1239 results about "Optimal control" patented technology

The invention provides a compressor energy-saving operation control method and system based on reinforcement learning, and belongs to the technical field of compressor control. The method comprises the steps that multi-dimensional data in the operation process of a compressor are collected through a multi-parameter sensor network; preprocessing the multi-dimensional data to obtain target feature data; inputting the target characteristic data into a state prediction model, and predicting an operation parameter prediction value in a future control period; splicing and fusing the target feature data and the operation parameter predicted value, and constructing state representation of the reinforcement learning model; inputting the state representation into a target reinforcement learning model based on a near-end strategy optimization framework to obtain an optimal control action; safety verification is conducted on the optimal control action based on preset compressor safety operation constraints, and an execution instruction is determined; and adjusting operation parameters of the compressor based on the execution instruction. According to the compressor energy-saving operation control method and system based on reinforcement learning, the energy-saving performance and the operation stability of the compressor are improved.

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 provides a high-speed magnetic levitation suspension system control method and system based on edge calculation and Transform prediction, and the method comprises the steps: constructing a Transform prediction model with a space-time attention mechanism and an autoregression mechanism based on obtained local low-delay calculation resources and train real-time sensing data, and deploying the Transform prediction model in a vehicle-mounted edge calculation unit; performing short-term high-precision prediction on the gap, the acceleration and the disturbance trend at a plurality of sampling moments in the future through a Transform prediction model to obtain a prediction result; processing actuator current saturation and gap safety threshold hard constraints in a limited prediction domain by using a model prediction controller, and solving an optimization control sequence in real time in combination with a prediction result; and overlapping a control barrier function as a safety filter of the model prediction controller, correcting the optimized control sequence to obtain an optimal control sequence, and controlling the high-speed magnetic suspension system. According to the method, the cloud communication delay and jitter are reduced, and the robustness and security of the system under uncertain disturbance are improved.

The invention provides a multi-parameter cold source intelligent control system and method based on big data, and the system comprises a data collection and control module which is used for collecting related parameters of a cold source system; the comprehensive energy consumption standardization module is used for converting the energy consumption parameters into unified standardized energy consumption indexes; the comfort degree algorithm construction module is used for generating a dynamic comfort degree index; the association rule mining module is used for mining a multi-dimensional association rule from historical data; the intelligent decision module is used for generating an optimal control strategy based on the index, the index and the association rule; according to the embodiment of the invention, based on a multi-parameter fusion cold source intelligent control technology of deep reinforcement learning and an improved FP-Growth algorithm, through deep mining and intelligent decision making of multi-source data, the optimal control strategy is converted into equipment regulation and control parameters, and the equipment regulation and control parameters are obtained. And high efficiency and energy conservation of the cold source system and accurate regulation and control of indoor environment comfort are realized.

The invention discloses a ship power system optimization control method and system based on a simulated annealing algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining operation parameters and historical energy consumption data in real time, and constructing a multi-objective optimization function of dynamic weight distribution; generating an initial temperature parameter and a solution set in combination with the navigation state and the environment data; a neighborhood search strategy disturbance solution set is improved, a new solution is evaluated by using a dynamic acceptance probability function, temperature parameters are adaptively adjusted for iterative optimization, and an optimal control parameter combination is output; and an adjustment instruction set is generated after multi-dimensional efficiency verification, and a propulsion device, a generator set and an energy storage module are cooperatively controlled, so that global energy consumption optimization is realized. According to the method, through data driving and intelligent algorithm fusion, energy efficiency and environmental adaptability are improved, and stable operation under complex working conditions is guaranteed. According to the ship power system optimization control method and system based on the simulated annealing algorithm, the energy efficiency level and the environmental adaptability of the ship power system are improved.

