528 results about "Adaptive control algorithm" 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.

A control method and system for precise landing of an unmanned aerial vehicle. The method comprises the following steps: on the basis of a multi-sensor fusion technique, using a lidar and a stereo vision algorithm to collect environmental data and perform primary processing, and generating comprehensive environmental perception data (step S1); on the basis of the comprehensive environmental perception data, using a digital elevation model algorithm to re-construct a three-dimensional terrain of a landing area, and generating a three-dimensional terrain model (step S2); on the basis of the three-dimensional terrain model, using an A* search path-planning algorithm to perform risk assessment and plan a safe landing path, and generating an optimized landing path (step S3); on the basis of the optimized landing path, using an adaptive control algorithm to adjust a flight attitude in real time by means of a fuzzy logic controller, and generating flight parameter adjustment (step S4); on the basis of the flight parameter adjustment, using machine vision and a decision tree algorithm to execute autonomous obstacle avoidance and emergency response, and generating a safe landing execution scheme (step S5); and on the basis of the safe landing execution scheme, using an ultrasonic sensor and a ground feedback system to confirm and fine-adjust a landing point, and completing final landing confirmation (step S6).

The invention relates to the technical field of intelligent agriculture, and discloses a visual intelligent agricultural planting control system. The system comprises an environment data acquisition module used for acquiring multi-source environment sensing data; the growth feature modeling module is used for extracting crop growth state features through a morphological analysis algorithm; the environment regulation and control decision module is used for generating an environment regulation and control instruction set by utilizing a dynamic threshold matching algorithm; the visual interaction module is used for generating a three-dimensional farmland live-action simulated diagram through multi-dimensional data fusion processing; the strategy execution module is used for driving agricultural facilities to execute actions by adopting a self-adaptive control algorithm; an abnormity early warning module is further arranged, and abnormity early warning signals are generated through correlation analysis. The system realizes comprehensive acquisition and analysis of agricultural planting environment data, precise environment regulation and control, visual and visual display and abnormal early warning, effectively improves the intelligent and precise level of agricultural planting, improves the crop yield and quality, and assists the development of intelligent agriculture.

The invention relates to the technical field of data processing, and discloses an intelligent bridge reinforcement decision generation method, device and equipment based on damage identification. The method comprises the following steps: carrying out bridge damage identification and evaluation on a bridge state data set to obtain a bridge damage characteristic spectrum; performing intelligent decision analysis on the bridge damage characteristic spectrum, generating a preliminary reinforcement scheme, and obtaining a candidate reinforcement strategy set; performing parametric modeling and multi-scale simulation on the candidate reinforcement strategy set, and performing coupling calculation evaluation on a reinforcement effect to obtain an optimized reinforcement scheme; virtual construction planning is conducted on the optimized reinforcement scheme, a construction process model is generated through an intelligent scheduling algorithm, and a target construction scheme is obtained; and monitoring and feeding back the target construction scheme in real time, and dynamically optimizing the construction process through an adaptive control algorithm to obtain a target intelligent reinforcement implementation scheme. The intelligent bridge reinforcement decision generation efficiency and accuracy based on damage identification are improved.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention provides a remote identity recognition method and system based on unmanned aerial vehicle management and control requirements. Wherein the preparation state of the unmanned aerial vehicle is determined by generating an inquiry code, sending the inquiry code to all online unmanned aerial vehicles in a target airspace, and receiving an identity response signal comprising a unique identifier, a task brief description and a flight path. A multi-dimensional data fusion algorithm is combined with historical flight data, meteorological conditions and geographic information to predict an unmanned aerial vehicle behavior mode, and a behavior prediction report is formed. The block chain technology is used for cross verification, and the real identity and authorization state of the unmanned aerial vehicle are verified through the dual key encryption technology and the consensus algorithm. And according to a verification result, using machine learning to construct a temporary virtual fence and plan an optimized route, and automatically adjusting an air corridor to form a traffic management scheme. And finally, analyzing actual flight parameters by applying a self-adaptive control algorithm, and further optimizing route setting. According to the technical scheme provided by the embodiment of the invention, the safety and efficiency of air traffic management of the unmanned aerial vehicle are improved.

