37261results about "Digital data protection" patented technology

A system and method for implementing a convergent intelligence fabric (CIF) for distributed artificial intelligence operations. The CIF architecture integrates tensor-theoretic foundations, probabilistic cache management, precision-aware memory operations, quantum-resistant security, and neural-based optimization within a unified framework. The system orchestrates asynchronous, multi-hop data flow among computational resources while maintaining data security through per-block encryption and identity-based access control. Key components include a universal multi-model KV cache subsystem, agent-parallel disaggregation pipelines, reinforcement learning-based orchestration, and neuromorphic memory integration. Advanced implementations incorporate graphon-enhanced memory for sparse graph sequences, multi-modal cognitive persistent memory, and quantum-resistant asynchronous multi-domain trust protocols. The system enables efficient cross-agent collaboration, sophisticated knowledge sharing, and secure cross-domain operations while optimizing computational resources and maintaining strict privacy guarantees across distributed AI deployments.

A context-dependent, privileged access control system for dynamic, risk-based authorization, consisting of: a context acquisition subsystem configured to ingest and normalize multimodal contextual data streams from a variety of sources, including endpoint telemetry, geolocation sensors, user authentication metadata, device status metrics, and network traffic descriptors; a behavior profiling processing unit communicatively coupled to the context detection subsystem, the engine configured to maintain per-user behavior baselines using unsupervised learning models and to compute behavioral deviation vectors in real time for each privileged access request; a dynamic risk assessment module operatively connected to the behavioral profile processing unit, the module configured to calculate a multidimensional risk score for each session by applying a weighted aggregate function to behavioral deviation vectors, device risk posture, threat intelligence indicators, and environmental context volatility; a policy decision engine configured to apply programmable access control policies to the calculated risk score using a context-sensitive policy scoring language, wherein the engine is further configured to dynamically determine whether to authorize, deny, elevate, or revoke privileged access according to the current trust thresholds and permission boundaries; A permission enforcement controller unit communicatively connected to one or more endpoints, cloud services, and virtual infrastructure resources. The framework is configured to implement policy decisions by generating ephemeral access tokens, providing just-in-time (JIT) permissions, and initiating permission revocation workflows upon contextual anomalies. a secure hardware appliance consisting of a plurality of tamper-evident modules configured to host the behavioral profile processing unit, the risk assessment module, and the policy decision control unit in a trusted execution environment isolated from general computing resources.

The present invention provides a facial recognition system using deep learning methodologies while integrating privacy-preserving capabilities. This system employs convolutional neural networks (CNNs) to extract and classify facial features, ensuring high accuracy in recognition tasks. Moreover, the system addresses privacy concerns by incorporating techniques such as facial feature encryption and anonymization, thereby enhancing user privacy and data security. This invention is applicable across various domains, including security, surveillance, access control, and personalized services, where facial recognition is utilized while preserving individual privacy.

The invention relates to the technical field of judicial data security, and discloses a judicial system confidential data security circulation method based on a block chain technology. Collecting multi-source judicial data, identifying sensitive information through a large language model, and performing differential privacy desensitization processing; constructing an SM4 encryption and TLS 1.3 end-to-end secure channel, generating a data hash fingerprint, and writing the data hash fingerprint into an alliance chain evidence based on a PBFT consensus mechanism; designing a multi-modal classification engine to perform feature extraction and intelligent classification; establishing a hybrid authority model to integrate an XACML strategy and a Kafka queue, and implementing dynamic authority control in combination with an RBAC / ABAC mechanism; a hierarchical encryption storage architecture is constructed, and homomorphic encryption retrieval and erasure code distributed storage are adopted; constructing a judicial knowledge graph based on a BERT model; and deploying a block chain auditing system, and combining LSTM anomaly detection and a DREAD risk assessment model to form a closed-loop risk control system. According to the method, the problems of security risk and privacy disclosure in judicial data cross-department circulation are solved.

The invention provides an ecological meteorology and satellite remote sensing combined dynamic environment monitoring method and system. Wherein spatio-temporal dynamic concentration data and spectral reflection characteristic data are obtained at a pollutant emission node; generating an associated data block by the spatio-temporal dynamic concentration data and the spectral reflection characteristic data according to a pollution concentration abrupt change event trigger time sequence, and performing chain storage on Hash fingerprints and pollution source geographic coordinate information through a distributed node consensus mechanism; integrating the ecological meteorological observation data to generate a pollutant migration path map; and establishing a pollution influence boundary judgment model according to the pollutant migration path map and the vegetation stress response characteristic data, and monitoring the diffusion range and influence boundary of pollutants in real time through the model to generate an environment monitoring report. According to the technical scheme provided by the invention, the industrial environment pollution dynamic monitoring precision and the decision response efficiency are remarkably improved.

