527 results about "Fuzzy control algorithm" patented technology

The invention discloses a battery charging monitoring and adjusting system based on multiple areas, and relates to the technical field of battery management. According to the system, electrochemical impedance spectroscopy, infrared thermal imaging and electric heating parameters are synchronously collected, a physical connection and heat conduction topological relation of a battery pack is constructed in combination with a graph neural network, and monomer aging distribution and a cross-regional heat diffusion path are accurately recognized. Based on multi-region data aggregation, battery health protection, temperature equalization and a power grid demand target are fused, and charging current constraint and equalization priority parameters are dynamically generated. And through a fuzzy control algorithm, an active energy transfer mode and a passive risk isolation mode are intelligently switched, the energy distribution efficiency is optimized, fault spreading is blocked, the risk level of the system is evaluated in real time, a hierarchical regulation and control instruction is triggered, and the weight is dynamically adjusted and optimized through a feedback mechanism. The problems of extensive monitoring, strategy stiffness and safety response lag of a multi-area battery system are solved, the charging safety and economy in a complex scene are remarkably improved, and the service life of the system is remarkably prolonged.

The invention discloses a charging chip real-time power scheduling control method, and relates to the technical field of new energy automobile charging pile power scheduling, and the method comprises the following steps: S1, obtaining the operation parameters of charging equipment in real time, including the voltage fluctuation value of an input power grid, the output current of a charging interface, and the temperature of a charging chip; according to the method, the power grid voltage fluctuation, battery SOC / temperature and chip working condition multi-source data are collected in real time, a multi-target optimization model of dynamic weight distribution is combined, the service life loss of the battery is minimized, the charging efficiency and the power grid load are balanced at the same time, the user charging mode and the battery state difference are taken into consideration by adopting a priority scheduling strategy, and the charging efficiency is improved. Millisecond-level power fine control is achieved through cooperative adjustment of the PWM duty ratio and the switching frequency, personalized charging requirements are met, the overall energy efficiency is improved, a dual safety defense line is constructed through a graded load reduction protection mechanism and a fuzzy control algorithm, and a power distribution scheme is intelligently reconstructed when a power grid is abnormal or a battery is overheated.

The invention discloses a building carbon emission monitoring system and method based on big data acquisition, particularly relates to the field of building carbon emission monitoring, and is used for solving the problems of energy use optimization and carbon emission dynamic regulation and control. Through combination of a recursive feature elimination algorithm and Kalman filter dynamic correction, efficient identification of an electric appliance operation state and accurate correction of load data are realized. Time-delay-free synchronization of distributed energy and load requirements is realized through a time sequence cross-correlation algorithm, and the efficiency and precision of energy allocation and carbon emission monitoring are greatly improved. The dynamic carbon load pressure index and the global energy consumption difference index cooperate to assess changes of load and carbon emission, rapid response to abnormal energy consumption and carbon emission is realized, generation and prediction optimization of a regulation and control strategy are carried out in combination with a fuzzy control algorithm and an autoregression model, the carbon emission of buildings is effectively reduced, and the construction efficiency is improved. And intelligent and dynamic load and carbon emission management is realized.

The invention relates to the technical field of temperature regulation and control, in particular to a liquid cooling control method and system of a battery control device, in the liquid cooling control method and system, through real-time collection and area screening of temperature and power data, accurate recognition of a high-temperature area is achieved, a clear cooling requirement partition is provided, and the cooling requirement of a battery is met. A dynamic adjustment strategy of pressure and flow parameters of the liquid cooling pump is optimized through a genetic algorithm, the cooling response speed and control precision are improved while energy consumption and cooling efficiency are effectively balanced, the pump speed and the valve opening degree are adjusted in real time, temperature changes are rapidly responded through automatic control, gradual implementation of a cooling target is ensured, and the cooling efficiency is improved. The hysteresis in the heat management process is reduced, the temperature control effect is continuously evaluated through a fuzzy control algorithm, the cooling intensity is dynamically adjusted according to the monitoring result, efficient cooling is ensured, meanwhile, equalization of temperature distribution is achieved, and a systematic heat management framework is established through long-term data monitoring and continuous optimization of cooling operation. And efficient and stable operation of temperature management is realized.

