491 results about "Weight distribution" patented technology

The invention discloses a trajectory optimization method based on a multi-source heterogeneous positioning data fusion algorithm, and relates to the technical field of intelligent navigation and high-precision positioning, multi-modal data are acquired through a multi-modal sensor array, positioning redundancy of scenes such as tunnels and indoor scenes is enhanced, a weight distribution strategy is dynamically adjusted through an Actor-Critic network architecture, and the positioning accuracy is improved. The state space input comprises an environment semantic tag, a historical error sequence and a real-time noise variance, the output action space is continuous weight distribution of each data source, a multi-target reward function optimization strategy is combined, scene adaptability is realized, a local SLAM map, inertial navigation error parameters and a weight distribution strategy are shared in real time based on a V2X protocol, and the real-time performance of the system is improved. According to the method, a single device accumulative error is compensated by using adjacent vehicle data, a terminal locally trains an error compensation model, parameters are uploaded to a cloud end through differential privacy encryption, the cloud end adopts a FedAvg algorithm to aggregate a global model and issue the global model, the error difference between devices is inhibited, and dynamic road network updating and scene differentiation model distribution are supported at the same time.

The invention relates to the technical field of unmanned aerial vehicle flight path planning, in particular to an unmanned aerial vehicle real-time path planning system and method based on dynamic weight distribution and multi-source data fusion. The system comprises a multi-source data fusion module, an integrated laser radar, a millimeter wave radar, a visual sensor and a Beidou positioning unit. The dynamic weight distribution module dynamically adjusts the weight coefficient of each sensor according to the environmental complexity, the threat level and the state of the unmanned aerial vehicle by adopting a mixed decision-making mechanism combining fuzzy logic and reinforcement learning; an improved RRT * algorithm and a Markov decision process are built in the real-time path planning module, and a global optimal path and a local obstacle avoidance track are generated by adopting a layered planning architecture; the unmanned aerial vehicle cooperative control module comprises a dual-redundancy flight control system and a dynamic obstacle avoidance unit; and the communication relay module supports 5G and low-orbit satellite dual-mode communication, updates an environment cognitive model of each unmanned aerial vehicle through federated learning, and realizes multi-source fusion real-time path planning based on dynamic weight distribution and the unmanned aerial vehicles.

The invention belongs to the technical field of unmanned aerial vehicle intelligent navigation, and discloses a low-altitude economic unmanned aerial vehicle data processing method and system based on a large model, and the method comprises the steps: calculating the scene adaptation weight of multi-modal data through a dynamic attention mechanism based on a space-time alignment feature package, and carrying out the fusion to generate a multi-modal joint feature matrix; based on the multi-modal joint feature matrix, constructing a three-dimensional topological model of an urban airspace, predicting a dynamic obstacle trajectory in combination with a space-time diagram neural network, and generating a hierarchical navigation instruction set; the unmanned aerial vehicle executes a flight instruction according to the hierarchical navigation instruction set, and generates a flight state monitoring log by collecting data in flight of the unmanned aerial vehicle in real time; according to the method, the multi-modal joint feature matrix is generated through environment parameter driving weight distribution, and the complex scene sensing precision is remarkably improved.

The invention discloses a road and bridge parameter anomaly detection method, and belongs to the technical field of civil engineering. Comprising the following steps: step 1, establishing space-time relevance and a data mapping relation between monitoring points; 2, self-adaptive updating of the judgment rule is achieved so as to adapt to state evolution of a bridge service stage; step 3, performing intelligent attribution analysis on abnormity in the monitoring data; step 4, carrying out joint identification and comprehensive evaluation on the sudden structural damage and the slow degeneration degradation process; and 5, carrying out quantitative analysis on the deviation degree of the bridge health state, and generating a standardized bridge health index. According to the method, the space-time correlation network and the environment-structure mapping function between the monitoring points are established, and a multi-dimensional feature extraction and dynamic weight distribution mechanism is combined, so that environment interference elimination, abnormal attribution intelligent analysis and joint identification of sudden damage and slow degeneration are realized, and finally, a standardized health index is quantified and generated.

