28 results about "Case analysis" patented technology

The invention belongs to the technical field of artificial intelligence, and particularly relates to an intelligent conciliation case analysis method and system based on an AI large model, and the method comprises the steps: inputting a text material, a voice transcription text and a picture evidence of a conciliation case into a preset multi-mode fusion large model; generating a high-dimensional vector representation; condensing the case core law semantic feature vector; calculating a flow failure risk index; judging whether the flow failure risk index exceeds a preset risk threshold value or not; a brand new workflow topological structure and node tasks are generated; solving a flow adaptability index; judging whether the flow adaptability index is lower than a preset adaptability threshold value or not; when the flow adaptability index is lower than a preset adaptability threshold value, a brand new workflow topological structure and a node task are generated again; and when the flow adaptability index is not lower than a preset adaptability threshold value, ending the flow. According to the method, the accuracy and comprehensiveness of the case core semantic features are improved.

The invention discloses an atmospheric pollution case system, a case analysis method and a medium, and the system comprises a basic information layer which is used for storing the whole-process feature data and treatment measure data of a pollution event; the logic analysis layer is used for carrying out quality control on the data, carrying out source analysis and cause analysis on the whole-process feature data of the pollution event after the quality control, and carrying out effect evaluation on the treatment measures after the quality control; and the comprehensive decision-making layer is used for comparing historical cases according to the retrieval conditions and matching similar historical cases, and finding out treatment measures corresponding to the similar historical cases according to the similar historical cases, or formulating targeted new treatment measures according to the corresponding treatment measures. The invention provides a'basic information-logical analysis-comprehensive decision 'three-layer progressive architecture case library, so that other modules can be conveniently expanded on the architecture of the case library subsequently; and multi-source data are systematically integrated, so that the problem of fragmentation of existing case data is solved.

The invention discloses a multi-mode large model police file and image association reasoning method, and belongs to the technical field of artificial intelligence and police information processing. The method comprises the following steps: preprocessing police affair texts and images; respectively extracting word-level and sentence-level features and local and global visual features by using a text encoder and an image encoder; realizing alignment and joint representation through a cross-modal fusion layer and an attention mechanism; generating a preliminary reasoning result by adopting a task output head; a heterogeneous evidence graph is constructed, and priori knowledge is injected in combination with the knowledge graph; and finally, joint reasoning is carried out through the graph neural network, and a cross-modal reasoning result and a complete evidence chain are output. According to the method, deep correlation analysis of police affair texts and images is realized, and the accuracy and interpretability of case research and judgment are improved.

The embodiment of the invention discloses a dispute focus recognition method and device, computer equipment and a readable storage medium. The method comprises the following steps: acquiring real case data to perform multi-modal feature extraction to respectively obtain text features, table features and image features; performing feature fusion on the text features, the table features and the image features to obtain cross-modal fusion features; constructing an element knowledge graph based on entities and relationships in the real case data; obtaining a statement text, and synthesizing the statement text, the cross-modal fusion features and the element knowledge graph to construct a multi-dimensional data set; mining potential dispute points in the multi-dimensional data set through a preset rule base; performing similarity calculation based on the statement text corresponding to the potential dispute point to obtain a similarity result; and judging whether the potential dispute point is a real dispute point or not according to a similarity result. According to the method, information splitting caused by single-modal data processing is avoided, case analysis is ensured to be based on a complete evidence chain, and the conclusion is more comprehensive and accurate.

The present application provides a case analysis method and system based on deep learning. The method includes: obtaining a target data set containing image data and text data; extracting features from the image data in the target data set based on a pre-trained convolutional neural network; inputting the extracted feature vectors into a pre-trained natural language processing model for analyzing the text data in the target data set; generating new feature vectors by linear interpolation fusion based on the image feature vectors processed by the convolutional neural network and the text feature vectors processed by the natural language processing model; expanding the target data set based on the generated new feature vectors, and training a deep learning model based on the expanded target data set to obtain a trained deep learning model; inputting the case to be analyzed as input into the trained deep learning model, and obtaining the case analysis results output by the trained deep learning model.

