2728 results about "Control effect" patented technology

The invention relates to the technical field of tobacco logistics and intelligent control, and discloses an intelligent temperature and humidity regulation and control system and method for tobacco transportation, and the system comprises a multi-dimensional environment sensing module, a self-adaptive strategy generation module and a closed-loop efficiency verification module. The method comprises the following steps: capturing three-dimensional space parameters in a transportation carrier in real time through distributed sensing nodes, outputting a structured environment situation matrix, and providing a space-time reference for a regulation and control decision; after receiving the environmental perception data, executing dynamic strategy optimization, establishing a transportation stage-spatial position two-dimensional regulation and control strategy library, processing sensitivity differences of tobacco leaves in different regions by adopting an asymmetric weighting algorithm, and generating a regulation and control scheme containing a gradient regulation instruction; real-time evaluation and strategy iteration of the regulation and control effect are achieved, microenvironment response detection is deployed, tobacco physical characteristic change data are collected, and a triangular verification model is constructed. The method effectively controls moisture content distribution, inhibits browning and mildew risks, and maximally retains tobacco leaf quality characteristics.

The invention provides a method for evaluating the grouting water control effect of an underground water-sealed cave depot project. The method comprises the steps that firstly, grouting and geological parameters and hydrological monitoring data of a project area are obtained; establishing a three-dimensional seepage-grouting coupling model, and simulating the dynamic state of a seepage field by using a finite element analysis algorithm; then calculating the equivalent permeability change rate of the grouting affected zone based on a simulation result; constructing an evaluation index system, and performing quantitative evaluation by using a fuzzy comprehensive evaluation algorithm; and finally, generating a grouting parameter dynamic adjustment scheme according to an evaluation result. According to the method, scientific and accurate evaluation and engineering optimization of the grouting water control effect can be achieved, and the safety and economical efficiency of engineering are improved.

The invention discloses a boiler combustion optimization method and device based on CFD numerical simulation and a medium, and belongs to the technical field of boiler combustion simulation. The method comprises the steps of obtaining operation parameters and historical operation data of a boiler combustion system; processing the data based on a three-dimensional modeling algorithm to generate a three-dimensional combustion model of the combustion area of the boiler; the three-dimensional combustion model is processed based on a CFD numerical simulation algorithm, and multi-physical field coupling data of the combustion process is generated; processing the multi-physical field coupling data and the historical operation data based on a multi-objective optimization algorithm to establish a combustion parameter optimization model; adjusting the ratio of the air speed at the inlet of the combustor to the fuel flow to update the model and optimize combustion parameters; verifying whether the optimized combustion parameters meet constraint conditions or not; and when the constraint conditions are met, finally optimized combustion parameters are output. By means of the method, multi-physical-field dynamic coupling optimization in the boiler combustion process is achieved, and the purposes of synchronously improving the combustion efficiency and the pollutant control effect are achieved.

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

The invention relates to the technical field of audio and video control, and discloses an audio and video player control method based on a voice instruction. The method comprises the steps that an original voice instruction stream of a user is collected, the instruction stream comprises a time domain audio signal sequence, an environment noise spectrum and user pronunciation characteristic parameters, and voice information can be comprehensively captured; multi-modal instruction analysis processing is carried out on the original voice instruction stream, a structured control instruction set containing acoustic control intention identification, semantic operation object description and context correlation parameters is generated, and the analysis precision is improved; then executing player state adaptation based on the set, generating a dynamic control response sequence containing an equipment state adjustment command, a media content positioning parameter and an interface interaction logic identifier, driving a player to execute a multi-dimensional control operation and generating real-time play control effect feedback data; and finally, multi-modal analysis parameters are optimized according to feedback data, a self-adaptive instruction analysis strategy is generated, and the control experience of a user on the audio and video player is optimized.

