4625 results about "Control algorithm" patented technology

The invention relates to the technical field of thermal power plant flue gas denitration, in particular to a thermal power plant APC advanced process control system and a denitration optimization method.The system comprises a data acquisition module for acquiring boiler combustion parameters, flue gas emission data and the operation state of a denitration system in real time; the multivariable predictive control module is used for optimizing the combustion efficiency and the NOx generation amount based on a dynamic matrix control algorithm; the denitration optimization decision module is used for dynamically adjusting the ammonia spraying amount through an ammonia escape feedback model; the intelligent coordination module integrates a unit load instruction and an environmental protection constraint condition and is used for cooperative control of combustion-denitration; according to the system, boiler combustion parameters, flue gas emission data and the operation state of the denitration system are obtained in real time through the data acquisition module, a dynamic matrix control algorithm of the multivariable prediction control module is combined, the NOx generation trend can be predicted in advance, the combustion efficiency can be optimized, and therefore the problem that adjustment is lagged when loads fluctuate in traditional PID control is effectively solved.

The invention relates to a tower crane operation control system based on complex scene three-dimensional real-time modeling. According to the system, a lifting hook is coarsely positioned through a lifting hook positioning and state sensing unit, a real-time position is positioned by combining a laser radar point cloud clustering algorithm with historical pose data, and visual tracking is synchronously performed by means of a tower top camera AI; converting the real-time point cloud data into a 3D voxel grid map, generating a global path by using a 3DA algorithm, and outputting a hoisting track after smooth processing and track optimization; establishing a sling-lifting hook double-pendulum dynamic model, predicting a state sequence based on a model prediction control algorithm, and adjusting a control signal through a feedforward compensation item and a feedback correction item; and the man-machine interaction and monitoring unit is used for displaying the cantilever angle, the lifting hook height and the three-dimensional map of the tower crane in real time and remotely intervening the operation state of the tower crane. According to the system, multi-source data are fused to construct a high-precision three-dimensional map, lifting hook positioning and full-view tracking are achieved, and lifting safety and trajectory tracking precision are improved through path planning and dynamics control.

The invention relates to a self-adaptive control rewinding machine tension and coiled material deviation collaborative optimization method in the field of intelligent manufacturing, and the method comprises the steps: deploying a distributed tension sensor network at a key position of a rewinding machine coiled material path, collecting the tension value of each measurement point in real time, and generating a multi-point tension distribution data matrix arranged according to a time sequence; processing the multi-point tension distribution data matrix by adopting a sliding window time sequence analysis algorithm, detecting tension fluctuation abnormity, and if a tension value exceeds a preset threshold range, recording a tension abrupt change timestamp and a change amplitude, and generating tension abrupt change data; based on the working condition state description, the rolling diameter real-time change data sequence and the tension sudden change data, a prediction model reflecting rolling diameter change and tension fluctuation is constructed in real time, and a predicted tension trend is obtained; and comparing the predicted tension trend with a preset ideal tension range through a model prediction control algorithm, and generating a multi-target optimization instruction which comprises a dynamic torque regulation and control quantity and a floating roller position set value.

The present disclosure belongs to a field of UAVs exploration technology, discloses a high-efficient autonomous exploration method, system, and terminal for UAVs, comprising: S1, heuristic waypoint generation: setting an exploration scope and waypoint spacing, and generating waypoints through waypoint generation algorithms; S2, global path planning: after generating heuristic waypoints, an A * algorithm is used to generate the global planning path; S3, real-time positioning and mapping: using point clouds for real-time positioning and mapping; S4, local B-spline trajectory generation: using B-spline parameterization method to generate local trajectories; S5, real-time obstacle avoidance and dynamic feasibility constraints: optimizing the trajectories to achieve fast convergence, generating smooth, collision-free, and dynamically feasible trajectories; S6, local real-time replanning: using a time sliding window for local replanning; S7, flight control: Using UAV control algorithms for controlling of UAVs robustly.