The invention relates to the technical field of question test paper composition, and discloses a medical theory examination intelligent test paper composition method based on an artificial intelligence technology, and the method comprises the steps: question information preprocessing, question similarity calculation, and intelligent test paper composition. In the topic processing link, the topics are subjected to disassembling and structured storage according to multiple modes of text, image and case analysis, and features are extracted in combination with technologies such as keyword extraction and local feature descriptor methods; in question similarity calculation, cosine similarity and a weighted summation mode are utilized to accurately quantify the similarity of questions of different question types, and question repetition in test paper is effectively avoided; during test paper composition, screening is performed according to parameters such as preset question type structures, knowledge point coverage and difficulty levels, reasonable difficulty distribution and comprehensive knowledge point coverage are ensured in combination with a similarity threshold judgment and replacement mechanism, a replacement object is determined according to historical use times of questions, test paper composition scientificity and question bank resource utilization rate are improved, and test paper composition efficiency is improved. And the medical theory examination paper composition flow is optimized.

The embodiment of the invention provides an auxiliary proof-giving method, an auxiliary proof-giving system, electronic equipment, a storage medium and a computer program product. The auxiliary proof-proof method comprises the following steps: acquiring right protection request content corresponding to a to-be-proof right protection event; performing case analysis on the right protection request content to obtain a right protection category positioning result matched with the right protection event; retrieving a preset checking template based on a right protection category positioning result to obtain a proof item list for the right protection event; and executing a verification task corresponding to the proof item list based on a mode of iteratively retrieving a preset knowledge base, and obtaining a proof suggestion report corresponding to the right protection event for guiding the user to provide a proof. According to the method, an iterative autonomous retrieval mechanism is driven by a task, an analysis process of manual gradual evidence collection is simulated through multiple rounds of query, and compared with a manual dominant evidence collection process, the evidence collection efficiency is improved, and the recall rate and the accuracy of key evidence are effectively improved.

The embodiment of the invention provides a method and device for evaluating an intelligent agent, equipment and a medium, and the method comprises the steps: obtaining to-be-evaluated data which at least comprises data for at least one bad case; based on the number of the at least one bad case, obtaining at least one first agent corresponding to the at least one bad case; and correspondingly processing the at least one bad case by using the at least one first agent so as to generate at least one bad case analysis result corresponding to the at least one bad case. According to the method, the multi-agent collaboration architecture for evaluating the performance of the agents is established. For each bad case of the to-be-evaluated agent, the corresponding agent can be adaptively acquired to carry out bad case-by-bad case analysis. According to the mode, the effects of high evaluation speed and comprehensive evaluation results can be achieved, and the context explosion phenomenon can be prevented.

This invention discloses a method and system for analyzing and outputting TCM acupuncture cases based on machine learning, belonging to the field of medical image processing technology. The method acquires facial image sequences from two pathological cycles of the subject, extracts spatiotemporal feature maps, and obtains local receptive field feature matrix pairs through sliding windowing. It calculates the cross-cycle joint entropy gradient and its matrix, and generates a two-dimensional deformation vector using optical flow. Using the coordinates of the first cycle as a reference, the deformation vector is set to zero when the joint entropy gradient does not exceed the rigidity threshold; otherwise, the joint entropy gradient matrix is ​​used as a weight matrix through a sigmoid activation function, and a Hadamard product is performed with the deformation vector to obtain a denoised deformation vector. The denoised vectors are superimposed to obtain corrected pixel coordinates, which are mapped to the target localization coordinate matrix, and the acupuncture case analysis results are output. This scheme achieves precise spatial decoupling of local tissue evolution, eliminates the contamination of rigid regions by global deformation, and significantly improves the robustness and accuracy of cross-cycle acupuncture target localization.

The invention discloses a case supervision system and method based on artificial intelligence, and the system comprises a data collection module, a case analysis module, an abnormity monitoring module, a risk early warning module and a man-machine interaction module, and improves the safety and efficiency of the case supervision system. A dynamically updated machine learning model is established through artificial intelligence, so that the accuracy and effectiveness of case supervision are ensured, the system adapts to the change and development of the judicial field, and the practicability and sustainability of the system are improved; the risk condition in the case handling process is comprehensively and objectively assessed through a comprehensive risk assessment and early warning mechanism, so that the potential risk is comprehensively found, and the case supervision effect and reliability are improved; and the usability and safety of the system are improved through man-machine interaction and visual display operation, so that decision making and case handling are assisted, comprehensive, intelligent and real-time supervision of cases is realized, and intelligent development in the judicial field is promoted.