The invention discloses a machine tool control method and system based on mechatronics, and the method comprises the steps: outputting an optimal cutting track sequence according to the three-dimensional model features and material attribute parameters of a machined workpiece; based on the optimal cutting track sequence, motion interference among shafts of the machine tool is eliminated through dynamic weight distribution, and an anti-interference optimization machining instruction is generated; constructing a nonlinear vibration wave propagation model according to the coupling relation between the spindle rotating speed and the feeding speed, and generating a steady speed regulation and control signal; generating an energy efficiency optimal parameter set including motor torque, cooling power and lubricating frequency based on the energy consumption efficiency constraint condition and the steady speed regulation and control signal; and outputting a closed-loop control signal and synchronously updating the three-dimensional machining precision thermodynamic diagram according to the real-time data in the machining process and the predicted deviation of the digital twin model. According to the embodiment of the invention, global optimization, dynamic adaptation and efficient operation of the machine tool can be realized, and technical support is provided for intelligent upgrading of the modern manufacturing industry.

The invention relates to the technical field of forklift risk management, in particular to a multi-scene-oriented forklift self-adaptive safety early warning decision-making method, which comprises the following steps of: through multi-scene dynamic modeling, constructing multi-dimensional scene feature vectors according to forklift operation environment spatial features, cargo physical attributes and driver behavior parameters; establishing a scene classification model by means of transfer learning; a multi-modal sensor data fusion technology is utilized, a heterogeneous sensor network is deployed to collect data, and data reliability is optimized through space-time alignment and confidence evaluation; a dynamic safety threshold value is generated through fuzzy logic and reinforcement learning, and a driver behavior correction factor is introduced for real-time adjustment; calculating a comprehensive risk index by using a dynamic weight distribution algorithm, and triggering graded early warning and response; a federated learning closed-loop optimization risk assessment model is adopted, robustness is verified in combination with digital twinning, and cooperative obstacle avoidance of multiple forklifts is achieved through inter-vehicle communication. According to the method, the operation safety and decision-making accuracy of the forklift in a complex and changeable scene are remarkably improved.

The invention discloses a starting control system and method for a main and auxiliary power hybrid ship, and relates to the field of ship power control, and the system comprises a multi-source data collection module which is used for obtaining a multi-source state data set of a ship power system; the fusion processing module is used for performing feature alignment processing on the multi-source state data set based on a multi-modal sensing data fusion algorithm to generate a hybrid power starting parameter set; the dynamic weight distribution module is used for constructing a dynamic weight distribution matrix according to the hybrid power starting parameter set; the cooperative control module is used for generating a main and auxiliary engine combined starting instruction set based on an improved cooperative control algorithm; and the execution and monitoring module is used for executing the main and auxiliary engine combined starting instruction set in a preset time window and monitoring the transient response characteristic of the propulsion system. According to the starting control system and method for the main and auxiliary power hybrid ship, the starting stability and the energy utilization efficiency of the hybrid ship are improved.

The invention provides an inertial navigation attitude resolving method and device based on sensor fusion. The method comprises the following steps: carrying out multi-clock domain synchronous calibration processing on a double-antenna GNSS system, an IMU inertial measurement unit and an antenna servo system in a vehicle-mounted communication-in-motion system; according to the time synchronization reference, carrying out quality evaluation and weight distribution processing on the GNSS satellite signal under the satellite communication in motion antenna pointing constraint; and carrying out multi-sensor fusion resolving processing on the inertial attitude of the vehicle body according to the time synchronization reference and the GNSS observation data weight distribution result. According to the method, millisecond-level time synchronization is realized by establishing a robust extended Kalman filtering model of a multi-dimensional extended state vector, GNSS signal quality is optimized by adopting an adaptive weight distribution strategy of antenna pointing constraint, and attitude fusion precision is enhanced by utilizing high-precision angle feedback of an antenna servo system. The problem that a traditional attitude resolving method in a vehicle-mounted communication-in-motion system is not high in precision is solved.

