Intelligent traffic method and device based on radar, computer equipment and storage medium

Abstract

The invention belongs to the technical field of radars, and relates to an intelligent traffic method and device based on a radar, computer equipment and a storage medium, and the method comprises the steps: scanning a traffic condition in real time through the radar, and obtaining the real-time data of vehicles and pedestrians; processing and analyzing the real-time data, and extracting traffic flow information data; analyzing the traffic state according to the traffic flow information data; according to the traffic state, a traffic control strategy is formulated in combination with traffic rules; and regulating and controlling signal lamps and traffic signs according to the traffic control strategy. Traffic conditions are scanned in real time through the radar, dynamic data of vehicles and pedestrians are accurately captured, and timeliness and accuracy of traffic information are ensured; through deep processing and analysis of real-time data, traffic flow information data can be efficiently extracted, and a solid foundation is provided for subsequent traffic state analysis; the road traffic capacity is remarkably improved, the urban traffic pressure is effectively relieved, and the overall operation efficiency of urban traffic is improved.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and in particular to a radar-based intelligent traffic method, device, computer equipment and storage medium. Background Art

[0002] The current road traffic system has many deficiencies that are not intelligent enough, which are mainly reflected in the following aspects:

[0003] First, the information perception capability is insufficient. Existing traffic systems often rely on traditional sensors and manual reports to obtain road condition information, which is not only inefficient but also easily affected by environmental and weather factors, resulting in inaccurate or delayed information. Intelligent transportation systems should be able to perceive key information such as traffic flow, vehicle speed, and congestion conditions in real time and accurately.

[0004] Secondly, data processing and analysis capabilities are limited. Even if a large amount of traffic data is collected, the existing system often lacks effective processing and analysis methods and cannot deeply explore the laws and trends behind the data. This limits the ability of traffic managers to accurately control and predict road conditions.

[0005] Furthermore, the decision support capability is insufficient. In traffic management, it is often necessary to make decisions quickly and accurately to deal with unexpected traffic conditions. However, existing systems often lack intelligent decision support functions and cannot provide effective decision suggestions based on real-time data, resulting in inefficient traffic management.

[0006] In addition, the lack of coordination in the transportation system is also a significant flaw. The lack of effective information sharing and coordination mechanisms between various transportation departments and road sections has led to the inability to smoothly dispatch and optimize traffic flow, exacerbating traffic congestion and delays. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides a radar-based intelligent traffic method, which adopts the following technical solution, including:

[0008] Scan traffic conditions in real time through radar to obtain real-time data of vehicles and pedestrians;

[0009] Processing and analyzing the real-time data to extract traffic flow information data;

[0010] Analyzing the traffic status according to the traffic flow information data;

[0011] Formulate a traffic control strategy based on the traffic status and in combination with traffic rules;

[0012] Adjust signal lights and traffic signs according to traffic control strategies.

[0013] Preferably, the step of scanning the traffic situation in real time by radar to obtain real-time data of vehicles and pedestrians specifically includes:

[0014] According to the application scenario, radars are set up at different locations to build a radar network;

[0015] Real-time data on vehicles and pedestrians is collected by a radar network.

[0016] Preferably, the step of processing and analyzing the real-time data to extract traffic flow information data specifically includes:

[0017] Cleaning the real-time data;

[0018] Deduplication of the cleaned real-time data;

[0019] Performing format conversion on the real-time data after deduplication;

[0020] The data after format conversion is archived, and the real-time data after archiving is the traffic flow information data.

[0021] Preferably, the step of analyzing the traffic status according to the traffic flow information data specifically includes:

[0022] Classifying vehicles and pedestrians according to the traffic flow information data;

[0023] Statistics on the number, density, and flow speed of vehicles and pedestrians.

[0024] Preferably, the step of formulating a traffic control strategy according to the traffic status and in combination with traffic rules specifically includes:

[0025] According to the traffic conditions and in combination with traffic rules, formulate traffic light control rules and pedestrian and vehicle traffic rules;

[0026] Dynamically adjust and optimize traffic rules and control strategies based on real-time traffic data.