The invention provides a case management method and system and related equipment, and the method comprises the steps: enabling the case management system to receive a case analysis request comprising a case number of a target case, obtaining the case information of the target case at a current stage according to the case number, carrying out the analysis of the target case according to the case information, and obtaining an analysis result, and finally, determining the next operation corresponding to the target case according to the analysis result. Wherein the analysis result comprises at least one risk and a risk level corresponding to each risk in the at least one risk. Through the case management system and method provided by the invention, standardized management of the case can be realized, the case management system can analyze the case according to the case information to determine the next operation of the case, and the efficiency of case management and case analysis is improved.

The present disclosure provides a forensic case data analysis model training method and a case data analysis method, wherein the training method comprises: obtaining a plurality of sets of sample forensic information based on a plurality of sample forensic case data; training the preset large language model based on each set of sample forensic information and the model fine-tuning strategy corresponding to each type of sub-sample forensic information included in the sample forensic information, to obtain the forensic case data analysis model; wherein the forensic case data analysis model is used to infer and analyze the corresponding target inference analysis result based on the input target forensic case data. The forensic case data analysis model obtained by the above training method can obtain corresponding case analysis results for different input conditions, improving the case analysis efficiency.

The application discloses a kind of based on general and special fusion model's judicial case retrieval method, it is related to data retrieval technical field, model scheduling module is based on case complexity and the preset complexity threshold value selects the preset domain professional model and general inference model to carry out semantic analysis, obtains professional feature vector;Input analysis module is preprocessed to original case text and completes feature vectorization, combines professional feature vector to generate paragraph level vector representation and corresponding keyword vector group;Case analysis module calculates cosine similarity by large-scale vector database, outputs preliminary similar case set, then based on model scheduling module carries out verification, outputs refined candidate case set;Result output module is structured and presents the final retrieval result of candidate case set using list structure.The application is more accurate case retrieval realized by combining general model and domain professional model.

The present invention relates to the technical field of question composition, and discloses an intelligent composition method for medical theory examinations based on artificial intelligence technology, comprising the steps of: question information preprocessing, question similarity calculation, and intelligent composition. In the question processing link, questions are decomposed and structured according to multiple modalities such as text, image, and case analysis, and features are extracted by combining techniques such as keyword extraction and local feature descriptor methods. In question similarity calculation, cosine similarity and weighted summation are used to accurately quantify the similarity of questions of different question types, effectively avoiding question duplication in the examination paper. When composing the examination paper, questions are screened according to preset question type structure, knowledge point coverage, difficulty level and other parameters, and similarity threshold judgment and replacement mechanism are combined to ensure a reasonable difficulty distribution and comprehensive coverage of knowledge points. The replacement object is determined according to the number of historical usage of the question, thereby improving the scientific nature of the composition and the utilization rate of the question bank resources, and optimizing the composition process of the medical theory examination.

The present application relates to the technical field of criminal case data analysis and early warning, in particular to a multi-technology coordinated criminal case intelligent early warning system and method fusing pre-training large model customization adaptation, dynamic weighted clustering and semantic vector retrieval, belonging to the cross technical field of judicial big data and artificial intelligence, through the coordinated technical path of "large model label extraction-weighted clustering-vector deduplication", a full-process intelligent system of "data input-intelligent analysis-accurate early warning-feedback iteration" applicable to judicial case analysis and processing scenes is constructed, not only effectively solving the core problems of low efficiency and early warning redundancy under the traditional manual analysis mode, but also completely solving the many common problems existing in the current technical system of criminal case analysis and early warning.

The application relates to the technical field of case analysis, and discloses a multi-case collision method based on a case-involved personnel archive, which comprises the following steps: integrating multi-source heterogeneous data such as case-involved personnel, time tracks and geographical positions; constructing a case three-dimensional digital twin through hierarchical feature extraction and cross-modal alignment technology; discovering the cause-effect correlation between case elements based on a data driving and field knowledge double channel; automatically filtering illegal logical connections through a reality constraint library after constructing an initial cause-effect graph to generate a credible cause-effect network conforming to law enforcement specifications. Through the construction of a constraint-enhanced deduction framework, the application realizes physical logic compliance guarantee of counterfactual scenarios on the basis of maintaining the full-element digital reconstruction and dynamic correlation mining capacity of cases, generates an investigation strategy scheme conforming to reality law enforcement constraints and possessing tactical innovation, and significantly improves the executability and decision credibility of the deduction result in a complex actual combat environment.

