125 results about "Defuzzification" patented technology

The invention discloses a solar radiation space-time prediction method and system based on physical information constraint and a neural network, and the method comprises the steps: collecting multi-dimensional time sequence meteorological data, extracting high-dimensional time sequence dynamic features, converting geographic space data into a fuzzy set, and carrying out the defuzzification of the fuzzy set through an inference rule, thereby obtaining geographic space features; and a gating mechanism is adopted to realize deep fusion of the space-time features to generate high-dimensional space-time fusion features. In a model training stage, an energy conservation equation is introduced into an optimization process, a physical residual error is constructed by calculating a time derivative and a space derivative of a predicted value, a physical constraint total loss function is formed in combination with a data loss item, and model parameters are updated by using a gradient descent method. According to the method, the accuracy and reliability of a prediction result are remarkably improved while the calculation efficiency is ensured, and the method is particularly suitable for solar radiation prediction under complex meteorological conditions; according to the method, abnormal prediction caused by data noise can be effectively corrected, and a solution with physical rationality and data adaptability is provided for the fields of solar resource evaluation, photovoltaic power generation power prediction and the like.

The invention provides a river crab feed feeding mixed monitoring method, system and device and a medium, and belongs to the field of aquaculture intelligent monitoring. Data is collected and normalized, and a chain type data collection system is constructed; the edge computing and block chain technology is adopted to realize data distributed storage and transmission; a fuzzy neural network hybrid model is constructed, fuzzification processing, feature mining and defuzzification output are integrated, and a feeding amount prediction value is generated; associating the feed proportioning library with the knowledge graph, and iteratively training the model and generating a multi-target feeding decision parameter; feeding equipment is controlled to execute actions according to the decision parameters, and the real-time monitoring model outputs and triggers an early warning signal; and updating the knowledge graph and the feed matching library based on the feedback data to form closed-loop self-adaptive regulation and control. According to the method, various factors can be comprehensively considered, scientific and intelligent feed feeding decision is realized, feed waste and insufficient feeding are effectively avoided, and the growth quality of the river crabs is improved.

The application discloses a network congestion control method and device based on fuzzy rules, and the method comprises the following steps: setting corresponding fuzzy rules according to network types, wherein the network types include a data center network and a wide area network; obtaining network parameters and performing fuzzy processing, fuzzy reasoning and defuzzification on the network parameters to obtain an adjustment factor ΔF, wherein the network parameters include a bandwidth utilization rate, an ECN marking rate and a queue length; obtaining the size of a current congestion window and adjusting the size of the congestion window in real time according to the adjustment factor ΔF; the steps of fuzzy processing and fuzzy reasoning further comprise detecting the current network type, and selecting corresponding fuzzy rules and dynamically adjusting the weight coefficients corresponding to each network parameter in the process of fuzzy reasoning according to the detected network type. The network congestion control method based on fuzzy rules can adjust the network congestion window in real time, realizes the intelligentization and automation of network congestion control, and improves the network management efficiency.

The invention provides an intelligent door and window control method and system, and the method comprises the steps: S1, obtaining multi-source environment data; s2, inputting the multi-source environment data into a fuzzification module, and converting the multi-source environment data into a fuzzy variable through a preset ambiguity function; s3, dynamically generating an environment parameter weight vector according to the real-time scene parameters; s4, inputting the fuzzy variable and the environmental parameter weight vector into a fuzzy inference engine, executing logical operation of a fuzzy rule base, and outputting comprehensive evaluation values of window opening suitability, window closing urgency and shading demand; and S5, generating a final control instruction through a defuzzification strategy, and executing the final control instruction. According to the method, the problem of frequent invalid operation caused by fluctuation of the environmental parameters near the boundary value is solved, the environmental parameter weight vector is dynamically generated based on the time parameters, the user preset mode and the meteorological data confidence factor, adaptation to seasons, time periods, user states and other scenes is achieved, and decision logic can be flexibly adjusted according to actual requirements; and a contradictory decision under a traditional fixed rule is avoided.

