2604 results about "Coordinate control" patented technology

The invention discloses a photovoltaic off-grid hydrogen production system and a control method thereof, and the method comprises the following steps: obtaining original data of an environment sensor, carrying out synchronous collection through a distributed data collection system, and obtaining standardized environment data through an STL algorithm, Z-score standardization and wavelet noise reduction processing; carrying out photovoltaic output power prediction by adopting an integrated learning method, and carrying out MPPT control optimization; an electrolytic cell dynamic model is constructed, multi-objective optimization and energy storage system state estimation are carried out, and energy storage optimization scheduling is achieved; and finally, realizing overall optimization of the system through a multi-time-scale coordination control framework. The system operation efficiency is improved, the system reliability is enhanced, the operation cost is reduced, and the system is particularly suitable for an off-grid hydrogen production scene with large photovoltaic energy volatility.

The invention discloses a park energy router multi-agent coordination control method based on a dynamic game model, and relates to the technical field of energy internet optimization control. The method comprises the following steps: according to real-time meteorological data and historical load fluctuation characteristics, identifying multi-energy supply and demand time sequence conflicts, constructing a multi-subject game framework, quantifying dynamic coupling strength of meteorological parameters and load fluctuation, and generating a dynamic constraint boundary; then, combining dynamic constraints and operation states, dynamically adjusting economic, reliability and low-carbon index weights, superposing a risk penalty term of a future supply and demand difference value, and constructing a coordination revenue function; when the photovoltaic output and the thermal load change rate exceed threshold values, an emergency game mode is triggered, Nash equilibrium is solved through a distributed optimization algorithm, and a coordination control instruction is output; and finally, collecting execution data, calculating a supply and demand deviation correction rate and an income balance degree, adjusting a coupling weight and a constraint boundary based on feedback, and realizing multi-subject benefit dynamic balance and stable and efficient operation of the park energy system.

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 discloses a micro-grid dynamic coordination control method and system for distributed energy, and relates to the technical field of micro-grid energy management. The method comprises the steps of 1, collecting multi-source micro-grid data in real time, performing edge calculation and data preprocessing operation, and performing early judgment of an island mode; 2, converging the structure and state of each micro-grid, dynamically constructing and updating a micro-grid topological graph, analyzing the structure change and health condition of the micro-grid topological graph, and evaluating the comprehensive risk of micro-grid nodes; 3, calculating the actual distributable power of each type of loads, and carrying out autonomous control and elastic mode switching; and step 4, on the basis of the actual distributable power of each type of loads, evaluating the collaborative energy scheduling capability of the micro-grid in real time, performing role identification and implementing an optimization strategy. The problem of island emergency scheduling response lag caused by sudden failure of a main network due to some extreme events along with continuous expansion of the access scale of distributed energy and a micro-grid is solved.

The invention discloses a deep sea underwater electro-hydraulic actuator (EHA) and a control system thereof. The system provides power through the power source module, balance of system oil pressure and pressure of external seawater is controlled through the pressure compensation module, oil circulation and oil pressure control are conducted through the integrated valve set module, a control instruction of the control module is executed through the execution mechanism module, and the control module conducts cooperative control over the whole system. According to the control system, the unbalanced pressure between the system and the deep sea high-water-pressure environment is compensated through the pressure compensator so as to reduce the influence of the deep sea high-water-pressure environment, meanwhile, the pressure compensator serves as an oil tank to supply oil, miniaturization of the system is achieved, and the reliability of the system during underwater work is improved; according to the deep-sea underwater electro-hydraulic actuator, the mounting positions and internal flow channels of all elements of the integrated valve bank are optimized, the pressure loss of fluid in the flow channels is reduced, and the energy utilization efficiency of the deep-sea underwater electro-hydraulic actuator is improved; the asymmetric hydraulic cylinder can operate in four working conditions of deep sea, and underwater high-precision movement can be completed.