The invention discloses an electromechanical installation engineering automatic debugging method and system based on intelligent control, and the method comprises the steps: enabling an intelligent control system to monitor the energy consumption of equipment in real time, optimizing an operation mode, adjusting the working state according to data, and enabling the equipment to work cooperatively through the intelligent control system; deploying temperature and humidity, pressure, current and voltage sensors for stable wiring, and configuring parameters by adopting an intelligent debugging tool; based on the preliminary data, optimizing the system by applying an adaptive control algorithm, adjusting a strategy, fusing multi-dimensional data, and identifying performance bottleneck and abnormality; the centralized monitoring platform monitors the equipment state in real time and sends out an early warning signal; after multiple rounds of debugging, the energy-saving effect and performance are evaluated, a control algorithm is optimized, and optimization is continuously carried out according to historical data and environmental changes; according to the electromechanical installation engineering automatic debugging method and system based on intelligent control, the adaptive control algorithm and the machine learning technology are introduced, and the strategy is dynamically optimized according to the equipment operation state, the environment change and the historical data.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle self-adaptive navigation system which comprises an inertial navigation system, a visible light sensor, a data fusion module, a database and a navigation control module. The inertial navigation system provides attitude, speed and acceleration data; the visible light sensor collects an image and obtains environment feature points through deep learning algorithm processing to realize visual positioning; and the data fusion module fuses two kinds of sensor data by using a dynamic weighted Kalman filtering algorithm, and dynamically adjusts the weight according to the environment to optimize the positioning information. The map library and the feature library store environment data to assist in positioning and dynamically update; the navigation control module generates an instruction according to the fused positioning information, and automatically adjusts a flight strategy by considering various factors based on an adaptive control algorithm. The system improves the positioning precision and environmental adaptability, enhances the flight safety and stability, is high in intelligence and autonomy degree, has flexibility and expandability, and can be widely applied to the field of unmanned aerial vehicles.

The invention relates to the technical field of spraying process control, and discloses a spraying process self-adaptive adjustment method based on temperature measurement. The method comprises the steps that target coating parameters and base material physical property data are obtained, and initial spraying control parameters are generated through a first intelligent calculation model in combination with historical process data and real-time environment monitoring data; when spraying is executed, heat distribution data of a spraying area are collected in real time through a temperature sensing device, and according to the difference between the heat distribution data and a preset target temperature interval, technological parameters are dynamically corrected through a first self-adaptive regulation and control algorithm; meanwhile, a machine vision system is used for capturing actual coating morphological characteristics, and after the actual coating morphological characteristics are compared with target parameters, excitation parameters are adjusted in a partitioned mode through a second self-adaptive regulation and control algorithm so as to optimize uniformity; and collecting whole-process data, comprehensively evaluating the whole-process data through the second intelligent calculation model to generate a process optimization instruction, and updating the first intelligent calculation model and the first self-adaptive regulation and control algorithm parameters according to the process optimization instruction.

The invention discloses an ATS cooling system control method based on whole vehicle VCU control, and aims to achieve intelligent scheduling of a cooling system by optimizing cooperative control of an engine, a battery and a transmission so as to meet the requirements of modern vehicles on the aspects of high efficiency, high reliability and low energy consumption, and achieve intelligent scheduling of the cooling system according to the temperature control requirement difference of different parts. A multi-objective optimization scheduling strategy is provided, the flow of cooling liquid, the rotating speed of a fan and the pump speed can be adjusted in real time, and the working temperature of an engine, a battery and a transmission is accurately controlled. Through a self-adaptive control algorithm and a real-time feedback mechanism, the cooling system can be dynamically adjusted according to the real-time working condition of the vehicle and the environment change, the response speed of the system is increased, the energy consumption is reduced, and overcooling is avoided. And a fault early warning and predictive maintenance method is adopted, so that the reliability and the safety of the system are effectively improved. By optimizing cooperative work of the cooling system, the energy efficiency of the vehicle can be improved, the service life of parts is prolonged, and energy loss is reduced.

