35 results about "Algorithmic complexity" patented technology

The invention provides a heterogeneous multi-core-based embedded numerical control system, which comprises a development system, an operation system and a bus servo driving unit, and is characterized in that the data output end of the development system is connected with the data input end of the operation system, and the data transmission end of the operation system is connected with the data transmission end of the bus servo driving unit; the development system is used for programming and monitoring tasks, and the operation system is used for executing control logic, controlling external equipment, receiving compiled control programs from the development system and controlling on-site input and output equipment according to instructions in the programs, so that industrial automatic control is realized. The embedded soft PLC is divided into the development subsystem and the operation subsystem to be deployed on different cores of the multi-core embedded processor, the embedded soft PLC independent of PC operation is achieved, a method for directly explaining and executing the ladder diagram is provided, the ladder diagram does not need to be converted into an instruction list program, and the operation efficiency is improved. The algorithm complexity of ladder diagram compiling and interpretive execution is reduced, and the execution efficiency of the numerical control PLC is improved.

The invention discloses a blockchain zero-knowledge proof collaborative credit risk control data privacy protection query method, and belongs to the technical field of financial science and technology and information security. According to the method, a four-layer architecture of a feature importance analysis layer, a zero-knowledge proof verification layer, a block chain intelligent contract layer and an audit log layer is constructed, so that the contradiction between privacy protection and data availability in a traditional credit risk control system is solved. According to the system, an SHAP value is adopted to calculate feature importance, a zk-SNARKs verification circuit is deployed for high-sensitivity features, zk-STARKs lightweight verification is adopted for low-sensitivity features, and differential privacy protection is achieved. And dynamically adjusting the complexity of a verification algorithm according to the credit risk score of the user, and realizing dynamic authority control based on a verification result through an intelligent contract. The verification metadata is recorded to the block chain to form a tamper-resistant auditing log, and it is ensured that the query process is transparent and traceable. According to the method, the real-time risk control requirement is met while the privacy of the user is protected, and a compliant and efficient data query solution is provided for financial institutions.

The invention relates to the technical field of power electronics and battery testing, discloses a charging and discharging control method and system for a multi-channel program-controlled battery simulator, and aims to solve the problems of response lag, high prediction algorithm complexity, timing alignment failure and poor dynamic adaptability in the prior art. The method comprises the following steps: synchronously acquiring current and voltage data with microsecond-level timestamps through hardware; predicting future current by using a lightweight gating cycle unit and transient attention mechanism hybrid model; and generating a feed-forward compensation quantity based on an inverse power level model, combining a PID feedback quantity, and dynamically fusing the feed-forward compensation quantity and the PID feedback quantity according to a transient activation factor to generate a final PWM instruction. The system comprises a synchronous acquisition module, a dynamic prediction module, a feed-forward compensation module, a closed-loop feedback module and a dynamic fusion module, and supports multi-channel independent control. According to the invention, the ultra-low delay response to the hundred-ampere / millisecond step load is realized, and the current tracking precision and the system stability are remarkably improved.

The present invention discloses a centralized intelligent spectrum management method for an unmanned system cluster network. This method constructs a weighted interference graph model and, based on node measurement information and communication performance feedback, employs reinforcement learning methods such as heuristic algorithms and probabilistic rules. This method can autonomously optimize and adjust the network's operating channels, reducing frequency conflicts within the system and the adverse effects of external interference. This method uses the communication performance actually perceived by communication nodes as feedback to guide spectrum decisions, adapts to complex and dynamic spectrum and wireless channel environments, and supports large-scale unmanned system network communication spectrum management. This method allocates channels for service data networks based on reinforcement learning algorithms such as probabilistic rules, and for wireless access networks based on reinforcement learning algorithms such as heuristic algorithms. This method has learning capabilities and low algorithmic complexity.

The present invention discloses a D2D communication resource reuse method, system, terminal and medium based on STACKELBERG game, relates to the field of wireless communication technology, solves the problem of low spectrum resource utilization in the prior art, and the key points of the technical solution are: pricing is performed based on the maximum number of times a channel can be reused and the number of times a D2D pair actually reuses the channel, and the levels of different channels are distinguished, so as to achieve the purpose of incentivizing D2D users to use resource blocks with a large number of reuse times as much as possible to improve the network spectrum efficiency. In the STACKELBERG game, the game between cellular users and D2D users is a non-cooperative game. When the game reaches Nash equilibrium, the utility of the base station and the utility of the D2D pair are both maximized. The centralized decision-making of the D2D pair uses a genetic algorithm, which greatly reduces the algorithm complexity of the number of decisions for reusing channel resources.

