1369 results about "Data graph" patented technology

The invention discloses an enterprise global data analysis method based on a knowledge graph and a large language model, and the method comprises the following steps: collecting internal and external multi-source data of an enterprise, cleaning, standardizing and mapping, and constructing a unified data graph; after a user inputs a natural language query, a reasoning target and task description are generated in combination with semantic understanding, two reasoning paths are formed through structural reasoning and semantic reasoning respectively, and the two reasoning paths are fused for interactive verification and optimization. Logic conflicts and data missing are detected in real time in the reasoning process, and supplementary knowledge is automatically called for local reasoning correction. Through the corrected reasoning result, the system carries out semantic alignment and correlation analysis on multi-source data, a unified enterprise data analysis view is dynamically generated, a reasoning path and evolution information are recorded in the reasoning and analysis process, and subsequent traceable query is supported. According to the method, the data integration, intelligent reasoning and decision support capabilities of an enterprise in a complex data environment are improved.

The invention relates to the technical field of state monitoring of wind generating sets, and discloses a fault early warning and service life prediction method and system for a wind generating set, and the method comprises the steps: obtaining first state data, second state data and image data of a target wind generating set, and forming multi-dimensional data; fusing the multi-dimensional data by using a multi-modal fusion model to obtain multi-modal data fusion features of the target wind generating set; and performing fault early warning and / or life prediction on the target wind generating set based on the multi-modal data fusion features. By integrating the multi-modal data, the problem that fault features are difficult to comprehensively capture by a single data source is solved, fault early warning and service life prediction are performed by utilizing the multi-modal data fusion features, the false report and missing report rate of faults is reduced, accurate quantitative prediction of the remaining service life of the wind generating set is realized in combination with the data driving model, and the prediction efficiency is improved. By improving the accuracy of fault early warning and life prediction, the wind generating set is effectively operated and maintained in advance.

The invention discloses an AI generation content detection and review method, device and equipment and a storage medium, and the method comprises the steps: receiving multi-modal input data which comprises text data, image data and video data; calling a large language model to carry out compliance analysis on the text data to obtain an analysis result; when the analysis result is compliance, calling a multi-modal model to identify and detect the image data and the video data, and respectively obtaining an image detection result and a video detection result; and aggregating the analysis result, the image detection result and the video detection result to obtain a content detection result. According to the method, the processing paths are automatically allocated according to the content types, redundant calculation is avoided, hardware consumption is remarkably reduced, the multi-modal detection results are aggregated into structured output, the large-batch content processing efficiency is remarkably improved, calculation resource occupation is greatly reduced, and seamless integration of the detection results and a downstream service system is achieved.

Systems, methods, devices, and computer readable storage media described herein provide techniques for generating and / or repairing query language (QL) queries. In an aspect, an embedding is determined based on a request to generate a QL query. The embedding is compared to a layer embedding(s) of a deep data map to determine a similarity between a layer embedding and the embedding satisfies similarity criteria. A prompt is provided to a large language (LLM) to cause the LLM to generate the QL query, the prompt comprising a description of an item associated with the layer embedding. In another aspect, an alert indicating an undefined variable of the QL query is received. A query embedding associated with the QL query is compared to the layer embedding(s) to determine a candidate variable. The candidate variable is substituted in for the undefined variable, and a response comprising the repaired QL query is generated.

The invention provides a semantic enhancement and dynamic completion method and system for a power market data graph. The method comprises the following steps: acquiring target entity information and external data of a power market data graph by responding to a data query instruction; converting external data into structured semantic tags, then traversing the topological structure, establishing a mapping relationship between the tags and entity attributes, and combining query instruction influence logic to calculate topological integrity indexes and mark missing entities or correlate to form a missing tag set; establishing attribute mapping based on a target entity and external data, generating a semantic vector fusing cross-domain features, and projecting the semantic vector to a topological space according to a missing mark set to form cross-domain feature topological nodes and connections; embedding a missing position to reconstruct a topological structure, and checking logic connectivity; the dynamic conduction chain is analyzed, and a feedback interface containing the semantic relation and the completion structure is generated to respond to query. According to the method, conduction logic closed-loop verification of the missing elements in the power market data graph is realized.