The invention relates to the technical field of ship propulsion load prediction, in particular to a ship propulsion load prediction method, system and device under a high-fluctuation working condition and a medium, and the method comprises the steps: obtaining a ship operation parameter sequence under the high-fluctuation working condition; inputting the ship operation parameter sequence into a pre-trained propulsion load prediction model, and outputting a ship propulsion load prediction sequence of a specified time scale; the propulsion load prediction model comprises an improved time sequence convolutional network, a bidirectional long short-term memory network, an attention mechanism unit and a full connection layer. According to the application, bidirectional multi-scale features are extracted by improving a time sequence convolutional network, a bidirectional long-short-term memory network separates forward and backward time sequence processing paths to analyze a bidirectional dependency relationship, an attention mechanism unit adopts three-stage weight distribution to enhance key feature focusing, a full-connection layer implements nonlinear mapping, and a full-connection layer implements non-linear mapping. And collaborative optimization of the propulsion load prediction precision and the real-time response capability under the high-fluctuation working condition is realized.

The invention discloses a road network abnormal event identification and risk evaluation early warning method based on vibration perception, and relates to the technical field of urban road safety monitoring. Comprising the steps of collecting road vibration data of a road network through a sensor; preprocessing the road vibration data through Kalman filtering and multi-dimensional feature extraction; abnormal event recognition is carried out on the processed road vibration data through a differentiation algorithm, wherein abnormal events comprise vehicle overload, illegal construction and road structure damage; and carrying out risk scoring on the abnormal events through dynamic weight distribution, dividing the risk scores according to the risk levels, and carrying out graded response. According to the invention, through differential algorithm design, a mixed model architecture and a hierarchical response mechanism are adopted, abnormal event identification, risk level quantitative evaluation and differential early warning processing are realized, the safety state of an urban road network is monitored in real time, and early warning information is provided.

The invention discloses a railway turnout defect detection method based on an SCG-DETR detection model. Firstly, a C2f-SMP feature extraction module is designed based on SMPConv and C2f modules, local and global information aggregation of features is optimized through continuous convolution and a gating mechanism, and the receptive field and feature extraction capability is enhanced. Secondly, a CGFM module is provided, and by introducing a CAA attention mechanism, weight distribution of a feature map is optimized, and fine-grained extraction and context information fusion are enhanced. And finally, replacing an original loss function with an Inner-GIoU based on an auxiliary frame, and improving the precision of the model while accelerating the convergence of the model. Experimental results show that the score of mAP50 of the improved model reaches 71.0% and is increased by about 4.6%, the detection speed reaches 80.5 FPS, and the requirement for real-time performance in engineering is met.

The invention discloses a tunnel ventilation control system and method based on artificial intelligence, and relates to the technical field of ventilation control, and the method comprises the steps: in a tunnel design stage, building an unsteady-state computational fluid dynamics model based on tunnel three-dimensional linear parameters, calculating turbulence structures under different traffic conditions through a large eddy simulation method, and calculating an unsteady-state computational fluid dynamics model; determining an optimal space configuration scheme of the fan group according to the distribution of the velocity field and the pressure field; in the tunnel construction stage, multiple types of sensor arrays are arranged along the vault and the side wall of a tunnel in a layered mode; in the tunnel operation stage, real-time vehicle tracks and speed distribution information of a traffic monitoring system are obtained; establishing a ventilation demand dynamic prediction model based on space-time correlation analysis, constructing a fan cooperative control model considering airflow organization optimization, and solving an optimal operation strategy by adopting a multi-target adaptive weight distribution algorithm; when a fire characteristic signal is monitored, the multiple sets of fans are coordinated to form a relay type smoke exhaust airflow organization.

