323results about How to "Meet real-time requirements" patented technology

The invention discloses a method for identifying virtual malicious nodes and a virtual malicious node network in social networks. The method includes the following steps: (1) obtaining the attribute data, behavioral data and communication data of unidentified accounts from target social networks; (2) calculating the creditworthiness of each unidentified account according to the credibility model feature vector calculated using the extracted data; (3) making a comparison between the user behavioral habit statistic data of each unidentified account with the credibility being lower than a set threshold value and that of a normal user, and judging whether the unidentified account is a virtual malicious node or not; (4) sorting the virtual malicious node set and establishing association for the virtual malicious node in each sort result, so as to form a virtual malicious node network; then evaluating by using the Bayesian network algorithm to determine the final virtual malicious node network. The method provided by the invention can be used to effectively identify highly-disguised malicious nodes and a collaborative virtual malicious node network.

The invention provides a method and system for achieving integrated camera automatic focusing, and provides a zooming tracking method based on a relationship type, namely for a shot object at a fixed object distance, according to the initial zooming multiple, the value of an initial focusing position and a plurality of zooming tracking curves stored in advance, the mapping relation between the object distance interval and the focusing position is constructed, and the position to be focused is calculated in real time. Then, a gradient control function obtained by the combination of the statistical law of a lot of experimental data is utilized to achieve mobility control over a zooming motor and a focusing motor, the value of the position to be focused is improved and amended to a certain degree, the value of the position to be focused is closer to a real zooming tracking curve, and an automatic focusing effect is more accurate. Meanwhile, the zooming tracking curves are not needed to be stored in advance, all the zooming tracking curves corresponding to different object distances are not needed to be calculated point by point, an algorithm is simple, it is possible that the zooming speed meets a real-time property, and the focusing effect can meet the requirement for clearness in the whole process.

The invention discloses a method for detecting a DOS/DDOS (denial of service/distributed denial of service) attack. The method comprises the following steps of: firstly extracting needed flow characteristic parameters from network stream data, determining abnormal time points and constructing a historical time window by analyzing the flow characteristic parameters, and then finding out first N destination IPs (internet protocols) with maximum flows for the abnormal time points, determining an abnormal destination IP by analyzing sub streams including all the selected destination IPs in the horizontal time window, and finally, confirming the attack and recognizing an abnormal stream. The method is different from a conventional packet-by-packet analyzing method, adapts to the characteristic of the huge flow of a backbone network, can meet the requirement on real-time performance in the abnormality detection of the Backbone Network, can detect the DoS/DDoS attack more precisely in the backbone network, and can recognize an attack stream in the backbone network, so that a network manager can set a router in time, filters the flow sent by an attacker, and prevents the flow from harming a destination host.

The invention relates to a redundant robot dynamic obstacle avoidance route planning method using a pre-selected minimum distance index under an obstacle random motion environment. The method includes steps of using an oriented bounding box (OBB) to model a redundant robot and establishing the pre-selected minimum distance index; rejecting safe rod pieces through an intersection testing before a real-time distance is calculated; using a local mapping distance calculation method and target point coordinate values to directly obtain a real-time minimum distance and determining impact point positions in a local coordinate system of corresponding rod pieces; and on this basis, establishing obstacle avoidance gains and escape velocities relevant to the minimum distance and using a null space mapping matrix of the redundant robot to plan obstacle avoidance routes. According to the redundant robot dynamic obstacle avoidance route planning method, the safe rod pieces are rejected before dynamic obstacle avoidance planning, unnecessary trivial calculations are removed, a distance calculation method is optimized, planning efficiencies of dynamic obstacle avoidance are improved, and real-time requirements are met.

The invention discloses a vehicle detection method based on laser and vision fusion. The method comprises the following steps of 1) acquiring target detection information for an input image and laserpoint cloud; 2) performing optimal matching on the images of the front frame and the rear frame and the point cloud detection frame, and establishing a tracking sequence of an image and point cloud detection target; 3) fusing the tracking sequences of the image and the detection frame thereof and the tracking sequences of the point cloud and the detection frame thereof; 4) classifying all the target detection boxes, outputting a fusion list, and outputting a fusion result; and 5) obtaining the accurate position of the surrounding vehicle relative to the vehicle in the current frame, reading the next frame of image and the point cloud data, circulating the steps 1) to 5), and outputting a fusion detection result. According to the method, on the basis of point cloud and image target detection, the detection result is subjected to information tracking, the detection result is optimally matched, and the fusion result is preferentially input into the final fusion list, so that compared witha single sensor target detection method, the target detection precision is improved, and the false detection rate is reduced.

