994results about How to "Reasonable distribution" patented technology

The invention provides a building construction potential safety hazard management method and system, and relates to the technical field of construction safety management, and the method comprises the steps: dividing a plurality of sub-regions, collecting data, classifying known potential safety hazard events, and obtaining a first classification; through comparative analysis of the reference features and the contrast features, corresponding data features and fusion features are selected, and risk assessment models of the first classification are established respectively; obtaining a first risk probability and a comprehensive risk index of each sub-region according to real-time monitoring data and the risk assessment model; according to the first risk probability, the comprehensive risk index and / or the risk change, performing early warning on the sub-region; according to an early warning processing result and field feedback; adjusting an early warning threshold value and / or optimizing the risk assessment model; real-time monitoring, early warning and management of potential safety hazards of the construction site are achieved, the efficiency and accuracy of potential safety hazard management can be improved, the labor cost can be reduced, and the safety level of the construction site can be improved.

The invention discloses a course recommendation method based on a learning track and a knowledge graph, and particularly relates to the technical field of education course recommendation, and the method comprises the following steps: collecting and integrating learning track data of students, mapping the learning track data to the knowledge graph to generate a learning track-knowledge joint graph, and carrying out knowledge point mastering and learning rate analysis; obtaining a knowledge mastering value and a learning rate of each knowledge point; based on the knowledge mastering value, the system divides the knowledge points into strong items and weak items; for weak knowledge points, weak recommendation degrees are calculated in combination with knowledge mastering values and learning rates, and courses are preferentially recommended according to the recommendation degrees; for the strong knowledge points, the strong recommendation degree is calculated based on the learning rate, advanced courses are recommended, and the learning effect and personalized learning experience of students are improved; according to the method, strong and weak knowledge points of students can be accurately identified, so that learning recommendation with extremely high pertinence is provided, the students can more efficiently learn on a personalized learning path, and the learning result is improved.

The invention discloses an evaluation method based on water ecological environment monitoring, and relates to the technical field of water ecological environment evaluation. Comprising the steps of data collection: obtaining a river basic information data set, a monitoring equipment information data set and a historical monitoring information data set, and building a cloud server to store the river basic information data set, the monitoring equipment information data set and the historical monitoring information data set. According to the method, each region is accurately monitored through region division of river landforms and a targeted monitoring method, the timeliness of monitoring data is ensured, the water quality condition can be evaluated in real time and the water quality of the corresponding region under unique weather can be evaluated by acquiring a region monitoring data set and a weather information data set in real time to cooperate with an evaluation model, and the water quality evaluation accuracy is improved. In addition, weather changes can be monitored and early warned, timely support is provided for dealing with emergencies, and by summarizing the method, related departments can formulate more reasonable treatment schemes, the water quality management efficiency and effect are improved, and the water quality problem is found and solved in time.

The invention discloses a VR-based unmanned aerial vehicle inspection method and system, a storage medium and a program product, and the method comprises the steps: calculating a low-polygon terrain model, and carrying out the subdivision smoothing processing, and obtaining a three-dimensional terrain image; matching and registering the panoramic image sequence and the three-dimensional topographic image to obtain a point cloud map; generating a virtual mining area environment map; calculating image quality parameters; when the image quality parameter is lower than a preset threshold value, generating a semi-real and semi-virtual fusion display picture; transmitting the semi-real and semi-virtual fusion display picture to a VR helmet for display, and synchronizing the attitude information of the unmanned aerial vehicle to the VR helmet; when it is determined that a fatigue feature appears according to the brain wave data, the unmanned aerial vehicle is controlled to be switched to a preset autonomous flight mode; constructing a virtual anti-collision boundary; and obtaining an optimized flight path according to the virtual anti-collision boundary, and controlling the unmanned aerial vehicle to inspect according to the optimized flight path. The continuity and reliability of the inspection task are improved, and the safety risk of the inspection task is reduced.