The invention relates to the technical field of control systems, in particular to an unmanned aerial vehicle flight control multi-sensor attitude confidence calculation method, which comprises the steps of multi-source sensor data acquisition, sensor independent attitude calculation, confidence coefficient dynamic evaluation, multi-modal data fusion, attitude error compensation and optimization and real-time verification and fault-tolerant processing. Compared with a fixed weight distribution scheme in the prior art, the method has the advantages that the attitude calculation precision is limited possibly, and particularly when the sensor data quality is influenced by environmental factors, the scheme adopts a strategy of checking the data quality in real time, judging the environmental state and dynamically distributing the weight; the validity of sensor data is effectively identified by verifying whether the acceleration modulus length is close to the gravitational acceleration or not and whether the magnetic field intensity is stable or not; meanwhile, the weight of each sensor is flexibly adjusted according to different environment states such as static, maneuvering or magnetic interference of the unmanned aerial vehicle, so that the self-adaptive capability and accuracy of attitude calculation are remarkably improved.

The invention provides a multi-dimensional driver capability evaluation and intelligent matching scheduling system, and the system comprises a data collection module which is used for obtaining driver driving behavior data, vehicle state data and environment data in real time; the preprocessing module is used for carrying out noise filtering, missing value filling and standardization on the acquired data; the multi-dimensional capability evaluation module is used for calculating a driving safety score, an efficiency score and an emergency response score of the driver through a dynamic weight distribution algorithm based on the preprocessed data; the demand analysis module is used for analyzing the route complexity, the time sensitivity and the special service demand of the passenger order; the matching scheduling module is used for generating a matching result according to the driver ability score and the passenger demand and outputting a scheduling instruction; the dynamic optimization module monitors the driver state and the road condition change in real time and adjusts the matching weight; and the interaction module is used for pushing real-time scheduling information and abnormal event early warning to the driver and the passenger. The scheduling efficiency and safety can be improved, and the passenger travel experience and the operation management level are improved.

The invention discloses a rush-repair vehicle path optimization method fusing meteorological data and vehicle parameters. The method comprises the following steps: S1, constructing a multi-dimensional database; s2, improving a path optimization algorithm, including dynamic weight distribution; performing multi-objective optimization; s3, dynamic path adjustment: deploying a lightweight model at a vehicle-mounted terminal, receiving meteorological and vehicle data in real time, and updating an optimal path; s4, man-machine cooperative intervention: when the vehicle deviates from the planned path or stops beyond a threshold value, path re-planning is started, and an alternative path is generated in combination with manual adjustment of a driver; according to the method, meteorological data and vehicle parameters are integrated, and a multi-dimensional database is constructed. Introducing a weather-vehicle parameter coupling factor in path planning, and carrying out dynamic weight distribution; the total driving time, the weather risk and the vehicle loss are brought into an objective function, and multi-objective optimization is carried out; and predicting an optimal path under different weather-vehicle combinations by using a historical data training model.

The invention belongs to the field of battery monitoring, and particularly relates to a battery health state on-line evaluation system considering a temperature-current coupling effect, which comprises an acquisition module, a coupling module, an on-line evaluation module and an early warning module, the acquisition frequency and the window length of current and temperature data are adaptively adjusted through reinforcement learning and a federated frame in the acquisition module; the coupling module constructs a consistency interval based on the current and temperature slope sequence, calculates a segmented current thermal stress factor and a segmented temperature gradient factor, and further generates a temperature-current coupling feature vector; the online evaluation module performs health state evaluation and life decay rate calculation in combination with dynamic weight distribution and principal component analysis; the early warning module gives an alarm in a grading manner according to an evaluation result, and feeds back an attenuation rate to the front-end acquisition module and the online evaluation module to realize closed-loop optimization; according to the method, the accuracy and reliability of battery health state evaluation of the electric two-wheeled vehicle and the tricycle under complex working conditions are effectively improved.

