195025 results about "Control engineering" patented technology

The invention relates to the technical field of cable operation state detection, and particularly discloses a multi-source data driven cable operation state comprehensive evaluation method, which comprises the following steps of S1, adopting a layered distributed sensing network architecture, and deploying three types of core sensors at key nodes of a cable, through space-time calibration of the multi-source heterogeneous sensor, data consistency is improved, fusion deviation is eliminated, the problem of data islands of a traditional system is solved, and a precise evaluation foundation is laid; noise suppression and dynamic correlation modeling are adopted, environmental interference is stripped, a vibration and displacement coupling relation is quantified, limitation of a single parameter is broken through, heterogeneous fault features are captured, and evaluation comprehensiveness and sensitivity are improved; a self-adaptive threshold mechanism is constructed based on environment weight and historical data, the bottleneck of a fixed threshold is broken through, an evaluation standard is corrected along with equipment aging and environment change, misjudgment is avoided, and diagnosis robustness in different scenes is enhanced.

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

The invention relates to the technical field of flow measurement calibration, in particular to a flow instrument intelligent calibration system based on multi-sensor fusion, which comprises a data acquisition unit, a deep coupling compensation unit and a closed loop verification unit, a data acquisition unit obtains a differential pressure value, an environment temperature, a pipeline pressure, a vibration frequency spectrum and sensor accumulated working time, a depth coupling compensation unit constructs an aging prediction model based on a Weibull distribution life model, and a temperature-pressure coupling equation and a vibration compensation mechanism are combined to obtain an aging real-time value and a time sequence deviation. Multi-parameter coupling characteristics are extracted through a neural network, an environment disturbance compensation coefficient matrix is constructed, a joint compensation amount is generated through dynamic weight distribution, a closed-loop verification unit optimizes model parameters, the problems that multi-source disturbance coupling analysis is insufficient and calibration precision is low in the prior art are solved, accurate calibration of a flow instrument under complex working conditions is achieved, and the calibration precision is improved. The metering stability is improved.

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

The invention relates to the technical field of power dispatching, in particular to a digital twin energy management method and system for source-network-load-storage cooperative dispatching, and the method comprises the steps: collecting source-network-load-storage multi-dimensional space-time operation data, and extracting space-time coupling features through a graph convolution-long and short-term memory network; establishing a simulation model of a digital twin environment, simulating an uncertain operation condition by using a Monte Carlo scene generator, and processing a power flow constraint by using a second-order cone relaxation technology; training an energy storage scheduling agent in a digital twin environment, and learning an energy storage charging and discharging strategy through a near-end strategy optimization algorithm; designing a source-network-load-storage hierarchical collaborative optimization framework, optimizing power output and load distribution by using an improved particle swarm optimization algorithm on the upper layer, and solving power flow distribution by using an alternating direction multiplier method on the lower layer; and establishing a self-adaptive feedback correction mechanism, and dynamically adjusting a cooperative scheduling strategy. According to the invention, intelligent collaborative scheduling of source network load storage is realized, and the operation efficiency and stability of a power system are improved.

The invention discloses an energy storage system operation and maintenance strategy optimization method based on digital twinning, and belongs to the technical field of electric energy storage and intelligent power grids. Establishing a digital twinning synchronous model of the energy storage system and calibrating the digital twinning synchronous model; generating prediction data at the current moment based on the digital twinborn model, performing residual analysis on the prediction data and real-time data, and generating a quantitative diagnosis index; according to the quantitative diagnosis index and the fault mode, adjusting parameters of the digital twinning synchronization model, and ensuring that the model and the actual state of the energy storage system are kept synchronous; inputting real-time data into the adjusted digital twinborn model, calculating a future operation index of the energy storage system, and generating simulation operation data; and generating a non-periodic operation and maintenance strategy according to the simulation operation data, the fault mode and the operation and maintenance rule base. According to the method, the adaptive calibration digital twinborn model is adopted, real-time diagnosis and prospective optimization can be fused, and the operation and maintenance efficiency and reliability of the energy storage system are remarkably improved.

