1329 results about "Data extraction" patented technology

The invention relates to the technical field of equipment testing, and discloses a motor operation state vibration noise combined diagnosis generation method, which comprises the following steps: when a motor operates, performing bimodal data extraction on operation state data to obtain a vibration signal and a noise signal; performing order tracking analysis on the vibration signal to obtain an electric vibration characteristic order component; calculating a time domain energy envelope line, and performing synchronous time domain segmentation on the noise signal to obtain noise signal slices; filtering non-related components of the vibration characteristic order component in the noise signal slice, and extracting a vibration synchronous noise component of the vibration characteristic order component synchronous with the current rotation period of the motor according to a filtered result; constructing a sound-vibration fusion spectrum; analyzing the distribution rule of the sound and vibration intensity in the sound and vibration fusion spectrum, identifying a correlation mutation point, and diagnosing a specific operation state fault based on the distribution mode of the correlation mutation point; according to the invention, the efficiency of vibration noise combined diagnosis generation of the motor operation state can be improved.

The invention relates to the technical field of artificial intelligence and large models, in particular to a multi-chain collaborative retrieval enhancement system, method and device based on a large model and a storage medium. The data extraction module is used for analysis; the mixed retrieval knowledge base construction module is used for cutting the document and constructing a mixed retrieval knowledge base by using the obtained document fragments; the query processing module is used for acquiring a query request of a user, performing mixed retrieval in the mixed retrieval knowledge base according to the query request, and outputting a document fragment corresponding to a retrieval result; the fusion sorting module is used for weighted fusion sorting; the answer generation module is used for inputting the document fragments and the query request into a large language model to generate answers; and the result output module is used for outputting an answer, wherein the answer comprises a traceability mark. According to the method, unified analysis and deep fusion can be carried out on the multi-modal heterogeneous data, multi-dimensional retrieval reasoning can be carried out, and the retrieval efficiency and the retrieval accuracy are improved.

The invention discloses an intelligent digital human training method and system based on multi-modal interaction, and belongs to the technical field of semantic indexing.The method specifically comprises the steps that voice, vision and text data are analyzed and converted into high-dimensional feature vectors through a modal exclusive encoder, the high-dimensional feature vectors are projected to a unified semantic space through a cross-modal semantic mapping model, and the high-dimensional feature vectors are obtained; generating a semantic primitive containing a modal identifier, a core semantic tag and a feature weight; semantic primitives are used as nodes, directed edges and edge weight table association strength are established based on semantic similarity, typical scene node connection weights are strengthened, and a mesh map containing intra-modal hierarchy and inter-modal cross association is formed; constructing a double-layer index on the basis of the mesh map; semantic primitives are extracted from newly added data, the position of a new node in an association graph is determined through a graph matching algorithm, an association edge with an existing node is automatically established, and a lower-layer modal exclusive index is synchronously updated.

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent collaborative data question-answering system and method based on a large model, and the system comprises an agent cluster module which is composed of five special agents, namely a data question-answering agent, a data extraction agent, a data analysis agent, a visualization agent and a quality examination agent, and achieves the task decomposition and collaborative execution through dynamic scheduling; the knowledge management module comprises a business knowledge base, a data element knowledge base and a user feedback base, and adopts a hierarchical knowledge fusion technology to provide domain knowledge support for the intelligent agent; and the supporting function module covers a front-end dialogue component and a verification and execution engine and is responsible for interactive interface rendering and result reliability verification. According to the method, the fine tuning requirement on the large model is remarkably reduced, the illusion of the large model is effectively intercepted through a dual verification mechanism, and the accuracy and reliability of question and answer results are improved.

The invention belongs to the technical field of artificial intelligence and information retrieval, and provides a multi-modal retrieval method and system based on a lightweight knowledge graph and an index table, and the method comprises the steps: obtaining multi-modal source data, and extracting a structured semantic tag set; constructing a lightweight knowledge graph and a metadata index table; analyzing a natural language query input by a user to obtain a semantic query vector and a keyword set, executing semantic retrieval in the knowledge graph to obtain a text candidate result, and executing keyword matching in the metadata index table to obtain a non-text candidate result; for each non-text meta record, fusing the cross-modal similarity between the non-text meta record and the text candidate result, performing index matching on an original score and a graph semantic evidence score, calculating a comprehensive correlation score, and performing reordering; and generating a natural language answer containing a non-text record link according to a reordering result. The method is suitable for efficient cross-modal knowledge retrieval in a high-security and low-resource scene.

