66309 results about "Data processing" patented technology

The invention relates to the technical field of financial data processing, in particular to a multi-source heterogeneous fund data processing method and system, and the method comprises the steps: recording source information through building a data source registry, and marking a unique identifier for data; performing differential analysis on the fund data in different formats to generate standardized column type storage data; traversing column type storage data to extract statistical characteristics, performing field classification through metadata analysis and financial dictionary matching, analyzing business connotations of fields difficult to classify in combination with a localized large language model, and mapping the business connotations to a header fusion knowledge graph; generating a mapping rule from the source field to the enterprise-level data model by applying a rule engine template on the basis of field classification and semantic recognition results; converting the data structure according to the mapping rule and executing standardization processing; the quality is further optimized through data cleaning; and finally, the data are verified, standardized fund data supporting data traceability are output, and the strict requirements of financial supervision application are met.

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention discloses a building ground real-time settlement monitoring device and a use method thereof, and belongs to the field of building structure safety monitoring. The monitoring device comprises a hierarchical sensor network which is used for carrying out multi-time-scale real-time data acquisition and comprehensively obtaining deformation data and related environmental parameters of a building structure; the data processing and analyzing module is used for performing real-time processing and intelligent analysis on the acquired data, and identifying and classifying abnormal deformation characteristics of the building structure in time; the deep learning prediction module is used for quantitatively predicting the probability state and the evolution trend of building settlement by constructing a multi-scale time sequence prediction model; the multi-factor analysis module is used for carrying out coupling modeling and comprehensive analysis on the environmental factors, the structural characteristics and the abnormal evolution process so as to identify key influence factors and action mechanisms thereof; and the risk assessment and early warning module is used for performing grading assessment on the building settlement risk based on the prediction and analysis result and generating corresponding early warning information and decision support schemes.

Disclosed in the present application are a multi-modal data processing method and apparatus, an electronic device, and a storage medium. The method comprises: acquiring a reference image and a reference text; extracting a reference visual feature of the reference image; by means of a multi-modal large language model, determining an embedding of the reference text, an embedding of a start mark of the reference visual feature, an embedding of the reference visual feature, and an embedding of an end mark of the reference visual feature; on the basis of the multi-modal large language model, splicing the embedding of the reference text, the embedding of the start mark, the embedding of the reference visual feature, and the embedding of the end mark into a target embedding sequence, performing attention processing on the basis of the embedding of the start mark, the embedding of the end mark, and an embedding selected by a sliding window in the target embedding sequence, and outputting a predicted sequence; and generating a predicted image and a predicted text on the basis of the predicted sequence.

The present invention discloses an artificial intelligence-based adaptive big data storage and retrieval optimization system and method designed to intelligently manage and optimize large-scale distributed data environments. The system integrates data acquisition, distributed storage, metadata processing, adaptive learning, and retrieval optimization units configured to work collaboratively for continuous self-optimization. The invention employs deep reinforcement learning and predictive neural network techniques to dynamically analyze system telemetry, workload behavior, and data access patterns in real time, enabling proactive adjustment of data placement, caching, replication, and compression parameters across distributed nodes. The metadata processing framework utilizes graph-based dependency modeling to maintain semantic and contextual relationships among datasets, facilitating intelligent and context-aware data retrieval. The retrieval optimization unit interprets user queries semantically and computes the optimal retrieval route using latency prediction models and dynamic routing techniques.

The invention relates to the technical field of data processing, and discloses a comprehensive evaluation method and system for the health degree of a distribution network cable. The method comprises the following steps: collecting cable joint multi-source monitoring signals, normalizing the monitoring signals to obtain a degradation degree feature vector, correcting multi-physics field coupling model parameters, obtaining a recessive degradation index through finite element calculation to obtain an enhanced feature vector, and performing time-frequency domain decomposition to extract multi-scale feature parameters to obtain a comprehensive feature matrix; a double attention mechanism calculates a feature weight and a time sequence correlation degree to obtain a deterioration trend prediction value, and fuzzy integral is fused with a multi-classifier output probability to obtain a health degree evaluation grade and an early warning result. According to the invention, the early defect identification accuracy and the degradation trend prediction precision are improved.

