879 results about "Service Interface for Real Time Information" patented technology

The invention relates to the technical field of network security situation generation, in particular to a network security situation generation method based on multi-view monitoring. The method comprises the following steps of: constructing a multi-view data acquisition system, acquiring real-time information from a multi-dimensional data source, and comprehensively capturing potential threats by utilizing an intelligent sensing technology; cleaning, noise filtering and feature extraction are performed on the acquired data based on a dynamic threshold self-adaption multi-factor cleaning method, so that the accuracy and consistency of the data are ensured; a dynamic analysis algorithm based on deep learning is adopted, the incidence relation between the security events is mined, and the development trend of potential threats is predicted; constructing a joint learning model, and comprehensively evaluating the security state of the network environment; the security situation is dynamically displayed by using a visualization technology, and a security policy is quickly generated in combination with an automatic early warning and response mechanism to resist threats. According to the method, accurate evaluation and prediction of the network security situation are realized, and support is provided for security decision-making in a complex network environment.

The invention discloses a multi-source data fusion method and system based on cloud computing, and particularly relates to the technical field of multi-source data fusion, and the method comprises the steps: obtaining the real-time information of multi-source data, generating a health score and a label through a health evaluation model, and marking a data state and an abnormality type; performing format conversion and semantic enhancement processing on the health data to generate structured data; based on a knowledge graph reasoning technology, extracting a logical relationship to generate a fusion rule, and calculating a rule weight and confidence in combination with a health label, a data source confidence and a semantic feature; a fusion result of conflict data is judged through a semantic consistency index, if the fusion result does not conform to expectation, historical data and rules are loaded according to a verification model, a verification score is calculated, an optimization process is triggered after the verification score is compared with a threshold value, health assessment model parameters, a semantic embedding model and fusion rule logic are optimized, and fusion accuracy and reliability are improved.

The invention discloses a multi-agent cooperation system and method based on spatial calculation and multi-modal AI fusion, and the method comprises the steps: collecting all real-time data of a construction site through a multi-modal data collection and fusion unit, and carrying out the processing and integration of all collected data through a multi-modal AI algorithm, and generating a unified semantic association model; a three-dimensional virtual environment of a construction site is constructed through a space calculation unit, real-time synchronous mapping is formed for the construction site, and dynamic changes in the construction process are simulated and predicted; task allocation and behavior planning are carried out on multiple agents through an agent behavior management unit; and the intelligent agents are configured to perform real-time information sharing and task cooperation through a communication protocol through the multi-intelligent-agent cooperation unit. According to the scheme, the problems of difficulty in multi-source data integration, low cross-department cooperation efficiency, insufficient construction dynamic adjustment and the like in intelligent construction in the building industry can be solved.

The invention provides a multi-path security inspection robot intelligent scheduling method, system and device and a medium thereof, and the method comprises the following steps: installing a plurality of sensors in a target inspection area, and collecting the basic parameter information of inspection robots and the inspection area in real time; according to the invention, multi-dimensional data, including various information of the inspection robot and the inspection area, are acquired through various sensors, and through preprocessing and fusion algorithms, noise is removed and multi-source data are integrated, so that an accurate and comprehensive data basis is provided for subsequent decision making, and the problem of information fragmentation is solved; a plurality of factors such as landforms and obstacles are considered through a path planning algorithm, an optimal path is calculated, updating can be carried out according to real-time conditions, and rapid correction is carried out by using an incremental algorithm, so that the planning precision and flexibility are improved, and the inspection efficiency is improved; the robot and obstacle information is acquired in real time through the sensor, so that the route and the task are optimized according to the real-time information, and the inspection effect and the guarantee capability are improved.

The invention discloses a supply chain intelligent management system based on dynamic collaborative optimization, and relates to the technical field of hotel supply chain management, and the system comprises a supply chain intelligent management platform which is in communication connection with the following modules: a multi-modal data sensing module, a multi-modal data processing module and a multi-modal data processing module. The multi-modal data acquisition module is used for acquiring multi-modal data in a hotel through a LoRaWAN + BLE hybrid sensor network, accessing an external data source and acquiring real-time information through an API (Application Program Interface); and the dynamic demand prediction module captures a time sequence trend through bidirectional LSTM based on an ASTGNN model. According to the method, external dynamic data such as social media public opinions and weather are fused, the multi-modal data analysis technology is combined, the accuracy of demand prediction is remarkably improved, the ASTGNN model is utilized, the time sequence trend and the demand association between the branches are analyzed in combination with bidirectional LSTM and GCN, and the feature weight is dynamically adjusted, so that the demand prediction error rate is greatly reduced, and the demand prediction efficiency is improved. A more reliable demand prediction basis is provided for enterprises, and optimization of inventory management and production plans is facilitated.