[0027] Preferably, the step of regulating signal lights and traffic signs according to the traffic control strategy specifically includes:

[0028] Set the parameters of the signal light controller;

[0029] When traffic flow changes, the traffic light timing scheme is dynamically adjusted through real-time detection of traffic data to achieve adaptive control;

[0030] Establish a signal light fault monitoring and alarm system to promptly detect and handle signal light faults and ensure the normal operation of signal lights;

[0031] Set and manage traffic signs.

[0032] Preferably, the steps of setting and managing traffic signs specifically include:

[0033] Set up traffic signs, including instruction signs, warning signs, and prohibition signs, based on road grade, traffic volume, and traffic direction;

[0034] Setting up variable electronic display screens on the road to provide traffic information to drivers based on real-time traffic conditions;

[0035] Maintain and update traffic signs regularly to ensure their clarity and accuracy.

[0036] In order to solve the above technical problems, the present invention further provides a radar-based intelligent traffic device, which adopts the following technical solution, including:

[0037] The acquisition module is used to scan the traffic conditions in real time through radar and obtain the real-time data of vehicles and pedestrians;

[0038] A processing module, used to process and analyze the real-time data and extract traffic flow information data;

[0039] An analysis module, used for analyzing the traffic status according to the traffic flow information data;

[0040] A formulation module, used for formulating a traffic control strategy according to the traffic status and in combination with traffic rules;

[0041] The control module is used to control signal lights and traffic signs according to traffic control strategies.

[0042] In order to solve the above technical problems, the present invention also provides a computer device, which adopts the technical solution described below, including a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the above-mentioned radar-based intelligent traffic method when executing the computer-readable instructions.

[0043] In order to solve the above technical problems, the present invention also provides a computer-readable storage medium, which adopts the technical solution described below, and stores computer-readable instructions on the computer-readable storage medium. When the computer-readable instructions are executed by the processor, the steps of the above-mentioned radar-based intelligent traffic method are implemented.

[0044] Compared with the prior art, the present invention mainly has the following beneficial effects: by scanning the traffic conditions in real time through radar, the dynamic data of vehicles and pedestrians can be accurately captured, ensuring the timeliness and accuracy of traffic information; the in-depth processing and analysis of real-time data can efficiently extract traffic flow information data, providing a solid foundation for subsequent traffic status analysis; in the traffic status analysis link, the method can fully understand abnormal situations such as traffic congestion and accidents, provide decision-making support for traffic managers, and combine traffic rules to formulate traffic control strategies that are more scientific and targeted, effectively improving the efficiency of traffic diversion; according to the real-time traffic control strategy, signal lights and traffic signs can be flexibly adjusted to achieve dynamic optimization of traffic flow, which not only significantly improves road capacity, but also effectively alleviates urban traffic pressure and improves the overall operating efficiency of urban traffic. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the scheme in the present invention, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 is a flow chart of an embodiment of the radar-based intelligent traffic method of the present invention;

[0047] Figure 2 is a schematic structural diagram of an embodiment of a radar-based intelligent traffic device of the present invention;

[0048] Figure 3 It is a schematic structural diagram of an embodiment of a computer device of the present invention. DETAILED DESCRIPTION

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present invention; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present invention or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0050] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0051] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

[0052] It should be noted that the radar-based intelligent traffic method provided in the embodiment of the present invention is generally executed by a server / terminal device, and accordingly, the radar-based intelligent traffic device is generally arranged in the server / terminal device.

[0053] It should be understood that the number of terminal devices, networks and servers is only illustrative. Depending on the implementation requirements, there can be any number of terminal devices, networks and servers.

[0054] Embodiment 1

[0055] Please refer to Figure 1 , shows a flow chart of an embodiment of a radar-based intelligent traffic method of the present invention. The radar-based intelligent traffic method comprises the following steps:

[0056] Step S1, scanning the traffic situation in real time through radar to obtain real-time data of vehicles and pedestrians.

[0057] In this embodiment, the electronic device (e.g., server / terminal device) on which the radar-based intelligent transportation method runs can receive the radar-based intelligent transportation request through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAXX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0058] In this embodiment, step S1, scanning the traffic situation in real time by radar to obtain real-time data of vehicles and pedestrians, may further include the following steps:

[0059] S11, according to the application scenario, radars are set up at different locations to build a radar network.

