74results about How to "Status accurate" patented technology

The invention relates to the technical field of data processing, and discloses a power distribution method based on AI artificial intelligence and an intelligent power distribution cabinet. The method comprises the following steps: carrying out real-time data acquisition on key nodes of the power distribution system through an intelligent sensor network to obtain original operation data of the power distribution system, and carrying out cleaning and standardization processing on the original operation data to obtain a standardized data set; performing analysis modeling on the standardized data set through multi-level hybrid model training to obtain an AI analysis model of the power distribution system; processing and analyzing the power distribution data collected in real time to obtain a load prediction result and a dynamic balance strategy; performing abnormal mode identification and multi-path reasoning on the system operation parameters to obtain equipment health state evaluation and maintenance suggestions; and based on the equipment health state assessment and maintenance suggestions, performing multi-objective optimization calculation on renewable energy source supply and power consumption demands to obtain an intelligent energy scheduling scheme. According to the invention, the efficiency and accuracy of the power distribution method based on AI artificial intelligence are improved.

The invention discloses an equipment operation evaluation method based on multi-source data fusion, and relates to the technical field of equipment operation evaluation, and the method comprises the following steps: S1, data collection: collecting multi-source data of equipment in a real-time operation state, the multi-source data comprising sensor data, operation logs, environment data and maintenance history of the equipment; according to the equipment operation evaluation method based on multi-source data fusion, accurate equipment state monitoring and fault prediction can be provided, and the performance and accuracy of the equipment can be continuously optimized according to actual equipment operation data and environmental conditions of the equipment, so that efficient and intelligent equipment management and maintenance strategies are realized; according to the method, specific steps and features of the equipment operation evaluation method based on multi-source data fusion are clearly defined, comprehensiveness and high efficiency of the method are ensured, and the method is suitable for various complex industrial environments so as to improve reliability and maintenance efficiency of equipment.

The invention discloses a power system false data injection attack detection method and system. Measurement data of a power distribution system are acquired; constructing an attack sequence, and injecting the constructed attack sequence into the obtained measurement data to simulate a false data attack; constructing a false data injection attack detection model based on the expansion causal convolution and the recurrent neural network to analyze the state change of the power distribution system; and judging an attack behavior according to the analyzed state of the power distribution system. According to the method, time sequence data can be processed, the long-term dependency relationship and the complex mode can be captured, and the response capability to the complex attack mode can be improved, so that the false data injection attack can be effectively detected and prevented, and the safety and reliability of the power distribution system can be maintained.

The invention discloses a new energy automobile lithium battery thermal runaway monitoring system and method. The system comprises a vehicle-mounted lithium battery, a multi-source data acquisition module, a multi-source data fusion module, a hidden Markov model monitoring module, a state identification module, a data analysis processing module, an early warning module, an emergency processing module, a wireless communication module, a cloud platform and a battery management system. The multi-source data acquisition module is responsible for acquiring multi-dimensional data such as voltage, temperature and internal resistance of a lithium battery, and the data fusion module processes the data and then transmits the data to the hidden Markov model monitoring module for state monitoring and prediction. According to the system, a high-order hidden Markov model and a hidden semi-Markov model are adopted, state retention time modeling and a self-adaptive online learning mechanism are combined, and conversion of a battery from a normal state to an abnormal state and a thermal runaway state can be accurately recognized. The system remarkably improves the safety and stability of the lithium battery of the new energy automobile, and is suitable for power battery management of the new energy automobile.

The invention relates to the field of data signal processing, in particular to a network algorithm cable terminal discharge early warning method, device and system based on dual-channel waveform identification. The method comprises the following steps of: firstly, acquiring a periodic ultrasonic signal and an ultrahigh frequency electromagnetic field signal generated by discharging of a cable terminal, converting the two signals into a corresponding time-frequency diagram by utilizing short-time Fourier transform, and performing normalization processing to serve as input of a subsequent network; a dual-channel high-order feature extraction network formed by cascading a ConvNeXt backbone network and a convolution block attention module is constructed, and is used for extracting time-frequency diagram detail fault features of ultrasonic signals and high-frequency electromagnetic field signals. And finally, by constructing an adaptive feature fusion network, fusing the fault features of the two channels, and obtaining cross-channel fault joint features by using an adaptive weight coefficient vector, thereby outputting a diagnosis result, and realizing accurate evaluation of a defect state of an adjacent cable terminal.