The invention discloses a multi-type energy complementary island micro-grid coordination control method and system, and relates to the technical field of energy coordination control, and the method comprises the steps: collecting an island micro-grid structure and a predetermined prediction time zone, and carrying out the historical data analysis; performing energy coordination control on the island microgrid based on the energy coordination control scheme; and performing simulation evaluation through a simulation tool. According to the island micro-grid coordination control method for multi-type energy complementation, the island micro-grid structure and the preset prediction time zone are collected for historical data analysis, the accuracy and consistency of data analysis are improved, different power utilization levels are set according to the actual power utilization condition and management requirements of the island, data are arranged according to the time sequence, and the power utilization rate of the island is improved. The power supply reliability and efficiency are improved, power generation prediction is carried out based on the prediction model, the perspectiveness and accuracy of energy management are improved, and better effects are achieved in the aspects of the power supply reliability, the power generation prediction precision and the power generation prediction efficiency.

The invention discloses a humanoid robot whole-body coordination control method based on a mass center dynamical model. The method comprises the steps that system modeling is conducted on a humanoid robot according to the mass center dynamical model; establishing a relationship between arm momentum and centroid momentum based on whole-body kinematics and a momentum conservation principle; a whole nonlinear optimization control problem is constructed and solved in real time, and a controller predicts the motion trail of the robot in a future period of time; feedforward torque is obtained through inverse dynamics calculation, and joint torque is issued in combination with a joint PD controller. The invention designs a nonlinear model predictive control problem for whole-body coordination control of the humanoid robot, reasonably plans a system state, an arm tail end state, a foot end state and a reference trajectory of control input, and designs equality constraints and inequality constraints necessary for whole-body coordination movement of the robot; task trajectory constraint and momentum conservation constraint on the tail end of the arm are added, so that the ability of the arm to assist in motion balance is more prominent.

The invention discloses a distributed energy coordination control method based on multi-agent reinforcement learning, and the method comprises the following steps: S1, constructing a distributed energy coordination control architecture, taking each distributed energy as an agent, and defining a state space and an action space of the agent; s2, economics, environmental protection and reliability indexes in the distributed energy coordination control process are defined, and comprehensive indexes and constraint conditions are given; and S3, constructing a reinforcement learning network, carrying out training, integrating local information and a global target of each agent through a consistency coordination algorithm, ensuring that decisions of all agents can be coordinated and consistent, and obtaining a consistency coordination control strategy of the distributed energy. According to the invention, a deep reinforcement learning algorithm is utilized to realize adaptive coordination control of distributed energy, and the overall operation efficiency and reliability of the system are improved.

The invention belongs to the technical field of distributed resource control of a power distribution network, and particularly relates to a distributed resource cooperative control method based on layering and partitioning autonomy of the power distribution network. The method comprises the following steps: S100, proposing an active power grid layering and zoning control strategy oriented to feeder autonomy; s200, establishing a power distribution network area power regulation and control information model of hierarchical and distributed control; and S300, realizing distributed resource cooperative control based on layering and partitioning autonomy of the power distribution network. According to the distributed resource cooperative control method provided by the invention, the distributed photovoltaic, user load and distributed energy storage coordinated control in the transformer area is realized by combining a feeder-autonomous-oriented active power distribution network layered and partitioned control strategy and utilizing the edge computing capability of the transformer area intelligent fusion terminal, the distributed photovoltaic absorption capability is improved, and the distributed resource utilization rate is improved. Energy supply and demand imbalance caused by distributed photovoltaic randomness is stabilized, and the economic operation level of a power distribution area is improved.

The invention relates to a shared energy storage virtual inertia compensation control method for wind power cluster collection, and belongs to the technical field of power system control. In order to solve the problem of insufficient cooperative control of a wind power system and an energy storage system in the prior art, a dynamic model is established by obtaining operation parameters, and the dynamic model comprises a wind turbine generator active power control model and an energy storage system charging and discharging model; designing a virtual inertia control strategy, and introducing virtual inertia control to simulate inertia response characteristics of the synchronous generator; shared energy storage coordination control is carried out, and a strategy is designed by using an MPC method according to an inertia demand and an SOC state; establishing a frequency dynamic response model and calculating inertia response time as a virtual inertia control start-stop condition; and optimizing controller parameters and designing locking logic by adopting an intelligent algorithm. The method can improve the system performance and efficiency, enhance the frequency stability, improve the energy utilization efficiency of the energy storage system, optimize the system performance, and improve the system stability and reliability.