The invention belongs to the technical field of ship path tracking, and discloses a ship path tracking method based on adaptive line-of-sight guidance and fuzzy adaptive PID, and the method comprises the steps: presetting a path point, and obtaining a real-time coordinate; obtaining an actual expected heading angle # imgabs0 # and a ship observation heading angle # imgabs1 #, subtracting the actual expected heading angle # imgabs0 # and the ship observation heading angle # imgabs1 # to obtain a heading deviation # imgabs2 #, and calculating a heading deviation rate # imgabs3 #; the course deviation and the course deviation rate are input into a fuzzy self-adaptive PID motion control module, and gain parameters are output through a fuzzy control algorithm; the gain parameter and # imgabs4 # are input into a PID controller to calculate a ship rudder angle # imgabs5 #; inputting # imgabs6 # into the motion mathematical model to update the motion state, and forming a closed-loop updated motion state to enable the ship to approach a preset path point; and judging a ship path after the state information is updated. The expected course of the ship is calculated in real time and the sideslip angle of the ship and the control parameters of the ship are adjusted in real time through self-adaptive sight guidance, and the control parameters are automatically adjusted in real time by means of the fuzzy self-adaptive PID motion control module to achieve accurate motion control, so that the ship travels according to a preset planned path.

The invention relates to a digital acupuncture dynamic control optimization method. According to the technical scheme, the method comprises the steps that real-time physiological data, including the heart rate, the oxyhemoglobin saturation, the skin temperature and the muscle tension, of a patient are collected through a wearable device and a sensor; performing cleaning, denoising and feature extraction on the multi-modal physiological data by using a data preprocessing algorithm; a personalized digital acupuncture model is established, and the physical condition and acupoint sensitivity of a patient are dynamically evaluated; adjusting acupuncture output parameters, control frequency, pulse width and current intensity according to real-time feedback of a patient by adopting a self-adaptive fuzzy control algorithm through the obtained personalized digital acupuncture model; multi-channel biofeedback equipment including EEG, EMG and ECG is integrated, and instant response of a patient is monitored; through a closed-loop control algorithm, biological feedback data are input into the control model in real time, and dynamic adjustment of acupuncture and moxibustion parameters is controlled; a deep reinforcement learning algorithm is introduced into acupuncture therapy.

The invention provides a load current and inverter load discharge combined remote discharge method and system, and relates to the technical field of discharge control, and the method comprises the steps: collecting the parameters of a battery pack through a remote monitoring system, evaluating the health state and dischargeable capacity of the battery pack based on a fuzzy control algorithm, and calculating the optimal discharge power and duration according to a prediction correction algorithm; and generating a load and inversion discharge power curve. When the battery pack is healthy and the dischargeable capacity is sufficient, the load and the inverter discharge unit are remotely controlled, the duty ratio of a load switch and the switching frequency and output voltage of an inverter are controlled according to respective power curves, and two discharge loops are formed. And collecting discharge current and battery pack parameters in real time, and stopping discharging when the state of charge of the battery pack reaches a preset threshold value. According to the invention, by combining the load current and the inverter load for discharging and performing intelligent control based on the battery state, efficient and safe remote discharging is realized, and the service life of the battery pack is prolonged.

The invention discloses an energy efficiency control method for a cooling fan of an industrial electric cabinet, and the method comprises the steps: firstly collecting temperature, humidity, equipment current / voltage and other multi-mode sensing data, carrying out the PCA algorithm dimension reduction and feature extraction, combining with Granger causality test, thermodynamic principle and machine learning algorithm, and carrying out the building and fusion, thereby obtaining a cooling efficiency model. Thirdly, preprocessing current and voltage data of the equipment, training and optimizing an LSTM model to predict load power, and calculating a fan rotating speed control value through a heat dissipation efficiency model; then, a PWM speed regulation signal is generated by adopting a fuzzy PID control algorithm, and a pulse width modulation and smooth speed regulation mixed strategy is applied to the duty ratio; and finally, energy efficiency indexes are collected to compare and update the weight of the LSTM model, knowledge distillation is introduced to optimize new model training, and self-adaptive adjustment of the rotating speed of the fan is achieved. The rotating speed of the fan can be accurately adjusted, energy consumption is reduced, the energy utilization efficiency is improved, performance reduction of equipment due to overheating is effectively avoided, and the service life of the equipment is prolonged.