The invention discloses a cooperative steering control method considering man-machine mutual reliability, and aims to solve the problem of man-machine conflict in an existing man-machine co-driving system. The invention relates to the field of intelligent driving. The system specifically comprises an expected path module, a man-machine steering conflict evaluation module, a driver experience evaluation module, a weight distribution module, a non-cooperative game module and a man-machine co-driving vehicle. The expected path module generates an expected path between the driver and the controller; the man-machine steering conflict evaluation module outputs the trust degree of the driver to the controller based on the steering action of the driver, the vehicle state and the expected path of the driver and the controller; the driver experience evaluation module outputs the driver trust degree of the controller based on the vehicle state; the weight distribution module is used for outputting a driver control weight; and the non-cooperative game module outputs an optimal turning angle in combination with the expected path of the driver and the controller and the control weight of the driver, and controls the man-machine co-driving vehicle to complete the cooperative steering action.

The invention discloses a federal learning data aggregation method for predicting traffic flow, a medium and equipment, and the method comprises the steps: setting a relation between a cloud server end and a vehicle end, and building a corresponding system; collecting vehicle data through the system for local training and data preprocessing; the vehicle side encrypts the preprocessed data through a homomorphic encryption technology and uploads the data to the cloud server side; carrying out weight distribution on the encrypted and uploaded data based on a kernel ridge regression technology; the encrypted update quantity is aggregated based on the adjusted weight, so that the update contribution to the global model is more similar, and the more reliable vehicle update quantity plays a more important role in the prediction process; and the aggregated encryption update quantity is decrypted, pre-training model updating is completed, finally, the server in the training wheel screens the weights of all vehicles, a part of vehicles with too low weights are eliminated, certain residual data similarity is ensured, and data aggregation of residual data is easier.

The invention relates to the technical field of aero-engine combustion chamber outlet temperature field monitoring, and discloses a multi-source data fusion method for an aero-engine combustion chamber outlet temperature field, and the method comprises the steps: unifying a multi-source data space-time reference through a standardized space grid and a precise time synchronization protocol; mapping the data to the same space-time grid by adopting a self-adaptive interpolation algorithm, and embedding a temperature gradient continuity constraint; the confidence coefficient weight is dynamically calculated based on a sensor drift error model and CFD residual feedback, and working condition self-adaptive weighted fusion is realized by using a fuzzy logic rule base; and the interpolation parameters and the weight distribution threshold values are optimized in a distributed mode through a federated learning framework, and fuel injection parameters are adjusted in combination with combustion chamber closed-loop control. The temperature field reconstruction precision and reliability are remarkably improved, the service life of turbine blades can be prolonged, the combustion efficiency can be optimized, and the method is suitable for real-time monitoring and regulation of the combustion state of an aero-engine.

The invention provides a charging pile energy consumption scheduling method based on a load prediction model, and the method comprises the steps: carrying out the data collection and preprocessing, obtaining multi-source data, such as charging, equipment, environment and electricity price, from a charging station management system, and cleaning abnormal values; and then using grey correlation analysis to determine weights of factors influencing energy consumption, constructing a grey prediction model, and combining preprocessing and weight distribution data to predict future charging pile load demands. And the control center receives a load prediction result and vehicle reservation information, preliminarily plans a charging distribution scheme according to the real-time state of the charging pile, and optimizes scheduling by taking the minimization of the total charging cost and consideration of the user satisfaction and the stability of the power system as targets through a simulated annealing algorithm. During operation, an actual state and an external environment are continuously monitored, a charging plan is rapidly adjusted when deviation occurs, finally, vehicle charging bills of all departments are generated according to an execution scheme, and system performance indexes are regularly evaluated to realize continuous optimization. The method can effectively reduce energy consumption cost and guarantee efficient and stable operation of the charging station.