The invention discloses a data anomaly detection system and method based on power equipment, and belongs to the technical field of power system data processing and fault monitoring, and the method comprises the steps: obtaining a multi-dimensional operation parameter sequence of voltage, current, temperature, harmonic waves, switching states, topological energy transfer vectors and the like; constructing a local disturbance response map, extracting a micro-disturbance driving factor, and generating a high-dimensional feature embedding matrix; constructing a multi-scale state density map on the basis of the embedded matrix, and mapping an equipment behavior evolution track; introducing a coupling evolution path tracking algorithm based on the density map, identifying an abnormal state set, and constructing a cross-time-period risk channel; matching the current monitoring data with the risk channel, and calculating a risk evolution value; when the value exceeds a set threshold value, abnormal early warning is triggered, and an evolution path model is output; the method has the advantages of high accuracy, strong trend identification capability and early warning, and is suitable for intelligent operation and maintenance of power equipment under complex working conditions.

The invention provides a dynamic graph neural network modeling method for space-time big data, and relates to the technical field of data processing, and the method comprises the steps: mapping a network function entity into a topology vertex and mapping a topology correlation characteristic into a weighted transmission link, and triggering a sequence through a signaling event to drive topology reconstruction, and generating a communication network topology model; inputting the communication network topology model into a dynamic graph neural network, executing state feature space aggregation of a topological vertex neighborhood through a spatial-temporal feature extraction layer, and fusing time evolution dependency of a historical topological sequence to generate a network node spatial-temporal state tensor; and based on the network node space-time state tensor, a particle swarm optimization algorithm is adopted to calculate a whole network risk level quantitative topology feature, and network resource strategy optimization is dynamically executed to suppress end-to-end risk conduction. The adaptive capacity of the network to the dynamic scene is improved.

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention provides a fatigue life prediction and residual life evaluation method for a high-power heavy-duty gearbox, and belongs to the technical field of intelligent operation and maintenance based on computer data processing. Comprising the following steps: acquiring dynamic data in an operation process, and performing multi-scale decomposition to form multi-source multi-scale data; inputting the multi-source multi-scale data into a designed multi-scale fatigue feature extraction module and a health state prediction module to obtain a multi-scale health index sequence and a health state label; establishing a fatigue damage evolution model, introducing the generated health index sequence for self-adaptive updating, outputting a comprehensive damage value, performing staged evaluation of fatigue degradation to obtain a damage label set, and performing multi-scale health index sequence and health state labels as well as the comprehensive damage value and the damage label set to obtain a multi-scale health index sequence and health state labels; inputting into a designed double-source fusion fatigue life prediction model, and outputting residual life prediction quantity; according to the invention, high-precision prediction and residual life evaluation of the fatigue life of the high-power heavy-duty gearbox are realized.

The invention provides a distributed storage resource intelligent scheduling method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the feature splicing of feature vectors of different modes, so as to obtain a multi-mode feature vector; predicting the user satisfaction based on the multi-modal feature vector and the resource adjustment parameter; establishing a resource demand priority mapping table under different service scenes according to the user satisfaction; constructing a multi-objective optimization model according to the resource demand priority mapping table; on the basis of the multi-objective optimization model, predicting the resource state and the load condition of each node in the distributed storage system; and according to the resource state and the load condition of each node, lightweight rule scheduling is carried out to obtain a preliminary scheduling scheme. According to the invention, intelligent scheduling management of distributed storage resources is realized.

