49 results about "Gradient network" patented technology

The invention provides a dynamic health adaptive monitoring method and system using artificial intelligence, and relates to the technical field of health monitoring, and the method comprises the steps: carrying out the noise reduction through a hierarchical diffusion probability model through multi-source heterogeneous health data, extracting preliminary features through combining with an improved attention auto-encoder, learning discriminative features through a contrast learning network, and carrying out the recognition of the discriminative features; inputting the discriminative features into an adaptive wavelet neural network for multi-scale feature decomposition, inputting the discriminative features into a dynamic graph neural network to obtain a health feature tensor, obtaining time sequence features by using a hierarchical memory enhancement network, and generating a health knowledge graph in combination with a dynamic routing capsule network and a causal reasoning network; and inputting the time sequence characteristics and the health knowledge graph into a deep Bayesian network for health risk assessment, and obtaining an adaptive monitoring scheme through a model prediction control network by using a hierarchical reinforcement learning network and combining a multi-agent collaborative optimization system and a deep deterministic strategy gradient network.

The invention provides a thermal power equipment real-time monitoring method and system based on edge computing, and relates to the technical field of thermal power equipment real-time monitoring, and the method comprises the steps: deploying an edge computing node array to collect multi-source heterogeneous data of thermal power equipment, and carrying out the preprocessing through data screening, multi-scale adaptive filtering and wavelet packet decomposition, a conditional variation auto-encoder is used to extract features, a hierarchical attention mechanism and a deep feature fusion network are combined to generate mixed feature representation, refined distribution estimation and abnormal mode recognition are performed on equipment states, cooperative monitoring modeling is performed based on a multi-scale spatial-temporal feature fusion network and a hierarchical depth deterministic policy gradient network, and a multi-scale spatial-temporal feature fusion network is established. The real-time monitoring accuracy and efficiency of the thermal power equipment can be effectively improved, the equipment failure rate is reduced, and safe and stable operation of a thermal power plant is guaranteed.

The invention relates to the technical field of servo motor control, and discloses an intelligent servo motor control method and device and computer equipment. The method comprises the following steps: carrying out multi-dimensional data acquisition and preprocessing on a position loop, a speed loop and a current loop of the intelligent servo motor to obtain standard structured parameter data; identifying a working condition characteristic parameter set of the intelligent servo motor; establishing a two-stage optimization architecture comprising a motor parameter layer and a control algorithm layer, and generating a dynamic parameter adjustment mapping table; a depth deterministic strategy gradient network is trained based on the characteristic parameter set and the dynamic parameter adjustment mapping table, and an optimal control strategy model for different working conditions is generated; and the optimal control strategy model is utilized, control parameters are automatically switched according to the working condition types recognized in real time, and smooth transition control of the intelligent servo motor among different working conditions is achieved. Smooth switching among different working conditions is achieved, system oscillation caused by sudden change of control parameters is avoided, and the disturbance influence in the working condition switching process is greatly reduced.

The invention relates to the technical field of process optimization control, and discloses a silk-covered enameled winding process optimization control method and system based on intelligent analysis. The method comprises the steps that multi-area production parameters are collected in real time and preprocessed; constructing parameter and product quality mapping through principal component dimensionality reduction, grey correlation and support vector regression; training an Actor-Critic structure by using a depth deterministic strategy gradient network to obtain an adjustment strategy; a non-dominated sorting genetic algorithm is combined with analytic hierarchy process to carry out multi-objective optimization, and a control scheme for balancing mass, efficiency and energy consumption is realized. According to the application, on the basis of considering the complex coupling relationship among the process parameters, multi-objective dynamic balance optimization of product quality, production efficiency and energy consumption is realized, the process parameters can be adaptively adjusted according to the production state and the task demand, and the stability and the optimization degree of the silk-covered enameled winding production process are improved.