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 discloses an automatic parameter adjustment test method for an electromagnetic angular displacement sensor, and the method comprises the steps: injecting a standard angle signal into a to-be-tested sensor, and collecting an output response signal; calculating a deviation value of the sensor parameter based on the output response signal and the standard angle signal, and judging whether the deviation value exceeds a preset threshold value or not; if yes, based on the deviation value, a control instruction used for compensation is generated through a self-adaptive control algorithm, and the mechanical zero position of the to-be-detected sensor or the relative position of an internal electromechanical conversion element is adjusted; based on the adjusted output response signal, the above steps are repeatedly executed, and sensor parameters are iteratively updated; and when the deviation value is lower than a preset threshold value, judging that the output precision meets a preset requirement, storing the current parameter, and completing the parameter adjustment process of the to-be-detected sensor. By constructing a closed-loop control flow of'detection-analysis-adjustment-verification 'and an adaptive control algorithm, full-automatic, high-precision and high-efficiency calibration of the zero position, sensitivity and excitation signal parameters of the electromagnetic angular displacement sensor is realized.

The invention relates to a dynamic cooperative path planning and autonomous landing control method for an unmanned aerial vehicle and an unmanned ship, and the method comprises the following steps: collecting the state data of the unmanned ship and the unmanned aerial vehicle and the high sea condition environment data, and carrying out the data fusion processing; an improved fast extended random tree algorithm is adopted to plan a safe navigation path for the unmanned ship, the generated path of the unmanned ship is optimized based on an unmanned ship motion model considering environmental interference, and a model prediction control algorithm is adopted to plan a path for the unmanned ship to fly to a predicted landing point of the unmanned ship. Dynamic cooperation between the unmanned ship and the unmanned aerial vehicle is realized through information interaction between the unmanned ship and the unmanned aerial vehicle; the unmanned aerial vehicle recognizes an unmanned ship landing identifier through a visual servo guide system, calculates the position and attitude deviation relative to a landing point, adjusts flight control parameters according to interference conditions by using an adaptive control algorithm, and performs trajectory prediction compensation in combination with an unmanned ship motion model to realize autonomous landing. Compared with the prior art, safe landing of the unmanned aerial vehicle on the unmanned ship under the high sea condition is achieved.

The invention belongs to the technical field of railway transportation safety, provides a railway contact net carrier cable tension online monitoring and early warning method and system, and aims at solving the technical problems that in the prior art, capturing is not comprehensive, and different environments are difficult to adapt. Acquiring a tension data stream by using a context awareness technique; analyzing the tension data flow by using a hidden Markov model to obtain a preliminary analysis result, processing by using a neural network, generating an abnormal mode recognition result, performing risk assessment by using a Bayesian network algorithm and a digital twin technology to obtain a preliminary fault estimation probability, and predicting a target fault estimation probability in combination with multi-context sensing data; a self-adaptive control algorithm is used for generating an adjusted alarm threshold value, when the monitored tension value exceeds the adjusted alarm threshold value, early warning information is generated in time, and processing is carried out through the block chain technology to obtain target early warning information; according to the technical scheme provided by the invention, the monitoring and early warning capabilities of the railway overhead line system carrier cable are comprehensively improved.

The invention discloses a motor intelligent control method and system based on a self-adaptive control algorithm, and the method comprises the steps: obtaining motor operation parameters, and determining an overall control target according to the motor operation parameters; on the basis of the overall control target and the current working condition information, initial control parameters are calculated through a self-adaptive control algorithm; monitoring interaction parameters between the motor and the load; the initial control parameters are dynamically adjusted, and the running state of the motor is optimized; in a preset motor prediction model, based on the historical operation data and the current real-time data of the motor, predicting the operation state change of the motor, obtaining a motor prediction result, formulating an adjustment control strategy, and triggering a motor control instruction; the actual operation state of the motor is collected, and a state difference value is calculated; if the state difference value exceeds the threshold range, triggering a generator exception processing instruction; adjusting control parameters are obtained; triggering an adjustment instruction based on the adjustment control parameter; the control effect and the self-adaptive capability of the motor control system are improved.