The invention belongs to the technical field of multi-source heterogeneous data fusion and intelligent decision, and discloses a multi-source heterogeneous information fusion and analysis method for an AI large model and data knitting, which comprises the following steps: accessing multi-source heterogeneous data through data knitting, calibrating time and space by taking a key event as an anchor point, generating time and space alignment data, and synchronously constructing a dynamic metadata network. Generating a metadata graph; constructing a dynamic hypergraph, and calling a modal exclusive coding cluster to generate a multi-modal fusion feature; edge-cloud collaborative reasoning is started, a collaborative reasoning intermediate result is generated, confidence data is output by the general large model to arbitrate cross-domain conflicts, and a final decision packet is generated; triggering two-way feedback closed-loop optimization, driving data source optimization and knowledge completion of a metadata graph by an AI large model, further dynamically scheduling the priority of a data stream, and encrypting and updating large model parameters; and iterating the metadata atlas, and feeding back to a link of constructing a dynamic metadata network and generating a multi-modal fusion feature.

The invention discloses a multi-modal knowledge-based automotive body innovative design system, which comprises a multi-modal data fusion module, a multi-modal data fusion module, a multi-modal data fusion module, a multi-modal data fusion module and a multi-modal data fusion module, wherein the multi-modal data fusion module is used for receiving text data, picture data, a three-dimensional CAD (Computer Aided Design) model file and an engineering symbol expression from an automotive body design process and is used for carrying out feature extraction and semantic alignment on input data of four modals; generating a unified semantic vector representation; the cross-modal knowledge mining and graph construction module is used for extracting multi-level entities and relationships from the unified semantic vector and constructing a dynamically weighted multi-modal knowledge graph; the large-model-driven multi-hop collaborative reasoning module is used for analyzing the multi-modal design requirement of a user, carrying out multi-hop reasoning on a knowledge graph, and outputting design parameter recommendation and an interpretable reasoning chain. The method aims at breaking through the limitation of an existing design system in the aspects of multi-modal processing and shallow semantic understanding, and deep fusion and intelligent application of multi-source heterogeneous design data are achieved.

The invention discloses a commerce and trade circulation supply chain optimization method based on multi-modal data fusion, and the method comprises the following steps: 1, multi-modal data collection: collecting the multi-modal data of the whole supply chain in real time through an Internet of Things device, an enterprise ERP system, a logistics tracking system and a third-party platform, the multi-modal data comprises text data, image data, voice data, time sequence data and spatial data. When the scheme is implemented, multi-source heterogeneous data of the Internet of Things, ERP and the like are integrated, so that the problem of an information island in a traditional supply chain is effectively solved; position coding and a cross-modal attention mechanism are introduced into the multi-modal fusion model, so that the data alignment precision is remarkably improved, for example, when'emergency order adding 'in a voice communication record is associated with a corresponding order image, the weight distribution accuracy is improved; the application of the block chain technology ensures the transparency and traceability of the supply chain decision.

The invention relates to the technical field of archive management, and provides an archive resource intelligent classification and association indexing method based on multi-modal ai analysis, which comprises the following steps: preprocessing collected archive data to form a multi-modal data set comprising text data, image data and audio data; extracting feature vectors of the text data, the image data and the audio data, and fusing the feature vectors into a unified archive representation vector through a cross-modal attention fusion mechanism; constructing a hierarchical classification model, and performing intelligent classification on the archives in combination with a domain knowledge graph; the method comprises the following steps: constructing an associated index of an archive from four dimensions of entities, semantics, time and space and events, establishing a feedback mechanism, dynamically updating and optimizing the associated index, and providing multi-modal retrieval and visual display based on a classification system and the associated index. According to the intelligent classification and association indexing method for the archive resources, the problems that in an existing archive classification and indexing method, the multi-modal processing capacity is insufficient, the association mining depth is insufficient, and the efficiency is low are solved.

The invention provides a gas turbine exhaust gas temperature prediction and fault early warning system and method, and relates to the technical field of gas turbine state monitoring and fault diagnosis. The system comprises a data acquisition and preprocessing module used for acquiring and preprocessing data; the dynamic graph construction module is used for constructing graph structure data; the graph neural network module is used for extracting spatio-temporal features through learning; the improved auto-encoder module is used for realizing feature reconstruction by learning normal working condition data and then outputting reconstruction errors to the fault early warning module; the temperature prediction module is used for predicting a future exhaust temperature value; the fault early warning module is used for performing fault judgment and decision; and the result display and output module is used for summarizing analysis results. According to the system, the prediction precision of the exhaust temperature is remarkably improved, the fault detection and early warning capabilities are enhanced, the system has better feature representation capability and adaptability to complex working conditions, and close integration and mutual enhancement of prediction and early warning are realized.

