121 results about "Sensor cluster" patented technology

The invention relates to the technical field of offshore wind turbine inspection, and discloses an offshore wind turbine inspection monitoring system based on digital twinning. A twinborn data synchronization module of the system collects fan structure state data and environmental hydrological data in real time through a multi-source sensor cluster, and constructs an offshore fan digital twinborn body; the routing inspection path dynamic generation module analyzes a fan fragile area and risk distribution based on the digital twinborn body, and generates a multi-unmanned aerial vehicle cooperative routing inspection path in combination with real-time marine weather forecast; the unmanned aerial vehicle cooperative control module schedules an unmanned aerial vehicle group to execute a task according to the cooperative inspection path, and dynamically responds to real-time data updated by the twin data synchronization module; and the real-time abnormity identification module fuses and analyzes unmanned aerial vehicle returned data, identifies fan defects, outputs abnormal information, calculates relative space deviation, generates an attitude control instruction, and sends the attitude control instruction to the unmanned aerial vehicle cooperative control module to realize unmanned aerial vehicle attitude real-time adjustment.

The invention discloses a mining equipment energy consumption prediction method, and relates to the technical field of mining equipment energy consumption prediction.The mining equipment energy consumption prediction method comprises the steps that through multi-dimensional data collection, operation, process and environment parameters are collected through a sensor cluster; noise reduction is carried out through a generative adversarial network in combination with empirical mode decomposition, and data is restored through a space-time interpolation network; identifying working conditions and extracting features by means of a hidden Markov model in combination with an attention mechanism; a cross-device transfer learning framework is constructed, and a cloud training general model is combined with local data fine tuning; the edge end deploys a lightweight model for real-time prediction, and the cloud end generates a global energy-saving strategy; through digital twinborn visualization, a model and a strategy are automatically corrected based on SHAP value analysis. The equipment idling rate is reduced; the unit energy consumption of the crushing link is reduced; the abnormal response time is shortened; and the prediction precision and the system adaptability are remarkably improved.

Systems and methods for battery system management and monitoring apparatus(es) and equipment using a sensor cluster are described. The systems and methods can be used to automatically repair or otherwise address actual and predicted failure modes of the apparatus(es), which include electrical systems, power systems, energy storage systems, and other systems.

The invention discloses a BIM building intelligent integrated management system based on Internet of Things control, and relates to the technical field of networking technology and building intelligent management, and the system comprises a sensing layer, a transmission layer, a platform layer and an application layer. The sensing layer comprises a multi-source heterogeneous sensor cluster and is divided into an environment monitoring unit, an equipment state acquisition unit, a personnel positioning unit and a structure health monitoring unit, and each unit is provided with a corresponding sensor; the transmission layer adopts a LoRaWAN and 5G fusion architecture and comprises an edge computing gateway, a TSN switch and a data security encryption unit, and the gateway can perform frequency domain analysis and the like; the platform layer is based on micro-service and is provided with a multi-source data fusion engine and a BIM model dynamic updating module; the application layer provides an equipment fault prediction module, an energy optimization module and a safety risk early warning module, and all the modules adopt corresponding algorithms. According to the invention, a hierarchical collaborative architecture is constructed, a multi-source sensor, a heterogeneous network and the like are integrated, deep integration of multi-dimensional management of a building is realized, and the problems of subsystem independence and data obstruction of a traditional system are solved.

The invention discloses a sensor-based agricultural information data acquisition system and method, and belongs to the technical field of agricultural information acquisition. Multi-type sensor nodes are arranged in an agricultural target area in a heterogeneous manner; the method comprises the following steps: constructing a multi-dimensional influence factor model according to crop growth stages and environmental historical fluctuation data, dividing initial sensing sub-regions, and configuring a sensor cluster; dynamically updating the sensing boundary based on the historical change rate and the spatial gradient information; fusing the heterogeneous data by adopting a multi-channel time synchronization mechanism to generate a standardized environment vector set W; calculating a sampling priority matrix according to a parameter change trend in the W, and adaptively adjusting a node state; integrating an energy consumption estimation model, and executing low-power-consumption scheduling; when any parameter exceeds the threshold, high-density sampling and remote early warning are triggered; according to the method, high-precision, low-power-consumption and dynamic-response data acquisition and intelligent early warning can be realized, and the efficiency and reliability of an agricultural sensing system are improved.