The invention discloses a distributed driving automobile control method. The method comprises the steps that 1, dynamic parameters of an automobile are collected in real time, and the equivalent adhesion coefficient of each wheel is calculated; 2, dividing types and optimizing torque distribution according to pavement adhesion coefficients: respectively adopting priority, uniform and dynamic weight distribution strategies for low, high and asymmetric pavements; 3, establishing a multi-objective optimization model, and solving an optimal torque distribution scheme considering the tire utilization rate and the yaw stability by using a quadratic programming algorithm; 4, compensating a torque error and motor response delay by adopting sliding mode control and a feedforward-feedback mechanism; and 5, the control parameters are dynamically corrected based on errors of the longitudinal acceleration and the yaw velocity, and torque limiting or safety mode switching is triggered. Through multi-strategy collaborative optimization, the stability and driving efficiency of the vehicle under different attachment road surfaces are improved, the tire slipping risk is reduced, and driving safety under extreme working conditions is guaranteed.

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

The invention discloses a foggy day driving augmented reality auxiliary method and system based on multi-mode perception, and relates to the technical field of intelligent driving. The method comprises the following steps: constructing a three-dimensional environment map to carry out real-time driving risk prediction, and carrying out preliminary adjustment on data source weight distribution in the three-dimensional environment map according to a set rule; the PPO reinforcement learning algorithm is adopted to readjust data source weight distribution, a new three-dimensional environment map is reconstructed based on the readjusted data source weight, and the risk probability, predicted in real time, of sudden deceleration of the front vehicle and the reconstructed three-dimensional environment map are projected to a windshield through a layered projection strategy to prompt a driver. According to the method, accurate and comprehensive multi-mode sensing data can be obtained in a foggy day environment, the accuracy of decision making and risk prediction is ensured, quick response can be made according to environment changes, the safety of a driver under complex weather conditions is improved, and the method can adapt to more changing environments in practical application.

The invention discloses a method and a system for evaluating the reliability of a contact network system under an icing galloping condition. The method comprises the following steps: collecting icing galloping data of the overhead line system in real time, and carrying out load conversion and statistics on parts of the overhead line system in icing galloping by adopting a rain flow counting method; the reliability of parts under the icing galloping condition is calculated through the Miner cumulative damage theory, and weight distribution of the overhead line system is conducted through an analytic hierarchy process; based on the reliability and the weight distribution result of each part under the icing galloping condition, evaluating the reliability of the overhead line system of the data acquisition anchor section until the evaluation task is finished; the method and the system provided by the invention are suitable for evaluating the operation reliability of the contact network system in the icing galloping section, can provide support for operation and maintenance of the contact network system after icing galloping occurs, and have wide application prospects.

The invention relates to a helicopter rotor crack fault identification method and device based on video semantic segmentation. The method comprises the steps that video collection and denoising and stability enhancement processing are carried out through an unmanned aerial vehicle; through designing a global-local feature interaction double-branch network, parallel computing and bidirectional fusion are carried out on context global feature extraction branches and detail mining branches, so that rotor wing crack feature extraction is realized; designing a hierarchical crack feature reconstruction decoder to realize cross-scale fusion and spatial precise alignment of crack features; and designing a multi-frequency-domain boundary sensing enhanced training head, and realizing multi-scale feature extraction and dynamic weight distribution of the crack edge through the synergistic effect of a multi-stage wavelet frequency domain decomposition module and a self-adaptive boundary weighting supervision module. Through the innovative modules, high-precision and robust crack detection is realized aiming at the problems of view limitation, global-local feature fusion difficulty, detail loss caused by down-sampling, edge blur, class imbalance and the like in helicopter rotor crack detection.

The invention provides a low-altitude flight accident intelligent identification method based on multi-source data fusion, and the method comprises the steps: carrying out the encryption processing of flight control data, dynamically mapping environment data through an airspace grid mapping model, carrying out the time-space correlation of the two kinds of data, and obtaining multi-source data; constructing a five-dimensional evidence evaluation system through the information entropy of the multi-source data, and carrying out dynamic weight adjustment on the evaluation system according to a real-time data triggering condition to obtain a causal contribution degree; an accident liability contribution degree is obtained through a weight modeling algorithm; then evidence conflict detection is carried out to obtain recombination credibility; and finally, training a dynamic weight distribution model by recombining credibility, and identifying a low-altitude flight accident to obtain a responsibility division result. According to the method, multi-dimensional data are integrated in real time, a complete flight accident evidence chain is constructed, and a dynamic evaluation and multi-party collaborative management mechanism is introduced, so that a scientific basis is provided for responsibility division, and the responsibility affirmation efficiency and credibility are improved.

