23 results about "Fuzzy inference engine" patented technology

The invention relates to an intelligent control method, system, equipment and medium for automatic discharging mining tunneling cutting picks, and the method comprises the following steps: monitoring the flowing state of coal rock slag in a spiral discharging channel in real time, collecting a channel pressure difference signal and a cutting pick vibration signal, and generating discharging state characteristic parameters; blocking risk quantification is carried out on the characteristic parameters, and a real-time blocking index is generated through weighted fusion calculation; based on the index, a cutting head rotating speed optimization adjustment amount is generated through a fuzzy reasoning mechanism; and finally, generating a rotating speed control instruction through dynamic amplitude limiting processing and sending the rotating speed control instruction to a variable-frequency driving system. By means of the method, real-time sensing and dynamic self-adaptive adjustment of the blockage risk of the discharging channel are achieved, the problem that the discharging channel is blocked due to coal rock property changes is effectively solved, the discharging efficiency and the stability of tunneling operation are improved, and the service life of cutting teeth is prolonged.

The invention relates to a multi-motor cooperative control method, system and device based on position and speed double feedback and a self-adaptive sliding mode and a medium, and relates to the technical field of multi-motor cooperative control. The multi-motor cooperative control method comprises the following steps: acquiring an actual position signal and an actual speed signal of each motor, and converting and calculating a position synchronization error and a speed synchronization error between the motors; calculating a position tracking error and a speed tracking error of each motor according to a preset reference position signal and a reference speed signal; fusing the weighted operation position synchronization error, the speed synchronization error, the position tracking error and the speed tracking error to construct a sliding mode surface function; iteratively optimizing the sliding mode surface function according to a preset fuzzy reasoning mechanism, outputting a self-adaptive adjustment amount, updating the original switching gain, and calculating a new switching gain; and calculating the final control quantity of each motor according to the new switching gain, the sliding mode surface function and the saturation constraint function, and outputting the final control quantity to a corresponding motor driver to drive the motor to move.

This invention relates to a cascaded fuzzy feedforward direct-drive speed control method and system based on LC filtering. The method includes: obtaining a speed error and error rate of change from a reference speed and a real-time measured speed; inputting the speed error and error rate of change into a Mamdani fuzzy inference engine to output a power setpoint coefficient; inputting the power setpoint coefficient and a normalized measured speed signal into a two-dimensional torque-current reference lookup table, performing bilinear interpolation calculation, and outputting an electromagnetic torque command; converting the electromagnetic torque command into a quadrature-axis command current, generating a PWM signal through current closed-loop regulation and SVPWM modulation, and driving a two-level voltage-type inverter; the output of the two-level voltage-type inverter drives a low-voltage permanent magnet synchronous motor through an LC sine wave filter with an RC damping branch. Compared with the prior art, this invention has advantages such as low computational load, improved dynamic response, and enhanced disturbance rejection.

The invention relates to the technical field of avionics, and discloses a friend behavior prediction and multi-platform autonomous decision-making method and system during communication interruption, and the method comprises the steps: constructing a multi-level fuzzy inference tree, and carrying out the fuzzy processing of multi-source heterogeneous input information, and forming a plurality of fuzzy sets; decomposing the complex input relation into a plurality of subsystems based on a hierarchical fuzzy reasoning mechanism, and obtaining a subtask response benefit value through layer-by-layer reasoning; input variables are fuzzified based on a triangular membership function, and a potential task allocation scheme of the friend formation is predicted in combination with a hierarchical rule reasoning mode. According to the method, centerless, low-dependence and high-robustness multi-platform autonomous task allocation can be realized, and the collaborative decision-making capability and task execution efficiency of formation members in a complex environment under an incomplete information condition are improved.