The invention discloses an image enhancement and partition method, which comprises the following steps of I, image enhancement: a processor and an image enhancement method based on fuzzy logic are adopted for performing enhancement processing on an image needing to be processed, and the process is as follows: i, converting from an image domain to a fuzzy domain: mapping the grey value of each pixel point of the image needing to be processed to a fuzzy membership degree of a fuzzy set according to a membership function (shown in the specifications); ii, performing fuzzy enhancement processing by utilizing a fuzzy enhancement operator in the fuzzy domain; iii, converting from the fuzzy domain to the image domain; II, image partition: partitioning a digital image subjected to enhancement processing, i.e. a to-be-partition image, according to an image partition method based on a three-dimensional fuzzy division maximum entropy. The method is simple in steps, reasonable in design, convenient to realize, good in processing effect and high in practical value, and an image enhancement and partition process can be simply, conveniently and quickly completed with high quality.

The invention relates to a reversing obstacle automatic detection and early warning method based on binocular vision. The method comprises steps that: 1), a binocular camera is calibrated; 2), a pavement area without abundant textures is removed from a binocular video frame image pair; 3), the contour at the right eye image is extracted, and cutting amount of a left eye image is determined; 4), a disparity image based on the left eye image is acquired according to the cut left eye image and the complete right eye image; 5), a splicing position of the right eye image is determined according to the disparity image of the left eye image, and the left and right eye images are spliced; 6), a suspected barrier contour and an external rectangle are extracted from the spliced image of the left and right eye images; and 7), pixel height of the suspected obstacle is calculated, the obstacle is screened, a distance to the obstacle is calculated, obstacle and distance marks are made on the spliced image, and grading early warning is realized. The reversing obstacle automatic detection and early warning method has advantages of simple and practical performance and high accuracy.

The invention provides a multi-node access detection and channel estimation method of an MMTC system based on deep learning. The method comprises the following steps: determining a pilot frequency sequence of each node according to a modulation scheme adopted by the MMTC, and determining the channel impulse response of each node; generating input data according to certain node activity, further generating a training set and a verification set for training a depth neural network and a test set for testing model performance, designing DNN and BRNN models for detecting active users, performing simulation verification, and performing channel estimation by solving a linear system of equations via a least square method according to a user activity detection result of the model. By adoption of the channel estimation method provided by the invention, under different pilot frequency lengths and different numbers of active users, the accuracy of user access detection is higher than that of the traditional method, and the time of the node access detection is greatly shortened.

The invention relates to a deep convolutional neural network based SAR image sea ice sorting method. The method includes steps of S01, segmenting a prior sea ice SAR image; S02, performing data pre-treatment; S03, performing model training and establishing a model; S04, performing treatment on a to-be-sorted sea ice SAR image; S05, combining sorting results. The method is advantaged in that the deep convolutional neural network model constructed in the method can realize characteristic automatic extraction based on images and excessive manual intervention is not needed; the method is an end-to-end SAR image sea ice sorting method and can reach sea ice monitoring service level and meet requirements on real time performance for offshore workers; image characteristics can be extracted automatically depending on a large amount of mark samples by the model and reliance on specialist knowledge is not needed; convergence is accelerated by utilizing a random gradient descending method and model training condition can be judged according to a loss function and accuracy; problems of gradient vanish or gradient diffusion in network parameter optimization counter-propagation are solved by utilizing an normative approach.

The invention provides a traffic sign detection method in a natural scene. The method comprises the steps that a detection image shot in the natural scene is acquired; luminance information of the detection image is subjected to statistical analysis, different luminance areas are divided according to grade luminance threshold values, pixel ratios of different luminance areas are calculated respectively, and the image is divided into a dark scene, a bright scene, a backlighting scene and a normal scene according to all the pixel ratios and scene classification threshold values; gamma parameter values are selected according to scene classification results, and an adaptive Gamma enhancement algorithm is adopted to perform image enhancement processing on a classification image; a partitioning algorithm is selected to perform image color partitioning according to different scenes in an RGB color space to obtain suspected target areas; a grayscale image obtained after color partitioning is subjected to binarization processing to obtain the suspected target areas after binarization; and the suspected target areas are screened through a feature screener to position traffic sign regions. Through the method, robustness and real-time performance of sign detection can be both considered.

