193 results about "Structured reporting" patented technology

The invention discloses an accounting data intelligent processing method and system for enterprise financial auditing, and relates to the technical field of accounting data intelligent processing, and the method comprises the steps: obtaining multi-mode enterprise financial data, and carrying out the preprocessing; carrying out multi-modal semantic understanding analysis on the unstructured text and image data; constructing an enterprise financial space-time knowledge graph containing time attributes; inputting into an anomaly analysis model, extracting spatial structure characteristics of the financial entity in the topological network, and extracting dynamic characteristics of the financial relationship evolved along with the time sequence; identifying an abnormal source, evaluating a systematic risk value and generating an abnormal propagation path; and integrating to generate a final audit report. According to the method, structured and bill images are fused, identifiers and time calibers are unified, abnormal source and propagation are positioned based on the space-time knowledge graph, closed-loop counter-knock and cross-period anomalies are identified, the auditing accuracy and coverage rate are remarkably improved, the workload of false report, missing report and manual recheck is reduced, and a traceable structured report is quickly generated.

The invention relates to the technical field of code security analysis, and discloses a control flow analysis and retrieval enhancement-based large model code security review method, which comprises the following steps of: analyzing an incremental code to generate an abstract syntax tree and data flow analysis information; retrieving local knowledge base association business rules and historical vulnerabilities based on grammatical features, and generating a context enhancement prompt; calling a large language model to jointly analyze codes and contexts, identifying vulnerabilities and outputting a structured report; and combining with a historical false alarm data optimization result and then integrating to a development assembly line. Multi-source information is fused through an RAG technology to enhance semantic understanding, and control flow node tracking and cross-version semantic association are combined, so that the problems of a traditional tool business logic vulnerability detection blind area, incremental code analysis failure and high false alarm rate are solved, synchronous improvement of security examination accuracy and efficiency is realized, and the method is adaptive to an agile development scene.

The invention discloses a report chart automatic generation method based on natural language analysis and data query, which comprises the following steps: acquiring a natural language text, and constructing a slot set and an initial semantic fragment; executing consistency verification and ambiguity identification, and constructing a semantic framework; executing field standardization and mode alignment, generating a data query statement and establishing a traceability chain identifier; querying a target database to obtain an original data set, completing data quality self-correction, and generating a cleaned data set; calling an improved Mamba model to construct a constraint judgment matrix and outputting a target chart subset; generating a chart description grammar and an image annotation template, and executing image rendering, feature point recognition and image sidebar construction; and performing layout optimization and double compilation, and outputting a structured report file. According to the method, the chart generation automation level and the report compiling efficiency are improved, and the accuracy, traceability and specialty of the output content are ensured.

The invention discloses an insurance claim settlement-oriented multi-modal image video evidence analysis method and system. The method comprises the following steps of: acquiring video / image and multi-source data such as metadata, audio, IMU (Inertial Measurement Unit), GPS (Global Positioning System), OBD (On-Board Diagnostic) and the like; calculating content Hash of the video and the audio according to frames, connecting the content Hash with time information in series to form chained Hash, and adding a verification digital signature and a credible timestamp; realizing cross-modal time sequence alignment based on self-adaptive time anchor-attitude coupling; tampering detection is carried out in combination with PRNU fingerprints, noise field consistency, dual compression, copy-movement and the like; multi-view geometry and monocular depth are fused, IMU scale constraint and micro rendering are introduced, three-dimensional reconstruction and re-projection optimization are completed, and collision dynamics verification is carried out; and constructing an event cause and effect graph, judging responsibility in combination with traffic rules, outputting a confidence coefficient vector and a structured report, and generating a verifiable evidence packet. The scheme has the advantages of high efficiency and traceability in the aspects of space-time restoration and interpretable responsibility judgment.

The invention provides an intelligent medical question-answering system and method based on hybrid retrieval and lightweight reordering, and is applied to the technical field of medical data processing. According to the method, five types of core entities are extracted through the preset Chinese medical NER model, and the structured knowledge base is formed through relation extraction modeling association. Analyzing user Chinese query, extracting medical entities, identifying four types of appeals and converting the four types of appeals into semantic vectors; a PubMedBERT is adopted to encode a medical document to generate a vector for storage, BM25 and vector retrieval are executed in parallel when query is received, and candidate documents are generated through fusion of an RRF algorithm. And generating a score data set based on the prompt template, and training the lightweight model to sort and output an evidence set. In combination with query semantics, a simplified context is generated through retrieval, sorting and compression, and FlashAttention optimization calculation is integrated. An optimized U-Net segmentation image is utilized to generate a structured report, and multi-modal information is integrated to generate an accurate answer giving consideration to the image and medical knowledge through LLM reasoning.

