62results about How to "Achieving joint optimization" patented technology

The invention provides a source-network-storage integrated power distribution network operation optimization method, which relates to the technical field of power distribution networks and comprises the steps of setting an intelligent sensor, collecting multi-dimensional power distribution operation parameters, performing semantic extraction and automatic labeling, determining logic association after conflict correction, obtaining a power distribution knowledge graph, constructing a dynamic prediction model and performing power distribution index prediction. Obtaining a prediction result and generating a key index prediction map; constructing a multi-temporal-spatial-scale collaborative optimization model, setting an objective function, constructing a physical optimization sub-model and a market game optimization sub-model, carrying out interactive coupling, and carrying out collaboration, interactive feedback and iterative convergence to obtain a multi-scale collaborative optimization scheme; and carrying out distributed evaluation, generating an evaluation result, if the evaluation result does not reach the standard, packaging the multi-temporal-spatial-scale collaborative optimization model into an optimization contract, triggering the optimization contract, generating a candidate optimization scheme, carrying out repeated evaluation until the standard is reached, and taking the candidate optimization scheme as a consensus scheme and executing the candidate optimization scheme.

The invention relates to the technical field of spherical display screens, and discloses a visual enhancement method and system for an LED creative spherical display screen, and the method comprises the steps: inputting an original display image of the LED spherical display screen into a multi-scale Transform layered CNN network for feature extraction, and obtaining a multi-dimensional feature image set; feature mapping is carried out, pixel points are projected into a spherical coordinate system, display deviation is calculated, and a comprehensive evaluation matrix is generated; calculating a space compensation coefficient through an N-order polynomial function to obtain a space-time compensation matrix; performing gradient calculation on the space-time compensation matrix to obtain a target vector field; inputting the target vector field into a U-Net structure generator for adversarial training to obtain compensation optimization parameters; according to the method, the real-time dynamic optimization of the display image of the spherical display screen is realized, and the display quality under different environment illumination conditions is ensured.

The invention relates to the technical field of air traffic control and voice recognition, in particular to an air traffic control instruction end-to-end voice recognition method under a high-noise condition, which comprises the following steps: preprocessing to-be-recognized voice, and extracting to obtain original voice frequency characteristics; carrying out noise reduction intensity control, frequency band gain control and fundamental tone tracking processing on the original voice frequency characteristics by adopting a self-adaptive attention noise reduction module to obtain noise-reduced voice; preprocessing the noise-reduced voice, and extracting to obtain noise-reduced voice frequency characteristics; a voice recognition module is adopted to carry out shared encoder coding, connection time sequence classification decoder decoding and attention-based encoder-decoder decoding on the noise reduction voice frequency characteristics, and an output voice recognition transcription text is obtained; according to the invention, the voice recognition precision of the air traffic control instruction under the high-noise condition can be improved.

The invention discloses an intelligent covert communication method and device based on artificial interference and 3D beamforming, and the method specifically comprises the steps: firstly building a multi-antenna covert communication system model based on channel state information and antenna gain network parameters; then, based on a radio frequency signal receiving principle, establishing a signal detection model of the monitoring machine, calculating an omission ratio and a false alarm rate of the monitoring machine, obtaining a minimum error detection probability of the monitoring machine, and establishing an optimization problem of hiding rate maximization; constructing a depth determination strategy gradient (DDPG) neural network; and finally, on the basis of the DDPG neural network, an intelligent covert communication algorithm based on artificial interference and 3D beam forming is adopted, and the covert rate of the covert communication system is optimized. According to the method, the transmission rate of the legal receiver can be maximized under the condition of meeting the concealment limitation, the communication performance and the concealment performance are enhanced, and the intelligent concealed communication requirements in various scenes can be met.

The present invention relates to an intelligent storage method and device based on cloud warehouse. The method: perform all-round scanning and cargo feature recognition on the goods entering the cloud warehouse to obtain cargo feature data; assign and bind RFID tags to each piece of cargo and perform initial positioning to obtain the three-dimensional coordinate value of the cargo; construct a multi-dimensional cargo feature vector and calculate the storage location allocation plan to perform path planning and multi-agent task scheduling on the AGV to obtain the storage task execution instruction of the AGV; obtain the RFID scanning data of the stored goods and the AGV on-board sensor data and perform fusion positioning to obtain the real-time and accurate location and storage status information of the goods; input the real-time and accurate location and storage status information of the goods into the encoder-decoder model to perform AGV path planning and sorting decision calculation to obtain the optimal sorting strategy and AGV motion trajectory. The implementation of the present invention improves the accuracy of AGV path planning and sorting decision of the cloud warehouse.