The invention discloses an assembly equipment health management system based on industrial Internet of Things, and particularly relates to the field of intelligent maintenance of industrial equipment, comprising a distributed sensing module, a cross-production-line transfer learning module, a dynamic maintenance strategy optimization module, a multi-modal man-machine cooperation module and an enhanced execution terminal module, multi-source sensor clusters and intelligent edge nodes are deployed, multi-dimensional operation data are collected and preprocessed at high frequency, an encrypted global fault feature library is constructed by using a federal transfer learning algorithm, cross-production-line group intelligent advanced early warning is realized, and the equipment health degree, the production plan and the resource state are comprehensively considered based on a deep reinforcement learning decision-making agent. A priority maintenance strategy is output, a digital twinning and AR technology is fused to generate an enhanced maintenance guidance package, real-time operation verification and health re-evaluation are realized through an execution terminal, accurate and efficient closed-loop health management is formed, and the equipment reliability and the maintenance intelligence level are remarkably improved.

The invention relates to the technical field of heat pump system control, in particular to a heat pump system control method and system based on compressor rotating speed feedback. The method comprises the steps that a refrigerant side parameter cluster, an air side parameter cluster and a heat load side parameter cluster of the heat pump system in a preset sliding window are obtained, and a first refrigerating capacity, a second refrigerating capacity and a third refrigerating capacity are determined through the different parameter clusters so as to determine fault source positioning indexes corresponding to the different parameter clusters; comparing the fault source positioning index with a preset fault threshold value, and determining a fault sensor cluster from a refrigerant side parameter cluster, an air side parameter cluster and a heat load side parameter cluster; and controlling the rotating speed of a compressor of the heat pump system according to a determination result of the fault sensor cluster. According to the technical scheme, the rotating speed of the compressor of the heat pump system can be effectively controlled.

The invention relates to the field of mine risk prediction, in particular to a mine dynamic risk prediction method and system based on multi-modal parameter fusion analysis. The method comprises the following steps: obtaining a multi-modal mine omnibearing data stream based on a multi-modal sensor cluster, carrying out adaptive filtering noise reduction and time-space synchronization space mapping, and constructing a cross-modal mine data stream model; performing mine geological micro-fluctuation behavior deep analysis according to the cross-modal mine data flow model, performing dynamic geological risk situation evolution, and constructing a multi-modal geological risk situation map; and obtaining a mine historical risk event log, performing risk event extraction, performing adaptive risk chain deep learning on the multi-modal geological risk situation map, and constructing a mine risk state dynamic evolution model. According to the invention, intelligent prediction and dynamic early warning of mine risks are realized, and the mine intrinsic safety level and the emergency response efficiency are improved.

The invention discloses an environmental event dynamic risk assessment method and system based on a Bayesian network, belongs to the technical field of artificial intelligence, and aims to solve the technical problems that in existing environmental risk assessment, dynamic data adaptability is poor, multi-source heterogeneous data fusion is difficult, and real-time performance is insufficient. Comprising the following steps: acquiring environment data through a sensor cluster deployed in an environment, and uploading the environment data to an edge computing node; performing data preprocessing on the environmental data through the edge computing node; extracting time series data fragments from the standardized data stream based on a predefined sliding time window; constructing a risk prediction model based on the dynamic Bayesian network; and taking the extracted time series data fragments as input, performing risk level analysis through a risk prediction model in combination with particle filter reasoning, predicting and outputting a risk level, a risk level probability value and a risk conduction path as prediction results, and constructing a visual risk conduction map based on the prediction results.

The invention discloses a building construction safety monitoring system and a monitoring method thereof. The building construction safety monitoring system comprises a sensing layer, a transmission layer, a data processing layer and an application layer. The sensing layer is composed of multi-dimensional sensor clusters of environment, structure, personnel and the like and can comprehensively collect various data of a construction site; the transmission layer adopts a wireless and wired combined mode, and ensures stable and safe transmission of data through encryption and redundancy transmission technologies; the data processing layer is deployed on a cloud platform, and performs data preprocessing, model training and risk decision making by using multiple algorithms; the application layer provides a monitoring center client and a mobile terminal APP for a user, and realizes data display, early warning and management functions. The system is high in intelligent degree, achieves the omnibearing, real-time and precise monitoring of the building construction process, improves the accuracy of safety risk assessment and the timeliness of early warning, provides a powerful guarantee for the safety of building construction, and has a higher practical value and a higher popularization prospect.

