259results about How to "Implement features" patented technology

The invention discloses an unmanned aerial vehicle routing inspection line adaptive obstacle detection method and system based on a monocular camera. The method comprises the steps of obtaining initial flight parameters, evaluating and optimizing the flight parameters based on scene complexity, collecting an original image sequence and carrying out quality evaluation and exposure compensation. Constructing a multi-resolution feature pyramid, extracting feature descriptors, and performing feature matching and verification; performing multi-constraint pose optimization and depth estimation based on a feature matching result, and reconstructing a three-dimensional point cloud; calculating direction enhancement response to extract a power line candidate area, performing multi-view line segment matching and three-dimensional reconstruction, and analyzing dynamic characteristics of the power line; and based on the power line three-dimensional model and the depth map, carrying out obstacle segmentation, calculating an obstacle distance, evaluating a risk level, and generating early warning information and a monitoring scheme. According to the invention, different scenes can be adaptively processed, the obstacle risk can be accurately identified and evaluated, and the inspection efficiency and reliability are improved.

The invention discloses a large container crane intelligent early warning system based on personnel behavior analysis, and the system comprises the following steps: collecting operator behaviors, equipment operation states and environment parameters in real time through a multi-mode sensor network, constructing a dynamic data set, extracting behavior characteristics through a time difference embedding algorithm, and carrying out the early warning of the operator behaviors, the equipment operation states and the environment parameters; a multi-level spatial-temporal characteristic matrix is generated in combination with dynamic scene influence factors, regional risk factors are dynamically calculated by using a Bayesian inference model, comprehensive risk values are generated through an adaptive multi-objective optimization algorithm, risk levels are divided into low, medium and high classes, and a system triggers graded early warning responses according to the risk levels. And an optimized behavior adjustment suggestion is generated in combination with a quantum probability behavior prediction algorithm, and a feature extraction rule and risk assessment logic are dynamically optimized through a progressive learning algorithm to form a closed-loop optimization process. According to the invention, intelligent analysis and real-time early warning of personnel behaviors are realized, and the safety and adaptability of the crane in an operation environment are improved.

The invention discloses an editable condition printing image generation method, and belongs to the field of image generation. The method comprises the following steps: constructing a printing image generation model, and training the model to obtain a generation model with editable conditions; and generating a printing design image meeting the condition requirements by using the model. According to the method, different advanced features of each element of the printed plane image are extracted through downsampling, element and style information in the printed plane image is captured through multi-scale feature extraction and information exchange, then hidden space vectors corresponding to different printed plane image features are analyzed, fusion of specific styles is achieved in the hidden space, and the image quality is improved. And the decoding of the printing design image is realized through the decoder, the decoupling of the element information is realized, the personalized design and diversified requirements can be met, and the expansibility is realized.

The invention discloses an intelligent remote follow-up visit method and system for tumor patients, and relates to the technical field of medical intellectualization, and the method comprises the steps: collecting follow-up visit data through a remote medical terminal, carrying out the processing of the follow-up visit data, obtaining a physical sign index sequence and a symptom feature vector, and generating a dynamic follow-up visit evaluation score through a time sequence prediction model; performing semantic analysis on the electronic medical record data of the patient based on the medical knowledge map, extracting tumor stage classification data, chemoradiotherapy measurement data, weight nutrition indexes and laboratory inspection data, determining a basic risk coefficient, a cumulative dose ratio and a body state score, and generating a baseline follow-up assessment score through a prediction model; and according to the dynamic follow-up evaluation score and the baseline follow-up evaluation score, calculating a comprehensive follow-up grade and generating a next follow-up plan. According to the invention, the accuracy of patient state evaluation is improved, accurate configuration of medical resources is realized, and the quality and efficiency of remote follow-up visit are effectively improved.

The invention discloses an intelligent online monitoring device for a transformer, and relates to the technical field of transformers. The intelligent on-line monitoring device for the transformer comprises a front-end acquisition layer, a data transmission layer, a data processing and analysis layer, a cloud monitoring and management layer and an emergency response layer, through the deployment of the high-precision sensor network and the edge computing nodes, the system can accurately collect and process the operation data of the transformer in real time, and the monitoring accuracy and efficiency are improved. And secondly, the data transmission layer adopts a self-adaptive communication protocol and a data encryption technology, so that real-time and reliable transmission and security protection of the data are ensured, and meanwhile, traceability and tampering prevention of the data are realized by utilizing a block chain technology. In the data processing and analysis layer, the system realizes intelligent early warning and continuous optimization of transformer data through an integrated learning and deep learning model, and improves the accuracy and reliability of early warning. And the emergency response layer is arranged, so that the system can quickly respond to faults.