The invention relates to a man-machine hybrid autonomous navigation system in an unknown dynamic environment, which comprises an environment sensing module for acquiring environment information by using sensors such as a laser radar, a camera and an inertial measurement unit and performing data preprocessing; the local target point selection module selects a local target point set meeting requirements in the environment according to the real-time information provided by the environment sensing module; the decision planning module is used for generating an optimal path and a navigation strategy based on mixed training of a deep reinforcement learning algorithm and artificial experience; and the learning optimization module improves the generalization ability of the algorithm through online learning and transfer learning technologies, and realizes effective introduction and strategy adjustment of human experience in combination with the man-machine interaction module. The man-machine interaction module can work in cooperation with the local target point selection module so as to dynamically adjust target point selection according to external input and optimize path planning. According to the method, high decision stability can be kept in an unseen environment, the training cost is reduced, and the method is more suitable for autonomous navigation application in the real world.

The invention relates to the technical field of satellite communication, in particular to a high-orbit and low-orbit satellite communication resource scheduling method and system, which collects real-time state information of high-orbit and low-orbit satellites, performs digital simulation of a satellite network based on the real-time information, constructs a satellite network management model and predicts satellite trajectory change. The method comprises the following steps: performing multi-dimensional evaluation on a satellite network management model by using a pre-constructed network evaluation framework, generating cross-orbit link evaluation information, analyzing parameters such as link quality and bandwidth, obtaining current service demand information, analyzing a communication resource allocation strategy in combination with a cross-orbit link evaluation result, and formulating a resource scheduling strategy according to strategy analysis. And a high-orbit and low-orbit satellite communication network is constructed through execution of a hierarchical network management mechanism and a cross-orbit data transmission protocol.

The invention provides an end-to-end automatic driving track evaluation method and system based on a world model, and the method comprises the steps: generating bird's-eye view state real-time information according to multi-mode sensor data obtained in a current automatic driving scene; inputting the aerial view state real-time information into a strategy network to obtain a plurality of real-time track anchor points output by the strategy network; according to the bird's-eye view state real-time information and the real-time track anchor point, constructing a real-time state-action pair, and inputting the real-time state-action pair into a bird's-eye view world model to obtain a predicted state-action pair output by the bird's-eye view world model; according to the real-time state-action pair and the predicted state-action pair, the predicted driving track corresponding to the predicted state-action pair is evaluated, and an evaluation result of the predicted driving track is obtained. According to the method, the predicted driving track can be evaluated more accurately, and the safety and reliability of the automatic driving system are improved.

Systems and methods for dynamic knowledge integration in LLM systems including receiving multimodal input data comprising text, image, audio, video, and / or code data, extracting information by processing the multimodal input data through a document processor, storing the extracted information in a dynamic knowledge base, receiving a user query at a query processor, identifying knowledge domains related to the user query using domain-specific agents, retrieving real-time information from the dynamic knowledge base responsive to the identified knowledge domains, integrating the real-time information into the processing of an LLM by a dynamic knowledge integrator, and generating a response using the LLM with the real-time information.

Described herein are systems, devices, methods, and media for an emergency response data exchange system. The system provides an operations center emergency management application with a textual and / or graphical notification of emergency communications initiated on a premises managed by the operations center. The system exchanges operations center data (e.g., floorplans, video feeds, point of contact information, building access controls) with an emergency communications center (ECC) and / or with a first responder device to facilitate reducing emergency response times. The system selectively establishes communications (e.g., messaging windows) between the operations center, ECC, and / or first responder device to facilitate real-time information sharing between relevant parties of an emergency.