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

The invention discloses a personal credit score real-time early warning system based on behavior chain mining, which relates to the technical field of financial data processing and comprises a data acquisition module, a behavior chain atlas module, a behavior mining module, a collaborative modeling module, a score fusion module, an early warning interpretation module and a safety optimization module. All the modules interact through an end-to-end data flow and a real-time message mechanism, and dynamic evaluation and intelligent early warning of personal credit risks are achieved. According to the method, diversified normal and high-risk virtual behavior chains are automatically simulated and generated through a generative AI technology, a federated learning mechanism is combined, multiple mechanisms are enabled to jointly confront novel fraud and complex risk behaviors, original data does not need to be transmitted during model training, privacy security and model generalization ability are greatly improved, unknown risk behaviors are virtualized through AI, and the method is high in practicability and easy to popularize. The capability of identifying unprecedented risks is improved, and the problems of data islands and privacy leakage are avoided through federal learning.

The invention relates to the technical field of image data processing, in particular to a cable surface defect detection method and system based on machine vision. The method comprises the following steps: acquiring a surface image of a cable, and converting the surface image into a grayscale image; determining the local complexity of each pixel point; obtaining a plurality of areas of the grayscale image, performing complexity determination, and dividing each area to obtain a plurality of windows of each area; determining a contrast limit threshold value of each window; performing image enhancement by using a CLAHE algorithm to obtain an enhanced grayscale image; and carrying out cable surface defect detection on the enhanced grayscale image by using a defect detection algorithm. According to the method, the CLAHE parameters are adaptively adjusted based on local complexity, the window size and the contrast threshold are dynamically determined in combination with gray and gradient information, discontinuity is corrected and eliminated through boundary similarity, and the quality and reliability of a cable defect detection image are improved.

An autonomous environmental regulation and behavioral monitoring system is disclosed, configured to adapt temperature, lighting, and acoustic conditions based on real-time emotional and physiological data. The system includes a dual-redundant central processor, hierarchical communication networks, multi-angle visual acquisition units, infrared thermometers, and modular environmental subsystems. It detects posture, gestures, facial expressions, and thermal signals to classify user states and apply individualized airflow, light, and sound modulation without relying on external internet connectivity. The system also monitors connected appliances using voltage-based pressure analysis to forecast device degradation. With integrated gesture recognition, privacy-preserving data handling, and predictive adaptation, the invention enables multi-user personalization, long-term learning, and uninterrupted operation within residential, administrative, or healthcare infrastructures.

The invention discloses a modeling method based on shield tunneling data feature analysis and parameter relevance, and relates to the field of tunnel engineering data processing. The method comprises the steps that shield tunneling time sequence parameters are obtained, and a non-uniform time sequence is resampled into a space-aligned standardized footage domain sequence through state cleaning and coordinate domain transformation; by means of mixed variable rejection and lagging correlation analysis, environment common cause interference is stripped, physical response delay among parameters is recognized, and a time-delay directed correlation graph model is constructed; and inputting the footage domain sequence and the graph model into a graph neural network, performing feature learning by using a time delay compensation aggregation mechanism, and outputting a key parameter influence degree set with symbols based on a prediction gradient. According to the method, the problem of data space-time dislocation caused by propelling speed fluctuation and the problem of parameter relevance misjudgment caused by physical response lag are solved, and accurate identification and explanation of shield tunneling key parameters are achieved.

The invention belongs to the field of data processing, and particularly relates to a dynamic cooperative control system and method for a gas turbine and a micro-grid, and the method comprises the steps: constructing a micro-grid real-time monitoring module, continuously collecting distributed energy real-time output, controllable load demands, bus voltage frequency and equipment state parameters, and carrying out the filtering and noise reduction through a preprocessing unit, thereby guaranteeing the data precision; calculating a real-time power difference value based on the preprocessed data, calling an adaptive neural fuzzy inference system, taking the power difference value, the bus voltage deviation and the frequency deviation as input, and judging whether the power difference value, the bus voltage deviation and the frequency deviation exceed a preset threshold value by means of a fuzzy rule base and a neural network model; if the threshold values are not exceeded, the current states of the gas turbine and the energy storage system are maintained; if any one exceeds the threshold value, a dynamic cooperative control instruction is triggered, precise cooperative control of the gas turbine and the micro-grid is achieved, and the operation stability, the operation efficiency and the reliability of the micro-grid are improved.