The invention provides a drug recommendation method fusing medical record features and risk control, and belongs to the technical field of medical information. Existing drug recommendation depends on external medical knowledge to a great extent, so that a recommendation system is poor in expandability and insufficient in clinical practicability. On the basis, the invention provides a drug recommendation method which does not depend on external medical knowledge and is only based on electronic medical record data. According to the method, an individualized illness state recognition and evolution modeling module is designed, records most representative for the current illness state can be automatically recognized from historical medical records of a patient, the disease course development process is described, and individualized medication preferences and treatment modes are mined. According to the method, semantic features of data are fully utilized, so that joint optimization of recommendation risks and effects is realized under the condition of no external knowledge intervention. Experimental results show that the method is obviously superior to existing mainstream methods in multiple evaluation indexes, and has good accuracy, robustness and clinical application prospects.

The invention discloses a hierarchical control network architecture based on an industrial software defined network (SDN), provides a dual-time scale network slicing method and a multi-domain resource collaborative management and control scheme, and shows a remarkable industrial application prospect. The hierarchical control architecture allows flexible configuration of the SDN controller according to a specific industrial environment, and realizes multi-level resource management and control. And the SDN controllers of different levels can be dynamically adjusted according to actual requirements to adapt to complex and changeable industrial scenes. According to the dual-time-scale network slicing method, resource reservation and arrangement are decoupled, and the resource scheduling efficiency is improved. By monitoring the network state in real time, the resource configuration is optimized, and the heterogeneous QoS requirement is ensured to be met, so that the utilization rate of resources is remarkably improved.

The present invention discloses a method, device and storage medium for suppressing overvoltage of SLCC converter valves, the method comprising: obtaining a first extreme working condition by traversing overvoltage fault scenarios through simulation, and optimizing the bridge arm reactor parameters according to the first extreme working condition to obtain a first scheme for suppressing overvoltage; when the first scheme cannot effectively suppress overvoltage, simulation is performed based on the first extreme working condition and the optimized bridge arm reactor parameters, and the valve-controlled unbalanced protection action constant is optimized to obtain a second scheme for further suppressing overvoltage; when the second scheme cannot effectively suppress overvoltage, simulation is performed based on the optimized bridge arm reactor parameters and the valve-controlled unbalanced protection action constant to obtain a second extreme working condition; based on the second extreme working condition, combined with engineering requirements, the converter valve assembly is optimized until a scheme that can effectively suppress overvoltage is obtained. The present invention realizes effective suppression of overvoltage of SLCC converter valves through the joint design of primary and secondary systems.

The invention discloses a production workshop intelligent material distribution method based on a time-varying station set, and the method comprises the steps: setting a station reorder point value interval based on the station information and distribution vehicle information of a production workshop, calculating a station service time window, and dynamically dividing the time-varying station set of each distribution period according to a cross relation; overall planning of a distribution path is realized by coordinating distribution decision-making elements and carrying out collaborative optimization with an adjacent distribution station set; and meanwhile, constructing a distribution optimization model under the time-varying station set, and performing hierarchical solution by adopting a double-layer genetic algorithm to obtain a joint distribution scheme including station reorder points and corresponding material distribution plans. According to the method, the time-varying characteristics of the distribution station set are considered, and the distribution path is dynamically planned according to the distribution decision-making elements, so that the distribution scheme can plan the dynamic change of the material demand of each station, the total cost of material distribution is reduced, and intelligent planning of the distribution scheme is realized.

This invention discloses a method for predicting energy consumption in the production of prefabricated building components, comprising the following steps: collecting and processing historical time-series data of prefabricated component production, establishing a time-series input matrix with multi-period feature enhancement; constructing a deep time-series quantile attention network, extracting local time-series features and global periodic dependencies in parallel through time-series quantile attention blocks to form a fused time-series representation; inputting the fused time-series representation into a progressive quantile decoder based on a quantile regression mechanism, outputting a component production energy consumption prediction interval with multiple confidence levels; adaptively adjusting the network configuration and training strategy according to data frequency and sample size to achieve model optimization and deployment; acquiring the target prefabricated component production time-series data to be predicted, and outputting the component production energy consumption prediction interval for the corresponding target period. This invention achieves joint optimization of prediction intervals and point predictions, and can output reliable intervals that meet the target confidence level, providing a quantitative basis for production scheduling risk decision-making.