The invention discloses an orthogonal fractured reservoir frequency variation AVO inversion and parameter prediction method, and relates to the technical field of exploration geophysics, and the method comprises the steps: deducing and reconstructing a fluid saturation horizontal-vertical orthogonal fractured medium rock physical model based on a multi-group fracture-pore medium Chapman model, realizing fluid saturation rock physical modeling containing two groups of orthogonal fracture media; based on an anisotropic reflectivity algorithm and a fluid saturation horizontal-vertical orthogonal fractured medium rock physical model, realizing frequency variation AVO forward modeling of the fractured reservoir, and calculating a frequency variation AVO reflection coefficient and a synthetic seismic record of the fractured reservoir under the interface reflection model; a frequency-varying AVO inversion theory based on a Bayesian framework is constructed by taking a forward modeling process of the frequency-varying AVO as a drive, and the frequency-varying AVO inversion based on the Bayesian inversion framework is realized and reservoir parameter prediction is carried out by utilizing pre-stack seismic AVO data and introducing a simulated annealing-particle swarm algorithm in an inversion process. The method provides a new method and a new means for fine description and prediction of the fractured reservoir.

The invention provides an engine pipeline measurement method based on laser scanning, which belongs to a non-contact measurement technology, and comprises the following steps: before measurement, selecting a three-dimensional laser scanner, configuring and calibrating the three-dimensional laser scanner, carrying out a scanning test, adjusting a scanning environment, and then obtaining pipeline point cloud data; the outlier error of the pipeline point cloud data is denoised by using a KD-tree (K-dimension tree), and the outlier error of the pipeline point cloud data is denoised by using the KD-tree (K-dimension tree); de-noising system errors and random errors of the pipeline point cloud data by using a Gaussian filter; segmenting the point cloud data of the pipeline, and identifying and isolating all parts (linear segments, elbows and joints) of the pipeline; performing reverse modeling on the pipeline point cloud data to obtain a digital model of the pipeline; and comparing and analyzing the original point cloud of the pipeline and the pipeline model, and evaluating the error of pipeline measurement. According to the invention, the feature extraction and processing analysis of the pipeline point cloud are realized, the time cost of engine pipeline assembly is reduced, and the wear of the pipeline is reduced.

The invention, which relates to the technical field of the cloud security system, discloses a monitoring method for a cloud security system comprising a data acquisition and preprocessing system, a threat detection and identification system, a behavior analysis and association analysis system, a risk assessment and early warning system, a response strategy making and execution system, and a monitoring report and log management system. The data acquisition and preprocessing system is used for acquiring network traffic, system logs and user behavior data in real time, and the threat detection and recognition system is used for performing threat detection and recognition on the preprocessed data. The data analysis and machine learning technology extracts significant features related to threats from a large amount of monitoring data, and the features can help a machine learning model better understand and classify potential threats, so that the accuracy and efficiency of threat detection are improved, the system automatically learns and updates threat features, and the threat detection efficiency is improved. And rapid identification and response to novel threats are realized.

The invention relates to the technical field of reservoir storage capacity measurement, in particular to a reservoir storage capacity measurement method and system based on remote sensing and point cloud data, and the method comprises the following steps: extracting a terrain surface point set based on input remote sensing image data and point cloud data, and calculating a three-dimensional coordinate value, a curvature value and a point density value of each point; and comparing and classifying curvature and density data in combination with the change of the reservoir boundary. According to the method, through comprehensive application of remote sensing image data and point cloud data, three-dimensional coordinates, curvature values and point density values are adopted to accurately extract topographic features, scientific division and feature description of sub-regions are achieved, refined three-dimensional grid parameters are generated through fitting and interpolation calculation, and the accuracy of the three-dimensional grid parameters is improved. The terrain modeling precision and the space geometry description capability of a complex area are improved, boundary node coordinates and normal vectors are dynamically adjusted, the grid continuity is optimized, the integrity of a boundary transition area is ensured, and the boundary complex volume is accurately described by adopting unit volume subdivision and boundary correction.