The invention discloses a metal plate laser cutting control method, and relates to the technical field of laser cutting, material parameters, thickness information and cutting path data are obtained, a mixed enveloping rectangle algorithm is combined with a genetic algorithm to optimize a cutting sequence, and a path sequence containing idle stroke avoidance and nested graph priority is generated; constructing a laser parameter prediction model based on the material, thickness and path characteristics, and dynamically adjusting the power gradient; the slag splashing state is judged through data fusion of a capacitive sensor and an encoder, a quality feedback signal is extracted in combination with machine vision, and laser parameters are dynamically adjusted through a fuzzy control algorithm; wavefront distortion is monitored in real time, the correction frequency is adjusted according to the slag splashing state, and a deformable mirror is used for compensating the phase; when the quality is abnormal, historical parameter combinations are retrieved through a cosine similarity algorithm for joint compensation. According to the method, the path cutting efficiency is improved, and the problems of low efficiency of path planning and parameter adjustment lagging in the prior art are solved.

The invention relates to the technical field of box-type transformer substations for energy storage, in particular to a temperature control system and a control method of a box-type transformer substation for energy storage. Comprising a temperature monitoring module, a grading threshold module, a speed regulation control module and a fault alarm module. The temperature monitoring module monitors the temperature in the transformer substation in real time to obtain accurate temperature, temperature deviation and temperature change rate, the grading threshold module is utilized to establish a grading temperature threshold model, the model is optimized and adjusted, and the speed regulation control module adopts a fuzzy control algorithm to control the speed of the transformer substation. The accurate correction of the initial rotating speed of the fan is obtained, the association rule of the temperature threshold value of hierarchical control and the speed regulation unit is set, the consistency of synchronous hierarchical switching and fan rotating speed regulation is achieved, hierarchical regulation and control of the fan are achieved, and on the premise that heat dissipation is guaranteed, electric energy consumption is reduced.

The invention provides an ecological circulation barrel-in-barrel culture data acquisition method and system, and relates to the technical field of intelligent control, and the method comprises the steps: carrying out the feature extraction of a historical data flow through a multivariable coupling analysis model, carrying out the mode matching through a preset environment threshold interval and a culture environment state matrix, and obtaining a culture environment state matrix; generating a water quality health degree evaluation index and an ecological imbalance early warning signal; and analyzing the water quality health degree evaluation index and the ecological imbalance early warning signal based on a fuzzy control algorithm, dynamically calculating a final feeding amount curve and a water change intensity function, and generating a regulation and control instruction set containing the feeding frequency, the bait particle size and the water pump rotating speed. According to the invention, real-time evaluation and accurate intervention of the water quality health state can be realized.

The invention relates to a winding tension control method and system. The method is applied to a winding tension control device, the device comprises a tension control part and a linear speed control part, and the method comprises the following steps: acquiring a real-time tension detection value and a tension set value of the tension control part, calculating a tension error and a tension error change rate, and performing fuzzy reasoning according to a fuzzy control algorithm to obtain a linear speed control value; pID output parameters are obtained through integral separation PID and used for indicating and controlling a first servo motor of a tension control part; and acquiring a real-time linear speed value and a linear speed set value of the linear speed control part, calculating a linear speed error and a linear speed error change rate, and inputting the linear speed error and the linear speed error change rate into the PID compensation model based on a fuzzy control algorithm to obtain a PID compensation parameter for indicating and controlling a second servo motor of the linear speed control part. According to the method, the tension and the linear speed are subjected to fuzzy control and PID control respectively, the control precision can be improved, and the stability, the response speed and the production efficiency of the winding process are effectively improved.