The invention discloses a twin network performance prediction method and device based on a graph neural network and a storage medium, and belongs to the technical field of communication networks, and the method comprises the steps: carrying out the preprocessing of the obtained historical information of a physical communication network, and obtaining a dependency graph data set; initializing the state of each node in the dependency graph data set to obtain the initial hidden state of each node; the initial hidden state is used to train a pre-constructed network performance prediction model to obtain a trained network performance prediction model, and the network performance prediction model obtains iteratively updated hidden state information and adjusts model parameters through cooperative work of an attention mechanism, a message passing neural network and a gating circulation unit; and inputting the obtained real-time information of the physical communication network into the trained network performance prediction model to obtain a performance prediction result, thereby improving the stability, accuracy and efficiency of performance prediction of the digital twin communication network.

A mobile software application implementing “live events,” via which a host of the event may sequentially offer one, two, three, four or more items to a plurality of guests in the event (e.g., via auction, via direct purchase, or via a giveaway). The host's device may obtain and transmit a real-time video stream, which is in turn provided to each guest's device in conjunction with real-time information about the item(s) being offered. Particular techniques further assist the host in sequencing two or more items to be offered in the same live event, thereby avoiding delays and cumbersome user interface interactions that might otherwise be involved in initiating the offering of a next item upon concluding an offering of a previous item.

The invention relates to the technical field of navigation, in particular to a bicycle refined intelligent navigation method, system and device and a storage medium, and the method comprises the following steps: obtaining information such as gradient and length of a ridable route; a physical exhaustion result is obtained through a physical exhaustion calculation model in combination with user weight, bicycle type and the like; calculating a time consumption result according to the riding ability of the user and evaluating a route difficulty level; acquiring real-time information of weather and road conditions, and generating detailed prompt information according to the real-time information; and sorting routes are screened according to user preferences, recommended and displayed. According to the method, refinement and intellectualization of bicycle navigation can be improved, physical power and time consumption are accurately calculated by integrating multiple factors, comprehensive and detailed route prompt and personalized recommendation are provided for users, different user requirements are met, and riding experience and efficiency are improved.

The invention relates to the technical field of Internet of Things security, and discloses an Internet of Things control system and a security early warning method, and the method comprises the steps: firstly collecting equipment basis and real-time information in an Internet of Things system, then evaluating equipment risk, network security and data encryption conditions, setting an early warning threshold according to multi-aspect evaluation results and historical security data, and finally, carrying out early warning. And finally, real-time monitoring is carried out, and early warning is triggered when the risk index exceeds a threshold value. The method has obvious advantages, equipment aging and safety risks can be accurately evaluated, equipment maintenance is planned in advance, and the maintenance cost is reduced; network attacks can be effectively identified, and network security is guaranteed; the data encryption security is enhanced, and the data leakage risk is reduced; early warning is given out in time, and the system response speed is increased; and the equipment safety state can be dynamically updated, the system safety management is continuously optimized, the safety and reliability of the Internet of Things system are comprehensively improved, and the stable operation of the Internet of Things system is promoted.

The invention relates to the technical field of water conservancy management, and discloses a water conservancy dynamic decision-making method and system based on digital twinning and multi-source heterogeneous data, and the method comprises the steps: carrying out the real-time information capturing of a target water conservancy system, and generating a system operation information scheduling matrix based on a grid geographic model and a time dislocation mark; performing hierarchical slicing on the system operation information scheduling matrix according to a multi-scale time window to form a multi-channel flow framework of regional hydrological dynamic behaviors; analyzing the evolution trends of the water level, the flow and the water quality by using a multi-channel flowing framework and a dynamic fusion analysis operator; dividing a river reach, a reservoir and a regulation and control facility corresponding to the potential risk event as candidate scheduling areas; and based on the local risk coupling network, dynamically optimizing a scheduling strategy, a pump gate operation sequence and a data acquisition frequency, and generating a real-time scheduling and emergency response scheme of the candidate region. The method has the advantage of improving flood early warning accuracy and water supply scheduling efficiency.

The invention relates to a production and logistics cooperative scheduling method for a dynamic flexible job shop, and the method comprises the following steps: obtaining the real-time information of the dynamic flexible job shop, inputting a production and logistics cooperative scheduling problem model for the dynamic flexible job shop, carrying out the solving through a deep reinforcement learning method, and obtaining a shop scheduling scheme; wherein the production and logistics collaborative scheduling problem model oriented to the dynamic flexible job shop sets respective optimization targets for production activities and logistics activities, fault scenes and corresponding processing strategies of logistics equipment are considered, and the solving process is as follows: S301, designing an adaptive multi-agent nested hierarchical framework; s302, designing Markov decision process key elements of each agent; and S303, performing cooperative training on each agent by using a multi-agent near-end strategy optimization algorithm. Compared with the prior art, a more reasonable and effective flexible job scheduling scheme can be obtained.