The invention provides a boundary flow control method and system based on a busy water area macroscopic basic diagram, electronic equipment and a storage medium, and the method comprises the steps: building a water area macroscopic basic diagram which can accurately reflect the macroscopic law of mixed traffic flows of different ship types through collecting and processing the data of a ship automatic recognition system; determining an optimal accumulated ship number target when the overall navigation efficiency of the system is highest; delimiting an easily-congested key area in the target water area as a core water area, and establishing a ship traffic flow dynamic balance model taking the optimal target as a balance point for the core water area as a control object; a fuzzy self-adaptive PID controller is designed and applied, the PID controller can sense the deviation between the actual number of ships in a core water area and a target value and the variation trend of the deviation in real time, control parameters are intelligently adjusted on line through a built-in fuzzy inference engine, the optimal boundary flow control rate is dynamically calculated and output, and therefore the flow of the ships entering the core water area is accurately adjusted. The number of the ships in the water area is actively and stably maintained in the optimal interval.

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 oil and gas field development, and discloses a crude oil treatment inlet liquid steady flow control method based on fuzzy cascade PID, which comprises the following steps: determining fuzzy cascade PID regulation loop control logic of an inner loop control loop and an outer loop control loop, the input and output of the outer loop control loop are respectively a pressure buffer tank target liquid level and an inlet pressure buffer tank flow control valve opening; the input and the output of the inner ring control loop are respectively the opening degree of a flow control valve of an inlet pressure buffer tank and the opening degree of a gulp valve; determining initial domains of discourse of input and output parameters of a fuzzy inference engine, and performing fuzzification processing; determining fuzzy rule tables of the inner and outer ring fuzzy inference engines; the target liquid level of the pressure buffer tank serves as loop input, initial parameters of an inner ring PID controller and an outer ring PID controller are determined, the parameters are continuously optimized under the action of a fuzzy inference engine, and steady flow control is achieved. The stability of crude oil liquid inlet flow can be greatly improved, the processing capacity of a process rear-end device is met, and light-load operation of the pressure buffer tank is guaranteed.

The invention discloses an efficient full feeding system and method for a high-pressure roller mill, and belongs to the technical field of high-pressure roller mill feeding control. According to the system, multiple parameters such as the ore feeding amount, the material level and the pressure are collected in real time through the sensing module, the optimal pressure set value is dynamically calculated through a fuzzy inference engine of the control module, then an adjusting instruction is generated through the cascade control module, and finally the roller is driven by the execution module to change the speed. According to the invention, the problems that the high-pressure roller mill is easy to cause material blockage and production halt due to unstable material level of the feeding hopper and the working pressure and frequency are difficult to accurately adjust manually are solved. According to the invention, the full ore feeding state can be automatically maintained, and the working pressure and frequency are stabilized in a high-efficiency interval, so that material blockage accidents are effectively prevented, continuous and stable production is guaranteed, the rolling efficiency is improved, the product quality is stabilized, and the energy consumption is reduced.

The application discloses a kind of intelligent high-voltage switch's circuit breaker body fault diagnosis method, device, equipment and storage medium, method includes: obtaining the historical fault data of circuit breaker in high-voltage switch, according to historical fault data determine suspected fault component set;Based on suspected fault component set, historical alarm information and fault reason are classified, determine the alarm information and fault reason corresponding to each fault component;Alarm timing information in historical fault data is checked with timing information constraint, and removes error fault information;The mapping relationship between the fault reason of each fault component and alarm information is analyzed using fault tree module;Based on the mapping relationship, the alarm information of target fault event is obtained by matrix form iteration operation by fuzzy inference mechanism, and the fault probability representation of high-voltage switch circuit breaker is obtained, and the fault diagnosis result is determined according to the fault probability representation of high-voltage switch circuit breaker.Effectively improve fault diagnosis efficiency.

This invention discloses an energy management method for hybrid energy storage systems based on fuzzy model predictive control. It constructs a linear variable parameter model predictive control architecture incorporating fuzzy decision-making for closed-loop optimization control. First, an LPV prediction model for the system is established, and the load power sequence is introduced as a measurable feedforward disturbance into the state equation. During the rolling optimization preparation stage, a fuzzy inference mechanism is used to analyze multi-dimensional operating conditions such as system power fluctuations, SOC, and SOV in real time, dynamically adjusting the weighting factors of energy tracking, battery loss, and current smoothing in the cost function to achieve an adaptive trade-off in the control strategy. Simultaneously, a hierarchical setpoint tracking strategy is adopted, intelligently switching the voltage state reference target based on the real-time energy level of the supercapacitor. Finally, based on the time-varying model, dynamic weights, and the reference target, a quadratic programming problem is solved, outputting the optimal control quantity applied to the power converter, thereby significantly improving the system's adaptive coordination capability and overall operating efficiency under complex and variable operating conditions.

