219results about How to "Improve COP" patented technology

The invention belongs to the field of network security, and discloses a network security evaluation system and method based on a dynamic attack and defense game model, and the evaluation system comprises four functional modules of attack and defense game construction, risk prediction, dynamic defense optimization and security situation awareness. According to the method, the idea of dynamic attack and defense game is introduced, multi-level and multi-scene attack and defense simulation is carried out according to historical states, strategy data and real-time data, and the optimal attack path and attack strategy are calculated and analyzed through a game equilibrium solution; according to the method, network resources and network security resources are dynamically adjusted by relying on reinforcement learning and deep learning technologies and a defense rule base generated by pre-training, network configuration is optimized according to a real-time evaluation result, and waste of system resources is reduced. By implementing the system and the method provided by the invention, the security of the target network can be dynamically evaluated, the security defense strategy of the network can be adjusted in real time, and unknown attacks such as advanced persistent threat (APT) and zero-day attack can be effectively resisted.

The invention discloses a data communication method and system based on information security, and belongs to the technical field of data communication, and the data communication method comprises the stages of preparation, transmission, intrusion detection and defense, receiving verification and cleaning ending. The two parties authenticate and exchange keys during preparation; encrypting data by using a symmetric key and an AES algorithm; automatic defense is performed after intrusion detection; the receiver verifies the data; and cleaning the resources at the end, and performing log optimization on the system. According to the invention, through innovatively improving the intrusion detection and defense method, based on real-time network flow monitoring, intrusion analysis based on machine learning and an automatic defense mechanism, the detection accuracy and efficiency are greatly improved, and false alarms and missing alarms are reduced; defense capability and flexibility are enhanced, attacks are intelligently blocked and isolated, and cooperative emergency response is rapid; data communication security technology progress and expansion are promoted, and network security is powerfully guaranteed.

The invention discloses a small watershed hydrological runoff and pollution load short-term prediction refinement method, which comprises the following steps of: constructing a modeling data set, constructing a small watershed foundation base model, segmenting hydrological response units according to land utilization types, crop planting structures and reservoir pond / wetland distribution data in a refinement manner respectively, and calculating the hydrological runoff and pollution load short-term prediction of the small watershed foundation base model. A small watershed foundation base model corresponding to the optimal segmentation scheme is screened out, and a small watershed depth base model is generated; constructing a reservoir pond / wetland simulation module; coupling the small-watershed depth base model and the reservoir pond / wetland simulation module to form a small-watershed hydrology and water quality early warning model, and optimizing the small-watershed hydrology and water quality early warning model; rolling to generate a hot start file for short-term prediction; constructing a future short-term weather enhancement forecasting model; the small watershed hydrology and water quality early warning model is driven to operate, the future water volume and load output are calculated in a rolling mode, and hydrology and water quality results are output; and calibrating and updating the small watershed hydrology and water quality early warning model. The precision and timeliness of hydrology and water quality prediction of the small watershed are remarkably improved.

The invention provides an RFID-based power supply management method for a tilt angle sensor of a power tower, and the method comprises the steps: employing a support vector machine and a decision tree, analyzing the historical operation data of the tower, building a tower fault prediction model, predicting the state of the tower through the tower fault prediction model, and obtaining the fault probability and time of the tower; according to a tower fault prediction result, a preventive maintenance plan is made, priority maintenance is arranged for the tower with the fault probability larger than a preset threshold value, and a power supply strategy is adjusted in advance for the tower predicted to have a fault, and load pressure is reduced; a tower group intelligent power supply management system is adopted, state information and a power supply strategy of a single tower are integrated into a whole power grid, the power supply load of each tower is dynamically adjusted through a load balancing algorithm, and for a tower in a dangerous state, the power supply load of the tower is reduced, and the load is transferred to a tower in a good state.

The invention discloses a multi-mode adaptive fusion navigation positioning method and system in a satellite navigation denial environment, the system adopts GNSS / IMU / visual sensors, and the method comprises the following steps: receiving data of the three sensors and completing modeling; initializing the three sensors; judging the strength of the GNSS signal by utilizing chi-square test; optimization variables are set, a residual matrix is constructed, and nonlinear optimization is carried out; setting a GNSS trust factor when the GNSS signal is weak or denial, and calculating a weight; and weighting and fusing sensor data, outputting optimal state estimation of the system, and realizing continuous navigation and positioning. According to the method, a self-adaptive weight adjustment mechanism is established, GNSS trust factors are introduced, and the weights of multi-sensor data are adjusted in a self-adaptive manner, so that the positioning difficulty under the conditions that the GNSS is subjected to deception interference, suppression interference, building blocking and the like can be overcome, and continuous navigation positioning of an unmanned system (such as a robot, an unmanned vehicle, an unmanned aerial vehicle, an unmanned ship, an unmanned underwater vehicle and the like) is realized.