The invention discloses a box-type substation transformer fault diagnosis method and system based on artificial intelligence, relates to the technical field of power equipment fault diagnosis, and solves the problems that a sensor mechanism lacks a dynamic redundancy and verification mechanism, the feature contribution degree difference is not considered in cloud feature fusion, and the fault diagnosis efficiency is low. The characteristic quality is difficult to guarantee; and the diagnosis model architecture is single. Comprising the following steps of firstly constructing a sensor cluster network, dividing a main unit, a standby unit and a verification unit through a multi-index dynamic weighting algorithm, performing cooperative work, then generating a lightweight feature set through edge node preprocessing, constructing a normalized multi-dimensional feature matrix through cloud data integration, and finally constructing an edge-cloud secondary diagnosis architecture. The edge quickly screens the fault through a random forest, and the cloud end dynamically adjusts the feature fusion proportion by adopting a double-branch fusion model in combination with a gating circulation unit, and outputs the fault type and confidence.

The invention relates to the technical field of intelligent automobiles, in particular to an intelligent cabin multi-AI Agent cross-domain collaborative super-brain system and an interaction method, and the system comprises a sensor cluster, a central decision AI Agent, a five-domain AI Agent and a cross-domain synchronization mechanism. The sensor cluster is used for collecting multi-dimensional data and transmitting the multi-dimensional data to the central decision AI Agent; the central decision AI Agent is used for processing the data, including analyzing fused data to establish a mapping relation corresponding to scenes and services, performing cross-domain priority arbitration and realizing inter-domain data synchronous scheduling; the five-domain AI Agent comprises a cabin domain AI Agent, a power domain AI Agent, a vehicle body domain AI Agent, a chassis domain AI Agent and an automatic driving domain AI Agent and is used for executing specific functions. According to the invention, the problem of function islands of a traditional intelligent cabin is effectively solved, deep coupling and dynamic linkage of cross-domain functions are realized, the function utilization rate is increased from less than 20% in the prior art to more than 78%, and the utilization rate of system resources is remarkably improved.

The invention relates to the technical field of mine safety production, in particular to a mine ventilation parameter dynamic optimization method based on industrial internet of things, which comprises the following steps: S1, data fusion perception and edge calculation; s2, modeling a digital twin ventilation network; s3, performing multi-objective optimization solution; s4, instruction issuing and feedback; and S5, model self-correction and knowledge base updating. According to the scheme, the multi-dimensional sensor cluster and the dual-mode communication network are constructed through the industrial Internet of Things, real-time sensing and differential transmission of mine ventilation parameters are achieved, the data cleaning and feature extraction technology of edge calculation is combined, the authenticity and effectiveness of input data are ensured, and true and effective data are provided for dynamic optimization of the ventilation parameters.

The invention discloses a signal positioning method, electronic equipment and a computer readable storage medium, and the method comprises the steps: collecting first signal data through a positioning system; acquiring second signal data through a multi-source data sensor cluster; wherein the first signal data and the second signal data are from the same signal source; the second signal data comprises various sensor data; performing weighted fusion processing on the first signal data and the second signal data; and positioning the signal source by using the data after the weighted fusion processing. Through the above method, the precision and reliability of positioning can be improved, the anti-interference capability is enhanced, and high-precision positioning can be provided in complex scenes and interference scenes such as cities, canyons, tunnels and the like.

The invention relates to the technical field of veterinary epidemic disease detection, and discloses a veterinary epidemic disease remote monitoring and intelligent diagnosis system, which comprises a multi-mode biosensor array including an implantable miniature biochip, a wearable flexible physiological monitoring patch and an environment sensing sensor cluster, the implantable miniature biochip is used for being implanted into a key tissue organ area in an animal body and collecting microscopic physiological data such as cellular metabolism signals and local immune reaction marker concentration in real time, and the wearable flexible physiological monitoring patch is used for being attached to the body surface of the animal. By means of the multi-mode biosensor array and the edge intelligent preprocessing unit, animal physiology and environment data are accurately monitored in real time. The multi-modal biosensor array covers the implantable miniature biochip, the wearable flexible physiological monitoring patch and the environment sensing sensor cluster, and can comprehensively collect in-vivo microscopic physiological data, body surface macroscopic physiological parameters and feeding environment information of animals.