The invention relates to the field of nonlinear system control, and provides a preset time reinforcement learning method and system of a continuous nonlinear system, and an electronic device, and the method comprises the steps: constructing a zero-sum game framework based on a kinetic model and a performance index of the nonlinear system; determining a value function and a Hamiltonian function based on a zero-sum game framework; applying a preset neural network model to carry out approximation on the value function, and determining an approximation error; based on the Hamiltonian function and the approximation error, an approximate optimal control strategy and a worst interference strategy are determined; constructing a Lyapunov function based on the value function and the weight error of the value function; and based on a Lyapunov function, an approximate optimal control strategy and a worst interference strategy, a reinforcement learning result is verified. The method and the device are used for overcoming the defects that convergence time cannot be dynamically adjusted, parameter complexity is high and robustness is insufficient in the prior art, and the scheme of the invention can meet dual requirements of a continuous nonlinear system on dynamic convergence and anti-interference performance.

The invention discloses a regulation and control system and method based on heat energy of an energy storage cabinet, and belongs to the technical field of intelligent heat management. Temperature data in the energy storage cabinet are collected, a temperature distribution matrix is constructed, and a thermal gradient vector and an overheating risk area are extracted based on a difference algorithm; further establishing a thermal power load mapping model, and dynamically generating an air circulation path optimization diagram; the opening angle of a flow deflector and the rotating speed of a fan are adjusted in combination with an optimized path, and self-adaptive regulation and control of a thermal field in the cabinet body are achieved; meanwhile, a machine learning model is trained by utilizing historical operation data, an optimal regulation and control strategy is output, and an execution layer is driven to realize collaborative response of a fan and a flow deflector; the method has the advantages of high regulation and control precision, high response speed, low energy consumption and the like, and is suitable for heat management requirements of various types of energy storage cabinets.

The invention belongs to the technical field of industrial flue gas purification, and discloses an ammonia desulfurization optimization control system based on a machine learning algorithm, which comprises a feature extraction module, a working condition clustering module, a dynamic optimization module and a self-adaptive feedback module, the feature extraction module is used for collecting multi-dimensional operation parameters in a coal burning process, performing data dimension reduction through a PCA algorithm, and extracting key working condition features; the working condition clustering module identifies different operation working condition modes; the dynamic optimization module can construct a multi-modal optimization neural network based on different operation condition modes, and generates optimal control parameters in real time. Through PCA dimension reduction processing of the feature extraction module and in combination with a working condition clustering algorithm, automatic mode recognition and strategy switching under complex working conditions are achieved, fluctuation of desulfurization efficiency is reduced, and meanwhile the problems that the ammonia water adding amount depends on experience setting, raw material waste and secondary pollution are likely to be caused, and the operation and maintenance cost is large are solved.

The invention relates to the field of heating ventilation air conditioner automatic control, and discloses an efficient central air conditioner cold station optimization control system and method based on physical AI. The system comprises a data acquisition module, an energy efficiency modeling module, a load prediction module, a rolling optimization module, an execution control module and a feedback correction module. The data acquisition module forms a time sequence data set; the energy efficiency modeling module adopts transfer learning to obtain a system energy efficiency model of a target domain cold station; the load prediction module outputs a predicted cold load sequence; the rolling optimization module is used for solving an optimal control sequence under the constraints of cooling capacity balance, an equipment operation boundary and a temperature difference threshold by taking a predicted cooling load sequence, a system energy efficiency model and a current equipment operation state as input under a model prediction control framework; the execution control module issues a first control action to control the water supply temperature, the water pump and fan frequency and unit start and stop; and the feedback correction module calculates a residual error and is used for closed-loop correction of the next period.