Provided is a robot posture optimization and follow-up control method and system for freeform surface repair. The system includes a worn surface reverse modeling module, a worn surface initial normal vector calculation module, an initial machining trajectory and normal vector extraction module, a normal vector rotation angle polynomial regression denoising module, a machining trajectory position and posture NURBS fitting module, a position and posture trajectory curvature adaptive segmentation module, a segmented trajectory position and posture synchronization acceleration and deceleration look-ahead module, and a robotic motion and laser process coordinated control module. The method and the system realize posture optimization and follow-up control in a cladding process of the robot, and ensures the processing quality of laser repair.

The invention provides a multi-agent control system based on a large language model. The system comprises an analogue simulation subsystem and a coordination control subsystem. The analogue simulation subsystem comprises a data acquisition module, an initial reward function generation module, an error correction module, a dense reward function generation module and a strategy network updating module, wherein the data acquisition module is used for acquiring a multi-agent reinforcement learning training code; the initial reward function generation module is used for generating an initial reward function code; the error correction module is used for generating executable reward function codes; the dense reward function generation module is used for generating dense reward function codes; and the strategy network updating module is used for obtaining the strategy network with the maximized reward. The coordination control subsystem comprises a data collection module and a strategy distribution module; wherein the data collection module is used for receiving observation data; and the strategy distribution module is used for guiding multiple agents to execute a control task by adopting a strategy network generation action for maximizing rewards based on the observation data.

A low earth orbit satellite (LEO)-based communication system and method. The system comprises a low-orbit satellite constellation, a ground station, a user terminal and a network management and control center. The network management and control center comprises a track and link prediction module, a link quality prediction and evaluation module, a predictive resource pre-allocation and dynamic adjustment module and a space-ground cooperative control module. According to the communication system, active resource optimization and dynamic adjustment before actual switching are realized through track and link prediction, link quality prediction, predictive resource pre-allocation and dynamic adjustment and air-ground cooperative control. According to the method, the switching time delay can be greatly reduced, the packet loss rate is remarkably reduced, and the system throughput and the access success rate are also remarkably improved. Through predictive management, the performance, reliability and user experience of LEO communication are comprehensively improved.

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 provides a big data intelligent decision analysis method and system. The method comprises the following steps: acquiring vibration data and mechanical node stress distribution data in power equipment transportation, and extracting high-frequency harmonic distortion characteristics aligned with a vibration data timestamp; performing cross-domain correlation analysis on stress data fluctuation and harmonic characteristic dynamic change, and identifying a symbiotic mode of mechanical stress abnormality and electrical harmonic distortion by using a machine learning model; based on logistics path planning and equipment constraints, a cooperative control instruction for adjusting the transportation path and the power supply loop parameters is generated through a multi-target cooperative optimization algorithm; and constructing a joint risk assessment matrix, and dynamically adjusting the cooperative relationship between the transportation path and the harmonic suppression parameter according to the quantitative analysis result and the cooperative control instruction to realize the closed-loop decision of the power equipment. According to the technical scheme provided by the invention, the power equipment transportation data processing efficiency is remarkably improved.

The invention relates to a construction method of a cooperative combat decision-making agent for ground unmanned equipment, and aims to solve the problem of space dimension disaster of a complex cooperative combat task. The DRL solution space is effectively compressed, the exploration efficiency is improved, and meanwhile intelligent decision making of tasks with different granularities in the whole combat process is achieved; aiming at the problem of generating a'task layer 'combat command action strategy, a cooperative combat decision-making agent network model is constructed to realize the unified coordination control from the effective representation of complex state input to the combat action; by constructing a distributed training framework, the sampling and training efficiency is improved, and high-throughput asynchronous parallel training of a cooperative combat decision-making agent network model is realized; by establishing a combat decision agent learning algorithm based on deep reinforcement learning and solving the combat decision agent learning algorithm, the learning efficiency and final performance of an agent network model are effectively improved.