The invention relates to the technical field of bidirectional PCS converters, and discloses a VSG control method and device for a DSP numerical control high-frequency bidirectional PCS converter, and the method comprises the steps: carrying out the multi-signal classification of an output voltage, an output current, a DC bus voltage and a PWM modulation waveform of the PCS converter through a DSP chip, and obtaining a frequency spectrum feature vector; calculating a VSG parameter group based on the spectrum feature vector, inputting the VSG parameter group into a depth deterministic strategy gradient network for parallel training, and generating a VSG real-time control strategy; applying phase-locked loop compensation to the VSG real-time control strategy to obtain a PWM modulation signal after synchronous compensation; and carrying out real-time fault early warning on the PWM modulation signal after synchronous compensation, and carrying out seamless switching on a grid-connected and off-grid mode by adopting voltage feed-forward decoupling control to obtain a smooth transition control sequence. According to the method, the control period and the calculation load of the PCS converter under the high-frequency switching condition are reduced, and the control precision under the complex working condition is ensured.

The invention provides a multi-unmanned aerial vehicle cooperative loading autonomous adjustment method under a sudden fault. The method comprises the following steps: establishing an unmanned aerial vehicle and load physical model according to unmanned aerial vehicle information and load information; according to the unmanned aerial vehicle and load physical model, establishing a multi-unmanned aerial vehicle cooperative hanging system virtual model; carrying out iterative optimization on the virtual model of the multi-unmanned aerial vehicle cooperative suspension system through a depth deterministic strategy gradient network; dispatching the multi-unmanned aerial vehicle cooperative suspension system to execute a task, and controlling the multi-unmanned aerial vehicle cooperative suspension system based on the optimized virtual model of the multi-unmanned aerial vehicle cooperative suspension system; and carrying out real-time fault detection on the multi-unmanned aerial vehicle cooperative suspension system through the fault detection model. Compared with the prior art, the unmanned aerial vehicles in the multi-unmanned aerial vehicle cooperative suspension system are trained, so that each unmanned aerial vehicle realizes the optimal action through local information, and the robustness, the stability and the control precision of the multi-unmanned aerial vehicle cooperative suspension system are effectively improved.

The invention discloses a multi-target bridge unmanned aerial vehicle inspection path optimization method and system, and relates to the technical field of crossing of bridge operation and maintenance and unmanned aerial vehicle cluster control. The method comprises the following steps: constructing a bridge three-dimensional point cloud model, extracting key inspection points, and establishing a multi-objective optimization function including path length, time consumption, coverage rate, risk coefficient, cluster safety and formation retention; a multi-target reward mechanism is fused through a double-delay depth deterministic strategy gradient network, and a deep learning model is constructed and used for generating an unmanned aerial vehicle cluster obstacle avoidance strategy; training a deep learning model; and based on real-time data, performing multi-target bridge unmanned aerial vehicle routing inspection path optimization by using the trained deep learning model, and adjusting a weight coefficient of a multi-target optimization function through an attention network dynamic weight distribution technology in the optimization process. According to the invention, multi-objective optimization of routing inspection safety, efficiency, quality and cluster cooperation is realized, and the method is suitable for automatic routing inspection of various complex steel structure bridges.

The invention relates to a multi-unmanned aerial vehicle formation control method based on offline sample correction reinforcement learning. The method comprises the following steps: constructing a depth deterministic strategy gradient network for offline training; the wingman interacts with the environment according to the current strategy to generate historical trajectory information of the wingman, and an off-line sample is obtained; off-line samples are randomly extracted, and importance weights used for correcting the off-line samples are calculated according to the difference between a current strategy and a previous strategy; performing offline training on the depth deterministic policy gradient network based on the importance weight so as to update network parameters of the depth deterministic policy gradient network; constructing a depth deterministic policy gradient network for online execution, and loading network parameters trained offline; the real-time interaction information of the wing aircraft and the environment is used as input, and the optimal action output is generated for the wing aircraft. The method can effectively improve the formation strategy learning efficiency of the multi-unmanned aerial vehicle system, and has a very high application value.

