116results about How to "Meet the accuracy requirements" patented technology

The present invention relates to a target tracking cooperative control system and method based on multiple unmanned surface vehicles. The system is formed by connecting a shore-based global location host and a single unmanned surface vehicle control system through a wireless communication module. The method comprises the operation steps of: 1) a formation generation process: employing an auction algorithm to find a multi-target distribution scheme of the multiple unmanned surface vehicles with the maximum income of an unmanned surface vehicle group; 2) motion of the unmanned surface vehicles to perform geometric path planning from any initial state to a target point; and 3) prediction of a target motion track through adoption of a prediction model based on a particle swarm to replace communication abnormal data and perform formation track tracking. The method reduces the calculation amount of the multiple auction processes, achieves the real-time demands of task distribution of the unmanned surface vehicles, employs the path planning method based on the geometric method and the track tracking method based on the neural network to meet the timeliness and the accuracy requirements ofsingle-vehicle track tracking control, employs the motion track predicted by employing the particle swarm optimization to perform compensation, improves the tracking capacity of the unmanned surfacevehicles in the limitation of the communication condition and allows the formation tracking to have high reliability and stability.

The invention discloses a host network abnormal behavior detection and classification method under a large flow environment, and belongs to the technical field of the Internet. The method comprises a first steps of creating and maintaining a network connection table; a second step of creating a connection record table; a third step of traversing a transcript of the network connection table; a fourth step of calculating the entropy and the largest proportion of a source/destination port of a host; and a fifth step of identifying abnormal network behaviors of the host. The method determines whether a node behaves abnormally or not based on several factors including the entropy and the largest proportion of the source/destination port of the source node, and further classifies the abnormal behaviors. The abnormal behavior detection method provided by the invention is practical, capable of adapting to large flow environments, simple in algorithm and easy to implement in various network devices, has certain real-time property, meets the accuracy requirement at the same time, can identify a variety of network scanning and DoS/DDoS attack traffic, provides detailed information and assists in further accurate interception.

The invention discloses an external control method and system of a mobile terminal. An induction earphone is connected to the outside of the mobile terminal, and at least one pressure sensor is arranged on the induction earphone. The external control method comprises the steps: connecting the mobile terminal with the induction earphone wirelessly or in a wired way, wherein the wireless connection includes Bluetooth connection; obtaining induction signals of the pressure sensor at preset time intervals, outputting the detected data according to the induction signals, wherein the detected data comprises the collected pressure values and the corresponding time values; and comparing the detected data with the preset command parameter, according to the compassion result, judging a control command which takes an application program of the mobile terminal as a target input, and executing the control command. With the adoption of the external control method and system, a user can accomplish compelled motions directly through the ears or the assistance of hands, and especially when the user is enjoying music or is engaged in a conversation, the user can pass on relevant operation command without taking out the mobile phone. Therefore, the external control method and system can bring brand-new convenient operation experiences to the user.

A remote network seal-affixing device able to automatically affix seal under the control of remote signal and with high safety is composed of a box consisting of upper cover, main body, hydraulic cylinder, interface and electromagnetic lock, a sealing moving unit, a sealing holder, a document bearing tray, a seal destroyer, a recognizer, a projector, a camera head, and a control circuit.

The invention discloses a channel estimation method and a device based on Doppler frequency shift. The method comprises the following steps: receiving a frame signal, and carrying out channel response estimation to subcarriers at pilot frequency symbol positions in the frame signal to obtain channel response values of the subcarriers of the pilot frequency symbol positions; computing relative velocity V of a terminal relative to a base station during receiving pilot frequency symbols; based on the velocity V, computing the shift quantity of channel response values, caused by the Doppler frequency shift; and utilizing the channel response values of the subcarriers of the pilot frequency symbol positions and the shift quantity of the channel response valves to estimate so as to obtain the channel response values of the subcarriers of data symbol positions between adjacent pilot frequency symbols. By the method disclosed by the invention, the requirement of a user on the accuracy of the system channel estimation under the condition of high-velocity moving can be met, the system error rate is reduced, and the good property of a system can be ensured.