The invention belongs to the field of power system engineering, and discloses a power distribution automation terminal diagnosis method and system based on multi-source wave recording feature fusion, and the method comprises the steps: obtaining the electric quantity data and equipment operation state data collected by a power distribution automation terminal; performing adaptive decomposition on the electrical quantity data by using a variational mode decomposition algorithm to obtain an intrinsic mode function; constructing a deep residual network model, carrying out fusion analysis on the time-frequency domain features of the intrinsic mode function, and generating a fault feature vector; establishing a multi-dimensional evaluation matrix based on the fault feature vectors, and integrating a plurality of indexes to output fault types and credibility scores; according to the fault type and the credibility score, generating a fault isolation strategy based on a Petri network model; and executing a dynamically adjusted self-adaptive self-healing control algorithm. According to the method, complex and changeable fault modes can be effectively identified, a complete collaborative verification mechanism is formed, seamless connection from fault diagnosis to self-healing control is realized, and the operation reliability of the power distribution network is remarkably improved.

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 discloses an equipment self-adaptive regulation and control method and system based on multi-mode perception, and relates to the technical field of equipment intelligent control. The method comprises the following steps: acquiring real-time operation parameters of medical equipment, and generating multi-modal equipment state data; performing depth state estimation based on the data and a pre-stored historical operation database, identifying a current equipment operation mode and a performance degradation trend, and generating a dynamic target set point interval; matching the dynamic target set point interval with a preset control strategy library to generate a self-adaptive control instruction set for driving an actuator group so as to achieve a target parameter; issuing an instruction set to drive an actuator group to execute regulation and control actions, and continuously collecting operation feedback data; and the feedback data is compared with the dynamic target set point interval to obtain the operation deviation, and the control instruction set is dynamically optimized by adopting a control algorithm based on the deviation, so that the self-adaptive monitoring and cooperative control of the operation state of the medical equipment are realized.

The invention relates to the technical field of electrical cabinet monitoring, in particular to an electrical cabinet active anti-condensation method and system based on a condensation mechanism mathematical model. According to the invention, a mathematical model based on a condensation formation mechanism is constructed, and environmental meteorological parameters, station room environmental parameters, electrical cabinet internal microenvironment parameters and cable trench state parameters are monitored through a four-stage monitoring system; and fusing the multi-source heterogeneous data, inputting a condensation mechanism mathematical model to calculate a condensation risk index, and adopting a hierarchical response mechanism to cooperatively control an execution mechanism according to the obtained condensation risk index value to realize active defense of condensation. According to the method, the condensation phenomenon with the space-time dynamic characteristic is quantitatively described through a mass transfer and heat transfer coupling equation, a grading prevention and control strategy and a cooperative control algorithm are developed by calculating the condensation risk index, setting the threshold condition and judging the condensation formation risk level, the prevention and control intensity is dynamically adjusted according to the risk level, and the control accuracy is improved. And the risk response speed and the resource utilization efficiency under the complex working condition are obviously improved.

The invention relates to the technical field of equalization control of a battery management system, and discloses a lithium battery hierarchical equalization control method based on a hierarchical model predictive control algorithm, comprising the following steps: constructing a hierarchical control architecture which comprises a state monitoring layer, an equalization decision layer and an execution control layer, each layer realizes cooperative control through closed-loop data interaction; the state monitoring layer collects multi-dimensional state parameters of the lithium battery system, pre-processes the collected data and then transmits the data to the equalization decision-making layer, the equalization decision-making layer constructs a multi-target optimization model based on a hierarchical MPC algorithm, and the model takes SOC consistency, equalization energy consumption minimization and cycle life maximization of the lithium battery system as optimization targets. The abnormal state of the sensor or the balancing module can be identified in time through a fault diagnosis mechanism, and when a fault occurs, a standby model is automatically switched or a balancing task is shared through an adjacent module, so that the system is ensured not to generate abrupt reduction of balancing performance due to the fault of a single component.

The invention provides a greenhouse environment adaptive regulation and control method and system, and relates to the technical field of environment control, and the method comprises the steps: collecting multi-dimensional environment parameters in a greenhouse; based on the environmental parameters, a preset crop growth period database and weather prediction data, taking minimization of a preset cost function as a target, and adopting a model prediction control algorithm to generate an equipment linkage instruction set in a future preset time period, the preset cost function fusing environmental regulation and control deviation and operation cost; issuing the equipment linkage instruction set to each execution equipment, and executing linkage regulation and control; wherein when the equipment linkage instruction set is generated, a conflict resolution mechanism based on a dynamic priority is adopted to determine an execution sequence of a plurality of equipment instructions; the adaptive regulation and control method integrating multi-source data verification, multi-scale prediction, dynamic priority conflict resolution and multi-target cost optimization improves the accuracy, economy and crop suitability of greenhouse environment regulation and control.