The invention relates to the technical field of data processing, and provides a cross-platform equipment management method and system based on a protocol converter, and the method comprises the steps: registering a unique identifier of target equipment in a first cloud platform, and connecting the corresponding target equipment in a second cloud platform; when the first cloud platform needs to obtain the real-time information of the target equipment, sending a unique identifier to the protocol converter for analysis, and finding the corresponding target equipment to obtain the real-time information; when the first cloud platform needs to control the target equipment, a control instruction containing the unique identifier is sent to the protocol converter, and the unique identifier and a control parameter are obtained; and finding a corresponding target device in the second cloud platform based on the unique identifier for control. Through deployment of the protocol converter, real-time information synchronization and instruction control between the first cloud platform and the second cloud platform are realized, protocol conversion and equipment positioning are supported, the management efficiency is improved, and the problems of multi-platform data synchronization and insufficient response are solved.

The invention discloses a multi-zone-area interconnection model prediction control system and method based on side-end interoperation, relates to the technical field of power distribution automation, and comprises the steps of designing an interconnection system, configuring equipment to realize system architecture construction, optimizing a communication architecture, realizing real-time information interaction, and performing collaborative optimization based on model control prediction. According to the method, by adopting a model predictive control (MPC) strategy, energy dynamic distribution and load balancing between transformer areas are achieved, and the power quality problem caused by load fluctuation and distributed energy access can be effectively solved; the optimized communication architecture enables the real-time information interaction to be more efficient, and the cooperative control capability among the transformer areas is improved; through the flexible direct current interconnection technology, the problems of difficulty in capacity increase and point distribution and voltage quality difference of a traditional power distribution network are solved, and the stability and the safety of the power distribution network are improved. And meanwhile, the flexibility of cooperative operation of multiple transformer areas is improved, and the self-recovery capability of the system under the fault condition is enhanced.

The invention discloses a digital economic risk assessment method and device based on computer statistics, and the method comprises the steps: S1, collecting enterprise economic data and market environment real-time information data, and carrying out the data cleaning; s2, dividing enterprise cycles according to the enterprise economic data in S1; s3, extracting enterprise internal stage features and market features according to the enterprise cycle in S2; s4, combining the enterprise internal stage features and the market features to construct an enterprise periodic knowledge graph; s5, defining risk indexes according to the enterprise internal stage features and the market features; s6, constructing a risk assessment model in combination with the enterprise periodic knowledge graph and the risk indexes; s7, analyzing a risk propagation path and evaluating an influence degree through a knowledge graph; s8, carrying out risk early warning according to a risk assessment result, and proposing countermeasures and suggestions in combination with a risk propagation path and an influence range; risk assessment is carried out through enterprise economic data and market environment real-time information in combination with enterprise cycle division, feature extraction and knowledge graph construction.

The invention relates to the technical field of digital twinning equipment management, in particular to a digital twinning scientific research equipment intelligent management system integrating the Internet of Things and artificial intelligence. The system comprises a multi-source heterogeneous data acquisition unit which is used for performing real-time information acquisition on the operation environment and the equipment state of scientific research equipment; the digital twin mapping unit constructs a temperature and humidity coupling dynamic model fusing a temperature dynamic model and a humidity dynamic model by introducing a temperature and humidity coupling coefficient, and maps data collected from multi-source equipment and a laboratory environment to the temperature and humidity coupling dynamic model; the AI multi-dimensional prediction unit predicts the future running state of the equipment and the environmental risk; and the risk early warning unit analyzes the operation state of the air conditioning equipment and the environment coupling risk thereof in real time based on the prediction result of the AI multi-dimensional prediction unit, and sends out an early warning signal before potential abnormity occurs. The method is used for predicting the equipment fault risk in advance, thereby achieving the purposes of advanced prediction and active intervention.