The invention relates to a humanoid robot double-arm body operation method based on reinforcement learning, belongs to the field of robot cooperative control, and is characterized in that a strategy of trajectory block prediction and time integration fusion is used for double-arm cooperative control, and continuity and stability of double-arm operation are improved; in reinforcement learning control, a three-dimensional pose track generation and correction module is introduced, condition generation and denoising correction of a three-dimensional space are carried out on a track block layer, and geometric consistency and naturalness of a generated track are guaranteed; the invention further provides a reinforcement learning optimization framework and a simulation-reality migration process, and through system integration of reward, value guidance and migration processes, strategy deployability and safety are guaranteed; compared with the prior art, the method has the advantages that the naturalness, the collaboration and the success rate of double-arm operation can be remarkably improved, the generalization ability is high, and the good simulation-to-reality migration ability is achieved.

The invention relates to the technical field of wind power integration control, and discloses a wind power integration system safe operation method aiming at subsynchronous oscillation and a system thereof. The method comprises the following steps: performing time-frequency feature extraction processing on an original data set to obtain an oscillation mode feature quantity; performing stability analysis processing on the oscillation modal characteristic quantity to obtain a subsynchronous oscillation risk early warning signal; performing multi-time-scale decoupling processing on the sub-synchronous oscillation risk early warning signal to obtain a mechanical-electrical coordination control instruction; performing task decomposition processing on the coordination control instruction to obtain a real-time control quantity of each execution mechanism; and performing evaluation processing and correction on the execution effect of the real-time control quantity of each execution mechanism to obtain a correction control parameter set, and performing real-time control on the wind power integration system through the correction control parameter set. According to the invention, coordinated control of the electrical subsystem and the mechanical subsystem of the wind power grid-connected system is realized, subsynchronous oscillation is effectively suppressed, and the operation stability of the system is improved.

The invention relates to an injection mold temperature control method and device and computer equipment. The method comprises the following steps: acquiring temperature data of a plurality of temperature measuring points of the injection mold to obtain a temperature data matrix; establishing a heating and cooling input matrix and a temperature response matrix, and solving to obtain a heat transfer path coefficient matrix; taking the temperature data matrix as state input, constructing a depth deterministic strategy gradient control network comprising a gating circulation unit and a full connection layer, and performing linear mapping to obtain a hybrid action space; inputting the heat transfer path coefficient matrix and the mixed action space into a training environment, performing iterative training on a depth deterministic strategy gradient control network based on an experience playback mechanism, and updating network parameters by calculating a reward function to obtain a temperature control model; and deploying the temperature control model to a target control system, and calculating a real-time control parameter set. According to the invention, coordinated control of the heating stage and the stabilizing stage is realized, the injection molding period is shortened, and the energy consumption is reduced.

The invention relates to the technical field of chip stacking, and discloses an intelligent temperature control method and device of a multi-chip stacking structure and computer equipment. The method comprises the following steps: acquiring target temperature data of each layer of chip in a multi-chip stacking structure, and establishing a nonlinear temperature response model; constructing a hierarchical control framework, and inputting the target temperature data into an agent auxiliary temperature control algorithm for processing to obtain temperature distribution characteristic data; performing optimal sampling analysis to obtain local temperature regulation and control parameters; constructing a temperature-flow multi-stage coordination control model, and generating a cooling liquid flow distribution scheme and a flow direction control instruction; the flow and temperature of cooling liquid are controlled, gradient cooling treatment is conducted on the temperature abnormal area, and temperature adjusting process data are obtained; and carrying out multistable distribution analysis to generate a temperature control optimization strategy. According to the invention, the problem of temperature balance in a multi-chip stacking structure is effectively solved.