The invention provides a chip production logistics network optimization method and system based on a graph neural network model, and relates to the technical field of chip production logistics optimizing.The method comprises the steps that logistics data of a plurality of process nodes of a chip production line are collected, node criticality scores are calculated, node types are determined, and the node types are determined; constructing a multi-level logistics network diagram comprising a material transmission edge and a priority-based resource competition edge; the method comprises the following steps: dividing logistics sub-networks, determining association strength among the sub-networks, constructing a sub-network state predictor and a cooperative scheduling module, and inputting a network topology structure and node features into a graph neural network for training to obtain an optimization model; and predicting a sub-network state change based on the optimization model to generate a resource allocation scheme and a node scheduling sequence, triggering an adjustment mechanism when a prediction state exceeds a threshold value, re-planning a material allocation and transmission path, and finally converting the scheme into an equipment control instruction and issuing the equipment control instruction to a process node.

The invention provides a logic modeling-based high-orbit satellite network dynamic optimization method and an SDN controller, and the method comprises the steps: constructing a logic model corresponding to a high-orbit satellite network according to the attribute information of physical equipment and satellite links in the high-orbit satellite network, collecting the real-time state data in the network, and carrying out the calculation of the real-time state data. Updating logic attribute information in the logic model based on the real-time state data; when it is determined that the resource adjustment requirement exists, generating a resource adjustment control instruction according to the updated logic attribute information, the topological structure of the logic model and a preset service priority; converting the resource adjustment control instruction into an operable command and issuing the operable command to physical equipment; monitoring execution results of the physical devices, and returning to the step of collecting the real-time state data of the physical devices and the satellite links and updating the logic attribute information in the logic model based on the real-time state data. According to the method, dynamic adjustment and closed-loop optimization of resources in the high-orbit satellite network can be realized, and the flexibility, reliability and service quality of the network are improved.

The invention provides a resource adjustment method and device for edge computing nodes, equipment and a medium, and belongs to the technical field of computers.The method comprises the steps that the use condition and service scene data of target resources associated with all the edge computing nodes are monitored, the target resource comprises at least one of a central processing unit (CPU), a memory and a network bandwidth; according to the monitored data, a target scheduling strategy is selected from a pre-established scheduling strategy library, and the scheduling strategy library comprises resource scheduling strategies for different service scenes and resource use conditions; and according to the target scheduling strategy and the current use condition of the target resource by each edge computing node, adjusting the resource currently allocated to each edge computing node.

The embodiment of the invention provides a resource management method and system based on cloud edge collaboration. According to the method, firstly, an intelligent AI management platform can be constructed to better allocate computing, storage and network resources; and then resource requirements of the static resources and the dynamic resources can be accurately estimated by monitoring calling frequencies of data interfaces of the static resources and the dynamic resources, so that dynamic allocation of the computing resources is realized, enterprises can be helped to better manage and utilize the computing resources, the stability and reliability of the system are improved, the operation cost is reduced, and the economic benefit is increased. And greater value is created for enterprises. Meanwhile, intelligent scheduling of resources can be achieved based on load balancing, tasks can be automatically distributed to nodes with low loads according to the load conditions of all the nodes, and therefore load balancing is achieved, and the overall performance of the system is improved. Through the dynamic resource allocation process, the resource allocation is more reasonable.

The invention relates to the technical field of operating systems, in particular to a process scheduling method, system and equipment based on a heterogeneous multi-core processor, and the method comprises the following steps: monitoring and recording an operation behavior of a user in real time, and generating a log file of the user behavior; analyzing the log file of the user behavior by using a machine learning algorithm, and predicting the next operation behavior of the user; dynamically loading the application predicted to be used into a memory based on the prediction result; determining the optimal processor core of the task by comprehensively considering the execution time, energy consumption, dependency relationship and processor resource utilization rate of the task according to the prediction result in combination with the state of each processor core in the system and the current process scheduling strategy; and allocating different tasks to the corresponding optimal processor cores for execution. The overall performance of the system is improved, energy consumption is reduced, and optimization of energy efficiency is achieved.

The invention discloses an intelligent online reservation management system, and relates to the technical field of reservation management, the system comprises a login module, an outpatient service information display module, an online reservation module, a data analysis module and a customer service module, the online reservation module comprises an intelligent triage unit, a number source distribution unit and a reservation priority unit, the intelligent triage unit recommends corresponding departments for patients, the number source allocation unit is used for allocating number source numbers for the patients, the corresponding departments are recommended for the patients through the intelligent triage unit, the number sources are dynamically allocated through the number source allocation unit according to the capacities of the departments, the reserved numbers and the illness state importance degrees, reasonable allocation of resources is ensured, and the number source allocation efficiency is improved. The appointment priority unit determines a doctor-seeing sequence according to factors such as illness state importance and fulfillment rate of the patients, preferentially processes emergency and important patients, and promotes the patients to fulfill the appointment on time by rating the patients, so that the effect of online appointment experience of the patients is improved.