The invention provides a zero-sum game-based distributed elastic consensus optimal control method under denial of service attack of a multi-agent system, and the method specifically comprises the steps: firstly, constructing a multi-agent formation model in which a leader and a plurality of followers cooperatively move through graph theory knowledge and a multi-agent second-order state equation; secondly, in order to reduce the influence of denial of service attack on communication topology, a time-varying weight distributed elastic observer is provided to estimate the state of a leader, and the attacked condition of the leader is considered; then, by constructing an augmentation system, a distributed consistency tracking problem with a leader is converted into a local tracking problem between each follower and a virtual leader thereof; and finally, in order to solve the zero-sum game problem, introducing a Hamiltonian-Jacobian-Ansaxophone equation to realize optimal control input under maximum external disturbance, and realizing algorithm design by using single-evaluation reinforcement learning with experience playback and combining a gradient descent method.

The invention discloses a numerical control machine tool vibration suppression self-adaptive control system and method, and relates to the technical field of intelligent vibration control, and the method comprises the steps: building a current-modal dynamic mapping relation between an electrical drive characteristic and a mechanical structure modal response based on a vibration state feature vector; real-time machine tool modal state vectors are obtained through online recursive estimation; the machine tool modal state vector is used as a boundary condition to be injected into the virtual prediction model; solving to obtain an optimal control parameter sequence and a corresponding vibration prediction result in a future time domain by taking multi-performance index collaborative optimization as a target through a rolling optimization control strategy; translating the control parameter sequence into a specific execution instruction and issuing the specific execution instruction; monitoring an execution process in real time and recording actual response data; according to the method, the accuracy of vibration prediction and the real-time performance of control response are improved by establishing the dynamic mapping relation from the electromagnetic driving characteristics of the servo main shaft and the feed motor to the dynamic response of the machine tool structure.

The invention relates to the technical field of rotary steerable drilling system dynamics analysis and control, in particular to a rotary steerable drilling system dynamics analysis system which comprises a data acquisition module used for acquiring ground engineering parameters and underground dynamic parameters in real time and intercepting drilling parameter adjusting instructions; the digital twin modeling module is used for outputting a complete state vector of a digital twin model; the noise prediction module is used for generating a control source noise signal; the signal reconstruction module is used for executing self-adaptive hedging processing on the original mixed signal so as to reconstruct a pure geological signal; the cooperative control module is used for predicting the vibration risk caused by the change of the front stratum and generating an optimal control instruction for actively avoiding the vibration risk; the occurrence probability of malignant vibration is remarkably reduced, the drilling efficiency is improved, and the service life of a drilling tool is prolonged.

The invention discloses an offshore rocket recovery platform attitude stability control method based on particle swarm optimization, and the method comprises the following steps: S1, collecting and preprocessing multi-source attitude data, and generating a time series data set; s2, constructing a fuzzy PID controller, and setting nine three-axis control parameters; s3, performing global optimization by adopting an improved particle swarm algorithm, and outputting an initial parameter solution; s4, introducing a zebra optimization algorithm to carry out local refined optimization; s5, constructing a collaborative optimization architecture, and fusing and outputting optimal control parameters; s6, the optimal parameters are input into a controller, and a servo system is driven to adjust the three-axis attitude; s7, monitoring disturbance amplitude, dynamically triggering a zebra strategy and feeding back the zebra strategy to the particle swarm; and S8, evaluating the control performance, and feeding back iterative optimization if the control performance does not reach the standard. According to the method, the improved particle swarm optimization algorithm and the zebra optimization algorithm are fused, so that self-adaptive optimization and accurate attitude stable control of attitude control parameters of the offshore rocket recovery platform are realized.

The invention discloses a feed pump closed-loop energy recovery system based on recirculation branch excess pressure driving, and relates to the technical field of energy recovery of a thermal power generating unit water supply system, a modular energy recovery assembly comprises a tandem type wide-area hydraulic turbine set which is formed by connecting a high-pressure micro-flow hydraulic turbine and a low-pressure large-flow hydraulic turbine in series, and the high-pressure micro-flow hydraulic turbine and the low-pressure large-flow hydraulic turbine are connected in series. Intelligent switching or cooperative work is carried out according to working condition requirements; the digital twinning intelligent control core comprises a prediction engine and a decision engine, the prediction engine identifies an operation mode through a behavior embedding vector and predicts a future state sequence, and the decision engine generates an optimal control instruction sequence through a strategy network subjected to optimal transmission theory regularization training. The system achieves cooperative control of the rotating speed of the water feeding pump and the opening degree of the recirculation valve by executing a control instruction, and triple closed-loop control of a data closed loop, an energy closed loop and a control closed loop is formed. The energy recovery efficiency is improved, the robustness of a control strategy is enhanced, and predictive maintenance is realized.