The invention discloses a scraper conveyor remaining service life prediction method, medium and equipment, and relates to the technical field of remaining service life prediction.The scraper conveyor remaining service life prediction method comprises the steps that multiple pieces of original graph data are constructed for obtained different sensor data of a scraper conveyor; taking graph data constructed after preprocessing as the input of a model through a convolution layer, extracting features, obtaining a dynamic dependency relationship between sensors through a graph structure learning layer, and fusing the spatial correlation of the sensors; spatial features of aggregation nodes are updated through a graph attention network in a space-time fusion layer, then a time attention mechanism and a bidirectional gating circulation unit are utilized to extract a dependency relationship between sequences, and finally fused space-time features are input into a full connection layer to determine the final remaining service life, so that life prediction of the scraper conveyor is realized.

The invention discloses a feature weighted fusion hyperspectral image classification method based on GAT and CNN. The method comprises the following steps: firstly, dividing a hyperspectral data set into a training set, a test set and a verification set; then, a double-branch feature weighted fusion network model combining CNN and GAT is constructed; inputting the divided training set into a classification network model, setting parameters, and training to obtain training indexes; and finally, evaluating indexes, data and image information of a classification result. According to the method, the Euclidean and non-Euclidean feature information of the hyperspectral image is fully extracted by utilizing the capture capability of the depth separable convolution and the GAT on the pixel information and the super-pixel data, and the accuracy and generalization capability of classified data are improved.

The invention discloses a 3D multi-target tracking method, a 3D multi-target tracking product, a 3D multi-target tracking medium and a 3D multi-target tracking system, and the method comprises the steps: S1, obtaining original point cloud data of a vehicle, and carrying out the preprocessing of the original point cloud data, and obtaining the preprocessed point cloud data; s2, extracting features in the preprocessed point cloud data to obtain a target detection result and generate a thermodynamic diagram; s3, performing motion compensation on the spatial position and orientation of the target by associating the thermodynamic diagrams of the multiple frames of point clouds; and S4, carrying out confidence evaluation on the corrected target in the thermodynamic diagram, and adjusting a detection tracking strategy according to an evaluation result. According to the invention, the tracking precision and stability can be obviously improved.

The invention discloses a charging pile electric energy consumption prediction method based on improved deep learning. The method comprises the following steps: preprocessing collected charging pile electric energy consumption data; extracting weather data, time features and special event features from the preprocessed data to form a feature data set; constructing a hybrid deep learning model by using a dynamic sparse connection network and a self-supervised learning deep pseudo-label generator, and generating a preliminary prediction result; a variational information bottleneck mechanism is introduced, and optimized feature representation is generated; an improved tensor fusion network is adopted to realize deep fusion and feature extraction of data; training the new feature representation by using a neural evolutionary strategy optimization algorithm to generate an optimized model; verifying the optimized model through a variational Bayesian verification method; and the reliability and the accuracy of the prediction result in real application are optimized by adopting an adversarial evaluation mechanism.

The invention relates to a fault prediction method for a mining high-voltage power distribution device. Temperature, current, voltage and gas leakage sensors are installed in the mining high-voltage power distribution device; integrating the multi-dimensional data by using a multi-sensor data fusion algorithm to form a real-time operation portrait of the power distribution device; extracting time sequence characteristics of temperature and voltage key variables by combining an operation rule of the high-voltage power distribution device; capturing abnormal fluctuation of the key characteristic parameters by using a sequence analysis model, and identifying a specific fault type in the high-voltage environment; constructing a feature mode database based on a time sequence, and matching new data with a historical fault mode; applying a feature selection algorithm to extract representative fault prediction features from the sensor data; establishing a fault prediction risk level model according to the hazard degrees of different fault modes; based on the fault risk level predicted in real time, an optimized self-adaptive maintenance scheme is generated, and the maintenance period and strategy are dynamically adjusted in combination with the actual operation state of the equipment.

The invention provides a pathological section image analysis method and device based on a large language model, and belongs to the field of computer vision, deep learning and medical image analysis. The method comprises the following steps: constructing an image coding large model which is composed of a pre-training model and an additionally-added task head and aims at a pathological section; freezing the pre-training model, training a task head by using a preset training set, and checking by using a preset verification set to obtain a task head after preliminary training; unfreezing the pre-training model, performing fine tuning training on the large model by using the training set, and performing inspection by using the verification set so as to obtain a finally trained large model; and inputting a to-be-analyzed pathological section image into the large model, and calculating corresponding features of the image based on a result output by the large model to obtain a final analysis result. According to the method, the features in the pathological section image can be automatically extracted, and the diagnosis report is generated based on the features, so that the accuracy and efficiency of pathological section image diagnosis are improved.