The invention discloses a high-precision voltage measurement method and system based on multi-parameter adaptive compensation, and relates to the technical field of intelligent voltage measurement. The high-precision voltage measurement method and system based on multi-parameter adaptive compensation comprises the steps that S1, voltage signal data and disturbance source characteristic data are collected, preprocessing is completed, and an analysis basis is constructed; s2, analyzing voltage excursion in combination with disturbance current characteristics, and dynamically adjusting a compensation path; s3, evaluating a compensation correction effect, and driving a compensation output strategy in real time; and S4, comprehensively analyzing the offset characteristics and the correction result, judging an abnormal section and triggering adaptive adjustment. The problems that disturbance components cannot be comprehensively extracted from multiple parameters such as PWM modulation frequency, wire harness impedance and working condition load change and a compensation function cannot be reversely constructed in an existing method, and a measured value does not have stability are solved.

The invention discloses a hydraulic pressure test tube machine and a tube body clamping centering device, in the tube body clamping centering device, two guide rods and a telescopic oil cylinder are arranged on a base side by side; corresponding ends of the two guide rods are respectively provided with a first sliding block, corresponding other ends of the two guide rods are respectively provided with a second sliding block; a fixed end of the telescopic oil cylinder is fixed on one end of the base, the fixed end of the telescopic oil cylinder is arranged corresponding to the second sliding blocks; a telescopic end of the telescopic oil cylinder is connected with the first sliding blocks, and is used for driving the first sliding blocks to slide along the guide rods, one of two clamping blocks is fixed on the two first sliding block, and the other one of the two clamping blocks is fixed on the two second sliding blocks; the first sliding block on one of the two guide rods is provided with a first rack, the second sliding block on the other one of the two guide rods is provided with a second rack, both sides of a linkage gear are respectively meshed with the two racks, the first sliding blocks and the second sliding blocks are adjusted by the telescopic oil cylinder to move towards each other or depart from each other. The scheme can solve the problems of high control precision and slow regulation efficiency of a plurality of oil cylinders of the tube body clamping centering device.

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 present invention relates to the technical field of circuit control, and relates to a direct-current high-voltage generator regulation and control method and system based on intelligent feedback. A fuzzy control logic operation is performed on an output current flux value and an input voltage fluctuation factor of a load experimental object, so as to obtain the disturbance amplitude of a voltage fluctuation state for a current flux change trend; if a step-up transformer can execute a PID control technology by adjusting a transformation ratio, PID real-time adjustment parameters are calculated, and a crossover mutation operation of individuals is performed on the basis of a fitness function, so as to generate a first boost regulation and control scheme; and if the step-up transformer cannot execute the PID control technology by adjusting the transformation ratio, a calibrated state feedback controller is constructed on the basis of a state variable of the current step-up transformer and an adjustment signal output amplitude, so as to generate a second boost regulation and control scheme. The present invention achieves intelligent feedback regulation and control by independently analyzing components of direct-current high-voltage generators, improving the stability and accuracy of outputting high-voltage direct current.

The invention relates to the technical field of energy storage systems, in particular to a source network load storage coordination control method for multi-configuration network type energy storage coordination operation. According to the technical scheme, the source-network-load-storage coordinated control method for multi-construction-network-type energy storage coordinated operation comprises the following steps: generating a new energy output and load change trend through a dynamic prediction model based on historical power data, real-time load requirements and meteorological information; optimization is carried out to obtain a power generation plan containing a clean energy consumption target and economical efficiency constraints; decomposing the power generation plan into a photovoltaic power adjusting instruction, an energy storage charging and discharging instruction and a load control instruction according to the power grid safety priority, wherein the emergency control instruction is executed prior to the economic dispatching instruction; and controlling parallel operation of a plurality of pieces of networking type energy storage equipment, and realizing multi-machine power distribution and circulating current suppression through a virtual synchronous control technology. And through layered optimization scheduling and multi-machine cooperative control, the new energy consumption capability is remarkably improved, and the phenomenon of abandoning light and wind is reduced.

