916 results about "Fuzzy rule" patented technology

The invention relates to an intelligent reflux valve adjusting method based on a self-adaptive control algorithm, and aims to improve the accuracy and response efficiency of fluid adjustment. The method comprises the steps that fluid flow, pressure, temperature and flow direction data collected by sensors installed at an inlet and an outlet of the reflux valve are obtained, and the initial target valve opening degree under the current fluid working condition is calculated through a working condition opening degree prediction model in combination with structural parameters of the reflux valve; based on the relation among the flow, the pressure and the valve opening, the initial target valve opening is adjusted through a fuzzy rule base, so that the final target valve opening is obtained, and therefore the dynamic change of the complex fluid working condition is adapted; according to the final target valve opening degree, an execution mechanism is controlled to adjust the operation state of the reflux valve, and precise adjustment is achieved; working condition prediction and fuzzy control are combined, the adjusting precision and stability of the reflux valve are improved, and the method is particularly suitable for fluid conveying systems with large flow and pressure fluctuation and has wide application prospects in the industries of energy, chemical engineering, environmental protection and the like.

The invention provides an intelligent incubation bin anti-interference control method and system based on error self-learning, and relates to the technical field of intelligent control, and the method comprises the steps: respectively calculating a temperature deviation value and a humidity deviation value according to an optimization parameter sequence of a temperature prediction error and an optimization parameter sequence of a humidity prediction error; performing normalized weighted summation on the temperature deviation value and the humidity deviation value, and performing dynamic scaling through a Gaussian kernel function to obtain a correction factor; dynamically adjusting the activation function slope of a neural network hidden layer according to the correction factor and a BP neural network, reconstructing the weight of the fuzzy rule base based on the numerical distribution characteristics of the correction factor, and generating the corrected weight of the fuzzy rule base; and based on the corrected fuzzy rule base weight, constructing a three-dimensional parameter adjustment curved surface, and dynamically adjusting proportion, integral and differential parameters of a PID controller through a curved surface gradient search algorithm to generate a control signal. According to the invention, the control accuracy is improved.

The invention discloses a distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: synchronously collecting a dielectric loss spectrum, a withstand voltage leakage current curve and a partial discharge spectrum of a distribution network cable through a phase resolution measurement device; processing the partial discharge spectrum by using a frequency domain adaptive filter to generate a denoised partial discharge characteristic spectrum; constructing a cable multi-dimensional parameter normal domain hyperspherical model, and calculating a deviation distance value; and inputting the deviation distance value into a fuzzy inference engine, performing nonlinear conversion according to a preset fuzzy rule base, generating an insulation state evaluation index and a fault risk probability distribution diagram of the distribution network cable, and realizing accurate evaluation of the cable insulation state. According to the invention, the accurate evaluation of the cable insulation state and the accurate positioning of the fault position are realized, and the operation reliability and maintenance efficiency of the power distribution network are improved.

The invention belongs to the field of data processing, and particularly relates to a dynamic cooperative control system and method for a gas turbine and a micro-grid, and the method comprises the steps: constructing a micro-grid real-time monitoring module, continuously collecting distributed energy real-time output, controllable load demands, bus voltage frequency and equipment state parameters, and carrying out the filtering and noise reduction through a preprocessing unit, thereby guaranteeing the data precision; calculating a real-time power difference value based on the preprocessed data, calling an adaptive neural fuzzy inference system, taking the power difference value, the bus voltage deviation and the frequency deviation as input, and judging whether the power difference value, the bus voltage deviation and the frequency deviation exceed a preset threshold value by means of a fuzzy rule base and a neural network model; if the threshold values are not exceeded, the current states of the gas turbine and the energy storage system are maintained; if any one exceeds the threshold value, a dynamic cooperative control instruction is triggered, precise cooperative control of the gas turbine and the micro-grid is achieved, and the operation stability, the operation efficiency and the reliability of the micro-grid are improved.

