801 results about "Historical record" patented technology

The invention discloses an intelligent dynamic triage system and method for emergency patients. The triage system comprises a data acquisition module used for acquiring basic information, physiological parameters and historical records of patients; the artificial intelligence analysis module generates a preliminary triage result; the Internet of Things connection module collects hospital resource data in real time; the central control processing unit is connected with each module to receive data; the dynamic grading decision-making module divides the patients into critical symptoms, severe symptoms, subcritical symptoms and common symptoms; the resource allocation module dynamically generates a resource allocation scheme according to the grading result; the diagnosis and treatment process management module tracks the whole process of the patient and updates state data; when the state of the patient changes, the system transmits data to the artificial intelligence analysis module again for evaluation, triggers the grading decision and resource allocation module, dynamically updates the triage priority and the resource allocation scheme, and achieves closed-loop feedback and continuous optimization.

The invention relates to the technical field of vacuum electric furnace monitoring, and discloses a state monitoring system suitable for a vacuum electric furnace. A multi-source sensor array of the system collects multi-dimensional physical signals such as temperature distribution, pressure change and vacuum degree fluctuation in a furnace in real time; a furnace cavity feature reconstruction module extracts sampling point feature parameters and correlates coordinates to construct a three-dimensional dynamic feature field; the process anomaly analysis module calculates a process deviation degree in combination with a preset reference parameter, and marks an anomaly coordinate area; the state transition evaluation module analyzes an abnormal trend according to historical records and predicts a state transition path and rate; the collaborative regulation and control decision-making module generates a multi-stage vacuum maintenance compensation strategy and a heating power regulation gradient scheme according to the multi-stage vacuum maintenance compensation strategy; and the running log feedback module records a strategy execution process, and associates the three-dimensional feature field data to generate a state tracing log. The system can realize comprehensive monitoring of the state of the vacuum electric furnace, accurate abnormity identification, trend prediction, cooperative regulation and control and state tracing, and helps to improve the operation management level of the vacuum electric furnace.

A device may receive, at a server system, a free-form query. The device may select two-way communication history records from a database associated with the free-form query to identify selected history records. The device may generate individual summaries of individual history records of the selected history records by processing a set of corresponding individual history records using a chunking algorithm, constructing language responses from outputs of the chunking algorithm using a large language model, and aggregating the language responses. The device may process the language responses using the large language model to generate an individual summary for corresponding individual history records.

The invention provides a self-adaptive recommendation method based on dynamic strategy optimization, and aims to solve the problems of memory bank update lag, retrieval strategy lag and insufficient long-tail article coverage of an existing recommendation system. A time sequence behavior coding module, a dynamic memory storage bank, a strategy control module and a heterogeneous fusion module are designed; and real-time optimized personalized recommendation is realized. Specifically, a time sequence behavior coding module is used for generating a user state vector with time decay, a dynamic memory storage library automatically updates historical records based on a mixed scoring mechanism, and a strategy control module outputs a learnable retrieval number, a weight coefficient and a fusion parameter. The heterogeneous fusion module realizes cross-channel feature enhancement through a dual-channel attention mechanism; according to the method, the problems of preference drift and long-tail recommendation are effectively solved, meanwhile, the online updating efficiency is improved, and more accurate and diversified recommendation results are provided for users.

The invention discloses an electrical equipment defect identification and alarm method and system based on deep learning. The method comprises the following steps: synchronously collecting and registering visible light and infrared thermal imaging images on the surface of power equipment, and constructing an instance segmentation network comprising a lightweight feature extraction network, a multi-scale feature fusion network and a frequency domain mask prediction branch; enhancing the diversity of training samples by adopting a generative adversarial strategy; based on the graph neural network, analyzing the incidence relation between the defects and the equipment topology and historical records, and deducing the defect causal relation and the risk level; generating interpretable alarm information including the thermodynamic diagram, the natural language report and the maintenance suggestion; real-time detection and deep analysis are realized by adopting an end-side cloud collaborative architecture; and the system performance is continuously improved through a closed-loop optimization mechanism. According to the method, high-precision defect detection under multi-modal data fusion is realized, the robustness and interpretability are high, and the operation and maintenance intelligence level of power equipment is remarkably improved.