The invention discloses a UV illumination control method and system for a reel-to-reel exposure machine. The method comprises the following steps: acquiring a real-time moving speed and a tension change value of a coiled material; when the tension change value is greater than the tension threshold value, predicting the deformation trend of the surface of the coiled material based on a nonlinear regression algorithm in combination with the real-time moving speed to obtain a deformation influence value; according to the deformation influence value, a PID control algorithm is adopted to adjust the UV light source, and a light intensity distribution value is obtained; according to the light intensity distribution value, a fuzzy control algorithm is adopted to calculate the output intensity requirement of the UV light source, and speed adjustment is carried out in combination with the real-time moving speed, so that optimized irradiation parameters are obtained; performing area judgment according to the optimized irradiation parameters to obtain an overexposure area and an underexposure area; and adjusting the intensity and distribution of the UV light source in the overexposure area and the underexposure area until the area exposure quality reaches a preset standard range. The method can realize real-time response regulation and control on tension and speed change of the coiled material, and solves the problem of non-uniform exposure dose.

The present disclosure provides a decision-making method and apparatus for water and fertilizer stress of crops, and a mobile phone terminal, and belongs to the technical field of intelligent agriculture. The method includes: determining a current crop coefficient and canopy temperature of a crop to be identified on the basis of a first crop image and a second crop image; determining a water stress state and a nutrient stress state on the basis of the current crop coefficient and canopy temperature of the crop to be identified; and processing the water stress state and the nutrient stress state with a fuzzy control algorithm, and determining a water and fertilizer stress decision result of the crop to be identified.

The invention discloses an automatic batching and stirring control system based on an industrial computer, belongs to the field of new wall material production control systems, and aims to solve the problems that the real-time performance is poor, and the influence of raw material characteristic changes on a fall value cannot be pre-judged. Weighing, humidity, infrared flow velocity, temperature, pressure, torque and airflow sensors are integrated, multi-dimensional parameters such as material weight, flow velocity, humidity and air pressure in a bin are monitored in real time, the influence of environmental interference on the sensors is dynamically compensated through multi-parameter collaborative feedback, the real-time performance and reliability of data acquisition are improved, a fuzzy PID control algorithm and a machine learning model are fused, and the real-time performance of the system is improved. A nonlinear mapping relation is established through a training data set, the initial valve opening degree and the fall value are predicted, errors are corrected in real time, dependence on historical data is reduced, the first-time use error of a new formula is reduced, the response time for coping with sudden environment changes is shortened, a single adjustable-caliber electromagnetic valve is adopted, the inner wall is coated with a nano anti-sticking coating, and the service life of the valve is prolonged. And the opening degree is dynamically adjusted in combination with a PID algorithm.

The invention relates to the technical field of automatic inspection, in particular to an automatic road surface inspection robot control method and equipment, and the method comprises the steps: collecting environment data and robot motion state data through integrating a plurality of sensors, carrying out the preprocessing, carrying out the data fusion through employing a Kalman filtering algorithm, and generating a comprehensive road surface state map; a disease area is accurately identified and positioned through a convolutional neural network, dynamic path planning and polling strategy optimization are performed through a reinforcement learning algorithm, adaptive control of a complex road surface environment is realized through a fuzzy control algorithm, and meanwhile, a targeted road surface maintenance suggestion and an emergency processing scheme are generated according to real-time polling data, so that the real-time polling of a road surface is realized. According to the method, the scientificity and timeliness of road surface maintenance and traffic management are effectively improved, the road surface diseases can be accurately recognized and the inspection path can be optimized in a complex environment, and the road surface inspection efficiency and the intelligent level of road maintenance are remarkably improved.