The invention relates to the technical field of electric power peak regulation of energy storage equipment, in particular to a dispatching energy management system for assisting electric power peak regulation, which comprises a load prediction unit, a dynamic adjustment unit and a model optimization unit. A dynamic adjustment unit monitors multiple parameters of an energy storage equipment power system through principal component analysis dimensionality reduction, a peak regulation threshold value of the energy storage equipment is dynamically adjusted according to working conditions and load prediction in combination with a fuzzy logic reasoning model, and a model optimization unit analyzes operation data, compares deviation, adjusts fuzzy rules and evaluates the peak regulation effect of the energy storage equipment. The method can flexibly adjust the threshold according to the system operation parameters and the load prediction, improves the peak regulation accuracy of the energy storage equipment, guarantees the stable operation of a power grid and the efficient utilization of energy, further optimizes the peak regulation effect of the energy storage equipment through the feedback improvement rule, and solves the problem of insufficient adaptability of the peak regulation strategy of the existing energy storage equipment.

The invention provides a centralized control automation strategy optimization method and system based on fuzzy logic, and the method comprises the steps: collecting the real-time operation state data of power grid operation equipment in a target centralized control region, covering the operation parameters of a power grid and a parameter fluctuation range, and carrying out the fuzzy membership processing of the data according to the parameter fluctuation range, and generating a fuzzy rule set containing a plurality of fuzzy rules and parameter dynamic adjustment logic corresponding to each fuzzy rule, optimizing a current control strategy by applying fuzzy logic based on the fuzzy rule set, and generating an optimized control strategy containing a control instruction and a trigger condition thereof, and then the strategy is sent to execution equipment according to a trigger condition, execution feedback data is monitored, the validity of the strategy is verified according to a matching result of the execution feedback data and a preset threshold value, the control strategy is dynamically adjusted and optimized, a centralized control automation strategy can be effectively optimized, and the power grid operation control effect is improved.

The present invention relates to the technical field of lithium battery state estimation, and in particular to a fuzzy EKF-AH algorithm-based state of charge (SOC) estimation and correction method for a lithium iron phosphate battery. The method comprises: by taking a certain startup of an energy storage device as a start, a BMS reading an SOC and a state of health (SOH) at the previous shutdown, and on the basis of a standby time, selecting an open circuit voltage (OCV) or an EKF algorithm to correct the SOC; using an EKF-AH algorithm to estimate the SOC, establishing, on the basis of fuzzy control, a fuzzy rule library associated with the SOC and the SOH, and dynamically adjusting a weight and a measurement noise deviation of the EKF-AH algorithm; and calculating estimation differences between the EKF algorithm and an ampere-hour integration method, and if the sum of the estimation differences is greater than a corresponding threshold, issuing an SOH correction warning. The present invention improves the estimation accuracy of the entire life cycle of the lithium iron phosphate battery, and assists the correction of the SOH.

The invention discloses an air environment comprehensive regulation system for a hospital purification area, and the system comprises S1, an environment parameter sensing node network which is used for obtaining data and generating sensing node data; s2, an area function identification and state labeling module which is used for generating an area state description vector; s3, a multi-modal environment state fusion module which is used for constructing an environment dynamic state map of the current area and extracting key influence factors; s4, an adaptive fuzzy control decision module generates an air conditioning parameter set through fuzzy logic and a dynamic weight adjustment mechanism; s5, the multi-channel air conditioning execution device is used for adjusting the regional air according to the air conditioning parameter set; and S6, a comfort level feedback and strategy optimization module which is used for performing closed-loop optimization on the fuzzy rule set in the adaptive fuzzy control decision module and the weight factor of each rule. The method has the advantages of being fine in response granularity, adaptive in control strategy and high in human factor comfort degree fusion degree.

The invention discloses an electric forklift multi-mode driving switching method and system based on multivariable cooperation, and relates to the technical field of industrial vehicle intelligent control. The method is used for solving the problems of one-sided working condition sensing, switching oscillation and safety fault tolerance deficiency in multi-mode driving. The method comprises the following steps: firstly, generating a dynamically weighted working condition feature vector through multi-source sensing data fusion, and quantifying a stability risk, an energy consumption constraint and a power demand; secondly, based on a stability risk trigger steering safety envelope, arbitrating a multi-mode priority and correcting a torque upper limit in combination with a fuzzy rule to form an anti-conflict execution sequence; then, torque distribution and a steering angle acceleration instruction are optimized in a rolling mode in the safe envelope boundary, center-of-gravity shift is synchronously restrained, the track precision is improved, and energy consumption is reduced; finally, the control authority is transferred adaptively according to the operation intensity of a driver, when the decision is abnormal, the load-ramp rule base is degraded, an instruction is transferred through an S-shaped curve, and a torque balancing strategy is executed at the high position of a pallet fork to maintain stable.