The invention provides a nuclear power safety report data extraction method and system based on multi-modal feature fusion, and belongs to the technical field of data processing, and the method comprises the steps: obtaining a nuclear power safety report document; carrying out OCR (Optical Character Recognition) and paragraph segmentation on the report document to obtain first text data; performing text semantic understanding on the first text data by adopting a dynamic template matching and semantic driving extraction mechanism to obtain a text feature vector; segmenting pages of the report document and identifying fonts and fonts to generate first format data; adopting a cross-page table reconstruction algorithm to detect table cells of the first format data and splicing a cross-page table to obtain format feature vectors; performing feature alignment on the text feature vector and the format feature vector, and inputting the text feature vector and the format feature vector into a large pre-training language model to obtain a semantic vector; and fusing the text feature vector, the format feature vector and the semantic vector to form a composite expression unit, and intelligently extracting structured data based on the composite expression unit. The semantic entity and structural relationship in the nuclear power report can be effectively identified.

System and methods are disclosed for matching extracted text data based on one or more similarity scores. The method may include receiving one or more documents from a plurality of data sources, utilizing an optical character recognition algorithm for extracting text data from the one or more documents, comparing, utilizing a fuzzy matching algorithm, the extracted text data to reference dataset(s) to determine one or more matches between the extracted text data and at least one of the reference dataset(s), wherein the one or more matches are based on at least one similarity score, inputting the determined one or more matches and the at least one similarity score into a trained machine-learning model to refine the one or more matches, and outputting a representation of the refined one or more matches and the at least one similarity score to a graphical user interface of a device.

The invention discloses a multi-source heterogeneous data flow real-time fusion and weight adaptive correction system and method, and relates to the technical field of multi-source data fusion. Comprising a path monitoring data extraction module, an initial fusion weight determination module, an abnormal transportation event analysis module, an initial correction strategy table generation module, a response updating module and a real-time weight adjustment fusion module. According to the method, a multi-dimensional data quality scoring system is established, so that strong association between weight distribution and data quality is ensured, and fusion precision loss caused by'fixed weight 'is avoided. For an abnormal monitoring point, through indexes such as a data missing rate, a deviation change rate, an auxiliary positioning deviation rate and the like, an abnormal data source type is accurately identified, a weight adjustment scheme of'target data source decreasing amplitude + increasing amplitude data source equal proportion complementation 'is formulated until abnormity is eliminated, and closed-loop correction of'abnormity positioning-weight adjustment-error elimination' is realized.

The invention discloses a multi-modal fusion bank receipt intelligent processing system and method based on vision and NLP, and the method comprises the steps: receiving a bank receipt picture or a PDF document, and completing the text detection, direction correction and character recognition through a visual processing engine; a three-level receipt independent segmentation mechanism is applied, and independent receipt records are divided through spatial clustering analysis, semantic analysis, visual verification and cross-page association processing; jointly extracting text features and layout features of each receipt through a double-flow multi-modal fusion model, and fusing the text features and the layout features; field-level data extraction is executed through a field extraction engine, and financial data verification including account number, amount, date and abnormity quadruple verification is carried out; and generating structured JSON output to obtain a bank receipt processing result. According to the invention, the innovative five-layer processing architecture realizes high-precision analysis of the bank receipts through an independently researched and developed receipt independent segmentation engine, a vision-semantic fusion model and a financial data verification system.

The invention relates to the technical field of deep learning, in particular to a building target intelligent extraction method and system based on multi-source surveying and mapping data fusion, and the method comprises the steps: obtaining multi-source surveying and mapping data of a target region; preprocessing the multi-source surveying and mapping data; based on a pre-constructed multi-source data fusion module, performing feature level fusion on the spectral feature map, the geometric feature map and the elevation feature map of the preprocessed high-resolution optical remote sensing image to generate a multi-dimensional fusion feature matrix; inputting the multi-dimensional fusion feature matrix into a trained model, and outputting an initial extraction result of the target region; and post-processing the initial extraction result to obtain a target extraction result. Compared with traditional single data extraction, high-resolution optical remote sensing images, laser point cloud and digital elevation model data are obtained synchronously, spectrum, three-dimensional geometry and elevation dimension information can be covered at the same time, the defect of single data is avoided from the source, and comprehensive data support is provided for subsequent accurate extraction.