The invention discloses a face posture recognition method, device and e apparatus and a storage medium, and belongs to the technical field of face recognition. The method comprises the following steps: acquiring a region image containing a face region; calling a posture angle recognition model to recognize a face posture in the area image, wherein the posture angle recognition model is a machine learning model with face posture angle recognition capability; wherein the posture angle recognition model is obtained by training a sample face image marked with a sample face posture angle, and the sample face posture angle is any angle selected in a circumferential angle range; and obtaining the human face attitude angle output by the attitude angle recognition model, wherein the human face attitude angle comprises a pitch angle, a yaw angle and a roll angle of the human face relative to a shooting position. The human face posture angle is any angle within the range of the circumferential angle.

The invention discloses a simulation system for virtual driving behavior of drivers of subway trains. The simulation system is characterized by comprising a database support system, a route simulation module, a power supply system simulation module, a signal system simulation module, a train simulation module and a simulation module for driving behavior of the drivers. The database support system is used for providing modeling data support, the route simulation module is used for outputting route status information, the signal system simulation module is used for outputting signal equipment status information, the train simulation module is used for outputting train status information, receiving a working condition and a level model which are outputted by the simulation module for the driving behavior of the drivers and simulating the working condition and the level model, and the simulation module for the driving behavior of the drivers is used for outputting the working condition and the level model. The invention further discloses a simulation method implemented by the simulation system. The simulation system and the simulation method have the advantages that the driving behavior of the drivers of the subway trains can be accurately simulated, requirements on simulation accuracy and diversity can be effectively met assuredly owing to a model grading idea, and effective functional support is provided for developing a train dispatching simulation system of a subway OCC (operation control center).

The invention relates to the field of computer vision and particularly relates to an improved MSER image matching algorithm. A speeded up robust feature (SURF) and a maximally stable extremal region feature (MSER) are combined to carry out image feature extraction and matching so as to generate a feature vector, and then the Euclidean distance is used to carry out the coarse matching of an image so as to preliminarily correct the space geometric distortion of the image. Then the scale invariance of an H-L feature is applied, and a feature point comprising a large amount of image structure information can be detected. According to the algorithm, the complementarity of feature extraction of two parties in the multiple transformation conditions of the image can be fully utilized, and the robustness of matching between images in a complex environment in a time condition acceptable range is achieved.

The invention brings forward a panorama image generation method and apparatus for a user terminal. The panorama image generation method for the user terminal comprises the following steps: obtaining multiple images shot by the user terminal, determining adjacent relations among the multiple images, and performing characteristic matching on adjacent images to obtain a matched characteristic point pair; according to the matched characteristic point pair and initial camera parameters, obtaining camera parameters after optimization; carrying out color adjustment on the adjacent images to obtain the adjacent images after the color adjustment; and according to the camera parameters after the optimization, splicing the adjacent images after the color adjustment so as to generate a panorama graph. The method provided by the invention can improve the image splicing speed.

The invention provides an image registration method and an image registration device based on a cascade structure. The method comprises the steps: carrying out bilateral matching processing on a characteristic point of a reference image and that of an input image to obtain a first coarse matching group; eliminating matched pairs in the first coarse matching group according to a predetermined confidence coefficient to obtain a second coarse matching group, wherein the similarity of the matched pairs eliminated from the first coarse matching group is lower than the predetermined confidence coefficient; eliminating the matched pairs in the second coarse matching group according to a predetermined threshold value to obtain a third coarse matching group, wherein the gray distance or the space distance between any two matched pairs in the matched pairs eliminated from the second coarse matching group is greater than the predetermined threshold value; processing the matched pairs in the third coarse matching group by adopting an RANSAC (RANdom SAmple Consensus) algorithm; and aligning the reference image with the input image spatially according to a transformational matrix so as to obtain an output image.