The invention discloses a regulation and control method and system for controlling the beef tallow crystallization morphology based on ultrasonic assistance, and belongs to the technical field of food production control, and the regulation and control method comprises the steps: deploying a sensor cluster on a beef tallow cooling crystallization tank, collecting process data of the beef tallow crystallization state in real time in the beef tallow crystallization process, and uploading the process data to a processing device; the processing device preprocesses the data collected by the sensor cluster, aligns the preprocessed data with a timestamp, and integrates the preprocessed data to construct a sensor historical database; a processing device is called to analyze data in the sensor historical database, a key index dynamic model capable of reflecting internal association between regulation and control parameters and crystal morphology is constructed, and an optimized regulation and control algorithm is designed based on the key index dynamic model; based on the model design optimization regulation and control algorithm, the technical problem that the crystallization rate and beta'crystal form control cannot be realized at the same time due to single regulation and control means in the traditional method is solved.

The application discloses a kind of sensor cluster management method, system and equipment based on industrial internet of things, it is related to industrial internet of things technical field, method includes: the physical space is divided into multiple subspaces and obtains internal perception node;Theoretical and actual coordinates and attitude angle of each node are extracted;Unit offset value is calculated in combination with sensitivity matrix, and then group offset value and anchoring drift rate representing mechanical stability are obtained;According to the time point of node reporting, a typical time difference is extracted, and a standard time difference is calculated in combination with bottom layer backoff and retransmission delay, to obtain network initial coordination value;According to the difference between actual and theoretical attitude angle, an attitude penalty value is obtained;Anchoring drift rate, attitude penalty value and initial coordination value are integrated to obtain the target coordination value of each subspace;Finally, the total coordination value is calculated in combination with space weight, and the bottom control instruction is triggered accordingly.The application has the advantages of precise quantification, multidimensional fusion and closed-loop intervention.

This invention relates to the field of data information processing technology, and discloses an intelligent operation and maintenance method and system based on data assets. The method includes: collecting device status data through a distributed IoT sensor cluster; standardizing the raw data and optimizing the data transmission path based on a dynamic topology network; dynamically adjusting the data model structure according to real-time data streams; performing multi-dimensional detection based on user-configured data quality rules to generate a quality detection report; cleaning abnormal data in real time based on the detection results to generate an interactive decision view; predicting operation and maintenance risk events using a multi-algorithm fusion model; and dynamically adjusting the workflow task execution path according to the risk level. This method optimizes the data acquisition and transmission process, improves the real-time performance and accuracy of data quality monitoring, and enhances the intelligence of abnormal data processing and operation and maintenance risk prediction, making operation and maintenance management more flexible and efficient.

This application provides a sensor data processing method and system based on big data, belonging to the field of data processing technology. In this method, when the target sensor cluster meets the security conditions, abnormal data in the original dataset uploaded by the target sensor cluster can be automatically identified and repaired to obtain a data-repaired target dataset, thereby ensuring data integrity and accuracy. Simultaneously, based on the update time of the sensor data in each target dataset, newly added sensor data in each target dataset can be stored in appropriate target leaf nodes in a Merkle tree. This achieves orderly storage of newly added sensor data while effectively reducing storage overhead. Furthermore, by achieving consensus on the newly added sensor data in a blockchain, it effectively prevents sensors from being illegally controlled or their data from being illegally tampered with, thus ensuring the security of data storage.

The application provides a control method and device of a sensor system, computer equipment, a system and a storage medium, and relates to the technical field of computers. The control method comprises the following steps: acquiring the types of a plurality of sensors, and performing group division on a sensor cluster according to the types of the sensors to obtain a plurality of sensor groups; acquiring the working requirements of the sensors, and determining the working modes of the sensor groups according to the working requirements of the sensors; if the working mode of a sensor group is a center mode, determining a target sensor corresponding to the sensor group; controlling the sensors in the sensor group to collect environmental data, so that the target sensor sends the environmental data to a server through a distributed soft bus; and if the working mode of the sensor group is a non-center mode, controlling the sensors in the sensor group to collect environmental data, and sending the environmental data to the server through the distributed soft bus. The application aims to improve the communication quality of the sensor system and improve the working efficiency of the sensor system.