The present invention provides a two-dimensional high-resolution imaging method for radar targets based on a rotating antenna. The method comprises the following steps: first, constructing a rotating antenna observation model, performing Fresnel approximation processing on the distance between the radar and the target; performing "Dechirp" processing on the echo data, and then performing Fourier transform on the obtained single-frequency pulse signal to obtain the corresponding sinc-shaped narrow pulse in the distance frequency domain, and extracting the peak of the narrow pulse to obtain the distance information of the target; second, extracting the signal S of each distance unit unit (t m ), perform time-frequency analysis on the signal and substitute the approximate distance into S unit (t m ) to obtain the Doppler shift expression. The target's elevation angle is estimated based on the time-frequency plot. This elevation angle is used to construct a sparse dictionary, and the target's azimuth is reconstructed using the orthogonal matching pursuit (OMP) algorithm. This imaging method boasts a simple model design, low cost, and low algorithmic complexity. It can achieve high-resolution two-dimensional imaging of targets at all elevation and azimuth angles, and can achieve stable and effective imaging of targets in low signal-to-noise ratio scenarios, demonstrating excellent robustness.

The application discloses a satellite slant beam inter-beam interference avoidance method and system based on cost flow and dynamic graph coloring, and accurately models the interference range of slant beams in a multi-satellite satellite hop-beam system, considers the influence of the slant beams when modeling the anti-beam inter-beam interference hop-beam pattern design problem, divides the dynamic graph coloring problem into two sub-problems of satellite-wave position matching and hop-beam time slot allocation which are coupled with each other, pre-solves the satellite-wave position matching problem by using a minimum cost maximum flow algorithm, obtains a satellite-wave position matching initial solution which maximizes the sum of satellite elevation angles of all wave positions in the system under the constraint of the maximum number of simultaneously opened beams of the satellite, and on the basis, combines a tabu search algorithm and a graph coloring algorithm to jointly optimize the two sub-problems, so as to further improve the inter-beam interference avoidance performance of the same hop-beam time slot opened beam. The application can obtain a multi-satellite hop-beam scheme with minimum inter-beam interference, and the algorithm has low complexity and is convenient for actual system implementation.

The disclosure relates to a method and system for extracting disparity information out of a pair of stereo camera images for processing images of camera sensors for Automated Driving or Advanced Driver Assistance Systems in a vehicle. The method includes: a) receiving a pair of unrectified stereo camera images, b) performing a 2D-matching of the received pair of images using a trained convolutional neural network and providing the 2D-displacement on a pixel-by-pixel level as disparity information, and c) outputting the 2D-displacement on pixel level. The method is robust which does not require a high precision rectification. Applying rectification on the disparity values after the matching process leads to reduced algorithmic complexity with simultaneous preservation of all information.

The present application provides a kind of port structure deformation monitoring method and system, belong to port safety technical field.Utilize GNSS sensor to the position information of port terminal is monitored to obtain the spatial coordinate information of port terminal;According to wharf horizontal coordinate information and wharf vertical coordinate information in spatial coordinate information, based on the first prediction method and the second prediction method, the lateral displacement and settlement displacement prediction of port terminal is carried out;Finally, based on lateral displacement prediction curve and settlement displacement prediction curve, port structure deformation early warning is carried out.The monitoring method of the present application only needs to utilize the spatial coordinate information of port terminal, and the required input data amount is small;Different prediction methods are used to predict the lateral displacement and settlement displacement of port terminal, which can take into account the relationship between algorithm complexity and prediction accuracy;When setting the prediction coefficient of the prediction method, the fluctuation of the data observation value is considered, and the stability and prediction sensitivity of the prediction method are considered.

This invention relates to a ground identification method and apparatus, mobile tools, and related products. The method includes: performing ring-direction alignment processing on a single frame of multi-ring point cloud transmitted by a multi-line lidar to generate a multi-ring aligned point cloud; for each ring aligned point cloud, forming data elements from each data point in the ring aligned point cloud and its forward, backward, and left points and storing them in a queue, and sequentially calculating the connectivity relationships of the data points in each data element; generating a connectivity graph of the ring aligned point cloud based on the connectivity relationships of the data points in each data element; determining suspected ground points from the multi-ring aligned point cloud based on the connectivity graph corresponding to the multi-ring aligned point cloud; fitting a ground equation based on the suspected ground points; and determining ground points from the multi-ring aligned point cloud based on the ground equation. This invention reduces the algorithmic complexity of ground identification in point clouds and improves computational efficiency by designing the connectivity graph of each data point in the multi-ring point cloud.