The invention relates to an offshore energy platform coordinated scheduling method based on multi-energy complementation and hierarchical optimization, which combines multi-energy complementation characteristic modeling, multi-target opportunity constraint optimization and rolling optimization, and realizes offshore multi-energy coordinated scheduling by constructing a hierarchical decoupling optimization and control system. Based on prediction and historical data of multiple types of energy such as offshore wind power, photovoltaic energy and tidal energy, complementarity and flexibility of the energy are quantified, high-quality data support is provided for scheduling optimization, a day-ahead layered optimization model containing renewable energy priority consumption and flexible standby configuration is constructed, and a medium-and-long-term output strategy is formulated. Output of various energy sources is dynamically adjusted through a rolling optimization mechanism, and flexible response to renewable energy fluctuation is achieved. And finally, second-level frequency and voltage support is realized by using a virtual synchronous machine and droop control, and the self-adaptive capability of the system is enhanced. According to the invention, the cooperative regulation capability and operation stability of the offshore platform multi-energy system can be effectively improved, and the dependence on a traditional standby power supply is reduced.

The present application relates to the technical field of power plant optimization, and discloses a thermal power plant auxiliary power system optimized dispatching method and system considering a wind-solar-storage system, and a device and a storage medium. The method specifically comprises: collecting thermal power plant auxiliary power system data, and establishing an auxiliary power system multi-objective function on the basis of the thermal power plant auxiliary power system data and a wind-solar power generation cluster model in an auxiliary power system; introducing constraint penalties and constraint conditions to the auxiliary power system multi-objective function, and constructing a thermal power plant auxiliary power system optimized dispatching model; and processing the thermal power plant auxiliary power system optimized dispatching model by using a dynamic learning factor-based particle swarm algorithm to obtain an optimized dispatching result, and completing thermal power plant auxiliary power system optimized dispatching on the basis of the optimized dispatching result. According to the present application, the optimal interactive output among wind turbine units, photovoltaic units, energy storage units, and a generating set can be determined on the basis of the optimized dispatching result, auxiliary power system low-carbon optimized dispatching is implemented, and the problem in the prior art of lacking dispatching in which new energy and thermal power plant auxiliary loads are integrated for analysis is solved.

The invention discloses an intelligent cable fault diagnosis and positioning method and system, and relates to the technical field of intelligent operation and maintenance of a power system. The method is used for accurately identifying and positioning high-resistance faults and external damage. According to the method, electric field, current, temperature and vibration signals are synchronously collected, a multi-source fusion enhanced signal flow is constructed, and multi-physical field features are extracted; based on a coupling mechanism of an electromagnetic-thermal field and a mechanical-electric field, generating a fault type label and a space coordinate; executing targeted impedance correction for different fault types, establishing a dynamic topology network, and inputting a time-space diagram neural network to output a preliminary positioning result; and multi-source verification is carried out by further fusing salinity dielectric, a harmonic thermal field, a vibration electric field and stress topological information, a high-confidence-coefficient fault positioning result is finally output, a closed-loop diagnosis mechanism is formed, and the fault recognition accuracy and the system adaptability under complex working conditions are improved.

The invention specifically relates to the technical field of big data analysis, and discloses a water conservancy gate multi-parameter cooperative intelligent monitoring system, which comprises a multi-parameter acquisition module, a multi-parameter processing module, a comprehensive analysis module, an intelligent decision module, an operation and maintenance early warning module and a man-machine interaction module, the multi-parameter processing module is used for calculating flood control and discharge indexes, structure safety indexes and equipment health indexes; the comprehensive analysis module is used for judging gate risk levels; the intelligent decision-making module is used for generating an optimal gate scheduling scheme; the operation and maintenance early warning module is used for constructing a multi-level early warning mechanism; according to the method, parameter coverage is comprehensive, a gate digital twinborn model and a gate opening comprehensive evaluation model are constructed, the gate risk level is evaluated, an optimization strategy is dynamically adjusted through an intelligent decision module, the accuracy of gate risk judgment is improved, and the self-adaptive capacity of the system is improved.