The invention provides an air-ground cooperative wetland air patrol method and device and electronic equipment, and the method comprises the steps: making a patrol flight plan based on an operation region and a flight limiting region defined by an operation control management center; daily first flight confirmation is carried out, the unmanned aerial vehicle is controlled to take off under the condition that the flight control right is obtained, and automatic patrol is carried out according to the patrol flight plan. In the automatic patrol process of the unmanned aerial vehicle, image video data are collected in real time, the operation condition is monitored, and the real-time equipment state and alarm information of the unmanned aerial vehicle are obtained. Analyzing according to the image video data to obtain an inspection result, and generating an alarm signal when an abnormal condition exists in the inspection result. And judging whether the unmanned aerial vehicle has a fault based on the real-time state of the equipment and the alarm information, and controlling the unmanned aerial vehicle according to a preset fault handling strategy when the unmanned aerial vehicle has the fault. According to the method, an air-ground cooperation technology and an intelligent technology are combined, the wetland inspection efficiency is effectively improved, and intelligent management of the wetland protection area is realized.

The invention provides an NPU instruction level context switching method and device, and relates to the technical field of data processing.The method comprises the steps that when a context switching instruction is received, a non-refresh instruction is forbidden, it is waited for completion of an instruction being executed, and context data is stored in a main storage module; after completion, recovering the context switching related instruction of the NPU module, and executing the target task; and after the target task is executed, recovering the context data to the NPU module, and recovering the execution of the non-refresh instruction. According to the method, optimization is carried out aiming at the problem that an existing NPU architecture generally can only support limited preemption at an algorithm boundary or a specific context switching point and even cannot preemption, so that a high-priority task must wait for a low-priority task to be completed, and remarkable priority inversion and / or key inversion are / is caused. A full-stack solution from a system on chip to an instruction set architecture and then to an operating system kernel is provided, and preemption of an NPU instruction level is achieved.

The invention is suitable for the field of pressure storage power station construction monitoring, and particularly relates to a BIM-based pressure storage power station underground chamber construction monitoring method and system, and the method comprises the steps: obtaining monitoring data used for representing the surrounding rock state of an underground chamber in a construction model; extracting feature distribution of the monitoring data in a time dimension by using a long-short term memory neural network; extracting feature distribution of the monitoring data in a spatial dimension based on a graph attention network; fusing the feature distribution of the monitoring data in the time dimension and the space dimension by adopting a cross attention fusion network structure, and constructing fusion features of the monitoring data; and taking the fusion features as input of a pre-constructed prediction model, and outputting a surrounding rock state prediction result. According to the invention, the BIM model and the monitoring data are combined, so that the construction state of the underground chamber can be comprehensively understood; and on the basis of feature extraction of monitoring data, monitoring and early warning of the surrounding rock state are realized, and the technical problem of poor accuracy of a monitoring result of a traditional monitoring method is solved.

The invention discloses a fracture diagnosis method and device, and belongs to the technical field of oil and gas reservoir fracture exploitation, and the diagnosis method comprises the steps: building a water hammer equation set through a water hammer continuity equation, a water hammer motion equation and a liquid elasticity equation; solving the water hammer equation set to obtain a distribution equation of the water head, the flow velocity and the density of the middle well section; initial conditions in the studied wellbore are determined, and expressions of water head, flow and density distribution are obtained in combination with the distribution equation; coupling the water hammer equation set with a wellhead flow boundary condition and a well bottom crack boundary condition, and calculating to obtain a water hammer pressure curve; and crack parameters are determined by adjusting crack related parameters in the well bottom crack boundary conditions. The device comprises a memory for storing a computer program and a processor, wherein the program comprises the steps corresponding to the method. According to the method, the related parameters of the fractures after fracturing are rapidly diagnosed based on the water hammer pressure in the fracturing process, a basis is provided for design and modification of an oil and gas resource reservoir transformation scheme, and therefore the fracturing transformation effect is improved.

The invention discloses a power distribution network fault early warning method, system and device and a storage medium, and the method comprises the steps: carrying out the processing of the operation state data of a power distribution network according to a minimum redundancy and maximum correlation algorithm, and obtaining a corresponding dimension feature tag; performing classification processing on the data according to the dimension feature tags to obtain a balanced data set; constructing a target data model based on a weak classifier through a multi-dimensional integration strategy; and optimizing and integrating the target data model based on a gradient lifting decision tree algorithm to obtain a fault prediction model for fault prediction. According to the method, the integrated prediction model is constructed by using the multi-dimensional weak classifier, so that the state in the operation process of the power distribution network can be accurately predicted, the fault risk of the power distribution network can be early warned in advance, and a more effective fault maintenance strategy can be formulated, thereby reducing the occurrence of power distribution network accidents and improving the economy and safety of the power distribution network.