The application provides a TBM jamming risk intelligent early warning method and system based on knowledge-data double driving, which comprises the following steps: acquiring multi-source parameter information collected in the TBM tunneling process of TBM tunneling cases under different stratum tunneling conditions, and constructing a multi-source parameter database; processing the data in the multi-source parameter database, and calculating data-driven jamming probability based on a robust time sequence anomaly detection model for abnormal changes; extracting expert experience knowledge based on TBM jamming literature research and experience knowledge in actual construction cases, and constructing a TBM tunneling jamming event knowledge base; determining the membership degree and the reliability of the fuzzy condition, constructing a TBM tunneling jamming event rule reasoning machine, and calculating knowledge-driven jamming probability based on a fuzzy reasoning machine model; multiplying the data-driven probability and the knowledge-driven probability by using a factor multiplication method, calculating the TBM tunneling jamming probability, and obtaining the TBM tunneling jamming risk prediction result based on the knowledge-data double driving.

Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, a reservoir model is received. The reservoir model includes a static model and a dynamic model. The static model includes one or more clusters of a three-dimensional volume of the reservoir and an uncertainty quantification generated using a neural network. The dynamic model includes pressure values and fluid saturation values propagated across the three-dimensional volume through a nodal connectivity of neighboring clusters. A set of input features is generated from the static model and the dynamic model. The set of input features is related to a drilling attractiveness of a target region of the reservoir using a set of rules executed by a fuzzy inference engine. A quantification of the drilling attractiveness is generated. A recommendation for drilling in the reservoir is output based on the quantification of the drilling attractiveness.

The invention relates to the technical field of photoelectric pods, and discloses a fuzzy PID control method and servo system for a photoelectric pod, and the method comprises the steps: 1, obtaining the speed error e, the speed error change rate ec and the speed gain Kv of a brushless motor in real time; 2, e, ec and Kv serve as three-dimensional input variables to be sent into a fuzzy inference engine, and proportional gain correction delta Kp, integral gain correction delta Ki and differential gain correction delta Kd are output; 3, performing gravity center method ambiguity resolution on the delta Kp, the delta Ki and the delta Kd to obtain real-time PID (Proportion Integration Differentiation) parameters Kp, Ki and Kd; and 4, calculating a PWM duty ratio through real-time PID parameters, and driving the brushless motor to drive the photoelectric pod frame to rotate. According to the method, the speed gain serves as fuzzy input, so that the control algorithm can output more reliable control quantity, and the requirements for high precision and rapidity when the photoelectric pod is controlled by using a brushless motor can be met.

This invention discloses a method, system, equipment, and medium for real-time quality assessment in workshop production processes, specifically relating to the fields of intelligent manufacturing and industrial automation control technology. The method includes: S1 collecting multi-source heterogeneous data and forming a fused feature vector through spatiotemporal matching; S2 constructing a digital twin benchmark model based on historical good product data to generate a dynamic time-varying tolerance band; S3 dynamically adjusting thresholds based on equipment health status and calculating a multi-dimensional comprehensive deviation index; S4 matching rules using a fuzzy inference engine to output normal, warning, and out-of-control status labels and potential root cause hints; and S5 storing data and incrementally updating the model using newly added good product data. This invention solves the problems of poor real-time performance, high false alarm rate, and difficulty in root cause localization in traditional assessment methods through multi-source data fusion, dynamic threshold adaptation, equipment status perception, and model iterative optimization. It improves the accuracy, adaptability, and intelligence level of quality assessment, making it suitable for real-time quality control in workshop production processes.