The embodiment of the invention relates to a scene reproduction method and system, electronic equipment and a computer readable storage medium. The method comprises the steps of obtaining an evidence material of a case, and generating a case description text based on the evidence material; the evidence material comprises one or more of text data, image data, audio data and video data; scene analysis information is acquired based on the case description text; generating an image and a video based on the scene analysis information; and generating a three-dimensional scene reconstruction model based on the image and the video. The generated three-dimensional scene reconstruction model is based on evidence materials such as text data, image data, audio data, video data and the like, can realize visual reproduction of case scenes, and can be applied to auxiliary case analysis in the fields of judicial, criminal investigation and the like.

This invention discloses an intelligent auxiliary method and system for qualitative and quantitative disciplinary proceedings. The method includes: acquiring detailed case information; analyzing the detailed information using a large-scale model to predict the type of violation; extracting key parameters needed to determine the qualitative and quantitative disciplinary action based on the violation type; supplementing necessary information through human-computer interaction; and combining the key parameters with regulations to generate the final case analysis, qualitative, and quantitative disciplinary results. By implementing this method, the powerful tagging, summarizing, and deep reading comprehension capabilities of the large-scale model can be utilized to effectively parse and integrate complex legal provisions and case scenarios, significantly simplifying development and improving the accuracy and efficiency of the qualitative and quantitative disciplinary process.

The invention relates to a public appeal case intelligent processing method and system, and the method achieves the analysis of a case through employing a government affair entity matching-oriented extraction model and combining with the comparison learning for optimization, and achieves the analysis of the case through a small computing power resource overhead. By introducing a small parameter quantization LLM model and adopting a verification method based on a thinking chain and evidence retrieval, the case analysis accuracy is improved while the model illusion risk is reduced; more accurate subject information is obtained by establishing a supervision object database and adopting a matching strategy of vectorization, weighted cosine similarity and cross comparison; and through a subsequent classification model and a clustering algorithm (such as DBScan and counting), macroscopic statistical analysis, hotspot map generation and high-risk object early warning of the case are realized. The problems that in the prior art, resource expenditure is large, precision is poor, subjects are complex, fuzzy and difficult to match, and a macroscopic analysis function is lacked are solved, and efficient and accurate automatic analysis, matching and statistical analysis of public appeal cases are achieved.

The specification discloses a case analysis method and device, a storage medium and an electronic equipment, wherein the method comprises: obtaining target case data corresponding to a target case type, inputting the target case data into a pre-trained feature extraction model, extracting features of the target case data by using the feature extraction model to obtain target features of the target case data, obtaining first historical data corresponding to the target features in a data center associated with the target case type, extracting features from the first historical data based on the target features to obtain first feature data, obtaining evaluation data based on the first feature data and the target case data, generating a first evaluation report based on the evaluation data, and using the embodiments of the specification, the features extracted based on the target case data are used to obtain historical data, and an evaluation report corresponding to the target case type is generated, thereby reducing the time required for analyzing cases and writing reports, improving the processing efficiency of case analysis, and ensuring the efficiency and accuracy of case analysis.

The disclosure provides a power facility case management and application system and method, comprising: a case uploading unit configured to provide a case uploading interface for each power facility substation, a management personnel of a management center, and a hidden danger, defect and fault management subsystem, and store uploaded case information, wherein the case comprises three types of cases of hidden danger, defect and fault; a case analysis unit configured to, for imported cases, select and associate countermeasures and standards related to the cases from a pre-constructed countermeasure library and a standard library based on the system, equipment and components to which the cases belong; meanwhile, divide the cases into typical cases and ordinary cases based on case characteristics; and a case display unit configured to graphically display the distribution of cases in each system, equipment or component and typical cases.

The invention discloses a case analysis method and device and a related product. Boolean processing is carried out on the to-be-analyzed case, and a plurality of different case element variables and a plurality of different case level variables are obtained; based on the multiple different case factor variables and the multiple different case level variables, multiple different hard constraints and multiple different soft constraints are constructed and obtained, the hard constraints indicate that the to-be-analyzed case is related to the case level variables, and the soft constraints indicate corresponding weights when the case factor variables and the case level variables are combined; based on the plurality of different case factor variables and the plurality of different case level variables, analyzing and processing the plurality of different hard constraints and the plurality of different soft constraints to obtain corresponding weights and values when the plurality of different case level variables are respectively assigned as true values; and obtaining a target case analysis conclusion corresponding to the to-be-analyzed case based on the case level variable corresponding to the weight sum value with the highest sum value. In this way, the case can be well analyzed.

