9308 results about "Control parameters" patented technology

The invention belongs to the technical field of circuit breaker management, and discloses a control system for managing a primary and secondary fusion circuit breaker, and the system collects the electrical parameters, mechanical states and environmental conditions of the circuit breaker through multiple channels, and constructs a standardized operation data set; establishing a circuit breaker health characteristic spectrum based on deep characteristic learning; establishing an environmental adaptability control parameter library through environmental factor correlation analysis and multi-scene simulation; performing fault mode identification and predictive diagnosis in combination with the health characteristic spectrum, and generating a fault risk early warning matrix; optimizing a multi-circuit-breaker cooperative control strategy based on the early warning matrix, and generating an optimal control instruction sequence; and reliable execution and effect feedback of the control instruction are realized through security encryption verification and a hierarchical execution mechanism. The problems that a traditional circuit breaker control system is difficult in data integration, insufficient in environment adaptability, weak in fault prediction capacity, incomplete in cooperative control and the like are solved, and the safety and reliability of power grid operation are remarkably improved.

The disclosed technology is generally directed to a method for controlling a satellite system comprising at least one satellite. The method may include receiving and processing a set of control parameters associated with an orientation of the at least one satellite via a classic control model to generate a set of actions to control the orientation of the at least one satellite and storing the set of actions as data in a buffer. The processing and storing are iteratively repeated until the data stored in the buffer exceeds a threshold. When the data stored in the buffer exceeds the threshold, the method may further include iteratively implementing, based on each of the set of control parameters and the data stored in the buffer, the machine learning model to control the orientation of the at least one satellite to stabilize the at least one satellite.

The invention discloses an energy storage battery pack equalization control method, which belongs to the field of energy storage battery packs, and comprises the following steps: S1, constructing a multi-physics field-aging coupling model; s2, based on a multi-physical field-aging coupling model, space-time-frequency domain joint state estimation and unbalanced pattern recognition are realized; s3, formulating optimal equalization topology and control parameters according to space-time-frequency domain joint state estimation and an unbalanced mode recognition result; s4, establishing an electric-thermal-fluid multi-physics field coupling control model, and optimizing equalization current regulation and control, liquid cooling flow velocity and cooling fan rotating speed based on the optimal equalization topology and control parameters; and S5, optimizing the full life cycle of the digital twin drive. By adopting the equalization control method for the energy storage battery pack, the efficient coordination of the energy storage battery pack from dynamic modeling, accurate state estimation and intelligent equalization decision to full life cycle management is realized, and the equalization efficiency and the system reliability are remarkably improved.

The invention relates to the technical field of data processing, and discloses a real-time data processing analysis method and system of an industrial PLC. The method comprises the following steps: transmitting temperature, pressure, current, vibration and acoustic parameters acquired by multiple sensors to an industrial PLC (Programmable Logic Controller) in real time to obtain multi-source heterogeneous original data; preprocessing the multi-source heterogeneous original data to obtain standardized fusion data; correlation calculation and anomaly recognition are carried out through the multivariate analysis model, and an abnormal state classification result is obtained; dynamically adjusting data interaction frequency and sampling rate between the edge nodes and the central PLC, and generating a real-time control decision instruction; and matching the real-time control decision instruction with the current motor load fluctuation state, and outputting the optimal frequency conversion control parameter. According to the invention, the response delay of the system is reduced, the control precision and reliability are improved, the dynamic balance between the safety and the energy efficiency is realized, and the system can intelligently adjust the control strategy according to the real-time safety situation.

The invention is suitable for the technical field of brushless motor control, and provides an intelligent brushless motor and a control method thereof, and the method is applied to a control system of the intelligent brushless motor. The system comprises a data acquisition module, a model construction module, a training tuning module, a model verification module, a parameter setting module, an operation application module, a wireless communication module, a memory, an alarm, a processing center and an intelligent mobile terminal. The data acquisition module, the model construction module, the training optimization module, the model verification module, the parameter setting module, the operation application module, the wireless communication module, the memory and the alarm are respectively connected with the processing center; the invention also provides an intelligent brushless motor. According to the method, the AI model is constructed and trained by collecting the daily motor operation quality data, and the operation control parameters are matched according to the model, so that various operation quality abnormalities are prevented, and accurate control of the motor is ensured.