The application provides a medicine cold chain logistics transportation whole-process tracking method and system, and relates to the technical field of logistics transportation monitoring. First, the original sensor tracking data stream output by the internal sensor cluster of a transportation carrier in a continuous collection time window is acquired. Then, the transportation carrier operation trajectory line is generated according to the geographic positioning parameters, and the multi-dimensional environment state marker set is generated by combining the physical parameters at the same time of each geographic position coordinate point. Meanwhile, the logistics transfer node topology connection network containing the logistics transfer node unit and the directed transfer path edge is constructed. The operation trajectory line, the multi-dimensional environment state marker set and the topology connection network are input into the pre-constructed space-time state feature fusion tracking model to generate the whole-process state tracking feature tensor. The abnormal state type identification and the space-time distribution feature are obtained by performing the abnormal state mode matching on the whole-process state tracking feature tensor, and the abnormal early warning instruction set is generated. The application can comprehensively and accurately track the whole process of medicine cold chain transportation, intelligently identify the abnormality and timely generate the early warning.

The invention relates to the technical field of satellite communication, and discloses a satellite communication data transmission method for deep and far sea net cages. The method comprises the following steps: collecting net cage environment parameters, biological behavior data and equipment operation state data through a multi-modal sensor cluster deployed on a deep sea net cage, and generating an original multi-modal monitoring data set; performing adaptive data cleaning and format standardization processing on the original multi-modal monitoring data set to generate a standardized multi-modal data stream; by establishing the satellite channel state evaluation model, predicting the channel quality change in real time and dynamically selecting the optimal satellite communication protocol and channel coding scheme based on the prediction result, the problem of channel quality degradation can be sensed and avoided in real time, the continuity and reliability of data transmission are ensured, and the risk of data transmission interruption is reduced.

The invention discloses a cluster head selection method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining a network environment parameter of a target sensor network, and determining at least one candidate sensor cluster head in the target sensor network based on the network environment parameter; acquiring evaluation parameters of each candidate sensor cluster head under different preset evaluation dimensions; determining an environment adaptive value of each candidate sensor cluster head under each preset evaluation dimension based on the evaluation parameters; and determining at least one target sensor cluster head in each candidate sensor cluster head based on each environment adaptive value corresponding to each candidate sensor cluster head. According to the method, the candidate sensor cluster heads are comprehensively measured through the multi-dimensional evaluation parameters, the reliability of target sensor cluster head selection can be improved, and stable operation of the target sensor network is guaranteed.

The invention provides a physical interactive simulation method and system for power grid equipment under an intelligent framework. The method comprises the following steps: acquiring operation data of power grid equipment through a sensor cluster in a physical layer, and transmitting the acquired operation data to an intelligent decision-making layer, so that the intelligent decision-making layer decomposes the operation data into first input data and second input data in time according to the demand of a simulation result, and then, sending the first input data and the second input data to the physical layer; and calling a corresponding hardware real-time simulation resource area or a software delay resource area in the interactive simulation layer to execute corresponding simulation operation, thereby performing simulation operation on different physical states of the power grid equipment through a heterogeneous resource dynamic scheduling architecture driven by an aging demand. According to the method, the timeliness requirement for physical transient simulation of the power grid equipment can be met, the accuracy requirement for physical steady-state simulation of the power grid equipment can also be met, operation resources are fully utilized, and the overall response efficiency of the system is improved.

The invention discloses a circular area sensor network communication method based on quantum-classical polarization entanglement, relates to the technical field of sensing communication, and solves the problem of how to realize efficient and reliable quantum communication in a circular area scene. The method comprises the following steps: initializing a network, and carrying out self-positioning on each sensor node; dividing the circular area into a plurality of annular subareas; electing part of sensor nodes as cluster heads; clustering other sensor nodes; establishing a quantum communication channel and verifying the quantum communication channel; taking the cluster head as a relay node, and performing quantum enhanced communication between the central node and any sensor node; and dynamically maintaining a cluster head and a sensor cluster thereof. In a word, the quantum polarization entanglement and the circular sensor network are deeply fused for the first time, through three innovations of spatial topological optimization, quantum annealing clustering and annular entanglement distribution, the problems of energy holes, multipath interference and quantum safety of circular area communication are solved, and a high-reliability anti-quantum-attack communication normal form is provided for the industrial Internet of Things.