[0060] Building a radar network involves optimizing the deployment of radar equipment at multiple locations to achieve efficient target detection, positioning, and tracking. First, the appropriate type and number of radars need to be selected according to the needs of the application scenario. For example, in areas where high-resolution detection is required, multiple high-precision radars can be deployed. Then, these radars are connected to the fusion processing center through effective communication means to form a unified and complete network system.

[0061] In the networked radar system, the fusion processing center is responsible for collecting the detection data of each local radar, and performs comprehensive processing to generate relevant intelligence information within the coverage area. Information sharing is maintained between devices, and communication between radars is also carried out through the fusion processing center. According to the intelligence obtained and the changes in the battlefield situation, the fusion processing center adaptively adjusts each local radar to make full use of the diversity advantages of radar.

[0062] S12, real-time data of vehicles and pedestrians are collected by the radar network.

[0063] Millimeter-wave radar and lidar are used to collect raw data. Millimeter-wave radar converts analog signals into digital signals through an analog-to-digital converter (ADC), and lidar obtains data by emitting lasers and receiving reflected signals. Radar emits electromagnetic waves and receives reflected signals, and obtains real-time data such as location and speed information of target objects such as vehicles and pedestrians through signal processing.

[0064] Step S2, processing and analyzing the real-time data to extract traffic flow information data.

[0065] In this embodiment, step S2, processing and analyzing the real-time data to extract traffic flow information data, may further include the following steps:

[0066] S21, cleaning the real-time data.

[0067] The collected real-time data is identified and filtered out through algorithms to remove abnormal data, noise data, etc. This ensures the accuracy and consistency of the data.

[0068] S22, deduplication is performed on the cleaned real-time data.

[0069] Real-time data deduplication after cleaning is a key step in data processing. Its main purpose is to eliminate duplicate data records in the data set, thereby retaining unique data records. This step plays an important role in improving data quality, reducing storage space usage, and improving data processing efficiency.

[0070] In practice, data deduplication can be performed in a variety of ways, such as using sets, list derivations, SQL's DISTINCT keyword or GROUP BY statement, Excel's "remove duplicate values" function, and the drop_duplicates() method in the Pandas library. These methods can be selected based on the characteristics and scale of the data to ensure deduplication efficiency and accuracy.

[0071] The role of data deduplication is mainly reflected in the following aspects: First, deduplication can avoid repeated calculations and reduce unnecessary computing overhead, thereby improving data processing speed. Second, deduplication can reduce storage space usage, making data sets more compact and easier to store and manage. In addition, deduplication can also improve data consistency and reliability, ensuring the accuracy and credibility of data analysis results.

[0072] In real-time data processing scenarios, data deduplication is particularly important. Real-time data is often time-sensitive and frequently updated. Deduplication can ensure the uniqueness of data and prevent duplicate data from interfering with analysis results. At the same time, real-time data deduplication can also improve the real-time and accuracy of data processing, providing strong support for data analysis and decision-making.

[0073] S23, converting the format of the real-time data after deduplication.

[0074] First, clarify the source and target format of the data. In actual operation, ETL (extraction, transformation, loading) tools such as Talend, Apache Nifi, etc. can be used.

[0075] The role of format conversion is mainly reflected in improving data compatibility, interoperability and processing efficiency. Different systems and applications may require different data formats. Through conversion, we can ensure the seamless flow of data between different platforms. At the same time, some data formats are more suitable for specific queries and operations. After conversion, the speed and efficiency of data processing can be significantly improved.

[0076] In addition, format conversion can also help optimize data storage efficiency and reduce storage costs. For example, converting text files to compressed binary format can significantly reduce disk space usage. In short, format conversion of real-time data after deduplication is an important means to ensure data quality and improve data processing efficiency.

[0077] S24, archiving the data after format conversion, the real-time data after archiving is the traffic flow information data.

[0078] Use a data archiving system to archive the converted data. The archiving process can include near-line archiving and offline archiving. Near-line archiving migrates data to another database table, while offline archiving backs up data as a disk file. In this process, the integrity, accuracy, and security of the data need to be ensured.

[0079] The archived data can be converted into traffic flow information data through specific processing and analysis. Through data mining, analysis and visualization, key information such as traffic flow and vehicle driving conditions can be extracted. This information is of great significance for traffic management and planning, and can be used to optimize traffic flow and improve road safety.