The invention relates to the technical field of software application testing, in particular to an automatic intelligent testing system and method for software application. The method comprises the following steps: acquiring test environment parameters of software to be tested, calculating a dynamic waiting time reference value, and generating waiting mechanism configuration data; performing element positioning information collection and element positioning strategy generation on a user interface of the software application based on the waiting mechanism configuration data to obtain element positioning configuration data; performing conditional waiting strategy processing according to the element positioning configuration data and the waiting mechanism configuration data to generate conditional waiting mapping data; and performing visual AI auxiliary positioning processing according to the conditional waiting mapping data to generate visual positioning feature data. Through real-time monitoring of test environment parameters, the system automatically calculates and configures appropriate waiting time, so that the test process can flexibly adapt to various operation environments.

The invention discloses an adjustment method and system for 5G network slices, and relates to the field of 5G networks, and the method comprises the steps: obtaining historical demand information of a target service; determining an initial network slicing strategy based on the historical demand information; determining an initial network slice based on the initial network slice strategy; monitoring performance data of each initial network slice; obtaining priority information of a target service corresponding to each network slice; the initial network slice is adjusted based on the performance data and the priority information to determine an optimized network slice.

The invention provides a UE5-based automatic construction and release method, and relates to the technical field of software development, the UE5-based automatic construction and release method comprises a construction module and a release module, and the construction module comprises the basic steps of construction triggering, pre-construction task execution, client and server packaging, log recording and automatic alarm. The release module comprises the basic steps of differential packet generation, version detection and automatic release, the intelligent prediction trigger carries out deep learning analysis on project development historical data, the peak period of code submission and the code conflict risk can be accurately predicted, construction is automatically triggered in the low-risk and resource idle period, and the construction efficiency is improved. The problem of low resource utilization efficiency caused by lack of multi-dimensional comprehensive judgment in a traditional construction triggering mode is effectively avoided.

The invention discloses a metal grain three-dimensional manufacturing method based on double-laser powder bed melting. The method comprises the following steps that a double-laser printing parameter and corresponding metal grain process database and a grain and part performance database are established; obtaining use performance requirements of the to-be-manufactured metal part, and obtaining grain characteristics and distribution of different areas of the to-be-manufactured metal part according to the grain and part performance database; layering and slicing the to-be-manufactured metal part to obtain a slice file; the slice file is imported into double-laser powder bed melting additive manufacturing equipment, corresponding double-laser collaborative printing process parameters are selected for different areas of the to-be-manufactured metal part according to a process database, and a printing file is formed; and the printing file is imported into double-laser same-breadth printing equipment, and the metal part to be manufactured is manufactured. According to the method, the metal grains are regulated and controlled by changing the dual-laser collaborative parameters, then integrated manufacturing of parts is achieved, and the machining difficulty is reduced while the performance of the parts is improved.

The invention relates to the technical field of data processing, in particular to a data transmission method and system for industrial edge gateways, and the method comprises the steps: obtaining industrial production equipment data, constructing a data queue, and obtaining the busy degree of each edge gateway based on the operation data of the data queue; according to the busy degree of each edge gateway, obtaining transmission results after the different data queues are transmitted to the cloud server, and calculating feedback rewards transmitted by the edge gateways by selecting the different data queues; and on the basis of the feedback rewards, using a UCB algorithm to calculate the confidence coefficient of each queue for transmitting data to the cloud server, and adjusting the data queue through which the edge gateway passes when transmitting the data to the cloud server according to the confidence coefficient. According to the method, the data transmission strategy is dynamically adjusted by monitoring the busy degree of the edge gateway and the transmission result of the data queue in real time, and rapid changes of task frequency, data volume and network conditions are better adapted, so that the data transmission efficiency is improved.