The invention relates to the technical field of road engineering, in particular to a silt roadbed filling quality detection method and system based on multi-source data, by collecting multi-source data such as humidity data, compaction degree data, mechanical construction dynamic data and roadbed deformation data in real time, key characteristic variables are extracted by adopting a data fusion technology and a principal component analysis method, and the filling quality of a silt roadbed is detected. And on the basis of the characteristic variables, constructing a silt roadbed quality evaluation model to realize real-time prediction and evaluation of roadbed quality in the construction process. And if the detection result does not reach the target standard, the system dynamically adjusts the construction parameters through a self-adaptive control algorithm to optimize the construction process and ensure that the roadbed quality meets the preset requirement. In addition, the system displays real-time monitoring results and prediction data through a visual means, and the intelligent level of construction management is improved. The method can be widely applied to infrastructure construction of roads, railways and the like, and has high application prospects and popularization value.

The invention discloses a dynamic model adaptive trajectory tracking control method for a deformable unmanned aerial vehicle. The method comprises the following steps: establishing a dynamic model of the deformable unmanned aerial vehicle; a control framework based on the model is constructed, flat mapping is performed according to a given reference trajectory, an expected state feed-forward item is calculated according to a current estimated or measured state, and flight control is realized according to a four-rotor geometric control theory; deformation control is achieved based on feedback control over the angle error of the servo motor; uncertain factors in the grabbing process of the deformable unmanned aerial vehicle are modeled into a residual term formed by external force and corresponding torque, an L1 self-adaptive control algorithm is used for conducting self-adaptive compensation on the external force, an incremental nonlinear dynamic inverse control algorithm is used for conducting compensation on the external torque, and therefore self-adaptive trajectory tracking control is achieved. Flight control is performed based on the deformable unmanned aerial vehicle dynamic model, and external force and torque are observed and compensated in real time, so that the uncertainty problem and the external disturbance influence are effectively solved.

The invention belongs to the technical field of aircraft control, relates to an aircraft gravity center real-time balancing method and system based on dynamic parameter fusion, and aims to solve the problems of insufficient real-time performance, accuracy and self-adaptability of a gravity center balancing method under complex flight conditions in the prior art. The method comprises the steps that real-time parameters are obtained, and the real-time gravity center position is calculated; if the real-time gravity center position is not within the preset range, calculating a first inertia moment and a first additional moment caused by fuel density change and a second inertia moment and a second additional moment caused by aeroelastic deformation; performing inertia moment correction based on the first inertia moment and the second inertia moment; additional moment correction is carried out based on the first additional moment, the second additional moment and the real-time pitching inertia moment; when the real-time gravity center position is within the preset range, gravity center balancing is completed. The fuel oil density and aeroelastic parameters are estimated in real time through a self-adaptive control algorithm, rudder deflection and fuel oil distribution are cooperatively adjusted, and the problem of center-of-gravity shift under complex disturbance can be effectively solved.

The invention relates to the field of industrial automation, and particularly provides a semiconductor photoetching system based on an intelligent industrial robot. And a multi-sensor fusion and real-time compensation algorithm is adopted, so that photoetching positioning errors are accurately sensed and corrected, the positioning precision is greatly improved, and the stable performance of the chip is ensured. According to a multi-physical field monitoring and self-adaptive control algorithm, process parameters are dynamically regulated and controlled according to a photoetching environment and a photoresist state, pattern transfer is optimized, pattern defects are avoided, and photoetching pattern quality is improved. According to the multi-modal data fusion and deep learning algorithm, photoresist coating parameters are automatically adjusted according to the surface characteristics of the wafer, uniform coating is realized, and the stability of the photoetching process is enhanced. The reinforcement learning algorithm assists the multiple robots in efficient collaboration, optimizes task allocation, improves the overall photoetching efficiency and the resource utilization rate, and promotes the progress of the semiconductor photoetching technology in all directions.