The invention discloses a moving object detecting method based on a graphics processing unit (GPU). The moving object detecting method comprises the steps of loading video training data to a GPU video memory, memorizing the video training data in a local Z-shaped block mode, extracting multi-feature data of the video training data, adopting a self-adaption weight model to integrate the multi-feature data so as to build a codebook model based on multiple features, loading video testing data to the GPU video memory, memorizing the video testing data in a local Z-shaped block mode, utilizing the codebook model based on multiple features to perform moving object detection to the video testing data, utilizing a moving object detection result to update the codebook model based on multiple features and memorizing the moving object detection result on the GPU side. The moving object detecting method has the advantages of being high in applicability, strong in expandability, high in efficiency and low in cost and enables moving object detection to meet the requirements for accuracy and timeliness.

The invention discloses a man-machine interaction method and system of an intelligent terminal. The intelligent terminal is in wired or wireless connection with induction headphones. The method comprises steps as follows: a digital gyroscope in the headphones collects action data in a moving process of the headphones and sends the action data to the intelligent terminal; the action data are analyzed, so that a control command matched with the action data is obtained and executed, wherein the control command comprises action of a focus identification on a user display interface; a pressure induction device in the headphones obtains an induction signal produced by tensioning action of user's ears and outputs induction data to the intelligent terminal according to the induction signal; and a control command matched with the control command is analyzed and executed after the induction data are analyzed, wherein the control command comprises triggering project pointed by the focus identification. Head action of a user and the tensioning action applied to the ears are detected by the induction headphones, so that the user can send an operation command without touching a telephone when listening to music, watching a video or even selecting a detailed menu.

The present invention discloses a kind of seal moving device for remote network seal machine, and aims at providing one kind of seal moving device capable of shifting the seal horizontally for remote network seal machine. The seal moving device includes fixing support, longitudinal driving mechanism, transverse driving mechanism, movable rack and seal holder fixed on the movable rack. The longitudinal driving mechanism connected to the fixing support includes a longitudinal motor; and the transverse driving mechanism includes a transverse motor. The present invention may be applied widely in seal management of different industry.

The invention discloses a 10kV feeder line fault prediction method based on a CNN (Convolutional Neural Network) and LightGBM (LightGBM), and the method mainly comprises the steps: 1), obtaining the original data of a power distribution network, and carrying out the preprocessing of the original data of the power distribution network; 2) extracting features from the original data of the power distribution network, and constructing a feature set f {f1, f2... F15, f16, L}, wherein L is a label and represents whether the feeder line has a fault or not, the element f1, the element f2, the elementf3, the element f4, the element f5 and the element f6 are inherent attribute characteristics, the element f7, the element f8, the element f9, the element f10, the element f11, the element f12 and theelement f13 are statistical analysis characteristics, and the element f14, the element f15 and the element f16 are depth time sequence features extracted by a convolutional neural network CNN; 3) establishing a power distribution network fault prediction model; and 4) inputting the real-time data of the 10kV feeder line of the power distribution network into the power distribution network fault prediction model to obtain a feeder line fault prediction result. The 10kV feeder line fault prediction method has the advantages that the rapidity and timeliness are better, and the prediction result can provide auxiliary decision support for the operation and maintenance management personnel of the power distribution network on the premise of meeting the accuracy requirement.

The invention discloses a calibrating method of a forward scattering type visibility meter. The calibrating method is characterized in that a plurality of standard measuring points of the forward scattering type visibility meter in a full scale are quantitatively simulated through combination of a diffuse scattering board and attenuation sheets with different transmittances, and a coefficient of the forward scattering type visibility meter is modified by using a modifier formula, and therefore, the forward scattering type visibility meter is calibrated. The calibrating method is applied to the calibration after the forward scattering type visibility meters are produced in batch and used for a long time; the coefficient of the forward scattering type visibility meter can be accurately, conveniently and rapidly modified, and thus the accuracy and consistency of the forward scattering type visibility meter are improved, and the difficulty that the forward scattering type visibility meter can not be rapidly and accurately calibrated indoors is solved. The calibrated forward scattering type visibility meter and a standard meter are experimentally compared, the measurement error of the visibility within 10kilometers is less than 10 percent, the measurement error of the visibility of more than 10 kilometers is less than 20 percent, and the forward scattering type visibility meter accords with the requirement of accuracy in requirements of functions and specifications of forward scattering type visibility meters issued by the China Meteorological Administration.