The invention relates to the technical field of agricultural internet of things and environment intelligent control, in particular to a greenhouse environment adaptive regulation and control system based on artificial intelligence, which comprises an environment acquisition module used for acquiring multi-dimensional environment data in real time through a distributed multi-source sensor; the central controller is used for generating an optimized regulation and control strategy; the regulation and control execution module is used for driving execution equipment to carry out regulation; the central controller comprises a multi-source data fusion unit, an AI decision-making unit and a dynamic optimization engine which are respectively responsible for data filtering and fusion, generating an initial regulation and control strategy based on a space-time joint AI model, and reconstructing and optimizing the initial regulation and control strategy through a multi-target optimization algorithm. According to the method, the response real-time performance is improved through multi-source sensing and data fusion, predictive regulation and control and multi-parameter cooperation are achieved through the AI model, balance of energy consumption, growth and carbon emission is achieved in combination with multi-target optimization, and long-term self-adaption and strategy iteration of the system are supported.

The invention belongs to the field of artificial intelligence, particularly relates to an intelligent regulation and control system for an injection molding process of an industrial control system, and aims to solve the problem that high-precision cooperative regulation and control are difficult under material batch fluctuation, mold state change and environmental disturbance. The system comprises a multi-source sensing module, a dynamic modeling module, a self-adaptive decision-making module, an execution feedback module and a knowledge evolution module, and high-stability and high-adaptability intelligent regulation and control of the injection molding process are achieved through a mixed digital twin model integrating a physical mechanism and data driving, confidence-guided multi-objective optimization and continuous evolution of a process knowledge graph.

The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The invention discloses a multi-mechanical-arm space-time synchronization control method for coiled pipe welding, and belongs to the technical field of automation control, and the method comprises the steps: in a primary laser and TIG hybrid welding execution stage, carrying out real-time data acquisition on a welding area of a workpiece, carrying out real-time deviation detection, and once real-time welding deviation is detected, carrying out time-space synchronization on the welding area of the workpiece; a local correction mechanism is triggered immediately, and deviation is prevented from being accumulated in the single welding process; after one-time welding is completed, the workpiece enters a transition area, global contour reconstruction is conducted on a welded section of the workpiece through three-dimensional scanning equipment, global deviation recognition is conducted, and unprocessed hysteresis deformation and accumulative errors are covered and corrected in real time; on the basis of the analysis result of the global deviation recognition, double correction is conducted on secondary welding, and cooperative control over space track correction and technological parameter adaptation is achieved; and after secondary welding is completed, the correction effect is evaluated through a double-layer evaluation mechanism, and the stability of the correction process is ensured.

The invention discloses a stamping part forming precision dynamic monitoring system based on digital twinning, and particularly relates to the field of general monitoring and adjusting systems, and the stamping part forming precision dynamic monitoring system comprises a digital twinning modeling module, a twinning simulation prediction module, an intelligent function coupling decision module and a physical execution closed loop feedback module; the digital twinning modeling module collects stamping related data through a multi-source sensor, and constructs and dynamically updates a geometric, physical and behavior three-level digital twinning body after preprocessing; the twinborn body simulation prediction module is based on three-stage twinborn bodies and is combined with parameter initialization, double-source simulation, result fusion and threshold decision to realize pre-judgment of forming precision; the intelligent function coupling decision-making module generates a targeted regulation and control instruction through a basic regulation and control function and a dynamic coupling mechanism according to the precision out-of-tolerance signal and the physical data; and the physical execution closed-loop feedback module completes instruction execution, state perception and model correction through iterative loop, realizes dynamic monitoring of the forming precision of the stamping part, and improves the forming precision stability and the production efficiency of the stamping part.