The invention discloses a virtual power plant intelligent regulation and control method and system based on artificial intelligence, and belongs to the technical field of electric power system intelligent regulation and control, and the virtual power plant intelligent regulation and control method based on artificial intelligence comprises the following steps: S1, aggregating equipment side data, desensitizing to generate topological codes, and constructing time scale matrix synchronization; s2, constructing a dynamic model by equipment parameters, and mapping real-time data to output a difference map; s3, adding equipment constraints, building a multi-objective function, optimizing a strategy and performing correlation analysis; s4, a wind and light fluctuation overrun trigger RL strategy and an abnormal switching base line generate a mixed instruction; s5, locally verifying the instruction, and correcting and feeding back parameters if the prediction is out of limit; s6, generating a three-dimensional thermodynamic diagram, and displaying an association report and a historical record by AR; s7, aggregating the data to reconstruct the training set, locally fine-tuning the strategy network and performing incremental updating; the method has the beneficial effects that the regulation and control pain point of the virtual power plant is systematically solved, the operation and maintenance cost is reduced, the new energy consumption capability is improved, and the equipment out-of-limit risk is reduced.

The invention provides a multi-source data management method based on machine learning and a related device, and the method comprises the steps: obtaining a multi-source data set in an emergency command rescue scene, carrying out the data preprocessing of the multi-source data set, obtaining a standardized data set with a unified data form, extracting a data element information set from the standardized data set, and carrying out the processing of the data element information set. And calling a pre-trained blood relationship modeling model to perform association analysis processing on the data element information set, generating a blood relationship description set among the data entries in the standardized data set, and generating a data blood relationship tracking result based on the blood relationship description set. The data consanguinity tracking result comprises a historical record of the full life cycle of the data and cross-data entry dependency graph information. According to the invention, the method can provide a governance support with a data source capable of being checked, a processing process capable of being traced and a clear dependency relationship for emergency command rescue, enables a rescue decision to be developed based on reliable data consanguinity information, and effectively improves the support capability of data governance for the rescue decision.

The invention discloses a network space security risk intelligent identification method and system. The method comprises the following steps: deploying traffic collection equipment at key network nodes and boundary equipment, and collecting network traffic data and log data; extracting network behavior features and log key fields, and constructing a data feature model; performing vectorization processing based on the data feature model, performing traffic classification by using a deep neural network, and identifying a device type and a device identifier; carrying out noise reduction, redundant information removal and standardization processing on flow data in the data feature model to generate standardized data and storing the standardized data in a vector database; entity extraction is carried out on the standardized data, a knowledge graph is constructed, data distribution is analyzed through a Gaussian mixture model, the probability of abnormal behaviors is calculated in combination with historical data, and attack risks are calculated based on Bayesian decision; and generating an alarm according to the calculated attack risk, tracing the historical record of the attack risk in the knowledge graph, sending a blocking instruction, and identifying the security risk of the cyberspace.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a task processing method, device and equipment based on multi-agent cooperation and a medium. And constructing a sub-task dependency graph, sequentially scheduling and executing the sub-tasks, and performing parameter completion to obtain the completed sub-tasks. And tracking the execution progress and the historical record of the completion subtask through a preset progress agent, and generating an executable operation decision by using a preset decision agent. If the task execution does not reach the expected effect, feeding back difference information and a correction suggestion, adjusting an operation decision and generating an updating operation; if the task achieves the expected effect, task completion information is sent to the progress agent, and the state is updated to be task completion. According to the method, the perception accuracy is improved, a task dependence tracking and feedback correction mechanism is provided, and a cross-application automatic task is successfully realized.

The invention relates to a gas leakage early warning method and system for a chemical industry park. The early warning method comprises the following steps: acquiring real-time temperature and pressure data of a reaction kettle area and a storage tank area; obtaining a historical leakage record of a pipeline joint, and determining a diffusion path prediction boundary; extracting a wind direction and a wind speed in a diffusion path prediction boundary, and determining space-time distribution characteristics; analyzing space-time distribution characteristics, determining an overlapping region and setting concentration monitoring nodes; identifying the concentration contribution ratio of the reaction kettle area diffusion characteristic and the storage tank area diffusion characteristic; integrating the operation data of the pipeline joint diffusion characteristics and the loading and unloading area diffusion characteristics, and determining a diffusion path deviation correction coefficient; combining the deviation correction coefficient with the humidity data of the peripheral diffusion characteristics of the cooling tower; and updating diffusion parameters of the separation tower area diffusion characteristics and the public engineering area diffusion characteristics according to a source intensity estimation result. According to the invention, accurate prediction and early warning of gas diffusion in a complex chemical device area are realized, and the safety management level is improved.