A genetic algorithm optimization-based fuzzy PID control method and apparatus, and a device. The method comprises: S1, acquiring a preset standard electrode plate quality, a measured electrode plate quality and a system default ray intensity, acquiring an ambient temperature parameter, retrieving a monitoring index, and monitoring the stability of a surface density measurement system according to the monitoring index; S2, on the basis of a preset system parameter of the surface density measurement system, setting a data sampling interval; S3, on the basis of the data sampling interval, continuously sampling a number N of pieces of data using a median average filtering algorithm, removing extreme values, calculating the arithmetic mean value of the N-2 pieces of data, and eliminating sampling value deviation of the arithmetic mean value, to obtain surface density sampling data; S4, performing fuzzification processing on the surface density sampling data, determining a fuzzy rule, and performing optimization in real time according to the fuzzy rule and by means of fuzzy reasoning, to obtain applicable PID parameters; S5, on the basis of the applicable PID parameters, using a genetic algorithm to control a servo motor of the surface density measurement system. Thus, the technical problems of parameter tuning being difficult and the robustness of closed-loop control being low are solved.

The invention provides a self-adaptive fuzzy control system for an inverter type arc welding power supply, and belongs to the field of welding process control and equipment automation, and the self-adaptive fuzzy control system specifically comprises the steps that a sensor module collects working numerical values corresponding to key parameters in the welding process in real time; the data processing unit converts the working values into a fuzzy set; the fuzzy controller executes fuzzy reasoning on the fuzzy set based on a preset fuzzy rule base so as to output a fuzzy control signal; the data processing unit performs defuzzification on the fuzzy control signal and converts the fuzzy control signal into an accurate control signal; the inverter power supply module receives the defuzzified accurate control signal to adjust the output power of the welding power supply; the feedback system monitors real-time parameters of welding seam quality in the welding process and transmits the real-time parameters to the self-adaptive module so as to realize closed-loop control; and the adaptive module dynamically adjusts the fuzzy rule base and the membership function according to the welding quality information provided by the feedback system. According to the processing scheme, the control precision and adaptability of the system are improved.

The invention discloses an intelligent heat supply two-network balance control method and system based on fuzzy reinforcement learning, and the method comprises the steps: obtaining real-time operation parameters which comprise the return water temperature, the heat load and the environment temperature; based on the real-time operation parameters, preliminarily generating a preliminary adjusting quantity of the opening degree of the valve; operation data are analyzed in real time, fuzzy rule parameters are adjusted according to feedback of the reinforcement learning module, and the fuzzy rule parameters comprise priority weights and membership functions of return water temperature deviation; and according to the preliminary adjustment effect, a deep learning algorithm is utilized to construct a state-action space equation, an energy consumption design reward function is considered, the priority weight is further optimized and fed back to a dynamic optimization module of a fuzzy rule, and secondary adjustment of the valve opening degree is output to an execution mechanism. The problems that a traditional fuzzy control method cannot adapt to complex working condition changes in the operation of the heat supply two-network, the adjusting strategy lags behind, the adjusting effect is not accurate enough, and the adjusting efficiency and energy consumption are affected are solved.

The invention discloses an equipment abnormity monitoring method and system based on the Internet of Things, and relates to the technical field of intelligent operation and maintenance of the Internet of Things, and the method comprises the steps: collecting monitoring data to generate a high-dimensional original matrix, carrying out the optimization through employing a GCN model and combining with ACO, carrying out the updating through a comparison learning model and an FCM algorithm, and carrying out the searching of global optimum through employing a VAE model and combining with a PSO algorithm. The method comprises the steps of performing classification optimization based on K-means clustering and BSO, performing MLE calculation, updating dynamic causal KG through Granger causal test, generating a multi-modal result array through NSM, a scoring formula, a naive Bayesian model, a Mahalanobis distance formula and a logistic regression model, and performing optimization by using a fuzzy rule and GWO. According to the method, the GCN model is combined with the adaptive optimization algorithm, the precision and response speed of anomaly monitoring are improved, optimization is carried out by using the fuzzy rule and introducing the GWO based on multi-modal causal reasoning, and the reliability and efficiency of anomaly monitoring are improved.

