884 results about "Pid control algorithm" patented technology

The invention discloses an air conditioner energy-saving control method and system based on multi-objective optimization, a medium and a product, and relates to the field of data processing. The method comprises the steps that optimization targets for conducting energy-saving control on the air conditioner and corresponding constraint conditions are determined, and the optimization targets comprise target air conditioner energy consumption and target comfort degree; acquiring current environment data of the air conditioner; the equipment operation parameter combination of the air conditioner is used as particles in the multi-target particle swarm algorithm; determining an optimal solution through a multi-target particle swarm algorithm; the air conditioner is controlled according to the parameter combination corresponding to the optimal solution; the actual air conditioner energy consumption and the actual comfort degree are obtained; based on the target air conditioner energy consumption, the target comfort level, the actual air conditioner energy consumption and the actual comfort level, the equipment operation parameters of the air conditioner are adjusted through a PID control algorithm, and final equipment operation parameters are obtained; and controlling the air conditioner to work according to the final equipment operation parameters. According to the method, both the energy-saving effect and the comfort level of the air conditioner are balanced.

The invention relates to the technical field of digital twinning, in particular to a multi-machine running-in test distributed control system and method based on digital twinning. The method comprises the steps that a sensing execution unit collects equipment operation state data based on a multi-source sensor module and carries out preliminary data processing and detection; the digital twinning unit receives multi-dimensional sensing data of the sensing execution unit based on a dynamic twinning modeling module, establishes an equipment state model, and performs simulation verification and parameter optimization on a control strategy through a Q-learning strategy to obtain optimized control parameters; the intelligent control unit constructs an optimal decision-making model of multi-machine cooperative control based on a cooperative decision-making module, adjusts parameters of an execution mechanism in real time through a PID control algorithm and feed-forward compensation, and generates a self-adaptive control instruction; and the fusion analysis unit constructs a multi-dimensional equipment operation knowledge base based on the knowledge graph module, and generates global optimization parameters through a multi-target genetic algorithm.

The invention relates to the technical field of cable production automation control, and provides a crosslinked cable production control method based on an improved PID (Proportion Integration Differentiation) control algorithm, which comprises the step of constructing an optimal reference model by establishing a cooperative control framework of temperature, pressure and traction speed and taking the temperature of a vulcanization pipe as a main control variable and the pressure and the traction speed of an extruder as slave control variables. A main controller tracks a temperature set value in real time, PID parameters are dynamically set according to technological parameters such as the thickness deviation of an insulating layer and the crosslinking degree of a material, and precise control is achieved through a three-ring cooperative coupling improved PID controller. A temperature control ring enables the steady-state error of the actual temperature and the set value to be converged to zero. The pressure and speed control loop converges the fluctuation amplitude to a process allowable range. The temperature stability, the pressure uniformity and the traction synchronism of crosslinked cable production can be improved, the process fluctuation and the defect rate are reduced, and the product quality and the production efficiency are improved.

The invention relates to the technical field of intelligent control, in particular to an intelligent control method and system for continuous basalt fiber drawing forming. The method comprises the following steps: acquiring temperature measurement point data including temperature data and flow data; temperature control, energy supply strategy adjustment and bushing partition heating parameter adjustment are conducted according to the collected temperature measurement point data, and a fuzzy self-tuning algorithm is adopted to conduct linkage adjustment on the temperature of an uptake, the temperature of an access and the temperature before bushing; dynamically adjusting an energy supply strategy based on the energy supply sequence; and the partition heating parameters of the bushing are adjusted by using a PID control algorithm. According to the invention, a longitudinal partition heating mode and accurate temperature control are adopted for the bushing, and a strategy of dynamically adjusting the wire drawing speed and the partition temperature according to the average value of the wire diameter is combined, so that the wire diameter of basalt fibers drawn by the whole bushing can be effectively ensured to be more uniform.

The invention relates to the field of intelligent mobile equipment control, and discloses a self-adaptive obstacle avoidance control system and an application method thereof, and the system comprises a data collection module, an environment modeling module, an obstacle segmentation module, a path planning module and a motion control module. The data acquisition module is used for acquiring multi-modal data of the surrounding environment of the vehicle; the environment modeling module fuses the multi-modal data, and generates a dynamic environment map including the spatial position, the dynamic characteristic and the free passing area of the obstacle; the obstacle segmentation module is used for segmenting the dynamic environment map and identifying an obstacle area and a free passing area; the path planning module calculates an optimal path with the minimum cost; the motion control module adjusts the motion state of the vehicle in real time through a PID control algorithm or a model prediction control method, and path tracking and dynamic obstacle avoidance are achieved. The method has high-precision environment perception capability, flexible path planning capability and efficient dynamic obstacle avoidance control capability, and can be widely applied to the fields of industrial automation and intelligent logistics.