The invention discloses a malicious encrypted traffic detection method and system based on incremental learning, and belongs to the technical field of network security. The method comprises the following steps: processing original traffic into a standardized feature sequence by adopting a double-layer nested data organization structure, and constructing a sample set; pre-training a malicious traffic detection model by using the sample set; model parameters of the pre-trained malicious traffic detection model are optimized in an incremental learning mode, an experience buffer area used for storing historical training samples, model output results of the historical training samples and sample labels is arranged in the incremental learning process, and the model parameters are continuously updated in the model parameter optimization process; the problem of disastrous forgetting of the model in the continuous learning process is solved by combining dynamic experience playback, stability constraint and a double-layer element updating mechanism; and finally, detecting data to be detected by using the trained model. According to the method, the detection accuracy of the malicious encrypted traffic can be effectively improved, the continuous adaptive capacity of the model is kept through incremental learning, and the method has good practical value.

The invention provides an interpretable fine-grained image recognition method, device and equipment and a medium, and relates to the technical field of fine-grained image recognition, and the method adopts a double-branch structure frame, and combines feature fusion and prototype learning to carry out fine-grained image classification. Wherein the feature extraction layer captures a complex mode and long-distance dependence in an image through a self-attention mechanism, meanwhile, a feature selection fusion module is introduced, information marks of prototype learning are effectively selected, and it is ensured that the learned prototype pays more attention to the prospect. The prototype learning layer designs a double-branch structure, processes and learns a global prototype and a local prototype of an image respectively, improves the accuracy of the network, and explains a model classification result from a global perspective. And the full connection layer realizes a final classification decision, and obtains a final classification probability through weighted sum. The objective of the invention is to solve the problem of black box characteristics of an existing deep learning model in fine-grained image classification and improve the application credibility of the model in the high-risk field.

The invention provides a pressure test control method and system for a universal pressure testing machine, and relates to the technical field of material testing machines, and the method comprises the steps: obtaining hydraulic cylinder parameters and servo valve parameters, building a pressure control mathematical model, calculating a transfer function, initializing a feedback controller, determining the number of hidden layer nodes, and setting an activation function and a network weight. A self-adaptive feedback controller is obtained; generating a closed-loop control system, constructing an objective function based on an integral absolute error and adjustment time, determining a Pareto optimal solution set in combination with a multi-objective genetic algorithm, selecting a neural network training sample, generating a training sample set, performing offline pre-training, and updating a network weight to obtain a pre-trained neural network; and the feedback controller is embedded into a pressure control system, outputs feedback controller parameters, sets an anti-saturation strategy of the feedback controller, judges whether the feedback controller is saturated or not, limits the amplitude of an integral item according to the relation between an error signal and a saturation direction, determines whether to stop integration or not, and generates a press control strategy under the current working condition.

The invention discloses a projection lens and a projection illumination system. The projection lens comprises a first lens, a second lens, a third lens and a fourth lens which are coaxially arranged in sequence from an image side to an object side, the diopter of the first lens is negative, the diopter of the second lens is negative, the diopter of the third lens is positive, and the diopter of the fourth lens is positive. By limiting the ratio of the focal length of an optical system to TTL * F.NO, the projection lens can be ensured to have the characteristic of large aperture, the size of the lens is effectively reduced, the ultrathin characteristic and miniaturization of projection are realized, the total length of the projection lens along the optical axis direction is reduced to be less than 23mm, the size of the projection lens is reduced, and the size of the projection lens is reduced. Therefore, the lamp is more suitable for the welcome lamp of the vehicle.

The invention discloses an instrument electric signal detection system, which comprises a signal acquisition module, a signal processing module, a data display module and a data storage library, and relates to the technical field of electric signal detection. According to the instrument electric signal detection system, the voltage waveform curve graph is established according to the converted digital signals, the change trend of the corresponding digital signals is determined according to the curve processing algorithm, a plurality of zero crossing points in the change process are searched, then curve segmentation operation is carried out with each zero crossing point as a node, and the segmented curves are covered. According to the method, the signal change period is determined, and the corresponding calculation period is compared with the set period of the output source, so that the electric signal detection efficiency is improved, meanwhile, the accuracy of a data result is verified, the detection operation can be quickly distinguished aiming at waveform change data, and actual application is facilitated.