The invention belongs to the technical field of robot control, particularly provides a force feedback teleoperation control system and method for the tail end of a mechanical arm of a quadruped robot, and aims at the key challenges that control errors are caused by communication time delay and soft obstacles are difficult to recognize in a dynamic environment. Modeling and judgment are conducted on the contact state of the tail end of the mechanical arm in advance, and feedforward control and buffer adjustment oriented to communication time delay are achieved. And meanwhile, a dynamic semantic map is constructed in combination with multi-source sensing information, and soft obstacle reasoning and path optimization are performed by fusing a tail end force sense change trend, so that the recognition and avoidance capabilities of the system in a complex and invisible obstacle environment are remarkably improved. The system has good perspectiveness, self-adaptability and high redundancy safety characteristics, is suitable for multi-task inspection operation of industrial sites such as a thermal power plant, and is especially suitable for a remote man-machine cooperative operation scene in a narrow space.

The invention discloses a self-generating and self-using distributed photovoltaic power station power anti-countercurrent control method and system, and relates to the technical field of photovoltaic anti-countercurrent control, and the method comprises the steps: obtaining the real-time load power of a grid-connected point of a power grid and the output power of a photovoltaic inverter, and calculating the net load power deviation; when the net load power deviation is smaller than a preset countercurrent risk threshold value, it is judged that a countercurrent risk exists, and an anti-countercurrent adjusting instruction is generated; in response to the anti-countercurrent regulation instruction, dynamically correcting the maximum output power limit value of the photovoltaic inverter, and synchronously activating the charge and discharge compensation mode of the energy storage system; an anti-countercurrent control coefficient is calculated, and the power reduction proportion of the photovoltaic inverter and the compensation power of the energy storage system are synchronously adjusted based on the anti-countercurrent control coefficient, so that the power flow of the grid-connected point of the power grid is kept to be zero or forward flow; according to the method, dynamic and accurate cooperation of photovoltaic reduction and energy storage compensation power in time and magnitude is realized, and the efficiency of a self-generating and self-using distributed photovoltaic power station is improved.

The invention relates to the technical field of equipment control, and provides an autonomous tracking anti-interference control method and system.The confidence coefficient of a sensor is determined by combining the historical precision of the sensor and the current signal quality through a sensing recognition module, and the fusion weight is dynamically adjusted based on the confidence coefficient, so that the target tracking state can accurately reflect the actual motion characteristics of a target; the trajectory planning module takes an interference type, an interference degree and a target tracking state as input parameters cooperatively, and combines an extended state space and a reward function containing an anti-interference reward, so that an explored tracking trajectory can actively adapt to an interference scene; meanwhile, the predicted collision probability is compared with a probability threshold value, so that the global updating triggering opportunity is ensured to be accurate, and the collision risk caused by an unreasonable track is effectively avoided; and the control optimization module generates a targeted feed-forward compensation amount according to the interference type and the interference degree, and generates a control instruction after superposing the basic control amount, thereby realizing cooperation of anti-interference compensation and trajectory tracking control.

The invention discloses a V2G-based dynamic discharge power optimization control method, and belongs to the field of smart power grid and electric vehicle cooperative control, and the method comprises the following steps: collecting the battery state information of an electric vehicle and the load demand data of a power grid in real time; determining a demand response priority through a multi-objective optimization algorithm based on the battery state information of the electric vehicle and the load demand data of the power grid; calculating a power distribution strategy scheme based on the response priority; when the power distribution strategy scheme does not meet the battery health constraint, a safe discharge power range is adjusted based on a battery state evaluation result; generating a correction parameter based on the adjusted discharge power range and the environment variable; and monitoring the operation state of bidirectional energy flow in real time, and when the operation state of bidirectional energy flow does not achieve the response effect, adaptively adjusting the power distribution strategy based on the correction parameter and updating the execution parameter.