The invention discloses a control method for a multi-modal information fusion intelligent robot with a body. The control method comprises the following steps: initializing a system, and collecting surrounding physical environment and object state information and a natural language instruction of a user; performing scene understanding and task analysis, processing data through a multi-modal information fusion mechanism and a cross-modal attention module, and generating unified multi-modal data; task planning and priority ranking are carried out, complex tasks are decomposed into subtask sequences, and a priority ranking layer dynamically adjusts the execution sequence; performing action execution and feedback adjustment, and generating a control instruction through a self-adaptive operation control algorithm; continuous learning and strategy verification are carried out, integrated execution is realized by using a hybrid AI system, and the robustness of an operation strategy is verified through a simulation environment; and closed-loop iteration is carried out to realize real-time response of the intelligent robot with the body. According to the method, the perception understanding precision and the task execution efficiency of the intelligent robot with the body are improved, the operation precision adaptability and the system robustness flexibility are guaranteed, and the method is suitable for multiple scenes.

The invention relates to the field of mold cooling optimization, and discloses an automobile mold cooling optimization system and method based on reinforcement learning, and the method comprises the steps: collecting the real-time temperature and flow data of each cooling loop, carrying out the preprocessing and abnormity filtering of the data, and constructing a multi-dimensional basic cooling state database through combining historical operation records and environmental parameters; performing spatial mapping and statistical analysis on the temperature and flow data in the basic database, identifying a high-temperature region with heat concentration and a part with remarkable temperature gradient, and forming a local heat feature model; establishing an independent control sub-model, and optimizing cooling water flow and temperature regulation parameters of each loop by adopting a simulation and reinforcement learning combined method; a local adjustment strategy and an overall cooling target are fused, and main loop parameters are optimized through a global control algorithm; in the mold cooling process, temperature and flow fluctuation are continuously monitored, and real-time data are input into an optimization system. The method has the advantage that the cooling precision and efficiency are improved.

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 an unmanned aerial vehicle self-adaptive wind-resistant flight control method and system based on multi-mode environment perception. The method comprises the steps that a miniature wind speed and direction sensor array obtains real-time wind speed and wind direction information of the surrounding environment of an unmanned aerial vehicle; the inertial measurement unit obtains real-time attitude angle, angular velocity and acceleration information of the unmanned aerial vehicle; the visual flow sensor obtains real-time image data of the surrounding environment of the unmanned aerial vehicle; the data fusion and wind field estimation module receives multi-source data collected by the miniature wind speed and direction sensor array, the inertial measurement unit and the visual flow sensor, carries out fusion processing on the multi-source data based on an extended Kalman filtering algorithm, and predicts to obtain predicted wind field information in a future short time window; the flight control core module adopts an adaptive sliding mode control algorithm to calculate a compensation control quantity for counteracting wind disturbance, and performs real-time control on the unmanned aerial vehicle based on the compensation control quantity; and the wind resistance and the operation reliability of the unmanned aerial vehicle in a complex dynamic environment are improved.

The invention discloses a carbon fiber composite material surface modification spraying system and a control method thereof. The system comprises a multi-axis robot, a plasma spray gun, a contact angle measuring probe, a 3D line laser scanner, a hyperspectral imager, an environment sensor and a controller. According to the method, the plasma power and the robot speed are adjusted in real time through contact angle measurement and plasma treatment feedback control, so that the CFRP surface can accurately reach a target value, and the coating adhesive force is improved; 3D line laser scanning and hyperspectral imaging are combined, and the posture, the distance, the wet film thickness and the component uniformity of the spray gun are monitored in real time; based on a prediction model fusing a physical model and a neural network, a self-adaptive fuzzy PID control algorithm is adopted, and the coating flow and the track posture of a spray gun are accurately regulated and controlled in real time. The problems of weak coating binding force, uneven thickness, orange peel, sagging and serious coating waste in traditional spraying are effectively solved, and self-adaptive, high-quality and green spraying of workpieces with complex curved surfaces is achieved.