The invention discloses an alloy field-oriented text and table double-track collaborative data extraction method and system. The method comprises the following steps of: obtaining an alloy related document DOI; taking the DOI as an index to obtain a full-text literature and storing the full-text literature in a JSON format; performing document screening based on an LDA topic distribution model and establishing a target topic document library; preprocessing the unstructured table, and obtaining structured table data by using a large language model; performing automatic text segment division on the JSON format text through rule matching analysis; and sorting the text segments by using a large language model to generate semi-structured text data, fusing and normalizing the text and table data, and outputting a component-process-organization-performance-strengthening mechanism alloy data chain. According to the method, through a double-track collaborative data extraction mechanism, the problems of multi-source heterogeneity, format fragments, information islands, semantic layering and the like of material subject data are effectively solved, structured treatment of literature data in the alloy field is achieved, and a unified technical path is provided for constructing a high-quality alloy database.

The invention discloses a material synthesis data extraction method and system based on a knowledge enhancement large model, and relates to the related field of artificial intelligence, and the method comprises the steps: retrieving literature data, and constructing a material synthesis knowledge text by executing data denoising and OCR text conversion; performing LoRA fine tuning on the basic large model, introducing a field instruction data set to perform fine tuning learning, and determining an extraction large model; performing semantic partitioning and vectorization on the material synthesis knowledge text, constructing a multi-level retrieval framework, performing retrieval enhancement in combination with a material science knowledge base, and determining an enhanced knowledge text; and constructing a data extraction prompt, combining the enhanced knowledge text with a material to synthesize a knowledge text, inputting the knowledge text into an extraction large model, and executing knowledge extraction processing. The problem that the accuracy of data extraction is insufficient in the prior art is solved, and the effect of improving the accuracy of data extraction is achieved. Meanwhile, manual work can be replaced to complete literature analysis extraction and domain knowledge association, and support is provided for material synthesis process recommendation.

The invention discloses a multi-wave chromatography and reflection combined detection method and device for a thin-covering-layer urban active fault and a medium. The method comprises the steps that longitudinal wave seismic data and transverse wave seismic data are collected; recording vertical component data based on the longitudinal wave seismic data for first arrival travel time pickup and tomography inversion to obtain a longitudinal wave velocity structure model Vp; extracting Rayleigh surface wave frequency dispersion through the vertical component data and carrying out multichannel surface wave analysis inversion to obtain a transverse wave velocity structure model Vs; calculating a longitudinal and transverse wave velocity ratio parameter structure model Vp / Vs, and calculating to obtain a Poisson's ratio parameter structure model v; obtaining a longitudinal wave (P wave) reflection profile and a transverse wave (S wave) reflection profile; identifying and summarizing into an interpretation fracture set Fc, and recording key elements; and by taking the interpretation fracture set Fc as a structural skeleton, completing unified registration and superposition display on Vp, Vs, Vp / Vs and v, a longitudinal wave reflection profile and a transverse wave reflection profile, and outputting a shallow underground structure comprehensive interpretation map of the thin covering layer region.

The invention relates to the field of drawing recognition, in particular to a PDF drawing data extraction method and system based on intelligent recognition. Comprising the following steps: reading an internal structure of a PDF engineering drawing to obtain a native text stream, a vector path and a grating image; identifying the native text flow through a shunt preprocessing framework to form structured text data; rendering the vector path and the grating image to obtain a background image; analyzing the structured text data by utilizing the intelligent recognition model through the character recognition and extraction sub-model, obtaining drawing metadata and recording the position, and obtaining a character recognition result; analyzing the background image through a graphic element recognition and classification sub-model, recognizing and classifying component elements, and obtaining a graphic recognition result; and performing fusion according to the visual space corresponding relation to form a drawing analysis result. According to the method, the adaptive capacity of engineering drawings with various sources and different qualities is improved through the shunting preprocessing framework and the intelligent identification model.

The invention discloses a heterogeneous document structured data extraction system and method based on multi-modal fusion, and the method comprises the following steps: S1, receiving a heterogeneous document, and carrying out the preprocessing; s2, extracting visual and semantic modal information based on the visual backbone network and an OCR module, and fusing spatial coordinates, text content and layout features; s3, receiving a dynamic target mode Schema, executing field-level semantic matching, and calculating semantic similarity; s4, performing consistency verification, verifying and filling the field values, and correcting the associated fields; s5, converting the format into a data file in a specified format, and reserving fields to be mapped with an original document semantic block; s6, automatically switching a cloud mode and a local mode according to a deployment environment; and S7, outputting the data file of the target field. According to the method, high-precision and low-delay structured data extraction of the heterogeneous format document can be realized, and the automatic processing efficiency and the data credibility are improved.