The invention relates to the technical field of environment monitoring, and discloses a completion process data-based environment monitoring parameter comparison evaluation method, which comprises the following steps of: obtaining completion process data and environment monitoring data, and constructing a multi-dimensional data graph, mapping the completion process data and the environmental monitoring data to generate a standardized data set containing a construction feature vector and an environmental parameter vector; calculating a dynamic weight coefficient through a multi-objective optimization model based on the construction feature vector and the environment parameter vector in combination with the construction progress and the environment sensitivity, and outputting a weight matrix; the intelligent contract automatically triggers an evaluation process, generates a dynamic evaluation report containing an overproof event identifier, a compliance repair suggestion and a digital signature, and links an AR visualization system to be superposed to a BIM model to generate a three-dimensional thermodynamic diagram; and in combination with anomaly detection and a time sequence prediction model, identifying potential risks, generating a dynamic risk early warning map, and finally outputting a predictive evaluation report. According to the invention, the accuracy and real-time performance of environment monitoring evaluation can be improved.

The invention discloses a construction elevator intelligent dynamic scheduling system and method based on multi-modal deep learning, and relates to the technical field of elevator scheduling management, the system comprises a multi-modal data acquisition module, a multi-modal deep learning processing module, a hierarchical decision module, an adaptive learning module, a hybrid reinforcement learning scheduling module, and a conflict prediction and solution module; time sequence data, image data and text requirements are processed at the same time through a multi-mode deep learning architecture, a three-layer neural network decision architecture is adopted to realize full-process intelligent decision from strategy to execution, and a learning strategy and decision weight are automatically adjusted according to a project progress stage in combination with an adaptive learning system. The optimal scheduling scheme can be automatically generated according to information such as material types, quantity and latest completion time submitted by a team, the use efficiency of the construction elevator is effectively improved, resource waste and scheduling conflicts are reduced, and the method is suitable for scenes such as building construction elevator scheduling and a multi-elevator cooperative working environment.

In a method for providing a visual representation of search results, a natural language query may be obtained from a user. The natural language query may be processed to obtain key query terms. Based on the key query terms, a relevance-ranked search on a plurality of documents may be performed to obtain a subset of the plurality of documents relevant to the natural language query. Answers to the natural language query may be extracted from relevant documents among the subset of documents, and graph data may be obtained using document links associated with the relevant documents from which the answers were extracted. The graph data may include entities and relationships identified in the relevant documents. The graph data may be used to generate a graph that may be displayed to the user to enable the user to discover information previously unknown to the user.

The invention discloses a civil aircraft PHM modeling method based on cross-modal coupling, a medium and equipment, and belongs to the technical field of aircrafts. The method comprises the steps of S1, multi-modal data preprocessing: performing feature extraction and time-space / event alignment for three modalities of flight time sequence data, text data and image data, and laying a foundation for subsequent fusion modeling; s2, cross-modal feature fusion and holographic data sample construction: constructing a holographic data sample library covering the whole life cycle through cross-domain fusion and dynamic optimization; and S3, PHM physical-knowledge fusion comprehensive modeling: constructing a double-layer collaborative framework combining physical simulation and knowledge reasoning, and realizing controllable fusion of physical simulation and multi-modal knowledge. The problems that in the prior art, multi-source heterogeneous data alignment is difficult, physical mechanism and data driving fusion is insufficient, and model interpretability is poor can be solved.

The invention discloses a building quality inspection management system, and the system comprises a distributed intelligent sensor network which comprises a multi-type sensor array disposed at a key node of a building structure, a mobile detection terminal, and an unmanned plane inspection unit; the multi-modal data fusion module is used for performing space-time alignment and feature extraction on the sensor data, the image data and the BIM model data; the digital twinborn visual platform comprises a quality evaluation engine, a defect traceability module and a three-dimensional visual interface; the defect detection rate in a dark light environment is obviously improved, full-coverage detection of floors can be realized in a short time, field sensor data and a BIM model are synchronized in real time through a 5G network, and when a quality problem is found, the system analyzes 12 types of influence factors such as materials, processes and environments through a Bayesian network, so that the quality problem is solved. The system can trace back to specific construction teams, equipment numbers and even meteorological data, and can reduce the requirements of personnel through intelligent detection, thereby reducing the working cost.