The invention relates to the technical field of power grid coordination control, and provides a coordination control method for a smart power grid, which comprises the following steps: S1, collecting voltage, current, frequency and load data in real time by deploying sensors and monitoring equipment, S2, processing the data by using a big data analysis technology, extracting load trend and fault early warning information, and carrying out early warning on the load trend and the fault early warning information. Real-time data collection is realized by deploying sensors and monitoring equipment, data analysis and load prediction are performed by using big data and a machine learning technology, an optimized objective function is constructed, fine coordination and a global optimal solution are realized by using a multi-agent system and a distributed optimization algorithm, and meanwhile, energy privacy is protected. The reliability of the power grid is improved through intelligent fault detection and a quick response mechanism; according to the technical scheme, interaction between the power grid and users is enhanced, demand side management is achieved, an efficient safety monitoring system is established, multi-level protection measures are taken, safe and stable operation of the power grid is ensured, and the problem that optimization and coordination are not comprehensive in the prior art is solved.

The invention discloses an intelligent control type expansion coating preparation reaction kettle device which comprises a reaction kettle body, the interior of the reaction kettle body is divided into a plurality of independent stirring cabins through partition plates, each stirring cabin is internally provided with an independent stirring assembly, a feeding assembly and an observation assembly, and each stirring assembly comprises a stirring shaft, stirring blades and a driver; the stirring blade is provided with a semiconductor chilling plate and a heating element, and the feeding assembly comprises a plurality of feeding channels connected with the stirring cabin, is provided with a flow meter, an electromagnetic valve and a dynamic adjusting module, and is used for independently controlling the feeding amount and the feeding speed of each cabin; the control system comprises a multi-channel data acquisition module, a learning module and a coordination control module, the multi-channel data acquisition module is provided with a sensor network for monitoring the temperature, the pressure and the material state of each stirring cabin in real time for each stirring cabin, and the control system performs coordination control on the stirring cabins according to preset parameters or autonomous learning results. And the stirring speed, temperature and feeding strategy of each cabin are dynamically adjusted.

The invention relates to the technical field of production line control, and discloses an intelligent control method and system for production line mold machining. According to the method, a pressure distribution state is determined by obtaining initial cutting parameters of a cutter and surface pressure data of a mold, mold node stress distribution is calculated by utilizing a three-dimensional finite element model, and a stress concentration position is identified. And predicting a mold deformation trend through a linear regression model according to the thermal expansion coefficient and the environment temperature data. Meanwhile, the initial cutting parameters are optimized by combining a cutting parameter optimization objective function, and the objective function comprehensively considers factors of tool wear, surface quality and residual stress. And on the basis of a die deformation prediction result, a dynamic compensation algorithm is adopted to adjust supporting structure parameters and optimize cutting parameters, and finally optimal cutting parameters and a target tool path are generated. According to the method, precise coordination control over pressure distribution and mold deformation under the complex working condition is achieved, the residual deformation risk is reduced, and the mold machining precision is improved.

The present invention relates to a multi-region collaborative control method and device for a high-precision pressure sensor array. The method: based on a first collaborative sampling parameter, data is collected from multiple sensor units in a pressure sensor array to obtain a pre-processed pressure data set and a sensor unit spatial position matrix; a spatial weighted pressure covariance matrix is ​​constructed; eigenvalue decomposition and information entropy calculation are performed, and the number of target pressure points is determined according to the minimum description length criterion and the information gain ratio; a pressure feature subspace and a local perception domain are constructed; the pre-processed pressure data set is projected onto the pressure feature subspace to generate a sensor unit state data set; a multi-layer attention encoder is input for processing, and the first collaborative sampling parameter of the pressure sensor array is adjusted to obtain a second collaborative sampling parameter. The implementation of the present invention improves the system's response sensitivity to local pressure changes, thereby realizing multi-region collaborative control of pressure sensing.

The invention discloses an unmanned aerial vehicle cluster collaborative target hunting method based on brain-like calculation, and belongs to the technical field of autonomous navigation robot and multi-robot coordination control, and the method comprises the steps: obtaining a multi-view image under a camera view, and obtaining the local pose and covariance of each unmanned aerial vehicle; obtaining a global consistent relative pose of the unmanned aerial vehicle cluster according to the local pose and the covariance of each unmanned aerial vehicle; performing state estimation on the target by adopting an unscented Kalman filtering algorithm to obtain target state information under a unified coordinate system; using a Bezier curve generation algorithm to obtain a target motion trail in a section of historical state; the method comprises the following steps: expanding an unmanned aerial vehicle cluster through a centroid extension method to obtain a surrounding queue of target surrounding, solving the minimum surrounding cost by adopting a Gaussian Newton method, and generating a surrounding position of each unmanned aerial vehicle; the motion trajectory of the unmanned aerial vehicle is generated by adopting mixed A * search, and a final surrounding trajectory sequence is generated, so that the accuracy of motion state estimation is remarkably improved.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