The invention provides a security event association analysis and question answering method and system and a medium, and the method comprises the following steps: context-aware query completion: receiving an original query input by a user, obtaining a historical record of a current dialogue, and forming a context enhanced query according to the original query and the historical record of the current dialogue; dynamic task identification: outputting a prediction task model according to the context enhancement query in combination with a dynamic task identification strategy; multi-dimensional retrieval: performing multi-dimensional retrieval on the prediction task model to obtain a final knowledge context packet; and knowledge-driven response generation: generating a knowledge-driven response based on the knowledge context packet, and outputting a final security analysis report after the generated content passes verification. According to the method, efficient, accurate and explainable intelligent association analysis of the multi-source heterogeneous security data is realized by constructing a multi-dimensional knowledge base, designing a dynamic task recognition mechanism and realizing context-aware query completion.

The invention discloses a PDF (Portable Document Format) conversion multiplexing method and device, computer equipment and a storage medium. The method comprises the following steps: extracting metadata features, logic structure features and visual layout features of a PDF document; performing dynamic partitioning according to the type of the PDF document to obtain a plurality of document blocks, and performing hash processing on each document block to generate a corresponding hash value; performing first-level comparison on the PDF document and a PDF document in a historical record according to the metadata features; performing second-level comparison on the PDF document and a PDF document in a historical record according to the logic structure feature; performing third-level comparison on the hash value of each document block in the PDF document and the hash value of each document block in the PDF document in a historical record, and determining whether different document blocks exist in the PDF document or not; if yes, incremental conversion processing is conducted on the difference blocks, the difference blocks obtained after incremental conversion are spliced with the converted parts of the unchanged content in the corresponding PDF document in the historical record, and a complete conversion result is formed. The method greatly improves the conversion efficiency.

The invention discloses a digital twin autonomous optimization system based on building structure quality monitoring. According to the system, structure state data, environmental influence data, material performance data and historical record data are comprehensively collected through a multi-source data collection module; feature data such as a structure degradation rate and a material aging coefficient are extracted through an edge calculation preprocessing module; a digital twinborn body is constructed through a multi-layer fusion module, geometric layer fusion achieves crack grading labeling, physical layer fusion corrects parameters such as material elasticity modulus, and behavior layer fusion updates the structural damping ratio to reproduce dynamic behaviors. Outputting a structure health index, residual life prediction and maintenance suggestion through an autonomous optimization decision module; and in combination with a dynamic iteration updating module, the system is ensured to continuously adapt to the structure change through validity evaluation and parameter adjustment. The accuracy of building structure quality evaluation and the scientificity of maintenance decision making are improved, and the method is suitable for full-life-cycle management of various buildings.

The invention discloses an equipment diagnosis and maintenance method and system based on a large language model, a terminal and a storage medium. The method comprises the steps that multi-source heterogeneous data of target equipment is collected and preprocessed; constructing and continuously updating a historical case vector database containing equipment knowledge; state data of target equipment are monitored in real time, after fault diagnosis is triggered, the most relevant knowledge fragment is retrieved from the historical case vector database according to real-time abnormal data, and a diagnosis context is constructed; inputting the diagnosis context into a large language model for reasoning, and outputting a fault diagnosis report and a maintenance scheme; the fault diagnosis report and the maintenance scheme are presented to a user, and user feedback is collected to optimize the system. According to the method, real-time data, historical records and structured knowledge of equipment can be automatically integrated, a dynamic knowledge base is constructed, accurate and efficient diagnosis of equipment faults is realized by utilizing the powerful reasoning capability of a large language model, an executable maintenance scheme is automatically generated, and the maintenance efficiency is improved.

The invention provides a multi-level page table traversal acceleration method, system and device and a medium, and belongs to the technical field of computers. The method comprises the following steps: inputting a virtual address into a path predictor and a dynamic weight hierarchical cache, and collecting an instruction access proportion, a TLB hit rate and a pre-fetching success rate in real time; performing hash calculation according to a preset bit of the virtual address through a path predictor, and querying a historical record table to obtain a prediction path and a confidence level so as to determine a query range; when the cache is hit, scores of hit entries are calculated, priority ranking and elimination decision making of the cache entries are carried out, and meanwhile, the cache entries are managed by adopting a hybrid replacement algorithm; under a preset condition, starting a prefetch operation through an adaptive prefetch engine, calculating and dynamically expanding a prefetch step length, and preventing memory bandwidth contention through two-stage monitoring; when the instruction access proportion, the TLB hit rate and the prefetching success rate meet conditions, an instruction priority mode is activated, and scores of hit entries are updated by adjusting related calculation parameters.