The invention discloses a method and device for searching the shortest path of a road network. The method comprises the following steps of dividing the road network into a plurality of sub-networks to generate the road network with a tree structure; calculating the shortest path between boundary nodes of the sub-networks at the same layer; inputting an enquiry point and a target point; according to the shortest path, implementing a dynamic programming algorithm to obtain the initial shortest path between the enquiry point and the target point; and supplementing the initial shortest path to obtain the complete shortest path between the enquiry point and the target point. According to the method, the road network is divided into the plurality of sub-networks so as to generate the road network with the tree structure, and the shortest distance between the boundary nodes of the sub-networks at the same layer is calculated, so that the shortest path between the enquiry point and the target point can be rapidly obtained when the enquiry point and the target point are input; the method and the device not only are high in efficiency, but also well meet the requirement of real-time property.

The invention discloses a navigation and remote monitoring system for a miniature agricultural machine based on a DSP (Digital Signal Processor) and binocular vision. The navigation and remote monitoring system comprises a binocular vision navigation subsystem and a remote monitoring subsystem, wherein the binocular vision navigation subsystem comprises an image acquisition device, a DSP image processing device and a control device. The image acquisition device is a binocular vision CCD (Charge Coupled Device) camera which is used for transmitting the acquired analog video signal to a video decoder and converting the acquired analog video signal into a digital video signal; the digital video signal is processed by the DSP image processing device according to a binocular vision technology to obtain three-dimensional coordinates of a navigation path and a barrier; the three-dimensional coordinates are calibrated by the camera to obtain a navigation parameter which is output to the control device, that is to say, the navigation parameter is decoded by a singlechip computer; and a control signal is transmitted to a hydraulic system for further controlling on/off of a corresponding hydraulic oil path so as to realize advance and left-right steering of the miniature agricultural machine and lifting of machines and tools. Meanwhile, image information can be transmitted to a remote PC (Personal Computer) in a real time; and if emergency situations (such as failure of an automatic navigation system, barrier appearing in front and the like) occur, the machine can be remotely controlled by a user.

The invention discloses a method for building a covered wire inner full-state turbofan engine vehicle-mounted real-time model. The method comprises the steps of firstly building a vehicle-mounted real-time dynamic model, indicating an applied force-included and above-idle-speed state, of a turbofan engine, then, building a starting simplified model of the turbofan engine, finally, utilizing a Kalman filter for estimating gas circuit performance characteristic parameters of the turbofan engine, and building a vehicle-mounted self-adaptation real-time model indicating the applied force-included and above-idle-speed state. The method can meet the requirement of a covered wire full-state general model of the turbofan engine for precision and vehicle-mounted real-time performance, the strong adaptive capacity for model mismatch caused by the engine individual difference and performance degradation is obtained, model foundation is provided for experimental verification of a modern control theory and a fault-tolerant control method, and the method plays a positive and promotion role in shortening the preparation time, reducing test risk and cost.

The invention provides signal processing equipment of a PET detector based on a neural network localizer. The equipment integrates and realizes procedures for processing a series of signals from a photoelectric switching signal outputted by a detector to the action position coordinate of the gamma ray counted by a neural network in real time. The equipment comprises a compression read processing circuit to an array pixel signal outputted by a photoelectric switching device, an analog-digital switching circuit, an energy identifying and timing circuit, a proper time judging circuit, a base line recovery circuit, a signal peak value estimating circuit, a neural network real-time computing circuit, a USB interface circuit, etc. Due to the realization of the technology for processing various digital nuclear signals based on FPCA and especially the real-time computing of the neural network, the instantaneity and the integration level of the system are greatly improved. The invention has compact structure, complete performance, strong on-line data processing performance, and can be conveniently assembled into an integral detector module with the PET detector, wherein the detector module is important intermediate equipment for researching and developing novel PET imaging equipment based on neural network positioning.

The invention discloses a license plate location method based on color change points and color density. The method mainly aims at reducing influence on locating a license plate caused by inconsonant background and size of the license plate and tilting of the license plate, thereby quickly and accurately capturing an image of a license plate region from a license plate image. The method comprises the following implementation steps of: firstly, classifying image colors in an HSV (hue saturation value) space according to the distance between the colors, and converting the image to only contain eight base colors; then, performing color binaryzation on the license plate image to get four binary images, and judging whether the image contains the license plate according to the number of white pixels; later, initially locating the position of the license plate according to the color change points and bottom color information of the license plate; removing small connected regions in the binaryimages; and finally, continuously shrinking the license plate region according to the color density and the connected region information, until the position of the license plate is accurately positioned. The whole location process has no operations with higher computational complexity, and is excellent in real time property.