The invention relates to the technical field of sewage treatment, and particularly discloses a sewage and wastewater treatment control system and method based on an intelligent optimization algorithm. Water quality data are collected through a data acquisition module, and a water quality characteristic matrix is generated through preprocessing. And the prediction module analyzes the feature matrix by using the trained water quality dynamic prediction model to obtain a water quality prediction result. And processing the prediction result by using a multi-objective optimization algorithm to obtain an initial control parameter. And the parameter optimization module calculates a water load fluctuation ratio, a model confidence coefficient and an equipment state according to the sewage and wastewater treatment data, inputs the water load fluctuation ratio, the model confidence coefficient and the equipment state into the adaptive fuzzy network and generates a multi-target parameter optimization suggestion. And the dynamic optimization module adjusts the multi-objective optimization algorithm parameters according to the parameters, and processes the prediction result again to obtain optimization control parameters. And the control module regulates and controls sewage and wastewater treatment according to the optimized parameters. The system realizes closed-loop management from data acquisition, prediction and optimization to control, can dynamically adapt to water quality change, and operates stably and efficiently.

The invention relates to the technical field of intelligent manufacturing and numerical control machining control, and discloses a machine tool dynamic characteristic sensing and intelligent machining control method and system based on a knowledge graph and a large model, and the method comprises the following steps: extracting frequency domain parameters through a vibration sensor, obtaining vibration displacement in combination with laser displacement, and comparing the vibration displacement with modal data to construct a graph; processing parameters and displacement are synchronously sampled, time sequence characteristics are extracted to generate tensors, dynamic characteristics are predicted and corrected, and frequency response adjustment rotating speed is matched to generate optimized track control parameters which are converted into G code instructions. According to the method, a dynamic characteristic map is constructed by fusing vibration signals and displacement, a sliding window synchronizes processing parameters and vibration data, LSTM extracts joint characteristics, GRU predicts rigidity and damping ratio, an attention mechanism dynamically corrects weight, characteristic coupling analysis and prediction precision is improved, nonlinear modeling captures dominant frequency offset and harmonic distribution, response speed is enhanced, and the method has the advantages of being high in precision and high in precision. Cutting vibration is inhibited, and the process stability is guaranteed.

The invention discloses a ship typical channel auxiliary navigation method and system based on unmanned aerial vehicle accompanying, and belongs to the technical field of intelligent shipping auxiliary control. A channel area multi-dimensional risk perception model is constructed, and historical accident, hydrology and obstacle information is extracted to generate a risk density matrix; forming a high-risk accumulation area based on risk spectrum dynamic clustering, and endowing an adjustable intervention weight; scheduling an unmanned aerial vehicle with self-positioning and differential recognition capabilities to carry out accompanying flight, and obtaining track deviation, a velocity vector and attitude information through multi-modal sensing and a low-delay link; calculating an intervention coefficient by using a risk decision engine and generating course, speed and steering point control parameters; and finally, local path dynamic optimization and feedback closed-loop control are realized. According to the invention, the course precision and risk avoiding capability of the ship in a complex typical channel are improved, and the method is suitable for intelligent shipping application in a high-density navigation environment.

The invention discloses a digital intelligent management method and system for hydraulic engineering construction, particularly relates to the technical field of hydraulic engineering construction management, and is used for solving the problems of regulation lag and insufficient environmental disturbance adaptability of an existing method under the action of multi-physics field coupling. According to the method, a concrete three-dimensional temperature field model is established, initial regulation and control parameters are generated, and a heat conductivity coefficient under the seepage influence is corrected based on space-time relevance of pore water pressure and temperature data; the constraint boundary is dynamically adjusted in combination with the construction progress and the material strength curve, a cooling water pipe coordinated regulation and control matrix is constructed, and target flow instructions of space differentiation are generated through the included angle between the distance and the water flow direction; setting a lag time threshold and optimizing an execution time sequence according to the thermal inertia parameter, splitting the threshold and allocating a weight coefficient when the environmental wind speed suddenly changes, and finally updating the model parameter and the regulation and control instruction; accurate temperature control response and abnormal heat conduction suppression under multi-field coupling are achieved, and the structural safety and construction efficiency under complex working conditions are improved.