The invention discloses a coal mine fire and gas coupling disaster multi-source information intelligent monitoring and early warning system, and relates to the technical field of coal mine safety. A data acquisition module is used for acquiring underground related data, and an image acquisition module is used for acquiring a roadway area image and a visible light video; the data image processing module is used for processing underground related data to obtain a comprehensive environment index, the regional analysis module is used for carrying out feature fusion on the processed data and images to obtain a fused feature index, and then the comprehensive monitoring module is used for collecting and calculating to obtain comprehensive equipment performance parameters. And performing comprehensive risk prediction calculation based on the comprehensive equipment performance parameters, the comprehensive environment indexes and the fusion feature indexes to obtain a comprehensive risk index coefficient, performing comparison through a preset dynamic risk threshold value, determining a risk state according to a comparison result, and performing execution operation according to different levels of early warning signals through an execution module. The function of determining whether the underground coal mine is safe or not through complete early warning based on the multi-source data is achieved.

The invention provides a cotton field growth state evaluation and prediction system based on an unmanned aerial vehicle RGB image, and the system comprises a data collection module which collects initial image data; the data processing module is used for denoising, distortion correction, orthographic splicing and illumination correction to obtain standard image data; the image mining module is used for carrying out image segmentation and target detection and then extracting cotton profile features and growth indexes; the comprehensive evaluation module is used for constructing a growth state evaluation model to obtain a comprehensive growth condition score and an evaluation index; the growth prediction module introduces yield related factors to carry out yield prediction and growth abnormity early warning; and the real-time monitoring and decision support module visually displays the evaluation prediction result and automatically generates decision support. The cotton field growth vigor can be comprehensively analyzed, the comprehensiveness, the accuracy and the high efficiency of evaluation and prediction are improved, real-time monitoring and intelligent decision making can be carried out on the whole cotton growth process, and the efficiency and the precision of agricultural management are improved.

The invention provides a multi-modal decision-making auxiliary method and system for law compliance examination. The method comprises the steps of S1, obtaining multi-source data of a to-be-examined object; s2, preprocessing the multi-source data; s3, extracting modal feature vectors of the text data, the voice data, the image data and the video data; s4, inputting each modal feature vector into a trained first convolutional neural network model, and outputting a unified feature representation matrix; s5, calling a law and regulation knowledge graph retrieval interface, and generating a law and regulation constraint vector corresponding to the associated law and regulation terms; and S6, inputting the unified feature representation matrix and the regulation constraint vector into a trained second convolutional neural network model, and outputting an illegal clause matching result and interpretable information. According to the method, the latest regulation terms are automatically matched, the visual risk evidence chain is output, and the review coverage, accuracy and timeliness are greatly improved.

The invention discloses a tunnel lining crack identification method and system based on panoramic space image splicing, and relates to the technical field of tunnel visual identification. The method comprises the following steps: acquiring a tunnel lining image array, and performing local splicing on tunnel lining images by using a splicing model based on cylindrical surface projection to obtain a local spliced image; fusing and splicing the local spliced images by using the global image space model to obtain a panoramic spliced data graph; and abnormal region detection is carried out on the panoramic splicing data graph according to the spatial local difference, and a tunnel lining crack identification result is obtained. According to the method, the lining panoramic space model based on image two-stage fusion is used for splicing, so that the visual coherence and naturalness of a global spliced image are improved.

The invention discloses an AI-based enterprise special asset diversified value-added service method and system, and the method comprises the steps: receiving multi-source heterogeneous data of enterprise special assets, and obtaining an enterprise special asset data graph containing asset attributes, market environments and historical disposal records; inputting the enterprise special asset data atlas into a pre-trained AI-based deep value evaluation model, and generating an asset value evaluation report including basic values, potential value-added paths and risk factors; according to the asset value evaluation report, utilizing a multi-target optimization algorithm to generate a plurality of preliminary value-added scheme sets for different value-added targets in parallel; and inputting the initial value-added scheme set into an intelligent matching engine, carrying out scheme feasibility verification and suitability screening, and outputting an optimal customized value-added service scheme package. By utilizing the embodiment of the invention, the accuracy of asset value analysis and the potential mining efficiency can be improved, and dynamic optimization and risk controllability of an asset value-added path are realized.