The invention discloses an adaptive modulation coding method and system based on fuzzy logic, and belongs to the field of wireless communication, and the system comprises a dynamic window module which dynamically adjusts the window size of a fuzzy set according to the received signal strength indication and the change trend of the packet reception rate; the input fuzzification module takes the actual received signal strength indication and the packet reception rate as input and converts the actual received signal strength indication and the packet reception rate into fuzzy variables through fuzzification processing; the fuzzy reasoning module is used for defining different membership functions, mapping continuous received signal strength indications and packet reception rate measurement values into a fuzzy set, considering membership value combinations of all RSSI and PRR fuzzy categories, calculating a weighted total score and outputting a fuzzy decision value; and the defuzzification module is used for converting into an actual modulation coding selection scheme through a defuzzification process. By adopting the method and the system, the adaptability and the response speed of the system to complex and changeable channel conditions are improved, and the accuracy and the reliability of adaptive modulation coding selection are improved.

The embodiment of the invention provides an intelligent bone drilling self-adaptive control method for a mechanical arm, which comprises the following steps: acquiring displacement deviation of the tail end of a drill bit of an orthopedic surgical robot, obtaining an error change rate through differential calculation, and combining a bone layer type as an input variable of a fuzzy controller and a feeding speed as an output variable; input variables are fuzzified through a membership function, and a fuzzy language variable set is obtained; according to the set and a preset rule base, constructing a multi-rule group structure comprising three types of scenes of free space, cancellous bone and cortical bone, and setting corresponding fuzzy rule subsets for different bone layers; designing a ''minimum-maximum'' rule activation and combination strategy for reasoning by adopting a Mamdani type fuzzy reasoning method according to fuzzification input and a multi-rule group structure, and generating a fuzzification output variable set; and defuzzification is performed through a gravity center method to obtain a continuous speed instruction, and dynamic regulation and control of the drill bit feeding speed are achieved. When approaching a penetration point, the device automatically decelerates and safely stops, so that the safety and accuracy of an operation are effectively guaranteed, and over-drilling injury is prevented.

The invention provides a fuel cell energy management method and device based on fuzzy control, and the method comprises the steps: carrying out the fuzzy processing of a current data value corresponding to an impact factor, and determining a target fuzzy set discourse domain corresponding to the current data value of the impact factor, and the membership degree of the target fuzzy set discourse domain in the target fuzzy set discourse domain; performing fuzzy reasoning in combination with a target fuzzy set discourse domain corresponding to the influence factor and a preset fuzzy rule table, and determining a fuzzy set discourse domain to which the output power of the fuel cell belongs; and according to the fuzzy set discourse domain to which the output power of the fuel cell belongs and the membership degree of the influence factor on the target fuzzy set discourse domain to which the influence factor belongs, performing sharpening processing on the output power of the fuel cell by using a preset defuzzification method, and determining a predicted value of the output power of the fuel cell. Fuel cell power output management is achieved through the fuzzy control theory, the energy utilization efficiency of the fuel cell hybrid power system is improved, and the driving experience of a driver is improved.

The invention provides an intelligent proportion control method and system for an antistatic agent synthesis process, and the method comprises the steps: fuzzifying real-time parameters through real-time and historical process parameters by using an asymmetric membership function constructed based on data distribution skewness and kurtosis; historical data samples are mapped into graph theory nodes, communities are divided through a community discovery algorithm to generate fuzzy rules, initial weights are set, and an initial rule base is constructed; using a recursive least square method to identify rule consequent parameters, combining redundancy rules according to cosine similarity, and combining a particle swarm optimization algorithm to optimize antecedent parameters; and inputting the fuzzification real-time parameters into the optimized fuzzy neural network, calculating the activation intensity of the rule, adjusting the weighted average weight based on the information entropy of the current activation intensity, and obtaining the proportion control quantity of each component after defuzzification.