The invention discloses a three-temperature test method and device for a semiconductor chip, and relates to the technical field of semiconductor chip testing, and the method comprises the steps: inputting an initialization parameter set into an impedance temperature mapping model, carrying out the phase angle analysis and multi-band feature fusion through a broadband impedance spectrum analysis method, obtaining a chip junction temperature instantaneous value, and carrying out the measurement of the junction temperature instantaneous value. A PID controller is utilized to dynamically compensate a junction temperature instantaneous value of a chip, a fuzzy rule base inference engine is utilized to map the junction temperature instantaneous value into an execution instruction set, the execution instruction set is converted into a bidirectional voltage signal through pulse width modulation, a semiconductor chilling plate is driven to perform bidirectional temperature rise and drop control in a three-temperature environment, and real-time temperature change test data is synchronously acquired. According to the method, the junction temperature instantaneous value of the chip can be more accurately obtained by combining broadband impedance spectroscopy analysis with a multi-band characteristic fusion technology, and meanwhile, the PID controller is combined with the fuzzy rule base inference engine, so that a response can be quickly made under the condition that the temperature is quickly changed, and the response capability is greatly improved.

The invention discloses an intelligent circuit breaker system based on a fuzzy control theory. In the intelligent circuit breaker, current changes are monitored in real time through a fuzzy controller, an input variable is converted into an input fuzzy quantity for expression according to a membership function, fuzzy reasoning is carried out on the converted input fuzzy quantity according to a preset fuzzy rule base to obtain an output fuzzy quantity, and finally the output fuzzy quantity is defuzzified. And a final circuit breaker action instruction is obtained. The method has the beneficial effects that the safety stability and the response flexibility of the system are improved, and the protection strategy is dynamically adjusted, so that various complex power failure scenes can be more flexibly and efficiently handled.

The present application relates to an output power adjustment method and apparatus, and a computer device and a storage medium. The method comprises: acquiring sampling data corresponding to sampling parameters, and determining a fuzzy set to which the sampling data belongs, wherein the sampling parameters are related to the maximum power point of a photovoltaic power generation system; on the basis of a fuzzy rule corresponding to the fuzzy set, performing fuzzy reasoning on the sampling data, and determining fuzzy control information matching the sampling data, wherein the fuzzy control information comprises a parameter value adjustment direction of working parameters and a step-size adjustment direction of parameter value change amounts; performing defuzzification processing on the step-size adjustment direction to obtain a step-size adjustment value; and outputting a control signal matching the parameter value adjustment direction and the step-size adjustment value, wherein the control signal is used for changing an output power of the photovoltaic power generation system by adjusting parameter values of the working parameters. By using the method, an output power adjustment effect can be improved.

The invention relates to a city planning checking method and system based on natural language processing, and the method comprises the steps: receiving a user request instruction, carrying out the analysis, and obtaining the checking intention information and target region information; retrieving and matching a corresponding region rule set based on the target region information, and generating a checking task list and a data demand list; obtaining multi-source heterogeneous data and preprocessing the multi-source heterogeneous data to obtain standardized check data and historical fusion features; executing a preset differentiation checking process according to the task type; generating a checking report based on the differentiated checking process result; in conclusion, according to the method, the user request is processed and analyzed through the natural language, the planning knowledge base is constructed, deterministic and fuzzy rule tasks are processed in combination with the differentiated checking process, cooperation of rule terms and case features is achieved, the problems that a traditional checking method is low in automation degree and insufficient in fuzzy term processing capacity are solved, and the efficiency of checking is improved. The method has the effects of improving the checking efficiency and enhancing the interpretability of decision suggestions.

