5773 results about "Feedback control" patented technology

The invention discloses a swivel bridge attitude monitoring system based on dynamic feedback control, and belongs to the technical field of bridge construction. Comprising the following modules: a multi-source sensor integration module for constructing an omnibearing attitude monitoring network and realizing high-precision real-time monitoring of key parts of a bridge; the edge calculation and data fusion module realizes real-time on-site processing and fusion of sensor data, effectively reduces data transmission delay and improves monitoring stability; the multi-physics field coupling modeling module is used for constructing a multi-physics field coupling model comprising structural mechanics, a temperature field, contact friction and fluid action, and realizing accurate prediction of the attitude change of the swivel bridge; the self-adaptive control strategy module is used for realizing high-precision self-adaptive control on the attitude of the swivel bridge; the grading early warning and cooperative control module automatically matches a corresponding control strategy and realizes risk grading response and intelligent cooperative control; and the digital twinning interaction verification module is used for constructing a high-precision digital twinning model of the swivel bridge.

The invention discloses a communication engineering construction dynamic optimization method based on multi-dimensional perception, and the method comprises the steps: collecting the multi-dimensional perception data of a construction site in real time through a multi-source heterogeneous sensor network, including environment parameters, equipment operation states, construction progress and personnel behavior data; performing space-time alignment processing on the multi-dimensional sensing data, and constructing a space-time associated dynamic construction digital twinborn body; establishing a dynamic optimization model based on a reinforcement learning algorithm, taking construction efficiency maximization, resource loss minimization and safety risk minimization as target optimization functions, and embedding risk constraint conditions; inputting the dynamic construction digital twin into the dynamic optimization model, and outputting a multi-dimensional parameter optimization instruction set comprising equipment scheduling parameters, construction path parameters and resource configuration parameters; and performing real-time performance evaluation on the multi-dimensional parameter optimization instruction set through edge computing nodes, and dynamically adjusting weight parameters of the model to form closed-loop feedback control. The purpose of cooperatively improving the construction efficiency, the safety and the economical efficiency is achieved.

The invention discloses a numerical control machine tool operation control system and a double-station sawing and drilling integrated machine tool, and relates to the technical field of numerical control machine tool operation. A multi-axis linkage state, a gas cylinder stress state, vibration frequency, cutting force and deformation data are acquired in real time through a data acquisition module; the dynamic modeling module constructs a digital twinborn model by using finite element analysis and real-time sensing data, and accurately simulates stress dynamic change and deformation trend; the intelligent error compensation module dynamically evaluates the multi-axis linkage precision by analyzing the machining data, generates an error compensation instruction and optimizes the operation parameters of the machine tool; the closed-loop feedback control module adjusts the cutting speed, the feeding depth and the clamping force in real time through a fuzzy logic algorithm, a closed-loop adjusting mechanism is formed, the machining precision and stability are greatly improved, the machining error and the workpiece surface damage risk are reduced, and the machining quality and efficiency of complex workpieces are effectively improved.

The invention relates to a welding quality detection system which comprises a process monitoring layer configured with a multi-mode sensor array and used for collecting original performance data in the welding process in real time; the intelligent analysis layer is provided with a multi-source data fusion module and a defect prediction module, and the multi-source data fusion module adopts an improved D-S evidence theory algorithm to perform fusion processing on the original performance data collected by the process monitoring layer to obtain multi-dimensional performance data; the defect prediction module analyzes and captures abnormal data according to the multi-dimensional performance data, dynamically predicts a defect development trend and outputs defect prediction information; the decision execution layer is provided with a self-adaptive control module and is used for dynamically adjusting welding process parameters according to the defect prediction information output by the intelligent analysis layer; and the data communication bus is used for realizing real-time data interaction and closed-loop feedback control among the process monitoring layer, the intelligent analysis layer and the decision execution layer. The method has the effect of effectively improving the detection precision and the detection efficiency.

The invention relates to the technical field of large language models, and discloses a voice data interaction feedback control processing method based on a large language model. The method comprises the following steps: acquiring an industrial voice instruction through an AMR main controller, and obtaining a standardized vector through industrial lexicon matching and intention classification; inputting an industrial large language model for reasoning processing, and generating an AMR execution scheme; carrying out distributed coordination and task allocation on the AMR cluster to form a control instruction sequence; and the motion controller executes monitoring, processes exceptions and outputs a feedback strategy. Accurate classification and semantic understanding of complex industrial instructions are achieved, the processing capacity of professional knowledge in the industrial field is improved, and meanwhile high-precision real-time state monitoring is achieved.