The invention discloses a frequency switching method and device of an embedded power terminal. The method comprises the steps that frequency switching is triggered by predicting frequency deviation and comparing the predicted frequency deviation with a preset frequency synchronization threshold value, and the transmitting power and the modulation mode in the switching process are optimized through a fuzzy control algorithm so as to suppress transient interference. During switching execution, the node synchronization state is monitored in real time, and an interruption mechanism is triggered when abnormity occurs. And after the switching is completed, calibrating the frequency parameter of each node, and updating the frequency drift compensation model. And dynamically optimizing the compensation model and the switching strategy by utilizing historical data and adopting a reinforcement learning algorithm, and synchronously updating the compensation model and the switching strategy to each node. By means of predictive triggering, intelligent parameter optimization, real-time monitoring and the like, the problems of transient interference, exception handling, model optimization and the like in the power terminal node frequency synchronization process are effectively solved. According to the method, the reliability and stability of power terminal node frequency synchronization are remarkably improved, and a powerful guarantee is provided for safe and stable operation of a power system.

The invention belongs to the technical field of shot blasting processes, and provides a self-adaptive aero-engine blade surface shot blasting control system and a shot blasting process. The system comprises a measurement module used for obtaining the three-dimensional geometrical shape of the complex surface of the blade and generating a digital three-dimensional model; the sensor module is used for monitoring and feeding back the shot blasting process condition in real time; the control module is used for dynamically adjusting shot blasting parameters by adopting a self-adaptive algorithm; the shot blasting execution module is used for executing a shot blasting process; the real-time monitoring feedback module is used for dynamically adjusting shot blasting parameters; the process comprises the steps that after the blade surface is cleaned, a three-dimensional model generated by a measuring module and surface data measured by a sensor module are fed back to a control module, a shot blasting injection parameter instruction is obtained through a fuzzy control algorithm, and a shot blasting machine execution module executes the shot blasting process. And the real-time monitoring feedback module dynamically adjusts shot blasting parameters until the shot blasting process requirements are met. According to the invention, accurate and uniform shot blasting treatment on the surface of the blade in a complex shape can be realized.

The invention provides a high-efficiency control method and system for a central air conditioner refrigeration machine room, and relates to the technical field of automatic control, and the method comprises the steps: 1, obtaining chilled water supply and return water temperature difference and flow data, and calculating the dynamic cold load demand of a building in combination with the personnel distribution thermal imaging data of each floor; and 2, on the basis of the dynamic cooling load requirement, a dynamic adjustment model for the number of running water chilling units is built, matching degree calculation is conducted on the actual cooling load value and the single refrigerating capacity of the water chilling units through a fuzzy PID control algorithm, and a final unit running combination scheme is generated. By calculating the cooling load requirement, dynamically adjusting the operation parameters of the water chilling unit, the chilled water pump and the cooling tower and balancing the equipment operation time, the system energy efficiency is effectively improved, the energy consumption and the operation and maintenance cost are reduced, and stable operation of the refrigerating system is guaranteed.

The invention relates to the technical field of electric vehicles, and particularly discloses a method for online estimating the capacity and health state of a battery pack based on random charging information, which comprises the following steps of: preliminarily estimating the capacity of the battery pack: selecting a charging section and utilizing an ampere-hour integral to estimate a model; temperature-based battery capacity correction: temperature data is corrected based on fuzzy Kalman filtering optimization algorithm processing, and because relatively large noise is inevitably generated after preliminary estimation, a fuzzy control algorithm is adopted to control observation noise of a Kalman filtering algorithm in real time, so that an estimation result is converged to a real trend; estimating and predicting the state of health of the battery: establishing an Arrhenius model based on the capacity attenuation of the battery pack to predict the service life of the battery; according to the method, the defect of inaccurate estimation of real-time measurement noise of a traditional Kalman filtering algorithm is effectively overcome, and the measurement noise is corrected in real time; the method is suitable for AI adaptive parameter adjustment models of different new energy heavy truck models, and is applied to state-of-health evaluation and life prediction of batteries.