The invention discloses a modeling and optimization control method for a gas supply system of a proton exchange membrane fuel cell, and belongs to the technical field of hydrogen energy power. The method comprises the following steps: establishing a nonlinear state space model of a proton exchange membrane fuel cell gas supply system; determining the optimal oxygen excess ratio of the system under different load currents through experiments, and fitting the optimal oxygen excess ratio into a reference function about the load currents; based on the nonlinear state space model, a model prediction control problem with tracking of the optimal oxygen excess ratio and minimization of the cathode and anode pressure difference as control targets is constructed and expressed as a constrained quadratic programming problem; and decomposing and iteratively solving the quadratic programming problem by adopting an alternating direction multiplier method to obtain the optimal control input of the current control period and act on the system. The method effectively solves the problem that traditional model predictive control is difficult to deploy in real time in a vehicle-mounted controller due to large calculated amount, so that efficient and accurate cooperative control of the proton exchange membrane fuel cell gas supply system is realized.

The invention relates to the technical field of industrial process control, and discloses a digital twinning-based fermentation process temperature adaptive control system, which comprises a data acquisition and state representation module, a probability generation type digital twinning module, an adaptive risk management module, an opportunity constraint model prediction control module and a model iteration trigger module, according to the method, probability distribution of future states is predicted by constructing probability generation type digital twinning, and the uncertainty of the model is quantified in real time; the system can dynamically adjust the risk tolerance, drives the chance constraint model prediction controller, seeks an optimal control solution on the premise of ensuring the process safety probability, triggers the model adaptive update by means of an online evaluation mechanism, and realizes the optimal control solution on the basis of effectively managing the process uncertainty. The fermentation temperature is subjected to high-performance self-adaptive control considering safety and economy, and the problem that a traditional deterministic model is insufficient in control robustness is solved.

The invention discloses a dynamic energy-saving control method and system for a cold storage based on deep learning and multi-source data fusion, and relates to the technical field of cold storage energy-saving control, and the method comprises the steps: constructing a thermal field sensing network through a fixed sparse sensor array and a mobile thermal field scanning robot, and collecting temperature, humidity and thermal imaging data; reconstructing a three-dimensional thermal pixel field through registration, interpolation and other processing, and performing space-time alignment on the three-dimensional thermal pixel field, equipment parameters and inventory thermal physical properties; constructing digital twinborn deduction thermal field evolution based on a physical information neural network, and recognizing a high-temperature hot spot and a low-temperature safe area in combination with a space-time diagram attention network; the method comprises the following steps: by taking power consumption cost minimization and over-temperature risk controllability as targets, solving an optimal control strategy containing tuyere parameters and compressor frequency by using a multi-agent depth deterministic strategy gradient algorithm, driving equipment to execute targeted cold supply, and enabling a system to comprise a thermal field sensing layer, a data fusion layer, a digital twinning and prediction layer, a decision optimization layer and an execution layer. And accurate temperature control and energy conservation are realized.

The invention relates to the technical field of aircraft trajectory planning, in particular to a dynamic gliding flight trajectory planning method for a fixed-wing aircraft, and the method comprises the steps: building an optimal control problem model with minimum system energy consumption as a target function; obtaining a nonlinear programming problem model; acquiring a corresponding single-time optimal trajectory and corresponding flight state data when the system energy consumption is minimum in the unpowered gliding flight stage and the powered recovery flight stage; and obtaining a multi-section type dynamic gliding flight path. According to the method, the iteration process of solving a nonlinear equation is avoided, the calculation burden of single iteration is remarkably reduced, the solving efficiency and robustness of the whole optimal control problem are greatly improved, and the optimal control effect is improved by designing the multi-section type dynamic gliding flight strategy of the unpowered gliding flight stage and the powered recovery flight stage. The stable periodic energy-obtaining gliding flight is realized by accurately compensating the flight energy deficit.