The invention discloses a coal mine water disaster prevention and control system and method based on artificial intelligence. The system comprises a data acquisition module, a data transmission module, a data preprocessing module, a geological modeling module, a water disaster prediction module, a risk assessment module, an artificial intelligence decision module, an equipment control module, an emergency response module and a data management updating module. Sufficient time is won for prevention and treatment work, and the possibility of accidents is effectively reduced; early warning is realized, and the timeliness and accuracy of disaster prevention and control are remarkably improved; sufficient time is won for prevention and treatment work, and the possibility of accidents is effectively reduced; personnel safety evacuation and smooth rescue work are powerfully guaranteed, and disaster loss is reduced to the maximum extent; the efficiency and success rate of prevention and control work are improved, and economic losses and casualties caused by water disasters are reduced; the control effect and the system performance are continuously improved, and the long-term safety production requirement is met.

The invention discloses a biped robot reinforcement learning control method which comprises the following steps: after a model prediction controller receives a walking instruction, outputting expected angle data of each joint of a robot to a reinforcement learning neural network, and meanwhile, returning joint angle data of an actual strategy of the reinforcement learning neural network to the neural network by a sensing system at a bottom layer; a joint angle taking time as a sequence and planned by model prediction is compared with a joint angle actually generated by a neural network, and model prediction control is fused into a reinforcement learning training process by setting a reward function for punishment, so that the reinforcement learning training efficiency is improved, and a stable gait is more quickly achieved. Excellent gaits planned by the MPC are transplanted into reinforcement learning control, and control robustness can be improved under the condition that the excellent gaits of the MPC are reserved; and meanwhile, the joint angle data which is obtained by taking time as a sequence and is obtained by taking MPC as a planner can accelerate the reinforcement learning training process, so that the training speed and the control effect of reinforcement learning are greatly improved.

The invention relates to the technical field of intelligent control of the welding process, and discloses an online monitoring system and control method for the friction stir welding process, and the system comprises a multi-mode sensor array module which is used for collecting temperature, pressure and displacement data in the welding process; the edge computing node module preprocesses the collected data and extracts spatial-temporal characteristics; the space-time diagram convolutional network module is used for mining a space-time association relationship between the sensors; the hierarchical reinforcement learning controller generates a control instruction according to the associated features; the execution mechanism module receives the control instruction and outputs a welding signal; the digital twinborn verification module performs simulation feedback on the control effect; and the cloud model optimization module fuses the multi-source feedback information to continuously optimize the control strategy. The welding process self-adaptive control technology based on reinforcement learning is adopted, the technical effect of adjusting the welding parameters in real time to optimize the welding quality is achieved, and the problem that in a traditional method, the welding quality is unstable due to working condition changes is solved.

The invention provides a distributed intelligent cooperative energy-saving control method and device for an industrial air conditioner, electronic equipment and a computer readable storage medium, and is applied to the field of data processing application. The method comprises the following steps: acquiring industrial air conditioner operation related data including environment parameters and equipment state data; an intelligent cooperative control mechanism is constructed based on multi-device cooperation and hierarchical adjustment requirements, and the intelligent cooperative control mechanism comprises a multi-device linkage strategy for dynamically distributing refrigeration resources according to regional cooling and heating loads and a hierarchical adjustment algorithm of a chilled water system and a cooling system, and a control framework for achieving global optimization is formed; processing the environment parameters and the equipment state data to generate a dynamic control strategy; generating target energy-saving control information based on the data analysis result and the dynamic control strategy; and all operation data and control effects generated based on the target energy-saving control information are integrated, a high-energy-consumption link optimization scheme is generated, energy consumption data credible tracing is achieved, and the distributed air conditioning system is aggregated to participate in power grid frequency modulation.

The invention provides a wide-voltage self-adaptive closed-loop vector swimming pool pump anti-interference method, and relates to the technical field of motor control and power electronics, and the method comprises the steps: collecting three-phase power supply voltage fluctuation characteristic parameters and motor operation state parameters in real time; performing amplitude compensation and phase synchronization correction on the voltage parameter based on the dynamic voltage compensation factor; a closed-loop vector anti-interference control model is constructed, and model parameters are adjusted in real time through a vector immunity coefficient; dynamically correcting control output by adopting a multivariable feedback regulation mechanism; executing an anti-interference instruction and synchronously updating PWM modulation parameters, and verifying a control effect in combination with hardware-in-the-loop simulation; and parameter self-learning optimization is realized through a genetic algorithm. The dynamic anti-interference capability under power grid voltage fluctuation and harmonic interference is improved, intelligent thermal management is realized through a temperature-voltage-current multi-dimensional coupling model, full-life-cycle self-adaptive optimization of the system is guaranteed, and the problems of compensation lag, insufficient harmonic suppression and poor long-term stability are effectively solved.