The invention relates to the field of intelligent operation and maintenance, and discloses a multi-source network data operation and maintenance system based on micro-service architecture and AI collaboration, comprising the steps of collecting multi-source data of a micro-service system, and performing cleaning, format unification and time alignment on the collected data; based on a data result of the data acquisition module, constructing a micro-service call chain and a dependency graph, embedding a real-time performance index and a log feature in each node, and dynamically updating a service relation graph; carrying out real-time anomaly detection on the multi-source data, and judging the alarm effectiveness in combination with a dynamic threshold and an AI alarm confidence self-learning mechanism; the AI conducts reasoning along the call chain anomaly map, causal relationship reasoning is added, and the anomaly propagation influence range is predicted; and feeding back a root cause positioning result and the optimized alarm information to an operation and maintenance system, optimizing an alarm threshold and decision parameters in combination with historical records, and outputting an updated operation and maintenance decision scheme. The method has the advantage of improving the operation stability of the system.

Systems and techniques are described for tracking and providing access reports for individual pieces of data managed by a data storage service. A service may generate and store a record of operations performed on a piece of data, such that may be classified as containing sensitive or important data, in a data store. The record may link representations of users and the operations performed by those users to instances of the piece of data, as it is found in one or more data objects within the data store. The data store may link other instances of the piece of data and other operations performed on the piece of data to the first instance of the piece of data. The service may access the data store to produce a history record of the various instances of the piece of data and operations performed on those instances of the piece of data.

The invention relates to the technical field of data processing, and discloses a data processing method and system based on liquid leakage detection. The method comprises the following steps: acquiring a sensor signal and processing to obtain leakage basic data; analyzing signal changes to determine leakage types and influence ranges; matching the risk threshold to determine a risk level; associating historical records to analyze leakage laws; calculating a failure probability to generate a maintenance strategy; and constructing a running state diagram to form a hierarchical management report. According to the invention, leakage management is converted from a traditional passive response mode to a data-driven active prevention mode, optimal configuration of maintenance resources is realized, and the leakage prevention and control efficiency is improved while the maintenance cost is reduced.

The application relates to the technical field of supply chain management, in particular to a supply chain management system based on big data, which comprises a data integration module, a demand prediction module, an inventory optimization module, a collaborative scheduling module and a feedback optimization module. The system realizes supply chain global state analysis and demand prediction through multi-source heterogeneous data collection and a deep learning model, combines dynamic inventory adjustment and cross-regional resource scheduling optimization, and improves the supply chain efficiency. The feedback optimization module further generates optimized control instructions by analyzing and intervening in the time delay of historical records. The application can improve the response speed and resource utilization efficiency of the supply chain and reduce the operation cost.

The invention relates to the technical field of operation and maintenance safety compliance, and discloses an operation and maintenance service automation safety compliance detection system and method. The system comprises an operation and maintenance event acquisition unit, a strategy execution unit, a compliance analysis unit, a strategy optimization unit and a violation tracing unit. The operation and maintenance event acquisition unit captures operation and maintenance operation events and execution environment parameters in real time, packages the operation and maintenance operation events and the execution environment parameters into event data packets and transmits the event data packets to the strategy execution unit; the strategy execution unit extracts a reference strategy rule from the security strategy library, performs matching verification on the event and generates a result; the compliance analysis unit analyzes compliance fluctuation characteristics through a multi-dimensional compliance deviation model in combination with historical records; the strategy optimization unit dynamically adjusts the event capture frequency and corrects a strategy matching rule according to the fluctuation characteristics; the violation tracing unit counts the risk cumulant and generates a violation tracing instruction when the risk cumulant exceeds a preset capacity. According to the system, automation and dynamic adjustment of operation and maintenance safety compliance detection are realized, and a risk accumulation process can be effectively tracked.