The invention discloses an automatic cruise system for a cleaning robot of a power insulator of a transformer substation, and relates to the technical field of intelligent operation and maintenance of the transformer substation, the automatic cruise system comprises a data extraction module, a processing module, a decision module and an execution module, and solves the technical problems of excessive shutdown when a wind field approaches danger and discontinuous operation when the wind field is safe. The decision-making module sets safety indexes according to the weights of the wind speed and the wind direction, divides the wind speed into four risk intervals, divides the wind direction into three grades, calculates the total safety score of the wind field through weighted summation, corresponds to three operation grades of safety, yellow early warning and red early warning, generates differential instructions according to different grades, and sends the differential instructions to the wind field. And real-time path updating during early warning level switching is supported, and safe operation under the dynamic change of the wind field is realized.

The invention discloses a social risk management method based on a large model and multi-modal data, and relates to the technical field of risk management, and the method comprises the following steps: accessing multi-source heterogeneous data from multiple parties, and carrying out unified space-time alignment and cleaning; analyzing data, extracting entities and relationships thereof, and constructing a basic map; the method comprises the following steps: quantifying mechanical operation into dynamic disturbance to a nearest pipe network, calculating health degree attenuation of the mechanical operation, simulating a dynamic process of spreading a risk to peripheral nodes along a pipe network connection relation, and updating a risk state of the whole network in real time; when the pipeline health degree is lower than a critical value, causal reasoning is triggered, and early warning information is automatically generated and pushed to a related responsible unit; and collecting a feedback result of each early warning to periodically optimize and adjust parameters in the risk propagation model. According to the method, the space-time knowledge graph is constructed, and the dynamic risk propagation model is researched and developed based on the space-time knowledge graph, so that the dynamic influence of construction disturbance on the pipe network system can be accurately quantified, and the chain evolution path of the pipe network system can be simulated.

The invention relates to the technical field of big data processing, and discloses a data extraction method and system for geological mineral exploration, and the method comprises the steps: carrying out the standardization preprocessing of original data containing space coordinates, lithology texts, mineral components, logging curves and mineralization labels; spatial, semantic, concentration and time sequence deep representations are extracted in parallel through a multi-modal geologic feature encoder and fused into high-dimensional vectors; applying structured sparse constraint to the features by using a graph attention mechanism guided by an expert knowledge graph, and strengthening a mineralization association dimension; a dynamic incremental learning engine is combined with an elastic weight solidification mechanism to realize local fine tuning of model parameters and historical knowledge retention; and finally outputting a mineralization potential score, a mineralization factor sequence and an abnormal element combination. According to the method, through triple mechanisms of multi-modal fusion, knowledge embedding and incremental evolution, the accuracy, interpretability and timeliness of data extraction are improved, and the real-time analysis requirement of large-scale mineral exploration is met.

The invention discloses a road drawing intelligent analysis and three-dimensional modeling method and system, and the method comprises the steps: obtaining drawing data, carrying out the content recognition of the obtained drawing data, carrying out the data screening and extraction based on the recognized data, carrying out the data cleaning and correction, converting the data into structured data, and carrying out the secondary correction. And finally, constructing a three-dimensional model according to the corrected data. According to the scheme, information extraction in the road drawing is carried out by using various different classification or recognition models before modeling, targeted design is carried out for table data and image data, data cleaning and correction are carried out after data extraction, and the data extraction efficiency is improved. According to the scheme, multi-dimensional road parameter information in the road drawing can be rapidly and accurately extracted, a solid foundation is laid for subsequent three-dimensional modeling, the modeling precision in a complex drawing scene is remarkably improved, and the problem that a traditional method depends on a low-efficiency mode of manual copying, trial calculation and repeated correction is effectively solved.

The invention relates to the technical field of text data processing, and discloses a text data extraction method, system, equipment and medium based on multi-modal fusion and self-evolution learning, which comprises the following steps of: performing feature extraction and spatial alignment on a printed text, a handwritten annotation and a dynamic table of a mixed format document to obtain a semantic feature of an image-text table, and inputting the semantic feature into a dynamic analysis layer; analyzing metaphor expressions and synonymous heterogeneous fields through field extraction and a context semantic reasoning mechanism, and outputting structured data; performing grammar compliance verification by adopting a regularization engine, and performing comparison verification through a federal learning mechanism; and inputting the verified data into the reinforcement learning model, updating the analysis rule and the model parameters through strategy iteration, and feeding back the updated analysis rule and model parameters to the dynamic analysis layer to complete closed-loop optimization. According to the method, the processing precision and efficiency of the complex document are greatly improved, the manual intervention requirement is remarkably reduced, and meanwhile, the privacy protection and compliance requirements are met.