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

The invention provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

The invention relates to an unmanned aerial vehicle aerial image small target detection method and a computer readable storage medium. The method comprises the steps of obtaining unmanned aerial vehicle aerial image data, and dividing the data into a training set and a verification set after processing; yOLOv8n is used as a basic model, traditional convolution structures of a shallow layer and a middle layer are replaced by full-dimensional dynamic convolution in a backbone network of the YOLOv8n model, an enhanced space attention mechanism based on routing is introduced into the backbone network of the YOLOv8n model, and an original SPPF structure of the backbone network is replaced by a multi-scale context modulation module. Performing collaborative design of a multi-scale structure and a detection head in a neck network and a head network of the YOLOv8n model to obtain an improved YOLOv8n model; training and verifying the improved YOLOv8n model by using the training set and the verification set to obtain a trained unmanned aerial vehicle aerial image small target detection model; and inputting a to-be-detected unmanned aerial vehicle aerial image into the trained unmanned aerial vehicle aerial image small target detection model to obtain a detection result. According to the invention, the small target detection precision and speed are improved.

The invention discloses a data import and field intelligent matching method for a low-code platform, and particularly relates to the technical field of low-code platform data processing. Constructing a field initial feature vector, extracting a standard field and historical matching features thereof from the target data model, and calculating a semantic matching score matrix by using a multi-source feature fusion model; screening the initial mapping table based on a dynamic threshold value, performing statistical deviation analysis in combination with a field value distribution difference measurement feature delta, and when delta exceeds a preset threshold value, calling a value mapping correction model to complete rematching to obtain an optimized mapping table; if it is detected that user history adjustment exists in the same source file type, re-correction is carried out based on a user behavior log, and a final field mapping table MP3 is output; finally, the MP3 is applied to data import, and automatic matching and binding are achieved. The method improves the accuracy and automation level of field matching, and is suitable for a multi-source heterogeneous data integration scene.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a fault diagnosis and recovery verification method and device, equipment and a medium. Inputting the candidate event and the context state into an intelligent diagnosis model to obtain a diagnosis result and a fault type, selecting a recovery path in a recovery strategy library based on the diagnosis result, generating a recovery execution instruction, creating a verification experiment based on the instruction, and injecting drill data to obtain verification data, and forming a verification conclusion according to the verification data, associating a recovery execution instruction, and issuing the instruction in the execution environment to complete recovery operation. According to the method, through multi-source monitoring data processing, intelligent diagnosis model reasoning, strategy library path selection, verification experiment verification and execution environment automatic recovery, a full-link automatic process from detection, diagnosis, recovery to verification is constructed, and accurate diagnosis and dynamic recovery in a complex environment are achieved.

The invention provides a water conservancy project safety monitoring method and system based on data processing, and relates to the technical field of monitoring, and the method comprises the steps: obtaining and carrying out the multi-dimensional preprocessing of water conservancy project multi-source heterogeneous monitoring data through the deployment of a sensor network, and extracting multi-scale space-time fusion features from the data; performing structural state modeling, anomaly prediction, risk assessment and early warning by using a long-short-term memory neural network model integrated with a multi-head attention mechanism; and intelligent suggestions oriented to maintenance decisions are generated, so that comprehensive, accurate and prospective evaluation and early warning of the structural state of the water conservancy project are finally realized, the exception identification and risk prediction capabilities are effectively improved, the false alarm rate is reduced, refined and initiative intelligent maintenance decisions are provided, resource allocation is optimized, and the service life of the project is prolonged.

In a described embodiment, a multi-agent system for processing information is provided including a data processing agent configured to ingest and normalize raw data inputs to produce standardized data and a standards integration agent configured to apply reporting standards into the standardized data thereby generating integrated reporting standards. The system further includes a performance alignment agent configured to align performance indicators based on the standardized data and the integrated reporting standards and an information synthesis agent configured to process narrative information from the standardized data and the integrated reporting standards. An orchestration framework configured to manage operations of the data processing agent, the standards integration agent, and the performance alignment agent to produce a regulatory repot compliant with regulatory requirements is further provided. The orchestration framework is further executable by a large language model.