The invention relates to a multi-cell control method for hydrogen production alkaline electrolytic cells based on nonlinear model predictive control, which belongs to the technical field of hydrogen production and comprises the following steps of: performing data acquisition on key positions of a hydrogen production alkaline electrolytic cell system in real time; based on the dynamically determined priority of each electrolytic cell, dynamic load distribution of each electrolytic cell is carried out, and a multi-dimensional cooperative regulation strategy is implemented to obtain a hydrogen yield target value of each electrolytic cell; constructing a nonlinear model comprising an electrolytic reaction model, a heat transfer model and an inter-tank coupling model, and performing parameter identification and verification; and designing a nonlinear model predictive control algorithm, performing discretization processing on the nonlinear model, introducing Kalman filtering to process the uncertainty of the model, taking a corresponding hydrogen yield target value reached by each electrolytic cell on the premise of meeting the constraint as a final control target, constructing an optimized target function, and performing solving to obtain an optimal control scheme. Compared with the prior art, the control precision and the system stability can be effectively improved.

The invention discloses an electrochemical descaling process ion concentration dynamic balance control method and system, and relates to the field of water treatment of a circulating cooling water system.The method comprises the steps that a multi-parameter sensor is deployed to collect calcium and magnesium ion concentration, pH, conductivity, flow and other multi-dimensional data in real time, and an electrochemical deposition dynamic model is combined; dynamically evaluating the time sequence change trend of the ion concentration; according to the method, an ion concentration dynamic balance control algorithm is constructed, and the current density adjusting quantity is calculated by synthesizing ion concentration deviation and historical integral deviation, so that the target current density of the electrochemical reactor is adaptively adjusted, and the system is always maintained in a target concentration interval under different inlet water quality and load conditions; precise electrolysis control is achieved by periodically adjusting power output, and the descaling efficiency and energy consumption are effectively balanced. Therefore, the self-adaptive adjustment of the electrochemical descaling process is realized, and the stability and the self-adaptability of the descaling process are remarkably improved.

The invention discloses an intelligent regulation and control method for a tea fermentation process and a regulation and control system based on microbial activity monitoring, and relates to the technical field of tea processing. According to the method, microbial activity and environmental parameters are monitored in real time, and a microbial metabolism model and a neural network algorithm are combined, so that dynamic optimization and accurate regulation and control of the tea leaf fermentation process are realized, firstly, microbial activity data and environmental parameters in a fermentation environment are collected by utilizing a sensor array; the method comprises the following steps: firstly, calculating a metabolite generation rate and a concentration change trend through a microbial metabolism model, accurately identifying abnormal fluctuation in a fermentation process, secondly, adjusting fermentation conditions in real time based on the metabolite concentration change trend and a dynamic regulation and control algorithm to ensure the stability of the fermentation process, and finally, carrying out real-time fermentation. Tea quality parameters after fermentation condition adjustment are predicted through a neural network algorithm, a regulation and control strategy is optimized, and stable improvement of tea quality is achieved.

The invention belongs to the technical field of metallurgical equipment intelligent control and digital twinning, and provides a metallurgical equipment twinning HMI control system based on multi-source data, and the system comprises a multi-source data collection module which is used for collecting the multi-source data; the data fusion processing module is used for performing data fusion processing on the multi-source data by adopting a space-time alignment algorithm and a feature extraction network; the digital twinborn modeling module is used for constructing a metallurgical equipment digital twinborn model by utilizing an improved deep belief network algorithm based on the fusion processing data; the HMI interaction module is used for dynamically presenting the fusion processing data and the operation state data of the metallurgical equipment digital twinning model based on the output of the digital twinning modeling module; and the intelligent control module is used for performing closed-loop control on the metallurgical equipment by adopting a self-adaptive predictive control algorithm. According to the invention, full-process intelligent management of the metallurgical equipment can be realized, and the operation efficiency, the control precision and the reliability of the metallurgical equipment are remarkably improved.

The invention relates to the technical field of inverter control, and provides an MBD-based reinforcement learning inverter control algorithm optimization method, which comprises the steps of S1, establishing a state space model of an inverter, designing a baseline controller of double-loop control, and constructing an MBD simulation platform containing multiple load models; s2, defining a state space of the 12-dimensional state vector, designing a continuous action space, and constructing an adaptive multi-target reward function; s3, training a control parameter optimization strategy by adopting an Actor-Critic algorithm architecture in combination with an experience playback mechanism and a hybrid exploration strategy; s4, establishing a security constraint mechanism of three-layer security protection, designing a fault detection and isolation strategy, realizing a closed-loop online learning strategy of pre-training-transfer learning-online fine tuning, and meanwhile, adopting a self-adaptive updating mechanism; and S5, verifying the optimization effect of the control algorithm through quantitative index evaluation, an experimental verification scheme and a real-time performance requirement test. And the upgrade of inverter control from model driving to data and model cooperative driving is realized.

