130 results about "Fuzzy logic inference" patented technology

The invention discloses an intelligent driving multi-sensor fusion data processing system, which belongs to the technical field of fusion data processing and comprises a data preprocessing unit, a data fusion unit and a data post-processing unit. The data preprocessing unit normalizes the original data of the laser radar, the camera, the millimeter wave radar and the ultrasonic sensor through a standardization, cleaning, noise reduction and calibration synchronization module; the feature level fusion module deeply digs the characteristics of each sensor, and fuses geometric, visual, motion and close-range obstacle features by using a deep neural network; the decision-making level fusion module generates a driving instruction through weighted voting and fuzzy logic reasoning through classification and situation evaluation; and the data post-processing unit combines the vehicle and road condition optimization instruction, evaluates the risk, and stores the data feedback optimization system. The system can improve the sensing precision and decision reliability, enhances the expansion adaptability of the system, and provides guarantee for safe and efficient operation of intelligent driving.

The invention discloses an electrical fire intelligent identification system based on multi-dimensional sensor fusion. The system comprises the following steps: constructing a reference environment model through multi-sensor scanning, data dimension reduction and intelligent node deployment; multi-sensor time sequence alignment is carried out through edge calculation denoising and dynamic time warping, and high-priority data is processed in real time through a layering mechanism; establishing a fire feature modeling system through LSTM time sequence analysis, mutual information correlation mining and self-supervised learning; through multi-level data fusion, GAN abnormal data generation and fuzzy logic reasoning; through grading alarm, an intelligent fire extinguishing strategy and remote control, full-process coverage from fire detection to emergency response is realized. A fire scene is visualized by means of a three-dimensional thermodynamic diagram, flame dynamic analysis and an augmented reality technology. The system is suitable for fire detection, alarm and response in a complex industrial scene, and can be widely applied to intelligent management of electrical fire.

The invention discloses a 5G message video monitoring method based on artificial intelligence. The method comprises the following steps: S1, constructing a multi-modal data set; s2, multi-modal fusion features are generated by using a joint convolutional neural network; s3, generating a target behavior trend curve and a target behavior prediction result by using the 3D convolutional neural network; s4, generating a target threshold curve by using an adaptive algorithm based on variable domain search, and obtaining behavior abnormality warning information; s5, generating comprehensive alarm information by using a method of combining fuzzy logic reasoning and a Bayesian network; s6, cross-camera data space-time correlation reasoning is carried out by using the graph neural network; s7, constructing an event severity curve by using a multivariable regression analysis and analytic hierarchy process, and generating a severity level and an event response measure of the event; and S8, pushing alarm information and event response measures to related personnel or equipment through the 5G message platform. The invention provides an efficient video monitoring method in combination with artificial intelligence and a 5G network.

The invention discloses an unmanned aerial vehicle cargo distribution management method and system based on strategy optimization, and particularly relates to the technical field of cargo distribution management. Through an improved incremental K-means clustering algorithm, a KD-Tree index is combined to optimize nearest neighbor search, order area grouping efficiency is improved, based on an order cluster set O, mixed integer programming and a Hungary algorithm are adopted to carry out unmanned aerial vehicle task matching, a task distribution moment M is optimized, a distribution path is dynamically adjusted to deal with real-time weather and airspace limitations, and in the distribution process, the distribution efficiency is improved. Data such as order liquidity index, battery consumption rate and task balance degree are acquired through an unmanned aerial vehicle sensor, and the scheduling delay condition is judged by using fuzzy logic reasoning, so that the system can still realize efficient, balanced and intelligent unmanned aerial vehicle scheduling in a high-load scene, the distribution delay is greatly reduced, the no-load rate is reduced, and the overall logistics efficiency is improved.