The invention discloses a coal-fired unit SCR denitration system ammonia injection prediction control method based on data driving, which is used for realizing better SCR denitration system ammonia injection control based on a data driving model in combination with an implicit generalized prediction control algorithm. The SCR inlet NOx concentration and the flue gas temperature serve as feedforward of an ammonia spraying amount instruction, the initial control instruction increment of the ammonia spraying amount is calculated through the SCR outlet NOx concentration, the SCR outlet NOx concentration set value and the ammonia spraying amount instruction at the previous moment through an implicit generalized predictive control algorithm, and the final ammonia spraying amount instruction is obtained through summation and amplitude limiting of the last three instructions. Wherein the feedforward instruction of the NOx concentration of the SCR inlet is obtained by a BP neural network prediction model, and the feedforward instruction of the SCR inlet is obtained by a long-short term memory network prediction model fused with an attention mechanism. According to the method, the problem that the concentration of NOx at an SCR outlet fluctuates severely in the frequent load changing process can be effectively solved, meanwhile, transient overshoot of NOx in tail flue gas of a coal-fired unit is restrained, and the SCR denitration control effect is improved.

The invention discloses a control method applied to self-piercing riveting, and relates to the technical field of self-piercing riveting control. According to the method, riveting material data are collected in real time through a multi-source data fusion interface, the high-dynamic deformation process of a riveting head is captured, deformation rate and rebound value characteristic values are extracted, and a multi-dimensional process parameter set is constructed. And comprehensively analyzing the hardness, thickness and plastic dissipation characteristics of the material, and establishing an anti-deformation capability evaluation function. By introducing the fluctuation characteristics of the punching force, the nonlinear relation between the punching force and the elastic deformation of the riveting rod is analyzed, a hybrid compensation model is constructed, and a riveting dynamic coupling compensation vector is output. And generating a compensation weight matrix through an adaptive optimization algorithm, and dynamically adjusting the stamping force and the displacement of the riveting rod in combination with an anti-saturation control strategy. And dynamically adjusting a gain parameter through multi-scale filtering and a Lyapunov stability criterion, and triggering iterative updating of a compensation weight parameter during out-of-tolerance so as to optimize a control effect. Riveting precision and stability are improved, and the service life of equipment is prolonged.

The invention belongs to the technical field of position tracking control of rigid multi-joint mechanical arms, and discloses a fuzzy self-adaptive visual servo control method and system for a multi-joint mechanical arm system for placing bobbins in a textile workshop, and the method comprises the steps: constructing an image error model according to the positions of feature points on an image plane; the fuzzy logic system is used for processing uncertain items in the system, the instruction filtering and fuzzy self-adaption technology is used for achieving tracking control over the expected trajectory of the system, the problem of calculation complexity is solved, and adverse effects caused by filtering errors are eliminated. In the process of changing the bobbins, the bobbins are placed on a creel through a multi-degree-of-freedom robotic arm, a clamping jaw at the tail end of the robotic arm is required to accurately grab the bobbins for feeding and discharging, and the requirement for accurate and rapid control of the robotic arm is met, so that research on visual servo control of the robotic arm is of great significance. The method has a good control effect, the problem of calculation complexity in the design process of the controller is solved, the trajectory tracking error is smaller, and the system robustness is higher.