The invention belongs to the technical field of image processing, and discloses a remote digital image analysis cooperation system. The virtual diagnosis module is used for collecting a continuous pathological section sequence and carrying out space registration to obtain a registration section; the model building module is used for performing three-dimensional voxel reconstruction on the registration slices to obtain a three-dimensional voxel reconstruction model, and performing semantic enhancement to obtain a three-dimensional pathological voxel model; the collaborative interaction module is used for generating virtual avatars of G experts based on the VR technology, labeling the three-dimensional pathological voxel model and obtaining space anchor point labeling data of the experts; the graph driving module is used for constructing a structured semantic tree according to the space anchor point annotation data, detecting annotation conflicts and obtaining an annotation conflict detection result; the conflict resolution module is used for performing conflict resolution on the marking conflict detection result to obtain a consensus suggestion; the collaborative efficiency and conclusion reliability of remote diagnosis are improved, and a systematic solution is provided for precise diagnosis of complex pathological cases.

The invention relates to the technical field of artificial intelligence, and provides a marketing data generation method and device based on portrait data, equipment and a medium, marketing association data can be collected and purified based on a three-level data gateway, feature fusion is carried out by using a star-shaped collaborative network constructed based on a dynamic weight mechanism and a federated learning mechanism, and the marketing data generation efficiency is improved. The problems of data dimension limitation and data island are solved; scene recognition is performed based on a marketing data graph constructed by a secondary scene classification tree including a gift scene, and the problems of low utilization efficiency of unstructured data and insufficient crowd portrait granularity are solved; the marketing strategy is generated by using the target engine matched with the scene, so that the problems of scene engine deficiency and gift scene adaptation imbalance are solved; and generating the target marketing data according to the target marketing strategy and the marketing data graph. The problems of low operation efficiency and insufficient content accuracy are solved.

The invention provides a retrieval enhancement generation method and system for multi-modal semantic alignment in the technical field of computer vision and natural language processing. The method comprises the steps that S1, a large amount of text data, image data and 3D model data are acquired to construct a multi-modal vector knowledge base; s2, acquiring an input query image and a natural language question, and performing joint semantic coding on the query image and the natural language question to obtain a multi-modal query feature vector; s3, evaluating complexity, dynamically adjusting retrieval parameters based on the complexity, and retrieving Top-K candidate knowledge from the multi-modal vector knowledge base through an approximate nearest neighbor search method based on the retrieval parameters and the multi-modal query feature vectors; and S4, in combination with multi-dimensional similarity and token-level interaction alignment, screening each candidate knowledge, and outputting a retrieval result. The method has the advantages that the reasoning precision, the answer credibility and the response efficiency in a complex question and answer scene are greatly improved.

The invention provides an intelligent mine open-pit coal mine measurement system and method, and relates to the technical field of coal mines, and the system comprises a browsing module which is used for obtaining coal mine information data of an open-pit coal mine, and carrying out the multi-window linkage display of the coal mine information data; the measurement module is used for measuring coal mine parameters of the open pit coal mine according to the coal mine information data to obtain target measurement data; the modeling module is used for performing three-dimensional modeling processing on the open pit coal mine based on the coal mine information data and the target measurement data to obtain topographic data; and the management module is used for carrying out data management and automatic coordinate conversion on the topographic data, and carrying out data level-to-level management and data security control according to a preset authority level. Compared with the prior art, according to the embodiment of the invention, through a unified data acquisition and data measurement mode, automatic nesting of the orthoimage data, the laser point cloud data, the image data and the topographic data is realized, and the measurement efficiency and accuracy of the open-pit coal mine are improved.

The invention relates to the technical field of video generation, in particular to a functional scene modeling and large model cross-dimension content generation method and system.The method comprises the steps that multi-modal scene description data input by a user are received, and the scene description data comprise text data, image data and audio data; analyzing the multi-modal scene description data, and extracting scene object entities, object attribute parameters and inter-object interaction logic; constructing a dynamically adjustable scene model, and instantiating the scene object entity into a functional unit; calling a pre-trained large language model or a multi-modal generation model, and executing cross-dimension content generation; dynamically optimizing output based on user feedback data; and outputting the optimized cross-modal content to the application terminal. According to the method, a scene entity is abstracted into a programmable function unit, real-time dynamic adjustment is supported through an internal parameter interface, the states of all associated units can be linked and updated in a short time by a dependency relationship topological graph, and the hysteresis of traditional script coding is avoided.

