multi-dimensional review report.

[0060] The human-machine co-intelligence report model generates a multi-dimensional review report through verification task generation, expert feedback, semantic analysis, and report integration, integrating flood scenario compliance, risks, and simulation results to guide rectification and enhance the practicality of the review. However, the efficiency of expert feedback is relatively low. For this reason, an improved method is proposed: add an intelligent suggestion generation module, use a knowledge graph and a generative model to pre-generate suggestions for difficult points in flood scenarios, and give priority to pushing high-risk tasks to improve the report generation efficiency.

[0061] Embodiment 2

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

[0063] Construct a multi-dimensional insight auxiliary brain model to process the safety evaluation report of the reservoir dam and generate a multi-dimensional review report;

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

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

[0066] Furthermore, the specific steps are as follows:

[0067] The multi-dimensional insight auxiliary brain model receives a safety evaluation report of a certain reservoir.

[0068] The layer meaning analysis layer first performs word segmentation, sentence pattern splitting, and grammar annotation on the reservoir dam safety evaluation report to generate a preliminary text structure; then analyzes the preliminary text structure to identify key semantic entities and generates a semantic entity set; the key semantic entities include engineering terms, technical parameters, geographical locations, and time nodes; finally, integrates the preliminary text structure and the semantic entity set based on the domain knowledge base to generate a structured semantic text. The layer meaning analysis layer transforms the report into a structured semantic text through word segmentation, syntactic analysis, and semantic entity extraction, precisely retaining engineering terms (such as "seepage gradient") and parameters, providing a reliable data basis for review and analysis. However, the ability to mine implicit semantic associations is limited. Therefore, an improved method is proposed: integrating a semantic association mining module, using a generative adversarial network to mine implicit semantic associations, combining real-time hydrological data, and dynamically generating a structured semantic text to improve the parsing depth and accuracy.

[0069] The multi-dimensional text analysis layer obtains preliminary review data by analyzing the structured semantic text; the preliminary review data includes compliance deviation marked text, assertion summary set, risk warning text, and comparison analysis report; the multi-dimensional text analysis includes compliance review, assertion extraction, risk preview, and scenario verification;

[0070] The multi-dimensional text analysis layer generates preliminary review data such as compliance deviations and risk warnings through steps such as compliance review and assertion extraction, improving the systematicness and depth of the review and assisting in engineering safety assessment. However, the analysis relies on a preset knowledge base with limited coverage. Therefore, an improved method is proposed: adding a dynamic knowledge base update module, using machine learning to incrementally learn from real-time cases and regulations, expanding the analysis coverage, and generating more accurate compliance deviation marks and risk warning texts.

[0071] Furthermore, the specific steps are as follows:

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

[0073] Assertion Extraction: Through semantic dependency analysis, the system extracts key facts such as "internal leakage in the fault has developed into piping" and "seepage occurs in the diversion tunnel". The system further extracts the original data declarations, verifies their consistency with the context through data verification, and generates a set of data declarations. Combining with the knowledge base in the dam field, the system generalizes facts and data, draws the conclusion "there are serious seepage safety hazards", and forms a set of assertion summaries. Assertion extraction extracts key facts and data declarations through semantic dependency analysis, verifies consistency, combines with the knowledge base to generate a set of assertion summaries, provides clear conclusions for flood scenarios, and reduces misjudgments.

[0074] Risk Preview Analysis: Through semantic analysis of the large language model, combined with historical dam accident cases (such as seepage leading to dam instability), the system identifies potential risk points such as "abnormal seepage may cause dam instability" and "insufficient downstream flood control facilities", and records their locations. The system performs feature matching on historical cases and risk points, extracts features such as "excessive seepage flow rate", and associates relevant cases. Through causal path analysis, the system deduces the risk path of "there are serious seepage safety hazards → dam break → causing serious inundation losses downstream", and generates a risk warning text. Risk preview analysis identifies potential risk points (such as "excessive seepage flow rate") through semantic analysis, matches historical cases, deduces the risk path of the flood scenario (such as "there are serious seepage safety hazards → dam break"), generates a warning text, and enhances the accuracy of risk prevention and control. However, the dynamic risk prediction ability is limited. Therefore, an improved method is proposed: adding a dynamic risk modeling module, using time series prediction and reinforcement learning, combining real-time flood data (such as flow rate changes), and adaptively generating risk paths to improve the accuracy of the warning text.

