15 results about "Fuzzy process" patented technology

The invention belongs to the technical field of intelligent control, and provides an air conditioner optimization control system and method based on temperature and humidity real-time monitoring, and the system comprises an air conditioner subsystem, a monitoring subsystem and a control subsystem; the air conditioner subsystem comprises a main machine assembly, a dry coil pipe and an electric adjusting valve. The monitoring subsystem is used for collecting temperature and humidity data in the clean room; and the control subsystem is used for performing difference operation on the temperature and humidity data and a preset temperature and humidity threshold value to obtain a temperature and humidity deviation value, performing fuzzification processing on the temperature and humidity deviation value to generate an initial control quantity, adjusting the initial control quantity through a PID controller to obtain an opening control quantity, and controlling the opening of the electric control valve according to the opening control quantity. According to the scheme, due to the fact that the fuzzy processing and PID adjusting modes are adopted in the control process, the more accurate opening control quantity can be obtained, and then the accuracy and reliability of the air conditioner control link are improved.

The application discloses a kind of semi-active suspension control method, vehicle controller, system, car and medium.The method comprises: obtaining the first detection data and second detection data collected in the process of vehicle driving;First detection data and second detection data are fuzzy processed, and the first fuzzy state corresponding to first detection data and the second fuzzy state corresponding to second detection data are obtained;According to the first fuzzy state corresponding to first detection data and the second fuzzy state corresponding to second detection data, determine target drive voltage;According to target drive voltage, control piezoelectric element in damper to work.The method does not need to use any accurate dynamics differential equation to carry out complex modeling calculation, not only can improve the efficiency of obtaining target drive voltage, and when realizing the active control of damper damping force in semi-active suspension, it is easy to engineering, with good real vehicle control effect, can improve the control effect of damper damping force in semi-active suspension.

The application provides a kind of intelligent textile printing and dyeing process optimization method and system, according to the parameter of membership function determined according to the characteristics of current batch material, fuzzy process characteristics are obtained by fuzzy processing of real-time process parameters using membership function;According to the correlation degree of the historical prediction accuracy of fuzzy rule and process benefit, the activation contribution of each fuzzy rule is calculated, and then the process coordination state is obtained;Sensor time series data and process coordination state are input into multi-modal time series main capsule layer to obtain main capsule output vector, calculate the confidence of the vector, and adjust the influence of the vector when it is routed to the upper capsule layer subsequently according to the confidence, the upper capsule layer generates a set of control parameters for the subsequent process;Obtain the quality detection data of subsequent process, obtain multi-objective deviation vector according to quality detection data and control parameter set, and use multi-objective deviation vector to optimize the upper capsule layer, to obtain the optimized control parameter set.

The application provides a vehicle fault diagnosis method and device based on multi-source data fusion and a storage medium. The method comprises: collecting real-time state data and real-time alarm data of a target part, obtaining external environment data and map navigation data; after data preprocessing, the multi-source data set target features are obtained by fusion, the target features extracted from the multi-source data set are input into the adaptive neural fuzzy inference system, the target features are mapped to multiple fuzzy sets, and the member function is used for fuzzification; the rule weight of each target feature is calculated to reflect the fuzzy strength; the neural network learning algorithm is used to train the inference system, the member function and the rule weight are adjusted, the fuzzy output is converted into a specific numerical value or a fault level in the defuzzification process by using the output member function, so that the health state of the part is judged. The application provides a fault diagnosis output with high interpretability, improves the accuracy, and can perform fault diagnosis in a wide range of vehicle operating environments.

The invention relates to the technical field of expressway foreign matter detection, in particular to an expressway foreign matter detection method and system based on a visual large model, and the method comprises a training stage and an application stage. In the training stage, virtual clear features are generated through a visual large model generator, a differentiable physical degradation module is combined to simulate a fuzzy process, and a cross-modal attention mechanism is utilized to align the features; in the application stage, a to-be-detected image is input, and foreign matter positions and categories are output. According to the invention, a dynamic fuzzy enhancement technology and an anchor-frame-free detection structure are adopted, the detection precision and efficiency are improved, and the reliability is enhanced through a confidence judgment mechanism. The problem of insufficient detection precision in the motion blurred image can be effectively solved, the hardware resource demand is reduced, and the method is suitable for real-time detection in a high-speed dynamic scene.