The invention relates to the technical field of bridge engineering, in particular to a cable structure bridge design method adopting a BIM (Building Information Modeling) model, which comprises the steps of three-dimensional bridge foundation model construction, cable structure parameterization configuration, nonlinear mechanical simulation, construction error modeling, feedback optimization and the like. Through introduction of structured geological data and NURBS curved surface reconstruction, a geological-structure integrated model is realized. Adopting a genetic algorithm and a particle swarm optimization method to intelligently configure pile foundation and cable parameters; a construction error closed-loop adjustment mechanism is constructed through dynamic simulation and real-time tension feedback control; and finally, parameter correction write-back and BIM delivery model integration is realized. According to the invention, the design precision, the construction stage adaptability and the digital delivery integrity of the cable structure can be improved, and the method is suitable for cable structure bridge engineering with complex geology and high-precision control requirements.

The invention relates to the technical field of liquid cooling servers, and discloses a liquid cooling server safety management system and method, and the method employs a multi-mode sensor network to synchronously collect the thermodynamic parameters of a liquid cooling system at 100 Hz, and constructs a three-dimensional thermal field digital twinborn model after the processing of an extended Kalman filtering algorithm. A distributed cooling strategy is designed based on a federated learning framework, and each node locally trains a thermal dynamic prediction model and is optimized by a central aggregator. A dynamic control instruction is generated by using a near-end strategy optimization algorithm, and cooling liquid flow distribution is optimized in combination with a quantum derivative simulated annealing algorithm. And designing a dual-threshold phase change control mechanism, establishing a block chain log to ensure traceability and tamper resistance of the instruction, and realizing closed-loop feedback control through a CAN bus. The system and the method can accurately monitor and intelligently control the liquid cooling system, improve the heat dissipation efficiency, reduce the power consumption, and guarantee the data safety and the system stability.

The invention relates to the technical field of safety monitoring, in particular to a bridge safety monitoring system based on sensor data. The method comprises the steps that a sensor measuring unit monitors key physical parameters of a bridge in real time; the data acquisition and transmission unit reads sensor data and transmits the read sensor data; the data processing and analysis unit extracts key features reflecting the health state of the bridge in the sensor data based on a multi-modal physical perception feature fusion method, identifies an abnormal mode in the sensor data through a support vector machine model, and evaluates the remaining life of the bridge based on a carbonization-corrosion-crack closed-loop feedback control rule; and the information management unit stores the analysis result of the data processing and analysis unit and displays the monitored bridge physical parameters. According to the design of the invention, a multi-modal physical perception feature fusion method is introduced, and adversarial learning, graph wavelet transformation and a three-dimensional convolutional neural network are combined, so that high-precision feature extraction of bridge structure response is realized.

The invention relates to the technical field of semiconductor manufacturing, and discloses a semiconductor wafer etching method with a detection function. The method comprises the following steps: acquiring initial image data of a wafer surface through an optical detection device, and extracting geometric features and defect distribution information; processing through a multi-scale feature fusion algorithm, identifying an abnormal region and classifying defect types; dynamically adjusting process parameters of etching equipment according to defect conditions, and planning a self-adaptive etching path; during etching, a real-time monitoring module is used for collecting data, and etching parameters are corrected through a feedback control mechanism; and after etching, the etching precision is secondarily detected and verified by means of a multispectral imaging technology. According to the method, defects can be accurately positioned, the etching process is optimized, the etching precision and quality are effectively improved, the rejection rate is reduced, and the efficiency and reliability of semiconductor wafer manufacturing are improved.

The invention discloses a material-based laser scanning system and method, and relates to the technical field of industrial automation, and the system comprises the following components: a laser scanning unit, an environment sensing sensor group, a dynamic compensation and calibration module, a feedback control unit and a visual monitoring interface. According to the invention, the environment change of a scanning site is monitored in real time through the integrated environment sensing sensor group, and the laser scanning data is processed in real time by using an advanced dynamic compensation and calibration algorithm, so that the measurement deviation caused by environmental factors such as vibration, temperature change and dust interference can be effectively reduced; the measurement precision of laser scanning in complex environments such as a coal yard is remarkably improved, meanwhile, the stability of long-term measurement is ensured by continuously optimizing and calibrating model parameters and automatically adjusting laser scanning parameters, and reliable data support is provided for material management.