The invention relates to the technical field of battery management, and discloses a battery management system with an active monitoring function, which comprises a processor, a battery state monitoring module, an active equalization control module, a performance evaluation module and a fault early warning module. The battery state monitoring module collects various parameters of a battery at different stages, and generates an abnormal signal by using a dynamic threshold algorithm. The active equalization control module adjusts an equalization strategy based on a fuzzy logic algorithm, and a fuzzy rule base can be updated. And the performance evaluation module tracks and evaluates an equalization process, analyzes equalization efficiency and quality, and optimizes an equalization strategy accordingly. The fault early warning module displays abnormal signals in a grading mode and carries out early warning, and the processor is further connected with the aging feature extraction module and the thermal runaway prediction module to achieve battery life prediction and thermal runaway early warning. According to the invention, comprehensive monitoring, efficient equalization control, accurate performance evaluation and reliable fault early warning of the battery are realized, the safety of the battery is improved, and the service life of the battery is prolonged.

The invention provides an energy storage system power grid allocation method based on a dynamic space-time diagram convolutional neural network. The energy storage management method comprises the following steps: S1, acquiring time sequence data of a power grid node; s2, sequentially executing a graph convolution layer operation and a time convolution operation by using a dynamic space-time graph convolution neural network, and extracting space-time features of the time sequence data; s3, calculating attention weighted features of the spatio-temporal features; s4, processing the new energy output uncertainty based on a fuzzy rule by using the spatial-temporal characteristics and the attention weighted characteristics; s5, realizing distributed model training based on a federated learning framework, and dynamically updating model parameters through an attention mechanism; s6, integrating the geographic positions and the connection relations of the nodes, and outputting enhanced features; and S7, the enhanced features pass through a full connection layer and an activation function, a power grid load prediction result is output, and a charging and discharging strategy of the energy storage system is dynamically regulated and controlled. Power supply and demand dynamic balance in a new energy high-permeability scene is realized, and the operation cost and the environmental influence are reduced.

The invention belongs to the technical field of laser welding, and discloses a real-time feedback control method and system for laser welding penetration stability, and the method comprises the steps: obtaining plasma electron temperature characteristics in a laser welding process in real time through a spectrum monitoring system, and enabling the plasma electron temperature characteristics to be associated with penetration fluctuation as a core input signal of feedback control. The signal has better real-time performance and more accurate feature extraction capability in real-time feedback control of laser welding by virtue of broadband coverage and multi-dimensional information acquisition capability of the signal in combination with a more efficient data analysis mode. A parallel type self-learning fuzzy neural network controller is used for executing real-time feedback control output of laser welding penetration fluctuation. On a control architecture, a traditional PD controller and a fuzzy neural network are connected in parallel and are respectively used as a PD control module and a fuzzy neural network control module. And the process database is embedded into the forepart structure of the neural network control module in a fuzzy rule form.

The invention relates to an MPPT (Maximum Power Point Tracking) control method based on dynamic optimization of a lightweight convolutional neural network, and belongs to the field of artificial intelligence control power tracking. The method comprises the following steps: S0, constructing a convolution-fuzzy neural network model which at least comprises an input layer, a convolution layer, a fuzzy control layer, a deep reinforcement learning layer and an output layer; s1, key parameters of photovoltaic equipment are collected in real time to serve as input data of an input layer; s2, performing convolution operation on the input data by using a convolution layer, extracting a feature vector, and transmitting the feature vector to a fuzzy control layer; s3, combining a fuzzy control layer with a deep reinforcement learning layer, and dynamically adjusting an optimization process of a fuzzy rule through a reward signal; and S4, the fuzzy control layer generates a control signal, and the control signal is transmitted to a DC / DC converter of the photovoltaic system through the output layer. The control method is suitable for a dynamic environment of a photovoltaic power generation system, can be expanded to a grid-connected system, and provides a new thought and technical support for efficient operation of photovoltaic power generation.