The invention relates to the technical field of load segmented transfer, in particular to a 10kV distribution line load segmented transfer method and device based on an electric energy meter and a storage medium. Comprising the steps that S1, electrical parameters of key nodes of a 10kV distribution line are monitored in real time based on an intelligent electric energy meter, and the electrical parameters at least comprise voltage, current and a power factor; s2, mapping the monitored electrical parameters to a predefined fuzzy set by using a fuzzy logic algorithm, and reasoning and quantitatively evaluating the overload degree of the equipment based on a fuzzy rule; selecting a to-be-transferred load path based on a Dijkstra algorithm; and after it is confirmed that the load verification of the to-be-transferred path is passed, carrying out load transfer. According to the method, the electrical parameters on the key nodes are monitored in real time, overload evaluation is carried out in combination with the fuzzy logic algorithm and the optimized defuzzification algorithm, and the overload condition can be recognized more accurately even under the condition that current and power factors fluctuate sharply due to frequent starting and stopping of large industrial equipment.

The invention relates to the technical field of virtual reality interaction, and particularly discloses a multi-modal fused virtual exhibition hall immersive interaction system, which comprises the following steps of: acquiring real-time multi-modal interaction data of a user and performing synchronous preprocessing; inputting the orientation semantic words into an interval type-2 fuzzy logic system to generate a three-dimensional space semantic membership field; according to the gesture pointing vector and the hand shaking amplitude, a pointing conical area is constructed, two-dimensional Gaussian distribution is established in a layered mode, and a gesture pointing probability field is generated; taking a semantic membership field, a gesture pointing probability field and a gaze point Gaussian kernel density field as independent evidences, introducing a wall boundary and a floor channel as spatial topology constraints, and fusing by adopting an evidence theory combination rule to generate a three-dimensional intention probability distribution field; carrying out gravity center defuzzification processing on the distribution field to extract a navigation target area, and planning a navigation path by taking the current position of the user as a starting point and taking a nearest floor channel entrance as a passing point; according to the method, the problems of multi-mode fuzzy intention understanding and space adaptation are solved.

The invention provides an air energy heat pump direct current motor intelligent speed regulation method and system based on self-adaptive fuzzy control. The method comprises the steps of obtaining original operation data; extracting dynamic characteristics of a short-time window from the original operation data set, and calculating to obtain a thermal load trend index; constructing a fuzzy controller, wherein the fuzzy controller comprises fuzzification, rule reasoning and defuzzification output; in the fuzzification, a thermal load trend index is mapped into a five-level fuzzy level by adopting a triangular membership function; combining the thermal load trend index with an operation mode to calculate a target wind speed; and the target wind speed is converted into a control signal, feedback data is collected after each regulation and control period is finished, a performance deviation index is constructed based on the feedback data, and correction parameters are output. According to the invention, a resonance frequency point avoidance mechanism and a voltage margin correction strategy are embedded, so that the system is kept stable, low in noise and high in efficiency under high-speed, low-speed or medium-speed operation conditions.

The invention discloses a software quality evaluation method based on a fuzzy theory. The method comprises the following steps: obtaining a quality factor set according to a Meiger software quality model; determining an evaluation grade set, and dividing the evaluation grade set into five evaluation grades; constructing a membership function by adopting a kernel density estimation method; constructing a judgment matrix through the triangular fuzzy number and calculating a defuzzification weight; and converting the score into a fuzzy synthesis value through fuzzy synthesis operation, and generating a fuzzy comprehensive evaluation score through weighted average and normalization processing. According to the method, the fuzzy theory and the Meicole model are combined innovatively, and uncertainty in subjective and objective evaluation is processed by applying fuzzy mathematics. The objectivity, scientificity and repeatability of an evaluation result are effectively improved through a membership function and a weight set constructed based on historical data of similar software. The method is suitable for quality evaluation and optimization decision making of various complex software systems.

The invention relates to the technical field of deep and far sea culture, in particular to an underwater intelligent light supplementing method and system for a deep and far sea culture net cage, and the method comprises the steps: obtaining video image data of fish school movement, and carrying out the preprocessing of the obtained video image data; taking the preprocessed video image data as input to construct a fish identification model, performing fish school behavior analysis by using an output result of the identification model, generating a dynamic light supplementing strategy through a fuzzy PID control algorithm based on a fish school behavior analysis result, converting the dynamic light supplementing strategy into electric signal driving, and performing fish school behavior analysis; the light supplement strategy is adaptively optimized according to the real-time illumination parameters, the weight of the fuzzy rule base is updated online through the recursive least square method, the light intensity error and the change rate of the light intensity error serve as input, the fuzzy rule base is constructed through the fuzzy PID control algorithm, the PID parameter adjustment amount is calculated in a defuzzification mode in combination with the gravity center method, and the light supplement strategy is optimized. The light supplementing strategy can be accurately adjusted according to the fish shoal behavior and the actual illumination condition.