The invention belongs to the technical field of power systems, and particularly relates to an audit data early warning method and system, and the method comprises the steps: collecting the node voltage and current of a power supply network and the charging and discharging efficiency data of an energy storage system in real time through a distributed sensor network, and generating an original data flow; carrying out localized cleaning and standardized format conversion through an edge computing node; generating a dynamic risk assessment result and a scheduling scheme based on fuzzy logic reasoning and digital twinborn simulation; and analyzing and predicting deviation through meta-learning, and realizing closed-loop optimization by adopting a model distillation technology. The system comprises a distributed sensor network, an edge computing node, a multi-source data fusion module and the like. According to the method, the anomaly detection real-time performance, the risk assessment accuracy and the energy storage scheduling economy of the power system are improved.

The invention discloses a fault prediction and health management system for a belt conveyor, relates to the technical field of intelligent monitoring and management of mechanical equipment, and aims to solve the problems of fault recognition lagging and high misjudgment rate caused by insufficient consideration of real-time performance and high reliability required by industrial equipment. Motor current fluctuation, roller temperature change rate, belt tension distribution and vibration spectrum data are acquired in real time through a data acquisition unit; the fault feature extraction unit extracts abnormal features based on wavelet transform, and calculates an operation stability index through a principal component analysis method; the risk assessment unit combines the abnormal features, the abrasion loss and the stability index, and adopts fuzzy logic reasoning to judge the fault risk level; and the intelligent regulation and control unit dynamically regulates and controls the operation state or gives an alarm according to grading processing, so that accurate early warning and intelligent response of early faults of the equipment are realized.

The invention discloses a ruins protection area tourist diversion system and method based on dynamic bearing capacity. The diversion system comprises a data acquisition module, a bearing capacity calculation module, a diversion strategy optimization module, a tourist guide module and a feedback adjustment module. Real-time tourist data and environment information including tourist flow, residence time, meteorological conditions, site physical conditions and the like are collected; calculating the dynamic bearing capacity of the current ruin area, and comprehensively considering factors such as tourist load and environmental influence; a fuzzy logic reasoning method is adopted to evaluate tourist density and define a congestion degree grade: an IPSO algorithm is adopted to optimize tourist shunting paths, the shunting paths are generated and tourists are guided, and accurate shunting is carried out by adopting modes of intelligent identification, dynamic indication boards, mobile applications and the like; based on historical data and a real-time monitoring result, error correction is carried out on bearing capacity prediction, and the prediction accuracy is improved.

The invention discloses an intelligent automobile lamp assembly which is used in the technical field of automobile lamps and comprises a sensor module, an instrument prompting module, a processing module, an intelligent light control module and an executing mechanism. The sensor module is used for collecting key data of a vehicle state and an external environment; the instrument prompting module is used for displaying the state of the vehicle lamp system and information related to driving in real time; the processing module is used for analyzing the vehicle state and the key data of the external environment and generating a vehicle lamp adjusting strategy; the intelligent light control module is used for receiving the vehicle lamp adjusting strategy and converting the vehicle lamp adjusting strategy into a specific control instruction; and the execution mechanism is used for executing the control instruction and adjusting the brightness and direction of the vehicle lamp based on the control instruction. Based on the fuzzy logic reasoning method, complex and fuzzy data input can be processed, the vehicle lamp adjustment strategy is flexibly generated according to different driving conditions, and vehicle lamp adjustment has better scene adaptability.

The invention discloses an electric vehicle charging and discharging method, device and equipment based on fuzzy logic reasoning and price excitation demand response and a medium. The method comprises the steps that modeling is conducted on loss of an energy storage battery of an electric vehicle; in combination with a membership function and a fuzzy rule, based on a Mamdani fuzzy logic algorithm, according to the charging time anxiety degree, the charging cost reduction degree and the residual SOC of the user to which the electric vehicle belongs, determining the intention of the user to participate in charging and discharging scheduling; and in consideration of the intention that the user participates in charge and discharge scheduling, combining a price demand response mode and an excitation demand response mode of the electric vehicle to optimize the operation state of a power grid which is used for charging the electric vehicle. The invention belongs to the field of electric vehicle charging and discharging. According to the method, a corresponding charging and discharging strategy scheme can be accurately formulated according to the requirements of an electric vehicle user, the peak-valley difference of a power grid is reduced while the charging cost and the charging time comprehensive cost of the user are reduced, and the load fluctuation is reduced.