The invention provides a postoperative drainage amount monitoring method and system for a drainage tube, and relates to the technical field of postoperative drainage monitoring. The method comprises the following steps: collecting drainage liquid time sequence monitoring data of a plurality of postoperative patients and carrying out adaptive wavelet packet decomposition to obtain sub-band coefficient matrixes corresponding to a plurality of decomposition layers of each group of drainage liquid time sequence monitoring data; determining a plurality of target demarcation points and drainage liquid monitoring stage division results of each postoperative patient, and fusing the drainage liquid monitoring stage division results of the plurality of postoperative patients to generate a target stage division scheme; obtaining physical sign monitoring data of each postoperative patient, and extracting drainage liquid stage monitoring features and physical sign stage monitoring features of each postoperative patient; and constructing a postoperative drainage monitoring model, and performing postoperative drainage monitoring on the target patient based on the postoperative drainage monitoring model. According to the invention, multi-stage refined monitoring and characterization analysis of the drainage volume of the postoperative patient are realized.

The invention relates to the technical field of photoelectric communication, in particular to a PWDM (Pulse Width Division Multiplexing) optical device compatible with high and low rates, which comprises a first controller, a second controller is arranged at one end of the first controller, a transmission mechanism is connected between the first controller and the second controller, a processing mechanism is mounted at one end of the first controller, and the processing mechanism is connected with the first controller. A mounting mechanism is mounted on the first controller; through installation of the transmission mechanism, the first controller and the second controller, data processing is facilitated, optical signal receiving and transmitting are achieved, high-speed data transmission is achieved, meanwhile, compatibility of low-speed data transmission is reserved, and therefore the experience that a user uses two rates mutually is met; different optical signals can be further processed, the stability of data transmission is achieved, through connection of the installation mechanism, the equipment is convenient to install and operate, and the equipment can work more stably.

The invention discloses a semantic segmentation method, a robot grabbing pose estimation method based on semantic segmentation, a computer program product and a terminal, and belongs to the field of semantic segmentation, and the method comprises the steps: constructing a semantic segmentation network, and enabling a decoder of the semantic segmentation network to comprise a lightweight feature fusion module which is used for carrying out multi-scale conversion processing on low-level features and high-level features, performing cross aggregation processing on different scale features, performing heterogeneous convolution processing, and finally performing feature fusion processing; training the semantic segmentation network; and inputting the to-be-segmented image into the trained semantic segmentation network to obtain a semantic segmentation result. Through the lightweight feature fusion module, the problem of feature fusion with inconsistent semantics and scales can be effectively solved, feature fusion and information interaction are better realized, pixel loss is reduced, and the quality of fused features is remarkably improved, so that the precision of semantic segmentation is improved.

The invention discloses an enterprise knowledge graph inference completion method and inference completion system, and the method comprises the steps: obtaining a to-be-completed knowledge graph of a target enterprise, inputting the to-be-completed knowledge graph into a trained knowledge graph embedding model, and predicting an incomplete triple to achieve the inference completion of the knowledge graph; wherein the knowledge graph embedding model comprises an entity node coding module, a multilayer graph convolutional neural network module, a Softmax pooling layer and a gating neural network based on a self-attention mechanism. According to the method, the knowledge graph embedding model can provide more accurate semantic information for feature interaction, multi-level feature interaction and aggregation of the triple on sub-tensor kernel granularity and global granularity can be realized, and hidden semantics behind entities and relationships of the knowledge graph can be more efficiently mined. Therefore, the knowledge graph completion accuracy, the semantic modeling capability, the interpretability and the like are obviously improved.

The invention discloses a broadband ultra-wide-angle metamaterial wave absorber and application, and belongs to the field of electromagnetic wave absorption. The broadband ultra-wide-angle metamaterial wave absorber is formed by periodically arranging a plurality of wave absorber units; each wave absorber unit sequentially comprises a metal layer, a first high-resistance surface layer, a dielectric layer, a lumped element layer and a second high-resistance surface layer from bottom to top; each of the first high-resistance surface layer and the second high-resistance surface layer is of a dielectric structure with a high-resistance surface coating on the surface; the lumped element layer is composed of a metal and a resistor. The lumped element and the high-resistance surface are combined, the broadband wave-absorbing characteristic and the wide-angle wave-absorbing characteristic are both considered, the wave-absorbing performance in a TE polarization state is enhanced by adopting the non-centrosymmetric design, the design process is simplified, the structure is simple, the cost is low, and the wide application prospect is achieved.