The invention discloses a multi-level energy management system based on multi-dimensional data, and relates to the technical field of energy intelligent management, and the system comprises a multi-source data collection module which collects power utilization, environment and equipment state data in real time; the data fusion processing module is used for processing abnormal values through an algorithm and fusing multi-scale data features; the energy state evaluation module is used for realizing equipment state evaluation and early warning by using a fusion algorithm and a prediction model; the multi-level energy scheduling module adopts an optimization algorithm to balance the energy cost, the production efficiency and the carbon emission, and dynamically adjusts the strategy; the energy performance analysis module is used for developing an analysis tool and an evaluation model; and the decision support module is used for configuring an expert knowledge base and developing a fault diagnosis system and a knowledge graph. Through multi-dimensional data acquisition and multi-level management, the energy data error is greatly reduced, the comprehensive energy cost and carbon emission are remarkably reduced, the cost of participating in enterprise operation is reduced, and the accuracy, safety and sustainability of energy management are improved.

A federated distributed computational system enables secure, privacy-preserving biological data analysis and engineering through interconnected nodes coordinated in a distributed graph architecture. A federation manager allocates resources, manages data flow and lineage, establishes privacy boundaries, and maintains cross-institutional knowledge relationships. Each node contains a processing unit for biological data analysis, privacy preservation protocols for secure multi-party computation, a knowledge graph structure with supporting data stores, and encrypted network connections. The federation manager enforces all computation and data exchange through secure channels while maintaining privacy, security, and contractual boundaries. This architecture enables research institutions to collaborate on complex biological analyses without compromising sensitive data, facilitating breakthrough discoveries through shared computational resources while maintaining strict data privacy and security controls.

An electronic device can comprise physiological sensors that can generate sensor data, a device processor, an actuator, and a controller electrically connected with the device processor and the actuator. The controller can monitor the state of the actuator; communicate a notification signal to the device processor to cause the device processor to prepare for a privacy mode responsive to determining that the actuator is in a privacy state, delay implementing the privacy mode until one or more conditions have been satisfied to allow the device processor to prepare for the privacy mode pursuant to communicating the notification signal to the device processor; and implement the privacy mode to inhibit access to the sensor data responsive to satisfaction of the one or more conditions.

The invention discloses a computer task scheduling method based on artificial intelligence, and the method comprises the following steps: 1, data collection: employing a double-flow feature fusion mechanism, and generating global feature representation containing long-term dependence and an instantaneous state; step 2, generating a global optimization scheduling strategy: constructing a hierarchical federal reinforcement learning system, dividing a cluster into a plurality of super nodes through an enhanced spectral clustering algorithm, independently training a Dueling DQN network by each super node, performing global strategy cooperation by adopting Shapley value weighted aggregation and differential privacy protection, and generating a scheduling strategy of global optimization; distilling a global strategy into a lightweight decision tree through a strategy distillation technology, and deploying the lightweight decision tree to a physical node; 3, task priority control and elastic resource allocation are carried out, wherein elastic control over resource allocation is carried out through a dynamic time slice bank mechanism; and 4, self-adaptive evolution: establishing a closed-loop optimization system, and carrying out strategy self-evolution by adopting a double-layer optimization architecture.

Systems and methods for decentralized data management across interoperable distributed platforms are disclosed. A computing system receives input data associated with a unique decentralized identifier (DID) representing an entity or event. The computing system segments the input data into encrypted data segments, each cryptographically linked to the DID, and distributes these encrypted segments across decentralized storage nodes according to a redundancy scheme. A cryptographic lineage record, including segment identifiers, timestamps, and hashes linked to the DID, is stored in a decentralized ledger. In response to authenticated access requests, the computing system reconstructs the input data by retrieving, decrypting, and cryptographically verifying the distributed data segments against the lineage record. Authorized entities access the reconstructed data through interfaces enforcing cryptographically secured access permissions defined within the decentralized ledger, providing enhanced security, provenance verification, and data resilience.