The invention provides an indoor positioning method and system based on dynamic mode switching and data fusion, and relates to the technical field of visible light indoor positioning, and the method comprises the following steps: deploying a VLC transmitting device and an auxiliary positioning point, constructing a VLC fingerprint database, carrying out dynamic signal detection and mode conversion, carrying out VLC fingerprint positioning, carrying out IMU data processing, and carrying out main positioning mode data fusion. VLC signal intensity distance estimation, ultrasonic ranging, IMU relative displacement estimation and standby positioning mode data fusion are carried out. According to the invention, by deploying the visible light communication transmitting device and the auxiliary positioning point, the strength and the stability of the VLC signal are monitored in real time, and dynamic positioning mode switching is realized through fuzzy logic reasoning. In the main positioning mode, VLC fingerprint matching is combined with IMU data, and accurate positioning is carried out through a Kalman filtering algorithm; and when the VLC signal is weak or unstable, a standby positioning mode is switched, and accurate positioning is carried out by fusing data through a particle filter algorithm according to VLC signal intensity distance estimation, ultrasonic ranging and I MU data.

The invention provides a hybrid model fault early warning method and system based on time sequence prediction and fuzzy logic, and the method comprises the steps: collecting operation and maintenance data of equipment in a continuous operation period, constructing an equipment state feature set, carrying out the feature correlation analysis of the equipment state feature set, generating an equipment state potential vector which reflects a dynamic coupling relation between parameters, and carrying out the fault early warning of a hybrid model. Calling a pre-trained fuzzy logic reasoning model to carry out fuzzy rule matching, and generating a fuzzy state set containing a multi-dimensional fuzzy subset; performing space-time correlation processing on the fuzzy state set to generate a multi-dimensional decision cloud picture, and performing spatial reconstruction on the multi-dimensional decision cloud picture through a quantization weight distribution mechanism to obtain an optimized decision matrix; and generating a fault early warning signal containing a fault early warning level and fault position positioning information based on the confidence distribution characteristics corresponding to the fault types. According to the invention, the accuracy and timeliness of fault early warning can be improved, and reliable decision support is provided for equipment operation and maintenance management.

The invention discloses a cardiovascular disease risk assessment system based on big data analysis, which relates to the technical field of medical big data and quantum and comprises a data acquisition module, a preprocessing module, a feature extraction module, a model training module, a risk assessment module and a result display module. Data acquisition integrates multi-source heterogeneous data, quantum encryption is used to guarantee security, and preprocessing is carried out by deep reinforcement learning cleaning and adaptive normalization; feature extraction is combined with quantum principal component analysis and an auto-encoder, and a quantum attention mechanism is introduced; the model training adopts quantum neural network ensemble learning of quantum annealing optimization; introducing fuzzy logic reasoning to correct probability and layering in risk assessment; results are displayed through virtual reality and augmented reality technologies, and report suggestions are automatically generated. The method has the advantages of prominent advantages, comprehensive multi-modal data acquisition, accurate advanced technology preprocessing and feature extraction, efficient and accurate quantum optimization model training, and combination of real-time monitoring and a knowledge graph, provides a basis for prevention and treatment of cardiovascular diseases, and promotes medical intellectualization and precision.

The invention relates to a salient scene graph generation method and device based on fuzzy logic reasoning relation importance, a scene graph generation model test method and device and computer equipment. The method comprises the following steps: step S100, calculating an importance score of a real relationship of each image sample in a target data set; step S200, constructing a target detector; step S300, constructing a semantic extractor and an instance feature refining module; step S400, constructing a bounding box modeling module and a feature splicing module; step S500, constructing a feature fusion module and a relation representation modeling module; step S600, constructing a relation loss weighting module; step S700, returning to the step S200 when the number of the training image samples reaches the batch processing size; and under the condition that all the image samples in the training set are read, entering the step S800 to output the scene graph generation model. According to the method, the importance of the relationship can be flexibly and comprehensively evaluated on the basis of not reducing the number of the relationship samples.