The invention relates to the technical field of workshop scheduling, in particular to a multi-online collaborative scheduling method and system for a flexible intelligent processing production line, and the method comprises the steps: building a numerical control parameter model for processing equipment, decomposing a processing program into basic process instruction units, combining the numerical control parameter model and the real-time operation state of the equipment to analyze the adaptation degree of the basic process instruction unit and the equipment, and generating a preliminary task allocation scheme; constructing a distributed control network among the devices, generating a local task sequence of each device based on the preliminary task allocation scheme and the adaptation degree, and obtaining a final task allocation scheme and a task execution plan set based on a contract network protocol algorithm; establishing a multi-constraint collaborative framework, and generating a collaborative production scheduling scheme under the multi-constraint collaborative framework; and establishing a heterogeneous equipment motion cooperative control model, and performing multi-level adjustment through event-driven feedback control. According to the invention, flexible cooperative scheduling of heterogeneous equipment can be realized.

The invention discloses a dissimilar metal laser welding device based on swing light beams and molten pool state monitoring. The dissimilar metal laser welding device aims at improving the welding quality and the joint stability. The device comprises a laser welding head with a light beam swinging function, and the laser welding head can implement nonlinear energy scanning in a welding area according to a preset track type, frequency and amplitude; the acquisition module can synchronously acquire a visual image, an excitation spectrum and an infrared thermal imaging signal of the molten pool at a high frame rate, and extracts interface diffusion and metal mixing characteristics through multi-modal fusion; the calculation module performs dynamic feature modeling according to the collected physical feature information and the swing parameters, and quantifies multi-dimensional state indexes of the welding quality; the control module carries out combined adjustment on the laser power, the welding speed and the swing parameters according to the state indexes, closed-loop feedback control is constructed, and therefore real-time stable regulation and control and defect suppression in the dissimilar metal welding process are achieved.

The invention relates to the technical field of robot autonomous navigation and control, and discloses a robot system based on a sensing system, which comprises a multi-mode sensing module for outputting data to a dynamic environment modeling module; the dynamic environment modeling module is used for transmitting the model to the path planning module; the path planning module outputs the path sequence to the neural motion control module; the neural motion control module outputs the control signal to the dynamic execution module; the dynamic execution module is used for receiving the pulse control signal and driving each joint of the robot to execute the path in combination with inverse kinematics solution and a feedback control mechanism; and the collaborative optimization module is in two-way communication with the modeling module, the path planning module and the neural motion control module. According to the method, the contradiction between real-time performance and integrity of environment modeling in a complex scene is solved by fusing cross-modal information complementary characteristics of binocular vision, millimeter-wave radar and inertial data and combining mathematical representation of a hypergraph topology model on a dynamic obstacle interaction relationship.

The invention discloses a task scheduling optimization and feedback control method and system based on a causal graph structure, and relates to the technical field of task scheduling, and the method comprises the steps: integrating multi-source information, identifying an entity, constructing a directed edge connection entity, calculating an edge weight, and forming a task causal graph; performing deep analysis on the causal graph, and identifying risk nodes and propagation paths thereof in the current project; combining the edge weight and the node attribute, quantifying the risk propagation possibility and influence range, and generating a risk thermodynamic diagram; on the basis of the identified risk nodes and propagation paths thereof, mining controllable variables in a task scheduling process, constructing a scheduling optimization objective function, searching a scheduling solution space under constraint conditions by using a search method, and evaluating the influence of different strategies on a task network through multiple rounds of simulation, so as to improve the task scheduling efficiency. A current optimal task adjustment strategy is screened out and practically applied; and collecting data generated in a project execution process, and updating the causal diagram. The method is suitable for intelligent scheduling and fine management scenes of projects.

The invention relates to the field of robot motion, in particular to a multi-modal information fusion robot motion planning method, which comprises the following steps: information acquisition: acquiring multi-modal data of a robot through various sensors; preprocessing: carrying out normalization, denoising and synchronization processing on the multi-modal data; fusion: coding and fusing the preprocessed data by adopting a feature level fusion method, and generating an environment model based on an information fusion algorithm; motion planning: combining an environment modeling and path planning algorithm, and generating a motion path according to the fused multi-modal information; and feedback adjustment: dynamically adjusting the motion track by adopting a feedback control mechanism based on the environment change sensed in real time. The invention provides a multi-modal information fused robot motion planning method and equipment, which are used for improving the perception capability of a robot to a complex environment, enhancing motion planning and precision and improving task execution efficiency and safety by fusing multi-modal information.