The invention provides a composite control method and system based on a fuzzy control algorithm and PID, and the method comprises the steps: obtaining an output error and an error change rate of a system in real time, and obtaining a state variable of the system; taking the output error, the error change rate and the state variable as input, and outputting a proportionality coefficient, an integral coefficient and a differential coefficient at the same time through a fuzzy inference engine; forming a parameter self-tuning PID (Proportion Integration Differentiation) controller by utilizing a proportionality coefficient, an integral coefficient and a differential coefficient, and calculating a control signal; and the control signal is output to a servo driver of the servo load simulator system to control an execution mechanism to act. All working conditions and nonlinear self-adaption of controller parameters are achieved through the fuzzy-PID fusion controller, and compared with a traditional PID controller, response can be made stably through self-adaption adjustment of the parameters in the face of load sudden change and other complex working conditions, overshoot is effectively reduced, the adjustment time is shortened, and the inherent high precision of the PID controller can be kept.

The invention discloses a design method of a fuzzy system with a center average defuzzifier, and the method comprises the steps: firstly determining a fuzzy rule number to obtain a fuzzy rule base, then defining an input fuzzy set membership function in a fuzzy rule, and determining the center and width of the membership function, and finally, determining the center of an output fuzzy set in the fuzzy rule to obtain a fuzzy system composed of a fuzzifier, a fuzzy rule base, a fuzzy inference engine and a defuzzifier. The method has the advantages that the fuzzy system principle is simple, the algorithm is easy to implement, and the universal approximation characteristic is achieved.

The application relates to an interpretable traffic cognition method based on fuzzy theory and belongs to the technical field of artificial intelligence. Real-time traffic data of a traffic scene is organized into an external attribute feature matrix, a traffic cognition feature matrix and an adjacency matrix. The preprocessed external attribute feature matrix is sent into a fuzzy reasoning mechanism, and an attribute influence feature matrix is output after feature calculation. The matrix, the traffic cognition feature matrix and the adjacency matrix are input into a graph convolutional neural network, and then input into a time characteristic capturing network based on a gated recurrent unit. Finally, a prediction result is output and used for attribute influence fuzzy classification and traffic cognition. The application can efficiently extract time and space dependence characteristics between multiple roads in a traffic scene, has higher traffic data cognition performance and interpretability, and solves the problems of low transparency, poor interpretability of a deep neural network and insufficient consideration of external attribute features of a traffic scene in a traffic cognition process.

The invention provides a distributed Internet of Things video data real-time analysis system and method, and the method comprises the steps: deploying video analysis units at a plurality of edge nodes, extracting the load index of each node, and achieving the quantification of the operation pressure of the nodes; an edge computing alliance is constructed based on the network topology distance between the nodes, the resource type and the historical cooperation efficiency, and periodic interaction between node loads and task metadata is achieved; when the node load exceeds a preset threshold value, migrating a part of video analysis tasks to low-load nodes for execution in the alliance in combination with task calculation feature similarity and node available computing power; in the migration process, node load changes are monitored, a collaboration degree score is calculated based on a fuzzy reasoning mechanism, and the collaboration degree score is used for dynamically adjusting weight parameters of a migration decision function so as to optimize task migration distribution and a node collaboration sequence. According to the technical scheme, dynamic collaborative scheduling of the video analysis tasks between the edge nodes can be realized, so that the overall operation robustness of the distributed Internet of Things video data analysis process is improved.

The invention discloses a monofilament production line tension cooperative control system and method based on neural network fuzzy adaptive PID. The system is composed of a distributed sensing layer, an execution driving layer and an intelligent control layer. The intelligent control layer adopts a neural network fuzzy adaptive PID control algorithm, optimizes a membership function and a rule weight of a fuzzy inference device on line through a neural network, dynamically sets parameters of a corresponding PID controller according to tension real-time deviation and a deviation change rate of each key process point, and executes an embedded multi-transmission-point coordination rule, so as to realize the optimal control of the tension of each key process point. And intelligent cooperative control of each transmission point is realized. According to the method, the self-learning ability of the neural network and the interpretability of fuzzy logic are fused, the problems that a traditional PID is poor in adaptability, a fuzzy controller depends on fixed expert experience, and multi-transmission-point collaboration is insufficient are solved, and the tension stability and uniformity and product consistency in the monofilament production process are remarkably improved.