The invention discloses a server heat dissipation device and a heat dissipation control method.The surface of a server mainboard is divided into a core heat dissipation area, a transition heat dissipation area and an edge heat dissipation area, and the method comprises the steps that real-time temperature data of all the areas are synchronously collected through a temperature detection module; constructing a three-dimensional temperature field distribution map containing temperature gradient and heat flow direction; then thermodynamic parameter calculation is carried out, temperature time sequence change characteristics of all the partitions are extracted, and a thermal diffusion path in a future set time period is predicted by fusing the temperature time sequence change characteristics and the thermodynamic parameters; a control instruction set is generated according to the predicted thermal diffusion path, the actual heat dissipation response is monitored through an acoustic sensor in the execution process, monitoring data and the predicted thermal diffusion path are dynamically compared, and control parameters of the control instruction set are updated till all the partitions reach the target temperature threshold value; and the sudden temperature change is pre-judged, the hot spot elimination response time is shortened, and the collaborative breakthrough of the heat dissipation efficiency and the energy consumption control of the high-density server cluster is realized.

The invention discloses a multi-modal wind turbine generator electromechanical transient modeling method based on artificial intelligence, and relates to the technical field of new energy power generation modeling, and the method comprises the steps: carrying out the causal association mining and dependence path recognition of a standardized spatio-temporal data cube through a causal discovery algorithm, constructing a causal topological graph, and carrying out the modeling of the new energy power generation. Performing cross-modal feature fusion through a space-time diagram attention network to generate a high-order feature tensor; dividing the high-order feature tensor into meta-learning task pools according to different models and environmental conditions, and training a cross-model general parameterization framework by adopting a double-layer optimization strategy; constructing a composite working condition generator based on the trained cross-model general parameterization framework, and performing constraint through a causal regularization loss function to form an extended test working condition set; and performing control parameter optimization on the extended test working condition set by adopting a multi-modal deep reinforcement learning algorithm to obtain optimized control parameters. According to the method, a foundation is laid for realizing electromechanical transient modeling with high robustness and high generalization ability through accurate causal modeling and cross-modal fusion.

The invention relates to the technical field of civil engineering safety monitoring, and discloses a multi-sensor fusion scaffold intelligent monitoring and early warning system and method, and the method comprises the steps: deploying a multi-source sensor group at a scaffold key node, collecting data in real time, carrying out the noise reduction fusion processing through an edge calculation module, and constructing a dynamic deformation feature vector; inputting a time sequence prediction model; a self-feedback adjusting unit is triggered to drive an executing mechanism to conduct deformation compensation, and data optimization control parameters are fed back in real time; and based on the compensated deformation state, the remote platform generates a hierarchical maintenance decision and synchronously pushes the hierarchical maintenance decision to the terminal. According to the invention, all-dimensional sensing of deformation, load and environmental parameters of the bridge supporting scaffold is realized through heterogeneous sensor cooperative networking and redundancy check. Local monitoring blind areas can be eliminated, cross validation of a physical model and a data driving algorithm is combined, the reliability of deformation monitoring and the robustness under environment interference are remarkably improved, and more comprehensive data support is provided for construction safety.