The invention discloses a hydraulic engineering data intelligent monitoring method based on digital twinning, and particularly relates to the technical field of hydraulic engineering intelligent monitoring, and the method comprises the following steps: obtaining control instruction data and entity hydraulic engineering real-time monitoring data, analyzing the time sequence characteristics and the change trend, and obtaining the real-time monitoring data of the control instruction data and the entity hydraulic engineering real-time monitoring data; detecting whether the control instruction data is returned and participates in system calculation, and identifying a data echo effect; after detection, calculating a short-term influence index and a long-term influence index based on the prediction error convergence degree, respectively evaluating the influence of the echo effect on the current operation state and the future scheduling precision, judging the level of the echo effect to be a low level, a middle level or a high level according to the influence degree, and processing by adopting a corresponding optimization strategy; according to the method, dynamic evaluation and grading judgment of the data echo effect are realized, and the early warning and adaptive capacity of the system is improved; fuzzy logic reasoning is adopted to optimize decisions, and regulation and control intelligence is enhanced; and through a differentiation strategy and a credible data source reconstruction model, the operation stability and the scheduling precision of the water conservancy project are guaranteed.

The invention discloses a rainfall type landslide early warning method based on multi-source environment threshold and nonlinear fusion. The method comprises the following steps: constructing a landslide disaster-inducing factor set; introducing soil humidity, vegetation indexes and evapotranspiration, and calculating a threshold parameter corresponding to the rainfall type landslide event to obtain an environment threshold point set; according to the landslide disaster-inducing factor set, constructing and training a landslide susceptibility model based on progressive learning; adopting a double-layer nonlinear fusion model based on multi-source data fusion and quality perception gating, and combining a landslide susceptibility model to obtain comprehensive risk probability distribution; and performing graded early warning based on the comprehensive risk probability distribution to complete rainfall type landslide early warning. According to the method, on the basis of traditional rainfall parameters, early-stage disaster-pregnant environment factors are introduced, and a multi-source environment threshold value is formed; fuzzy logic reasoning is adopted to realize nonlinear fusion of the threshold information and the landslide susceptibility base map; and dynamically reflecting a regional environmental condition evolution process in combination with an annual iterative updated susceptibility layer, and realizing finer and more adaptive space grading early warning.

This disclosure relates to automatic lane and and / or terrain detection and recognition under various ambient and driving conditions for robotic / vehicular control purposes / applications. In one aspect, a rule-based detection system is configured to perform lane recognition using optical images. Various edge detection thresholds are adaptively tuned from frame to frame using a feedback signal derived from a fuzzy logic inference mechanism. Such detection techniques may also be adapted for drivable region detection, which may be fused together with drivable region detection based on LiDAR cloud point. Such detection techniques may be further adapted for a preprocessing of optical images of road to generate preprocessed images that may then be processed by learning-based lane detection models. In another aspect, an intelligent terrain classification system may be configured to include a fusion of both a vision-based terrain classification convolutional neural network (CNN) and a parallel proprioception-based terrain classification CNN. These CNNs may be configured to process a small number of selected set of proprioceptive signals and to adaptively detect / classify the terrain as platform-independent as possible.

The invention relates to the technical field of injection mold monitoring, and discloses an injection mold cavity state monitoring method, system, equipment and medium, the injection mold cavity state monitoring method comprises the following steps: multiple sensors collect mold state data in real time, and the mold state data are preprocessed and fused to generate a comprehensive data set; training a machine learning model based on historical samples, and updating model parameters in real time through an incremental learning algorithm; analyzing current data by adopting dynamic fuzzy logic reasoning, and outputting a state evaluation result; real-time data and results are uploaded to the cloud for deep analysis and distributed storage; a user feedback mechanism is integrated, and model parameters and a fuzzy inference rule base are optimized. According to the method, model parameters are updated in real time through an incremental learning algorithm, a multi-parameter nonlinear coupling relation is analyzed in combination with dynamic fuzzy logic reasoning, and based on cloud collaboration and a user feedback closed-loop mechanism, self-adaptive monitoring and continuous optimization of the injection mold state are achieved, and the anomaly detection precision and the system robustness are improved.