The invention relates to the technical field of injection molding, and discloses a plastic part injection mold regulation and control system and method based on a complex curved surface and a thin-wall structure, and the plastic part injection mold regulation and control system based on the complex curved surface and the thin-wall structure comprises a mold modeling module, a temperature modeling module, a control modeling module, a control optimization module and a control execution module; the plastic part injection mold regulation and control method based on the complex curved surface and the thin-wall structure comprises the steps that a mold three-dimensional model and thermal control area mapping are built, a heat conduction model is built, modal dimensionality reduction is conducted, a target temperature field and a performance function are combined, optimal temperature regulation and control are achieved through a predictive control algorithm, real-time feedback is matched, and a heating and cooling unit is driven. And dynamic closed-loop control of the mold thermal field is realized. According to the method, thermal control precision and energy consumption efficiency are optimized through Galerkin modal dimension reduction, model predictive control and a weighted performance function, global closed-loop temperature control is achieved by combining mapping of a three-dimensional area and a heating and cooling unit of the mold, and system safety and stability are enhanced.

The invention discloses a glass bottle grabbing method and system based on visual inspection, and the method comprises the steps: carrying out the real-time recognition of a bottle opening, a bottle body contour and defect features through an image recognition algorithm, and outputting a detection data set containing the precise three-dimensional pose and feature identification of a glass bottle; based on the detection data set, generating an action parameter set containing the expansion and contraction amount, the rotation angle and the grabbing time sequence; according to the action parameter set, the movable guide rail is controlled to slide to a target area along the fixed guide rail, and self-adaptive adjustment of grabbing force is achieved through an impedance control algorithm; and after grabbing is completed, the conveying path is dynamically corrected through a model prediction control algorithm in combination with the real-time movement speed of the conveying belt, the conveying belt is accurately placed on a preset detection station, and a closed-loop grabbing-conveying process is formed. By utilizing the embodiment of the invention, high-precision identification, dynamic task allocation and self-adaptive flexible grabbing of the glass bottle can be realized, and the grabbing success rate and the production efficiency are improved.

The invention discloses a VGA (Video Graphics Array)-based 5G miniature optical transceiver module self-calibration method, which belongs to the technical field of optical transceiver module self-calibration, and comprises the following steps: S1, establishing a self-calibrated benchmark reference value through an initialization module and collecting environment parameters and benchmark optical parameters; s2, dynamically collecting and receiving optical power and environment change data, and judging whether deviation exceeds a threshold value or not to trigger model retraining by collecting actual operation data of environment parameters and driving parameters and combining a neural network model prediction target value; integration of a new data set and re-fitting of a non-linear relation enable the model to continuously adapt to the influence of long-term factors; key parameters such as a gain coefficient or a temperature compensation curve of a proportional-integral-differential control algorithm are dynamically corrected, long-term precision and reliability of a self-calibration system are guaranteed, and the problem of calibration failure caused by failure of a static model is avoided.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle dynamic obstacle avoidance method based on multi-sensor fusion. Comprising the steps of receiving an image sequence, a depth point cloud set, a pose state parameter and a flight speed parameter; calculating an illumination distortion gradient value and a speckle noise entropy value based on the image sequence and the depth point cloud set; performing coordinate registration and feature extraction on the image sequence, the depth point cloud set and the pose state parameter, and outputting a visual feature set and a radar feature set; generating a weighted visual feature set and a weighted radar feature set through a weight adjustment function, and generating a joint heterogeneous feature tensor through alignment splicing; and generating a body attitude and thrust control instruction by using the combined heterogeneous feature tensor through a probability prediction model and a model prediction control algorithm device. According to the method, through cross-domain multiplexing and deep coupling of the body physical parameters in the data stream, error accumulation and decision delay caused by multi-stage series calculation are avoided, and global performability of an obstacle avoidance task is facilitated.