The application discloses a kind of information system access risk assessment methods based on access context logical inference, according to the security element of access subject context, the accessible asset set of access subject is obtained by logical inference of trust calculation and access policy set, then the risk degree of single access subject's comprehensive trust degree, access environment and its actual access asset value degree are carried out fuzzy processing, fuzzy inference and defuzzification, obtain the session access risk of single subject, after processing, the session access risk of entire information system is obtained.The application obtains the access permission of access subject by using trust calculation and logical inference, and introduces access context and reasoning mechanism into risk assessment, carries out fuzzy processing to these contexts related to access, such as environmental risk degree, asset value degree, by quantifying each element of access context process in information system, combined with logical inference and fuzzy inference, the comprehensive evaluation of information system session access risk is realized.

This application relates to the field of stationary time series analysis and prediction technology, and discloses a fuzzy prediction method for non-stationary time series, including: determining the fuzzy posterior probability corresponding to the current sequence; updating the fuzzy parameters of the fuzzy set based on the fuzzy posterior probability; generating a rule base associated with the updated fuzzy parameters; performing probabilistic defuzzification prediction based on the rule base to obtain the prediction result; updating the model parameters of the Gaussian mixture model based on the prediction error set corresponding to the prediction result; determining the KL divergence corresponding to the prediction error set based on the parameters in the model parameters, the first half error set corresponding to the first half window, and the second half error set corresponding to the second half window; wherein the current window consists of a first half window and a second half window; the prediction error set consists of the first half error set and the second half error set; and performing prediction based on the KL divergence to obtain the output sequence. This solution can meet the practical prediction needs of non-stationary time series.

The application relates to an embedded fuzzy voice control method and system based on language operator quantification, and relates to the field of embedded voice control, which comprises the following steps: collecting a voice instruction of a user; performing voice recognition on the voice instruction to generate corresponding text instructions; performing semantic analysis on the text instructions to extract control variables, action directions and language operators; determining a basic fuzzy set based on the control variables and the action directions; determining a deformation operator according to the language operator; combining the deformation operator and the basic fuzzy set to generate an input fuzzy quantity, and collecting a current physical state value; and obtaining an aggregated fuzzy set based on the input fuzzy quantity, the current physical state value and a fuzzy rule library; performing defuzzification calculation on the aggregated fuzzy set to determine a physical control signal value, and driving a physical device to perform corresponding actions according to the physical control signal value. The application has the effect of meeting the actual needs of users for the delicacy of device adjustment.

The invention discloses an acquisition terminal test case priority ranking method based on a fuzzy analytic hierarchy process, which comprises the following steps of: applying the fuzzy analytic hierarchy process to the field of acquisition terminal test, and quantizing fuzzy evaluation opinions of experts by triangular fuzzy through constructing a'target-criterion-scheme 'hierarchical structure; fusing a multi-expert judgment matrix by adopting a weighted geometric averaging method, and optimizing a criterion weight and performing defuzzification in combination with consistency check; and comprehensive priority scores of the test cases are calculated through weighted summation, a sorting sequence from high to low is generated, and meanwhile, dynamic adjustment during demand change is supported. According to the method, the fuzzy demand quantification accuracy can be improved by 60%, the expert dispute rate is reduced to 8% or below, the regression test error detection efficiency is improved by 40%, and the method adapts to the multi-scene test demand of the acquisition terminal.