The invention discloses an operation risk analysis method and system for a virtual power plant, relates to the field of risk analysis, and solves the problem of insufficient operation stability of an existing virtual power plant, and the method comprises the steps: S1, collecting the operation data of various devices of the virtual power plant; s2, acquiring historical fault data, and constructing a fault rule base; performing fuzzy logic reasoning based on historical fault data to obtain membership values of the abnormal parameters; real-time equipment parameters are collected, calculation is carried out, and a fault judgment value is obtained; fault equipment is judged; s3, calculating the fluctuation condition of the equipment parameters, constructing a fluctuation interval, analyzing and calculating a fault risk value according to the fluctuation interval, judging pre-fault equipment, counting the fault equipment and the pre-fault equipment, and constructing a maintenance equipment list; s4, adjusting the energy supply of the equipment according to the energy supply plan in combination with the maintenance equipment list; the stability of the virtual power plant can be effectively improved.

The invention discloses a greenhouse CO2 concentration partition regulation and control method based on fuzzy logic reasoning. The method comprises the following steps: S1, acquiring an environment parameter data set corresponding to each partition in real time through a sensor network deployed in a greenhouse; s2, performing data preprocessing on the acquired environment parameter data set of each partition; s3, determining priority weights of the partitions; s4, based on a greenhouse partition priority model and the preprocessed environment parameter data set of each partition; s5, calculating the target CO2 concentration range and the regulation and control amount of each subarea; s6, converting the target CO2 concentration range and the regulation and control quantity of each subarea into executable control signals, and executing CO2 concentration subarea regulation and control; and S7, monitoring the environmental parameter data of each partition in real time through the sensor network until the target range is stable. The adaptability to the crop growth environment is improved.

The invention discloses a personalized co-driving type intelligent vehicle man-machine sharing control method and system, a medium and equipment, and relates to the field of intelligent auxiliary driving, and the method comprises the steps: obtaining a risk tolerance coefficient and a sensitivity zooming coefficient of a driver; acquiring a personalized driving risk field according to the risk tolerance coefficient and the sensitivity zoom coefficient of the driver; using the personalized driving risk field to calculate driving risks of different types of drivers; according to the driving risks of the different types of drivers, the driver control weights of the different types of drivers are obtained through fuzzy logic speculation; and establishing a man-machine non-cooperative game model, and realizing personalized man-machine sharing control based on the driver control weights of different types of drivers and the man-machine non-cooperative game model. When the man-machine conflict exists, the driving requirements of different types of drivers can be met while the vehicle driving safety is guaranteed, and personalized man-machine sharing control is achieved.

The invention discloses a personalized precise fitness scheme intelligent pushing method and system, and relates to the technical field of intelligent fitness, and the method comprises the steps: collecting motion data of a user in real time, and carrying out the preprocessing; the method comprises the following steps: collecting a fitness target of a user, converting the fitness target into a target vector, activating a task sub-network in a dendritic neural network according to a matching degree between motion data of the user and the target vector, dynamically adjusting weights and importance of different tasks in the task sub-network through an attention mechanism, and generating and optimizing a preliminary fitness scheme through multiple agents. The most relevant sub-networks are activated in real time, task priorities are adjusted through an attention mechanism to adapt to environment changes and user states, the exercise intensity is optimized through fuzzy logic reasoning, training tasks are automatically adjusted according to real-time feedback of users, the balance between the tasks is optimized through co-evolution between agents, and the training efficiency is improved. The personalized fitness scheme can be continuously optimized, and more accurate exercise recommendation is provided under the dynamic environment and user demand changes.