The invention discloses an intelligent heat supply pipe network optimization control method and system based on the Internet of Things, and relates to the technical field of heat supply pipe network optimization control, and the system comprises environmental parameter collection modules which are distributed in different areas covered by a heat supply pipe network and are used for collecting environmental data of each area in real time; and the Internet of Things control platform is used for receiving the environmental data from the environmental parameter acquisition module. According to the intelligent heat supply pipe network optimization control method and system based on the Internet of Things, flow and temperature data are monitored in real time, the valve opening degree is accurately controlled according to a PID control algorithm, a closed-loop control system is formed, heat supply demand changes are quickly responded, energy waste is reduced, heat supply quality is guaranteed, meanwhile, energy consumption is reduced, and the energy utilization efficiency is improved; environmental parameters and user heat using habit changes are monitored in real time, a heat supply strategy is adjusted in time, heat supply demand fluctuation is quickly responded, and it is ensured that heat supply always meets the actual demand of a user.

The invention provides a sewage treatment aeration control system and method based on multi-parameter collaborative optimization. The method comprises the following steps: acquiring multi-sensor data of COD (Chemical Oxygen Demand), BOD5 (Biochemical Oxygen Demand), DO (Dissolved Oxygen Demand), ammonia nitrogen, ORP (Oxidation Reduction Potential), pH (Potential of Hydrogen), water inlet flow and sludge concentration of wastewater in an aeration tank by utilizing a multi-parameter sensor module; according to the dynamic correction oxygen mass transfer coefficient and the feedforward parameter, establishing a relationship between the water inlet load and aeration; calculating the air volume of an air blower and the fine adjustment value of the opening degree of a butterfly valve through a PID control algorithm and feedback parameters, and correcting the aeration quantity to obtain a total air volume target value; and adjusting the opening degree and the rotating speed of a guide vane of the air blower according to the self-adaptive PID control parameters and the corrected total air volume target value by utilizing a self-adaptive PID controller, and adjusting the opening degree of a butterfly valve according to a gradient butterfly valve adjusting strategy. The output of the air blower is optimized by adopting self-adaptive PID, and the opening degree of the butterfly valve of each biological tank is dynamically adjusted through a gradient valve adjusting strategy, so that the accurate control of the aeration rate is realized; the sewage treatment effect is ensured.

The invention relates to the technical field of intelligent equipment monitoring, and discloses a sandstone separator monitoring control method and system based on multi-source data processing. According to the method, equipment operation parameters, real-time image data and vibration sensing data of the sandstone separator are collected in real time, data preprocessing and feature extraction are carried out, a monitoring model based on a multi-modal deep learning framework is constructed, and accurate joint characterization of the equipment state is achieved. The system can dynamically generate a control instruction according to a monitoring result, adjust equipment operation parameters in time, and avoid production interruption and quality problems. Besides, the multi-modal data fusion technology and the self-adaptive PID control algorithm are adopted, so that the operation efficiency and stability of the sandstone separator are further improved, and powerful technical support is provided for the field of intelligent manufacturing.

The invention belongs to the technical field of physical and chemical analysis, and particularly discloses a high-precision full-automatic titration system and method based on fusion of a spectrum sensor and a PLC, a multi-channel spectrum sensor is adopted to capture fine spectrum characteristics of solution color change in real time, and the flow rate of titration liquid is dynamically adjusted through a PLC embedded PID control algorithm, so that the titration accuracy is improved. The method realizes submilliliter-level precision control, constructs a real-time compensation mechanism in combination with temperature, humidity, pH and other environmental parameters, improves the accuracy and robustness of titration end point judgment, automatically updates an end point judgment threshold and control parameters in combination with an AI self-learning module, realizes self-adaptive adjustment and sustainable evolution of the system, and improves the accuracy and robustness of titration end point judgment. The device has the characteristics of modular structure, high detection precision, high control response and high operation stability.