The invention relates to a boiler combustion optimization control system of a thermal power plant, relates to the technical field of thermal power generation control, and aims to solve the problems of inaccurate multi-source data perception, slow control response and insufficient multi-target collaborative optimization capability under fire coal quality fluctuation and load change. The system comprises a data perception and fusion module, a working condition self-adaptive identification module, a multi-target dynamic optimization decision module and a distributed execution control module, and through multi-source data acquisition and feature extraction, depth time sequence mode identification, multi-target optimization model solving and distributed coordination control, the multi-target dynamic optimization decision module and the distributed execution control module are subjected to multi-source data fusion. Dynamic balance of boiler heat efficiency improvement and nitrogen oxide emission reduction is achieved, and adaptability and control precision of the system under complex working conditions are enhanced.

The invention discloses a hierarchical coordination control system and method for an AC / DC hybrid micro-grid group, and relates to the technical field of smart grids and micro-grids, the system comprises a dynamic partition aggregation unit, a flexible interconnection control unit and a wide-area coordination control unit. The method is advantaged in that firstly, operation efficiency and reliability of the micro-grid group are substantially improved, stable supply of electric energy is guaranteed, secondly, the system has high flexibility and adaptivity, dynamic partition aggregation and flexible interconnection control can be carried out according to different source load scene characteristic parameters, various complex conditions can be effectively handled, response speed and stability of the system are improved, and the method is suitable for large-scale popularization and application. And finally, the implementation of the system also contributes to reducing the operation cost, realizes double improvement of economy and reliability by optimizing energy scheduling and energy storage management, and lays a solid foundation for sustainable development of the microgrid group.

The invention discloses a processing control method and system for a carbon fiber composite transmission shaft, and relates to the technical field of intelligent transmission shaft manufacturing, and the method comprises the steps: dynamically adjusting the control current of a magneto-rheological damper through a PID controller based on a process control parameter set, and recording tension fluctuation and temperature gradient data; inputting tension fluctuation and temperature gradient data into an ANSYS coupling simulation engine, establishing a three-dimensional temperature field model, calculating a resin flow compensation coefficient, generating a temperature-pressure cooperative control curve through an NSGA-III multi-objective optimization algorithm based on the resin flow compensation coefficient, and calculating the resin flow compensation coefficient and the temperature-pressure cooperative control curve according to the resin flow compensation coefficient and the temperature-pressure cooperative control curve. And integrating through a time sequence control engine to generate a solidification control instruction. According to the invention, a multi-source data fusion algorithm is combined with the industrial Internet of Things edge computing gateway, so that accurate monitoring and synchronous processing of the process control parameter set are realized, and the dynamic behavior of the carbon fiber composite material in the processing process is comprehensively reflected.

The embodiment of the invention discloses a double-truck-crane cooperative hoisting monitoring method and device, electronic equipment and a storage medium. The method comprises the steps that component parameters and operation environment parameters of hoisting operation are collected, a reference data model is constructed, and an action expected value and a safety control threshold value are set; state acquisition devices are arranged on the two truck cranes to acquire multi-source data in real time and transmit the multi-source data to a central control system so as to realize synchronous sensing of the two trucks; processing multi-source data based on a data fusion algorithm, and constructing a dynamic attitude model and a collaborative operation state model; a state prediction correction value in the model is compared with the reference data model in real time, deviation is judged, and safety early warning is triggered; and generating a feedback control instruction, dynamically adjusting the hoisting operation, correcting the coordination deviation, and realizing the coordination control of the hoisting posture. In this way, through multi-source data fusion and real-time feedback control, the safety and coordination of cooperative lifting operation of the double truck cranes are remarkably improved.