The application relates to a target detection pre-tracking method and system based on fuzzy control optimized particle filtering, and belongs to the field of target tracking. Particle states are predicted, and particle weights are calculated, the particle with the largest weight is regarded as an optimal particle, and the space near the optimal particle is regarded as an optimal region; the distance between the particle and the optimal particle and the particle ratio in the optimal region are calculated and taken as inputs of a fuzzy controller, a moving coefficient is output after a process of fuzzification, fuzzy reasoning and defuzzification; the particle state is updated according to the moving coefficient, and the particle with a lower weight is moved to a high-likelihood region; the iteration is performed for multiple times until the number of particles in the optimal region exceeds a threshold value; and the state of the target is estimated according to the particle state. The application can effectively alleviate the particle impoverishment problem, improve tracking accuracy, control particle movement through the fuzzy controller, be adaptively applied to different environments and scenes, and has good robustness and interpretability, and the rules can be expressed in natural language.

The invention provides a wind and light storage micro-grid control optimization method and system based on adaptive fuzzy reinforcement learning, and relates to the field of micro-grids, and the method comprises the steps: building a wind and light storage micro-grid control optimization model based on an Actor-Critic neural network; acquiring real-time change information of the wind power and the illumination intensity, and performing fuzzy processing on the real-time change information of the wind power and the illumination intensity; determining a current reinforcement learning parameter based on the real-time change information of the wind power and the illumination intensity after fuzzy processing; the wind and light storage micro-grid control optimization strategy is generated based on the current reinforcement learning parameters through the wind and light storage micro-grid control optimization model, and the method has the advantage that the system stability and the robustness in the aspect of the fluctuation coping capacity are improved.

The invention provides an intelligent control method and system for a semi-autogenous grinding-ball milling grading loop. The method comprises the steps that input variables and output variables of the intelligent control system for semi-autogenous grinding-ball milling grading are obtained; on the basis of data in a pre-constructed historical data sample library and a typical working condition sample library of the semi-autogenous grinding-ball milling grading process system, fuzzification processing is conducted on the input variables and the output variables, and the subordination relation between the basic discourse domains of the input variables and the output variables and the corresponding states is obtained; determining the current related working condition based on the basic universe and membership of the input variable and the output variable; determining a target control rule in a plurality of control rules in a pre-constructed core control logic algorithm library in combination with expert knowledge and the working condition; and controlling an execution mechanism in the semi-autogenous grinding-ball milling grading loop to operate based on the target control rule.

The application relates to an intermittent sampling and forwarding interference threat evaluation method and relates to the field of radar anti-interference. The application is aimed at solving the problem that the existing interference threat evaluation method has slow recognition speed and low accuracy when applied to intermittent sampling and forwarding interference threat evaluation. The application comprises the following steps: establishing an evaluation parameter index set; fuzzy processing the evaluation parameter index set based on the membership function of the evaluation parameter index to obtain an evaluation parameter index intuitionistic fuzzy subset; establishing a reasoning rule base based on the mapping relationship between the intermittent sampling and forwarding interference and the interference threat grade; obtaining a total intuitionistic fuzzy relationship by using an intuitionistic fuzzy reasoning synthesis algorithm, and obtaining the relationship between the interference threat grade intuitionistic fuzzy subset, the total intuitionistic fuzzy relationship and the evaluation parameter index; and de-fuzzifying the interference threat grade intuitionistic fuzzy subset to obtain the clear quantity of the intermittent sampling and forwarding interference threat. The application is used for evaluating the intermittent sampling and forwarding interference threat.

The application provides a kind of detection method, system, storage medium and computer of vehicle plastic parts, the method comprises: real-time acquisition vehicle plastic parts is detected image, and corresponding separation threshold is constructed, and according to separation threshold, feature separation is carried out to the image to be detected to obtain feature image;Increase regression prediction in convolution neural network model to obtain deep learning model;Get the labeled image of detection completed, and the labeled image is fuzzy processed, the obtained fuzzy image and labeled image are calculated, the obtained fuzzy degree is input into deep learning model for optimization, to obtain deep learning optimization model;Feature image is input into deep learning optimization model for image processing, and the detection result of image to be detected is generated according to image processing result.The application optimizes deep learning model using fuzzy degree, and uses the constructed deep learning model to carry out image processing to feature image, to generate corresponding detection result.