The application discloses an adaptive tuning device and method of an anti-interference satellite communication antenna, and relates to the technical field of satellite communication, wherein the device comprises a multi-mode reconfigurable surface antenna module, an environment sensing module, a prediction module, a cooperative tuning module and a closed-loop feedback correction module; the application constructs a multi-mode radiation system through the multi-mode reconfigurable surface antenna module and the environment sensing module, obtains an electromagnetic environment and a link state, the prediction module predicts interference and signal deviation based on multi-source data, the cooperative tuning module synchronously executes radiation mode switching and array amplitude and phase pre-tuning according to a prediction result, preset nulls are formed before interference arrives, and the closed-loop feedback correction module collects signal quality in real time and corrects a prediction model and a tuning strategy, so that a prediction type, closed-loop self-optimizing active anti-interference tuning mechanism is formed, interference is not arrived, and tuning is performed in advance, and the anti-interference capability and communication stability under a dynamic complex electromagnetic environment are significantly improved.

The application is suitable for the technical field of power market dispatching, and provides a multi-market coordinated dispatching optimization method and system for wind-solar-water storage integration, which comprises the following steps: obtaining wind-solar-water storage system operation parameters, adjustable industrial load response characteristic parameters and power market rule parameters, constructing an adjustable load aggregation model, and establishing a multi-market regulation capacity mapping relationship for electricity energy, frequency modulation and standby auxiliary service markets; based on the mapping relationship and the operation parameters, a multi-market day-ahead joint coordinated dispatching model is constructed, with the optimal system operation comprehensive index as the target, and the base load output of various resources, the frequency modulation and standby capacity reservation, the energy storage charging and discharging and the load power regulation plan as the decision variables, and the coordinated dispatching scheme is obtained by solving the model and executed. The application effectively improves the overall operation economy, regulation flexibility and coordinated dispatching efficiency of the high-proportion renewable energy power system in the multi-market environment.

The invention discloses a bogie fault identification method based on an agent model and significance sorting, and the method comprises the steps: carrying out the preprocessing of the collected original state information in the operation process of a bogie, and obtaining the processed information; performing factor calibration on the processed information to obtain a fault factor vector; establishing a mapping relation between the fault factor and an actual physical fault state; the fault factor vector is used as an input variable, the fault severity quantitative index is used as an output variable, and a fault function meeting monotonicity or segmented monotonicity constraint is constructed; and constructing an improved Kriging model, fitting a nonlinear relationship between the fault factors and the fault function, and quantifying the proportion of the main effect and the interaction effect of each fault factor. According to the method, multi-factor significance sorting and high-precision prediction capability can be considered, and technical support is provided for intelligent fault diagnosis of the railway vehicle bogie.

The invention provides an oil-water separation effect prediction method and device based on a Lonion model. The method comprises the following steps: defining an input independent variable vector f (x) = [x1, x2,..., x8] T; performing feature engineering extension on the input independent variable vector to generate an extended feature vector f (z); the extended feature vector f (z) comprises an original feature vector, a polynomial feature vector and a physical interaction feature vector; inputting the extended feature vector f (z) into a pre-trained prediction model to obtain a predicted value, output by the pre-trained prediction model, of the oil content of the effluent of the air flotation synergistic device; inputting the predicted value into an integrated prediction framework to obtain a final predicted value and uncertainty estimation; displaying the final predicted value and the uncertainty estimation to a user through a display interface; according to the technical scheme, the accuracy, robustness and physical consistency of oil content prediction of the effluent can be improved.

This invention relates to the field of autonomous navigation technology for indoor mobile robots, specifically disclosing a SLAM method for indoor mobile robots, comprising: acquiring environmental image information of the indoor mobile robot; performing ray sampling and attention-based encoding fusion on an RGB-D image sequence to obtain first optimized parameter information; performing pose optimization processing on the initial pose information according to a target total loss function to obtain tracked frames with pose information; determining keyframes for mapping based on the tracked frames; performing ray sampling and attention-based encoding fusion on the keyframes to obtain second optimized parameter information; and performing joint pose and mapping optimization processing on the second optimized parameter information according to the target total loss function to obtain pose and map construction results. The SLAM method for indoor mobile robots provided by this invention can improve positioning accuracy in complex indoor environments.