The invention discloses a multi-source heterogeneous data flow real-time fusion and weight adaptive correction system and method, and relates to the technical field of multi-source data fusion. Comprising a path monitoring data extraction module, an initial fusion weight determination module, an abnormal transportation event analysis module, an initial correction strategy table generation module, a response updating module and a real-time weight adjustment fusion module. According to the method, a multi-dimensional data quality scoring system is established, so that strong association between weight distribution and data quality is ensured, and fusion precision loss caused by'fixed weight 'is avoided. For an abnormal monitoring point, through indexes such as a data missing rate, a deviation change rate, an auxiliary positioning deviation rate and the like, an abnormal data source type is accurately identified, a weight adjustment scheme of'target data source decreasing amplitude + increasing amplitude data source equal proportion complementation 'is formulated until abnormity is eliminated, and closed-loop correction of'abnormity positioning-weight adjustment-error elimination' is realized.

The invention discloses a man-machine dynamic cooperative steering control method integrating reinforcement learning and a game theory, and aims to optimize dynamic weight distribution of a driver and a controller so as to guarantee driving safety and path tracking precision of a man-machine co-driving vehicle. The invention relates to the field of intelligent driving. The system comprises an expected path module, a reinforcement learning module, a non-cooperative game module and a man-machine co-driving vehicle module. The expected path module is used for generating an expected path sequence of the driver and the controller and an expected path at the current moment; the reinforcement learning module receives the expected path at the current moment, the vehicle dynamic information and the optimal rotation angle of the driver and the controller, and training is carried out to generate a driver control weight; and the non-cooperative game module combines the driver control weight, the expected path sequence of the driver and the controller and the state information of the vehicle, optimizes the optimal steering angle of the driver and the controller through the non-cooperative game, and realizes the cooperative steering control of the man-machine co-driving vehicle.

The invention provides an air-jet loom rack structure optimization method based on a BESO method, and relates to the field of air-jet loom design, and the method comprises the following steps: S1, building a three-dimensional model of an air-jet loom rack, and setting material parameters, grid division, load application and boundary conditions; s2, setting an objective function, BESO parameters and volume constraints; s3, finite element stress analysis is carried out, and a weak area of the air jet loom rack structure is found; s4, constructing a weight distribution model, and dividing an optimization region; s5, performing BESO optimization iteration according to the weight distribution model; s6, when the preset optimization target and constraint conditions are met, the optimization process is stopped, and a final air jet loom rack optimization model is obtained. According to the method, the rigidity priority in the specific direction is strengthened by building a directional flexibility objective function, different optimization areas are divided in combination with a weight distribution model, efficient lightweight and dynamic stability collaborative optimization of the air jet loom rack structure is achieved, the rigidity of the air jet loom rack is improved, the weight of the rack is reduced, and the stability is improved.

The invention provides a cross-coupling cooperative control method for double permanent magnet synchronous motors of an aviation airborne suspension lifting system. The cross-coupling cooperative control method comprises the following steps: acquiring a global instruction and real-time state feedback of the double motors; based on the global instruction and the real-time state feedback, constructing a dynamic topology and dynamically adjusting the weight to obtain the weight of the dynamic topology; generating a collaborative correction based on the weight of the dynamic topology and a consistency protocol; dynamic error balanced distribution under load disturbance is carried out based on the collaborative correction amount; and controlling the double motors based on an error balanced distribution result. According to the method, dynamic weight distribution and dynamic error balanced distribution are combined, multi-source interference working conditions such as mechanical unbalance loading, dynamic coupling and parameter disturbance can be dealt with, synchronous errors and disturbance fluctuation in a dual-motor system can be accurately monitored and coordinated, and high-precision stability of cooperative driving is guaranteed.