The present application relates to a kind of semi-supervised learning image defogging method based on physical imaging model, belong to computer vision and image processing technical field.The method includes: S1: design the light weight dense feature fusion network with attention mechanism, using feature extraction network sharing mechanism, using adaptive weight distribution module to estimate physical model parameters from degraded image;S2: atmospheric scattering model describes the imaging process of haze degraded image, proposes an improved physical model with adaptive information compensation, calculates and restores clear image;S3: design semi-supervised learning strategy, enhance the defogging performance of network in actual haze scene, weaken the domain difference between synthetic domain and real domain.The present application can solve the problem of single image defogging recovery with light algorithm complexity, and the superiority of the present application is proved by experiment verification in real data set.

The invention discloses a linear frequency modulation signal adaptive constant false alarm detection method based on delay autocorrelation. The method comprises the following steps: multiplying a received signal by a delayed signal of the received signal, converting a chirp signal into a single-tone signal, performing fast Fourier transform, converting the single-tone signal into a frequency domain signal, and performing modulus to obtain frequency domain data; taking each sampling point of the frequency domain data as a to-be-detected unit, selecting a front group of reference units and a rear group of reference units for each to-be-detected unit, and splicing to obtain a reference sequence; the stability of the reference sequence is judged, a minimum constant false alarm detection algorithm or a maximum constant false alarm detection algorithm is adaptively selected to be used, and a judgment threshold of each to-be-detected unit is obtained; judging whether the frequency domain data of each to-be-detected unit is greater than a judgment threshold, and if so, determining that an LFM signal exists in the received signal; otherwise, the information does not exist. According to the method, the false alarm rate is reduced while the detection rate is improved under the condition of low signal-to-noise ratio, and the algorithm complexity is low.

The present invention discloses a vehicle visual positioning method, system, device and readable storage medium. The rotation matrix in the vehicle motion process is parameterized as an angle variation, and the translation vector in the vehicle motion process is parameterized as a translation component to be represented by graph theory concepts. A feature detection algorithm based on robust feature point AKAZE and a feature point matching algorithm combining zero-mean normalized cross-correlation and multi-strategy are used to ensure the accuracy of feature point extraction and feature point association. An improved RANSAC algorithm's external point removal algorithm removes noise points and erroneous matching points, ensuring the accuracy of motion calculation. The vehicle motion information is solved by a nonlinear weighted least square method and a filtering processing technology based on Sigma points is used to further optimize the motion result. The positioning accuracy is high. The improved RANSAC algorithm based on initial value estimation reduces the complexity of external point removal, ensures the real-time performance of the algorithm, reduces the number of feature points, thereby reducing the complexity of the algorithm and ensuring the real-time performance of the entire algorithm.

The present invention discloses a multimodal medical image fusion method based on a cross neural network and ECA‑S, belonging to the field of medical image analysis. The method uses two single-modal medical images as input in a fusion network model, cross-extracts image feature information through a structural function module and a functional structure module, can realize the interaction between structural information and functional information, thereby well extracting texture detail information of medical images, and adopts the ECA‑S attention mechanism to fuse the extracted feature information by continuously adjusting the weights of structural information and functional information. In the decomposition network model, the decomposition network composed of a residual network forces the fused image to contain richer information. The present invention realizes multimodal medical image fusion by combining a cross network, an improved attention mechanism and a decomposition network. The texture details and contrast processing of the fused image are more detailed, the algorithm complexity is lower, and the optimization of existing methods and the upgrading of existing technologies are realized.