The invention discloses a combined electric appliance health analysis method and system based on multi-source data fusion, and the method comprises the steps: cleaning text data, carrying out the semantic mapping of the text data to a preset state quantity index, extracting a basic feature value from image data, and generating a standardized evaluation index set; calculating a weight vector of each evaluation index by constructing a judgment matrix, and performing consistency verification; weibull distribution fitting is carried out based on historical state quantity data, and grading threshold values of the positive degradation index and the negative degradation index are calculated; dividing a state interval based on the normal cloud model and calculating a membership degree vector of each state level; the membership degree vector is converted into a basic probability distribution function, evidence fusion is carried out in combination with the weight vector, and a comprehensive state level is output; and identifying the high-conflict evidence according to the Pignistic probability distance, performing refusion after correction, and outputting a final state level. Deep fusion and intelligent evaluation of multi-source heterogeneous data are realized, and the accuracy and robustness of health state judgment of the combined electric appliance are improved.

Techniques for identifying a document layout are disclosed. In one embodiment, attribute data associated with an electronic document is accessed. A machine learning model is then applied to the attribute data. The machine learning model is configured to classify the electronic document based on the attribute data and feature sets of a plurality of document classes. Based on the document class predicted by the machine learning model, the system identifies a layout associated with the document class. The layout specifies layout elements and content types associated with the layout elements. The system extracts and stores information from the electronic document according to its content type.

The invention discloses an electric power field intelligent portrait generation method and device based on a multi-modal knowledge graph, and belongs to the technical field of artificial intelligence. The method comprises the steps that a knowledge representation framework is adopted to perform standardized semantic representation on entities and relationships extracted based on electric power field multi-source heterogeneous data, and a graph database is adopted as a storage platform; constructing a multi-modal electric power knowledge graph; metadata extraction and cross-modal semantic alignment are carried out based on the multi-source heterogeneous data, and cross-modal feature fusion is carried out on the multi-modal metadata set based on the multi-modal electric power knowledge graph to obtain a plurality of multi-modal feature vectors; constructing a multi-modal graph neural network based on the multi-modal electric power knowledge graph, the multi-modal metadata set and each multi-modal feature vector; and performing multi-modal portrait fusion based on each node feature of the multi-modal graph neural network to generate a multi-modal intelligent portrait. Therefore, by implementing the method and the device, the intelligent portrait fused with the multi-dimensional power metadata can be generated.

The invention provides a method and device for extracting report data based on a large model technology, and relates to the field of batch data processing, and the method comprises the steps: carrying out the intelligent table splitting processing of obtained report image data through a pre-constructed intelligent report prompt word project, and obtaining an effective target report; carrying out metadata dynamic extraction processing on the effective target report by utilizing a pre-constructed data extraction cue word project to obtain a report label; performing multi-dimensional data integration processing on the effective target report by utilizing a pre-constructed data integration prompt word project and the report label to obtain a report data dictionary; and performing subject semantic mapping on the effective target report according to the report data dictionary to obtain a required data report. According to the method, a complete automatic processing flow can be constructed, and intelligent report splitting, metadata dynamic extraction, multi-dimensional data integration, subject semantic mapping and the like are realized by combining an optical symbol recognition technology with a large model technology.

The invention discloses a BIM and quota database linkage deep foundation pit full life cycle carbon emission automatic calculation method. The method comprises the following steps: S1, a BIM model intelligent analysis module; s2, a dynamic quota database; s3, calculating carbon emission; s4, performing visualization and optimization output; through BIM model intelligent analysis, dynamic quota database linkage, phased carbon emission calculation and collaborative design of a visual optimization module, accurate quantification, dynamic adaptation and efficient management of deep foundation pit full-life-cycle carbon emission are achieved; precise data extraction is realized through intelligent analysis of a BIM model, flexible adaptation of factors is ensured by a dynamic quota database, calculation precision and optimization efficiency are improved through staged calculation and adaptive weight distribution, carbon emission data are converted into an operable low-carbon scheme through visualization and multi-objective optimization, and the method is suitable for popularization and application. And the comprehensiveness, the real-time performance and the practicability of carbon emission management of deep foundation pit construction are remarkably improved.