The invention discloses a charging pile power management system based on the Internet of Things, and particularly relates to the technical field of the Internet of Things, and the system comprises a data collection module which collects the real-time operation data of a charging pile through a sensor, and forms a data set; the data processing module is used for processing the data according to a preset or optimization strategy and generating a processed data set; the scene analysis module performs analysis based on the processing data set and divides the operation scene of the charging pile; the data feature extraction module is used for extracting and analyzing features when an optimization mechanism is triggered; the logic judgment module is used for formulating an optimization strategy through fuzzy logic reasoning and sending the optimization strategy to the data processing module; the power energy efficiency management module is used for adaptively adjusting power distribution and improving the energy efficiency of the system; the data transmission delay can be reduced, the response speed can be improved, a user can obtain smoother experience when using the charging pile, transmission of redundant information is reduced through an optimization strategy, and efficient power management is achieved.

The invention discloses a photo-thermal power station key equipment failure consequence assessment method, device and equipment and a medium, and relates to the technical field of risk assessment, and the method comprises the steps: collecting and integrating the historical accident data of a photo-thermal power station, building an accident scene model, and recognizing key nodes such as equipment failure factors, response measures and states. The model is converted into a Bayesian network, the device state is a child node, and the influence factor is a father node; and determining the weight and conditional probability of each factor according to expert opinions by applying an interval type-2 fuzzy logic reasoning model, and evaluating the severity of the equipment failure consequence. According to the method, the historical accident data of the photo-thermal power station are collected and integrated, and the Bayesian network and the interval type-2 fuzzy logic reasoning model are combined, so that the potential risk of equipment failure can be identified and evaluated more accurately, and a scientific basis is provided for safe operation of the power station.

The invention relates to the technical field of agricultural Internet of Things, and discloses an edible mushroom growth environment regulation and control method and system based on multi-source sensing data, and the method comprises the steps: deploying a multi-source sensor network, unifying time synchronization, and carrying out event-driven space-time alignment and three-dimensional mapping. Carrying out multi-dimensional reliability evaluation and environment correction, and obtaining an environment parameter fusion data set by adopting a fuzzy logic reasoning method; performing space-time Kriging interpolation, and obtaining a heterogeneous microenvironment feature database by adopting a multi-source data collaborative correction method; carrying out cross-modal attention fusion; performing multi-scale convolution and time sequence attention classification; carrying out deep reinforcement learning and multi-objective optimization; carrying out online incremental learning optimization to obtain a self-adaptive optimized partition collaborative regulation and control strategy; according to the method, the technical problems of insufficient multi-source data fusion precision, lack of environment spatial and temporal distribution modeling, inaccurate parameter coupling prediction, lack of self-adaptability of regulation and control strategies and the like are solved.

The invention provides a quality detection method and system for magnesium ingot production. The method comprises the following steps: acquiring a visible light image, three-dimensional point cloud data and an infrared thermal image of a magnesium ingot to be detected; constructing a three-dimensional voxel model representing surface geometry and color information of the magnesium ingot; identifying and outputting the type, three-dimensional position and size information of the surface defect of the magnesium ingot; taking an infrared thermal image as a surface temperature boundary condition of a heat conduction model, and taking a defect position and a defect size as a heat conductivity disturbance source in the model; obtaining an equivalent heat conductivity distribution field in the magnesium ingot; on the basis of the spatial non-uniformity of the equivalent thermal conductivity distribution field, calculating to obtain an index for representing internal density; calculating contour conformity parameters; and inputting the surface defect severity parameter, the contour conformity parameter and the index of the internal density into a fuzzy logic reasoning system, performing reasoning according to a preset fuzzy rule library matched with a magnesium ingot downstream application process, and outputting the magnesium ingot quality grade.