The invention relates to the technical field of data processing, can be applied to business system platforms of medical health, financial science and technology and the like, and discloses a payment business processing method based on a process engine, which comprises the following steps: standardizing an input / output interface, packaging business logic into business nodes, and storing the business nodes in a node library. And selecting nodes, configuration information and a connection relationship according to service requirements, and generating a process model. And starting the process engine, and converting the process model to obtain a process instance. And extracting node information and a connection relationship, scheduling node execution, obtaining real-time data, and generating a dynamic adjustment decision. And according to the decision and the real-time data, adjusting a process instance execution path and node parameters, scheduling node execution, completing payment service processing, and storing process instance information in a historical record library. By standardizing the interface, packaging the service nodes, generating the process model and dynamically adjusting the execution path and parameters, the payment service processing is efficiently completed, the flexibility and efficiency are improved, and the process information is convenient to store and trace.

The embodiment of the invention relates to the technical field of data storage, and discloses a cross-K8S cluster configuration change storage method and device based on a block chain, and the method comprises the steps: obtaining a transaction request message passing the compliance verification of an intelligent contract engine, and the transaction request message comprises a configuration change request; obtaining log information of the configuration change request in the transaction request message executed by the target K8S cluster, wherein the log information further comprises configuration change cluster difference information; the log information is stored in a fragmented mode based on the IPFS network, and content hash codes of the log information are obtained; and taking the transaction request message and the content hash code as complete transaction information, and storing the transaction request message and the content hash code in a transaction pool. Decentralized storage is achieved based on an IPFS network fragmentation storage mode, the problem that a centralized database or a log system is maliciously modified or historical records are deleted easily in centralized storage is solved, and auditing integrity and data storage safety are guaranteed.

The invention discloses an acute pancreatitis complication assessment method based on artificial intelligence, and belongs to the technical field of medical information, and the method specifically comprises the steps: firstly, obtaining a clinical data set containing real-time clinical indexes and basic disease historical records; performing time sequence analysis on the historical records of the basic diseases, extracting long-term influence characteristics of the historical records of the basic diseases and generating basic disease influence factors; carrying out standardization processing on the real-time clinical indexes and the basic disease influence factors, and generating a comprehensive feature vector after calculating association weights among features; inputting the vector into a trained artificial intelligence evaluation model, and outputting a complication risk probability through layer-by-layer nonlinear transformation; and finally, mapping the risk probability value into a risk level, and integrating patient information to generate a structured risk assessment report. And an acute pancreatitis complication evaluation scheme integrating the acute stage index and the chronic basic disease influence is established.

The invention provides an intelligent infusion system and an intelligent liquid control method based on database dynamic feedback regulation and control, and relates to the technical field of medical equipment intelligent control. The system comprises an infusion terminal execution module, a multi-parameter monitoring module, a data transmission module, a central control server module and a dynamic database module; patient information, an infusion scheme, a threshold range and multi-dimensional data of historical records are stored through the dynamic database module, the central control server module calls the data and compares the data with real-time physiological parameters and infusion state parameters collected by the multi-parameter monitoring module for analysis, and the target infusion speed is calculated. Early warning is carried out according to the target infusion speed and the real-time monitoring parameters, accurate monitoring of the regulation and control parameters and the individual condition of the patient is ensured, the infusion speed is always in the optimal range adaptive to the physiological state of the patient, and the infusion adverse reaction caused by parameter errors and exceeding the physiological range is avoided.

The invention provides an intelligent decision-making method and system for a job progress based on big data, and the method comprises the steps: obtaining a real-time data stream of a job site, and carrying out the unified format processing of the real-time data stream; segmenting and cleaning the data in the unified format to generate cleaned data fragments; performing aggregation processing on the cleaned data fragment set to generate an aggregated data set, and generating an early warning signal and a scheduling instruction according to the aggregated data set; carrying out data packaging on the early warning signal and the scheduling instruction, and generating a preliminary scheduling scheme in combination with historical records; and dynamically comparing the preliminary scheduling scheme with the dynamic input parameter sequence, and if a deviation value is detected to exceed a preset threshold value, optimizing the scheduling scheme based on the deviation value to obtain a final optimized scheduling scheme. The scheduling strategy is continuously optimized through dynamic comparison and a feedback mechanism, intelligent management and continuous optimization of the production process are achieved, and the production efficiency and the resource utilization rate are improved.