This invention relates to the field of construction monitoring technology, specifically to a method and system for monitoring structural offsets using BIM technology. The method involves using a multi-source sensor cluster to collect real-time location information of multiple components within the construction structure, obtaining real-time monitoring data. Based on the real-time monitoring data and the preset coordinates of the components in the BIM model, a first offset evaluation index for the target component is calculated. The component importance is then obtained. The first offset evaluation index is corrected based on the component importance score to obtain a second offset evaluation index. This second offset evaluation index is compared with a preset offset threshold to determine monitoring and early warning information. This approach enables dynamic assessment of offset risks, establishes an early identification mechanism for potential problems, and optimizes the early warning system. It can dynamically capture offset trends and comprehensively consider them, achieving early risk identification and optimized early warning, thereby improving the accuracy and efficiency of construction structures.

This invention discloses a safety processing algorithm and system for dual-Hall collaborative verification position sensing of an electric wheelchair joystick, relating to the field of electromechanical control technology. The algorithm includes acquiring a first output signal from a triaxial Hall sensor and a second output signal from a linear Hall sensor, and performing dynamic baseline calibration to obtain two anti-drift position signals. Co-occurrence difference calculation and consistency determination are performed on the two anti-drift position signals to obtain a sensor cluster health status value. Signal fusion and fault reconstruction are performed based on the sensor cluster health status value and the two anti-drift position signals to obtain a fused position command. A confidence-based safety joystick command is generated based on the fused position command and the sensor cluster health status value, and simultaneously sent to the microcontroller module. This provides complete information, including reliability assessment, for subsequent control decisions, thereby constructing a full-link safety protection system from signal perception to control decision-making.

This invention relates to the field of cofferdam stability prediction technology, and specifically to an intelligent prediction method and system for the stability of double-walled steel cofferdam structures. The method includes: acquiring the installation locations of sensors; arranging a sensor cluster according to the sensor locations; collecting data from the sensor cluster to obtain sampling information, including strain data, water flow velocity data, and soil pore water pressure data of the double-walled steel cofferdam structure; preprocessing the sampling information to obtain preprocessed sampling information; correcting a preset numerical model of the double-walled steel cofferdam based on the preprocessed sampling information to obtain a corrected numerical model; and predicting the stability level of the double-walled steel cofferdam structure based on the corrected numerical model to obtain the prediction result. This invention can detect potential safety hazards in advance, assisting engineers in taking timely measures to ensure the safety of construction and use of double-walled steel cofferdams and reduce accident risks.

The invention discloses a path planning method and device for an unmanned aerial vehicle and electronic equipment, and the method comprises the steps: carrying out the cluster division of at least one wireless sensor in a target patrol area according to the task load of a sensor, and obtaining at least one sensor cluster; on the basis of cluster head sensors in all the sensor cluster sets, cluster head sensor sets corresponding to all the to-be-planned unmanned aerial vehicles are constructed; for each to-be-planned unmanned aerial vehicle, determining at least one to-be-optimized flight path corresponding to the current to-be-planned unmanned aerial vehicle according to the positions of all cluster head sensors in a cluster head sensor set corresponding to the current to-be-planned unmanned aerial vehicle; and performing path optimization on the to-be-optimized flight path of each to-be-planned unmanned aerial vehicle based on the total unmanned aerial vehicle energy consumption corresponding to all the to-be-optimized flight paths to obtain a target flight path corresponding to each to-be-planned unmanned aerial vehicle. The effects of reducing the energy consumption of the wireless sensor and the unmanned aerial vehicle, improving the communication range and efficiently completing large-scale data transmission are achieved.

The invention relates to the technical field of power distribution devices, in particular to a power distribution protection measurement and control device supporting cloud edge cooperative computing. The system comprises a front plate assembly, a display screen, a control button cluster, a warning lamp cluster, a peripheral sensor cluster, a data sensing module, a data processing module, a cloud side cooperation module, a strategy generation module, a strategy execution module and a power distribution protection measurement and control device supporting cloud side cooperation calculation. According to the method, the multi-source power distribution data and the man-machine interaction data are collected, the protection strategy is dynamically generated based on the edge nodes and the cloud nodes, strategy control is executed after network optimization and queue correction, closed-loop optimization is achieved through man-machine interaction, accurate generation and self-adaptive execution of the protection strategy can be achieved, and the safety of the system is improved. And the system response speed and operation reliability are improved.