The invention discloses a power efficiency optimization method based on intelligent load management. The method comprises the following steps: S1, generating an initial load characteristic matrix; s2, decomposing the initial load characteristic matrix into a short-term behavior characteristic matrix and a long-term trend characteristic matrix through pseudo-symmetry decoupling; s3, multi-level load demand prediction is carried out through a fusion structure of the multi-scale convolution and the recurrent neural network; s4, coding a cognitive gene sequence, triggering gene mutation and recombination operation when significant change occurs, and generating a load distribution strategy; s5, a load distribution strategy is transmitted into a causal reasoning and self-supervision feedback control module, and if the deviation between feedback data and a prediction result exceeds a threshold value, a self-learning mechanism is triggered; s6, dynamically distributing the load priority, and generating a decision path of load management; and S7, performing adaptive optimization through a multi-layer feature feedback closed-loop optimization system. The intelligent load management method is adopted, and the power efficiency is optimized through multi-layer feature decoupling and closed loop.

The invention discloses a multi-welding-robot collaborative operation system based on digital twinning, and belongs to the technical field of intelligent manufacturing and robot control, the system comprises a physical space module, a virtual space module, a twinning data layer and a control module, and communication connection is established among the physical space module, the virtual space module, the twinning data layer and the control module; the physical space module comprises a plurality of welding robots, a sensor group and a data transmission network; the virtual space module comprises a multi-welding robot twinning body and a workpiece twinning model; the twin data layer is used for connecting the physical space module and the virtual space module; and the control module generates a welding path optimization scheme, a multi-welding-robot collaborative collision avoidance strategy and a welding quality feedback control instruction based on the twin data layer. According to the method, a closed-loop control framework of'physical-virtual 'bidirectional mapping is constructed, so that dynamic collaborative optimization of multi-robot motion trails, welding seam forming and thermal deformation is realized.

The invention belongs to the technical field of intelligent equipment fault diagnosis, and particularly relates to a motor fault real-time diagnosis method and system based on LSTM and random forest. The method comprises the steps that a vibration signal, a current signal and a temperature signal of a motor are collected and preprocessed; performing feature engineering, extracting time domain, frequency domain, time-frequency domain and cross-modal correlation features, and determining an optimal static feature subset through a hybrid screening strategy; constructing a hybrid fault prediction model comprising a random forest model and an LSTM sequential network model; fusing prediction results of the two models by adopting a dynamic credibility weighted fusion mechanism; and performing real-time decision and hierarchical feedback control based on a fusion result. According to the method, advantages of multi-source information and the model are fused, the classification precision of the lag type is remarkably improved, real-time fault diagnosis and active protection are realized, the equipment maintenance cost is reduced, and the method is suitable for motor health management of intelligent agricultural equipment such as mowers.

The invention relates to the technical field of electrical design and intelligent optimization calculation, and discloses a capacitor structure design optimization method and system and a storage medium. The method comprises the following steps: carrying out modeling processing on capacitor structure parameters, and dividing a parameter space to obtain an initial model set; obtaining a multi-physical field simulation model according to the geometric model and the material attributes; obtaining performance indexes such as capacitance value, heat distribution and stress based on the simulation model; setting a target function and constraint conditions, and operating an optimization algorithm to obtain an optimal solution set; performing feedback control processing on an optimization result, and analyzing a convergence path to obtain a structure parameter; according to the invention, the execution efficiency of capacitor structure design optimization is improved, and the consistency and stability of the optimization process and the performance prediction result are improved.

The invention provides an industrial robot control system and method. The system comprises a trajectory planning module, a dynamic compensation module, a feedback control module, an execution module, a sensor module and a data storage module. The trajectory planning module generates an expected trajectory of an end effector of the robot; the dynamic compensation module calculates a dynamic compensation torque based on the expected trajectory and a robot dynamics model; the feedback control module generates feedback control torque according to the deviation between the real-time pose data acquired by the sensor module and the expected trajectory; the execution module drives robot joints to move according to the synthesized moment of the dynamic compensation moment and the feedback control moment; the sensor module collects robot joint angle, angular velocity and torque data in real time; the data storage module stores robot dynamic model parameters, control algorithm parameters and historical motion data. High-precision and smooth robot motion control can be achieved, the dynamic response and stability of the system are improved, and energy consumption and motion errors are reduced.