The embodiment of the invention discloses a sleep monitoring method and device, electronic equipment and a storage medium. The sleep monitoring method comprises the steps of receiving a reflection signal sent by a millimeter wave radar monitoring user, processing the reflection signal, generating multi-frame point cloud information, and determining sleep information of the user according to the change degree of the point cloud information in a monitoring area. According to the embodiment of the invention, the reflected signal of the position of the target user is acquired through the millimeter-wave radar, so that the user does not need to wear any equipment, the use is more comfortable, the reflected signal acquired by the millimeter-wave radar generates the point cloud information accurately representing the user state, the sleep condition of the user is accurately identified, and the user experience is improved. Therefore, the sleep health condition of the user can be accurately evaluated, and the user experience is further improved.

The invention discloses a data linkage early warning analysis system based on a GYK operation record and a vehicle-mounted video, and relates to the technical field of rail transit, and the data linkage early warning analysis system comprises the steps that vehicle-mounted equipment collects vehicle operation data and vehicle-mounted video monitoring data respectively and sends the vehicle operation data and the vehicle-mounted video monitoring data to a ground storage analysis server; the ground storage analysis server performs timestamp synchronization and data fusion on the vehicle operation data and the vehicle-mounted video monitoring data, realizes real-time anomaly detection based on fused data, gives an alarm according to a preset early warning level, and transmits alarm information to the vehicle-mounted equipment; in addition, the WEB client is connected with the ground storage analysis server, and supports real-time data synchronous search and manual selection of alarm information. Through linkage analysis of the GYK operation record and the vehicle-mounted video file, intelligent early warning of rail car operation is achieved, and the operation safety and the fault early warning capacity of the rail car are effectively improved. And meanwhile, the operation process is simplified, and the working efficiency is improved.

The invention discloses a live pig breeding equipment full life cycle monitoring management system, a medium and a terminal. The system comprises a user login module used for user authority management and data query; the data acquisition module is used for acquiring full-life-cycle data of intelligent equipment used for pig breeding; the data preprocessing module is used for preprocessing operation data of various intelligent devices monitored in real time by using the Internet of Things technology; the data storage module is used for storing various data information; the data analysis module is used for predicting and evaluating the state of the intelligent equipment by adopting a deep neural network model; the equipment state early warning module is used for setting an early warning rule and establishing an early warning mechanism according to the characteristics and business requirements of the equipment, and sending out early warning and corresponding measure notifications; the intelligent decision-making module is used for generating an equipment state evaluation report and generating a multi-dimensional data analysis report; and the display module is used for displaying the generated multi-dimensional data analysis report. The intelligent equipment is supervised, and the breeding efficiency and the management level are improved.

The invention relates to the technical field of visual detection, in particular to a real-time visual detection method and system for diamond wire fracture, and the method comprises the steps: receiving a detection instruction of a diamond wire, starting a diamond wire detection unit according to the detection instruction, setting a cutting speed and a feeding speed, calculating the twisting deformation of the diamond wire, and if the twisting deformation of the diamond wire is not in a deformation range, determining that the diamond wire is broken; if yes, obtaining a diamond wire vibration signal, converting the diamond wire vibration signal to obtain a diamond wire frequency domain signal, calculating a signal energy spectrum based on the diamond wire frequency domain signal, obtaining a collection image set by using an image collection unit, obtaining an optimal image based on the collection image set, calculating the wear degree, and performing signal decomposition on the diamond wire frequency domain signal; and obtaining a signal component set, calculating an energy value of the signal component set, calculating a fracture risk value based on the wear degree and the energy value, and completing real-time visual detection. The fracture detection capability in the diamond wire cutting process can be improved, and the stability of the production process is ensured.