The present invention discloses a joint optimization method for compression and reconstruction of ultra-high-definition video, and belongs to the technical field of ultra-high-definition video compression and reconstruction. The technical problem of blind reconstruction caused by the separation of ultra-high-definition video compression and reconstruction process due to the inability to obtain effective information of ultra-high-definition video at the decoding end is solved. The technical scheme is as follows: It includes the following steps: S1, establishing a bidirectional mapping relationship between ultra-high-definition video compression and reconstruction based on mathematical modeling; S2, designing an ultra-high-definition video spatiotemporal feature map information enhancement interaction mechanism based on Transformer; S3, constructing an ultra-high-definition video compression and reconstruction integrated framework based on the spatiotemporal feature map information enhancement interaction mechanism. The beneficial effects of the present invention are as follows: The present invention is oriented to ultra-high-definition video, breaks through the original video encoding and decoding framework, and realizes the joint optimization of ultra-high-definition video compression and reconstruction from the perspective of architectural design.

The application discloses a kind of quantum circuit enhanced knowledge graph reasoning method;Belong to knowledge graph technical field;Method includes: knowledge graph entity is mapped to complex vector space quantum state;For the triple to be reasoned, the parameterized quantum gate of the head entity quantum state from three rotation angle parameters is constructed to carry out relationship transformation, and the quantum exchange test is carried out with tail entity quantum state, the inner product real part is calculated to generate semantic correlation score, is converted into the probability of triple establishment;With the first-order logic rule, the joint probability distribution is constructed with the probability of triple establishment as priori, and the posterior distribution of unknown fact is solved by variational quantum circuit;Based on the joint probability distribution definition variational objective function, the entity amplitude / phase parameter, relationship rotation parameter and rule weight are optimized jointly by gradient descent, until the posterior distribution converges;Based on the posterior distribution of convergence prediction unknown fact, complete knowledge graph completion.The application realizes high-precision, low-complexity, explainable knowledge graph reasoning.

The application provides a pig behavior recognition method and device based on skeleton topology constraint and an electronic device. The method comprises the following steps: S1, obtaining a pig image to be recognized; S2, inputting the pig image into a trained pig behavior recognition model to obtain a pig behavior recognition result; the pig behavior recognition model comprises a backbone network, a neck network and a detection head; the backbone network is used for multi-scale feature extraction on the input pig image; the neck network is used for feature fusion and enhancement on the multi-scale features extracted by the backbone network to generate a multi-scale enhanced feature map; the detection head comprises a key point prediction branch, a visual classification detection branch and a behavior classifier. The application deeply fuses the skeleton space topology features between key points and visual priori, so that the behavior discrimination can explicitly utilize the geometric dependency relationship between key points, and effectively solves the problem of accurate pig behavior monitoring in a complex pigsty environment.

The invention discloses a nerve-mechanics coupling software multi-mode motion control method based on a pre-training language model. Comprising a mass point-spring-based soft robot body structure module, a rhythm oscillation signal generation and parameter control module modulated based on a pre-training language model, a muscle driving and mechanical calculation module based on nerve-mechanical mapping, and a simulation and debugging module based on physical simulation. The method is oriented to gait planning and intelligent control of the bionic soft robot in an unstructured environment. In order to solve the problem that gait switching depends on artificial parameters and is difficult to quickly adjust along with instructions, a pre-training language model is introduced, and high-level semantic instructions are mapped into neural controller characteristic parameters. The robot can smoothly switch wriggling, steering and rolling behaviors under the working conditions of a hard plane, viscous fluid and the like through a natural language without remodeling, and a control and analysis method for effectively improving the autonomous environment adaptability of a software system is provided.

The present invention belongs to the technical field of augmented reality, and discloses a dynamic synchronous positioning and mapping method and system for augmented reality. The present invention integrates binocular event cameras, binocular grayscale cameras and inertial sensor data to improve the real-time performance and accuracy of positioning and mapping. Through event noise elimination and block motion compensation, the blurring problem of traditional cameras under high-speed motion or complex lighting conditions is solved. A feature fusion strategy based on dynamic weight adjustment is innovatively proposed to fuse the event time surface with the grayscale image. The system effectively processes motion information at different scales through a variable scale joint optimization algorithm, and dynamically adjusts key frames and optimization windows to improve the robustness of the system in augmented reality scenes. The system has low computational load and high accuracy, and is suitable for augmented reality applications in real-time environments.