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 invention discloses a low-temperature-resistant heat-insulating composite material and a preparation method thereof, and relates to the technical field of composite materials, and the material comprises an outer protective layer, a middle reinforcing layer and an inner heat insulating layer. The inner heat insulation layer is composed of aerogel, polytetrafluoroethylene micro powder and the like, and good heat insulation performance is provided. The middle reinforcing layer contains ethylene-propylene rubber and the like, so that the material is endowed with elasticity, mechanical strength and flame retardance. And the outer protective layer is made of PVC resin and the like, so that the overall strength and wear resistance are enhanced. The low-temperature-resistant anti-cracking additive is prepared from polyimide resin and the like, so that the anti-cracking performance is improved. The modified filler is prepared through a specific method, and the material performance is improved. During preparation, all the layers are prepared respectively and then subjected to hot-pressing compounding. The internal mixer control system guarantees preparation parameter control of the middle enhancement layer, and a PID control algorithm is adopted. The composite material has the advantages of reasonable structural design, synergistic effect of all layers, mature preparation method and process, excellent performances of low temperature resistance, heat insulation, cracking resistance and the like, and is suitable for being applied to various low-temperature environments.

The invention relates to a control method, a stabilizer bar system and a vehicle, and the control method comprises the steps that when the action state between a first stabilizer bar and a second stabilizer bar is a semi-coupling state, target parameters of a hydraulic system are determined according to the vehicle running state; and according to the target parameter and the actual parameter of the hydraulic system, a PID control algorithm is adopted to control the hydraulic system so as to adjust the rigidity of the stabilizer bar, and the technical problems that the stabilizer bar cannot effectively cope with various working conditions and cannot give consideration to the operation stability and comfort due to the fact that the rigidity is single and cannot be adjusted in the related technology are at least solved.

The invention belongs to the field of robot control, and particularly relates to a robot self-adaptive impeller welding control method and system fused with multi-modal data, and the method comprises the steps: obtaining an impeller workpiece image and three-dimensional point cloud data through an industrial vision subsystem, and extracting a three-dimensional welding seam position point set representing a welding seam track; generating a candidate welding path set based on welding seam geometric feature analysis; in combination with welding seam morphology and process requirements, optimal welding process parameters are predicted through a regression model, and a welding gun attitude angle is calculated through quaternion conversion according to a curved surface normal vector; in the welding process, the relative position deviation of a robot and a workpiece and the parameter deviation of welding current / voltage and a planned value are monitored in real time, when the deviation exceeds a preset tolerance interval, a PID control algorithm is adopted to dynamically adjust robot motion parameters or welding process parameters, and closed-loop control over the welding process is achieved; according to the method, precise planning of the welding path and self-adaptive regulation and control of the process are achieved, and the welding quality and the forming consistency are remarkably improved.

The invention relates to the technical field of industrial robot path control, and discloses an industrial robot real-time path correction method, which comprises the following steps: acquiring current position information, speed information and acceleration information; calculating and generating a path error value by adopting an Euclidean distance; an error feedback control method and an inverse kinematics algorithm are combined to generate a correction matrix, and the angle, angular velocity, acceleration and angular acceleration parameters of the industrial robot joint are adjusted in real time; and further adjusting the motion trail parameters based on an improved Kalman filtering algorithm. Compared with the technical problems that in the prior art, path correction lags behind, precision is insufficient, and real-time path correction is difficult to achieve especially in a complex dynamic environment, due to the fact that multi-sensor fusion, a feedback control algorithm and an improved PID control algorithm are combined for use, the path is corrected in real time, error accumulation is reduced, and therefore the situation that path deviation is too large is avoided; and the production efficiency of the industrial robot in a complex environment is improved.

The invention relates to the field of intelligent control based on image recognition, and discloses a Lyocell fiber production continuous feeding control method and system based on image recognition, and in the method, a feeding and conveying device continuously feeds a pulp raw material into a swelling machine at a preset feeding speed without shutdown; moreover, a camera arranged in the swelling machine obtains a to-be-detected liquid level image in the swelling machine containing the circular liquid level boundary in the non-stop continuous feeding state, then the current liquid level height in the swelling machine is obtained according to the to-be-detected liquid level image, the current liquid level height is compared with the target liquid level height, and a liquid level difference value is obtained; and the liquid level difference value serves as a control factor, the opening degree of a solvent conveying pump at a solvent inlet of the swelling machine is controlled based on a PID control algorithm, and the current liquid level height in the swelling machine is kept within a set range. Waste of production time is avoided, the large-yield requirement is met, and the overall production efficiency is improved; in addition, it is also ensured that the produced pulp porridge has good uniformity.