The invention discloses a geological information real-time sensing and decision-making method for intelligent mining of a coal mine, and the method comprises the following steps: S1, deploying geological sensing equipment at a working surface and an adjacent region of the coal mine, and collecting original geological data; s2, performing standardization, alignment and denoising on the original geological data, and constructing a tensor structure; s3, inputting the tensor structure into a tensor manifold learning model, extracting an evolution trend and an abnormal region, and generating a state vector; s4, constructing a multi-objective optimization model according to the state vector, and solving an optimal control parameter by adopting an improved badger optimization algorithm; s5, the control parameters are converted into operation instructions, and the operation strategy of the tunneling equipment is dynamically adjusted; and S6, combining operation feedback and a new data updating model to realize closed-loop coupling control. According to the method, the tensor manifold learning model and the improved badger optimization algorithm are fused, and the geological information real-time sensing and decision-making method for intelligent mining of the coal mine is realized.

The invention discloses a novel power system primary frequency modulation optimization method based on deep learning, and relates to the technical field of power optimization. The method comprises the following steps: collecting state data of a hydroelectric generating set running in a power grid, constructing a three-dimensional feature tensor, and extracting spatio-temporal features through a TCN-GRU hybrid network; constructing a deep reinforcement learning decision layer, and defining an action space and a reward function; constructing a PID neural network with a time-varying forgetting factor, and outputting a dynamic weight; establishing a dual-time scale updating mechanism; a safety verification module is arranged, and when the system frequency deviation value is larger than a threshold value, the traditional PID mode is switched. Characteristics are extracted from state data of operation of a hydroelectric generating set in a power grid, the time sequence dependency relation of the characteristics is processed through deep learning, the frequency change trend of the power grid is captured, the optimal control strategy is explored by applying the DRL technology, PID controller parameters are adjusted in a dynamic environment, and the optimal control strategy is obtained. And the primary frequency modulation response speed and the control precision of the hydroelectric generating set during power grid frequency fluctuation are improved through self-adaptive control.

The invention relates to the technical field of ground source heat pumps, and discloses a ground source heat pump buried pipe zoning layout and cold and heat balance control method which comprises the following steps that the current three-dimensional ground temperature field state of the area where a ground source heat pump buried pipe is located is obtained; predicting a future state sequence of the ground temperature field under different control strategies in a future prediction time domain; solving to obtain an optimal control sequence based on a multi-objective optimization function which aims at minimizing the comprehensive operation cost of the system in the prediction time domain; the multi-objective optimization function at least comprises an energy consumption item representing system short-term operation energy consumption and a balance item representing a ground temperature long-term balance state; and executing the first control action in the optimal control sequence. The prediction model constructed by the invention can combine a physical rule with a data residual continuously learned from actual operation, and ensures that prediction information on which a control decision depends always keeps high fidelity in long-term operation of the system.

The invention discloses a precise air conditioner energy-saving control optimization method based on reinforcement learning, which comprises the following steps of: acquiring environmental parameters such as temperature, humidity, thermal load, electric energy consumption and running state of an air conditioner system, generating a vector reflecting the current environmental condition, scoring a plurality of control actions through an improved Banditron perceptron model, and calculating the control parameters of the air conditioner system according to the scored control actions. Selecting an optimal control action through a Thompson sampling mechanism, executing the selected control action, collecting environment feedback information after execution, forming a feedback parameter set, dynamically adjusting parameter values of exploration behaviors through data analysis of a continuous control period, and finally jointly inputting a current environment state and the control action into a decision model. And generating a parameter set for setting operation of the air conditioner. Intelligent optimization and energy-saving regulation and control of the air conditioner control process are achieved.