The invention discloses a data stream anomaly detection system based on empirical characteristics and convolution neural network. The system includes an empirical feature extraction module, which is used to identify statistical features and header features as features based on artificial experience, which play a more important role in data packet anomaly recognition; a bit stream conversion picture module used to convert the data stream into the form of two-dimensional gray-scale picture, and then through the convolutional neural network perception, the global high-level perception features are extracted; a fusion splicing module used for fusing the above modules as the data stream characteristics and identifying abnormal data streams by using the full connection layer of the neural network; a distillation model module that replaces complex networks in actual deployment; a concept drift fine-tuning module updates the detection model of concept drift; an update experience database module adding new network attacks or hidden attack instructions to the artificial experience database. The invention accurately and efficiently detects abnormal behaviors such as network failure, user misoperation, network attack and the like.

The invention discloses a real-time multi-modal medical image fusion method. The method comprises the steps of conducting Laplacian Pyramid decomposition on multiple registered medical source images to obtain low-frequency components and high-frequency components corresponding to each source image, conducting fusion on the low-frequency components of all the source images based on the sparse representation method and conducting fusion on the high-frequency components of all the source images based on the coefficient absolute value method, and conducting Laplacian Pyramid reconstruction on the low-frequency component and high-frequency component obtained after fusion to obtain a multi-modal medical fused image. The method well overcomes the defects of the traditional method based on Laplacian Pyramid conversion and the traditional method based on sparse representation, and can obtain a result obviously better than that obtained with the traditional methods. Meanwhile, the method is high in calculation efficiency and good in real-time performance, and has high application value in occasions such as clinical diagnosis.

The invention relates to a cooperative transmission method of uplinks of secondary users in a cognitive radio system. The cognitive radio system comprises primary users, secondary users and a base station serving both the primary users and the secondary users. The cooperative transmission method comprises the following steps: selecting cooperative users undergoing cooperative forwarding from idlesecondary users according to the channel information; setting beamforming factors for the selected cooperative users according to the spectrum access mode of a secondary source user; and finding out the normalized eigenvector corresponding to the maximum eigenvalue to serve as the beamforming vector of a receiving array antenna of the base station by decomposing the H2H1PsW (H2H1PsW)H eigenvalue.Compared with the existing technical schemes, the technical scheme adopted by the invention has the following advantages: the cooperative transmission of the uplinks is realized through integration and reutilization of the idle secondary user resources, thus improving the transmission performance; and the scheme is simple and easy to realize and can meet the requirements of the cognitive radio system for high flexibility and instantaneity.

The invention relates to a rock fragment size classification method based on multiple features and segmentation recorrection. The method comprises the following steps that (1) an original rock fragment size image is cut and blocked to generate subblock images of 64*43; (2) color feature extraction is carried out on the subblock images; (3) texture feature extraction is carried out on the subblock images; (4) feature selection is carried out on color features and texture features; (5) a classifier is designed through a support vector machine (SVM), and rough classification on the subblock images is completed; (6) watershed segmentation is carried out on the original rock fragment size image, and each obtained rock area is marked as blob; (7) on the basis of segmentation, a result obtained through rough classification is corrected and adjusted to improve classification performance and obtain a final classification result. According to the classification method, different classification algorithms are synthesized, efficiency and accuracy are high, cost is low, and the real-time performance requirement of a mining field is met.

The present invention provides a rapid distortion correction method based on a Bayer image. The method comprises the steps of firstly, inputting the Bayer image acquired by a fish-eye lens and a CMOS sensor in an FPGA module, adopting an equidistant internal reference model to map a fish-eye image to a three dimensional spherical surface, utilizing a longitude and latitude mode to describe the three dimensional spherical surface, and obtaining a longitude and latitude coordinate transformation relation of the fish-eye image plane coordinate points and the corresponding mapping points of the three dimensional spherical surface; secondly, adopting a vertical fish-eye projection method to obtain a calculation formula of projecting the mapping points on the three dimensional spherical surface to the corresponding projection points in a two dimensional plane, and projecting the three dimensional spherical surface to the two dimensional plane again; and finally, adopting a transverse scanning interpolation method to transform the two dimensional Bayer image interpolation into a one-dimensional image interpolation, utilizing the multi-channel correlation of the Bayer image to divide an interpolation section into a plurality of segment sections, solving a cubic polynomial curve of the to-be-fitted variables in each section separately to obtain the pixel values of the to-be-interpolated points, and carrying out the interpolation on a corrected two dimensional plane image, thereby improving the Bayer image distortion correction precision.