The invention relates to the technical field of energy storage cooperative control, and discloses an improved energy storage converter grid following control algorithm, which comprises the following steps of: 1, extracting positive sequence and negative sequence components of three-phase voltage of a power grid through a double-sequence decoupling phase-locked loop, and acquiring a positive sequence axis component signal by adopting forward rotation coordinate transformation; and 2, when frequency locking is carried out based on the positive sequence axis component signal, extracting a voltage amplitude corresponding to the positive sequence axis component signal, and inputting the voltage amplitude to a feed-forward passivation module. An active power regulation technical scheme based on a frequency deviation threshold value is adopted, the technical effect of simulating the primary frequency modulation characteristic of a network-forming converter is achieved by detecting the power grid frequency deviation in real time and generating an active power compensation signal, and the defect that traditional network-following control lacks frequency active supporting capacity is overcome. And the converter can spontaneously increase the active power to stabilize the power grid frequency when the power grid frequency drops.

The invention relates to the technical field of chip test engineering control, in particular to an active temperature control method and system for a chip aging tester, and the method comprises the steps: analyzing a test process according to test task parameters, and evaluating the expected thermal load change rate of a test chip, so as to dynamically generate a chip target junction temperature control curve; based on the chip target junction temperature control curve, the actual chip junction temperature and the current actual furnace temperature, the theoretical furnace temperature is obtained through a junction temperature closed-loop control algorithm, the furnace temperature compensation amount is calculated through a feedforward compensation algorithm in combination with the test state parameters, and the ideal furnace temperature is obtained according to the theoretical furnace temperature and the furnace temperature compensation amount; the actual temperature control quantity is obtained according to the basic temperature adjusting quantity and the temperature control compensation quantity, a preset chip target junction temperature control curve can be tracked more accurately, transient and steady state errors caused by test program switching and environment fluctuation can be more effectively restrained, and control over the chip junction temperature with higher precision and faster response is achieved.

The invention discloses an intelligent interpretable traffic signal adaptive control method. The method comprises the following steps: training an intelligent agent through reinforcement learning according to environment state information of an intersection; a timing decision of each phase of the intersection is generated by using the intelligent agent; guiding the first large language model to generate pre-training data by the timing decision and the cue word to perform LoRA fine tuning on the second large language model, and inputting the cue word into the fine-tuned second large language model to enable the second large language model to generate a plurality of reasoning tracks to generate positive samples and negative samples; performing all-parameter fine tuning on the second large language model to obtain a traffic control signal decision model; and inputting the constructed cue word into a traffic control signal decision model to obtain each phase timing scheme of the intersection. According to the method, the defects that an intelligent traffic signal control algorithm based on deep reinforcement learning lacks interpretability and the cross-scene generalization ability is poor are overcome, and the method has important significance in improving the decision credibility and the deployment efficiency.

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 relates to the technical field of phosphoric acid production control, solves the problem that in the prior art, accurate control over the whole process of phosphoric acid production cannot be achieved, and provides a multivariable model prediction control method and system for the whole process of phosphoric acid production. The method comprises the following steps: acquiring key process data of a plurality of process units in the whole phosphoric acid production process in real time; preprocessing the key process data to obtain real-time standardized data; establishing a soft measurement model according to a pre-collected historical standardized data set; inputting the real-time standardized data into the soft measurement model to obtain a soft measurement predicted value; constructing a multivariable dynamic model according to the historical standardized data set and the soft measurement predicted value; according to the multivariable dynamic model, in combination with a model prediction control algorithm, obtaining target set values of a plurality of preset operation variables; and according to the target set value, realizing control of the whole process of phosphoric acid production. According to the invention, accurate control of the whole process of phosphoric acid production can be realized.

The invention relates to the technical field of energy storage, and discloses a transformer substation storage battery pack differential preheating method, which comprises the following steps of: classifying batteries with health indexes lower than a threshold value I as high-priority preheating areas based on single battery health index hierarchical management, and needing to be quickly recovered; the areas with the health indexes higher than the second threshold value are classified as low-priority preheating areas, and conventional preheating is conducted; differentiated preheating power is configured for the two types of areas, the high priority power is high, the low priority power is low, and an optimization strategy is generated through a model prediction control algorithm with the lowest total preheating energy consumption and the fastest whole set performance recovery as targets. According to the transformer substation storage battery pack differential preheating method, differential preheating is carried out according to the health state of each single battery, it is ensured that the maximum potential capacity of the whole storage battery pack is exerted in the low-temperature environment, system operation and maintenance energy consumption is effectively reduced, and economic operation is achieved.