The invention discloses a vehicle target detection tracking and trajectory data extraction method based on deep learning, and relates to the technical field of unmanned aerial vehicle aerial photography. The method comprises the following steps: carrying out stable frame processing on an unmanned aerial vehicle video, extracting feature points and feature vectors through an SURF algorithm, matching and screening reliable matching pairs through an FLANN algorithm, calculating a homography matrix through an RANSAC algorithm when a condition is met, carrying out perspective transformation to eliminate jitter, and outputting a stable video sequence; vehicle target detection: introducing an AIFI module to construct an improved YOLOv5OBB model, and outputting vehicle rotation bounding box parameters and categories after training; vehicle tracking and trajectory extraction are carried out, cross-frame tracking is realized based on a DeepSORT model, original trajectory data are preprocessed, and the speed, the acceleration, the additional lane number and the ID of an adjacent vehicle are calculated. According to the method, the problems of video jitter and insufficient detection precision are effectively solved, and the accuracy and continuity of track data extraction in the highway scene are improved.

The invention discloses a virtual pre-assembly method for a bridge closure section. The method comprises the following steps: acquiring three-dimensional point cloud data of the closure section and a closure gap; carrying out closure section connection section area data extraction and closure gap connection section area data extraction based on a bisection method, and obtaining connection end face area data; extracting connection end face point cloud data from the connection end face region; extracting outer boundaries of the connecting end faces and fitting feature angular points to obtain all key angular points on each connecting end face, and finally obtaining a feature angular point data set of the closure section and the closure gap; and feature point three-dimensional model construction and virtual pre-assembly: establishing a feature point three-dimensional model based on the feature angular points of the closure section and the closure gap, simulating the final state of hoisting butt joint by using a virtual pre-assembly method, and calculating the matching and cutting amount of the closure section. According to the method, the cutting amount of each feature point position can be automatically calculated, and the precision and efficiency of closure construction are greatly improved.

The invention provides a safety detection method and device for cross-border data, equipment, a storage medium and a program product. The method comprises the following steps: acquiring transmission data of a cross-border network environment; performing cross-border data extraction processing on the transmission data to obtain cross-border session data; performing protocol analysis processing on the cross-border session data to obtain a protocol analysis result of the cross-border session data; performing dual-channel privacy data identification processing on the protocol analysis result to obtain a privacy data identification result; generating a security detection result according to the protocol analysis result and the privacy data identification result; performing data desensitization processing on the privacy data according to the security detection result to obtain desensitized privacy data; establishing a secure communication link according to the desensitized privacy data; and sending the desensitized privacy data to a preset compliance decision subsystem through the secure communication link to determine a compliance evaluation result of the desensitized privacy data. The accuracy of safety detection can be improved.

The embodiment of the invention discloses an alarm method and device based on a telemedicine conference, a medium and a program product. The method comprises the following steps: performing data extraction and classification on consultation records of a telemedicine conference, collected historical medical records and environment detection records, and obtaining medical data and context awareness data of a patient; acquiring disease probability distribution based on the noise gate Bayesian network and the medical data; acquiring a feature vector matrix for the context awareness data and the disease probability distribution through an interface layer; acquiring a disease type, a severity parameter and an emergency degree parameter of the patient based on the connection heuristic network and the feature vector matrix so as to determine whether the patient has a health risk, and if yes, selecting an optimal receiver; and sending alarm information to the optimal receiver. The method can dynamically adapt to complex and changeable medical situations and environmental factors in combination with the use of a remote medical conference, and carries out intelligent alarm, so as to improve the accuracy and response speed of patient health risk identification.

The invention relates to the field of artificial intelligence, in particular to a part quality detection plan generation method based on a CAD drawing and a storage medium. The method comprises the steps that data are extracted from a CAD drawing and converted, and a geometric element set containing three-dimensional coordinates and a labeling text set are generated; identifying geometric features of the geometric elements, analyzing semantic information of the annotation text, and generating a geometric feature set with semantics and an annotation object set; according to the geometric feature set and the annotation object set, constructing a semantic association graph, and carrying out annotation legality check and correction to obtain a final semantic association graph; according to the final semantic association graph, mapping the geometric features and the annotation objects into a hierarchical tree structure according to an industrial standard organization data structure; and generating a part quality detection plan according to the hierarchical tree structure, wherein the detection plan comprises detection points and tolerance zones of dimensional tolerance. According to the method, the efficiency of extracting the data from the CAD drawing is improved.