The invention relates to the technical field of power generation equipment control, and discloses an intelligent control method and system for a hydroelectric generating set speed regulator, and the method comprises the steps: employing an introduction variable structure and variable parameter control technology, and dynamically adjusting the control parameters of a power closed loop and an opening closed loop; real-time calculation and feedback control are carried out, and a speed regulator responds to a power grid frequency modulation Operating data of key parts of the speed regulator are collected in real time through a sensor, feature extraction and mode recognition are conducted on the data in combination with an artificial intelligence algorithm, and the operating state and potential faults of the speed regulator are recognized; constructing a fault tree model based on the speed regulator state monitoring parameters, carrying out hierarchical analysis on the speed regulator, and positioning the specific position and reason of the fault; and verifying and optimizing the fault diagnosis result, and generating a targeted fault processing suggestion. The system comprises a dynamic adjustment module, a state evaluation module and a fault analysis module. According to the invention, the operation efficiency and safety of the hydroelectric generating set speed regulator in a complex power grid environment are obviously improved.

The invention relates to the technical field of optical engineering, in particular to an AI silicon carbide lens-oriented aspheric laser polishing precision online detection and feedback system. Comprising a detection module; the data processing module is electrically connected with the detection module and used for constructing a lens surface three-dimensional model and calculating polishing precision parameters; the feedback control module is electrically connected with the data processing module, is used for generating a feedback control strategy and comprises a strategy generation unit and a parameter optimization unit; an execution module; and an intelligent learning module. According to the design, a laser measuring head, an energy sensor array and an environment sensor are integrated through the detection module, synchronous acquisition of multi-dimensional data such as surface profile, laser energy distribution and environment parameters is realized, and three-dimensional point cloud data with consistent time and space are generated through multi-modal fusion processing (time domain synchronization, space registration and feature enhancement) of the data preprocessing unit; the physical essence of the polishing process is completely represented, and the detection comprehensiveness and the data relevance are improved.

The invention belongs to the technical field of intelligent driving, and particularly relates to an intelligent driving method and system with a body, and the method comprises the steps: fusing the V2X data of a vehicle-mounted laser radar, a millimeter wave radar and a road side unit, and obtaining the vehicle driving information; constructing a centimeter-level dynamic environment model by adopting a space-time alignment algorithm, and realizing multi-source data space-time synchronization and obstacle real-time prediction; designing a hierarchical reward function including security, efficiency and comfort rewards based on a user risk cognition mechanism, and generating an end-to-end reinforcement learning decision in combination with a Transform architecture; optimizing the dynamic environment model through reinforcement learning decision; inputting vehicle driving information into the optimized centimeter-level dynamic environment model, and predicting obstacles and vehicle tracks in a vehicle driving path; an MPC-Hybrid feedforward-feedback control system is constructed, a feedforward module is adopted to calculate a front wheel turning angle and acceleration parameters according to obstacles in a vehicle driving path and a vehicle track, and a feedback module is adopted to optimize transverse and longitudinal errors; controlling the driving direction of the vehicle according to the front wheel turning angle and the acceleration parameter transverse and longitudinal errors; according to the invention, through deep fusion of vehicle-road collaborative perception and reinforcement learning decision, the reliability and adaptability of the automatic driving system are significantly improved.

The invention provides a cardio-pulmonary resuscitation training system based on multi-modal artificial intelligence in combination with a virtual reality technology, and relates to the technical field of virtual reality, and the system comprises a multi-modal sensing module which is used for collecting posture data, mechanical data and environment data; the multi-source data collaboration module is used for generating an operation quality evaluation parameter set and establishing a mapping relation between the operation posture and the pressing efficiency; the virtual scene construction module is used for generating a virtual first-aid environment according to a preset first-aid scene template and the operation quality evaluation parameter set; the decision module is used for generating a feedback control signal; and the interactive closed-loop execution module is used for inputting the feedback control signal into the virtual first-aid environment and updating the operation quality evaluation parameter set according to the operation data after feedback response. According to the invention, standardized, intelligent and scene-diversified cardiopulmonary resuscitation skill training can be realized, and the technical problems of inaccurate posture evaluation, non-personalized feedback, single scene, high resource dependence and the like in traditional training are solved.