The invention relates to the technical field of electrical equipment diagnosis, and provides an insulator state early warning method based on full-frequency-domain leakage current characteristics, which can comprehensively reflect the state of an insulator from multiple angles, accurately identify the aging and degradation conditions of the insulator and realize early warning by analyzing the full-frequency-domain leakage current characteristics. Through characteristic stage division of a high frequency band and a low frequency band and by combining comparison of a frequency characteristic value and an intensity value, accurate state evaluation and early warning judgment can be provided in each stage, and careful analysis is performed in each stage from an initial stage to a rapid development stage, so that accidents are prevented. According to the invention, through visual presentation of leakage current characteristics, an insulator state monitoring diagram is formed, frequency and intensity changes of discharge signals are visually displayed, and rapid determination is facilitated. The early warning mechanism is subdivided into two-level early warning and first-level emergency early warning levels, so that timely maintenance of the insulator before fault deterioration is ensured, and the safety and reliability of operation of a power system are improved.

The invention discloses a translational photon optical shutter device capable of being combined with multiple inserts, and the device is characterized in that the device comprises an optical shutter switching assembly, a vacuum chamber, a driving mechanism, and a hinge assembly; the optical shutter switching assembly and the hinge assembly are installed in the vacuum chamber, and the driving mechanism is located outside the vacuum chamber. The driving mechanism drives the optical shutter switching assembly to do reciprocating translation through the hinge assembly and is used for switching control over light passing and light blocking of synchrotron radiation light. An absorber is arranged in the optical shutter switching assembly, the top of the absorber is a light receiving face, and the light receiving face comprises a plurality of inclined faces which are obliquely upward in the incidence direction of synchrotron radiation light and used for blocking the synchrotron radiation light generated by various insertion pieces at the same time. The design of the multi-inclined-plane absorber can block synchrotron radiation light generated by various inserts at the same time, so that the valuable axial space occupied by the device in a light beam line is effectively shortened, and a solid technical basis is provided for the design and layout optimization of the light beam line combined by multiple inserts.

The invention relates to a line insulator health state analysis method based on a Transform technology, and belongs to the technical field of insulator monitoring. Comprising the steps of identifying input features from images and video data polled by an electric power line unmanned aerial vehicle, analyzing pixel distribution and colors of the images, calling an encoder assembly of a Transform technology, executing sequence embedding conversion, and obtaining feature embedding representation. According to the method, classification and regional clustering of the insulator state are carried out by combining a decoding technology, the monitoring precision and the response speed of the insulator state are improved, the change of the insulator state is predicted through deep learning of features and trend analysis of a time sequence, the maintenance period is effectively planned, and potential faults are prevented in advance; potential insulator operation abnormity can be identified more accurately, power failure events are reduced, and the stability and reliability of power grid operation are improved through real-time monitoring and dynamic threshold judgment.

The invention relates to the field of unmanned aerial vehicle groups, and aims to solve the problem that an existing detection means cannot quickly and accurately classify and judge anti-unmanned aerial vehicle cluster low-slow small targets. A grouping detection and group parameter estimation method based on a photoelectric detection unmanned aerial vehicle group target is provided, a single-frame image of the unmanned aerial vehicle group target is collected, and the pixel position coordinate and the pixel gray value of each unmanned aerial vehicle in the single-frame image are extracted; setting the pixel gray scale of each unmanned aerial vehicle in the single-frame image to be 1, setting the gray scale of other background pixels to be 0, outputting pixel position coordinates with the pixel gray scale being 1, and performing normalization processing to obtain target point coordinates; the obtained target point coordinates serve as input of a clustering algorithm for clustering processing, and a final clustering center of the multiple group targets is obtained; and fitting the sample points of the group targets through a principal component analysis method to form an elliptical shape, and obtaining the center position, main axis direction, long and short axis length and eccentricity information of each group target, thereby completing the initial grouping detection of the unmanned aerial vehicle group targets.

The invention relates to a distributed intelligent rice transplanter cluster navigation method based on deep reinforcement learning and speed obstacles, which comprises the following steps: acquiring a reciprocal speed obstacle and a speed obstacle vector, inputting the reciprocal speed obstacle and the speed obstacle vector into a BiLTSM network model, and acquiring an environment perception vector; the BiLTSM network model is obtained through training of a training set; and combining the environment perception vector with an ontology perception measurement value to generate an observation space, and setting a reward function for iteratively updating a multi-intelligent-vehicle obstacle avoidance strategy so as to optimize driving control of an individual level in an intelligent vehicle cluster and generate an optimal navigation scheme. The method still faces many challenges in application in the agricultural environment, the defects in the aspects of environment state representation, action mapping and reward design are overcome, and the problem of multi-machine collision avoidance in the complex dynamic environment is effectively solved.