The invention relates to the technical field of network resource scheduling and artificial intelligence, and provides a computing power routing method based on multi-intelligence collaboration, which comprises a satellite, an unmanned aerial vehicle, a ground base station, a user terminal and other multi-layer heterogeneous nodes. Network nodes are dynamically networked through a wireless link, and node movement, topology change and multi-hop communication are supported. Each node has certain computing power and storage capability, and can cooperatively complete data processing and task distribution. The core routing nodes are deployed in a network backbone in a distributed manner and are responsible for intelligent routing and resource scheduling. The system integrates key technologies such as link quality quantification, distributed election, reinforcement learning of intelligent agents, virtual wireless interface simulation, flow control and route issuing. The method provided by the invention supports large-scale simulation, dynamic routing optimization and computing power resource collaboration, is suitable for scenes such as satellite internet, unmanned aerial vehicle clusters, emergency communication and intelligent transportation, and realizes wide-area, intelligent and high-reliability communication and computing power services.

The invention discloses a three-dimensional human body posture estimation method and device and a medium, and belongs to the technical field of computer vision. The method comprises the following steps: extracting two-dimensional human body posture key points based on an acquired video picture to obtain a two-dimensional human body posture key point sequence; projecting the two-dimensional key point sequence to a feature space through nonlinear high-dimensional mapping to obtain a high-dimensional feature space matrix; inputting a three-dimensional human body posture estimation model based on the high-dimensional feature matrix to obtain a three-dimensional human body posture key point sequence; based on the three-dimensional human body posture key point sequence, a three-dimensional human body posture estimation result is obtained through the three-dimensional coordinate point positions. According to the method, through the three-dimensional human body posture estimation model, the anti-interference capability of feature extraction is enhanced, and the robustness of three-dimensional posture estimation in a complex dynamic scene is remarkably improved.

This invention discloses a method for predicting the unsteady acoustic quality of electric drives based on intelligent sensors. Specifically, it includes: acquiring noise signals and extracting sound pressure level, loudness, sharpness, speech intelligibility, and priority speech interference level to form a feature vector sequence and dataset; constructing a neural network to determine nodes and form network parameter vectors; establishing an information entropy budget allocation model, mapping each feature to an information budget variable vector, setting constraints to construct an objective function containing budget constraint terms and prediction error terms, and embedding the budget variables into the feature representation; constructing a particle swarm, uniformly encoding the information budget vector and network parameter vector into particle state vectors and then initializing them; introducing an information budget modulation factor during iteration to weight the velocity update component, achieving co-evolution of the two; updating the optimal particle based on fitness, outputting the optimal information budget and network parameters to construct a prediction model; and inputting the feature vector sequence to output the prediction result. This invention assigns independent budgets to each acoustic feature, dynamically co-optimizes, and achieves high accuracy and fast response.

The invention discloses a semantic communication optimization method and system based on sparse reward re-optimization, and belongs to the technical field of wireless communication, and the method comprises the steps: constructing a semantic communication optimization model; establishing a reward prediction model, and predicting a reward value at a future moment in reinforcement learning; reconstructing a reward sequence based on a reward value prediction result; and performing reinforcement learning on the semantic communication channel based on the reconstructed reward sequence to realize transmission strategy updating. According to the method, the problems of sparse rewards and unstable training in semantic communication reinforcement learning can be effectively relieved under the conditions of complex channels and limited feedback, joint optimization of a semantic layer and a channel layer is realized, and an intelligent communication system is promoted to develop towards an efficient, self-adaptive and interpretable semantic transmission direction while low delay and high reliability are ensured.

The invention belongs to the technical field of augmented reality, and discloses an augmented reality-oriented dynamic synchronous positioning and mapping method and system, which integrates data of a binocular event camera, a binocular gray camera and an inertial sensor, improves the real-time performance and precision of positioning and mapping, and improves the positioning and mapping accuracy through event noise elimination and block motion compensation. The problem that a traditional camera is blurred under the condition of high-speed motion or complex illumination is solved, a feature fusion strategy based on dynamic weight adjustment is innovatively proposed, an event time surface and a grayscale image are fused, and the system effectively processes motion information under different scales through a variable-scale joint optimization algorithm, so that the accuracy of the system is improved. And the key frame is dynamically adjusted and the window is optimized, the robustness of the system in an augmented reality scene is improved, and the system is low in calculation load, high in precision and suitable for augmented reality application in a real-time environment.