The invention discloses a gradient optimization control method for an aeration system of a sewage plant. The gradient optimization control method comprises the following steps: S1, acquiring key monitoring multi-source data of the aeration system of the sewage plant in real time through an online sensor; s2, setting a security threshold value of each monitoring data to form a security constraint condition; s3, taking unit nitrogen removal energy consumption minimization as an optimization target, and constructing a comprehensive loss function fusing an energy consumption item and a water quality deviation item; s4, iteratively updating the air volume of the aeration system by adopting a self-adaptive gradient algorithm; s5, the optimized and updated air volume is issued to the aeration equipment to be executed, and water quality data and energy consumption data after execution are collected in real time, a control effect is calculated, an award signal is generated, and the award signal serves as feedback to be input into an optimization module; and S6, dynamically adjusting key parameters including the learning rate and the gradient threshold according to a feedback result. According to the invention, the energy consumption can be effectively reduced and the control precision can be improved while ensuring that the effluent stably reaches the standard, and the stable operation of the aeration system of the sewage plant under a complex working condition is ensured.

The invention provides a multi-rotor unmanned aerial vehicle adaptive attitude stability control algorithm and device, and relates to the technical field of unmanned aerial vehicle control. The control algorithm comprises a geometric phase driving control process, a fractional order chaos game control process, a manifold resonance self-adaptive control process, a topology manifold self-adaptive control process and a multi-scale disturbance prediction control process, and all the processes of the control algorithm are executed through the main control processing unit. In order to solve the problems that a traditional PID control attitude error is 0.02-0.05 degree and is difficult to adapt to a dynamic environment, a geometric phase drive control flow is combined with a plasma pneumatic fine tuning array and an inertial measurement unit, a high-precision 3 * 3 rotation matrix is generated by adopting Lie algebraic transformation and a Newton-Rafson iteration method, the rotation speed of a rotor wing is optimized, and the precision of the rotor wing is improved. The attitude error is controlled to be 0.006 degrees, and the effect is improved compared with a traditional PID control effect.

The invention discloses a fishery pond water temperature control optimization method based on a swarm intelligence algorithm, and belongs to the technical field of control optimization, and the method comprises the following steps: S1, constructing a fishery pond water temperature PID control system introducing an improved single-corner whale optimization algorithm module; s2, introducing an improved single-corner whale optimization algorithm, and constructing an improved single-corner whale optimization algorithm module; s3, setting and optimizing the pond water temperature PID control parameters by using an improved single-corner whale optimization algorithm to obtain optimal control parameters; and S4, setting an optimal control parameter obtained through optimization by using the improved single-corner whale optimization algorithm as a parameter of a pool water temperature PID controller, and optimizing a pool water temperature regulation control effect. According to the method, a swarm intelligent optimization algorithm, namely an improved unicorn whale optimization algorithm, is introduced, high-performance control over the pond water temperature is achieved, and reliable control technical support is provided for efficient, stable and safe operation of aquaculture.

The invention provides an AI-driven industrial decarburization process control method and system based on feedback regulation, and relates to the technical field of industrial control, and the method comprises the steps: executing industrial decarburization process monitoring, and obtaining a process operation data stream sequence; performing process working condition time sequence iterative analysis; matching is carried out in a pre-constructed causal knowledge base; traversing the associated causal clue set to perform historical data backtracking exception investigation to obtain an exceptional associated causal clue set and an exception degree set; and performing feedback control scheme identification to obtain a target feedback control scheme, generating a feedback adjustment instruction, and issuing the feedback adjustment instruction to the control execution module to execute the target feedback control scheme. According to the method and the device, the technical problem that in the prior art, due to the fact that static model prediction or empirical rule-based judgment is mainly adopted, real-time tracing and dynamic intervention of process-level carbon emission abnormity are difficult to achieve, and the carbon emission control effect in a complex environment is not ideal is solved, and the control effect of the industrial decarburization process is improved.

The invention relates to the technical field of control systems, and discloses a thick broad-bean sauce production control system and method, which are used for solving the problems of quality fluctuation and pollution risk caused by insufficient precision control in the existing thick broad-bean sauce fermentation process. The method comprises the following steps: acquiring temperature, humidity, pH value and microbial activity indexes in a fermentation process in real time to form a dynamic parameter data set; analyzing the deviation degree of the data set, calculating the matching degree with a standard fermentation curve, and identifying potential unstable factors; generating a preliminary adjustment instruction based on the deviation result, and performing automatic fine adjustment on the temperature and humidity to stabilize the environment; monitoring the activity change of the adjusted microorganisms, feeding back and verifying a control effect through a sensor, and calculating an activity recovery rate; optimizing fermentation model parameters, and iteratively adjusting a control algorithm according to a recovery rate to realize self-adaptive precision improvement; and outputting a final control report, and feeding back optimized parameters to the system to realize closed-loop precision control.