The invention relates to the technical field of automatic control, in particular to a multi-parameter cooperative control method and device for a clamping system of a precision boring and milling machine. According to the method, an industrial camera is used for collecting and processing a workpiece image and extracting visual features, and the visual features are fused with process parameters of a workpiece and geometric distribution parameters of a workpiece key area obtained from a process database; generating a comprehensive state vector; based on the vector, outputting a target clamping force adjustment coefficient by a pre-trained MLP decision model, and calculating a final target clamping force in combination with a safe clamping force range; in the main clamping stage, vibration, temperature and pressure signals are synchronously collected, multi-modal disturbance characteristics are extracted, observation vectors are constructed, the observation vectors are input into a preset fuzzy rule base for reasoning, and compensation activation factors are obtained; and finally, a current correction instruction is dynamically generated according to the clamping force deviation and the compensation factor, and an electro-hydraulic proportional valve is driven to achieve accurate compensation. According to the invention, the stability and the machining reliability of the clamping system are obviously improved.

The invention discloses a time sequence-based fuzzy PID (Proportion Integration Differentiation) control algorithm dynamic thermal assistance method, which comprises the following steps of: acquiring temperature data of a drilling and riveting area in real time through an infrared sensor, mapping temperature deviation and a change rate thereof to an adjustment weight of a PID parameter based on a fuzzy rule base, and solving the problem of parameter mismatch of the traditional PID in a nonlinear system. Process parameter database matching: establishing an association rule base of a material combination such as a metal-composite material and a preset temperature threshold value, and dynamically setting a temperature control target by combining a thermal physical property heat conductivity coefficient and a melting point of the material so as to realize differential regulation and control of dissimilar materials; the temperature-vibration coupling model based on the LSTM neural network is constructed to precisely and cooperatively regulate and control internal correlation of temperature and vibration, vibration disturbance brought in the temperature rising process is accurately obtained, and dynamic adjustment of the temperature and vibration in the drilling and riveting process is achieved by finely adjusting the power of the active damper.

The invention discloses a visual inspection method for the surface quality of a precise injection mold blank, relates to the field of visual inspection of quality inspection, and is used for solving the problems that the efficiency and quality of weld surface detection of the injection mold blank can be reduced due to image light spots or apertures, and the precision of a final detection result is reduced due to multi-angle image processing. The method comprises the following steps: randomly collecting an initialized image sample, obtaining an image surface light spot area ratio mean value and an image light spot appearance probability, determining an analysis angle of a further detection image, and collecting the similarity of a current angle image welding position and the current angle mold blank reflected light intensity; constructing a support vector machine model to perform angle positioning judgment on the injection mold base, screening out corresponding angle images according to an output result, making a group of fuzzy rules for the reserved angle images according to a surface defect detection coefficient and a surface smoothness coefficient to perform fuzzy reasoning, determining a surface quality result of the injection mold base, ensuring production consistency, and improving the quality of the injection mold base. And the mold blank surface quality detection difference is reduced.

The invention discloses a multi-drive cooperative control method for high-precision electric drive assembly equipment, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of insufficient cooperative control precision of a multi-axis system under parameter mismatch and disturbance. By constructing a multi-source data fusion and dynamic coupling analysis mechanism, the system collects the motion state and control parameter data of multiple driving shafts, generates a multi-shaft collaborative dynamic response sequence, and calculates the dynamic coupling degree between the shafts to identify the uncertainty mode of the system; positioning a performance degradation shaft section by combining the temperature field and the vibration signal characteristics, and generating a system stability evaluation coefficient; and establishing a space mapping relation to construct a dynamic compensation factor matrix, and generating a control parameter adjusting quantity meeting a collaborative matching condition through fuzzy rule reasoning. According to the method, control instability caused by traditional global adjustment is avoided, precise cooperative control of the multi-axis system is achieved, and the control precision, operation stability and reliability of equipment under the dynamic working condition are remarkably improved.

The invention relates to the technical field of environmental science and engineering, and discloses a regional carbon emission accounting system, which adopts a plurality of sensors and intelligent equipment to collect carbon emission data in real time, and transmits the data through wireless communication. And the data integration module performs cleaning and standardization processing on the carbon emission data, and evaluates the reliability of a data source based on a fuzzy rule to form an accurate comprehensive data set. And the dynamic evaluation module analyzes the comprehensive data set by adopting a machine learning algorithm to generate evaluation feedback, and feeds back the evaluation feedback to the intelligent monitoring module to optimize the monitoring strategy and the data acquisition frequency. And through a visual display module, an analysis result is presented in a chart form, user interactive data query and retrieval are supported, and scientificity and real-time performance of carbon emission management are improved. The system provides an important basis for policy making and effective emission reduction while improving the carbon emission accounting precision, and assists in coping with global climate changes.