This invention discloses a method for collaborative search and capture of unmanned aerial vehicle (UAV) swarms in a denied environment, belonging to the field of UAV swarm technology. The method includes the following steps: The method comprises two phases: a search phase and a capture phase. In the search phase, the UAV swarm, based on jointly solved stimulus values, covers the target area as efficiently as possible, exploring both dynamic and static targets. In the capture phase, the alpha UAV leads the arrived UAVs to attack the target until it is eliminated, completing the mission. This invention introduces a UAV collaborative localization method based on positional confidence, which is more realistic, allows for more accurate target localization, and facilitates decision-making applicable to real-world scenarios.

The application discloses a kind of financial special network optimization method and system for AI big model, it is related to communication network technical field.The method is by collecting AI business demand and network state, using the SID programming ability of SRv6, generates exclusive path strategy for AI training, inference and sensitive data flow;Real-time monitoring of computing power node load, link computing power state and network scheduling, realize the collaborative optimization of "network-computing power";Introduce the hop-by-hop path authentication mechanism based on dynamic authentication segment identifier Authen-SID, enhance the security of data transmission path;End-to-end visualization operation and maintenance and closed-loop feedback of service flow are realized by using stream detection technology.The application solves the problems of traffic scheduling mismatch, network and computing power collaboration deficiency, path security guarantee deficiency and weak operation and maintenance visualization capability in the prior art, realizes the deep adaptation of network resources and AI business demand, improves the computing power collaborative efficiency, path security and operation and maintenance intelligent level.

The invention discloses a large-scale MIMO (Multiple-Input Multiple-Output) communication-sensing integrated joint precoding and power distribution method and system, relates to the field of wireless communication, and aims to solve the problem that the existing method cannot give consideration to both improvement of communication rate and high-precision estimation of a target. The method is technically characterized by comprising the following steps of: 1, establishing a large-scale MIMO (Multiple Input Multiple Output) general sensing integrated base station downlink joint beam forming pre-coding system model; step 2, establishing minimization interference of joint power distribution and a Cramer-Rao bound ISAC downlink precoding optimization model; 3, initializing quantum positions of particles, and constructing a fitness function according to the established optimization model; 4, selecting an optimal quantum position; 5, judging the decay type of the particles, and generating new particles; 6, mapping the updated quantum positions of the particles into positions, calculating fitness function values of all the updated particles, and updating the global optimal quantum position; and step 7, performing iterative optimization to obtain an optimal ISAC joint precoding and power distribution scheme.

The invention belongs to the technical field of electric quantity transaction data quality verification, and discloses an electric quantity transaction data quality verification method, system, device and medium, and the method comprises the steps: extracting all feature data contained in an electric quantity transaction data curve, carrying out the feature selection, and obtaining the feature data after redundant information and irrelevant features are removed; reconstructing the feature data after feature selection through an integrated depth auto-encoder to obtain a low-dimensional vector and a reconstruction error feature; and inputting the low-dimensional vector and the reconstruction error feature into an estimation network, performing density estimation and energy calculation of an abnormal sample, judging whether a threshold value is exceeded or not, and completing quality verification. According to the method, the feature vectors are screened, redundant information and irrelevant features are eliminated, only features which contribute to anomaly detection remarkably are reserved, sample noise can be suppressed, and the robustness of the model is improved. Data abnormity can be effectively identified and processed, and the efficiency and reliability of power market settlement are improved.

The invention provides a dual unmanned aerial vehicle (UAV) trajectory optimization method and system applied to a multi-eavesdropper and no-fly zone coexistence scene. The method comprises the following steps: constructing a communication system model; constructing an optimized mathematical model with user scheduling, unmanned aerial vehicle beamforming and unmanned aerial vehicle flight path as constraints by taking maximization of the user safety rate as a target according to the communication system model; a block coordinate descent method is adopted to decouple the optimized mathematical model into sub-problems only about user scheduling, unmanned aerial vehicle beam forming and flight tracks of the unmanned aerial vehicle U and the unmanned aerial vehicle J; for each non-convex sub-problem, a convex approximation method is adopted to convert the non-convex sub-problem into a convex optimization form; an iterative algorithm is adopted to sequentially solve user scheduling, unmanned aerial vehicle wave beam occurrence and convex subproblems of flight paths of the user scheduling and the unmanned aerial vehicle wave beam occurrence, and finally successive approximation is carried out and a global optimal solution of an original overall optimization problem is obtained; according to the method, the waveform and the track are jointly optimized, and the eavesdropping capability of an eavesdropper is effectively interfered by using a radar signal, so that safe communication between the unmanned aerial vehicle and a ground user is ensured, and the method has obvious innovativeness and uniqueness in system design and application scenes.