The invention relates to the field of navigation signal processing, and discloses a passenger car battery temperature control intelligent regulation and control method and system based on multi-source sensor data, environment data and battery operation state data are acquired by constructing a multi-source sensing array, and fusion processing and feature extraction are performed on the acquired data by using an edge calculation method; analyzing and predicting the charging and discharging behaviors of the battery based on the extracted characteristic parameters, judging the charging and discharging states of the battery, dynamically optimizing the switching threshold value of the refrigerating or heating mode of the battery, switching the mode according to the optimized switching threshold value, and performing matching analysis on the photovoltaic power generation energy and the temperature control energy consumption, so as to determine the temperature control energy consumption of the battery. According to a matching analysis result, an exclusive temperature control matrix is generated through a geographic position and meteorological data, and time-sharing gradient control is performed by using an adaptive PID control algorithm in combination with instantaneous wave cloud characteristics of photovoltaic energy storage, so that customized temperature control strategies can be provided for different environmental conditions, temperature fluctuation is avoided, and the temperature control stability is improved.

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 mixed emulsion explosive one-key production control method and system, and relates to the field of automatic and intelligent control. By means of automatic and intelligent control means, the problems of low efficiency, high cost, quality fluctuation, process uncertainty and the like in traditional mixed emulsion explosive production are solved, accurate feeding of raw materials is achieved through a quality sensor array and a PID control algorithm, and the production efficiency is improved. The stability of production parameters is ensured through multi-sensor data fusion and an advanced control algorithm, so that the consistency of product quality is improved; meanwhile, a feedback control loop and a continuous learning mechanism are established, closed-loop control in the production process is achieved, technological parameters are optimized, the production stability and reliability are ensured, the production efficiency and the product quality are remarkably improved, and the labor cost is reduced.

The invention discloses a bone grinding reinforcement learning system and method based on near-end strategy optimization, and the method comprises the steps: learning a relation between a tail end cutting force state and a robot motion in a human trial and error growth mode, and controlling a Markov decision process problem through a robot force; contact force control is optimized through a near-end strategy; designing a reward function and a penalty function of the bone grinding action; performing accelerated training on a robot action decision strategy; and factors influencing the bone cutting effect of the robot are analyzed. On the basis that the robot bone resection control process has the Markov property, the PPO algorithm is adopted to train a controller of the grinding robot, and the robot is guided to obtain a positive value reward; parallelization calculation and closing of graphic display are helpful for increasing the training speed; by coordinating a reinforcement learning strategy of the robot and combining and utilizing priori knowledge of human or a PID control algorithm for guidance, invalid exploration behaviors are avoided, and an efficient learning process is realized.

The invention relates to the technical field of lithium battery manufacturing equipment, and discloses a rolling thickness control method and device, electronic equipment and a storage medium. According to the method, in the working process of the roller press, the real-time thickness of a machined object of the roller press is measured, the real-time thickness is compared with the target thickness, all adjusting parameters in a PID control algorithm are updated according to the comparison result, and the adjustment amount of the rolling parameters of the roller press is calculated through the PID control algorithm with the updated adjusting parameters. And therefore, closed-loop control on the rolling thickness is realized. In the working process of the roller press, the adjusting parameters of the PID control algorithm are updated on line according to the real-time condition, extra manpower and time do not need to be occupied for updating the adjusting parameters of the control algorithm, manpower and time consumption is saved, the adjusted adjusting parameters can better adapt to the real-time working condition of the roller press, and the working efficiency of the roller press is improved. And the accuracy of controlling the rolling thickness is improved.

The invention relates to the technical field of agricultural fertilization control, in particular to an accurate fertilization flow control method and system for a water and fertilizer all-in-one machine for economic forest tree species, and the method comprises the steps: collecting water and fertilizer pressure data and water and fertilizer flow data in a water and fertilizer mixing pipeline in real time at a fertilization nozzle of each tree; analyzing the fluctuation condition of the water and fertilizer flow data of each tree within a preset duration before each collection moment, and determining the fluctuation trend influence coefficient of each tree at each collection moment; determining an upstream trend influence coefficient and a water and fertilizer flow deviation degree of each tree at each collection moment; determining the same-row fluctuation difference value of each tree at each collection moment; determining a proportionality coefficient adjustment value of each tree at each collection moment; and determining proportional parameters of a PID control algorithm, and controlling the fertilization flow of each tree. The invention aims to improve the control precision of the fertilization flow of each tree.