The invention discloses an adaptive O-OFDM symbol decomposition noise suppression method based on a rough set information decision system, which suppresses additive Gaussian white noise at a system receiving end, and comprises the following steps: establishing a rough set theory noise suppression model by using an information decision system of a rough set theory and an indistinguishable relationship; constructing an information decision-making system according to the time domain sampling value of the decomposition symbol of the receiving end, making a decision-making rule according to the information decision-making system, and performing classification decision-making on the time domain sampling value which is originally equal to an amplitude limiting threshold and a zero value; the method comprises the following steps: exporting an indistinguishable relationship according to a formulated decision rule, carrying out attribute reduction on an information decision system by using the indistinguishable relationship, carrying out attribute reduction to obtain time domain sampling values between amplitude limiting thresholds, reconstructing the time domain sampling values, and finally carrying out bitwise merging and adding to recover an O-OFDM symbol. According to the method, the noise of self-adaptive O-OFDM symbol decomposition is suppressed by using a rough set theory, and a suppression algorithm is designed, so that the bit error rate performance of the system can be improved, and the complexity of the algorithm can be effectively reduced.

The present invention discloses an MZM non - linear equalization compensation method based on the LM - BP neural network algorithm, belonging to the field of communication. First, at the transmitting end of the system, the generation of random signals and the modulation of PAM4 signals are implemented in Matlab. A PAM4 signal IM / DD back - to - back transmission system is built using the VPI simulation platform to simulate the non - linear damage caused by MZM and transmit the signals. The signal data after transmission is collected, and digital signal processing is performed through Matlab after going offline. The compensation algorithm of the present invention is to put the offline data into the neural network model, generate a large amount of empirical data through learning and exploration of the data to train the model, and finally extract the trained neural network model to verify the data signal. This method has an extremely fast operation speed, high accuracy after training, low scheme cost, good effect, can ensure the stability of the IM / DD system transmission, achieve precise compensation for MZM; and while not increasing the algorithm complexity, reduce the transmission error rate and improve the transmission efficiency of the system.

The application discloses a kind of intelligent vehicle trajectory tracking robust control method and system, method includes the following steps: the vehicle model of target vehicle is simplified as bicycle model, establishes two degrees of freedom vehicle model;Based on two degrees of freedom vehicle model, establish vehicle trajectory tracking model;Using orthogonal decomposition method and state feedback method, establish the feedback dissipative Hamilton system of vehicle trajectory tracking model;According to the feedback dissipative Hamilton system, combined with Hamilton robust controller, design generalized Hamilton robust controller.The Hamilton robust controller designed in the application is compared with sliding mode controller and LQR controller, greatly reduces the algorithm complexity, improves the calculation efficiency, reduces the error of control vehicle tracking reference trajectory.

The invention provides a port structure deformation monitoring method and system, and belongs to the technical field of port safety. Monitoring the position information of the port wharf by using a GNSS sensor to obtain the space coordinate information of the port wharf; according to wharf horizontal coordinate information and wharf vertical coordinate information in the space coordinate information, predicting transverse displacement and settlement displacement of the port wharf based on a first prediction method and a second prediction method; and finally, carrying out port structure deformation early warning based on the transverse displacement prediction curve and the settlement displacement prediction curve. According to the monitoring method, only space coordinate information of a port wharf needs to be utilized, and the needed input data amount is small; different prediction methods are adopted to carry out transverse displacement prediction and settlement displacement prediction of the port and the wharf, and the relation between the algorithm complexity and the prediction accuracy can be considered; the fluctuation condition of a data observation value is considered when the prediction coefficient of the prediction method is set, and the stability and the prediction sensitivity of the prediction method are considered.

The application provides a path planning cooperative control method for ship-borne heterogeneous strong coupling unmanned equipment, relates to the technical field of intelligent control and path planning, and comprises the following steps: considering the longitudinal, transverse and yaw motion of an unmanned tractor and an unmanned carrier aircraft on the deck plane of an aircraft carrier, a three-degree-of-freedom coupling motion mathematical model of the heterogeneous unmanned equipment is established; a virtual force field is constructed according to an improved artificial potential field method, a field strength adjusting operator and a heading disturbance injection term are introduced, and discrete path points are generated; the discrete path points are converted into continuous expected trajectories by using a Gaussian smoothing function; precise trajectory tracking of the unmanned equipment is realized through an adaptive neural network double-layer control architecture, the upper layer controls the unmanned carrier aircraft, the lower layer controls the unmanned tractor, and cooperative control is realized by using the coupling relationship. The application introduces a first-order filter (dynamic surface control technology), effectively suppresses the differential explosion phenomenon, reduces the algorithm complexity, and improves the engineering applicability of the controller.