The invention relates to a control system for a water-jet loom. A data acquisition module acquires and transmits weft tension, humidity and temperature data in real time. The parameter calculation module calculates the optimal parameters of the water spraying time, the water pressure and the nozzle opening degree through a fuzzy control algorithm according to data changes and transmits the optimal parameters to a water spraying execution mechanism. The tension adjusting module adjusts the nozzle opening degree and the water spraying time according to the comparison result of the weft yarn tension and the preset threshold value. The nozzle optimization module optimizes the nozzle design by means of a computational fluid dynamics approach. The pressure adjusting module monitors pipeline pressure through a pressure sensor, and alarms and adjusts the rotating speed of a water pump and the opening degree of an electromagnetic valve if the pipeline pressure exceeds a threshold value. The state detection module monitors knitting needles and shuttles through the machine vision technology, and water spraying parameters are adjusted when abnormity occurs. The data processing module uploads data to a cloud platform, and big data and machine learning are utilized to optimize a control strategy so as to realize real-time monitoring and accurate control of various parameters of the water-jet loom and guarantee weaving quality and efficiency.

The invention discloses a highway tunnel lighting intelligent control method and system based on the Internet of Things, a sensing module is composed of a millimeter wave radar vehicle detector, a tunnel interior brightness sensor, a tunnel exterior brightness sensor and a vehicle speed detector, and a transmission module is a dual-mode communication module composed of a Zigbee wireless network module and a power line carrier communication module. A fuzzy control algorithm library is built in an edge computing gateway of the control module and is used for fusing data of the sensing module in real time and generating a dimming instruction, and each Internet of Things segmentation controller of the control module manages a plurality of lamps and is internally provided with a fault isolation circuit; and an intelligent management platform is arranged in the application module and is used for receiving an instruction of the control module and generating a dynamic dimming strategy. According to the invention, the millimeter-wave radar and video tracking data fusion, Zigbee + PLC dual-mode transmission and edge gateway self-learning algorithm are combined; according to the invention, the defects of the traditional scheme in the aspects of real-time performance, reliability, energy efficiency ratio and operation and maintenance cost can be systematically solved.

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 relates to the technical field of hybrid energy storage management, in particular to a hybrid energy storage fast charging station energy management method based on a decommissioned battery, which comprises the following steps of: comparing a load demand with a battery capacity based on the load demand, the residual capacity of the decommissioned battery and a health state parameter, dividing a load interval according to time, and determining the load interval of the decommissioned battery; and calculating the load change rate in each time period, performing battery health state evaluation and power response selection, and generating a time interval battery pack distribution result. According to the method, the fuzzy control algorithm is used for clustering and dividing the retired batteries, comprehensive calculation is carried out by combining the health states of the batteries and power distribution, the power output in the battery pack is optimized, the service life of the retired batteries is prolonged, overload or unreasonable use of the batteries is avoided, the energy storage cost is reduced, and the real-time power regulation scheme is optimized. The battery pack can be adjusted in time according to power grid load fluctuation, and the stability in a high-load period is ensured.

The invention discloses a target tracking method fusing visual perception and motion control. The method comprises the following steps: improving a YOLOv8 model and pre-training the improved YOLOv8 model; s2, preprocessing an image acquired by a camera, inputting the preprocessed image into the YOLOv8 model trained in the step S1, and outputting a target bounding box and a target center pixel coordinate; target center pixel coordinates are transmitted to a development board of a pre-burning algorithm program through a serial port, the development board is designed according to a stepping motor mathematical model, sine acceleration and deceleration serve as the basis, PID control, sliding mode control and fuzzy control algorithms are fused, and code burning is generated after in-loop simulation optimization; and the development board drives a stepping motor to adjust the posture of the holder according to the target center pixel coordinate control instruction, so that the target stops moving after being kept in the view center. The problems that a traditional YOLOv8 model is large in target recognition error under the complex background, PID control is prone to oscillation under the nonlinear working condition, buffeting exists in sliding mode control, and response lags when fuzzy control is independently used are solved.

The invention discloses a spray drying device for boron carbide production, the spray drying device comprises a spray drying tower, a hot blast stove, a cyclone separator and a system controller, an atomizer and an online detection module are arranged in the spray drying tower, and the online detection module is used for monitoring temperature parameters in the spray drying process; comprising a first temperature sensor, a second temperature sensor and a temperature signal processing unit, the temperature signal processing unit adopts a multi-stage filtering and error compensation technology, noise and interference are effectively suppressed, and the accuracy of detection data is ensured. The air inlet temperature and the air outlet temperature are monitored in real time through the online detection module, the fuzzy-PID control algorithm is combined, the temperature control precision and the response speed are remarkably improved, and temperature fluctuation is reduced. The device dynamically adjusts the heating power of the hot blast stove according to real-time working conditions, the energy utilization efficiency is optimized, and the energy consumption is reduced.