The present disclosure includes computer-implemented methods of guardrails for securely using large language models (LLMs). The method comprises monitoring user data flow using an application programming interface (API) and receiving an administrative policy from an administration communication interface. The method involves dynamically applying a plurality of LLM input inspectors to LLM input data. The application of the plurality of LLM input inspectors is based on the administration policy. The dynamic application of the plurality of LLM input inspectors is in sequence for latency optimization. The plurality of LLM input inspectors serve as LLM input guardrails for a plurality of secure deployed large language models (LLMs). The plurality of LLM input inspectors are configured by the administrative policy and validate the LLM input data to validated LLM input data based on the administration policy. Additionally, the method comprises dynamically applying a plurality of LLM output inspectors to LLM output data.

The invention relates to a digital integrated quality management system based on multi-source data fusion, and belongs to the technical field of industrial internet and quality management. A data acquisition layer of the system obtains real-time and static multi-source heterogeneous data through a multi-source adapter; the data processing layer is used for cleaning, converting and standardizing the acquired data; the intelligent analysis layer performs deep analysis and prediction on the data by using an adaptive quality prediction model, an anomaly detection module and a root cause analysis engine; the application service layer displays a quality trend and an anomaly detection result through a visual billboard, and provides credible tracing and collaborative decision-making functions; and the feedback closed layer adjusts system processing logic according to the decision support data to form closed-loop quality control. According to the method, real-time fusion and efficient utilization of multi-source data are realized through a dynamic routing technology, an adaptive quality prediction model and a block chain evidence storage mechanism, and the intelligent level and decision-making efficiency of quality management are remarkably improved.

The present invention discloses a system and method for performing artificial intelligence (AI) safety red-teaming with integrated policy fuzzing and adversarial prompting to systematically identify, characterize, and mitigate unsafe or non-compliant behaviors in AI models. The disclosed invention automates the process of generating, executing, and analyzing adversarial test cases through coordinated functional units comprising a policy fuzzing unit, an adversarial prompting unit, an execution sandbox, a telemetry processing unit, a scoring and triage processor, and a cryptographic provenance processor. The system applies grammar-driven and reinforcement-based fuzzing techniques to vary policy descriptors, model configuration parameters, and instruction hierarchies, while a learned adversarial prompt generator synthesizes contextually coherent adversarial prompts optimized for maximum policy violation likelihood. The generated prompts and policy vectors are executed in an isolated, instrumented sandbox that records input-output interactions, timing characteristics, and intermediate representations.

The invention belongs to the field of intelligent edge systems and privacy enhancement computing, and particularly relates to a privacy protection-oriented robot large model cloud edge end collaborative reasoning and federated learning system, which comprises a cloud server layer used for deploying a large-scale pre-training model and executing complex reasoning and global federated learning coordination; the edge calculation layer is used for deploying an intermediate layer model and executing local data aggregation, privacy protection processing and intermediate feature calculation; the terminal equipment layer is used for deploying a lightweight model and executing data acquisition, primary processing and lightweight reasoning; the federated learning framework is used for optimizing the model; the privacy protection module is used for integrating data localization, differential privacy, homomorphic encryption, secure multi-party computing and a block chain verification mechanism; the adaptive allocation module is used for dynamically adjusting computing resources. According to the method, the problems of privacy leakage risk, computing resource limitation, network delay, insufficient data isolation and the like of the traditional AI service in a robot scene are solved, and efficient privacy protection and data security isolation are realized.

The invention discloses a teaching data management method and system based on artificial intelligence, and the method comprises the steps: obtaining a standardized time series data stream according to a heterogeneous data stream generated by a multi-source teaching platform in real time; based on the standardized time sequence data stream, performing classified encryption on the teaching data through a dynamic hierarchical storage strategy based on attribute-based encryption to obtain a security hierarchical storage topological structure; according to a user query request and a teaching scene label, extracting a target data set from the security hierarchical storage topological structure to obtain an enhanced multi-modal teaching data set; based on the enhanced multi-modal teaching data set, generating an interpretable teaching mode graph through a dynamic sub-graph evolution algorithm; and according to the teaching mode map and the real-time teaching feedback data, generating a personalized teaching recommendation strategy through a course-learner dual-channel adaptive recommendation model. According to the embodiment of the invention, the utilization efficiency of teaching resources can be improved, and personalized and intelligent teaching recommendation and decision can be realized.