The invention relates to an automatic calibration method, an automatic calibration system, terminal equipment and a computer readable storage medium. The method comprises the following steps: obtaining target parameter information of a target calibration object when a calibration instruction of the target calibration object is detected; matching the target parameter information with preset parameter information pre-stored in a calibration database, wherein the calibration database stores calibration compensation information generated by using the collected preset parameter information of a plurality of preset calibration objects; determining preset parameter information corresponding to the target parameter information according to a matching result; and executing calibration compensationoperation on the target parameter information by utilizing the calibration compensation information of the preset parameter information. The calibration operation process can be simplified, the test rate is improved, the accuracy requirement of the test system can be met, and the test efficiency and accuracy are improved.

The invention discloses a leveling device for suspension equipment. A panel is supported on four supporting legs. Each supporting leg comprises a motor fixing box, a motor, a ball screw pair and a cylindrical supporting leg body. The motor fixing boxes are fixed to the end portion of the bottom face of a fixing plate of a sliding screw base. The motors are arranged in the motor fixing boxes through motor fixing frames installed in the motor fixing boxes. Screws in the ball screw pairs are connected with main motor shafts through couplers. The inner cavities of the cylindrical supporting legs are provided with the screw pairs. The screws in the ball screw pairs penetrate out of the motor boxes and extend into the screw pairs in the cylindrical supporting legs. The leveling device for the suspension equipment can greatly lower the work intensity, save labor and materials, improve the work efficiency by 88%, improve the installation process quality, meet the precision requirement, prevent the equipment from breaking down or being damaged irreversibly in the debugging or operating process, and save the operating cost for enterprises.

The invention belongs to the technical field of geophysical exploration, relates to a method for detecting chemical weapons abandoned by Japan and buried in land, and is suitable for detecting single-shot or multi-shot chemical weapons which are abandoned by Japan and buried in various depths. The method comprises nine steps of arranging a metal detection work area, carrying out metal detection and marking a shell reaction point, excavating and removing a shell in an excavating while detecting manner, arranging a magnetic method detection work area, carrying out magnetic method detection data acquisition, analyzing a magnetic method detection result, determining the analyzed result, excavating while detecting by using a metal detector and excavating to remove a magnetic anomaly point, and excavating and thoroughly confirming detection. A known shell the buried depth of which is no more than 1 meter and the surface of which is free of metal object interference, a known shell the buried depth of which is more than 1 meter and the surface of which is free of metal object interference, and an area where the surface of a shell is provided with strong metal object interference are distinctively excavated. The method for detecting the chemical weapons abandoned by Japan and buried in land is advantaged by being high in sensitivity, high in safety, high in detection result accuracy, timeliness and reliability and the like.

The invention discloses a portable bus hydraulic punching machine and a punching method. The hydraulic punching machine at least comprises a hydraulic punching machine main body, a hydraulic punching machine bottom plate, a scale mother blank side bracket, a movable scale set I, a movable scale set II, a scale mother blank for positioning scales, and a protective upright post; a perforated fixed hydraulic punching machine and the scale mother blank side bracket are arranged on the hydraulic punching machine bottom plate; after supporting and accurately positioning the scale mother blank, the scale mother blank side bracket properly prolongs the length of the scale mother blank to the positive direction of the hydraulic punching machine main body, and keeps the height of the scale mother blank accordant with the punching plane of a hydraulic punching machine static impact mill; the scale mother blank is provided with two inverse trapezoidal positioning tenons for accurately positioningthe scale set I or the scale set II; and the scale set I is used for a vertical alignment method, and the scale set II is used for a planar alignment method. The portable hydraulic punching machine punches a bus based on the punching requirement of a bus connection form by using a scale I or a scale II with graduation as a reference of a bus punching circle center.