The invention provides an unmanned aerial vehicle flight attitude self-adaptive imbalance control method, device and equipment, and aims to accurately obtain an ideal control track and recognize a beneficial imbalance mode to generate a controllable imbalance strategy library by constructing a reverse track analysis and controllable imbalance strategy generation mechanism. A dynamic balance reference establishment and track reconstruction technology is adopted to construct an unbalance-recovery control track, and dynamic response characteristics are accurately obtained; an unbalance recovery space-time manifold construction and topology analysis technology is introduced, a core unbalance control feature set is extracted through space-time folding transformation, and unbalance control key parameters are determined; establishing an unbalance-recovery mapping rule and self-adaptive parameter fusion mechanism, balancing the control effect and the safety requirement, and generating a disturbance control parameter and a self-adaptive mapping parameter; a real-time control output regulation mechanism is constructed to generate a flight control signal in combination with reverse unbalance combined control and a multi-axis cooperative control mechanism; finally, a self-adaptive unbalance control system is formed, and precise unbalance control over the attitude of the unmanned aerial vehicle is achieved.

The invention relates to the technical field of water resource purification, in particular to a control method and equipment of a reverse osmosis water purification system.The control method comprises the steps that firstly, on the fluctuation dimension of the pre-membrane pressure, the adjusting requirement of the frequency of a high-pressure pump is preliminarily determined on the dimension of the pre-membrane pressure according to an obtained pressure fluctuation index; determining a membrane resistance increase factor representing the pollution state of the reverse osmosis membrane on the basis of the transmembrane pressure difference on the dimension of separation function failure of the reverse osmosis membrane; finally, a membrane separation abnormal value is comprehensively represented by combining a pressure fluctuation index and a membrane resistance increase factor, and a purified water recovery rate is introduced to a membrane separation result end, so that the finally obtained frequency reduction demand degree is more accurate, and the control effect of the reverse osmosis water purification system is improved; therefore, the stable, efficient and sustainable operation of the reverse osmosis water purification system can be ensured while the membrane surface pollutant deposition driving force can be effectively reduced and the membrane pollution speed can be slowed down.

The invention discloses an intelligent management and control system and coordination control method for an indirect cooling air cooling island, and belongs to the technical field of intelligent operation and maintenance of equipment, and the method comprises the steps: collecting key operation parameters in real time through building an intelligent sensing network, and calculating a fin heat exchange efficiency attenuation coefficient and a back pressure early warning value; when the attenuation coefficient of the heat exchange efficiency is lower than a threshold value or the back pressure exceeds a limit, a linkage control mechanism of the intelligent cleaning system and the intelligent spraying system is synchronously started; constructing a control effect evaluation matrix based on the back pressure-attenuation coefficient coupling degree, dynamically adjusting the spraying intensity and the number of cleaning times, and switching to a conventional monitoring mode until the parameters return to normal and are continuously stable; according to the method, cooperative intelligent control of cleaning and spraying is creatively achieved, the heat exchange efficiency of the air cooling island is improved through multi-parameter fusion evaluation and a closed loop feedback mechanism, energy consumption is reduced, meanwhile, the self-adaptive anomaly monitoring and quick response capacity is achieved, and an efficient intelligent operation and maintenance solution is provided for a thermal generator set air cooling system.