The invention relates to an electric power meteorological disaster risk early warning method and system based on artificial intelligence. The method comprises the following steps: cleaning and preprocessing basic meteorological data including temperature, humidity, wind speed and precipitation to obtain a cleaned basic meteorological data set; on the basis of satellite cloud picture or radar image data, meteorological changes are modeled through a neural network model, assimilation features are obtained, and the basic meteorological data and the assimilation features are spliced to form a joint feature vector; and constructing a comprehensive disaster risk assessment model based on the meteorological factors, the power grid factors and the geographic information, carrying out training and multi-objective optimization on the comprehensive disaster risk assessment model, and carrying out real-time electric power meteorological disaster risk early warning by using the optimized comprehensive disaster risk assessment model. According to the invention, meteorological data, power grid equipment states and geographic information can be monitored in real time, meteorological disasters possibly suffered by power equipment can be accurately warned, and the safety and stability of a power system are improved.

The invention provides a path planning method and system for an automatic driving trolley, and relates to the technical field of path planning, and the method comprises the steps: obtaining the data of an acoustic sensor of an underwater environment, and obtaining the contour of the underwater terrain and the position information of an obstacle; carrying out dynamic environment modeling processing; the Euclidean distance is used as a heuristic function to calculate the dynamic model of the underwater environment, and a second optimized path conforming to kinematics and dynamics constraints of the automatic driving trolley is obtained; carrying out smoothing processing on the second optimization path through a dynamic motion primitive technology; constructing a three-dimensional map for path planning and obstacle avoidance; predicting an action track of the robot in a period of time in the future; and through real-time adjustment processing of the self-adaptive control system, path planning of the automatic driving trolley is completed. According to the method, the dynamic characteristics of the underwater environment and the motion constraint of the automatic driving trolley can be comprehensively considered, the three-dimensional map containing the obstacle and topographic information can be constructed in real time, and the future motion trail of the obstacle can be predicted.

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses an information processing method based on sentiment analysis, and the method comprises the steps: collecting data and information which comprise text data, user behavior data and event information; performing natural language processing and sentiment analysis on the text data, and identifying sentiment features of the text data; analyzing the user behavior data to determine a behavior pattern; analyzing the event information to determine an event change trend; generating an emotional state index based on the emotional characteristics, the behavior pattern and the event change trend; monitoring the online platform to obtain user emotion feedback; comprehensively analyzing the emotion state index and the user emotion feedback to generate an emotion analysis result; and generating target output content based on the emotion analysis result. Through combination of emotion analysis, behavior data and event change trends, the emotion state of the user can be captured in real time, decision suggestions are dynamically adjusted, and market or environment changes are effectively coped with.

The invention discloses a regional heavy pollution discriminating and forecasting system and method based on big data, and relates to the technical field of environmental pollution monitoring. Comprising a multi-source data acquisition module, an adaptive weight distribution module, a feature extraction module, a heavy pollution discrimination module, a pollution diffusion path simulation module, an intelligent early warning and protection suggestion module, an event-driven dynamic monitoring module and a multi-scale meteorological prediction optimization module. And the pollution diffusion path simulation module performs prediction analysis on a pollution diffusion path and an influence range by using a diffusion path simulation algorithm. By designing the pollution diffusion path simulation module and the intelligent early warning and protection suggestion module, the pollutant diffusion path prediction and early warning functions are realized, and the problem that early warning lags due to the fact that the pollution diffusion path simulation and early warning module is separated and cannot realize timely linkage of prediction results and protection suggestions is solved; and a more timely and effective prevention and control strategy is provided, and the capability of coping with heavy pollution events is enhanced.

The invention relates to the technical field of plant cultivation, and discloses a dynamic regulation and control method, system and device for a plant growth environment and a storage medium. Inputting the image data into a growth recognition model to obtain a growth stage and a growth state of the plant; according to the growth stage, obtaining a soil temperature and humidity optimal value of the plant growth environment by adopting a response surface method, and inputting the environment data into the temperature and humidity prediction model to obtain a soil temperature and humidity prediction value; and comparing the predicted soil temperature and humidity value with the optimal soil temperature and humidity value, and regulating and controlling the soil temperature and humidity of the plant growth environment according to a comparison result. According to the method, through refined environment management, temperature and humidity, illumination and pest control required by plant growth are dynamically regulated and controlled, global management and accurate regulation and control of the cultivation process are achieved, the intensification and intelligence level of the whole cultivation method is improved, and cost reduction and efficiency improvement of plant cultivation are achieved.