The invention discloses a motion control method and system for five-axis series-parallel processing equipment, and relates to the technical field of processing equipment control, and the method comprises the steps: obtaining the real-time state data of the five-axis series-parallel processing equipment, and carrying out the data preprocessing; constructing a kinematics model based on deep learning, and calculating the motion trail of each joint of the processing equipment; planning an energy consumption optimal path by using a multi-objective optimization algorithm; generating a joint driving signal according to a path planning result by utilizing a self-adaptive fuzzy control algorithm; and detecting external disturbance in real time and adjusting control parameters based on the disturbance observer to realize motion control. According to the method, the path planning precision of the five-axis series-parallel processing equipment is improved, the energy consumption is obviously optimized, the anti-interference capability is enhanced, the control response speed is improved, the method has the technical advantages of high precision, high efficiency and high robustness, and the method is suitable for a precise motion control scene in a complex dynamic environment.

The invention discloses a hybrid power engineering machinery control method and system based on operation load identification, and belongs to the technical field of engineering machinery. Target torque of a driver and predicted torque of a hydraulic pump at the target moment are calculated; calculating a torque difference value; based on a fuzzy control algorithm, considering the optimal fuel consumption torque interval of the engine and the optimal efficiency torque interval of the ISG motor, and determining the target torque of the engine and the target torque of the ISG motor according to the target torque of the driver, the difference value between the target torque of the driver and the predicted torque of the hydraulic pump and the SOC of the battery; and jointly controlling the pressure of the hydraulic pump by using the target torque of the engine and the target torque of the ISG motor. The method has the advantages that the operation load prediction model is built through Markov, the operation load at the future moment is predicted, the driver target torque and the difference value of the two torque are introduced, the torque distribution fuzzy controller of the engine and the ISG motor is built by considering the SOC state of the battery, and the target torque of the engine and the ISG motor is dynamically adjusted.

The invention relates to a motor power self-adaptive control method and system based on multi-dimensional sensor data, and the method comprises the steps: collecting multi-dimensional signals, such as ground resistance, motor temperature and battery voltage, in real time, carrying out the self-adaptive filtering and noise reduction, analyzing the correlation of all parameters through a multivariable decoupling algorithm, and determining a power demand weight; when it is detected that the ground resistance is a dominant factor, the system generates a load feature vector through weighted fusion, a database is matched to determine an optimal power target value, and PWM output is dynamically adjusted through a fuzzy control algorithm; and a closed-loop feedback mechanism continuously optimizes control parameters to ensure that the system quickly responds to sudden change of ground resistance. According to the method, intelligent adaptation of the motor power is achieved, the problems that adjustment lags behind and multi-parameter coupling processing is insufficient in the traditional technology are effectively solved, the stability of the sweeping robot under the complex ground working condition is remarkably improved, and the service life of the motor and the service life of a battery are remarkably prolonged.

The invention discloses a control system and method for an automatic beer production line, relates to the technical field of automatic control, is used for solving the problems that state judgment is lagged and abnormal trend is difficult to identify in time, identifies electronic tags of key equipment through an RFID reader-writer, imports models and serial numbers into a database, and automatically associates operation parameters. The distributed sensors collect temperature, pressure, flow and vibration data in real time, the analysis unit calculates the operation deviation and the health degree, a multi-condition control strategy is configured through the programmable logic controller based on the health degree, a time window and a task priority algorithm are combined, real-time data and historical data are compared to recognize abnormity, and the real-time data and the historical data are analyzed. And a fuzzy control algorithm is fused to generate an adjustment instruction, the equipment state is automatically adjusted, time sequence modeling is utilized to analyze whole-process data, a start-stop strategy is formulated, energy distribution and process optimization are performed, and intelligent scheduling and self-adaptive optimization of the beer production line are realized.