The application provides a vulnerability risk assessment method and device, constructs a vulnerability attack graph based on an attack path of a system vulnerability, determines an attack income degree in a Nash equilibrium situation based on a fuzzy vulnerability game model as a vulnerability importance degree of a target vulnerability in the vulnerability attack graph, represents the income degree through a triangular fuzzy number, solves the Nash equilibrium after defuzzification, determines a risk parameter of the target vulnerability in combination with the vulnerability importance degree of the target vulnerability, a vulnerability propagation loss and a vulnerability cumulative probability, the attack graph can correlate and comprehensively analyze the vulnerabilities in the network, the game theory can consider the restriction relationship of the attack and defense sides, therefore, on the basis of analyzing the logical relationship of the vulnerabilities, the attack and defense restriction relationship is considered, the defuzzification of the fuzzy number solves the problem that the attack and defense income degree is difficult to quantify due to the uncertainty and fuzziness of the information system, thereby the cumulative probability and the risk parameter of the vulnerability are quantitatively analyzed, and the accuracy of the vulnerability risk assessment is improved.

The application discloses a vehicle active tail wing attack angle control method based on fuzzy logic. The application adopts double active controller design, the input of the main fuzzy controller is vehicle speed and acceleration request value, the output is active tail wing attack angle, the membership function is used for processing, fuzzy rules are made, and the fuzzy quantity of the output of the main fuzzy controller is obtained through fuzzy reasoning; the output of the slave fuzzy controller is tail wing attack angle correction, the input is the difference between the actual and reference of the front and rear wheel slip rate difference and its change amount; the fuzzy rules of the slave fuzzy controller are made, and the fuzzy quantity of the output of the slave fuzzy controller is obtained through fuzzy reasoning; the output of the main and slave fuzzy controllers is de-fuzzied by using the barycentric method, the accurate quantity of the main and slave fuzzy controllers is obtained, finally, the attack angle of the active tail wing can be obtained by combining the output of the main and slave fuzzy controllers, and the slip rate of each tire and vehicle speed are measured as feedback.

The application discloses a driving intention determination method, device and equipment and a readable storage medium, and applies to the technical field of intelligent driving, and comprises the following steps: determining a turning membership degree corresponding to each turning intention information and a lane-changing membership degree corresponding to each lane-changing intention information based on a fuzzy membership method; combining fuzzy relations by using a defuzzification function based on all the turning membership degrees to determine a left-turning probability and a right-turning probability; the defuzzification function is a function for determining an intention probability based on a membership degree and a weight coefficient; combining fuzzy relations by using the defuzzification function based on all the lane-changing membership degrees to determine a left-lane-changing probability and a right-lane-changing probability; and determining a target driving intention based on the left-turning probability, the right-turning probability, the left-lane-changing probability and the right-lane-changing probability. The application determines the membership degrees corresponding to multiple types of intention information, combines all the membership degrees based on the defuzzification function, determines the target driving intention, and improves the accuracy of driving intention determination.

The invention is suitable for the technical field of automation control, and provides a follow-up control method, device and equipment and a storage medium, the method comprises the steps that the actual height between a machining head and a plate is obtained, and the current error and the error change rate between the actual height and the preset target height are determined; performing fuzzification processing on the current error and the error change rate to obtain an error membership degree and a change rate membership degree; according to a preset fuzzy rule table, performing fuzzy reasoning on the error membership degree and the change rate membership degree to obtain a proportion-integration-differentiation (PID) gain membership degree; performing defuzzification processing on the gain membership degree to obtain a PID gain increment; and carrying out superposition processing on the gain increment and the PID fixed gain to generate an adjusted control signal. According to the follow-up control method provided by the embodiment of the invention, the PID gain can be adaptively adjusted so as to cope with follow-up control under complex working conditions, and overshoot or insufficient follow-up response is avoided.

The invention discloses a fuzzy control power dynamic adjustment method of a charging pile node and a related device. The method and the device are used for improving dynamic adaptability and operation stability when the charging pile node is controlled. The method comprises the following steps: carrying out preprocessing calculation on feedback data to obtain a characteristic quantity; performing nonlinear composite deviation calculation on the characteristic quantity of each charging pile node to obtain a comprehensive state deviation; performing fuzzification processing on the comprehensive state deviation and the characteristic quantity to obtain a membership degree value; calculating the trigger intensity of each fuzzy rule according to the membership value, updating the weight of each fuzzy rule based on the trigger intensity to obtain a target weight, carrying out weighted aggregation on the output of each fuzzy rule, and carrying out nonlinear output correction after defuzzification through a centroid method to obtain a target adjustment coefficient; calculating adjustment power corresponding to the charging pile node according to the target adjustment coefficient; and performing unification correction on the adjustment power of each charging pile node according to the total demand power to obtain target power.