The present invention discloses a method and an apparatus for 2D regularized planar projection of a point cloud. The method includes: obtaining original point cloud data; initializing a planar structure of 2D projection of the point cloud; calculating horizontal azimuth information of the point cloud based on the original point cloud data; and determining a mapping relationship between the original point cloud data and the planar structure of 2D projection based on the horizontal azimuth information, to obtain a planar structure of 2D regularized projection of the point cloud. In the present invention, a large-scale point cloud may be projected to a 2D regularized planar structure without 2D local search. Therefore, complexity of an algorithm can be reduced, time spent on 2D regularized planar projection of the point cloud can be reduced, and algorithm performance can be improved.

A repository replica management method. The method comprises: receiving a backup command, which backup command is used for backing up a primary repository of a user, wherein the primary repository comprises at least one piece of object data and reference data of the at least one piece of object data, and the reference data is used for pointing to the at least one piece of object data; and in response to the backup command, compressing the at least one piece of object data in the primary repository to generate a compressed file, and storing replicas of the compressed file in a replica storage medium corresponding to the primary repository. In the method, replicas are created only for object data that occupies a large storage space and has a low update frequency in a repository, and reference data that occupies a small storage space and is frequently updated remains shared, such that an object-level repository replica of the repository is implemented. A synchronization and consistency algorithm for the object-level repository replica is simple, and thus both the high algorithmic complexity associated with strong consistency algorithms used to resolve repository divergence due to concurrent writes and the risk of repository corruption are avoided.

The invention provides a rotor position estimation method of a high-order sliding-mode observer based on a variable reaching law, and the method comprises the steps: obtaining an alpha-axis stator voltage, an alpha-axis stator current, a beta-axis stator voltage and a beta-axis stator current of a permanent magnet synchronous motor in an alpha-beta coordinate system; the obtained data are input into the preset high-order sliding-mode observer with the variable reaching law, the rotor position is estimated through fusion of the variable reaching law and the high-order sliding-mode observer, and rotor position estimation can be achieved under the condition that the rotor position does not need to be measured through a sensor; and the hardware cost required by the rotor position determination scheme is effectively saved. The high-order sliding-mode observer based on the variable reaching law under the alpha-beta coordinate system achieves estimation of the rotating speed and the rotor position, the number of times of coordinate change can be reduced, the algorithm complexity is reduced, a low-pass filter is avoided, and the dynamic performance and the observation precision are improved.

The invention discloses a method and a device for analyzing the influence of distributed new energy output on power distribution network electric energy quality. The method comprises the following steps: acquiring a correlation model between the output power of distributed new energy power generation equipment and a power distribution network electric energy quality index; performing simplification processing on the correlation model based on the short-line characteristics of the low-voltage power distribution network, and representing the electric energy quality index as a function relationship between the active power of the distributed new energy power generation equipment, the reactive power of the distributed new energy power generation equipment and the engineering coefficient; fitting the engineering coefficient through actual measurement data of the power distribution network to obtain an engineering simplified correlation model; and determining the influence of the distributed new energy output on the power quality of the power distribution network based on the engineering simplified correlation model. According to the method, the technical problems of large calculation data demand, high algorithm complexity and poor engineering practicability of the existing analysis method for the influence of the distributed new energy output on the power quality of the power distribution network are effectively solved.

The invention discloses a data intelligent application system based on large model driving, which comprises a multi-source data processing module, a large scene application module, a small and medium scene application module, an optimization feedback regulation module and an intelligent management information output module, relates to the technical field of data analysis, and solves the problems of accurate classification of small and medium scenes and large scenes and high efficiency. According to the method, a lightweight tool and a simplified model are adopted for small and medium-sized scenes, the data analysis cost is reduced, and the large-scale scenes are subjected to real-time streaming through the cooperation of a distributed computing framework and a real-time streaming engine, so that the real-time streaming capability of the large-scale scenes is improved, and the real-time streaming capability of the large-scale scenes is improved. The real-time requirements of complex scenes such as smart cities and industrial internet are met, the processing efficiency is improved, feedback is collected from multiple dimensions of a data end, a service end and a model end, data hysteresis and tool parameters are corrected for medium and small scenes, flow engine delay and algorithm complexity are optimized for large scenes, and the long-term precision retention rate of the model is improved.