The invention discloses a realizing method of a multi-mode GNSS (Global Navigation Satellite System) software receiver based on a multi-core processor. One of N available cores is distributed to be used as a scheduling core for scheduling and controlling a multi-mode GNSS receiver, and the rest N-1 cores are used as processing cores for processing intermediate-frequency signals of the receiver. The realizing method comprises the steps of: firstly, distributing all the processing cores to form a related operation module by the scheduling core for completing satellite search of a GNSS, feeding the searched result back to the scheduling core; and then scheduling the processing cores by the scheduling core according to the searched result, reserving partial processing cores to maintain the work of the related operation module, forming a data channel consisting of a satellite tracking channel module and a positioning resolving module through the rest processing cores to complete the processing of the intermediate-frequency signal of the GNSS for realizing positioning navigation. By using strong processing capacity of the multi-core processor, the traditional GNSS hardware baseband chipis replaced with the multi-core processor for processing navigation signals of different types of satellites of multiple systems, and flexible configuration and scheduling can be realized.

The invention provides a vehicle license character feature extracting and classifying method using projection symmetry as the precondition for judgment. The method comprises the following steps: firstly extracting character feature to 26 letters and 10 numbers which may appear in vehicle license characters by using projection symmetry as precondition to divide into four classes, namely vertical projection class, horizontal projection class, central point projection class and dissymmetric property class and realize the coarse classifications of vehicle license characters; and fining, performing normalization transformation, then extracting features of points and rings and completing the fine classification of vehicle license characters. The invention combines the projection symmetry with the feature extraction method of points and rings to set a vehicle license character classifier, thus laying the foundation of realizing the identification of vehicle license characters finally. The method has better identification effect on confusable characters, such as '0' and 'D', '8' and 'B', '7' and 'T' and the like, thus increasing the identification speed and accuracy of vehicle license characters.

A digital device for accurately controlling frame servo system of torque gyro is featured as obtaining current, rotary speed and position signal of frame servo motor by DSP system through interface circuit; carrying out operation as per certain control algorithm to generate out control amount to be PWM-modulated; transmitting modulated PWM signal to single-phase contra variant bridge circuit by driving circuit for generating control current required by frame servo motor so as to realize accurate control of frame servo system.

The invention discloses a traffic flow information acquisition terminal based on Zynq-7000, and belongs to the technical field of a traffic control system signal device. The traffic flow information acquisition terminal based on Zynq-7000 uses a Zynq-7000 chip as a carrier to build an architecture including a video image acquisition sensor, an external memory module and an HDMI display, uses an AXI4 bus to internally interconnect a PS (Processing System) module and a PL (Programmable Logic) module, designs an IP core for accelerating convolutional neural network calculation, implements real-time data interaction between the PS module and the PL module by using an MCU to drive a communication architecture of an AXI4-VDMA IP core and an AXI4-DMA IP core, and integrates the functions of videoimage acquisition, storage, target detection, traffic flow statistics, output display and the like into a single chip, so that digital image processing and data transmission with a high-integration level, high-speed and low-latency can meet the real-time requirements for traffic flow statistics.

The invention discloses a wave front correction method based on defocused image training, and a device used by the method comprises an image acquisition module, a neural network module, a voltage conversion module and an actuating mechanism. The method comprises the following steps that the incident light is converged into defocused light spots through an optical prism; the image acquisition module is responsible for acquiring defocused light spots; after a large number of defocused light spot image training, the neural network module fits corresponding Zernike coefficients according to the image characteristics transmitted by the image acquisition module; the voltage conversion module establishes the relationship between the Zernike coefficient and a driving voltage; and finally, an execution mechanism realizes the correction of the wave front distortion generated by the atmospheric turbulence according to the fitted Zernike coefficients. The wave front correction method based on defocused image training does not need to use a Hartmann wave front detector and the like, and improves the utilization rate of light beam energy. The loop iteration process is not needed, the bandwidth is high, and the requirement of real-time performance is met. Only a single defocused image is needed to be collected for neural network training, so that the workload of image collection is reduced, and the information quantity of sample light spots is increased.