The invention relates to the technical field of centrifugal machines, and particularly provides a self-adaptive fuzzy PID control disc type centrifugal machine rotating speed adjusting method and system, and the method comprises the steps: obtaining a rotating speed error, an error change rate, a load torque change rate and a friction coefficient variable quantity; a fuzzy inference engine is constructed based on a preset parameter self-adjustment rule base, and PID parameter adjustment amount is obtained with the rotating speed error, the error change rate, the load torque change rate and the friction coefficient change amount as input variables of the fuzzy inference engine; and the PID control parameters are corrected according to the PID parameter adjustment amount, and the rotating speed of the disc type centrifugal machine is adjusted in real time according to the PID control parameters. According to the method, the load torque change rate and the friction coefficient change quantity are introduced to serve as input of the fuzzy inference engine, the load torque change rate serves as a disturbance feed-forward item, the working condition adaptability can be enhanced, and the defects that PID parameters are fixed, variable working conditions are difficult to adapt, and overshoot or slow response is likely to be caused in traditional PID control are overcome.

The application relates to the field of avionics, and discloses a method and system for friendly behavior prediction and multi-platform autonomous decision-making in communication interruption, which comprises the following steps: constructing a multi-level fuzzy reasoning tree, fuzzy processing multi-source heterogeneous input information to form multiple fuzzy sets; decomposing complex input relations into several subsystems based on a hierarchical fuzzy reasoning mechanism, and obtaining a subtask response benefit value through layer-by-layer reasoning; and fuzzy processing input variables based on a triangular membership function, and predicting a potential task allocation scheme of a friendly formation in combination with a hierarchical rule reasoning mode. The application can realize multi-platform autonomous task allocation without a center, with low dependence and high robustness, and can improve the cooperative decision-making ability and task execution efficiency of formation members under non-perfect information conditions in a complex environment.

This invention discloses an intelligent diagnostic expert system for underground drainage pipe networks based on fuzzy rules for water flow velocity, comprising external factors, internal pipe network processes, water flow velocity data, and a working condition analysis and diagnostic system. The working condition analysis and diagnostic system in this expert system is implemented using fuzzy rules for water flow velocity, including two parts: establishing a fuzzy rule base and fuzzy inference. Establishing the fuzzy rule base includes the steps of: determining the fuzzy inference engine structure, determining the fuzzy subset membership functions, establishing fuzzy control rules, and identifying water flow velocity parameters. Fuzzy inference includes the steps of: fuzzifying water flow velocity data, establishing a fuzzy inference engine, and defuzzifying the data. This invention, using fuzzy rules for water flow velocity to implement an intelligent diagnostic expert system for underground pipe networks, has the characteristic of requiring no manual operation. It relies on water flow velocity data to analyze and diagnose the status of underground drainage pipes and can be applied to the complex working conditions of underground pipe networks.

A kind of fuzzy inference-based zero trust system subject trust degree dynamic evaluation system, the fuzzy class and membership degree initialization module is calculated according to the preset classification and membership degree function, and the fuzzy class and corresponding membership degree of subject information are obtained;Session security fuzzy reasoning module calculates the subject session security fuzzy class by fuzzy reasoning;The session security value of session security fuzzy class is calculated by de-fuzzification through zero trust session security calculation module;Through zero trust subject credit dynamic adjustment module, the comprehensive security value is compared with the requirement of user application resource, and access credentials are issued for user according to the comparison result, and trust degree reward and punishment are carried out on it in the security risk and high-risk operation in the process of user access.The present application introduces fuzzy inference mechanism into zero trust architecture, processes quantitative security elements;Effectively introduce the historical data of access subject trust degree into trust evaluation as trust benchmark, and realize dynamic adjustment of its trust degree according to the security of user access process, realize the dynamic adjustment and quantitative evaluation of zero trust system access security.