The invention provides a linkage early warning method based on security situation assessment and application thereof. The linkage early warning method comprises the following steps: data preprocessing and feature extraction; an abnormal evaluation model and a normal evaluation model are constructed, and training and parameter adjustment are carried out; evaluating the data acquired in real time by using a safety evaluation function, judging the current safety state of the hydraulic power plant, and outputting a safety parameter; constructing a decision function, and determining whether the current state is abnormal or not based on the safety parameters and environmental factors EnvFactors; dynamically selecting and determining to select an abnormal evaluation model and a normal evaluation model for real-time evaluation, and judging whether the current state needs early warning or not; when early warning is needed, an emergency command system and early warning information are linked to carry out early warning and emergency response, and possible safety risks of the hydraulic power plant are prompted. According to the invention, by constructing the security evaluation function and the decision function and combining the neural network model, the problem that the existing system lacks enough flexibility and adaptability and cannot effectively cope with variable network security threats is solved.

The invention provides an unknown environment adaptive navigation control system based on deep learning, and aims to improve the autonomous navigation capability of a machine in a complex and unknown environment. According to the system, an advanced sensing module is adopted, multi-sensor data are combined, and a dynamic environment is quickly and accurately sensed through a deep learning algorithm. The online environment modeling mechanism can update the environment information in real time without depending on a pre-constructed map. And by utilizing a reinforcement learning algorithm, the system can autonomously learn and optimize path planning, and meanwhile, energy consumption, time efficiency and safety are considered. An innovative multi-mode obstacle avoidance strategy is adopted, and obstacle avoidance behaviors are dynamically adjusted according to environment complexity and machine states. The intelligent decision support system combines a decision tree and fuzzy logic, and provides fast and accurate decision ability. The adaptive motion control algorithm optimizes path tracking performance and adapts to dynamic characteristics and environmental conditions of a machine. In addition, the collaborative job mechanism supports efficient communication and task collaboration in a multi-machine system.

The invention discloses an intelligent medical pre-inquiry method and system based on a multi-modal large model and a medium, and the method comprises the steps: obtaining user input data to recognize a target intention, and when a knowledge question and answer intention is recognized, obtaining a knowledge question and answer module; based on the medical knowledge graph, combining with knowledge keywords in the user input data and utilizing a multi-modal large model to obtain medical answers; when the pre-inquiry intention is recognized, inquiry keywords in the user input data are extracted for slot filling to obtain slot information, and a slot filling strategy is dynamically adjusted based on the slot information and user feedback data; and performing visual display on the medical answers and the health management suggestions based on the user interaction interface, and obtaining user interaction data for human-computer interaction. According to the invention, a natural language processing and multi-modal fusion mode is adopted, so that the information input process of a patient is more convenient; and meanwhile, by introducing dynamic medical history information acquisition and interaction, real-time interaction with the patient is realized, and the user experience of pre-inquiry is improved.

The invention discloses an AI-based network security risk identification and management and control system, which is characterized by comprising network security equipment and a server, the network security equipment is used for monitoring flow data flowing into / out of the server, and the AI-based network security risk identification and management and control system also comprises an interface conversion module which is used for connecting different network security equipment, original operation logs of different network security devices are collected; data collection: collecting multi-source data from multiple channels of network traffic, server logs, application program logs and user behavior records; preprocessing the data; preprocessing the data, removing noise data, filling missing values, and extracting useful features from the original data; simulation training: training the model to identify normal and abnormal behaviors through machine learning by utilizing supervised learning, unsupervised learning or semi-supervised learning methods; the deep neural network is used for modeling a complex data mode, and the detection precision is improved.