[0075] Scenario Verification: Through semantic parsing, the system extracts the flood scenario description and records its location. The simulation parameters are extracted from the scenario description and converted into the simulation input format. The system uses external simulation tools to verify the flood scenario and generates results. By comparing the simulation results with the report content, the system finds that the report does not mention the overflow height, records this difference, and generates a comparison analysis report. Scenario verification extracts flood scenario parameters, drives the simulation, generates a comparison analysis report, reveals omissions in the report, and improves the depth of flood scenario review. However, the adaptability of the simulation parameters is insufficient. Therefore, an improved method is proposed: integrating an adaptive parameter optimization module, using Bayesian optimization to dynamically adjust the flood scenario simulation parameters, combining real-time data (such as rainfall), and generating a high-precision comparison analysis report.

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

[0077] Furthermore, the specific steps are as follows:

[0078] The preliminary review data, including compliance deviation marked text, summary set of assertions, risk warning text, and comparison analysis report, are input into the natural language processing layer. Through semantic association mapping, the natural language processing layer identifies the correspondence between non-compliance items and the original report text. Through cross-text reference analysis, the mapping relationship between review data and structured semantic text is established. Integrating the association points and mappings, problem points such as "lack of emergency plan" and correction points such as "formulate a seepage emergency plan" are extracted to generate a semantic link traceability set, recording problems, correction suggestions, and regulatory bases.

[0079] The comprehensive review layer extracts features from the semantic link traceability set to obtain difficult points, and combines with the human-machine collaborative intelligence report model to generate a multi-dimensional review report.

[0080] Furthermore, the specific steps are as follows:

[0081] The comprehensive review layer extracts features from the semantic link traceability set to obtain difficult points, such as "lack of emergency plan", classifies them as compliance problems, generates verification tasks and assigns them to domain experts. The experts analyze the tasks and give feedback, such as "it is necessary to formulate an emergency plan and improve the downstream evacuation plan". The system generates a preliminary review result through semantic analysis to confirm the problem points. Finally, the system integrates the expert feedback and the review result to generate a multi-dimensional review report.

[0082] The human-machine collaborative intelligence report model generates a multi-dimensional review report through verification task generation, expert feedback, semantic analysis, and report integration, integrating flood scenario compliance, risks, and simulation results to guide rectification and enhance the practicality of the review. However, the efficiency of expert feedback is relatively low. Therefore, an improved method is proposed: add an intelligent suggestion generation module, use the knowledge graph and generative model to pre-generate suggestions for difficult points in flood scenarios, and give priority to pushing high-risk tasks to improve the report generation efficiency.

[0083] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-dimensional review method for the safety evaluation report of reservoir dams based on large language models, characterized in that, Including: Construct a multi-dimensional insight assistant brain model to process the reservoir dam safety evaluation report and generate a multi-dimensional review report; The multi-dimensional insight assistant brain model includes a layer meaning analysis layer, a multi-dimensional text analysis layer, a natural language processing layer, and a comprehensive review layer; The multi-dimensional text analysis layer analyzes the structured semantic text to obtain preliminary review data; the analysis steps include performing risk path deduction on the structured semantic text and outputting risk warning text; parsing the structured semantic text to obtain an operating condition scenario description, extracting a simulation input parameter set, and outputting multi-dimensional simulation data including a confidence interval; Perform simulation scenario verification on the multi-dimensional simulation data and the scenario description to generate a comparison analysis report; The natural language processing layer performs semantic integration and cross-text correlation analysis on the preliminary review data to generate a semantic link traceability set; The comprehensive review layer extracts features from the semantic link traceability set to obtain difficult points, and combines with a human-machine co-wisdom report model to generate a multi-dimensional review report.

2. The multi-dimensional review method for the reservoir dam safety evaluation report based on the large language model according to claim 1, wherein The layer meaning analysis layer includes: Perform word segmentation, sentence pattern splitting, and grammar annotation on the reservoir dam safety evaluation report to generate a preliminary text structure; Analyze the preliminary text structure to identify key semantic entities and generate a semantic entity set; the key semantic entities include engineering terms, technical parameters, geographical locations, and time nodes; Integrate the preliminary text structure and the semantic entity set based on the domain knowledge base to generate a structured semantic text.