The present invention provides an optimal consistent method and device for approximate dynamic programming of heterogeneous multi-UAV data collaboration, and relates to the field of multi-agent control technology. The method comprises: obtaining the state data of the heterogeneous multi-UAV system, constructing an identifier with a compensation signal, approximating the dynamic model information of the heterogeneous multi-UAV system, constructing a compensator with a dynamic signal, and obtaining a weight update rule of a neural network with a compensator identification; constructing a Hamiltonian function according to the weight update rule; constructing a cumulative square error with a collaborative signal according to the Hamiltonian function; using a gradient descent method to obtain a weight update rule of a cumulative execution-collaborative judgment neural network, and calculating the constant weights of two adjacent iterations; judging whether a preset threshold condition is met according to the error of the constant weights of two adjacent iterations; if met, stopping iteration, and outputting the optimal consistent solution of the heterogeneous multi-UAV system. The present invention can improve the scalability of the algorithm and reduce the complexity of the algorithm.

The present invention relates to a positioning method and system based on trackside components. The method includes marking the relative positions of the components beside the track to be detected; selecting a component in a picture under normal conditions as a reference target, marking the absolute coordinates of the reference target and the target to be located in the picture according to the relative position, and using this as a training set; fitting the mathematical relationship between the target to be located and the reference target based on a neural network according to a deep learning regression algorithm based on the training set; training and establishing a positioning prediction model based on deep learning based on the mathematical relationship; obtaining the absolute position of the target to be located using the positioning prediction model according to the absolute position of the reference target. By marking the positions of the trackside components of various models, fitting the mathematical relationship between the reference target and the target to be located using a regression network, and establishing a positioning prediction model for each specific model, the data demand is greatly solved, the algorithm complexity is reduced, and the calculation speed is improved.

The present application relates to the technical field of optical fiber communication, and particularly relates to a GRU neural network nonlinear equalization method based on a regression coupling value, which first reshapes the data structure of a received signal sequence and matrixes it; then in the training stage, a nonlinear damage error factor based on a nonlinear Schrodinger equation is used to guide network updating, and in the application stage, a signal is input into a GRU neural network, and a signal nonlinear damage compensation result is obtained through the propagation algorithm of the network. The method of the present application uses a GRU neural network algorithm based on a regression coupling value to realize the capture and compensation of signal nonlinear damage, solves the problems of high algorithm complexity, multiple iteration numbers and limited nonlinear compensation capacity in digital back propagation and learning type equalization algorithms, further improves the effectiveness and practicability of the nonlinear equalization algorithm, and has an important application prospect in the field of digital signal processing related to optical communication.

The present invention belongs to the field of neural network technology, and discloses a method for maintaining high performance of a ternary neural network when a memristor fails, comprising: pre-training the ternary neural network on software to obtain weight matrices of each layer; detecting whether a memristor device in a memristor array has a stuck fault and obtaining a fault map; adjusting the memristor device in the memristor array according to the weight matrix and the fault map, that is, initializing the memristor array; performing row and column flipping operations on the memristor array according to the weight matrix and the fault map; performing row and column permutation operations on the memristor array according to the weight matrix and the fault map; and optimizing the original memristor array by adding a redundant memristor array. The present invention does not require retraining, and requires low computing power; the Hungarian algorithm used in the present invention has lower complexity than the existing KM algorithm. The present invention can enable a ternary neural network based on a memristor array to exhibit good classification accuracy even when a certain proportion of memristor devices have stuck faults.

The invention discloses a ship cable intelligent arrangement method based on an improved Dijkstra algorithm. According to the method, through main path optimization, the number of branches is reduced, and the cable laying concentration ratio and the maintenance efficiency are improved; node capacity constraint is introduced, and cable overload and physical damage are avoided; three-dimensional space dynamic adjustment is supported, and modular ship design change requirements are met; the algorithm complexity is low, the optimal solution can be quickly generated, and the manual design cost is reduced.

The application provides a 2.5D global positioning method, system, equipment and medium for exhibition visitors, relates to the technical fields of meta universe and artificial intelligence, and comprises the following steps: S1, a zero Z-axis plane of a global coordinate system is arranged in a positioning domain, global grid points are divided for the whole scene, and the global grid points are projected into each camera to obtain image grid points of a video image; S2, a nearest neighbor point is acquired according to the image grid points; and S3, the mean value of the neighbor points acquired by a plurality of cameras is taken as the global position of the target. The application can adjust the positioning accuracy and avoid the algorithm complexity caused by three-dimensional reconstruction.