The invention relates to the technical field of text processing, and provides an engineering management report generation method and system based on natural language processing, which are used for realizing deep analysis of construction logs and providing a comprehensive and accurate decision basis for engineering management. The method comprises the steps that a construction log text set generated in the engineering construction process is acquired, natural language processing is conducted on the construction log text set, key entities and progress events in each construction stage description unit are extracted, and a construction feature set containing entity identification information and event time sequence information is obtained; performing semantic alignment processing on the construction feature set and a preset engineering specification knowledge base to generate specification association information between the construction stage description unit and an engineering specification requirement; and performing structured integration processing on the standard associated information based on a hierarchical template matching algorithm to generate a structured report text meeting the engineering management requirements.

The invention provides a disease screening system based on a large model. The disease screening system is used for solving the technical problem that an existing disease screening system is intelligently used for single special disease screening. The system comprises a scheduling model and a plurality of AI auxiliary diagnosis models, the scheduling model is connected with the plurality of AI auxiliary diagnosis models, and the scheduling model is connected with a big data disease library. According to the method, a high-level scheduling model is utilized, multiple single AI auxiliary diagnosis models are managed in a centralized mode, automatic calling of a multi-disease AI system is achieved, automatic structured report generation is achieved through a large language model, historical medical history is combined, progress is predicted, treatment suggestions and reference cases are automatically given, an existing clinician film reading workflow is fitted, and the efficiency is improved. The whole process of actual diagnosis decision making of doctors is greatly fitted, and the working efficiency of the doctors can be improved to the maximum extent.

The invention discloses an intelligent interaction system based on a large language model and a knowledge graph, and relates to the technical field of artificial intelligence and intelligent interaction. According to the system, data processed by a data acquisition and preprocessing module is stored in a local knowledge warehouse in a three-layer nested structure; the multi-modal semantic understanding module constructs a knowledge system in which a triple knowledge graph and a vector database are complementary by optimizing a BERT model and performing dual-channel analysis; the intelligent interaction module realizes natural language interaction and business task automatic triggering based on an RAG technology and a dialogue memory mechanism; the information change identification and analysis module generates a personnel change risk report and performs early warning; and the summary report automatic generation module outputs a structured report. According to the method, the problems of data splitting, complex interaction and slow response in traditional enterprise management are solved, the functions of system intelligent question answering, personnel change risk analysis, structured report automatic generation and the like are realized, and the management efficiency and the information consistency are improved.

The invention discloses an auxiliary film reading method and system based on artificial intelligence, and the method comprises the steps: 1, collecting a pathological WSI, an electronic medical record, detection data and equipment parameters, correcting the equipment difference through adaptive dyeing normalization, and constructing a structured data package associated with an ID-timestamp of a patient; 2, developing a dynamic branch CNN, migrating teacher model knowledge through knowledge distillation, and introducing federated learning; step 3, the edge generates a thermodynamic diagram to mark a suspicious area, and the cloud outputs a structured report; step 4, constructing a normal tissue feature space by the variational auto-encoder, detecting abnormal slices and triggering expert re-checking; a reverse automatic encoder generates a pseudo-health image to compare and position a pathological area, and dynamic weight adjustment balances the federal learning convergence speed; 5, integrating the thermodynamic diagram, the gene data and the clinical indexes by a three-dimensional platform, and supporting multi-dimensional superposition display; webGL realizes browser end rendering, and NLP automatically generates a report abstract marked with a key evidence chain and is in butt joint with an international diagnosis and treatment guide.

The invention discloses an intelligent text report automatic generation method and system based on safety production data, and belongs to the technical field of big data and artificial intelligence, and the method comprises the steps: multi-source heterogeneous data collection and structured processing: collecting safety production event data including violation operation, violation command and violation of labor discipline, carrying out structured processing on the data; semantic enhancement prompt engineering and large language model reasoning: constructing prompt words, inputting structured data into a large language model, and performing semantic understanding and text generation on the data by utilizing a prompt engineering technology guide model; and dynamic report template adaptation and multi-format output: realizing structured report output through template matching, outputting a structured text report by the model, filling the structured text report into a preset template, and exporting the structured text report. The enterprise safety management efficiency is remarkably improved, the manual writing burden is reduced, informatization, standardization and intelligent processing of safety production data is achieved, and the safety management digitization and automation level of enterprises is improved.