The invention provides a reply information acquisition method based on a civil aviation big language model. The reply information acquisition method comprises the following steps: acquiring to-be-replied information input by a user; if the target intention is a civil aviation knowledge type intention, determining a target knowledge type intention from a preset knowledge type intention tree; if the target knowledge type intention carries the target identifier, calling a real-time information acquisition port corresponding to the current target knowledge type intention according to account information of the user to acquire target real-time information corresponding to the user; and splicing the to-be-replied information, the target real-time information and the target civil aviation knowledge information, and inputting the spliced information into the civil aviation large language model to obtain reply information output by the civil aviation large language model. According to the reply information acquisition method based on the civil aviation large language model, when the determined target knowledge type intention carries the target identifier, the target real-time information is queried according to the real-time information acquisition port, and the target knowledge type intention is determined to carry the target identifier. Therefore, the civil aviation large language model can further accurately determine the real intention of the user according to the target real-time information.

The invention provides an information resource matching recommendation method and system based on context awareness, and the method comprises the steps: firstly obtaining a context data set which is generated by real-time interaction of a target user and comprises user operation behavior data and scene awareness parameter data, and then carrying out the demand feature extraction of the context data set, the method comprises the following steps: acquiring a dynamic demand feature and a context association feature, matching the dynamic demand feature and the context association feature with a pre-stored information resource library to generate a real-time information resource matching result set, and optimizing a matching strategy of a target user matching model in real time according to a dynamic feedback parameter of the real-time information resource matching result set. According to the method, a resource matching strategy is optimized to obtain an optimized resource matching strategy, and finally, a target information resource set related to the dynamic demand characteristics is pushed to a target user terminal device based on the optimized resource matching strategy, so that more accurate and personalized information resource recommendation can be realized, and the recommendation quality and the efficiency of obtaining effective information by a user are improved.

The invention belongs to the technical field of emergency rescue management, and discloses a multi-disaster-point emergency rescue scheduling method, which comprises the steps that a multi-disaster-point emergency rescue dynamic network is constructed according to real-time information of disaster-affected points, and the real-time information of the disaster-affected points comprises positions, disaster-affected time and emergency rescue decisions of the disaster-affected points; formulating an initial rescue path of each rescue vehicle in the multi-disaster-point emergency rescue dynamic network based on a preset constraint condition; and drawing a directed graph based on the formulated initial rescue path, and solving an optimal scheduling strategy of the multi-disaster-point emergency rescue dynamic network under the minimum number of vehicles in the directed graph based on a Hopcroft-Karp algorithm. According to the technical scheme of the invention, by constructing the multi-disaster-point emergency rescue'time-space-decision 'dynamic network, the problem of'minimum vehicle number' in the multi-disaster-point emergency rescue dynamic network can be solved.

The invention provides a building informatization system management method and device based on digital twinning, and the method comprises the steps: obtaining the building information of a building, so as to construct a BIM basic model; the method comprises the following steps: collecting construction site data in real time, and constructing a building digital twinborn body by using a BIM basic model; and detecting the state change of the digital twin of the building, performing data analysis on the state change, and when the state change represents that the building has a potential problem, outputting early warning information. According to the invention, the state change of the digital twin of the building can be continuously monitored, potential problems can be timely found and early warning is carried out through data analysis, and real-time information support is provided for operation and maintenance management; moreover, predictive maintenance analysis is carried out on the building equipment by using an AI algorithm, and a maintenance plan is made in advance by deeply mining operation data of the equipment and predicting the time and probability of occurrence of equipment faults, so that the non-planned downtime is shortened, and the maintenance cost is reduced. And performing building energy efficiency analysis in combination with the BIM model and the energy consumption data.

The embodiment of the invention provides a port logistics management and control method and system, and belongs to the technical field of port logistics management and control, and the method comprises the steps: predicting the arrival time of a ship and the required unloading duration through employing historical data and real-time information; according to the predicted ship arrival time, an optimal unloading berth is arranged, and a truck connection sequence is planned; synchronously regulating and controlling operation time windows of the ship and the truck based on the predicted interaction time of the ship and the truck; and optimizing the vehicle scheduling path and sequence in the harbor district according to the synchronous regulation and control result to improve the overall efficiency. Through the scheme of the embodiment of the invention, the problem of how to synchronously regulate and control the shipping date and the truck to improve the overall efficiency can be solved.