The invention discloses a privacy enhanced intelligent search method and system based on multi-round iteration. The method comprises the following steps: performing hierarchical semantic analysis on a query input by a user; splitting the complex query into sub-queries based on a task dependency graph algorithm; according to the sub-query, retrieving an evidence fragment from the multi-source data, constructing a semantic element coverage matrix to detect a knowledge gap, and if an uncovered element exists, generating a supplementary sub-query for iterative completion until a preset termination condition is met; integrating cross-modal data through a federated learning technology, and generating a structured knowledge graph fragment in combination with semantic vector alignment and an evidence fusion algorithm; performing dynamic desensitization processing on the retrieval result; and a closed-loop iterative updating mechanism is formed based on a user explicit and implicit feedback optimization retrieval strategy. The problems of traditional intelligent search in the aspects of semantic understanding depth, complex problem reasoning, search result accuracy and integrity, user privacy security and the like are effectively solved.

The invention relates to the technical field of financial data security, and discloses an intelligent ERP financial system data security management and authentication method. The method comprises the following steps: acquiring an original transaction data stream in an ERP system, extracting key financial fields, and dividing the key financial fields into a sensitive data set and a common data set according to a preset rule; a dynamic encryption strategy framework is constructed based on sensitive data set attributes, the framework comprises multiple levels of encryption strength parameters, and the corresponding encryption strength can be automatically matched according to the authentication level of an access request. And monitoring a system data access behavior in real time, collecting feature data, inputting the feature data into the anomaly detection model, and triggering access blocking when an output anomaly access probability exceeds a threshold value. And generating a periodic integrity verification instruction according to the sensitive data updating frequency, performing integrity verification by using a hash chain technology, recording a result and marking a tampering risk level. And based on the association relationship between the tampering risk level and the abnormal access probability, generating an updated security policy and synchronizing the updated security policy to each data access node.

The present disclosure provides a method and system for orchestrating automated data processing and transformation. A centralized orchestrator receives a request to process a client dataset and initiates a data ingestion process to obtain sample data. A semantic analysis module analyzes the sample data to determine semantic types of data fields. A transformation module generates data transformation instructions based on the determined semantic types. The orchestrator deploys a data processing pipeline to a client-controlled environment and configures privacy preservation parameters to identify and obfuscate potential personally identifiable information. The pipeline applies the transformation instructions and privacy parameters to the dataset. A configuration module determines data storage configurations for the transformed dataset. The transformed dataset is stored according to the configurations in a client-controlled or cloud environment. A machine learning module generates a model based on the transformed dataset, which is stored in a model repository accessible to the client.

The invention discloses a digital asset secure transaction system and method based on a block chain, and belongs to the cross field of block chain technology and financial science and technology. The system adopts a layered architecture design and comprises an intelligent contract execution layer, a distributed account book storage layer and a cross-chain interaction layer. The transaction method comprises the following steps: generating a digital identity certificate based on asymmetric encryption, and realizing privacy protection through zero-knowledge proof; a secure transaction channel is constructed by adopting a multi-signature mechanism, and sensitive data processing is performed in combination with a trusted execution environment; and designing a dynamic fragmentation strategy to optimize the transaction throughput, and establishing an on-chain and off-chain collaborative verification mechanism. The innovation point is that a double-layer verification model combining a verifiable delay function and a threshold signature is provided, and millisecond-level confirmation is realized while the transaction irreversibility is ensured. The system supports multi-chain asset atomic exchange, and interoperability of different block chain networks is realized through a heterogeneous cross-chain gateway. The scheme has the characteristics of high transaction confirmation speed, high privacy protection level and high system expansibility.

The invention discloses an intelligent management method and system for port and navigation Internet of Things data, and the method comprises the steps: generating a standardized data flow through a multi-modal data fusion model according to the heterogeneous features of ship navigation data, port equipment operation data and cargo information; generating an anti-interference transmission channel based on the standardized data stream; according to the real-time data received by the anti-interference transmission channel, dynamically generating a tamper-proof storage index through a trusted execution environment; extracting multi-source data based on the storage index, and generating a ship arrival time prediction model and a port resource scheduling strategy; and according to the port resource scheduling strategy, constructing a cross-department data sharing network through a federated learning framework and a zero-knowledge proof protocol, and generating a verifiable shared data set. According to the embodiment of the invention, port and navigation Internet of Things data management with reliable transmission, safe storage and collaborative intelligence can be realized, and the data management efficiency is improved.