The invention provides an image data and pattern spot management system and method and electronic equipment, and relates to the technical field of geographic information data processing, and the image data and pattern spot management system comprises an image data indexing module which is used for carrying out three-dimensional indexing on a remote sensing image to obtain a space-time-data source association chain of the remote sensing image, the remote sensing image contains pattern spot data with an identifier; the pattern spot complete cycle construction module is used for performing multi-temporal analysis on the pattern spot data to obtain an analysis result, and constructing a pattern spot change life cycle map of the pattern spot data in combination with the association chain; and the tracing module is used for positioning the target pattern spot when the target pattern spot needs to be traced to obtain a change node of the target pattern spot and a space-time-data source index corresponding to the change node, and searching in the association chain according to the change node and the space-time-data source index to obtain a traceable data chain. According to the invention, the storage and management of the geographic image data and the pattern spot data are more systematized and refined.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle detection tracking device, method and equipment, which comprises a data sensing unit, a calculation unit, a mechanical and power supply unit, a communication and storage unit and a countering execution unit, and is characterized in that the data sensing unit is used for acquiring image data and servo turntable rotation angle data; the calculation unit is used for deploying a lightweight neural network model and a multi-algorithm fusion process, processing sensor data and realizing unmanned aerial vehicle target detection and tracking logic control; the mechanical and power supply unit is used for providing physical support, servo motion capability and energy supply for the device; the communication and storage unit is used for real-time data transmission and storage and encryption archiving of historical data and image videos; and the countering execution unit is used for implementing signal interference or physical interception on the detected and tracked target. Therefore, the problems of easy target loss, low small target identification precision, poor complex environment adaptability, insufficient countering means and the like of a traditional device are solved.

The invention belongs to the field of geotechnical engineering, and provides a geotechnical engineering investigation data analysis method and system based on machine learning, and the method comprises the steps: obtaining geotechnical engineering investigation data; taking the sampling points as nodes of the rock-soil data graph; according to the spatial proximity relation and the geological similarity, determining edges of the rock-soil data graph, and endowing the edges with weights, so that nodes with similar spaces and similar geological characteristics are connected; carrying out sparse processing on the rock and soil data graph; performing weighted fusion according to the spatial distance weight and the geological similarity weight to form a final rock-soil data graph; a graph convolutional neural network is adopted, and the initial features and the adjacency relation of the nodes are used for information propagation; neighbor information of each node is aggregated through multi-layer graph convolution calculation, and node features are updated step by step; a semi-supervised learning or supervised learning method is adopted to train a GNN model, and prediction error minimization is taken as a target; and deducing rock and soil parameters of an unknown area through the trained GNN.

The invention discloses a dynamic form generation and cross-database adaptation method based on metadata driving, and belongs to the technical field of data architectures. The method comprises the steps that an original metadata attribute graph is established; on the basis of the original metadata attribute graph and user roles and equipment parameters for calling the forms, generating an optimal equipment form interaction data graph corresponding to each user permission; verifying an interaction path between the forms and a cross-database demand of a target database type to carry out cross-database mapping adaptation analysis, and generating an optimal form data adaptation database of the corresponding equipment of each user permission; and performing rule compiling based on multi-source heterogeneous data input by a user in real time and execution logic in the original metadata attribute graph, marking an incremental propagation path in the optimal form interaction data graph of the corresponding equipment of each user permission, and updating the optimal form data adaptation database of the corresponding equipment of each user permission. The method has the advantages that the data interaction efficiency is improved, and the interaction round-trip times and redundant data transmission are reduced.

The invention relates to the technical field of intelligent medical monitoring, in particular to a personalized health monitoring and early warning system for elderly people living alone fused with multi-dimensional data, and the system comprises a data collection and preprocessing module which collects multi-dimensional physiological parameter signals of a user and converts the multi-dimensional physiological parameter signals into multi-dimensional medical features; the health prediction module performs health category prediction on the multi-dimensional medical features of the current time node based on a pre-trained deep learning model, generates a data graph under each time node, and stores the data graph and a corresponding health category prediction result in a specified directory; the early warning module analyzes whether the multi-dimensional physiological parameter signals under each time node are abnormal or not in a mode of combining a hard threshold value and a soft threshold value, and if yes, an alarm is given out; and the abnormity output module calls and outputs the data maps of the preset duration before and after the abnormity alarm time node and the corresponding health categories. According to the invention, personalized monitoring and risk early warning of the health state of the elderly can be realized more accurately from multiple dimensions.