The present invention relates to the field of robotics, and more particularly to a quadruped robot gait control method and the quadruped robot thereof. The quadruped robot gait control method comprises the following steps: S1: acquiring source domain and target domain data, performing fuzzy processing on the source domain data using a fuzzy controller, and training an error back propagation neural network model using the fuzzy processed source domain data and target domain data; S2: deploying the optimized error back propagation fuzzy neural network model to a simulation model of the quadruped robot for verification; S3: deploying the optimized error back propagation neural network model to an algorithm platform for verification; and S4: deploying the optimized error back propagation neural network model to an algorithm verification machine for verification. The error back propagation neural network model is trained using the fuzzy processed source domain data and target domain data to obtain an optimized error back propagation neural network model, so that the gait parameter adjustment function in the virtual model controller has self-learning and self-adaptive capabilities.

The invention relates to the technical field of sensitive data processing, and discloses a sensitive data processing method, device and equipment and a storage medium. A structural sensitive data set stored in a sensitive data server and sensitive data calling demand information of a data calling terminal in a target calling period are obtained; in consideration of storage space limitation and fuzzy decryption efficiency of a sensitive data server, the sensitive data server is driven to construct a sensitive data fuzzy encryption list and a fuzzy decryption key corresponding to each data calling terminal in a target calling period based on sensitive data non-full element encryption and dynamic key management principles; the sensitive data element fuzzy encryption and fuzzy decryption processing based on the structural sensitive data difference under the service operation system is realized, and the server processing load and the data calling delay are reduced while the sensitive data fuzzy processing fine granularity and the data calling adaptability are improved; and the sensitive data security and availability requirements of the service operation system are balanced.

The invention provides a vehicle plastic part detection method and system, a storage medium and a computer, and the method comprises the steps: obtaining a to-be-detected image of a vehicle plastic part in real time, constructing a corresponding separation threshold value, and carrying out the feature separation of the to-be-detected image according to the separation threshold value, and obtaining a feature image; adding regression prediction in the convolutional neural network model to obtain a deep learning model; obtaining a detected marked image, carrying out fuzzy processing on the marked image, carrying out ambiguity calculation on the obtained blurred image and the marked image, and inputting the obtained ambiguity into a deep learning model for optimization so as to obtain a deep learning optimization model; and inputting the feature image into the deep learning optimization model for image processing, and generating a detection result of the to-be-detected image according to an image processing result. According to the method, the deep learning model is optimized by using the ambiguity, and the feature image is processed by using the constructed deep learning model, so that the corresponding detection result is generated.

The present application relates to a multi-dimensional value evaluation method and system for scientific and technological information viewpoints based on fuzzy cumulative prospect theory, the method comprising the following steps: S1: constructing a scientific and technological information viewpoint value evaluation index system and calculating the accurate scores of several dimensions; S2: triangular fuzzy processing the accurate scores of several dimensions and calculating the comprehensive prospect value based on cumulative prospect theory; S3: linear normalization of the comprehensive prospect value of several dimensions to obtain the standardized score; S4: generating the star rating of the scientific and technological information and the corresponding decision suggestion. The system comprises an index calculation module, a fuzzy processing and prospect value calculation module, a linear normalization module and a grade evaluation module. The present application can realize comprehensive, objective and intelligent evaluation of the value of scientific and technological information viewpoints, and provides a scientific, intelligent, practical and interpretable value evaluation solution for the field of scientific and technological information analysis and decision support.

The invention provides a method and a system for controlling the bonding strength of a grid interlayer and paper. The method comprises the following steps: acquiring input parameters such as environment temperature and humidity, paper porosity, adhesive viscosity and production line speed; fuzzy processing is carried out on each parameter, a discourse domain is divided into non-equidistant fuzzy level subsets, the division density of a target bonding strength sensitive interval is improved, an asymmetric membership function is constructed, a Gaussian function is adopted in an ascending section, and adjustment is realized through historical data weighted smooth fitting according to a confidence index of a previous control period in a descending section; performing fuzzy reasoning on the basis of a preset fuzzy rule base, adjusting the weight of each rule, and generating a fuzzy output quantity in combination with the weight and a rule of rule triggering intensity aggregation activation; and performing defuzzification and correction on the fuzzy output quantity to generate a final control quantity, and updating the fuzzy rule weight with the maximum contribution by using a difference value.