[0080] Step S3: analyzing the traffic status according to the traffic flow information data.

[0081] In this embodiment, step S3, analyzing the traffic status according to the traffic flow information data may further include the following steps:

[0082] S31, classifying vehicles and pedestrians according to the traffic flow information data.

[0083] During the classification process, the machine learning algorithm extracts features from the image, such as shape, size, color, etc., and then classifies the object based on these features. For example, vehicles are usually large in size and regular in shape, while pedestrians are relatively small and have variable shapes. By training a large amount of sample data, the machine learning model can gradually learn these features and achieve accurate classification of vehicles and pedestrians.

[0084] S32, count the number, density, and flow speed indicators of vehicles and pedestrians.

[0085] The classified targets are counted using counting algorithms. For vehicles, they can be counted based on information such as license plate recognition or body shape; for pedestrians, they can be counted based on information such as body outline or movement trajectory.

[0086] Time window: Set a time window to count the number of vehicles and pedestrians passing through the time window. The size of the time window can be adjusted according to actual needs.

[0087] Density refers to the number of vehicles and pedestrians per unit area or unit volume. In the traffic flow data obtained by radar scanning, density can be estimated by calculating the size of the space occupied by the target. The specific implementation methods include:

[0088] Spatial division: Divide the target area into multiple small grids or pixels and count the number of targets in each grid or pixel.

[0089] Density calculation: Calculate the density value in each grid or pixel according to the size of the grid or pixel and the number of targets. Then average or weighted average the density values ​​of all grids or pixels to get the density value of the entire target area.

[0090] Flow speed refers to the distance vehicles and pedestrians move per unit time. In the traffic flow data obtained by radar scanning, the flow speed can be estimated by calculating the position change of the target at consecutive time points. The specific implementation methods include:

[0091] Position tracking: Use the radar's continuous scanning data to track the target's position changes at continuous time points.

[0092] Speed ​​calculation: Calculate the target's moving speed based on the target's position change and time interval at consecutive time points. Then average or weighted average the moving speeds of all targets to get the flow speed value of the entire target area.

[0093] Step S4, formulating a traffic control strategy according to the traffic status and in combination with traffic rules.

[0094] In this embodiment, step S4, formulating a traffic control strategy according to the traffic status and in combination with traffic rules, may further include the following steps:

[0095] S41, according to the traffic status and in combination with traffic rules, formulate traffic light control rules and pedestrian and vehicle traffic rules.

[0096] Phase refers to the different release states of the traffic light in one cycle. Phase design should be determined based on the number of lanes and the proportion of turning vehicles in the traffic volume. For example, a four-phase signal control system can include states such as straight, left turn, right turn and pedestrian crossing.

[0097] The cycle time refers to the total time it takes for all phases to appear in sequence. It is determined based on traffic flow and saturation, while taking into account the loss of the green light phase. Traffic management personnel need to use traffic flow data to analyze the traffic demand of each phase and reasonably set the cycle time to achieve efficient operation of traffic flow.

[0098] The green-to-light ratio is the ratio of the green light time of each phase to the cycle time. It needs to determine the effective green light time according to the traffic volume of each phase. In actual operation, it is also necessary to consider factors such as green light loss time, green light interval time, slope, right-turn vehicle ratio, left-turn turning radius, lane width, and non-motorized vehicles, and make flexible adjustments.

[0099] In order to achieve more intelligent traffic control, traffic flow detectors can be used to obtain traffic data in real time, and algorithms can be used to dynamically adjust the cycle duration and green-to-light ratio of traffic lights to adapt to changes in real-time traffic conditions.

[0100] Set pedestrian traffic rules, such as pedestrians should walk on the sidewalk, or walk close to the roadside if there is no sidewalk, etc.

[0101] Set vehicle traffic rules, such as vehicles should follow traffic lights, etc. If the road is divided into motor vehicle lanes, non-motor vehicle lanes and sidewalks, motor vehicles, non-motor vehicles and pedestrians should each go their own way.

[0102] Traffic control strategy is the basis for setting traffic light parameters, which can include timing control, induction control and adaptive control.

[0103] Timing control: Based on historical traffic flow data, preset the green light time, red light time and yellow light time for each direction. The timing control strategy is simple and reliable, but lacks flexibility and is difficult to cope with real-time changes in traffic flow.