The invention relates to the technical field of photovoltaic cleaning control, and discloses a spraying cleaning control method of a photovoltaic cleaning unmanned aerial vehicle. The method comprises the following steps: receiving photovoltaic panel surface state data and environmental parameters, comparing dirt grade standards to obtain dirt distribution characteristics and stubborn stain positions, and determining initial spraying parameters through a self-adaptive control algorithm in combination with the environmental parameters; after the spraying executing mechanism is driven to conduct preliminary cleaning, an image collecting device is used for obtaining surface image data, and a deviation value is calculated by comparing a cleanliness threshold value; and in combination with flight attitude data of the unmanned aerial vehicle, a fuzzy decision system is adopted to dynamically adjust initial spraying parameters to generate a real-time control instruction, an execution mechanism is controlled to perform fine cleaning until surface image data accords with a cleanliness threshold, and a final scheme is generated. According to the method, through real-time feedback and dynamic adjustment, spraying parameters are made to adapt to different dirt states and environment conditions, targeted cleaning is achieved, and the intelligence and accuracy of photovoltaic panel cleaning are improved.

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 temperature compensation system of a magnetic sensor, which comprises a temperature sensor, a heating element, a cooling module and a temperature control unit, and is characterized in that the temperature sensor monitors the environment temperature and the surface temperature of the magnetic sensor in real time and transmits temperature data to the temperature control unit, and the temperature control unit carries out processing through a self-adaptive control algorithm. According to the temperature compensation system of the magnetic sensor disclosed by the invention, by combining real-time data feedback and self-adaptive control calculation of the magnetic sensor, accurate temperature control is realized, and high-precision measurement of the magnetic sensor in a complex environment is ensured; the self-adaptive control algorithm can automatically adjust working parameters of the temperature control system according to magnetic field data and temperature data fed back by the magnetic sensor in real time, and by introducing the fuzzy control algorithm and PID control, the system can adaptively adjust heating and cooling power in different environments and quickly respond to temperature fluctuation.

The invention belongs to the technical field of wind power generation, and discloses a wind power plant digital twin modeling system and a use method, the system comprises an equipment module, the equipment module comprises a biological bionic adaptive fan blade system, a quantum sensing network, a laser radar system, a high-resolution multi-modal imaging system and an intelligent autonomous flying robot group system; the network module comprises a 5G private network, a quantum communication network and an edge computing network; the data module comprises a distributed database system, a big data processing platform and an AI training platform; the AI module comprises a wind regime prediction model, a power prediction model, an equipment health state monitoring model and a self-adaptive control algorithm model; the application module comprises a digital twinborn visualization platform, a holographic mixed reality operation and maintenance system and an intelligent scheduling system. Modeling and intelligent management of the wind power plant can be realized, the power generation efficiency can be improved, the operation and maintenance cost can be reduced, and the service life of equipment can be prolonged.

The invention discloses an automatic control etching system and method for a printed circuit board, and relates to the technical field of circuit boards, and the method comprises the following steps: 1, an operator puts a to-be-etched PCB into an etching groove, and inputs target etching parameters through a human-computer interaction interface; 2, the multi-dimensional sensor module continuously monitors key parameters in the etching tank, and data is transmitted to the central controller; 3, according to the data provided by the sensor, the central controller calculates a real-time etching rate by using a nonlinear kinetic model; 4, when the real-time etching rate deviates from the target rate, the system starts an adaptive control algorithm for adjustment; 5, when the system detects that the set etching depth or width reaches a target value, the etching process is automatically stopped, and key data are recorded; and step 6, the system analyzes based on the stored historical data, provides optimal etching parameters and adjustment strategies for different types of PCBs, and displays the data in real time through a human-computer interaction interface module.

The invention discloses a method and equipment for constant temperature control of a printer nozzle and a storage medium, and relates to the technical field of printer nozzle temperature control, temperature data is collected in real time through a distributed temperature sensor array, and PID parameters are adjusted by combining environment temperature and humidity and ink flow and utilizing a dynamic self-adaptive control algorithm model, so that the temperature of the printer nozzle is controlled. The method comprises feedforward control, partition gradient heating, a pulse type low-power-consumption heating mode and an abnormal temperature processing mechanism, precise control over the temperature of the spray head is achieved, the electronic equipment comprises a multi-sensor fusion module, a data processing module, a power driving module and a state monitoring module, efficient execution of a control strategy is ensured, and the control efficiency is improved. According to the method and the equipment, the control strategy is continuously optimized through the self-adaptive learning function, and the printing quality and the equipment stability are improved.