The invention discloses an intelligent scheduling and order dispatching management method and system, and relates to the technical field of intelligent scheduling, and the method comprises the steps: collecting original data, and carrying out the preprocessing; performing feature extraction on the preprocessed original data to obtain task priority related features and employee condition related features; based on historical data, constructing an intelligent scheduling model, inputting task priority related features into the intelligent scheduling model, calculating task priority scores, and performing task priority ranking according to the task priority scores; on the basis of task priority ranking and employee condition related characteristics, optimal matching personnel are automatically selected for task allocation; and monitoring execution condition data of the task in real time, and optimizing the intelligent scheduling model by utilizing the execution condition data. According to the method, the intelligent scheduling model is constructed, the health and skills of employees are considered, efficient and reasonable distribution is ensured, the model is optimized in real time, and the task execution quality and efficiency are improved.

The invention discloses an intelligent nursing data task scheduling management system based on cloud computing, and relates to the technical field of nursing scheduling management. The cloud computing platform is in communication connection with a data acquisition module, a data storage module, a data processing and analysis module, a nursing evaluation module, a task scheduling module, a report generation module and a user interaction module, and all the modules are in electric signal connection. According to the intelligent nursing data task scheduling management system based on cloud computing, through calculation of the task scheduling evaluation model and the emergency degree evaluation value, the emergency degree and priority of the nursing tasks are evaluated, priority ranking is carried out on the nursing tasks, intelligent scheduling of the nursing tasks is achieved, it is ensured that the emergency and important tasks are processed preferentially, and the task scheduling efficiency is improved. The nursing efficiency and quality are improved, meanwhile, reasonable allocation can be carried out according to the priority of the tasks and available nursing resources, and resource utilization is further optimized.

The invention relates to the technical field of video processing, and discloses a video analysis task scheduling method and system based on an intelligent scheduling mode, and the method comprises the steps: determining the analysis priority of a to-be-analyzed video task according to task type information; determining an initial resource allocation quantity of the to-be-analyzed video task; whether the initial resource allocation amount is optimized or not is judged based on the content complexity; when it is judged that the initial resource allocation amount is optimized, determining an optimization coefficient of the initial resource allocation amount according to a comparison result, and obtaining an optimized resource allocation amount; acquiring current computing power resource state information of the system, analyzing the computing power resource state information, and judging whether to adjust the optimized resource allocation quantity or not based on an analysis result; and when it is judged that the optimized resource allocation quantity is adjusted, determining an adjustment coefficient of the optimized resource allocation quantity according to the resource allocation influence index, and obtaining an adjusted resource allocation quantity. According to the method, resources can be reasonably distributed and used according to the requirements of different tasks and actual conditions.

The invention discloses a phishing security verification method, system and device and a storage medium, and relates to the field of network security. In the method, a domain name of a webpage link to be verified is collected, risk assessment is performed on the domain name to identify a target domain name, the risk assessment comprises blacklist comparison, domain name feature analysis and historical record query, and the target domain name refers to the domain name with the risk higher than a first threshold value; performing phishing risk verification on the target domain name to determine a risk level of the target domain name, wherein the phishing risk verification comprises assessment of domain name registration information, historical behaviors and similarity; determining the priority of the target domain name according to a sorting parameter, wherein the sorting parameter comprises a risk level, a user access history and a domain name reputation; and sorting the target domain names according to the priorities from high to low, and displaying the domain names with different priorities by adopting different colors and icons. By implementing the technical scheme provided by the invention, the accuracy and effectiveness of phishing protection are improved.

The invention discloses a computing power aware routing distribution method and system based on SRV6, and the method comprises the steps: constructing a network topology undirected graph based on a target distributed cluster; in response to the service request, obtaining real-time resource state data of each node in the target distributed cluster, and analyzing the service request to obtain resource request data; matching each node in the network topology undirected graph with the resource request data to obtain a target node; calculating an optimal path from the source node to the target node in the network topology undirected graph by using an improved Di jkstra algorithm; and according to the optimal path, configuring a corresponding routing rule in the target distributed cluster through an SRV6 network programming technology. According to the method provided by the invention, the problem of insufficient computing power resource routing distribution flexibility is solved.