The invention discloses a laboratory quality management document intelligent generation method and system based on retrieval enhancement, and relates to the technical field related to data processing.The method comprises the steps that semantic coding is conducted on a preset standard text, and a vector knowledge base is constructed; retrieving associated standard terms according to the document theme, and extracting structured data from a laboratory business system; embedding the standard terms and the business data into a Prompt template, and calling a preset large language model to generate a text; and performing paragraph splicing and hierarchical control on the generated text, automatically checking compliance by utilizing term consistency of rule model fusion and a numerical value comparison algorithm, and outputting a quality management document. The technical problems that in the prior art, standard term retrieval and matching are not accurate, laboratory business data fusion is difficult, and consequently document compiling efficiency and quality are poor are solved, and the technical effects that minute-level automatic generation of laboratory quality management documents is achieved, and document compiling efficiency, quality and compliance are improved are achieved.

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

The invention belongs to the field of civil aviation, provides a civil aircraft field service operation scheduling control system, and aims to solve the problem of low cooperative efficiency of multiple links of ground service scheduling. File management is based on an Ali cloud OSS development interface, supports multi-format files, and realizes functions of permission access, encryption and the like; communication adopts an MQ / MQTT architecture to construct an IM system, and supports multi-type message transmission and http network communication; data processing packages a multi-language database interface, and internal and external network secure intercommunication is realized through a gatekeeper agent. Flight functions cover and guarantee node management and control, dynamic updating, cross-department communication, real-time flight data butt joint, task distribution of unified operation according to work types, and support of multi-terminal such as a web terminal and a handheld terminal and nailing access. Ground service multi-scene requirements can be met, and the operation efficiency and the management level are improved.

The invention relates to a water quality real-time detection system which comprises the following modules: a multi-source sensing module which is based on a multi-parameter sensing array, adopts a self-adaptive sampling strategy, realizes sensor time sequence synchronization through a state estimation algorithm, completes water body multi-dimensional parameter acquisition in combination with a micro-fluidic chip, generates a multi-modal sensing data set, and transmits the multi-modal sensing data set to a data processing module; the multi-source sensing module comprises a multi-source sensing sub-module, a signal conditioning sub-module, a time sequence synchronization sub-module and an anomaly capture sub-module. The method has the advantages that through the synergistic effect of the adaptive sampling strategy and the state estimation algorithm, the multi-sensor time sequence synchronization precision is remarkably improved, the phase deviation problem caused by traditional fixed frequency sampling is effectively eliminated, the sliding window polynomial fitting is combined with the wavelet threshold de-noising technology, and the multi-sensor time sequence synchronization precision is improved. High-frequency noise interference is greatly suppressed on the premise that effective components of the signals are reserved, and meanwhile, the abnormal value detection accuracy is improved through a dynamic threshold mechanism.

The present invention relates to adaptive multi-state encoding and processing in virtualized computing environments. The system includes a virtualized state selection module that dynamically transitions between binary, ternary, quaternary, and higher-order encoding states based on workload, bandwidth, security posture, and compliance requirements. A virtual encoding engine utilizes hardware-accelerated components, such as vFPGAs, vGPUs, and cTPUs, to enhance encoding throughput. A compliance-driven feedback controller continuously monitors encoding efficiency, threat levels, and adherence to mandates such as GDPR, HIPAA, and FIPS 140-3. Additional features include AI-based anomaly detection, federated model refinement, distributed ledger-backed audit trails, and quantum-resistant encoding techniques. By integrating intelligent encoding decisions with scalable compliance enforcement, the system enables high-performance, secure, and regulation-ready data processing across distributed cloud and edge environments, delivering measurable improvements in system responsiveness, data integrity, and operational trust.

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.