The invention discloses a fault identification method for generating vibration data based on frequency domain transformation under a finite sample. The method comprises the following steps: collecting multiple types of fault vibration signals and extracting frequency domain spectrum characteristics; a forward diffusion stochastic differential equation based on mirror image Brownian motion is constructed, and original spectral distribution is converted into Gaussian prior distribution through progressive noise injection; training a gradient network by adopting a fractional matching criterion, and realizing spectrum feature reconstruction by combining a reverse stochastic differential equation and a Langevin dynamic sampling strategy; and the generated sample and original data are spliced and then input into a spectrum feature lexical element Transform model, local resonance frequency band feature extraction is enhanced through a multi-head attention mechanism, and finally fault identification is realized through coding, decoding and linear classification. According to the method, the problem of low fault identification precision of the rotating machinery in a data scarce scene is effectively solved, and a high-reliability data enhancement and feature decoupling scheme is provided for an industrial equipment intelligent diagnosis system.

The invention relates to the technical field of fire safety monitoring and early warning, in particular to a fire risk dynamic assessment and early warning method based on deep reinforcement learning, comprising the following steps: acquiring multi-source sensing data of a target area; a dynamically updated fire risk assessment state space is constructed based on multi-source sensing data, and a real-time state in the state space is processed and a risk assessment decision is generated through a deep reinforcement learning agent optimized based on an accumulated award function weighted by an accuracy award, a false alarm punishment and a missing alarm punishment; the deep reinforcement learning agent adopts a deep deterministic strategy gradient network structure; mapping the continuous output of the risk assessment decision into a discrete dynamic fire risk level; and triggering an early warning signal matched with the dynamic fire risk level according to the dynamic fire risk level. Through multi-source data standardization, dynamic state space, reinforcement learning optimization and grading early warning binding, accurate dynamic evaluation, reliable early warning and efficient emergency response are realized.

The invention provides an AGV scheduling method and related equipment integrating path planning and traffic control, the method comprises the steps of obtaining task information, vehicle dynamic factors and topological map data of an AGV vehicle, the vehicle dynamic factors comprising a cargo state, a storage location state, spinning prohibition and point location prohibition; based on the task information, the vehicle dynamic factors and the topological map data, a depth deterministic strategy gradient network is adopted to optimize the weight, and an improved A star algorithm formed by the sum of basic cost and real-time congestion cost is adopted to plan the driving path of the AGV; continuously monitoring the running state of the AGV cluster in the process that the AGV runs based on the planned path; and conflict prediction is carried out based on the operation state of the AGV cluster, and traffic control is carried out on the AGV cluster by using a preset conflict prevention mechanism and a heuristic deadlock removing strategy. According to the method, vehicle dynamic factors are considered, an A star algorithm is optimized, traffic control is configured, and the AGV scheduling efficiency is improved.

The invention discloses a joint optimization method for time slot allocation and power allocation of an underwater acoustic communication network, which comprises the following steps of: designing a deep Q network, namely DQN, based on reinforcement learning in a global optimizer, and respectively designing a deep deterministic policy gradient network, namely MADDPG, in each underwater transmitting node and the global optimizer; time slot allocation is carried out through a deep Q network, a time slot allocation decision is fed back to each underwater transmitting node, and a power allocation strategy is generated through a deep deterministic strategy gradient network; and alternately training the depth Q network and the depth deterministic strategy gradient network until convergence, and outputting optimal time slot allocation and power allocation results. Through combination of the DQN and the MADDPG, joint optimization of time slot allocation and power allocation is realized. The joint optimization can consider the influence of the time slot and the power on the system performance at the same time, and avoids the step-by-step optimization in the traditional method, thereby improving the resource utilization efficiency and the overall performance of the system.