This invention relates to the field of battery testing technology and discloses an ice-water impact testing system, comprising: a temperature control unit for regulating the temperature of the testing environment, the temperature control unit including a liquid nitrogen cooling device and an electric heating device, and using a PID algorithm to regulate temperature fluctuations within ±0.5℃; an impact unit for simulating water flow impact and mechanical vibration of battery samples during testing, the impact unit including a high-pressure water pump and a mechanical vibration device, capable of adjusting water flow pressure and vibration acceleration; and a data acquisition and analysis module for real-time acquisition of temperature, humidity, pressure, and battery voltage data during the testing process, and evaluating battery performance through intelligent analysis algorithms. By combining the liquid nitrogen cooling device and the electric heating device, and employing a PID control algorithm, precise adjustment of the temperature control unit is achieved, enabling rapid temperature switching within the range of -40℃ to 80℃, while maintaining temperature fluctuations within ±0.5℃.

The invention discloses an emulsion paint production temperature control management system and method, and relates to the technical field of automatic temperature control. According to the emulsion paint production temperature control management system and method, by arranging the emulsion paint temperature monitoring module, the temperature abnormity module and the temperature adjusting module, accurate temperature control in the emulsion paint production process is achieved. The temperature change of the latex paint in different production stages is monitored in real time through a temperature sensor, a temperature fluctuation curve is generated through time sequence analysis, periodic fluctuation and amplitude characteristics of temperature are recognized based on a spectrum similarity analysis technology, a temperature fluctuation intensity index is calculated, and whether temperature abnormity exists or not is judged by comparing the temperature fluctuation intensity index with a preset threshold value; when the temperature abnormity is identified, the temperature adjusting module dynamically adjusts the power output of the temperature control equipment through a PID control algorithm, and precise temperature control is achieved. The temperature control precision and the stability in the production process are obviously improved, and the quality consistency and the production efficiency of the latex paint are ensured.

The invention provides a microbial fermentation temperature control method based on PID (Proportion Integration Differentiation), which relates to the field of temperature control, and specifically comprises the following steps: constructing microbial fermentation temperature control models including a temperature monitoring model, an improved PID control model, a temperature driving model, a temperature error model and a target function model; the maternal line optimization algorithm is improved, and the improved maternal line optimization algorithm is guided through the target function to set the coefficient of the traditional PID control algorithm to obtain an improved PID control algorithm; normalizing the deviation value of the target temperature and the real-time temperature in a range of [0, 1] to obtain a normalized temperature deviation value of the real-time temperature and the target temperature; the normalized temperature deviation value is input into an improved PID control model, a temperature control signal is output after adjustment through an improved PID control algorithm, the temperature control signal controls and adjusts the microbial fermentation temperature through a temperature driving model, and the microbial fermentation temperature is accurately and rapidly adjusted.

The invention discloses an automatic focusing control method based on a displacement sensor, and relates to the technical field of motion control, and the method comprises the steps: collecting a distance signal of a detection target, and converting the distance signal into a physical distance value through a calibration coefficient; the motion control card performs difference operation on the physical distance value and the standard focusing distance to obtain a position deviation value, performs data smoothing processing by using a Kalman filtering algorithm, establishes a state prediction model by using latest N frames of cache data, generates position compensation data, converts the position compensation data into a pulse control quantity, and when the pulse control quantity exceeds a preset threshold value, outputs the position compensation data; generating a motor driving instruction according to a PID control algorithm; and controlling the stepping motor driver to drive the lens assembly to perform position compensation according to the motor driving instruction, so that a standard focusing distance is kept between the lens assembly and the detection target. According to the invention, the problems of response lag and poor anti-interference capability in a traditional automatic focusing system are solved, and the dynamic response characteristic and the control precision of the system are improved.

The invention provides a battery pack temperature equalization control method. According to the method, multi-zone temperature and heat dissipation distribution data acquisition is carried out, a temperature distribution model is established in combination with K-means clustering and a heat conduction equation, heat dissipation parameters are optimized by using a least square method, differential temperature control instructions are generated by using a PID control algorithm, and Q-learning reinforcement learning is introduced to dynamically adjust control parameters. According to the method, accurate monitoring and intelligent regulation and control of the internal temperature of the battery pack are achieved, the problems that a traditional temperature control system is lagged in response and poor in equalization effect are effectively solved, the temperature equalization performance of the battery pack is remarkably improved through multi-level data analysis and a self-adaptive control strategy, the service life of the battery is prolonged, and the service life of the battery pack is prolonged. And reliable guarantee is provided for safe and efficient operation of the new energy automobile power battery.