A BIM-based engineering information management system of the present invention relates to the field of constructional engineering informatization management, and comprises a data processing module, a model construction module, a block chain evidence storage module, a verification module, a data fusion module and an authority management module. The data processing module receives original engineering data and generates a structured data signal. And the model construction module generates a BIM model according to the structured data signal. And the block chain evidence storage module encrypts and stores the metadata signal, generates an evidence storage completion signal and feeds back the evidence storage completion signal to the model construction module. And the verification module analyzes the dynamic operation log signal, and generates a risk early warning signal and a repair suggestion signal when abnormality is detected. And the data fusion module generates a fusion data signal, transmits the fusion data signal to the model construction module and triggers BIM model updating. And the authority management module adjusts the user authority according to the risk early warning signal. According to the invention, the problems of data islands, safety risks, low cooperation efficiency and insufficient intelligence in engineering information management can be solved.

The invention relates to the technical field of power system monitoring, and discloses a wire and cable fault early warning system based on intelligent monitoring, which comprises a data sensing module, a multi-mode fusion module, a characteristic evolution module, an abnormal early warning module and a dynamic optimization module. The data sensing module collects multi-source heterogeneous data, the multi-modal fusion module processes the data to generate a spatial-temporal feature matrix, the feature evolution module extracts cable degradation features, the abnormity early warning module performs fault early warning based on the cable degradation features, and the dynamic optimization module optimizes system parameters by using federal learning. In addition, the system also comprises a digital twin mapping and topology analysis module for assisting decision making and enhancing positioning. According to the invention, real-time monitoring, accurate fault early warning and system performance optimization of the operation state of the wire and cable are realized, the stability and reliability of power transmission are improved, and the system has the advantages of comprehensive multi-source data acquisition, efficient data processing, accurate early warning, data privacy protection and the like.

The invention provides a precision injection mold accessory production quality traceability management method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting material, process, equipment and environment data in real time through a distributed sensor; fusing multi-source data based on dynamic material characteristic parameters and process stability indexes, and quantifying melt flow and process fluctuation characteristics; constructing a mixed kernel function anomaly detection model to identify quality deviation; establishing a cross-process association map to reveal the space-time relationship among the raw materials, the process and the finished product; generating a three-dimensional traceability identifier containing the material hash, the process compression code and the block chain address; a block chain enhanced database is adopted to realize tamper-proof storage; and generating a visual traceability report through reverse analysis. Through dynamic modeling, cross-process association and block chain technologies, the problems of data isolation, detection lag and low traceability credibility in a traditional method are solved, the quality traceability efficiency and precision are remarkably improved, and precise injection molding full life cycle management is supported.

The invention provides an electronic certificate management and supply chain quality tracing method and system based on a block chain, and belongs to the technical field of information. According to the method, encryption storage and tamper-proof protection are carried out on the electronic certificate information through the distributed account book of the block chain and the Hash algorithm, and verification, approval and data updating operations are automatically executed based on the intelligent contract. Fine-grained authority management and abnormal access detection of all parties of the supply chain are realized through a user access control module; and through a tracing query module, performing multi-condition combination query on the electronic certificate information stored in the block chain, and generating a quality tracing report meeting supervision requirements. According to the method, through tamper-proofing, distributed storage and intelligent contracts of the block chain, trusted storage, automatic verification and full-chain quality tracing of the electronic certification are realized, data security and supply chain transparency are improved, trust cost is reduced, and supervision and tracing efficiency is improved.

The invention discloses a block chain-based medical data management method, system and device, and a medium, and relates to the field of information management. The method comprises the following steps: acquiring medical data, analyzing content attributes and context information of the medical data through a first smart contract on a block chain, and generating a first sensitivity level identifier; based on the first sensitivity level identifier, performing differential encryption on the medical data through a second smart contract on the block chain, and generating verification information; partitioning the differentially encrypted medical data, constructing a data structure together with the verification information, and storing the data structure into a block chain; in response to the received access request, verifying an authority certificate in the access request and the type of the access request through a third smart contract on the block chain, and generating an access authorization certificate; and outputting target medical data corresponding to the access request based on the access authorization certificate. By implementing the technical scheme provided by the invention, the full-life-cycle safety management and control of the medical data from collection, storage to sharing is realized.