The invention relates to the technical field of intelligent manufacturing, and discloses an extreme manufacturing process technological parameter optimization method and system fused with machine learning. The method comprises the following steps: acquiring multi-source data from a manufacturing equipment sensor, and fusing to generate a material state vector; inputting a pre-training model to obtain a material coefficient transition trend; judging whether the trend fluctuation amplitude exceeds a preset threshold value or not, and if yes, marking key nodes and extracting feature parameters; for the key nodes, according to the characteristic parameters and the real-time data of the key nodes, a control algorithm is adopted to calculate the parameter adjustment amount; optimizing the control parameters based on the parameter adjustment amount, generating a control instruction sequence and transmitting the control instruction sequence to an actuator; and obtaining adjusted feedback data, comparing the adjusted feedback data with the transition trend, and if the deviation exceeds an allowable range, updating the pre-training model. Through deep fusion of predictive monitoring and intelligent control, accurate optimization and adaptive control of process parameters are realized, the stability of the extreme manufacturing process and the product quality are improved, and the energy consumption and the defect rate are reduced.

The invention relates to the technical field of non-electrical variable control, in particular to a digital visual control system and method for a glass bottle production line. The method comprises the following steps: acquiring digital image data of molten glass; molten glass bubble distribution is identified based on the molten glass digital image data, the quality state of the molten glass is evaluated, and micro-defect distribution map data is generated; collecting the internal temperature of the smelting furnace through a temperature sensor array, and constructing a three-dimensional temperature field distribution model of the smelting furnace; and predictively optimizing and adjusting the heating control parameters of the smelting furnace based on the three-dimensional temperature field distribution model of the smelting furnace, and generating a regional heating power distribution strategy. According to the invention, through fusion of a plurality of advanced technologies, accurate sensing and whole-process linkage control of the molten glass state are realized, intelligence, quality uniformity and process visualization level of glass bottle production are significantly improved, and glass manufacturing is promoted to develop towards the intelligent and efficient direction.

The invention relates to the technical field of robot joint control, and discloses a robot joint control method and system based on multi-sensor fusion and a medium. The method comprises the following steps: preprocessing angle, current, acceleration and force / torque data; joint kinetic parameters are calculated through sine trajectory testing; performing state estimation according to the kinetic parameters; evaluating the reliability of the sensor and calculating the weight; inputting the fusion data with the weight into a cascade controller to generate a current instruction; performance index calculation is executed, and control parameters are optimized. According to the method, high-precision self-adaptive control of the robot joint can be realized under the condition that the sensor has uncertainty and faults, so that the system can keep stable performance and continuously perform self-optimization under various working conditions.

The invention discloses an intelligent cooperative switching control system and method for multi-path valve runners, and the method comprises the steps: collecting all-condition operation data of a multi-path valve system, extracting valve switching characteristic parameters, and building a runner mapping relation between paths; a cascade action chain containing time sequence dependence is constructed based on the mapping relation and the switching characteristics, and the action sequence and the trigger condition of each drift diameter are determined; by analyzing the pressure distribution and the flow velocity change in the cascade action chain, predicting the water hammer strength and designing a reverse pressure wave parameter, and generating a water hammer counteracting control instruction; high-low pressure area distribution in the flow field is identified, a flow field complementary cooperation scheme is constructed, and a pressure balance control strategy is generated; analyzing a pressure wave propagation path to determine a superposition elimination point, extracting pressure pulse resonance characteristics and generating time sequence control parameters; and the sequential control parameters are matched with the cascade action chain, synchronous control nodes are determined through delay compensation, and a time-sharing control sequence is generated to achieve intelligent cooperative switching of the multi-drift-diameter valve flow channels.

The invention discloses a temperature control system and method for a hot runner of an injection molding machine mold, and the method comprises the steps that the temperature control system collects the temperature data of each region of the hot runner in real time through a temperature sensor, and carries out the adjustment according to the data feedback; a built-in self-adaptive algorithm automatically adjusts a temperature control strategy according to different injection molding materials and production requirements, such as temperature upper limit, temperature lower limit and heating and cooling opportunities, and the production efficiency is optimized; when the system recognizes temperature fluctuation, a heating mode or a cooling mode is automatically switched, and the cooling system is dynamically adjusted for temperature balance; a plurality of temperature sensors are arranged in a key area of the mold for regional monitoring, and temperature control output is adjusted in real time; meanwhile, an embedded learning algorithm gradually optimizes a temperature control strategy according to historical production data, adjusts control parameters, and improves the production precision and efficiency. According to the temperature control system and the temperature control method for the mold hot runner of the injection molding machine, the problems of low temperature control precision, poor energy efficiency and slow response in the prior art are solved, and efficient and intelligent temperature control capability is provided.