The invention belongs to the technical field of drilling, and provides an active vibration reduction method and system based on self-adaptive adjustment of a drill rod load in order to effectively reduce vibration of a drill rod. A real-time feeding oil pressure signal is converted into real-time feeding thrust, and a real-time rotating oil pressure signal is converted into real-time rotating torque; filtering the original vibration signal by adopting a singular value subspace decomposition method to obtain a real vibration signal; according to the preset initial value of the feeding thrust and the preset initial value of the rotating torque, a neural network self-adaptive PID control algorithm is used for conducting self-adaptive comparison calculation, the vibration adjustment amount is obtained, and active vibration reduction is conducted according to the vibration adjustment amount; the system comprises six closed-loop control modules including a preset parameter input module, a signal acquisition module, a signal filtering module, a control algorithm module, a control output module and an execution module, accurate vibration reduction control is achieved, the bounce phenomenon of a drill bit is eliminated, the deviation error of a drill hole is reduced, meanwhile, stick-slip vibration is restrained, and the clamping stagnation failure rate of a drill rod is greatly reduced.

The invention provides a greenhouse intelligent temperature control automatic adjustment optimization method based on multi-parameter dynamic coordination, and relates to the technical field of agricultural facility environment control. The method comprises the following steps: acquiring temperature, air humidity, wind speed and airflow distribution environment data inside and outside a greenhouse in real time; the environment state is quantified through a dynamic evaporation efficiency coefficient DEEC and an airflow distribution uniformity index ADUI; the flow of a water pump, the rotating speed of a fan and the opening degree of a ventilation system are dynamically adjusted in combination with an improved PID control algorithm, and the weight of an integral term is dynamically adjusted along with the ADUI to enhance the response efficiency; a time sequence prediction model based on LSTM is adopted to fuse historical and real-time data, a parameter correction amount is predicted, and the adaptation capability of the system to a new environment is improved in combination with a transfer learning algorithm; system hardware comprises a low-power-consumption wireless sensor, a frequency conversion actuator and an embedded control terminal. Through combination of multi-parameter perception and cooperative control, accurate temperature and humidity control and energy efficiency optimization are realized, resource consumption is reduced, and automation and intelligence levels of environment adjustment are improved.

The invention discloses a laser accurate positioning system and method based on an improved PID control algorithm, and relates to the technical field of laser positioning, a holder laser module is constructed and comprises a laser emission unit and a holder execution unit and is used for executing a control strategy, and a multi-sensor array is constructed to collect parameter data of the holder laser module; a control module is constructed and comprises a dynamic bimodal PID unit, a space-time decoupling learning unit and a frequency domain anti-interference unit and is used for analyzing parameter data to generate a control strategy of a holder execution device, a fault processing module is constructed and is used for detecting whether a fault occurs in the process of a laser precise positioning system, and when the fault occurs, the fault processing module is used for processing the fault. Executing a three-stage shutdown process; according to the invention, by improving the PID control algorithm and the frequency domain anti-interference design, the anti-interference capability and the system stability of the laser positioning system in a complex dynamic environment are significantly improved.

The invention discloses an internet of things-based amusement equipment light dynamic control method and system, and the method comprises the steps: collecting the acceleration, angular velocity and coordinate data of equipment, carrying out the wavelet denoising and Z-score standardization processing, and constructing a dynamic topology model based on a graph attention network and a graph convolution network. A spectral clustering algorithm is utilized to carry out space partitioning on equipment nodes to generate a topological sub-graph, and an LSTM network is combined to predict an equipment motion track and generate a continuous track sequence. Track correlation features are extracted through a graph attention network, a genetic algorithm is fused to optimize and generate a light conversion sequence, dynamic time warping alignment timestamps are synchronously adopted, and phase synchronization of the light sequence and the motion track is ensured. And finally, brightness, color and flicker frequency are adjusted in real time based on a PID control algorithm, and a closed-loop feedback mechanism is formed. According to the method, high-precision dynamic matching of light and equipment movement is realized, and immersive experience and visual interactivity are improved.