The invention relates to the technical field of intelligent fuel supply, in particular to an intelligent optimization method and system for fuel supply of a thermal power plant based on multi-source information fusion. The method comprises the following steps: collecting multi-source heterogeneous data and carrying out space-time alignment and normalization processing; constructing a coal as fired calorific value soft measurement model by adopting a physical constraint corrected LSTM network; generating a transportation delay predicted value and probability distribution thereof through an XGBoost regression model; calculating a comprehensive risk score in combination with a Bayesian risk network; solving a Pareto optimal solution set under the constraint of an inventory safety threshold by adopting a depth deterministic strategy gradient network, and generating a fuel scheduling instruction; and updating the as-fired coal calorific value soft measurement model on line. Through combination of the physical constraint corrected LSTM network and multi-source time sequence data fusion and regularization training, the fire coal calorific value prediction precision is remarkably improved, and the problems that a traditional method depends on off-line assay, response is lagged, and the real-time dynamic state of the combustion process is difficult to reflect are solved.

The invention relates to the technical field of intelligent control, and discloses a special line painting control method and system based on an intelligent algorithm. The method comprises the following steps: collecting painting line sensor data to construct a process digital model; training the depth deterministic strategy gradient network based on the model, and generating a multi-parameter cooperative control instruction; executing the control instruction and generating a feedforward signal through multi-stage comparison of quality data; the feedforward signal and the control instruction are fused, and a real-time control strategy is formed through network correction. According to the method, the problem of digital modeling based on multi-dimensional parameter mapping in special line painting control is solved, and the problem of insufficient precision of painting process multi-parameter cooperative control is overcome. Through an intelligent algorithm of a depth deterministic strategy gradient network and a feedforward control signal fusion mechanism, the problems of lag detection of quality abnormity and insufficient dynamic response in the painting process are effectively solved.

The invention discloses a network data security and privacy protection method and device, and the method comprises the steps: obtaining network behavior data features in real time, calculating a behavior entropy value, judging whether to trigger privacy protection or not based on the behavior entropy value, and continuing a next step if the privacy protection is triggered; dynamic privacy budgets of different features are generated based on the behavior entropy in combination with an LSTM model; generating a feature sensitivity grade through a dynamic privacy budget; performing mask processing on the gradient network behaviors of the corresponding features based on feature sensitivity classification; the main technical scheme and effects are as follows: 1, through a dynamic privacy quantification engine, a network behavior entropy value is calculated in real time, and a risk is predicted by using an LSTM model, so that a dynamically changing privacy budget is generated; in this way, the privacy protection intensity can be adaptively adjusted according to the real-time threat situation, and the rigid mode of fixed budget in the existing differential privacy technology is thoroughly broken through.

The application relates to the technical field of intelligent control, and discloses a special line painting control method and system of an intelligent algorithm. The method comprises the following steps: collecting painting line sensor data to construct a process digital model; training a deep deterministic policy gradient network based on the model to generate a multi-parameter collaborative control instruction; executing the control instruction and generating a feedforward signal through multi-level comparison of quality data; fusing the feedforward signal and the control instruction, and forming a real-time control strategy through network correction. The application solves the problem of digital modeling based on multi-dimensional parameter mapping in special line painting control, and overcomes the problem of insufficient precision of multi-parameter collaborative control of the painting process. Through the intelligent algorithm of the deep deterministic policy gradient network and the feedforward control signal fusion mechanism, the problems of lagging detection and insufficient dynamic response of quality abnormalities in the painting process are effectively solved.

A distributed power adaptive voltage control method based on multi-agent reinforcement learning: according to the selected distribution network containing distributed power, input the basic parameter information of the distribution network; form a Markov decision process for voltage control of the distribution network containing distributed power, and construct each distribution network regional agent and a distributed power reactive output dynamic boundary mask method based on a multi-agent deep deterministic policy gradient network; conduct offline training on each distribution network regional agent to obtain each trained distribution network regional agent; regulate the distributed power in the region, and each distribution network regional agent gives a control strategy for the distributed power in the region based on the real-time input distribution network status in the region, and is processed by the distributed power reactive output dynamic boundary mask method and sent to the distributed power converter in the region for execution. The present invention can improve the voltage operation level of the distribution network, improve the voltage quality, and enhance the economic efficiency of power system operation.