The invention relates to a planet bearing fault feature enhancement and diagnosis method and device. VMD (variational mode decomposition) and MNAD (minimum noise amplitude deconvolution) are combined; firstly, a vibration signal of a shell of the planetary reduction gearbox is collected, the output rotating speed of the planetary reduction gearbox is measured, and a fault characteristic frequency interval is introduced by calculating the theoretical fault characteristic frequency under the working condition; inputting a vibration signal into the VMD, improving the VMD by taking the maximum envelope spectrum amplitude in the interval as an optimization target so that the VMD can adaptively decompose an optimal component, and taking the frequency under the maximum envelope spectrum amplitude as the actual fault characteristic frequency; inputting the optimal component and the actual fault feature frequency into the MNAD to realize further fault feature enhancement; and finally, identifying a fault type through an envelope spectrum. According to the method, the fault features can be effectively extracted from complex planetary bearing fault signals in an enhanced mode, then fault diagnosis is achieved, meanwhile, the proposed method can process the signals in a self-adaptive mode, and intelligent diagnosis can be achieved in industry.

The invention belongs to the technical field of medical diagnosis, and discloses an MRI lumbar sagittal view disease image diagnosis method based on a YOLOv8s algorithm, and the method comprises the following steps: S1, screening collected MRI lumbar sagittal view image data, self-building a VOC2007 data set, and marking the data set; s2, performing data enhancement and other processing on the existing data set; s3, establishing a YOLOv8s deep learning neural network detection model; dividing the data set into a training set, a verification set and a test set according to a proportion of 8: 1: 1, training the YOLOv8s network until convergence, and obtaining a lumbar disease image detection model based on the YOLOv8s network and a weight file; and S4, testing the randomly extracted MRI lumbar vertebra sagittal view image of the patient by using the trained YOLOv8s detection model, outputting a detection result, and evaluating the detection result. According to the invention, the diagnosis of the MRI lumbar sagittal view disease can be realized, the detection precision and efficiency are greatly improved, and the diagnosis accuracy is improved.

The invention discloses a gas turbine oscillation combustion fault detection method and system, and relates to the technical field of gas turbine fault detection, and the method comprises the steps: collecting the data of a gas turbine for preprocessing; performing multi-scale analysis on the gas turbine data by using continuous wavelet transform, and analyzing and extracting a wavelet coefficient; classifying and distinguishing fault features and stable features; and taking the classified wavelet coefficient as the input of the convolutional neural network, and training the model until the diagnosis effect is optimal. According to the method, time-frequency analysis is carried out on flame pressure pulsation data by adopting continuous wavelet transform, wavelet coefficients are extracted, and data dimension reduction and feature extraction are realized. A convolutional neural network deep learning algorithm is used for flame feature classification, and compared with a traditional fault diagnosis method, the fault diagnosis time is shortened, the fault diagnosis speed is increased, and the calculation cost is reduced. The oscillation combustion fault detection algorithm provided by the invention has higher detection speed and higher accuracy.

The invention discloses a combined type car running vehicle pneumatic flight intelligent device and method. According to the device and method, technologies such as multi-rotor wings, especially turning, folding and pneumatic layout of a vehicle door into wings, 5G communication, Beidou and a Beidou enhancement system, a composite new material and the like are integrated to form a multifunctional carrying tool; ict technology detection and 5G remote audio and video interactive consultation are used for monitoring life dynamic information and fatigue state indexes of a pilot in real time, and six symmetrical and obliquely upward rotary wings are used for assisting flight and hybrid braking; a rotary aeronautical instrument cross pod is additionally arranged, and a one-key starting and airline-selecting take-off flight control system is arranged; a mobile phone navigator and a support are arranged in a cockpit, an intelligent mobile phone, a CAN bus and an IMU inertial unit are used for detecting the acceleration and the flight attitude, and an aerocar joystick, an airspeed tube and an empennage structure of the airspeed tube are iteratively upgraded and energized, such as an elevator, a navigation light and a rudder; the system is integrated, and 200-meter short-distance take-off and landing and run-up take-off are carried out; therefore, the hovercar can stably fly on land, sea and air and safely drive.

The invention relates to the technical field of tumor immunotherapy, in particular to a dual-targeting chimeric antigen receptor, a recombinant expression vector, an anti-tumor natural killer cell and application, and provides a PD1-DAP10-41BB-P2A-NKG2D-Ig4-CD28-41BB-CD3 zeta fusion receptor.The dual-targeting chimeric antigen receptor can target PD-L1 on the surface of a tumor cell, can also recognize NKG2DLs and can be used for preparing anti-tumor natural killer cells. The broad spectrum of the NK cells is greatly improved, and meanwhile, the immune escape of tumor cells is also reduced.