The invention relates to the technical field of electric power facility fault detection, and discloses an electric power facility multispectral remote sensing inspection and fault diagnosis method, and the method comprises the steps: carrying a multispectral remote sensing device through an unmanned plane platform to obtain multispectral image data of an electric power facility, and carrying out the preprocessing to extract spectral features; and constructing a spectral feature analysis model to evaluate the health condition of the electric power facility, constructing a fault diagnosis model in combination with historical fault data, and determining the fault type and position. In order to optimize the fault diagnosis process, fault diagnosis is modeled as a Markov decision process, and the accuracy and efficiency of fault diagnosis are improved by defining a state space, an action space, a reward function and an output strategy and continuously optimizing decision actions by using a reinforcement learning algorithm. According to the invention, intelligent and automatic inspection and fault diagnosis of the electric power facility are realized, powerful technical support is provided for safe and stable operation of the electric power facility, and the method has wide application prospect and important practical value.

The invention discloses an energy storage grid-connected cabinet control management system, and particularly relates to the technical field of energy storage grid-connected cabinets. The intelligent power dispatching module is used for optimizing a charging and discharging strategy of the energy storage system and dynamically adjusting power output; the fault protection management module is used for detecting, preventing and processing a fault intelligent protection mechanism in real time to guarantee the safety of the system; and the intelligent monitoring communication module is used for collecting operation data of the grid-connected cabinet in real time and providing remote monitoring and communication support. By introducing the intelligent power dispatching module, the fault protection management module, the intelligent monitoring communication module and the energy optimization integration module, intelligent management and efficient dispatching of the energy storage system are achieved, the system can automatically adjust the charging and discharging strategy of the energy storage system by predicting the future power grid load, and the energy efficiency is improved. The power resource of the energy storage unit is reasonably distributed when the power grid demand changes, and the resource waste is avoided.

The invention relates to the field of computer storage, and discloses a solid state disk data prefetching method based on an attention mechanism, which comprises the following steps: S1, collecting access track data of a solid state disk, and performing fixed time window segmentation; s2, related labels are generated according to the content, and the labels are converted into numeric vectors; s3, combining the tag vector with a traditional access mode feature to form a multi-mode feature vector; s4, processing the multi-modal feature vector by using a gating cycle unit network, evaluating the importance of data of different time windows through an attention layer, and calculating a weight; s5, predicting data blocks which may be frequently accessed in the future based on the attention weight and the gated loop unit network output; and S6, according to a prediction result, selecting a prediction result with high confidence coefficient for prefetching. By using the solid state disk data prefetching method based on the attention mechanism, which data blocks may be frequently accessed in the future can be more accurately identified and predicted.

The invention belongs to the field of artificial intelligence, finance and medical treatment, and relates to an algorithm model verification and precision evaluation method, which comprises the steps of calling a pre-constructed original algorithm model to process an original sample file set to obtain a to-be-evaluated model result, and associating an original sample file with the to-be-evaluated model result to obtain a to-be-evaluated model result; performing annotation task allocation according to the generated annotation task allocation scheme, receiving a truth value result submitted by the annotation personnel, comparing a to-be-evaluated model result with the corresponding truth value result to obtain an initial model precision evaluation result, optimizing the model according to the initial model precision evaluation result, and evaluating the optimization algorithm model to obtain an evaluation result. And generating a model performance comparison report. The invention further provides an algorithm model verification and precision evaluation device, equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the original sample file can be stored in a block chain. According to the method, the model iteration efficiency can be greatly improved, and the precision and performance of the model are improved.

The invention discloses an equipment scheduling method and system based on dynamic priority and multi-objective optimization, and the method takes the minimization of a waiting time mean square error, the minimization of total waiting time and the minimization of maximum completion time as optimization objectives. A task dependency relationship, an equipment maintenance time window, a time window constraint, a student learning progress priority and a delay compensation mechanism constraint condition are comprehensively considered, a dynamic priority calculation mechanism is introduced, the priority is dynamically adjusted in real time according to task waiting time, deadline and urgency, and a high-quality initial population is generated through CMA-ES; a non-dominated sorting framework optimized by NSGA-II is combined, crossover variation of a traditional genetic algorithm is replaced by PSO updating, and an optimal solution is searched by using an elitism mechanism. The method can effectively improve the utilization rate of equipment resources, reduce the task completion time and optimize the fairness of task allocation, is suitable for equipment sharing and dynamic scheduling scenes, and has high practicability and popularization value.