The invention relates to the technical field of tunnel defect identification, in particular to a tunnel defect identification method based on a dielectric distribution diagram. According to the method, on the basis of AI rock-soil perspective radar field monitoring, gprMax simulation and laboratory electromagnetic data, a two-dimensional dielectric distribution diagram is generated through variational Bayesian inversion fusion. Through multi-scale spectral clustering and expert knowledge, a potential abnormal region is automatically identified, and then accurate classification and identification of a defect region are realized by adopting graph form constraint propagation and integrating a Transform-graph neural network model. And finally, projecting an identification result to an original image, and generating a visual defect labeling layer and a structured report. According to the invention, high-precision, automatic, visual and structured detection of tunnel structure defects is realized, the intelligent level and risk early warning capability of tunnel safety operation and maintenance are significantly improved, and the digitization and intelligent process of tunnel operation and maintenance management is promoted.

The invention relates to a digital archive intelligent processing method, a storage medium and a system, which are suitable for multi-source heterogeneous archive management scenes such as colleges and universities. The method comprises the following steps of: classifying structured and unstructured data such as paper archive scanning pieces and database views, and extracting metadata and entity information by adopting a scanning and OCR (Optical Character Recognition) technology; a complex table and document content are analyzed through a model, semantic analysis (entity recognition, relation extraction and event abstract) is achieved in combination with a language model of a Transform architecture, and a structured report containing a data abstract, an entity relation graph and abnormal annotations is generated. The system is internally provided with a parameter template automatic generation module, supports cross-page content continuous restoration and sensitive data encryption desensitization, and realizes safe sharing through an API interface. The method solves the problems of low efficiency, difficulty in multi-source data fusion and the like of traditional archive processing, improves the automation level and data value mining capability of archive management, and is suitable for intelligent upgrading of complex archive scenes.

The invention belongs to the technical field of mechanical property testing, and particularly discloses and provides a coal rock sample mechanical property intelligent testing system and method.The coal rock sample mechanical property intelligent testing system comprises a consistency verification module, a test piece dividing module, a density testing module, a triaxial testing module and a report generating module; standardized screening is carried out on the size, morphology and structural integrity of the test piece, physical characteristics are ensured to be unified, individual difference interference is avoided, then density and triaxial testing are integrated, density, triaxial compressive strength and shear strength parameters are synchronously obtained, a multi-dimensional data system is constructed, the problem that in the prior art, parameters are single is solved, and the test efficiency is improved. The method comprehensively supports comprehensive evaluation of coal rock mechanical properties, and meanwhile, by outputting a structured report containing a measured value, an adaptive threshold value, a judgment result and an unqualified item and clearly marking the unqualified item, the limitation that manual secondary processing is needed in the prior art is solved, seamless connection of testing, evaluation and engineering application is achieved, and the engineering application value is improved.

The invention relates to a multi-dimensional aging test data analysis system and method, and aims to realize real-time acquisition and intelligent analysis of multiple performance indexes in the aging process of electronic products. The system comprises an information acquisition module, a test scheduling module, a data processing module, an analysis module, an abnormity judgment module, a report module and a communication module. Cooperative work among the modules is realized based on task driving and data circulation, accurate association of test data is realized through bar code binding, and unified analysis is performed in combination with multi-source information such as USB read-write performance, network transmission state and display resolution. The system realizes multi-task concurrent acquisition through a resource-aware scheduling strategy, and the analysis module identifies performance abnormity and outputs a structured report by combining preprocessing, feature extraction and time sequence prediction. According to the method, the test automation level and the anomaly recognition accuracy are improved.

The present invention discloses a computer-implemented system and method for automated evaluation of Environmental, Social, and Governance (ESG) disclosures and detection of greenwashing. The system integrates artificial intelligence and machine learning techniques to extract, segment, and analyze ESG content from structured and unstructured sources. A retrieval and segmentation module identifies ESG-relevant sections, which are then vectorized and stored for semantic search. A semantic retrieval module fetches contextually relevant content, which is evaluated against predefined benchmarks using an analysis and validation module. The scoring module assigns ESG scores based on rule-based criteria including key strengths, specificity, and supporting evidence. A report generation module creates structured ESG reports with visual analytics, while an orchestrator module dynamically coordinates all processing steps based on ESG framework requirements.