The invention relates to the technical field of flight control, in particular to a hybrid control FPV unmanned aerial vehicle flight control system and method. The method comprises the following steps: acquiring triaxial angular velocity data of the unmanned aerial vehicle in real time by using an inertial navigation system, eliminating high-frequency noise and performing zero offset calibration; constructing a corresponding linear extended state observer to adjust a gain parameter in real time based on an absolute value of an observation error, and filtering tiny noise disturbance through a dead zone function to realize high-precision estimation of the system state and total disturbance of the unmanned aerial vehicle so as to obtain an error and an error change rate; and designing a self-adaptive PID control law to dynamically adjust PID control quantities corresponding to proportion, integral and differential gains, carrying out dynamic weighted stacking according to a flight mode, generating a three-axis control quantity, carrying out flight control on the FPV unmanned aerial vehicle, and meanwhile, realizing real-time visual verification of a control effect corresponding to the flight control. According to the invention, high-precision and high-robustness attitude control can be realized.

The invention discloses an elevator and building energy system fusion energy-saving management and control method based on the Internet of Things, and relates to the technical field of energy management. In order to solve the problems that traditional elevator operation management and building energy system control are mutually independent and lack of an effective cooperation mechanism, so that energy waste is serious, the operation states of an elevator and a building energy system are difficult to comprehensively master in real time, and an energy-saving strategy cannot be accurately formulated. Elevator operation and building energy system data are collected in real time through the sensor network, the remote supervision center deeply integrates, analyzes and excavates a correlation mode and a time sequence relation between operation characteristics of the elevator and the building energy system data, an operation characteristic analysis model is constructed, an accurate energy-saving strategy is formulated, energy consumption is remarkably reduced, and the energy utilization efficiency is improved; the energy-saving effect is quantitatively evaluated, a closed-loop optimization mechanism is formed, and the energy-saving management and control effect is continuously improved; by establishing a fault prediction model, equipment faults are predicted in advance, a maintenance scheme is generated, and stable operation of the system is guaranteed.

The invention discloses a sewage process parameter regulation and control method and system based on artificial intelligence, and relates to the technical field of data analysis, and the method comprises the steps: analyzing the interaction relationship between basic parameters and the interaction relationship between the basic parameters and evaluation parameters; the relationship between the basic parameters and the relationship between the basic parameters and the evaluation parameters are accurately analyzed, and a reliable basis is provided for subsequent multi-objective optimization and regulation. And the weight of each evaluation parameter is set according to the action relation of the basic parameters on the evaluation parameters and the fluctuation condition of the evaluation parameters. The inertia weight in the MOPSO algorithm is set, and the optimal solution of the multi-target optimization model is solved through the MOPSO algorithm, so that the local and global search capabilities are balanced, and the quality of the optimal solution is improved. Cooperative regulation and control are carried out on sewage process parameters based on an adaptive control strategy and an optimal solution of a multi-objective optimization model, and the process parameter regulation and control effect is comprehensively and effectively improved in combination with adaptive control and multi-objective optimization.

The invention relates to the technical field of thermal management control, and particularly discloses a thermal management controller regulation and control method and system based on dynamic thermal inertia prediction. According to the method, the dynamic thermal inertia model is constructed through system identification, and the future temperature field evolution and thermal accumulation risks of the system are accurately predicted. On the basis, a multi-layer nested compensation strategy composed of pre-compensation, main compensation and residual compensation is generated in three dimensions of a compensation angle, a compensation order and a compensation domain, and prospective accurate intervention is achieved. In order to evaluate the control effect, an artifact intensity entropy value is introduced as a quantitative index, and a model and a strategy are corrected in real time through an outer ring optimization mechanism to form self-correction. When continuous thermal unbalance caused by hardware design defects is detected, the system can further start hardware cooperative regulation and control, local heat capacity and a heat dissipation path are optimized fundamentally, cross-level cooperative optimization from a control algorithm to a physical structure is achieved, and finally high-precision, low-energy-consumption and self-adaptive system-level thermal management is achieved.

The invention provides a low-voltage ride-through control method for a grid-forming type grid-connected converter based on reactive power synchronization, and belongs to the technical field of converter low-voltage ride-through control. When the voltage of a power grid drops, a current amplitude limiting module in the power grid sends a control switching signal to trigger low-voltage ride-through control, and the low-voltage ride-through control is switched to a reactive power synchronization mode; a reactive power synchronous control gain KQs is calculated according to an inner loop gain coefficient Kcp, a current amplitude limiting value, a power grid voltage Vg and a grid side inductance Lgi, a virtual power angle reference value is generated by combining a set reactive power direct current component reference value QN, so that a power angle delta is stabilized in a range close to a normal working point, and a control effect is verified by comparing with a traditional method. According to the method, the system can quickly recover to a normal working state regardless of the duration of the fault and the severity of the fault, meanwhile, the system can effectively work under different power grid fault levels, and the robustness of the system is further enhanced.