The present invention discloses a method for testing the most sensitive electromagnetic waveform based on reinforcement learning, which includes the following steps: S1. Establish a reinforcement learning architecture for electromagnetic sensitivity testing, and construct a state space and an action space; S2. Establish a reinforcement learning model based on the dual-delay deep deterministic policy gradient network; S3. Build an automated closed-loop test environment for sensitivity thresholds; S4. Construct an electromagnetic sensitivity reward function, and design a basic reward function and a shaping reward function; S5. Start the test of the most sensitive electromagnetic waveform and reinforcement learning training to obtain the most sensitive waveform. The present invention can quickly search for the most sensitive waveform that minimizes the sensitivity threshold of the test item in the defined modulation waveform set.

The invention belongs to the technical field of intelligent automobile lighting, and particularly relates to a dynamic glare suppression method and system based on multi-automobile cooperative perception, a vehicle and a computer readable storage medium. According to the method, a glare sensitivity distribution diagram which is sent by an opposite vehicle and based on the visual angle of the vehicle is received through V2X vehicle wireless communication, vehicle-vehicle information sharing is achieved, and the sensing limitation of a single vehicle is broken through; constructing a human eye glare sensitivity model based on the CIE standard, converting the physical brightness into subjective glare perception to generate a smooth weight map, and performing curve and ramp dynamic geometric compensation on the map to eliminate perspective distortion; and finally, generating a pixel-level dimming instruction by using the multi-agent depth deterministic strategy gradient network in combination with the lighting requirement of the vehicle, and driving the pixelated headlight to execute accurate brightness adjustment. According to the method, the problems of multi-vehicle meeting game conflicts, rough control strategies and failure of complex road conditions are effectively solved, and collaborative and accurate glare suppression and remarkable improvement of subjective comfort are achieved.

The application discloses a chiller group control optimization method based on layered reinforcement learning, and aims at the problems of poor dynamic adaptability of traditional rule control, high difficulty of single reinforcement learning in processing mixed action space and existence of equipment operation risk, wherein the control task is divided into high-layer discrete decision and low-layer continuous control; the high layer adopts a deep Q network to output the number of chiller units in operation and a water outlet temperature setting value, and the safety timer is used to limit the high-layer action update frequency; the low layer adopts a deep deterministic policy gradient network to output a chiller load distribution ratio and the operation frequency of each variable frequency equipment, and the safety mapping is used to guarantee that the equipment constraint is in line with the regulations; the high and low layers are cooperatively optimized through window cumulative rewards, and the multi-objective reward function is combined to balance the energy saving, stability and safety; and the application can effectively reduce the energy consumption of the chiller room, reduce the number of chiller unit start and stop, and improve the cooling control stability and system energy efficiency.

The invention provides a kiln state optimization method and system based on deep learning, and relates to the technical field of state optimization, and the method comprises the steps: collecting the operation parameters of a kiln in real time through a sensor, and the operation parameters comprise temperature, pressure, gas concentration and energy consumption data; a deep reinforcement learning model is constructed, the deep reinforcement learning model comprises a state encoder and a strategy gradient network, the deep reinforcement learning model is trained based on the historical operation parameters and the historical adjustment amount, and the state encoder adopts a convolutional neural network and a long-short-term memory network to perform feature extraction on the historical operation parameters; the strategy gradient network is used for learning the mapping relation between the extracted features and the historical adjustment amount; and inputting real-time data into the trained deep reinforcement learning model to obtain a real-time adjustment amount, and adjusting kiln operation parameters according to the real-time adjustment amount. According to the method, the reliability of the kiln state optimization result based on deep learning can be improved.