The invention relates to the technical field of knowledge maps, in particular to a steel manufacturing knowledge map construction method based on multi-source heterogeneous data, which comprises the following steps: converting message data into a standardized semantic triple through a semantic mapping rule, and complementing missing attributes by using physical and chemical component balance logic to form a standardized entity; meanwhile, a semantic processor is used for extracting unstructured report text logic, cross-modal coupling is carried out on the unstructured report text logic and SEM image features, and a failure mechanism enhanced entity is generated. Then, full-process dynamic parameters are collected and regularized into equal-interval time sequence parameters; and finally, taking the normalized entity as an index feature and the mechanism enhanced entity as an association constraint, executing semantic fusion by utilizing a topological feature mapping engine, and constructing a dynamic weight graph structure by combining with a dynamic probability distribution correction operator iteration parameter weight. According to the method, efficient association and semantic organization of manufacturing whole-process multi-modal data are realized, and the retrieval precision and knowledge discovery capability of steel manufacturing data are greatly improved.

The invention discloses a service approval method, device and equipment based on multiple agents and MCP drive, and the method comprises the steps: obtaining a policy information file, carrying out the semantic analysis of the policy information file, and storing the obtained policy meta-information to a knowledge base; selecting a subject to be approved and a target policy in a knowledge base according to the approval task request, analyzing the target policy by using a preset DQN model, and generating a condition verification sequence; performing service approval according to the target policy and the condition verification sequence through a cooperative multi-agent, and generating a service approval result; and according to the service approval result, generating a structured report including an identifier of the subject to be approved and an approval failure reason. Distributed efficient execution of tasks is realized, and the efficiency and accuracy of examination and approval decision making are improved.

The invention discloses an image text report quality control method and system based on LLM and a structured report, relates to the technical field of medical text report quality control, and solves the problems that a current text report quality control method is difficult to accurately extract key fields and is difficult to judge complex diagnostic reasoning logic. According to the technical scheme, the method is characterized in that iconography feature description related to a diagnosis conclusion is extracted from a text report, cue words are constructed according to a matched structured report template to analyze the iconography feature description into structured data, and an interface is called to fill the structured data into the structured report template. And obtaining a template diagnosis conclusion according to the built-in judgment logic of the structured report template, and comparing the template diagnosis conclusion with the current diagnosis conclusion to realize quality control auditing of the text report.

The invention discloses an unplanned secondary operation decision support method and system based on EMR analysis. According to the method, firstly, multi-source EMR data are collected in real time through an HL7 FHIR standardized interface, differential analysis is conducted through a time sequence analysis algorithm and a BioBERT model fused with a medical knowledge graph, and standardized clinical indexes are generated; secondly, the indexes are matched with an unplanned secondary surgery thematic knowledge graph in real time, multi-path probabilistic reasoning is carried out based on a Bayesian network, and a risk conclusion with confidence and a reasoning chain are output; and generating a structured report including risk early warning, core basis, possible reasons and processing suggestions, and pushing the structured report in multiple channels. And finally, collecting clinical feedback, optimizing the knowledge graph and the analytical model, and forming a closed loop. According to the invention, dependence on experience of doctors is reduced, and accuracy and timeliness of risk research and judgment are improved.

The invention relates to the technical field of medical report generation, and discloses a medical image automatic annotation and report generation system which comprises a preprocessing module, a hierarchical feature extraction module, a semantic annotation module, a guide generation module, a diagnosis reasoning module and a man-machine cooperation interface. According to the method, through standardized preprocessing, hierarchical feature extraction and automatic generation of the annotation and the report, the processing efficiency is greatly improved, and the manual load is reduced; secondly, on the basis of hierarchical feature extraction and semantic and visual embedding alignment, feature support and semantic mapping precision of annotation and reporting are enhanced, and result accuracy and cross-scene consistency are improved; meanwhile, multi-modal alignment processing and multi-dimensional information fusion enhance the adaptability of the system to different image types, generate a structured report conforming to the clinical process and improve the clinical applicability; in addition, a man-machine cooperation feedback mechanism forms a parameter optimization closed loop, so that the system dynamically adapts to new scenes and specifications, and continuous improvement of generalization ability is realized.