The invention discloses an intelligent material path planning method and device in a powder conveying system. The method comprises the steps that multi-source sensing data sent by a sensor is obtained, the sensor is used for monitoring operation of a powder conveying system, the multi-source sensing data comprises material state data, environment parameter data and equipment operation data, feature extraction and space-time alignment are conducted on the multi-source sensing data based on a pre-established Gaussian edge network, and the multi-source sensing data are obtained; obtaining a space-time alignment feature matrix; constructing and generating an intervention model according to the space-time alignment feature matrix; training a material path planning strategy based on the equal variance local neural network and the generation intervention model to obtain a path planning strategy model; and generating a powder conveying path by adopting the path planning strategy model, wherein the powder conveying path is used for controlling the powder conveying system to convey powder. According to the invention, precise modeling and intelligent path planning of a complex powder conveying environment are realized, and the conveying efficiency and the system stability are improved.

The invention discloses a flight space directional training auxiliary system and method, and the system comprises a flight task simulation module; the meteorological environment simulation module is used for simulating a meteorological scene corresponding to a flight task, and simulating the meteorological scene by establishing a multi-dimensional meteorological model and combining meteorological parameters; the meteorological environment parameter control module comprises a time step length control module which adopts a preset time step length to input meteorological parameter observation data collected in real time to the meteorological parameter setting module; the specific meteorological event control module is used for transmitting meteorological parameters of a specific meteorological event area to the meteorological parameter setting module when the airplane triggers a specific meteorological event; and the training scheme generation module is used for generating a proper training scheme according to the historical training information of the flight personnel. According to the system and the method, a real and personalized simulation training environment can be provided for the flight personnel, and the professional skills and the capability of coping with various flight conditions of the flight personnel are effectively improved.

The invention provides a high-precision object pose estimation and fine operation method based on visual and tactile fusion, and the method comprises the steps: obtaining visual information and tactile information through the physical interaction of a robot and an environment, thereby completing the precise pose estimation in a dynamic scene; according to the method, a hand object pose correction algorithm based on particle filtering is constructed, the pose variation when a held object slides and when a contacted object moves can be estimated, and the method is suitable for most high-precision operation scenes, including challenging scenes with dynamic attribute changes, and can be applied to the field of high-precision operation. The coping capacity of the robot in a dynamic interaction environment is obviously improved; according to the method, high-precision operation tasks are executed on 13 objects with different geometrical shapes including the FMB reference, the result shows that the method achieves accurate pose estimation, the translation error is 1-2 mm, the rotation error is about 2 degrees, and meanwhile the method can cope with dynamic situations such as object sliding.

The invention relates to a virtual power plant regulation and control method and system for realizing new energy consumption. The method comprises the steps of performing statistics according to a wind and light power generation module to obtain predicted power generation amount E1 in each time period; according to an electric vehicle scheduling module, predicting the actual charging demand electric quantity QS1 of the electric vehicle in each time period; predicting actual load demand electric quantity QS2 in each time period according to a load prediction module; according to the energy storage module, calculating the storable electric quantity C and the standby residual capacity CS of each time period in real time; and according to the virtual power plant global scheduling module, the electric quantity difference value E difference required by each time period is calculated according to the predicted electric quantity E1 of each time period, the actual charging demand electric quantity QS1, the actual load demand electric quantity QS2 and the storable electric quantity C, and according to the electric quantity difference value E difference, a scheduling power generation instruction is issued to the heat power unit module for time-phased power generation. The distributed new energy consumption can be realized, and the stability and economy of grid-connected scheduling operation of the distributed new energy equipment can be improved at the same time.

The invention relates to a regulation and control method and system suitable for wastewater multistage purification of a green factory and a terminal, and belongs to the technical field of factory wastewater treatment.The regulation and control method comprises the steps that according to historical wastewater discharge data of the factory, the predicted total discharge amount of wastewater discharge of the day is predicted; generating a first expected water inflow of the wastewater multi-stage purification system according to the predicted total discharge amount; periodically acquiring the actual buffer capacity of the stored sewage discharged into the flow buffer pool by the factory on the same day; whether the actual buffering capacity exceeds one time of the first expected water inflow or not is judged; and if yes, controlling the sewage in the flow buffer pool to enter the wastewater multi-stage purification system at the first expected water inflow. The wastewater multi-stage purification system has the beneficial effects that the treatment capacity of the wastewater multi-stage purification system adapts to the discharge amount of sewage, so that the wastewater treatment effect of the wastewater multi-stage purification system is improved.