The invention discloses a traditional Chinese medicinal material standard base pest and disease damage intelligent monitoring and precise control method and control system, and relates to the technical field of agricultural intelligence. The Internet of Things technology, the block chain and the artificial intelligence algorithm are integrated to improve monitoring accuracy and prevention timeliness, environment and plant states are acquired through the sensor and image data, safe storage of data is ensured by using the block chain, early recognition of diseases and pests is performed by using the convolutional neural network, and the accuracy of disease and pest control is improved. A disease risk index is calculated in combination with environmental parameters, a reinforcement learning algorithm dynamically optimizes a control strategy, control measures are automatically executed through Internet of Things equipment, an execution result is recorded on a block chain, in addition, the strategy is continuously adjusted according to a control effect evaluation result, intelligent and precise disease and pest management is realized, resource consumption is effectively reduced, and the economic benefit is improved. The traditional Chinese medicinal material production safety and efficiency are improved.

The embodiment of the invention provides an underground tool face dynamic control method and system based on reinforcement learning, and belongs to the technical field of directional drilling. The method comprises the steps of collecting drilling state data of a target well, performing data preprocessing on the drilling state data, and constructing a corresponding state vector based on the preprocessed drilling state data; calling an action decision model obtained by training based on a DDPG algorithm based on the state vector to obtain an action instruction; wherein the network strategy parameter of the action decision model is obtained by correcting the previous network strategy parameter based on the deviation between the current drilling state data and a pre-constructed reward function; analyzing the action instruction to determine a target physical action; and executing top drive torsional pendulum based on the target physical action, and recycling an execution result until the execution result indicates that the tool face angle meets the expectation. According to the scheme, the stability and the real-time control effect under the working conditions of complex well sections and severe disturbance are improved.

The invention discloses a high-load regulation and control method and system for a power distribution network, and relates to the technical field of power distribution network regulation and control, and the method comprises the following steps: obtaining first data of the power distribution network in real time, carrying out the dynamic partitioning of the power distribution network based on the first data, and generating a sub-region set; establishing a multi-time scale optimization model, and obtaining an optimal regulation and control instruction set by taking the sub-region set as a unit; executing the regulation and control instruction through a master-slave game coordination mechanism, and verifying a regulation and control effect; and when voltage out-of-limit or reverse power is detected, rolling correction is triggered, and constraint conditions of the optimization model are updated. According to the method, communication delay of centralized regulation and control and global mismatch of distributed regulation and control are avoided through a master-slave game mechanism, when voltage out-of-limit or reverse power is detected, distributed power supplies with high influence nodes are preferentially adjusted based on voltage-power sensitivity sorting, and robustness and economical efficiency of a power distribution network in a high-fluctuation scene are improved.

The invention relates to the technical field of intelligent regulation and control, and discloses a data-driven workshop production equipment flow intelligent regulation and control method. Comprising the steps of S1, equipment state data acquisition in a data acquisition stage, S2, data cleaning abnormal value processing in a data preprocessing stage, S3, equipment health degree analysis model in a data analysis and modeling stage, S4, equipment scheduling decision in an intelligent regulation and control decision stage, and S5, regulation and control effect feedback and effect evaluation index production efficiency in an iteration stage. According to the data-driven workshop production equipment flow intelligent regulation and control method, the data acquisition and processing efficiency is improved, the integrity of equipment state data is improved to 98% through multi-dimensional sensor deployment (vibration 100Hz sampling and temperature 30s / time), the time consumed for automatic correction of an abnormal value is shortened from 30min to 1min, and the efficiency of automatic correction of the abnormal value is improved. And after data standardization, the multi-dimensional fusion analysis efficiency is improved by 4 times, and a foundation is laid for accurate regulation and control.