The invention belongs to the field of artificial intelligence and big data analysis, and particularly relates to an AI-based drive type data analysis method and system.The method comprises the steps that S1, on the basis of natural language query input by a user, candidate SQL and / or DSL statements are generated through a pre-trained big language model; s2, generating a parallel query plan and optimizing the parallel query plan based on the candidate SQL and / or DSL statements and the metadata knowledge graph; s3, extracting authorization data; s4, fusing the extracted data to generate an analysis feature wide table, and identifying key features, abnormal modes and text abstracts in the analysis feature wide table; s5, executing controlled retrieval in the business knowledge graph based on the key features and the abnormal mode, and generating a security evidence sub-graph; s6, inputting the text abstract of the analysis feature wide table and the security evidence sub-graph into an AI inference engine, and outputting a structured report; and S7, displaying the structured report. The problems of high technical threshold and weak safety and interpretability in a data analysis process are solved.

The invention discloses a medical image diagnosis report quality control system based on artificial intelligence, and the system comprises a report obtaining module which is used for obtaining medical image diagnosis report data, and the medical image diagnosis report obtaining modes comprise on-line transmission and off-line import; the content identification module is used for identifying the content of the medical image diagnosis report to obtain the report content; the checking module is used for checking and correcting the content of the report to obtain a checked and corrected report; the report generation module is used for analyzing the check and correction completion report to generate a structured report; the feedback module is used for collecting feedback information of the user on the structured report and comprehensively processing the feedback information to generate optimization management information; and the intelligent evaluation module is used for comprehensively analyzing all the generated structured reports to generate overall quality evaluation information. According to the invention, more comprehensive quality control of the medical image diagnosis report can be carried out, so that the quality of the medical image diagnosis report is effectively improved.

The invention belongs to the technical field of aviation flight training, particularly relates to a multi-module collaborative flight simulator control panel and method, and solves the problems that an existing system is low in interaction efficiency, subjective in evaluation, lagged in early warning and time-consuming in comment. The system depends on traditional IOS hardware, and is provided with an intelligent voice interaction module which recognizes teacher voice through ASR, generates an instruction through intention recognition and three-level slot completion, and cooperates with double-layer flight stage recognition logic to guarantee interaction accuracy; the flight quality monitoring module operates a rule engine and an enhanced LSTM neural network in parallel, and outputs a comprehensive flight quality score in combination with an adaptive fusion algorithm; the real-time reminding module dynamically adjusts the early warning priority according to the event level and the flight stage, and multi-mode early warning is provided; and the intelligent comment module fuses the multi-source data and generates a personalized structured report according to the priority of the template. According to the invention, intelligent interaction, quantitative evaluation and automatic commenting of training are realized, and the training efficiency and evaluation objectivity are improved.

The invention discloses a file disassembling method based on multi-dimensional file association analysis and automatic interpretation, and relates to the crossing field of natural language processing and big data analysis. Comprising the steps of (1) carrying out multi-source heterogeneous data collection, (2) carrying out intelligent cleaning on the collected data and constructing a knowledge graph, (3) carrying out deep correlation analysis, (4) carrying out automatic disassembly and file simulation, and (5) carrying out intelligent application and feedback: automatically generating a Markdown document containing a file abstract, clause comparison and an enterprise portrait through a structured report engine, and carrying out intelligent application and feedback on the Markdown document. GIS map overlay analysis is supported, clauses in an applicable file are matched based on an enterprise tag system, declaration materials are pre-checked through an OCR + rule engine, user feedback cases are collected, model parameters are incrementally trained monthly, it is ensured that adaptation is completed within 24 hours after a new file is released, and a collection-analysis-application-optimization closed-loop iteration mechanism is formed.

The invention discloses an identification document full-process intelligent generation system and method. The system realizes end-to-end automatic processing from data acquisition to document generation through original report screenshot sampling, file filtering, authorization document analysis, data extraction and structuring, report content generation, equipment information analysis and judicial document template filling. The system adopts a multi-stage fault-tolerant and verification mechanism, integrates a large-model intelligent analysis and extraction technology, can automatically repair a non-standard data format, and generates standardized document content according to judicial specifications. According to the method, the judicial expertise document generation efficiency, accuracy and normalization are remarkably improved, and the evidence effectiveness of the output document is ensured.