The invention discloses a multiple-subarea matching method constraint by a space relation. A plurality of subareas are selected in a matching template as sub-templates, and the sub-templates are separately and simultaneously matched with to-be-matched images. Space position relations among the sub-templates are utilized to realize the accurate matching of the images. The method comprises the following steps: the plurality of subareas are selected in the matching template respectively as the sub-templates; the space relations among the sub-templates are determined; the plurality of sub-templates keep the space position relations unchanged to form combined templates; matching is carried out by utilizing the mobile searching performed by the combined templates on the to-be-matched images, and a plurality of similarity values of the combined templates are obtained; the plurality of similarity values are compared, and a position where a maximum similarity value exists is regarded as an optimum matching position to complete the matching. The method of the invention utilizes the mutual space relations among the plurality of sub-templates to reach the requirements for accuracy and precision of matching. The object identifying performance of the method is substantially improved in real-time performance compared with the conventional large-template gray scale or contour matching algorithms.

The invention discloses a fault diagnosis method of a traction transformer based on model diagnosis. The method comprises the steps of building a two-layer structure model of the traction transformer by knowledge of a structure model of the traction transformer, measuring actual operating parameters of the traction transformer by a voltage transformer and a current transformer, and inputting the parameters into a diagnostic system to diagnose. The diagnosis is divided into an offline diagnosis part and an online diagnosis part by the fault diagnosis method at the same time. Therefore, the diagnosis speed is improved, and a fault element and the fault type are finally diagnosed by consistency diagnosis and abductive diagnosis. A simulation result indicates that the faults such as short circuit, open circuit, and interturn grounding of a winding of the traction transformer can be rapidly and accurately diagnosed, and the fault diagnosis method is a new fault diagnosis method of the traction transformer.

The invention provides an unmanned vehicle vision recognition system based on dual cameras, which comprises a first image acquisition module, a second image acquisition module, a lane recognition module, a vehicle recognition module, a traffic sign recognition module, and a traffic signal lamp detection and recognition module. According to the unmanned vehicle vision recognition system based on dual cameras provided by the invention, the technical problems that a large amount of data needs to be processed due to a single camera in a vehicle vision processing system in the prior art, and the real-time performance is influenced can be solved.

The invention relates to a communication method of an application layer of a FlexRay bus for a small satellite. Transmission information of an electronic system on the satellite is divided into two types firstly, one type of the information belongs to information transmitted in a normal mode of the electronic system on the satellite and is transmitted through a static section of the FlexRay bus, and the other type of the information belongs to information which is transmitted in an abnormal model of the electronic system on the satellite and is transmitted through a dynamic section of the FlexRay bus. According to the amount of information flow of the electronic system on the satellite, speed of the FlexRay bus, the size of a time slot in the static section, the size of a time slot in the dynamic section, and distribution of the time slots are reasonably formulated, and then a time dispatch list of the FlexRay bus is formulated to provide basis for authoring satellite-borne FlexRay bus software. The communication method resolves the problem of communication interaction of satellite application of the FlexRay bus, software programming of each node of the FlexRay bus has rules to follow, and the using rate of transmission of the FlexRay bus is also improved.

The invention discloses an arbitration device for a double computer redundancy hot backup computer. The arbitration device for the double computer redundancy hot backup computer comprises an arbitration controller (7), a state control monitor (5) and a network switcher (8), and further comprises a milestone information synchronizer (6), an IO switching module (10), an RS422 switching module (11), a KVM switching controller (9), a work host computer dual port RAM interface (3) and a backup host computer dual port RAM interface. In the initial phase of booting the double computer redundancy hot backup computer, the arbitration device firstly confirms a host computer on duty between a work host computer and a backup host computer, and secondly performs booting synchronization. In the running process after booting the double computer redundancy hot backup computer, the arbitration device firstly performs task synchronization and data synchronization on the work host computer and the backup host computer, and secondly performs switching operation so as to obtain a work shift between the work host computer and the backup host computer. The arbitration device for the double computer redundancy hot backup computer meets the requirements of a display and control platform of a modern naval ship auxiliary device for manipulation ability and timeliness of the double computer redundancy hot backup computer.