The invention provides a liquid cooling fusion energy consumption adjustment optimization method and system based on dynamic adjustment, and the method comprises the steps: S1, obtaining the thermal load data of calculation equipment, the state data of a liquid cooling system, the state data of an air cooling system, and the constraint data of the system, and constructing a cooling fusion real-time state map; s2, performing calibration operation on the cooling prediction model based on the cooling fusion real-time state atlas, after calibration is completed, extracting cooling key dynamic feature vectors, obtaining short-time cooling prediction data based on the cooling prediction model and the cooling fusion real-time state atlas, and constructing a cooling conflict objective function; s3, obtaining a cooling random sample set, performing initialization operation on a PSO algorithm based on the cooling random sample set, and generating an optimal control parameter combination; and S4, the cooling system is controlled in real time based on the optimal control parameter combination, the step S1 to the step S4 are executed in a circulating mode, and by means of the method, optimal energy consumption control can be found in real time under the condition that the cooling capacity of the cooling system is kept.

The invention provides an intelligent grabbing control method and system based on tactile perception, and the method comprises the steps: obtaining a tactile feedback signal through a sensor array of a humanoid robot, carrying out the frequency domain decomposition of the signal through a Fourier transform method, and carrying out the analysis of the pressure and deformation data of each contact point on the surface of a to-be-grabbed object, calculating to obtain frequency characteristic distribution of the tactile feedback signal; if the initial estimation value of the object rigidity gradient exceeds a threshold value, performing finite element analysis on the object rigidity gradient, and combining a time domain processing result of the tactile feedback signal to obtain object rigidity gradient distribution; carrying out probability modeling on the deformation field by adopting a Bayesian inference method according to the gradient distribution of the rigidity of the object, and carrying out iterative optimization on a modeling result to obtain dynamic update parameters mapped by the deformation field; and regenerating an action sequence of the end effector of the humanoid robot based on the optimized strategy according to a trigger condition of grabbing failure detection, and determining a final grabbing control parameter.

The invention relates to the technical field of vehicle lateral stability control, and discloses a multi-source sensing fusion vehicle lateral stability coordination control system which comprises a multi-source sensing module, a fusion decision module, a control execution module, a state evaluation module and a coordination optimization module. The multi-source sensing module is provided with multiple types of sensors, and vehicle transverse motion parameters, tire attachment parameters and the like are collected and preprocessed. The fusion decision module constructs a data fusion model and a control rule, and dynamically simulates control parameters; the control execution module transmits the rule to a steering mechanism, a braking mechanism and the like, adjusts the output power and returns feedback data; the state evaluation module performs data standardization processing, calculates a first evaluation index, compares the first evaluation index with a reference threshold value, and preliminarily evaluates the stability; and the coordination optimization module calculates a second evaluation index in combination with the road environment parameters when being unstable, and performs secondary evaluation and adjusts the control instruction. The system is suitable for the field of active safety control of various vehicles.

The invention relates to the technical field of steel production and processing, and discloses an electrical steel strip annealing temperature real-time monitoring and regulation system. The system comprises a temperature data acquisition module which is responsible for acquiring temperature and cooling rate data in an annealing process and acquiring expected parameters; the temperature deviation analysis module is used for calculating a deviation coefficient through a dynamic weighting algorithm to construct a matrix; the dynamic regulation and control module is used for generating regulation and control parameters based on a fuzzy logic control algorithm; the abnormity grading module is used for grading annealing abnormity; and the real-time feedback module transmits the regulation and control parameters and the abnormal identifier to the control end. The system also has the functions of adjusting a rule base according to the characteristics of the steel strip, backtracking historical data, optimizing regulation and control, adaptively updating an abnormal threshold value and the like. Through cooperative work of all the modules, real-time monitoring and accurate regulation and control of the annealing temperature are achieved, the annealing quality and production efficiency of the electrical steel strip are improved, and the cost is reduced.