The invention relates to the technical field of power grid defect recognition, in particular to an Agent-based automatic power grid defect recognition system. The system comprises the following steps: acquiring real-time acquisition data, analyzing the real-time acquisition data to obtain a data analysis result, and performing tagging processing on the real-time acquisition data according to the data analysis result to obtain tag data; based on a D-S evidence theory, according to a preset historical defect graph, performing classification processing on the label data; and performing defect identification on the classified label data by utilizing a defect identification Agent, generating a defect report according to an identification result, and feeding back the defect report to an equipment maintenance system. And the classified label data directly triggers a defect identification algorithm of the corresponding Agent, so that a communication link of traditional cross-system data calling is shortened, and the defect upgrading risk caused by processing delay can be reduced. A structured report is automatically generated, a traceable decision basis is provided for an equipment maintenance system, and artificial transcription errors are reduced.

The invention belongs to the field of medical image analysis, and provides a multi-modal semi-supervised image analysis and structured report generation method and application for solving the problems that standardized report generation efficiency of chronic obstructive pulmonary disease patients is low and the patients highly depend on artificial experience. Performing pretreatment; constructing an improved variational auto-encoder to obtain latent variable representation of the lung medical image data; using domain adaptive adversarial learning to align the data in the unlabeled data set to the distribution of the data in the labeled data set on the latent variable and the segmentation result; generating a preliminary report text by using a text decoder according to a hidden variable output by an encoder of the variational auto-encoder; knowledge distillation is carried out based on a pre-trained Teacher network and a text decoder, and medical terms and abbreviations in the preliminary report text are modified; and outputting the structured report. According to the invention, the lung focus can be segmented with high precision, the disease degree is quantified and the structured report is automatically generated.

The invention provides a block chain-based second-hand vehicle detection evaluation report traceability method and system, and relates to the technical field of traceability detection, and the method comprises the following steps: detecting a second-hand vehicle to obtain structured report data, the structured report data comprising a plurality of pieces of traceability information and a plurality of pieces of feature information; performing encryption processing on the structured data to generate a first verification code and a feature information set integrity check code, and generating a second verification code for each type of traceability information; storing the structured report data in a distributed storage system, and obtaining a global content addressing identifier of the structured report data in the distributed storage system; organizing the multiple key data according to a predefined sequence and format, calculating abstract fingerprints, and writing the abstract fingerprints, the structured report abstract and the multiple verification codes into blocks of the block chain; and performing traceability verification in the second-hand car exit transaction. The method has the advantages of high reliability and capability of ensuring that detection report data is real, complete and traceable.

The invention provides an industrial anomaly classification method and system based on a multi-modal large language model, and belongs to the technical field of visual detection. The visual pre-screening module adopts a BYOL unsupervised feature extractor and a local block matching technology to calculate the minimum Euclidean distance between an image to be detected and a normal sample so as to generate an abnormal thermodynamic diagram, and efficient preliminary screening is realized; the coordinate set is converted into a binary mask through an abnormal contour marking module, and an accurate single-pixel contour is generated through morphological gradient and SIFT displacement correction; fusing a CLIP visual encoder and a GPT text encoder in a multi-modal decision engine, realizing visual-semantic alignment through cross-modal fusion and a multi-head attention mechanism, and supporting alpha hybrid dynamic rule update; and a dynamic classification executor calculates an abnormal region risk index based on the risk grading model, and generates a grading response action and a structured report. According to the invention, the classification precision is improved, so that the method can adapt to complex and diversified industrial abnormal scenes.

The invention provides a geological disaster intelligent early warning and rapid report generation method based on a multi-modal large model, which is applied to the technical field of geological disaster prediction and comprises the following steps: acquiring geological monitoring data acquired by integrated geological disaster monitoring equipment; inputting the preprocessed image data into a deep convolutional neural network model to obtain image features; inputting the preprocessed time sequence monitoring data into a bidirectional long short-term memory network model to obtain a feature association relationship output by the bidirectional long short-term memory network model; performing multi-modal fusion based on the image features and the feature association relationship to obtain cross-modal joint features; inputting a preset influence factor and the cross-modal joint feature into the long short-term memory network model to obtain a disaster prediction result; and performing natural language generation on the disaster prediction result to obtain a structured report corresponding to the disaster prediction result. According to the invention, accurate prediction and rapid disaster report generation of geological disasters can be realized.