3. The multi-dimensional review method for the reservoir dam safety evaluation report based on the large language model according to claim 1, characterized in that The multi-dimensional text analysis also includes: Analyze the structured semantic text against the regulations and standards in the dam domain knowledge base, identify non-compliant items, and generate a compliance deviation marked text; Extract and analyze the key facts and original data declarations in the structured semantic text to generate a judgment summary set; The preliminary review data includes the compliance deviation marked text, the judgment summary set, the risk warning text, and the comparison analysis report.

4. The multi-dimensional review method for the reservoir dam safety evaluation report based on the large language model according to claim 3, characterized in that The specific process of obtaining the judgment summary set: Obtain key facts by semantic dependency analysis of the structured semantic text, where the key facts include operating state descriptions and abnormal data; record the key facts and their positions in the structured semantic text to generate a key fact set; extract the original data declarations in the structured semantic text, verify the consistency of the original data declarations with the context using data verification, eliminate redundant data and contradictory data, obtain normal data declarations, and generate a data declaration set; the original data declarations include project overview and basic parameters; the data declaration set includes original data declarations, verification results, and normal data declarations; combine with the dam domain knowledge base to perform conclusive descriptions on the key fact set and the data declaration set to form a judgment summary set.

5. The multi-dimensional review method for the reservoir dam safety evaluation report based on the large language model according to claim 3, wherein The specific process of obtaining the risk warning text: Obtain potential risk points by semantic analysis and interpretation of the structured semantic text; record the potential risk points and their positions in the structured semantic text to generate a risk entity set; Perform feature matching on the dam domain knowledge base and the risk entity set, extract the risk features and historical related cases in the risk entity set, and generate a risk feature set; Combined with the knowledge base in the dam field, perform risk path deduction on the risk entity set and the risk feature set to form a risk warning text; The specific steps of the risk path deduction are as follows: Construct risk points as nodes, and obtain risk propagation paths through temporal relevance, spatial proximity, and causality; Classify the risk feature set into three levels: triggering conditions, intermediate conduction, and final consequences, and calculate the state transition probability between levels through a Markov chain model to generate potential causal chains; Based on historical accident data, comprehensively considering the three dimensions of risk probability, severity, and urgency, obtain the risk impact range through a weighted fusion algorithm; Conduct multiple rounds of scenario simulations on critical paths exceeding the threshold, and output a risk evolution time series diagram including a confidence interval; Generate a risk warning text with risk propagation paths, potential causal chains, and risk impact ranges.

6. The multi-dimensional review method for the reservoir dam safety evaluation report based on the large language model according to claim 3, wherein The specific process of obtaining the comparison and analysis report: Analyze the structured semantic text through semantic parsing to obtain a description of the operating condition scenario; Use parameter extraction to extract simulation parameters from the operating scenario description, and convert the simulation parameters into a simulation input format to generate a set of simulation input parameters; Use an external simulation tool to verify the simulation scenario, specifically including: Invoke hydraulics and structural mechanics to calculate the set of simulation input parameters, and output multi-dimensional simulation data including a confidence interval; Convert the multi-dimensional simulation data into a natural language description, calculate the matching degree between the multi-dimensional simulation data and the scenario description using a semantic similarity algorithm to obtain a deviation; Establish a numerical mapping relationship to quantify the deviation level and severity, and generate a comparison and analysis report.

7. The multi-dimensional review method for the reservoir dam safety evaluation report based on the large language model according to claim 1, wherein The human-machine collaborative intelligence report model includes a verification task generation layer, an expert opinion analysis layer, a review information interaction layer, and a report generation layer; The verification task generation layer classifies the difficult points to obtain verification tasks and assigns them to matching experts; The expert opinion analysis layer analyzes the verification tasks to obtain expert task feedback; The review information interaction layer performs semantic analysis on the verification tasks to generate a preliminary review result; The report generation layer fuses the expert task feedback and the preliminary review result to form a multi-dimensional review report.

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