The invention provides a machine room equipment control method and system based on a virtual PLC, and the method comprises the steps: building a virtual PLC, connecting a sensor and an equipment control cabinet in a machine room to remote IO equipment, and enabling the remote IO equipment to communicate with the virtual PLC; the sensor collects environment parameters and equipment state information; the control logic program performs analysis based on the data, and when the environmental parameters exceed a threshold value, a control instruction is generated to adjust the operation state of corresponding equipment in the machine room; the virtual PLC is connected with the cloud computing platform, uploads historical data and real-time information for big data analysis, and optimizes control logic; carrying out fault prediction and generating early warning; encrypting operation data of the storage device by using a block chain technology; and a monitoring platform is constructed by adopting virtual reality and augmented reality technologies, so that managers can check the equipment state in real time and the maintenance efficiency is improved. By means of the scheme, efficient machine room equipment control and management can be achieved, data processing and communication are optimized, data safety and traceability are enhanced, and maintenance and monitoring experience is improved.

Various embodiments described herein disclose an endpoint modeling and grouping management system that can collect data from endpoint computer devices in a network. In some embodiments, agents installed on the endpoints can collect real-time information at the kernel level providing the system with deep visibility. In some embodiments, the endpoint modeling and grouping management system can identify similarities in behavior in response to assessing the data collected by the agents. In some embodiments, the endpoint modeling and grouping management system can dynamically model groups such as logical groups, and cluster endpoints based on the similarities and / or differences in behavior of the endpoints. In some embodiments, the endpoint modeling and grouping management system transmits the behavioral models to the agents to allow the agents to identify anomalies and / or security threats autonomously.

The invention provides a data physical node isolation scheduling method and system, and belongs to the technical field of distributed storage and cloud computing. The method comprises the steps of collecting attribute information of tenants and establishing a tenant demand model; monitoring in real time and predicting the resource load of the physical node by using machine learning, and generating a resource evaluation report; formulating differentiated isolation strategies according to the data security levels of the tenants, adopting physical isolation for the tenants with high security levels, and distributing independent physical nodes for the tenants; the scheduling priority is allocated based on the business emergency degree and the resource demand of the tenant, the node load condition is combined, the high-priority tenant is preferentially scheduled to the node with the load lower than the preset threshold value, and meanwhile, the strategy is dynamically adjusted according to real-time information. The corresponding system comprises a tenant management module, a resource monitoring module, an isolation control module and a scheduling decision module. Unification of data security isolation and efficient and dynamic resource scheduling in a multi-tenant environment is achieved, data security and service quality are effectively guaranteed, and the resource utilization rate is increased.

The invention discloses an intelligent shooting range training system based on real-time data acquisition, relates to the technical field of intelligent shooting range training based on real-time data acquisition, and is used for solving the problem that an existing intelligent shooting range training system based on real-time data acquisition only pays attention to a single aspect of a training effect. The system is difficult to provide personalized training suggestions according to specific conditions of trainees; and a traditional training method may lack scientific verification. Comprising a real-time information acquisition module, an information analysis module, a scene analysis module and an optimization processing module. The real-time information is acquired through the real-time information acquisition module, and the behavior and physiological data are analyzed by the information analysis module to obtain comprehensive and deep analysis, so that the pertinence and effectiveness of training are improved; the method comprises the following steps: identifying different scene mode factors and setting corresponding evaluation weights; personalized improvement suggestions are provided for trainees, the best physiological state can be kept, the training effect is comprehensively improved, and the training experience is optimized.

The invention provides an electronic acupuncture closed-loop control method and system based on multi-modal information feedback. The method comprises the following steps: acquiring long-term, short-term and real-time representation information of a testee under an initial stimulation scheme; the long-term and short-term characterization information is input into a collaborative generative network model for learning, a target stimulation scheme is output, the collaborative generative network model comprises traditional Chinese and western medicine agents, the traditional Chinese and western medicine agents learn the long-term and short-term characterization information, and a corresponding traditional Chinese medicine treatment scheme and an electrical stimulation scheme are output. The collaborative generation network model carries out collaborative training on traditional Chinese and western medicine agents; calculating an index deviation between the real-time characterization information and the standard characterization information to generate a real-time information deviation matrix; inputting the target electrical stimulation scheme and the real-time information deviation matrix into a parameter deviation prediction agent for learning and prediction; and performing parameter adjustment on the target stimulation scheme based on the predicted stimulation parameter deviation matrix to realize long / short time and real-time double-layer closed-loop control of the electronic acupuncture.