The invention belongs to the field of sensor calibration, and in particular relates to an angle displacement sensor automatic calibration system and method. The system comprises an angle displacementcontrol module, N angle displacement sensor excitation input modules, an electrical signal acquisition module and a calibration module, wherein N is a natural number greater than or equal to 1. The angle displacement control module is used to fix a calibrated angle displacement sensor and control the calibrated angle displacement sensor to rotate. The output end of the angle displacement sensor excitation input module is connected with the excitation input end of the calibrated angle displacement sensor. The excitation output end of the calibrated angle displacement sensor is connected with the input end of the electrical signal acquisition module. The angle displacement control module, the angle displacement sensor excitation input module and the electrical signal acquisition module are all connected with the calibration module. The problems of low automatic angle displacement sensor calibration efficiency, easy error and low versatility in the prior art are solved.

The invention discloses a quality control method of upper-air wind data and a quality monitoring device of the upper-air wind data. The quality control method of the upper-air wind data comprises the following steps that: whether thermal wind residual errors of sounding data collected by each sounding station, in corresponding standard constant-pressure surfaces, is in a pre-set maximally-permissible error range, is judged; whether dynamic coordination exists in wind fields and temperature fields of the sounding data collected by each sounding station on two adjacent standard constant-pressure surfaces is judged; and therefore, corresponding quality control codes can be provided for the sounding data collected by each sounding station. With the quality control method of the upper-air wind data of the invention, a situation that large scale dynamics balance constraints are mainly adopted in current upper-air data quality control are changed. The quality control method of the upper-air data is based on statistical distribution of thermal wind residual errors. According to the quality control method of the upper-air wind data, full consideration is given to temporal and spatial distribution characteristics of difference values between thermal wind and actual wind shear, and requirements of work such as increasingly-refined climate statistics and numerical value forecast for accuracy and integrity of upper-air observation data can be satisfied.

The invention discloses a method for simultaneously determining 20 organic amines, biogenic amines and cations in a fruit juice beverage, belonging to the field of analytical chemistry. In the method, a conductivity inhibition-ion chromatography method is adopted, and under the condition of multi-stage gradient elution, the experimental determination conditions are optimized by researching a plurality of experimental influence factors, so that the method for simultaneously determining 20 organic amines, biogenic amines and cations (methylamine, dimethylamine, trimethylamine, diethanolamine, butylamine, histamine, cadaverine, putrescine, agmatine, heptanediamine, spermidine, spermine, Li+, Na+, NH4+, K+, Mg2+, Ca2+, Sr2+ and Ba2+) in the fruit juice beverage is established. The method is rapid, simple and convenient, high in high in sensitivity and good in stability, and achieves the aims of rapid analysis, green analysis and multi-component simultaneous analysis.

The invention provides a complex scene-oriented license plate recognition method, which is used for performing license plate recognition on a to-be-detected image in a complex scene so as to recognizea character recognition result of a license plate, and is characterized by comprising the steps of S1, preprocessing the to-be-detected image to obtain a preprocessed image set; S2, building a YOLOv3license plate detection model; S3, training a YOLOv3 license plate detection model of the license plate detection training set; S4, building an end-to-end license plate recognition convolutional neural model; S5, training an end-to-end license plate recognition convolutional neural model; S6, inputting the preprocessed image set into the trained YOLOv3 target detection model to perform license plate target detection so as to obtain a license plate position; S7, processing the corresponding to-be-detected images according to the license plate positions in sequence to obtain a license plate image set; and S8, inputting the license plate image set into the trained end-to-end license plate recognition convolutional neural model to obtain a character recognition result of the license plate.