[0104] Inductive control: The traffic flow is detected in real time through the vehicle flow detector, and the signal light parameters are adjusted dynamically according to the traffic flow changes. The inductive control strategy can better adapt to the real-time changes in traffic flow, but it may interfere with the traffic flow of adjacent intersections.

[0105] Adaptive control: Based on real-time traffic data, traffic light parameters are adjusted in real time through advanced algorithms to achieve global optimal traffic control.

[0106] S42, dynamically adjust and optimize traffic rules and control strategies based on real-time traffic data.

[0107] Dynamic timing can be performed, and the duration of the signal light can be dynamically adjusted according to the real-time traffic flow and queue length to optimize the green wave band. For example, when the traffic flow in a certain direction is large, the green light time in that direction can be extended to reduce the waiting time of vehicles and improve the road traffic efficiency.

[0108] It is also possible to carry out multi-intersection linkage control: through green wave coordinated control, congestion linkage control and other methods, the desired control effect can be achieved at different time periods. This requires the use of real-time detected traffic data on appropriate sections to optimize the intersection cycle, green light time, and phase difference in real time, and improve the traffic efficiency of single intersections and trunk lines.

[0109] Traffic guidance can also be provided. Through electronic display screens, mobile phone applications, etc., real-time road information and optimal travel route suggestions can be provided to travelers. For example, when a road is congested, vehicles can be guided to choose other unobstructed roads in a timely manner, thereby dispersing traffic flow and alleviating congestion.

[0110] Based on real-time passenger flow data, the bus departure frequency and route planning are dynamically adjusted to improve the service level and attractiveness of buses, encourage more people to choose public transportation, thereby reducing the use of private vehicles and alleviating road traffic pressure.

[0111] Step S5: adjusting the traffic lights and traffic signs according to the traffic control strategy.

[0112] In this embodiment, step S5, the step of adjusting the traffic lights and traffic signs according to the traffic control strategy specifically includes:

[0113] S51, setting parameters of the signal light controller.

[0114] The traffic light controller controls the display time and sequence of the red, yellow and green lights of the traffic lights through a preset program, thereby guiding and controlling the traffic flow. Its basic working principle can be summarized as follows: receiving input signals (such as vehicle flow detection, pedestrian crossing requests, etc.), outputting control signals according to preset control strategies (such as timing control, induction control, adaptive control, etc.), and driving the traffic lights to display the corresponding light colors.

[0115] The parameter settings of the traffic light controller mainly include green light time, red light time, yellow light time, phase setting and cycle duration, etc.

[0116] Under the timing control strategy, the setting of signal light parameters mainly depends on historical traffic flow data. By counting the traffic flow during peak and off-peak periods in each direction, reasonable green light time, red light time and yellow light time can be preset. For example, for the intersection of main roads and branch roads, different signal light cycle durations and phase sequences can be set according to different traffic flows.

[0117] In the timing control strategy, phase setting also needs to be considered. Phase refers to the time period in which vehicles in different directions obtain the right of way in a complete cycle of signal light control. Phase setting needs to comprehensively consider factors such as vehicle volume, number of lanes, and pedestrian crossing needs to ensure smooth and safe traffic flow.

[0118] Under the induction control strategy, the setting of traffic light parameters depends on real-time traffic flow data. Traffic flow detectors detect the traffic flow in each direction in real time, and the traffic light parameters can be adjusted dynamically. For example, when a sudden increase in traffic flow in a certain direction is detected, the green light time in that direction can be extended to reduce vehicle waiting time.

[0119] The parameter setting under the induction control strategy also needs to consider the layout and sensitivity of the sensors. The layout of the sensors needs to cover the traffic flow in each direction to ensure the accuracy and integrity of the data. The sensitivity of the sensors needs to be adjusted according to the actual traffic conditions to avoid false detection and missed detection.

[0120] S52, when traffic flow changes, the traffic light timing scheme is dynamically adjusted by real-time detection of traffic data to achieve adaptive control.

[0121] Under the adaptive control strategy, the setting of traffic light parameters depends on real-time traffic data and advanced algorithms. By collecting and analyzing data such as traffic flow, vehicle speed, and vehicle type in real time, traffic light parameters can be adjusted in real time to achieve global optimal traffic control.