The invention discloses an intelligent air conditioner water chilling unit control method and system based on predictive optimization. Operation data and weather forecast data are collected to establish a dynamic response reference, building cold load sudden change opportunity is recognized, the mismatching relation between magnetic suspension frequency and wet bulb temperature is detected, and energy-saving opportunity recognition data is generated to determine a cooling starting judgment table; identifying equipment response delay characteristics by using a dynamic response reference, extracting time sequence advantage parameters to determine dislocation configuration among multiple pieces of equipment, and generating coordination control parameters; cloud-local transmission delay analysis is carried out according to the coordination control parameters, and a coordination control sequence is generated through buffer opportunity identification and delay compensation; mechanical refrigeration suppression data is generated through energy efficiency mode classification, free cooling potential mining is implemented to form a cold source optimization factor, and an emergency response strategy is generated; an emergency response strategy is used to identify a multi-device linkage trigger critical zone, a prediction correction feedback network is constructed, a global collaborative optimization instruction is generated, and the system operation efficiency and the control precision are improved.

The invention discloses an electric power analysis method and system based on artificial intelligence, and relates to the technical field of electric power system intelligent analysis. Establishing a hierarchical depth map neural network model, and setting a cross-layer information interaction channel to connect each sub-network; executing a bidirectional knowledge distillation algorithm, extracting a rule set from neural network output, and forming a parameter-rule bidirectional mapping matrix; deploying a federal reinforcement learning architecture, performing parameter aggregation on the local models on the distributed power nodes, and generating a power system state evaluation result and a risk coefficient matrix; and starting the self-repairing intelligent agent network, calculating a regulation and control parameter set of the multilevel power system, and outputting a regulation and control instruction. According to the invention, through the hierarchical depth map neural network and a cross-layer information interaction mechanism, collaborative optimization among all levels of the power system is realized, and the operation efficiency and stability of the system are improved.

The invention relates to a dynamic load distribution method, device and equipment based on a vehicle body controller and a medium, and the method comprises the steps: collecting the mechanical load, electrical load and thermal load data of a vehicle body in real time through a distributed sensor network, and generating multi-source heterogeneous initial load data; performing standardization processing on the initial load data, and constructing a three-dimensional data cube type standard load data set; comprehensive characterization features are extracted through feature space mapping, and a fusion load feature set containing a threshold range is dynamically generated in combination with historical data; constructing a load correlation model based on the fusion features, and mapping to generate a steering and braking control parameter combination; and according to the real-time load threshold cross-border state, dynamically adjusting the task priority, allocating real-time calculation core resources to the chassis system, and generating an optimal allocation scheme. According to the method, the problems of multi-source load data fusion failure, resource allocation rigidity and safety task response delay in the prior art are solved, and dynamic load optimization of the vehicle body control system under complex working conditions is realized.

The invention relates to the technical field of heat management of energy storage systems, and discloses a heat management method of an intelligent distributed liquid cooling energy storage system. The method comprises the following steps: acquiring operation state parameters of a plurality of single batteries and optimizing timestamp synchronization; and processing the temperature data by adopting small-batch time series decomposition, and extracting change trend characteristics and abnormal hot spot positions. And based on the information, optimizing a heat exchange control rule base parameter set through a non-dominated sorting strategy, and generating optimized control parameters and priority rules. And in combination with inlet temperature and flow parameters of the liquid cooling system, a tabu search mechanism is utilized to decide a cooling liquid flow distribution scheme, and a branch control instruction is output. And finally, a flow constraint boundary is dynamically adjusted by adopting an approximate gradient projection technology, and a branch and bound strategy is fused to discretize and optimize a distribution path, so that the branch flow is finely regulated and controlled, and the thermal management precision and reliability of the energy storage system are improved. According to the method, the accuracy, adaptability and stability of thermal management of the liquid cooling energy storage system are improved.