The invention provides a concrete spraying rebound rate optimization decision-making method and a concrete spraying rebound rate optimization decision-making system. The method comprises the following steps: firstly, collecting and extracting time-frequency characteristics in a jet impact acoustic emission signal, collecting laser Doppler array measurement data in a concrete jet process to obtain concrete flow velocity distribution, then carrying out multi-source fusion processing on the time-frequency characteristics and the concrete flow velocity distribution, and extracting principal component characteristic vectors associated with springback; the method comprises the following steps: firstly, constructing a main component feature vector, then constructing a space velocity field model to obtain an identification result representing a concrete rebound state, then carrying out clustering analysis on the main component feature vector and the identification result, identifying a high rebound mode category and a low rebound mode category to generate a risk partition map, and finally, dynamically generating a jet parameter adjustment instruction by applying a self-adaptive control algorithm. Outputting a concrete rebound rate optimization decision scheme; according to the technical scheme provided by the invention, the problem of feature drift caused by space-time asynchronization of physical field data is solved, and the overall rebound rate prediction precision is greatly improved.

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 belongs to the technical field of mobile mechanical arm grabbing, and provides a mobile mechanical arm grabbing control method and system based on machine vision, and the method comprises the steps: collecting an image in the moving process of a mobile mechanical arm according to a preset grabbing path, recognizing a dynamic obstacle in the image through an RT-DETR algorithm, and generating a prediction track of the dynamic obstacle, collision detection is conducted on a preset grabbing path of the movable mechanical arm, an obstacle avoidance track switching instruction is generated by combining the current time point of the mechanical arm with the predicted collision time point of the dynamic obstacle, trajectory tracking is conducted on the obstacle avoidance track of the mechanical arm, a model reference self-adaptive control algorithm is adopted, a reference model is established, and obstacle avoidance is achieved. According to the method, mechanical arm vibration caused by obstacle avoidance track switching is effectively restrained, interference of vibration on grabbing stability is reduced, and the operation precision and reliability of the mechanical arm in a dynamic obstacle avoidance scene are remarkably improved.

The embodiment of the invention provides an oxidation kettle temperature self-adaptive control system and a control method. The control system comprises a temperature detection module; the self-adaptive control algorithm module is used for dynamically adjusting the output of the heating or cooling device through data provided by the temperature detection module based on a self-adaptive control algorithm of a snow ablation algorithm; the feedback control module compares the output of the temperature detection module with a set temperature, the feedback control module adjusts control parameters and ensures that the temperature reaches a set value, the feedback control module adopts a double-closed-loop PID control system, and the double-closed-loop control structure comprises a self-adaptive PID controller of an outer loop and a rapid PID controller of an inner loop which are combined; and the man-machine interaction module allows an operator to set a target temperature and a control parameter, and displays a temperature change curve in real time. Through dynamic adjustment of PID controller parameters combined with real-time monitoring, intelligent analysis and an intelligent optimization algorithm and design of a double-closed-loop feedback system, quick response and stable control of temperature control are realized.

The invention discloses a high and low temperature environment test box thermal load verification method and system, and relates to the technical field of environment test, and the method comprises the steps: initializing a target temperature range, a cooling rate, a heating rate, a preset temperature fluctuation range and a thermal load demand, and carrying out the preliminary setting of a thermal load; adjusting a liquid nitrogen adjusting unit in real time by adopting a dynamic optimization algorithm, and adjusting internal thermal load parameters of the test box under the condition of no external thermal load to obtain reference data; based on a self-adaptive control algorithm, dynamically adjusting the matching relationship between the liquid nitrogen release rate and the internal thermal load of the test box, and verifying the operation state of the test box under different thermal load conditions; a self-adaptive thermal load adjusting algorithm is adopted, the relation between the liquid nitrogen release rate and thermal load change is adjusted in real time, and thermal load verification of the test box is carried out according to the states under different load conditions. According to the invention, through the dynamic heat exchange coefficient model and the high-order injection optimization algorithm, the thermal load verification precision of the high and low temperature environment test box is improved.