[0122] The parameter setting under the adaptive control strategy requires the support of the intelligent transportation system (ITS). By integrating a variety of sensors, communication equipment and control systems, ITS can collect and analyze traffic data in real time and automatically adjust the signal light parameters according to the analysis results. For example, based on the intelligent fuzzy control theory, the green light time and red light time can be adjusted in real time according to the traffic vectors in each direction to reduce vehicle waiting time and improve traffic efficiency.

[0123] S53, establish a traffic light fault monitoring and alarm system to promptly detect and handle traffic light faults and ensure the normal operation of traffic lights.

[0124] The traffic light fault monitoring and alarm system can include the following parts: front-end sensing equipment, video fusion management platform, intelligent analysis gateway, alarm push module, terminal viewing equipment, etc.

[0125] Front-end sensing equipment: mainly includes electronic police cameras and checkpoint cameras at intersections, which are used to collect video image data at traffic intersections in real time.

[0126] Video fusion management platform: The SkeyeVSS video fusion management cloud platform is used. Based on advanced technologies such as video surveillance technology, network transmission technology, GIS geographic information technology, and AI image intelligent recognition technology, the platform aggregates and manages the transmission data of all front-end sensing devices, performs intelligent analysis, processing and distribution, and pushes early warnings.

[0127] Intelligent Analysis Gateway: Based on deep learning algorithms, it conducts intelligent analysis of video images and can identify common faults such as signal lights being on at the same time, off, insufficient brightness, always on, yellow flashing, occlusion, missing display, displacement, etc., and achieve detection within seconds.

[0128] Alarm push module: When a signal light failure is detected, the system can push alarm information in real time and notify relevant personnel to go to the scene to handle it. Alarm information can be sent through SMS, email, APP push and other methods.

[0129] Terminal viewing equipment: Managers can remotely view and manage traffic lights anytime and anywhere through PC, mobile phone, tablet, WeChat, electronic large screen / TV wall, WEB H5 and other terminals.

[0130] S54, setting and managing traffic signs.

[0131] In some optional implementations of this embodiment, S54, setting and managing traffic signs further includes:

[0132] S541, set up traffic signs, including instruction signs, warning signs and prohibition signs, based on road grade, traffic volume and traffic direction.

[0133] S542, a variable electronic display screen is set on the road to provide traffic information to the driver according to the real-time traffic conditions.

[0134] S543, regularly maintain and update traffic signs to ensure their clarity and accuracy.

[0135] By reasonably setting traffic signs, we can improve traffic safety, optimize traffic order, and improve road traffic efficiency, creating a safer and more convenient travel environment for the public.

[0136] The beneficial effects of implementing this embodiment are as follows: by scanning the traffic conditions in real time through radar, the dynamic data of vehicles and pedestrians can be accurately captured, ensuring the timeliness and accuracy of traffic information; the in-depth processing and analysis of real-time data can efficiently extract traffic flow information data, providing a solid foundation for subsequent traffic status analysis; in the traffic status analysis link, the method can fully understand abnormal situations such as traffic congestion and accidents, provide decision-making support for traffic managers, and combine traffic rules to formulate traffic control strategies that are more scientific and targeted, effectively improving the efficiency of traffic diversion; according to the real-time traffic control strategy, the signal lights and traffic signs can be flexibly adjusted to achieve dynamic optimization of traffic flow, which not only significantly improves the road capacity, but also effectively alleviates urban traffic pressure and improves the overall operating efficiency of urban traffic.

[0137] The present invention can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0138] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0139] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0140] Embodiment 2

[0141] Further references Figure 2 , as a response to the above Figure 1 The present invention provides an embodiment of a radar-based intelligent traffic device, which is similar to Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0142] like Figure 2 As shown, the radar-based intelligent traffic device 60 described in this embodiment includes: an acquisition module 61, a processing module 62, an analysis module 63, a formulation module 64 and a control module 65. Among them:

[0143] An acquisition module 61 is used to scan traffic conditions in real time through radar to acquire real-time data of vehicles and pedestrians;

[0144] A processing module 62, used to process and analyze the real-time data and extract traffic flow information data;

[0145] An analysis module 63, used for analyzing the traffic status according to the traffic flow information data;

[0146] A formulation module 64, for formulating a traffic control strategy according to the traffic status and in combination with traffic rules;

[0147] The control module 65 is used to control the traffic lights and traffic signs according to the traffic control strategy.