The present disclosure provides a multi-task model training method, a promotion content processing method and a related device. The multi-task model includes an auxiliary network, a core network and a forward-looking gradient network. The method includes: obtaining a sample feature vector, the sample feature vector is used to characterize the user's behavior on the promotion content; inputting the sample feature vector into the auxiliary exclusive network to obtain an auxiliary feature vector; inputting the auxiliary feature vector into the forward-looking gradient network to obtain a gradient feature vector, the gradient feature vector characterizes the contribution of the auxiliary feature vector to the optimization target of the core network; after fusing the gradient feature vector with the auxiliary feature vector, inputting the auxiliary output network to obtain the output of the auxiliary output network; updating the weight of the auxiliary network according to the output of the auxiliary output network and the optimization target of the auxiliary network and the optimization target of the core network, until the auxiliary network meets the first training stop condition. The auxiliary task in the multi-task model can be biased to optimize the core task.

The invention discloses a measurement data dynamic correction method based on deep reinforcement learning, and the method comprises the following steps: carrying out the self-adaptive correction of an error caused by environment change and noise in measurement data through combining a depth deterministic strategy gradient network and a Bayesian linear regression model. According to the method, a state vector is constructed through acquisition time, equipment state and environmental parameter information, an initial correction action is generated by using a depth deterministic policy gradient network, and confidence interval parameters are output through Bayesian linear regression to evaluate correction uncertainty. And performing weighted adjustment on the correction action according to the confidence coefficient and then correcting the measurement data, and generating a joint reward signal based on the error change and the confidence coefficient for updating a strategy network and a Bayesian model so as to realize stable optimization and strategy output in a dynamic environment. The invention aims to solve the problem of measurement errors caused by environment change and noise interference.

The invention discloses a multispectral ambient light adaptive compensation method and system based on reinforcement learning, and belongs to the technical field of image processing and intelligent control, and the method comprises the following steps: a multispectral environment perception step: collecting a visible light image and an infrared image through an RGB-IR four-channel optical perception system, carrying out multispectral feature fusion and estimating environment illumination intensity; a reinforcement learning decision-making step: outputting parameter adjustment actions of light source intensity and exposure time based on the depth deterministic strategy gradient network; light compensation execution: adjusting light source and camera parameters according to the action vector; and an image quality evaluation step: calculating the gradient intensity, gradient uniformity and noise standard deviation of the structured light stripes as reward signals and feeding back the reward signals to the decision network, thereby forming an intelligent closed loop of perception-decision-execution-evaluation, and effectively solving the problem of unstable image acquisition quality caused by illumination fluctuation and metal reflection in a complex industrial environment.

The invention discloses a camouflage detection method, system and device based on an improved depth gradient network, and a medium, and belongs to the technical field of picture detection, and the method comprises the steps: obtaining a camouflage target image; processing the camouflage target image by using the improved context encoder to obtain an initial feature; processing the initial features according to an integrated attention mechanism to obtain enhanced features; performing context exploration on the foreground and the background of the enhanced feature prediction to obtain refined features; and outputting a camouflage target detection result according to the refining features, and verifying the authenticity of the detection result through indexes. According to the invention, the problem that the accuracy of the current camouflage target detection algorithm and model cannot reach the expectation when some camouflage targets in actual situations and real life are detected in the prior art is solved.

The application relates to a multi-AUV bearing-only perception cooperative hunting control method based on reinforcement learning, which comprises the following steps: multiple autonomous underwater vehicles interact in a hunting environment; each autonomous underwater vehicle acquires its own state and passive sonar bearing-only observation of a target at the current time, receives delayed communication information from a neighbor autonomous underwater vehicle, constructs observation and state input for strategy learning, and stores samples obtained through interaction into an experience replay pool; under a centralized training and decentralized execution (CTDE) framework, a multi-agent deep deterministic policy gradient network sharing parameters is constructed; sequence samples are sampled from the experience replay pool, and trained network parameters are obtained through joint optimization of each network; the trained network parameters are loaded, each autonomous underwater vehicle generates input for a strategy network based on its own state, passive sonar bearing-only observation and delayed communication information, and realizes multi-AUV cooperative hunting control based on output actions.