The invention relates to a blast furnace molten iron temperature prediction and regulation method, and belongs to the technical field of blast furnace ironmaking production methods. According to the technical scheme, the method comprises the steps that relevant parameters of the blast furnace molten iron temperature are collected, preprocessed, normalized and subjected to feature engineering processing, and a KAN-LSTM-Attention molten iron temperature prediction model is constructed; relevant regulation and control rule knowledge of the blast furnace molten iron temperature is collected, and a rule base containing fuzzy judgment rules and operation rules is constructed in combination with production data and expert experience; and dynamic optimization of the rule base is realized through a fuzzy control method, a molten iron temperature decision model is constructed, fuzzy reasoning and defuzzification operation are performed based on a predicted difference value and related parameters, and an optimal control strategy is selected to realize closed-loop regulation and control of the molten iron temperature. The method has the advantages that the model structure is simplified, deployment feasibility is improved, and accurate control over the temperature of the molten iron in the iron and steel industry is achieved.

The invention discloses a multi-AGV formation keeping control method based on IPSO-fuzzy PID. The multi-AGV formation keeping control method is characterized by comprising the following steps: (1) establishing a dynamic self-adaptive multi-AGV pilot following formation control model; (2) constructing a fuzzy PID controller, setting a fuzzy domain of input and output variables, a membership function and a fuzzy control rule, and obtaining the correction of PID parameters through fuzzy reasoning and defuzzification; (3) constructing an improved particle swarm optimization algorithm with dynamic balance of global search and local optimization, improving the inertia weight and learning factor of a particle swarm, and setting a fitness function; and (4) optimizing the input quantization factor and the output scaling factor of the fuzzy PID controller by using an IPSO algorithm, assigning optimized parameters to the controller, and adjusting the linear velocity and the angular velocity of the following AGV. The formation keeping precision, the response speed and the anti-interference capability can be improved.

The invention discloses a building environment self-adaptive regulation and control method and system based on a space-time attention mechanism, and the method comprises the steps: collecting environment data, such as temperature, flow density, humidity and air pressure, and constructing a three-dimensional thermodynamic model through a dynamic weighted Bayesian collaborative fusion algorithm; the method comprises the following steps: processing environment data by virtue of a space-time cross attention network, and generating a regulation and control priority map with a dynamic space-time weight through operations of dynamic multi-feature embedding, parallel space-time self-attention, cross attention fusion and the like; and the fuzzy-PMV decision engine fuzzifies the atlas and PMV comfort indexes, outputs an equipment regulation and control instruction set through expert rule matching and defuzzification, and realizes closed-loop control from environmental data to equipment actions. Compared with the prior art, the method can accurately analyze the space-time correlation of building environment parameters, adaptively regulate and control equipment, improve the environment comfort and regulation and control efficiency, has the advantages of energy conservation and intelligence, and is suitable for the field of intelligent building environment control.

The invention discloses a permanent magnet synchronous motor sensorless control method for a variable universe fuzzy sliding mode observer, and the method comprises the steps: obtaining an error equation of the stator current of a permanent magnet synchronous motor based on a stator voltage equation of the permanent magnet synchronous motor under a coordinate system; designing a sliding mode surface function as an error of stator current of the permanent magnet synchronous motor, constructing a sliding mode control rate based on a power reaching law and a switching function, and obtaining a back electromotive force according to an equivalent control principle of a sliding mode; acquiring the position and the rotating speed of a rotor; designing a two-dimensional fuzzy controller, and controlling the change of a power reaching rate parameter by adopting fuzzy reasoning and defuzzification processing; designing a discourse domain scaling factor, and adjusting the discourse domain of the input quantity and the output quantity of the two-dimensional fuzzy controller in real time according to the operation state of the system; and dynamically adjusting parameters of the power reaching rate to realize self-adaptive optimization operation of the sliding-mode observer. According to the embodiment of the invention, the buffeting phenomenon of a traditional sliding-mode observer can be inhibited, and the observation precision and robustness of the sliding-mode observer are improved.