The invention provides an array detection and positioning method combined with multi-welding-head battery welding, which comprises the following steps: monitoring the distribution of a welding head temperature field in real time by adopting an infrared thermal imaging technology, judging whether the welding head temperature field deviates or not by combining a preset temperature threshold value, and judging that the welding head temperature field deviates if detecting that the temperature of a certain welding head exceeds a threshold value range; according to the offset of the welding position, motion control parameters of the digital welding system are adjusted in a self-adaptive mode, and the offset error of a welding head is compensated in real time through a closed-loop feedback control algorithm; the welding spot quality online monitoring technology is adopted, the welding spot fusion depth and the welding spot diameter are extracted, whether the welding spot quality is qualified or not is judged in real time in combination with a pre-established welding spot quality evaluation model, and if the quality of a plurality of continuous welding spots is not qualified, it is judged that the welding spot quality is affected by welding head temperature field deviation.

The invention discloses a system and a method for predicting the fatigue life of a flange by utilizing ultrasonic flaw detection, and belongs to the field of artificial intelligence. Comprising an aggressive frequency domain spot feature extraction module, a microdefect enhancement recognition module, an acoustic echo database training module, a damage accumulation-stress wave mapping model construction module, an active echo disturbance injection module, a fatigue life progressive prediction module, a real-time feedback closed-loop control module and a multi-scale self-checking redundancy mechanism module. Spot feature extraction and defect identification share high-frequency phase data to generate a focusing map, and registration with historical defect features is carried out in a database; the mapping model constructs a stress-damage relation according to the stress concentration coefficient and the frequency domain characteristics; the disturbance injection module applies directional excitation to potential fatigue points, and the prediction module constructs a life evolution curve based on response; the feedback control module fits and corrects the database and the disturbance strategy; the self-checking mechanism improves the prediction robustness and self-adaptability through waveform deviation and mismatch correction. The beneficial effects are that the structure safety and the service life management intelligence are improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle task automatic planning and situation display system based on artificial intelligence. Comprising a task demand identification and priority dynamic adjustment module, a multi-task path optimization module, a multi-dimensional situation awareness and data fusion module, a three-dimensional situation display module, a task dynamic adjustment and feedback control module, a task data storage and historical analysis module and an emergency task response and environment interference adaptation module. By collecting task requirements and environment parameters in real time, the system can dynamically generate a task priority queue and optimize path planning based on priorities, it is ensured that the unmanned aerial vehicle has efficient response ability when executing tasks such as reconnaissance, monitoring, searching and rescue, the system combines a multi-source data fusion technology, the three-dimensional situation of the task environment is displayed in real time, and the system has good application prospects. Visual support is provided for decision making; the self-optimization capability and the adaptability to emergency situations lay a foundation for long-term intelligent development of unmanned aerial vehicle tasks.

The invention discloses a ship control auxiliary method based on machine learning, and relates to the technical field of ship intelligent control, and the method comprises the steps of ship data collection, fuel oil energy consumption prediction, dynamic navigational speed planning and energy-saving feedback control. According to the method, the fuel energy consumption is predicted by adopting a prediction method combining multi-mode and disturbance self-adaption, modeling multi-dimensional data is unified, perception of environmental disturbance is enhanced, accurate prediction of the fuel energy consumption under complex navigation conditions is realized, and the reliability of subsequent dynamic navigational speed planning is effectively improved; dynamic speed planning is carried out by adopting a speed planning method combining multiple targets and segmented constraints, three core factors of fuel energy consumption, navigation time and safety risks are comprehensively considered, a multi-target cost function is constructed, segmented constraints are introduced, the physical feasibility and risk control of speed adjustment are guaranteed, the speed is optimized through a reinforcement learning mechanism, and the speed planning efficiency is improved. Intelligent self-adaptive navigational speed adjustment is achieved, and the energy-saving capacity, the navigation efficiency and the operation safety of the ship under the complex navigation condition are improved.

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

The invention discloses a prefabricated building integrated safety monitoring method based on BIM, and relates to the technical field of BIM, and the method comprises the steps: S1, building a BIM model and managing information of prefabricated parts; s2, safety assessment and simulation before construction; s3, real-time monitoring in the construction process; s4, performing quality safety evaluation after construction; the technical key points are as follows: in the construction hoisting process of the prefabricated part, the accurate control on the hoisting speed can be realized by monitoring the deviation angle of the prefabricated part in real time and dynamically adjusting the proportionality coefficient according to the deviation value by adopting a feedback control principle, so that the stability of the prefabricated part in the hoisting process is ensured; dynamic adaptability is achieved through proportion adjustment, the method is an effective means for guaranteeing hoisting safety, meanwhile, the problem that errors exist in the operation result of the structural analysis construction sub-model is solved, and the method can adapt to prefabricated parts in different environments.