The invention discloses a power transmission line icing intelligent identification method and system based on multi-source data, and relates to the technical field of icing intelligent identification, and the method comprises the steps: estimating the rough sag of a lead, carrying out the differential interference processing of an SAR complex image, calculating a winding gradient, constructing a target function, and obtaining an optimal integer flow field; and calculating the bus density of the wire after icing, generating the icing weight on the wire, and converting the icing weight into the equivalent icing thickness. According to the method, the quantitative inversion capability of the continuous distribution icing thickness along the power transmission line is improved through the combination of the low-orbit satellite SAR differential interference deformation inversion result and the fiber grating strain and span data, and the wrapping gradient network flow optimization unwrapping method is combined with the expected vertical deformation field constructed based on the rough sag and atmospheric phase correction. And the robustness of interferometric phase unwrapping and the inversion precision of the deformation quantity are improved.

The present invention relates to a multi-UAV formation control method based on offline sample correction reinforcement learning, comprising: constructing a deep deterministic policy gradient network for offline training; the wingman interacts with the environment with the current strategy to generate the historical trajectory information of the wingman and obtain offline samples; randomly extracting offline samples, and calculating the importance weights for correcting offline samples according to the difference between the current strategy and the previous strategy; offline training the deep deterministic policy gradient network based on the importance weights to update the network parameters of the deep deterministic policy gradient network; constructing a deep deterministic policy gradient network for online execution, and loading the offline trained network parameters; taking the real-time interaction information between the wingman and the environment as input, generating an optimal action output for the wingman. The present invention can effectively improve the efficiency of learning formation strategies of multi-UAV systems, and has high application value.

The invention provides a commercial vehicle double electric drive axle torque distribution model generation method and device, and belongs to the technical field of motor control, and the method comprises the steps: obtaining multi-dimensional working condition variable data at the current moment in the vehicle operation process; inputting the multi-dimensional working condition variable data into an Actor network of a depth deterministic strategy gradient network model to obtain a double-bridge torque distribution proportion; based on the torque distributed to the double electric drive axles of the vehicle, multi-dimensional working condition variable data and instant rewards at the next moment are obtained; constructing a training sample based on the multi-dimensional working condition variable data at the current moment and the next moment, the double-bridge torque distribution proportion and the instant reward; and training the depth deterministic strategy gradient network model based on the training sample to obtain a double-electric-drive-axle torque distribution model. According to the method, the technical problem that an existing double-electric-drive-axle torque distribution strategy is low in adaptive capacity and generalization capacity of a new scene can be solved.

The invention discloses an air-ground network anti-interference transmission and deployment method and system based on a GAT, and belongs to the technical field of communication signal processing. Firstly, a communication scene is constructed, each unmanned aerial vehicle base station performs legal communication with a legal user served by the unmanned aerial vehicle base station, and meanwhile, a plurality of interference sources are randomly distributed in an area to interfere with legal communication; obtaining the instantaneous receiving rate of each legal receiver under interference, and forming a problem model which takes maximization of the anti-interference receiving rate of each legal user of the system as a target and takes beamforming transmitted by each unmanned aerial vehicle base station and position deployment of each unmanned aerial vehicle as variables; then, a framework based on a graph attention network is developed, a beam forming vector is solved through neural network training, and unmanned aerial vehicle base station position deployment is solved by training a multi-agent depth deterministic strategy gradient network nested with the graph attention network; neural network parameters are optimized by adopting a stochastic gradient descent method, so that the purposes of effectively suppressing interference and improving the communication performance of a physical layer are achieved.