This invention discloses a fuzzy logic energy-saving control method for a mechanical jigging screen, relating to the field of mineral sorting equipment control technology. The method includes real-time acquisition of raw multi-source state data from the equipment, preprocessing to generate a standardized state feature set containing normalized feed load characteristics, particle size distribution feature vectors, bed thickness variation trend characteristics, and motor power spectrum characteristics. This set is then input into a preset fuzzy inference engine, where fuzzy logic operations are performed using a predefined and tuned fuzzy rule base. The output is a fuzzy set of optimal driving parameters covering fuzzy suggestions for screen stroke, stroke frequency, and acceleration. This set is then defuzzified and converted into deterministic driving command parameters. This method enables precise adaptation of the equipment's multi-source operating states and driving parameters, enhances the collaborative adjustment capability of multi-dimensional driving parameters, optimizes equipment operating adaptability, and achieves the technical effects of dynamic matching of operating conditions and energy-saving regulation.

The invention belongs to the technical field of air-ground collaborative target recognition, and particularly relates to an air-ground collaborative target recognition method based on fuzzy integral decision-level fusion, which comprises the following steps: S0, acquiring acquired data; forming a set Gkcv by the data of each source for the same possible result in the same aspect of the same target; s1, setting a fuzzy density interval gi of the unmanned vehicle and the unmanned aerial vehicle; s2, solving a value of a correlation parameter lambda by using a Newton iteration method; s3, defining a subset Ai as a subset composed of the first i elements of the set Gkcv; calculating a fuzzy density interval w'i of the subset Ai; s4, sorting the confidence values in the set Gkcv from small to large; s5, performing data fusion by combining the sorted confidence values and the fuzzy density interval w'i of each subset Ai to obtain a corresponding fusion confidence interval; and S6, performing defuzzification on the fusion confidence interval to obtain corresponding information. According to the method, air-ground collaborative multi-dimensional heterogeneous target information can be effectively fused, and a high-confidence identification decision is output in a complex environment.

The invention provides an automatic flood monitoring method and system based on SAR (Synthetic Aperture Radar) data. The method comprises the following steps: calculating a basic SAR membership degree according to dual-polarization SAR data by utilizing fuzzy logic; calculating a radar reflection property membership degree according to the ground feature classification result and the monitored SAR decibel value change data by using fuzzy logic, and calculating a hydraulic membership degree according to the hydrological parameters by using the fuzzy logic; defuzzification is carried out on the basic SAR membership degree, the radar reflection property membership degree and the hydraulics membership degree by using a product method, the probability that each pixel is a water body is determined, and a water body probability graph is generated; and predicting a water body range based on the water body probability graph. According to the automatic flood monitoring method and system based on SAR data provided by the invention, the influence of ground objects such as vegetation and buildings on water body extraction is solved, and the interference of other ground objects (such as roofs, playgrounds and roads) with relatively low reflectivity on a water body recognition result is reduced.

The application relates to the technical field of automatic driving, and discloses a control method, a system, equipment and a medium for a mine area automatic driving vehicle, the method comprising the following steps: determining a first road area where a vehicle is located, and acquiring preset road condition parameters of the area and a second adjacent area; combining vehicle multi-type information, outputting a comprehensive fuzzy set through a preset fuzzy rule base, and generating dynamic weights for the two types of road condition parameters through a defuzzification algorithm; based on dynamic weight weighted fusion, obtaining a current road section target basic risk index and a next road section target predicted risk index; acquiring instant vibration data of the vehicle in the first area, and processing the instant vibration data to obtain an instant jolt index; and weightedly fusing the three types of indexes to obtain a fusion control index, and adjusting vehicle driving parameters according to the fusion control index. The application can comprehensively perceive and integrate mine area road conditions and vehicle states, thereby effectively improving the safety and adaptability of automatic driving.