The invention provides a steering-by-wire control method and system based on particle swarm-sliding mode control and a fuzzy radial basis function, and the method comprises the steps: carrying out the adaptive approximation of an uncertain item and unknown disturbance of a state-space equation based on a radial basis function neural network in combination with an adaptive law, and obtaining the real-time estimation values of the uncertain item and the unknown disturbance; a sliding mode surface is obtained based on the wheel rotation angle error, the sliding mode surface is corrected and compensated based on the real-time estimation value, parameters of the sliding mode surface are optimized in combination with a particle swarm algorithm, and an optimized sliding mode surface is obtained; on the basis of a fuzzy logic controller, the optimized sliding mode surface and the change rate of the optimized sliding mode surface are converted into membership degrees of a fuzzy set for fuzzy logic reasoning, and the fuzzy set of control behaviors is obtained; obtaining a control signal based on the fuzzy set of control behaviors; and completing steering-by-wire control of the vehicle based on the control signal. According to the technical scheme, the response speed and the anti-interference capability of the steer-by-wire system can be improved.

The invention relates to the technical field of robot intelligent control, and discloses an inspection robot task scheduling and state switching method for quantifying a state machine, which comprises the following steps: acquiring state machine operation configuration data; performing health degree evaluation on the sensor, navigation, communication and battery systems in an initial state; in the idle state, a segmented charging strategy is adopted, and task priorities are calculated through a neural network; a breakpoint resume and event-driven architecture is adopted to execute tasks in the inspection state; in a warehouse returning state, a deep neural network is adopted to predict return flight energy consumption, and a path is re-planned through multi-objective optimization when the electric quantity is insufficient; processing a control instruction by adopting speed limitation in a manual or mapping state, and starting an SLAM module in the mapping state; and in an abnormal state, a fuzzy logic reasoning system is adopted to calculate an abnormal grade, and recovery waiting, degradation protection or alarm is executed according to the grade. According to the invention, autonomous operation and intelligent decision making of the inspection robot in a complex dynamic environment can be realized.

The invention discloses a new energy station 5G network security risk assessment system, and relates to the technical field of network security, and the system comprises the following components: a data collection module, a data analysis module, a threat prediction module, an early warning module and a database module. The detection and prediction accuracy of the network security threats is improved, the system can more accurately evaluate the currently occurring security threats through the hybrid model of the multi-layer perceptron and the fuzzy logic reasoning, and the prediction model based on the combination of the time sequence analysis and the Bayesian network can more accurately evaluate the security threats generated at present. And potential network security threats in the future can be predicted more accurately.

The invention relates to an electric carbon metering detection method and device based on an electric carbon coupling model and a medium, and the method comprises the following steps: collecting electric carbon data of an electric power system, and constructing an initial electric carbon coupling model based on the electric carbon data of the electric power system; expanding the initial electricity-carbon coupling model, and optimizing the expanded electricity-carbon coupling model based on a fuzzy logic reasoning mechanism to obtain an optimized electricity-carbon coupling model; generating a simulation scene based on the optimized power-carbon coupling model according to the collected power system power-carbon data and different preset operation conditions; and performing matching analysis on the simulation scene and the power system electricity and carbon data to obtain comprehensive evaluation parameters, and judging the precision of the power system electricity and carbon metering equipment based on the comprehensive evaluation parameters.

The invention discloses a touch screen for an unmanned aerial vehicle and an instruction decomposition method of the touch screen. The method comprises the following steps: S1, performing standardization processing on task semantic data; s2, generating a complete unmanned aerial vehicle task instruction set; s3, generating an initial population solution according to the complete unmanned aerial vehicle task instruction set; s4, optimizing the initial population solution based on an elk optimization algorithm to generate an optimal task decomposition scheme; s5, generating an unmanned aerial vehicle executable subtask set; s6, collecting feedback data in the task execution process; and S7, dynamically adjusting the task decomposition scheme by using the collected task execution feedback data, and when the feedback data indicates that an obstacle or deviation exists in the execution of the sub-tasks, re-calling the elk optimization algorithm and the fuzzy logic reasoning module to optimize the optimal task decomposition scheme to generate a new unmanned aerial vehicle executable sub-task set. According to the method, the change of the task scene can be dynamically responded, and higher flexibility and execution efficiency are shown in the execution of the multi-target task.