The invention discloses a dynamic authority management system and method based on multi-source salary data integration, and relates to the technical field of salary data management, and the method comprises the following steps: collecting role names, authority boundaries and operation granularity information from a plurality of business systems, and generating structured role semantic ontology entries based on a field-level semantic annotation mode; and disassembling the to-be-mapped role according to the authority dimension, calculating a semantic similarity index between the to-be-mapped role and the standard role based on the role semantic ontology library, and outputting a role consistency scoring matrix. According to the method, the authority difference is identified through the role semantic ontology and the similarity score, the minimum authorization judgment is realized in combination with the rule engine and reinforcement learning, the audit traceability and anomaly detection are guaranteed by using the block chain and the dynamic graph, and finally a strategy self-evolution closed loop is constructed. And the problems of permission mismatching and data leakage caused by role semantic inconsistency are effectively solved.

The invention relates to the technical field of agricultural information management, and particularly discloses an agricultural information management system and method based on a big data platform, and the method comprises the steps: firstly deploying a multi-source data collection module at an edge calculation node, and obtaining and standardizing the soil moisture content, meteorological environment and equipment operation data in real time; secondly, constructing a local dynamic irrigation strategy model, and realizing multi-objective optimization through a reinforcement learning algorithm; establishing a federated learning framework at the cloud, dynamically distributing node weights by adopting an attention mechanism, and realizing model aggregation of privacy protection in combination with secure multi-party computing; an optimal irrigation instruction is generated through a multi-source data fusion engine, and a three-level response exception handling mechanism is established; and finally, a closed-loop feedback system containing short-term incremental learning and long-term architecture optimization is formed. The corresponding management system comprises six functional modules, namely a data acquisition module, a local modeling module, a federated learning module, a real-time decision-making module, an abnormal monitoring module and a closed-loop optimization module.

The invention provides a BIM intelligent management platform oriented to a whole life cycle of project construction. A building information model, Internet of Things sensing data and a block chain evidence storage mechanism are integrated through a multi-source data fusion technology, and a whole-process data chain of association planning, design, construction, operation and maintenance is associated. The platform adopts space optimization Huffman coding to realize model lightweight, combines a constraint genetic algorithm to optimize a construction path, and applies a bidirectional long-short-term memory network to analyze an equipment state. A three-chain block chain system is reconstructed on the architecture, intelligent association of engineering quantity and payment nodes is realized through cooperation of a main chain, a calculation quantity side chain and an auditing side chain, and mobile terminal offline interaction is supported based on a digital-analog separation technology. The platform covers an intelligent design management unit, a block chain investment management unit, a dynamic correction management unit, a quality safety responsibility tracing unit, an NLP risk management unit and a digital twin operation and maintenance unit. The units achieve cross-system cooperation through a unified data bus, and a closed-loop management architecture covering the whole life cycle of project construction is formed.

Disclosed herein are systems and methods for enabling decentralized, schema-driven interoperability across distributed computing environments through the use of a Standard Knowledge Language (SKL). An Enterprise Mesh Platform (EMP) interprets and executes SKL specifications—such as capabilities, objects, mappings, policies, and workflows—as composable, machine-interpretable contracts that define data structures, logic, and governance protocols. The system supports dynamic versioning, validation, semantic linking, and recursive execution of SKL-defined components. A mesh-wide analytics server coordinates execution, issue detection, and resolution propagation. Capabilities can be orchestrated, remediated, and adapted in real-time based on SKL-defined relationships, while preserving compliance and traceability. The disclosed architecture facilitates federated development, adaptive system integration, and fine-grained policy enforcement across complex digital ecosystems.

The invention discloses a medical whole-course intelligent management system based on a large model, and belongs to the technical field of large models. Comprising a multi-modal data acquisition module, a privacy calculation preprocessing module, a dynamic knowledge enhancement module, a time sequence data analysis module, an intelligent decision engine module, a multidisciplinary collaboration module, a patient interaction platform module, a dynamic intervention feedback module and a system security center module. The cross-mechanism data security sharing is realized, and the compliance of sensitive information processing is also ensured; a two-channel medical knowledge base is constructed, authoritative guidelines can be synchronized, newest clinical research data can be analyzed in real time, the knowledge base is kept in the newest state all the time, and the frontier scientific basis is provided for clinical decisions; dynamic modeling and trend prediction are carried out on long-term monitoring data of a patient by adopting a hybrid neural network model, and potential health risks and development trends can be identified more accurately.