The invention provides an elevator operation abnormity prediction and diagnosis method and system based on the Internet of Things, and relates to the technical field of elevator fault diagnosis. The method comprises the following steps: acquiring historical audio data, performing time-frequency conversion to generate a plurality of first time-frequency diagrams, and clustering the historical audio data to obtain a plurality of elevator running state class clusters; generating a plurality of second time-frequency diagrams of each elevator running state class cluster; performing event flow detection on each second time-frequency diagram to obtain event flow characteristic data of each second time-frequency diagram, performing time sequence abnormity diagnosis on the second time-frequency diagrams to generate an abnormity diagnosis result, and performing characteristic optimization on the second time-frequency diagrams to generate third time-frequency diagrams corresponding to the second time-frequency diagrams; and a training set is obtained through construction, the elevator operation analysis model is trained, the elevator operation audio data are analyzed through the trained elevator operation analysis model, and an elevator operation anomaly detection result is generated. According to the invention, efficient prediction and fault diagnosis of abnormal operation of the elevator are realized.

This invention discloses a method, system, device, and storage medium for early warning of power battery degradation. The method includes: determining a usable energy state boundary threshold and a capacity absolute deviation boundary threshold based on current vehicle usage parameters, including equivalent mileage and service life; comparing and analyzing the current power battery state parameters with the usable energy state boundary threshold and the capacity absolute deviation boundary threshold to output analysis results; generating corresponding early warning information based on the analysis results; and, by fusing the comparison results of the two key parameters, outputting the true degradation state, pseudo degradation state, good state, or normal state with a degradation trend of the power battery according to a preset strategy, and generating corresponding maintenance, repair, or normal prompt information. This invention can accurately identify the degradation state of power batteries, effectively avoid misjudgment, and provide reliable technical support for the safe operation and maintenance of electric vehicles.

The application provides a seven-day period factory cyclic Megalobrama amblycephala fry behavior domestication method and system, relates to the Megalobrama amblycephala breeding technical field, and includes: domesticating the fry into a domestication pond on the first day; starting vertical water flow impact before feeding fresh bait fish according to a preset first feeding frequency on the second day; feeding electric shock disabled live bait according to a preset second feeding frequency on the third day; feeding mixed live bait according to a preset third feeding frequency on the fourth day; feeding mosaic bait according to a preset fourth feeding frequency on the fifth day; feeding pure artificial compound feed using a composite feeding mode and starting water flow impact on the sixth day; feeding pure artificial compound feed on the seventh day, and screening domestication successful individuals based on behavior indexes after the period ends, so that the Megalobrama amblycephala is rapidly domesticated from feeding live bait to feeding artificial compound feed.

The invention discloses an on-line electrochemical impedance spectroscopy measurement method and device for a fuel cell and a storage medium, and the method comprises the steps: obtaining an initial value of an influence parameter which influences an EIS test result when the fuel cell is started, the influence parameters at least comprise the stack temperature, the stack voltage, the stack current and the low-frequency impedance; acquiring an actual measurement value of the influence parameter when the fuel cell runs; determining a target output value of each influence parameter according to the initial value of each influence parameter and the measured value of each influence parameter; and coupling the target output values of the influence parameters to obtain a high-frequency impedance value. By adopting the method, accurate online EIS diagnosis can be realized, and the diagnosis reliability of the battery state is improved.

The invention discloses a power grounding state monitoring method and system, which is applied to the field of power equipment state monitoring, and comprises the following steps: synchronously obtaining a time sequence mechanical state signal and a time sequence electrical state signal of a grounding terminal; extracting and constructing a multi-dimensional feature vector containing static and dynamic feature components; performing differentiation evaluation and integration on the feature components by using a pre-constructed hybrid analysis model to generate an initial monitoring result; and introducing environmental parameters to dynamically calibrate the initial result to obtain a calibrated grounding state monitoring result. According to the electric power grounding state monitoring method provided by the invention, the mechanical tightness and the electrical connectivity information are fused, and the intelligent model is adopted to carry out collaborative analysis and environment adaptive calibration, so that the comprehensive and accurate sensing of the connection state of the grounding terminal is realized.

This utility model provides a device for indicating the operating status of an equipment and a control box, relating to the field of electrical automation, including: an indicator unit for indicating the status based on an indicator voltage; and an anti-interference unit for filtering the indicator voltage input to the indicator unit, wherein the anti-interference unit is connected in parallel with the indicator unit.