The invention relates to the field of intelligent control of marine navigation, and discloses a formation dynamic interception path planning method and device based on a differential game theory, and the method comprises the steps: constructing a differential game model comprising a formation and intercepted ships; designing revenue functions for the two parties; obtaining a Nash equilibrium strategy by solving a Hamiltonian-Jacobian-Bellman equation of the game, and solving an individual optimal control instruction of each formation member; and fusing state information among formation members through a consistency protocol, adjusting individual instructions to meet anti-collision and formation cooperative constraints, and generating a final control instruction. According to the method, historical adversarial data is learned by using the neural network, and the weight of the revenue function is adaptively and dynamically adjusted to cope with different tactical scenes; and the whole decision-making process is set under a model prediction control framework for rolling optimization, so that the real-time performance, the collaboration and the intelligent level of interception path planning are greatly improved, and the interception efficiency is effectively improved.

The embodiment of the invention relates to an intelligent control method and system for a data-driven aluminum extruding machine and the aluminum extruding machine, and belongs to the technical field of electronic data process.The method comprises the steps that multi-source time sequence data are collected and converted to obtain two-dimensional depth characteristics, data distribution is detected, and the data distribution is calculated; a generative adversarial network and dynamic time warping oversampling are used to enhance an unbalanced sample, a domain invariant feature model is trained to extract cross-working-condition robust features, state evaluation information output by the model is accessed to a non-parameterization prediction control framework, a multi-objective optimization function is constructed based on real-time sliding window data at each beat, and a multi-objective optimization function is constructed based on real-time sliding window data. And an optimal control sequence is issued to an execution mechanism after solving, and online closed-loop adjustment of parameters such as the extrusion speed and the temperature is achieved. According to the embodiment of the invention, data processing, AI and automatic control are fused, self-adaption to working condition changes is realized, the control precision and the quality stability can be improved, and the energy consumption and the cost are reduced.

The invention discloses an intelligent analysis and management method and system for an integrated controller, relates to the field of integrated control, and aims to complete complex air conditioning system identification model training and off-line optimization of an optimal PID parameter strategy by using historical data at a cloud end with sufficient computing power. And generating an expert strategy library containing various working conditions and corresponding optimal control parameters thereof. Then, a lightweight proxy model with small calculation amount and high reasoning speed is trained based on the strategy library; the proxy model is deployed on a resource-constrained edge controller. Therefore, the edge end does not need to execute complex online optimization calculation, and only needs to input the real-time working condition data into the agent model, so that the current optimal PID parameter can be quickly deduced. According to the cloud training and edge reasoning architecture, online, real-time and intelligent self-tuning of controller parameters is realized, and a gap between an advanced algorithm and industrial landing is effectively filled up.

The invention provides an energy storage electrical intelligent system of a self-adaptive control strategy, and relates to the technical field of electric power grids. According to the invention, multi-source operation data of a power grid, a load, new energy and an energy storage system are collected in real time through the sensing layer, the decision-making layer realizes accurate synchronization and updating of the system operation state by using a digital twin model, and distributed optimization of swarm intelligence and extreme scene testing of confrontation deduction are fused. Dynamically generating and screening out an optimal control instruction which shows robustness under various working conditions; and finally, the execution layer accurately controls the energy storage system to form a sensing-decision-execution-feedback adaptive closed loop, so that the control strategy of the energy storage system can be automatically and accurately adjusted along with the fluctuation of the running state of the power grid, the stiffness defect of a fixed strategy is overcome, the capacity of the power grid for dealing with source load fluctuation and sudden faults is enhanced, and the energy storage system is ensured to be more stable. The self-adaptive matching of the energy storage system control strategy and the power grid operation state is realized, and the dynamic stability and the operation safety of the power grid are improved.