The invention relates to the technical field of meteorological monitoring and lightning protection, and discloses a radio signal-based lightning protection detection method for a meteorological station, which comprises the following steps of: receiving electromagnetic wave signals generated by lightning discharge in real time through radio receiving equipment of a plurality of meteorological stations, and acquiring characteristic data of time, frequency and intensity of the signals; an effective light velocity model is adopted to correct the influence of the propagation velocity of the lightning signal in the atmosphere, and the effective propagation velocity of the lightning signal is calculated by considering the ionospheric and atmospheric refraction effects; the position of the lightning power supply is estimated by measuring the arrival time difference and the time difference of lightning signals received by different sites and utilizing the geometric positions of the multiple sites. The positioning precision of the lightning power supply is greatly improved by adopting the cooperative work of the radio receiving equipment of a plurality of meteorological stations and combining the technologies of accurate lightning signal propagation speed correction, time synchronization error correction, geometric precision optimization and the like.

The invention discloses a network security vulnerability detection method and device based on a knowledge graph, terminal equipment and a medium. The method comprises the following steps: acquiring a time sequence knowledge graph of a vulnerability; according to a pre-trained vulnerability detection model and a time sequence knowledge graph of the vulnerability, a security level corresponding to the vulnerability is determined, and the pre-trained vulnerability detection model is obtained by training a TCGFormer model by adopting the time sequence knowledge graph of the sample vulnerability; the pre-trained vulnerability detection model at least comprises an entity coding module, a relation coding module, a joint sequence coding module and a time sequence decoder, and the pre-trained vulnerability detection model is determined according to the correlation and dependency of entities and relation nodes of a time sequence knowledge graph of vulnerabilities. According to the method, the correlation between the entity and the relation node and the long-term dependency relationship are more effectively captured, so that the early warning and coping capacity for potential security threats is improved, and the accuracy and efficiency of vulnerability detection are improved.

The invention relates to a vector propulsion spherical airship with an adjustable structure. The spherical airship is mainly composed of a vector propelling device, an airship capsule, an airship framework, a flight control module, a posture detection module, an environment monitoring module, a battery and the like. By adjusting the thrust direction of the vector propulsion device and the included angle between the airship framework and the capsule body, accurate control over the attitude and the flight path of the airship is effectively achieved. The design has the advantages of being small in size and compact in structure, and the propelling device is installed on the framework with the adjustable included angle, so that the control process is more flexible. Meanwhile, the propelling device achieves dynamic position adjustment through a framework structure and is prevented from being located in the airship movement direction, and potential safety hazards of a traditional external propeller are overcome. Although only two propelling devices are configured, the system can still provide fine control capability through seven controllable degrees of freedom, and the precision and flexibility of flight control are remarkably enhanced.

The invention relates to a multi-robot unknown environment collaborative exploration method based on a dynamic layering and zoning strategy, which comprises the steps that a plurality of robots are distributed in different areas of an unknown environment for collaborative exploration, each robot constructs a local map in real time through an SLAM algorithm, and a global map is constructed based on information sharing among the robots; based on the exploration area in the global map, segmenting the two-dimensional exploration area corresponding to the global map into a plurality of sub-areas through a dynamic layering and partitioning strategy to obtain a layering and partitioning result; wherein the layering and partitioning result is a sub-region set when the two-dimensional exploration region reaches the optimal hierarchy; based on the starting point position of each robot, distributing the layering and partitioning results among the robots, and balancing the task load between any two adjacent robots in the multiple robots to obtain a task unit set of each robot; and a minimum coverage path is planned on the optimal level based on the task unit set of each robot, and collaborative exploration is carried out based on the minimum coverage path.

The invention discloses a computer network security transaction optimization system, and belongs to the technical field of network transaction security. The system comprises an identity verification authorization module, a transaction request processing module, a security monitoring protection module, a transaction intelligent optimization module, a transaction auditing management module and a transaction report analysis module, protection is enhanced by analyzing a user behavior mode, additional verification is triggered when abnormality is detected, the risks of identity false use and fraud are reduced, and the safety of the transaction is improved. Network traffic is monitored in real time, potential attack behaviors are identified and blocked, through transaction request checking and dynamic request scheduling strict auditing, node loads are monitored in real time, transaction requests are intelligently scheduled, system overload or bottleneck is avoided, standby nodes are automatically started, stable operation under the high concurrency condition is guaranteed, and the system reliability is improved. The method enhances the adaptation to the increase of the transaction flow and the change of the network environment, improves the security, efficiency and transparency of the transaction through the transaction audit management and report analysis, and guarantees the stability and compliance of the financial transaction environment.