The invention belongs to the technical field of accident analysis, and particularly discloses a hazardous chemical substance storage explosion accident cause analysis and risk diagnosis method, which comprises the following steps: analyzing an accident report text by combining an improved HFACS model and related regulatory standards, and constructing a cause factor set; calculating the grey relational degree of each cause factor and the explosion accident through a grey relational degree method, and substituting the grey relational degree into an Apriori algorithm to mine cause factor association rules; establishing a Bayesian network model based on a strong association rule to obtain key cause factors and cause paths; predictive analysis is carried out by focusing key cause factors, a high-risk scene and the key cause factors which have the most obvious influence on an accident are identified, and targeted prevention measures are put forward; through reasoning and analyzing specific accidents, potential cause factors are identified, and targeted prevention measures are provided. According to the method, the accuracy and comprehensiveness of dangerous chemical substance storage accident case analysis are improved, and a theoretical basis is provided for realizing accurate risk grading prevention and control and optimizing resource allocation.

The invention provides a forensic case data analysis model training method and a case data analysis method, and the training method comprises the steps: obtaining a plurality of groups of sample forensic information based on a plurality of sample forensic case data; training the preset large language model based on the model fine tuning strategies corresponding to each group of sample forensic medical information and each type of included sub-sample forensic medical information, so as to obtain the forensic medical case data analysis model; wherein the forensic case data analysis model is used for performing reasoning analysis based on input target forensic case data to obtain a corresponding target reasoning analysis result. The forensic medicine case data analysis model obtained through the training method can obtain corresponding case analysis results according to different input conditions, and the case analysis efficiency is improved.

The invention discloses a legal consultation multi-mode key information structured extraction and identification method and system, and relates to the field of legal services. The method comprises the steps of obtaining a legal consultation request submitted by a user; analyzing the multi-modal evidence file, and establishing a triple mapping relationship among text contents, visual elements and spatial positions; constructing a legal evidence ontology dictionary, performing cross-modal semantic understanding on visual-text joint information with a spatial mapping relationship, and extracting a structured evidence tuple comprising legal entities, entity attributes and relationships among entities; and carrying out logic merging on the structured evidence tuple according to the case element template to generate a standardized case fact structural body. The method has the advantages that unstructured evidence files such as contracts, borrows and discriminants are automatically converted into structured case fact structural bodies which can be directly used for case analysis, document generation and legal reasoning.

The invention discloses an atlas inference analysis system for cell antigen treatment defects, relates to the technical field of knowledge atlas inference, and is used for solving the problem of slow explaining and positioning of defect presentation. According to the method, a detection likelihood set is constructed by fusing HLA typing, somatic cell variation, transcriptional expression, immunopeptidomics and surface HLA detection, direct presentation and cross presentation sub-graphs are assembled by adopting a mode gating and loading complex integrity rule, a reachable path and observation consistency matrix is generated, structural contradictions and detection limitations are distinguished, and a detection result is obtained. According to constraint propagation, node cutting and link shrinkage are performed to form an individualized map, defect nodes and defect paths are searched and output based on a minimum interpretation path and a cause and effect graph cost, and a verification and intervention list and clinical decision support output are generated, so that unnecessary detection and intervention attempts are reduced, and the detection efficiency is improved. And the timeliness and consistency of case analysis and strategy making are improved.

The invention discloses a photovoltaic case analysis quota inference method based on case reasoning, and the method comprises the following steps: S1, carrying out the classified storage of each historical project according to the information of each historical project and the feature dimension, and constructing a case library; s2, establishing an overall difficulty coefficient, a construction feature dimension and an environment feature dimension of any historical project and an association rule model of key parameters and quota indexes in a construction process; s3, inputting a plurality of related parameters of any new project into the case index system, and dynamically adjusting the matching weight coefficient of each related parameter according to the importance degree of each related parameter; and S4, the case index system retrieves cases in the case library according to the input related parameters and the corresponding weight coefficients, and outputs an optimal quota index scheme of the current new project. According to the method, the historical engineering data is converted into the structured case library, so that accurate prediction and efficient management of the construction quota can be improved.

An object of the system according to the embodiment is to model a thought pattern of a judge and propose an optimal strategy.SOLUTION: A system includes a precedent collection part, a precedent analysis part, a thinking pattern modeling part, and a strategy proposal part. A case collection part collects past case data of a judge. A case analysis part analyzes the case data collected by the case collection part. The thought pattern modeling unit models a thought pattern of a judge based on the data analyzed by the judge case analysis unit. The strategy proposing section proposes an optimum strategy based on the thinking pattern modeled by the thinking pattern modeling section.SELECTED DRAWING: Figure 1