The invention discloses an operation control method for a wind power generation system, and relates to the technical field of operation control, and the method comprises the steps: obtaining the historical operation data of a wind power generation system in a target sea area, taking an offshore environment factor as an interference factor, and coupling the time-space parameters of ocean turbulence intensity, wave load and salt spray corrosion rate; establishing a fan-marine environment digital twinborn body; operation data is acquired in real time, synchronous mapping and dynamic updating of the digital twin are realized, and a state prediction model is established in combination with historical operation characteristics; taking the maximum power generation efficiency and the minimum mechanical load as a multi-objective optimization function, and introducing a model prediction controller to obtain an optimal control sequence of the wind power generation system in a future time domain of each control period; and selecting a first control instruction in the optimal control sequence, issuing the first control instruction to a physical fan torque controller and a variable pitch system in real time, realizing rolling optimization of the wind power generation system, and obtaining a self-adaptive operation control scheme of the wind power generation system. The power generation benefit of the whole life cycle is improved.

The invention discloses a building energy consumption subentry metering optimization regulation and control method and system, and relates to the technical field of building energy consumption management. The method comprises the following steps: collecting multi-source data through a sensor network, performing event-level segmentation processing based on a power transition event to obtain an energy consumption sub-sequence, determining the category and confidence of an energy consumption unit through an interpretable classification model, and performing non-stationary correction to obtain a stabilized sub-item energy consumption sequence; constructing a temperature condition embedded sequence prediction model, and performing scale calibration and time sequence consistency correction in combination with building features to obtain a calibrated sub-item energy consumption prediction model; when the predicted deviation exceeds the threshold, a multi-strategy regulation process is triggered, an optimal control strategy is dynamically selected, and a regulation instruction is generated; and carrying out transfer learning updating on the prediction model based on virtual-real consistency feedback. According to the invention, closed-loop optimization from fine perception and accurate prediction to intelligent regulation and control is realized, and the accuracy of building energy consumption metering and the intelligent level of regulation and control are effectively improved.

The invention discloses a water surface unmanned ship control method and system under path tracking, and relates to the technical field of attitude control, and the method comprises the steps: building a kinetic model of a water surface unmanned ship; establishing a path tracking error equation for describing a system error of the unmanned surface ship relative to an expected path under a Serret-Frenet coordinate system; constructing a path tracking controller based on a linear active disturbance rejection controller; inputting the system error and a water surface unmanned ship control signal into a fuzzy RBF neural network, and outputting an optimal control parameter of a path tracking controller by taking minimization of the system error as a target; updating a path tracking controller according to the optimized control parameters; and re-estimating external disturbance by using the updated path tracking controller, outputting a control signal for optimizing the water surface unmanned ship, and controlling the water surface unmanned ship to advance along an expected path. According to the invention, the attitude control precision under strong ocean current and changeable weather is improved, and the path tracking precision of the water surface unmanned ship is greatly improved.

The invention provides a high-temperature liquid cooling cabinet control system and method based on intelligent energy efficiency optimization, and relates to the technical field of intelligent adjustment control. And meanwhile, constructing a digital twin containing a heat flow coupling simulation model and multi-target dynamic optimization to process real-time operation data of the liquid-cooled cabinet acquired in real time, deducing a heat dissipation result, an energy consumption result and a heat recovery result of the liquid-cooled cabinet, and performing multi-target dynamic optimization by taking the deducing result as a basis. Simulation solution is carried out by taking the purposes of ensuring the safety of a computing chip, reducing the energy consumption and stabilizing the waste heat quality as targets, an optimal control instruction set is obtained, a global dynamic balance point is actively generated in each control period, and low-energy-consumption and stable high-temperature heat output are achieved while the optimal heat dissipation cooling efficiency is ensured. Multi-target maximization cooperation is achieved, the energy utilization rate and the cooling efficiency are improved, waste is avoided, and the cost is reduced.