The invention discloses a driving safety monitoring method based on eye movement tracking and a target perception technology, which is suitable for a motor vehicle driving scene. The method comprises the following steps: S1, collecting eye movement data of a driver in different driving scenes through eye movement tracking equipment, and converting the data into a standard format which can be understood by a fusion model; s2, collecting traffic environment data including traffic object information, distance information, speed information and lane line information by using traffic environment sensing equipment integrated with an environment sensing algorithm; and S3, inputting the eye movement data and the traffic environment data into the fusion model, and judging whether a driver notices all traffic objects with interaction risks through calculation so as to realize driving safety monitoring. The method has the advantages that by fusing the eye movement data and the traffic environment information, the attention distribution of the driver can be accurately monitored, and the traffic safety management level is improved.

The invention relates to the technical field of intelligent building construction path planning methods, in particular to an AI-based intelligent building construction path planning method and system, and the method comprises the following steps: mapping site construction data into a virtual scene graph which comprises construction machinery information and environment information; on the basis of an AI algorithm and the virtual scene graph, a reasonable construction sequence and path are automatically planned for all machines in construction, and a construction scheme is generated; simulating the construction scheme to verify feasibility and risk points of the construction scheme; the construction scheme is optimized according to the risk point, and an optimal construction scheme is generated, so that the dynamic change of the construction site can be captured in real time, and intelligent path planning and resource allocation can be carried out based on the information; the method provided by the invention has self-learning and optimization capabilities, can continuously accumulate experience from practice, and improves the accuracy and efficiency of planning.

The invention discloses a dangerous chemical accident analysis system and analysis method based on digital twinning. The method comprises the following steps: establishing a jet fire accident consequence model of a fire spread analysis unit; establishing a toxic gas leakage accident consequence model; establishing a pool fire accident consequence model; establishing a steam cloud explosion accident consequence model; setting coordinates, proportion and position attributes of the model in the process of importing the three-dimensional data of the chemical industry park; sensor real-time monitoring data are accessed through a data interface and serve as input parameters, and dynamic updating and visual effect display are achieved. Three-dimensional scene restoration under high resolution is realized through a digital twin technology, model influence parameters are interactively set in combination with a Web end interface, physical states and environmental factors of hazardous chemicals are monitored in real time, and dynamic simulation and analysis are performed through a digital twin model, so that early recognition and early warning of accident risks are realized. And the prevention and coping capability of dangerous chemical accidents is improved.

The invention discloses an online fire-fighting training simulation training system and a training method thereof. The system comprises a sensing module, a virtual environment module, an interaction and feedback module and a learning recording and suggestion module. According to the invention, multiple sensory experiences such as smell, temperature, wind feeling and the like are comprehensively introduced into fire-fighting simulation training, the limitation that a traditional VR / AR system purely depends on vision and hearing is broken, the sense of reality and immersion of training are greatly improved, and the coping ability of students in a real fire scene is effectively improved; the state of a trainee is analyzed in real time through an AI algorithm, a self-adaptive threshold intervention mechanism is combined, a virtual AI coach can provide dynamic and personalized guidance in different training scenes, and intelligent intervention based on real-time data greatly improves the fire-fighting training effect; the system not only pays attention to the short-term training effect of the student, but also makes a personalized continuous learning plan through the learning recording and suggestion module, and promotes the long-term progress of the student.

The invention relates to the technical field of Internet of Vehicles, in particular to an Internet of Vehicles federal semi-supervised learning method and system, and the method comprises the steps: in each iteration, a vehicle client firstly generates a pseudo label of unlabeled sample data, then carries out the training of a local model, and uploads the trained model to a server; and after the server side aggregates the local models of all the vehicle clients, the local models and the supervision model trained by using the labeled sample data are aggregated again to obtain a global model of the current iteration, and the global model continues to be used for training the local model in the next iteration. According to the invention, the model training efficiency is improved, the real-time performance and adaptability of the system are further improved, and the coping ability of the vehicle in a complex and changeable traffic environment is enhanced.

The embodiment of the invention provides an electric power emergency material prediction and material guarantee risk assessment method and system, and belongs to the technical field of emergency material prediction. The evaluation method comprises the following steps: acquiring emergency management data information; performing feature processing on the data information; constructing an emergency material supply risk quantitative evaluation model by using a multi-layer perceptron neural network; inputting the data information into an emergency material supply risk quantitative evaluation model to obtain safety inventory data; and comparing the safety inventory data with data of material data indexes in emergency management data information to obtain an emergency material supply risk level. According to the method, the emergency management data information is arranged, the emergency material prediction model is established, a practical tool is provided for the power grid operation to cope with natural disasters and emergencies, and the method has good generalizability and usability.