The utility model relates to the technical field of power supply management, in particular to a TVS (Transient Voltage Suppressor) state acquisition circuit. Comprising a transistor and a divider resistor, a collector electrode of the transistor is connected to an anode of the TVS through a first resistor, a base electrode of the transistor is connected to the anode of the TVS through a second resistor, and a cathode end of the TVS has working voltage; a divider resistor is connected in series between the emitter of the transistor and the ground, a divided voltage value is output from one end of the divider resistor, and the state of the TVS is determined according to the divided voltage value. The state judgment of the TVS tube can be realized through the divided voltage value, so that the state of the TVS tube can be accurately judged without the cooperation of a software algorithm.

The invention provides a mutual inductor operation and maintenance method based on an improved K-means algorithm, and the method comprises the steps: collecting the operation time and an operation time ratio difference sequence of a mutual inductor, calculating the reliability of the mutual inductor through three-parameter Weibull distribution, and evaluating the stability of the mutual inductor through ADF statistics; the reliability and the stability are used as key indexes, a mutual inductor data set is constructed, kernel density estimation (KDE) and a local outlier factor (LOF) are adopted to optimize initial cluster center selection of a K-means algorithm, and particularly, by calculating a cluster center variation coefficient and a local reachable density, it is ensured that the dispersity and the density of cluster center distribution are reasonable. After a mutual inductor data set is subjected to clustering analysis through an improved K-means algorithm, the reliability and stability mean value of each cluster is calculated, weights are given according to user requirements, comprehensive scores are calculated, and the comprehensive scores are divided into five grades to evaluate the state of the mutual inductor. And finally, different operation and maintenance strategies are made according to the evaluation result, and the accuracy and reliability of the operation and maintenance of the mutual inductor are improved.

The present application discloses a ground state detection method, device, equipment and medium based on a companion robot. The method includes: obtaining a ground state image and infrared data within a preset range of the companion robot through an image acquisition device and an infrared pair tube; comparing the ground state image with a pre-stored sample image to determine a first ground state of the ground state image; filtering the infrared data to obtain ground feedback data, and identifying the difference based on the ground feedback data to determine a second ground state; determining whether the first ground state is consistent with the second ground state, and if so, determining the ground state; if not, obtaining the current motor working state of the companion robot, and determining the contact friction coefficient between the companion robot's travel device and the ground contact surface through the motor working state; determining the ground state within the preset range based on the contact friction coefficient, the first ground state and the second ground state; and determining a moving path based on the ground state.

The utility model provides an automatic direction finding tool for a coaxial pipe, and belongs to the technical field of automobile part machining. The axial adjusting assembly and the angle adjusting assembly are installed on the moving table at the same time, the axial adjusting clamping jaw is installed on the axial adjusting driver of the axial adjusting assembly, and meanwhile the angle adjusting clamping jaw is installed on the rotating piece of the rotating base of the angle adjusting assembly. The clamping center of the axial adjusting clamping jaw and the clamping center of the angle adjusting clamping jaw are both located on the rotating axis of the rotating piece, so that the position of the coaxial pipe to be machined can be adjusted in the axial direction after the coaxial pipe is clamped through the axial adjusting clamping jaw, circumferential rotation is achieved after the coaxial pipe is clamped through the angle adjusting clamping jaw, and the adjusting requirement is met; therefore, the accuracy of the position of the through hole in the coaxial pipe is ensured, the automatic direction finding operation of the coaxial pipe is realized, the state of the coaxial pipe subsequently clamped in a bending die can be more accurate, the product quality is ensured, the processing effect is improved, and the industrial development of coaxial pipe processing is better promoted.

The invention discloses an accurate roadside parking detection method of a high-position camera based on monocular three-dimensional vision. The method comprises the following steps: 1, fixing the attitude of a roadside camera, and carrying out camera calibration and distortion correction; 2, acquiring an original video by using a high-position camera, preprocessing and making a vehicle data set; 3, inputting the collected vehicle data set into a 3D target detection network model for training to obtain a target detection model, selecting an optimal detection model to detect the vehicle, and outputting 3D information of the vehicle in the space; and 4, inputting each piece of vehicle information obtained in the step 3 into a 3D vehicle tracking module to realize real-time tracking. After a roadside camera completes calibration, when a vehicle is monitored, an obtained monocular image is input into a feature extraction network, and a two-dimensional detection result is input; monocular three-dimensional detection is carried out through camera parameters on the basis of two-dimensional detection.