The invention provides an accident consequence simulation calculation method based on mathematical physical model coupling solution, and the method comprises the steps: constructing an accident chain knowledge graph and a physical trigger graph, and building a causal relationship and physical constraints among equipment, states, events and consequences; gathering and mapping historical records, expert rules and online observation data into map entities and relationships, forming a baseline accident scene and calculating a baseline index; generating candidate paths by utilizing graph reasoning and coupled multi-physics field simulation, and realizing a closed loop of graph reasoning and physical solution through consistency check; performing scoring and disturbance simulation analysis on the candidate paths, and screening robust target paths; and carrying out high-fidelity simulation on the target path, identifying key nodes in combination with sensitivity analysis and a minimum cut-off set, and generating disposal suggestions and action priorities. According to the method, high-credibility prediction of accident evolution and emergency response closed-loop linkage are realized, and the method has high precision, high robustness and engineering implementability.

The invention provides an intelligent conference memo generation method based on a robot, and the method comprises the steps: collecting a multi-channel voice signal through a microphone array, and enhancing the voice of a target speaker through a beam forming technology; background noise is separated by adopting a self-adaptive filtering algorithm, and the voice signal quality is improved; speech features are extracted, language model parameters are adjusted, and a transliteration text is generated; analyzing the text structure, and extracting conference themes, participants and decision contents to form structured information; calculating semantic similarity among the knowledge graph nodes, and if the semantic similarity is higher than a preset threshold value, associating historical records to generate extension information; constructing a structured memorandum based on the extended information, organizing a conference theme, participants, decision contents and associated historical records, and generating an initial memorandum; and monitoring a memorandum editing operation, updating a knowledge graph node relationship, and generating a final memorandum document. According to the method, the accuracy, integrity and availability of conference records are effectively improved, and the conference efficiency is remarkably improved.

The invention relates to the technical field of data information operation and maintenance management, and discloses a data information operation and maintenance management method based on an AI chain trust mechanism, and the method comprises the steps: obtaining multi-source heterogeneous data through a distributed collection terminal composed of an edge calculation node, an Internet of Things sensor and a third-party interface module; and after format conversion, a data trust value is calculated by using a multi-dimensional trust evaluation model containing a source reliable item, a content consistent item and the like, then the data trust value and a historical trust record in a block chain distributed account book are subjected to association verification, and a trusted data identifier is generated through a consensus algorithm. And constructing an on-chain storage index based on the data type, the timestamp and the source node, encapsulating the data by adopting a hierarchical structure of a metadata layer, a content layer and a verification layer, and outputting a storage address. A monitoring layer of the dynamic operation and maintenance control model identifies abnormity in real time, a decision-making layer generates an operation and maintenance strategy in combination with credible data identification and historical records, and an execution layer triggers repairing and updating operation through an intelligent contract, so that on-chain credible operation and maintenance management of data information is realized.

The invention discloses an intelligent Internet of Things home energy management system, and relates to the technical field of intelligent homes, the system comprises a multi-dimensional data sensing module which uses a sensor and an intelligent equipment interface to collect human physiological data, obtains environment and external data through an environment sensor and a weather API, and sends the environment and external data to a cloud server; historical physiological records, environmental regulation logs and user feedback data are called, timestamps and space coordinate information are added to all kinds of data at the same time, and the data are converted into data vectors and uploaded to a system database; through the multi-dimensional data sensing module, human body physiological data, environmental data and external data can be comprehensively collected, and a physiological state prediction model and a health risk identification model exclusive to a user are constructed in combination with historical records and user feedback, so that the personalized level of health management is improved, and the health risk of the user is improved. And through a chaos embedding biological rhythm prediction formula and a dynamic Bayesian health risk assessment formula, accurate prediction of the physiological state of the user and identification of the health risk are realized.

The invention relates to the technical field of artificial intelligence and biological characteristic analysis, in particular to an AI-based personalized postpartum rehabilitation medical beauty evaluation system and method. The method comprises the following steps: generating a standardized action sequence according to a rehabilitation stage and a historical record of a user and guiding execution; collecting dynamic biological characteristic data in real time; aligning data and distributing dynamic weights by using a multi-modal fusion model guided by time sequence semantics, constructing a three-dimensional response graph by combining a tension response function, a collaborative exponential function and a stable fluctuation function, and generating individual embedded vectors through a graph neural network; identifying a potential high-risk time period through map potential energy offset analysis and disturbance sensitivity factors; integrating a user state, a historical response and an intervention knowledge graph, recommending a dynamically adaptive intervention scheme by using a multi-channel attention network, and continuously optimizing through a feedback closed loop; precise evaluation and personalized intervention recommendation of the postpartum rehabilitation process are realized through multi-modal fusion and dynamic modeling, and the rehabilitation efficiency and safety are improved.