The invention relates to the technical field of press monitoring, in particular to a digital twinning-based three-dimensional monitoring system for a precision servo press, which comprises a physical layer sensing module for acquiring real-time operating parameters, environment variables and workpiece processing data of the press; the dynamic twin construction module constructs a total-factor digital twin, and simulates a force-heat-deformation coupling effect by using finite element analysis and a multi-body dynamics algorithm based on physical attributes and process parameters; the intelligent analysis center identifies a potential fault mode of the press machine and locates an abnormal source through multi-physics field simulation data in combination with an improved CNN-LSTM model; the three-dimensional visual interaction unit constructs an interactive immersive three-dimensional virtual scene, renders a running state and a processing process in real time, and generates a maintenance strategy; and the self-adaptive regulation and control unit predicts the residual life of the key component and dynamically adjusts parameters according to a maintenance strategy and real-time monitoring data. Therefore, the problems of single monitoring dimension, disjunction of maintenance strategies and the like in the prior art are solved.

The invention discloses a dynamic path planning and self-adaptive control method and system for a coating robot, and relates to the technical field of coating automation. The method comprises the following steps: acquiring point cloud data through three-dimensional scanning equipment, constructing a dynamically updated workpiece curved surface model, and extracting curvature, edge and high-curvature mutation region features; a spraying path is generated based on a reinforcement learning algorithm, and path density, speed and coating supply are dynamically adjusted for a high-curvature area; distance, force feedback and environment parameters are fused, and mechanical arm and spray gun parameters are dynamically adjusted; dividing operation sub-areas and distributing tasks based on robot capability characteristics to realize multi-machine cooperation; and fault redundancy control and multispectral imaging are added to optimize the coating quality. The system comprises a sensing module, a decision-making module, an execution module, a redundancy control module and a communication module. According to the invention, the uniformity of the complex curved surface coating, the multi-machine cooperation efficiency and the system anti-interference capability are improved, and the method is suitable for spraying large workpieces such as aircraft fuselages and high-speed rail vehicle bodies.

The present invention belongs to the technical field of building integrated energy. Disclosed is a virtual energy storage-considered double-layer optimization scheduling method for a building integrated energy system, the method comprising: constructing an energy hub-based low-carbon building integrated energy system containing wind-solar energy storage and energy conversion devices; comprehensively analyzing characteristics of loads of the system to improve the demand response capability thereof; further providing a double-layer optimization model containing an upper-layer energy operator pricing layer and a lower-layer building user optimization layer, building virtual energy storage and building user comfort indicators being considered in said model to improve the system scheduling flexibility so as to construct an overall user satisfaction indicator; and finally, solving the double-layer optimization model to optimize device contributes, demand responses and electricity purchasing and selling plans of the building integrated energy system, so as to obtain an optimal scheduling policy. The present invention can finely regulate and control various loads of the building integrated energy system, thus improving the energy utilization efficiency, alleviating the power supply pressure of the system, and achieving the purposes of energy conservation and emission reduction of buildings.

The invention discloses an accurate flow control method and system for a two-phase cold plate cooling data center, and relates to the technical field of two-phase cold plate cooling, and the method comprises the following steps: S1, collecting multi-mode operation monitoring data in real time, and carrying out the data preprocessing; s2, constructing a multivariable short-time-sequence prediction model, predicting the cooling demand, and performing optimization regulation and control on a cooling demand prediction result; s3, the target regulation and control flow of the cooling liquid is predicted, and flow and cooling execution measures are taken according to the target regulation and control flow prediction result; the opening degree of the valve is accurately adjusted and evaluated in real time, and accurate flow control is achieved; s4, integrating multi-mode operation monitoring data, a cooling demand prediction result, a target regulation and control flow prediction result and a valve opening accurate regulation evaluation result, and constructing a parameter optimization and safety fault-tolerant mechanism; the problems of chip safety and energy consumption risks caused by cold plate temperature overshoot and cooling capacity regulation lag under high-load fluctuation of the server are solved.