The invention relates to the technical field of automobile metal part drilling machining, in particular to an automobile steering knuckle drilling pre-machining machine tool. The automobile steering knuckle machining equipment comprises an installation machine base, a drilling device, a tool clamp and a control system, the drilling device and the tool clamp are installed on the installation machine base and are important components of the equipment, and preprocessing of an automobile steering knuckle is achieved; the control system comprises a hydraulic system, a pneumatic control system, a cutting fluid circulating system, a lubricating system, a chip removal system and an electric control system. The drilling device, the tool clamp, the material pressing device, the control device and other functional components are efficiently matched, the efficiency of automobile steering knuckle drilling pre-machining is remarkably improved, through precise movement of the drilling device, stable fixation of the tool clamp and the material pressing device and continuous lubrication of the lubricating device, high precision of the machining process is guaranteed, and the machining efficiency is improved. The problems of workpiece machining defects, equipment overheating and the like are reduced, and potential safety hazards are reduced.

The invention discloses a comprehensive management background operation and maintenance system, and the system comprises an automatic charging management module which is used for providing an automatic payment service; the abnormal behavior detection module is used for detecting an abnormal behavior and triggering an alarm; the energy management module is used for automatically adjusting the state of the illumination and ventilation system; the parking space distribution management module comprises an entrance recommendation unit used for recommending an entrance for the vehicle; the parking space distribution unit is used for acquiring idle parking spaces, vehicle positions, access destination positions and charging pile position information when the visitors are identified and the electric vehicles are driven, and establishing a multi-target optimization model; a target parking space is distributed to the vehicle owner through solving; and the navigation positioning unit is used for providing navigation service for the vehicle owner and updating the optimal driving path in real time by adopting a dynamic path planning algorithm. According to the invention, proper entrances and parking spaces can be rapidly and accurately provided for vehicle owners, the optimal path is provided for driving reference of the vehicle owners, the rationality of parking space distribution of the parking lot is ensured, and the utilization rate of parking space resources and the management energy efficiency are improved.

The invention provides an intelligent traffic data transmission method and system based on an industrial switch, and relates to a data processing technology. The method comprises the following steps: acquiring traffic monitoring data through each traffic monitoring device in a traffic monitoring device cluster, and transmitting the traffic monitoring data to an industrial switch to form a traffic monitoring data set; the industrial switch determines the transmission priority of each traffic monitoring data in the traffic monitoring data set by using a preset transmission priority determination model, and then the industrial switch obtains an available communication link set, and determining a target communication link of each traffic monitoring data from the available communication link set according to the transmission priority of each traffic monitoring data in the traffic monitoring data set, so as to transmit the corresponding traffic monitoring data to the intelligent traffic management cloud platform through the target communication link, thereby dynamically adjusting the transmission priority. Emergency and important traffic monitoring data can be transmitted preferentially, and the response speed and decision-making efficiency of the system are improved.

The invention belongs to the field of software monitoring and finance, and relates to a cluster GPU utilization rate management method, which comprises the steps of dividing a cluster into namespaces, setting a business GPU resource quota and a public GPU resource quota of a public namespace for each business namespace, and allocating GPU resources for each business namespace, resource monitoring data of service GPU resources of all service namespaces are collected in real time, when idle service namespaces exist, the resources of the idle service namespaces are recycled to the public namespace, and the resources of the idle service namespaces are allocated according to the GPU resource application, the current resource use condition and a preset resource allocation priority rule. And allocating the GPU resources in the public namespace to the target business namespace. The invention further provides a cluster GPU utilization rate management device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the resource monitoring data can be stored in a block chain. According to the method, the GPU resources can be dynamically scheduled, and the utilization rate of the GPU resources is maximized.

The invention discloses a database security secrecy system based on storage encryption, and relates to the technical field of information security. The database encryption platform is in communication connection with a key management module, an encryption and decryption management module, a performance monitoring module, a load evaluation module, a distributed storage optimization module, an early warning analysis module and an audit log module. According to the system, the early warning analysis module is combined with burden evaluation and security vulnerability detection of encryption and decryption performance, the abnormal degree of encryption and decryption processes is comprehensively analyzed, the early warning level is set, potential abnormal conditions are analyzed, and early warning information is sent out, so that a system administrator can respond in time, and the system safety is improved. A preposed risk management strategy enables a system administrator to take corresponding countermeasures before the risk actually occurs, thereby effectively reducing the influence of security threats, and meanwhile, according to different early warning levels, the countermeasures can be flexibly adjusted, and reasonable allocation of resources is ensured.