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 relates to a self-adaptive pressure regulation vacuum pump closed-loop control system and method. The system comprises a model building module which is used for acquiring real-time data flow of a vacuum pump and building a nonlinear dynamic model to obtain a pressure-flow-power feature mapping relation; the predictive analysis module calculates a pressure-flow predictive value based on the relationship and compares the pressure-flow predictive value with the real-time power data for analysis. And generating a parameter adjustment instruction once the deviation value exceeds a preset threshold value. And the control scheme generation module corrects the control parameters according to the parameter adjustment instruction and generates an optimized power adjustment scheme. And if the data is abnormal, generating a state update value. And the control scheme updating module fuses the state updating value and the pressure-flow prediction value to obtain an optimized model parameter so as to adjust a weight coefficient of an adaptive control strategy and generate a control scheme. The system effectively improves the accuracy of vacuum pump pressure control, greatly enhances the ability of the system to deal with abnormal conditions and equipment performance changes, and ensures long-term stable operation of the vacuum pump.

The invention relates to the technical field of ground mobile unmanned equipment control, and discloses a ground mobile unmanned equipment autonomous obstacle avoidance control system optimized by artificial intelligence. The system comprises an environment perception layer, a bimodal risk assessment layer, a dynamic decision-making layer, a trajectory optimization layer and a feedback optimization layer. The environment sensing layer adopts a retina fovea centralis imitating mechanism to perform non-uniform sampling on laser radar point cloud data to generate dynamic point cloud partitions; the bimodal risk assessment layer fuses two types of radar data to generate static and dynamic obstacle risk assessment diagrams; the dynamic decision-making layer establishes space-time mapping and generates an obstacle confidence coefficient matrix through a graph neural network; the trajectory optimization layer converts the matrix into a control parameter based on a multi-objective evolutionary algorithm, and issues the control parameter through a time-sensitive network protocol; and the feedback optimization layer monitors environment change, calculates deviation, generates an effectiveness index, and dynamically adjusts a point cloud acquisition strategy until the index is optimal. According to the system, the autonomous obstacle avoidance capability and adaptability of the ground mobile unmanned equipment in a complex environment are enhanced.

The invention provides a cold-rolled sheet shape multivariable cooperative control optimization method and system. The method comprises the steps that rolling tension set value parameters are determined on the basis of a dynamic coupling model so as to be matched with dynamic tension fluctuation in the high-speed strip steel rolling process of a cold-rolled sheet, the magnetic conductivity change in the strip steel rolling process is detected through the magnetoelastic effect, and a magnetic conductivity gradient map is generated; residual stress distribution data are inverted based on the magnetic conductivity gradient map, and target axial roll shifting control parameters influencing the roll gap shape are generated; and multivariable cooperative matching is conducted on the rolling tension set value parameter and the target axial roll shifting control parameter, a matching result is obtained, the rolling tension and the roll gap shape are adjusted, and strip steel deviation and fluctuation caused by dynamic coupling of the composite material tension and the cold-rolled sheet shape in the cold-rolled sheet high-speed strip steel rolling process are restrained. According to the technical scheme, through cooperative control of the dynamic coupling model and the magnetic conductivity gradient map, deviation and fluctuation of the strip steel are effectively restrained, and the shape stability and rolling precision of the cold-rolled plate are improved.

The invention discloses a pipe network operation and maintenance management system based on data analysis, which relates to the technical field of municipal infrastructure operation and maintenance management and comprises an equipment feature modeling module, a group anomaly identification module, a common-mode risk tracing module, a dynamic risk modeling module, a regulation and control strategy optimization module and a closed-loop self-adaptive updating module. Based on historical operation data and real-time acquisition parameters, feature coding is performed on the model, batch, position and time sequence of the sensor, and an equipment attribute mapping matrix is constructed. According to the method, the equipment attribute mapping matrix and the group anomaly identification mechanism are constructed, and external induction factor positioning and the dynamic risk weight map are combined, so that intelligent adjustment of regulation and control parameters and anomaly interference avoidance are realized, a closed-loop optimization process is constructed, and the identification accuracy, the control safety and the operation toughness of a pipe network system in a multi-disturbance scene are improved.