The invention discloses a parking space recommendation and reservation method, which comprises the steps of normalizing acquired multi-source heterogeneous data to generate parking lot static attribute data, real-time parking space state data and dynamic user request data; constructing an objective basic potential field by using fuzzy reasoning according to a fuzzy rule set capable of performing adaptive evolution based on performance feedback; modeling long and short term preferences of the user by adopting methods such as hierarchical Bayesian and an attention mechanism so as to generate a user subjective preference modulator; an objective potential field, a subjective modulator and a multi-agent rejection potential field with dynamically adjustable parameters are fused, a total potential energy function of the system is constructed, and a collaborative guidance strategy for all users and conflict avoidance is determined and output by solving the minimum value of the function. According to the invention, efficient and personalized parking guidance with macroscopic cooperation capability can be provided, and the user experience and the operation efficiency of the parking lot are significantly improved.

The invention discloses a road risk grading early warning method and system based on a Beidou satellite system, and the method comprises the steps: firstly carrying out the real-time positioning of a vehicle through Beidou dual-frequency signals, inertial navigation data, road side unit differential data and vehicle-mounted sensor data, and obtaining the precise position information; fusing the real-time position of the vehicle with meteorological data, vehicle-mounted OBD parameters and social media public opinion data to generate a dynamic risk factor matrix; a fuzzy rule base is constructed based on expert experience, the fuzzy weight of each risk factor in a matrix is obtained, meanwhile, the time sequence weight of each factor is predicted by means of an LSTM model, and a final road risk score is obtained through dynamic weighting. And performing graded early warning on the road risk in combination with the driver portrait, and feeding back and updating the fusion parameter, the fuzzy rule base or the LSTM model parameter according to the early warning effect to form a closed-loop optimization mechanism. Dynamic coupling analysis of multi-dimensional risk factors is realized, and the real-time performance and accuracy of road risk early warning are improved.

The invention relates to the technical field of environment monitoring, in particular to an intelligent water quality monitoring method and system for collaborative sampling of a bionic fish school and an unmanned aerial vehicle and computer equipment. The method comprises the following steps: dividing monitoring grid units by using GIS data, establishing a layered communication architecture, dynamically deploying a bionic fish school to carry out real-time water quality monitoring, and scheduling and optimizing resource allocation through a fuzzy rule; historical pollution event data and real-time sensing data are integrated to construct a water quality evaluation index system, and a Nash equilibrium strategy is adopted to determine an optimal weight to generate a dynamic pollution situation cloud chart; constructing an inferior fingerprint database to realize rapid identification of pollution events and trigger a two-stage response mechanism; and finally, combining the bionic fish school sampling data and the unmanned aerial vehicle pre-diffusion coordinate to dynamically simulate the effect of the treatment measure. Air-water integrated efficient monitoring is achieved, the accuracy and timeliness of water quality anomaly detection are improved, and comprehensive technical support is provided for water area environment protection.

The invention discloses a high-precision multi-angle workpiece production detection system based on simulation vision, relates to the field of simulation, and is used for solving the problem that when a workpiece is disassembled or assembled, deviation exists between multi-angle image data fusion and illumination characteristics in a real scene, so that the detection accuracy is reduced. Illumination characteristic information and material characteristic information in a simulation environment and a real scene are collected, a data analysis model is established, deviation evaluation coefficients are obtained and compared with a deviation threshold value, a comparison result is obtained, the deviation evaluation coefficients smaller than the deviation threshold value are defined as fine deviations, and the fine deviations are calculated. Deviation evaluation coefficients larger than or equal to the deviation threshold value are defined as deviation angles, a set of fuzzy rules are formulated according to the alarm threshold value trigger frequency for fuzzy reasoning, a deviation adjustment scheme is determined, the detection frequency is set according to production operation of workpieces, deviation of all the angles can be effectively adjusted, and the detection accuracy is improved. And the detection decision flexibility and efficiency are higher.