[0148] The beneficial effects of implementing this embodiment are as follows: by scanning the traffic conditions in real time through radar, the dynamic data of vehicles and pedestrians can be accurately captured, ensuring the timeliness and accuracy of traffic information; the in-depth processing and analysis of real-time data can efficiently extract traffic flow information data, providing a solid foundation for subsequent traffic status analysis; in the traffic status analysis link, the method can fully understand abnormal situations such as traffic congestion and accidents, provide decision-making support for traffic managers, and combine traffic rules to formulate traffic control strategies that are more scientific and targeted, effectively improving the efficiency of traffic diversion; according to the real-time traffic control strategy, the signal lights and traffic signs can be flexibly adjusted to achieve dynamic optimization of traffic flow, which not only significantly improves the road capacity, but also effectively alleviates urban traffic pressure and improves the overall operating efficiency of urban traffic.

[0149] Embodiment 3

[0150] To solve the above technical problems, the embodiment of the present invention also provides a computer device. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0151] The computer device 7 includes a memory 71, a processor 72, and a network interface 73 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 7 having components of a memory 71, a processor 72, and a network interface 73, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital processor (DSP), an embedded device, etc.

[0152] The computer device may be a desktop computer, a notebook computer, a PDA, a cloud server, etc. The computer device may interact with the user through a keyboard, a mouse, a remote control, a touch pad, or a voice control device.

[0153] The memory 71 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 71 may be an internal storage unit of the computer device 7, such as a hard disk or memory of the computer device 7. In other embodiments, the memory 71 may also be an external storage device of the computer device 7, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 7. Of course, the memory 71 may also include both the internal storage unit of the computer device 7 and its external storage device. In this embodiment, the memory 71 is generally used to store the operating system and various application software installed on the computer device 7, such as computer-readable instructions of the radar-based intelligent traffic method, etc. In addition, the memory 71 can also be used to temporarily store various types of data that have been output or are to be output.

[0154] The processor 72 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 72 is generally used to control the overall operation of the computer device 7. In this embodiment, the processor 72 is used to run computer-readable instructions stored in the memory 71 or process data, such as running computer-readable instructions for the radar-based intelligent traffic method.

[0155] The network interface 73 may include a wireless network interface or a wired network interface. The network interface 73 is generally used to establish a communication connection between the computer device 7 and other electronic devices.

[0156] The beneficial effects of implementing this embodiment are as follows: by scanning the traffic conditions in real time through radar, the dynamic data of vehicles and pedestrians can be accurately captured, ensuring the timeliness and accuracy of traffic information; the in-depth processing and analysis of real-time data can efficiently extract traffic flow information data, providing a solid foundation for subsequent traffic status analysis; in the traffic status analysis link, the method can fully understand abnormal situations such as traffic congestion and accidents, provide decision-making support for traffic managers, and combine traffic rules to formulate traffic control strategies that are more scientific and targeted, effectively improving the efficiency of traffic diversion; according to the real-time traffic control strategy, the signal lights and traffic signs can be flexibly adjusted to achieve dynamic optimization of traffic flow, which not only significantly improves the road capacity, but also effectively alleviates urban traffic pressure and improves the overall operating efficiency of urban traffic.

[0157] Embodiment 4

[0158] The present invention also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the radar-based intelligent traffic method as described above.

[0159] The beneficial effects of implementing this embodiment are as follows: by scanning the traffic conditions in real time through radar, the dynamic data of vehicles and pedestrians can be accurately captured, ensuring the timeliness and accuracy of traffic information; the in-depth processing and analysis of real-time data can efficiently extract traffic flow information data, providing a solid foundation for subsequent traffic status analysis; in the traffic status analysis link, the method can fully understand abnormal situations such as traffic congestion and accidents, provide decision-making support for traffic managers, and combine traffic rules to formulate traffic control strategies that are more scientific and targeted, effectively improving the efficiency of traffic diversion; according to the real-time traffic control strategy, the signal lights and traffic signs can be flexibly adjusted to achieve dynamic optimization of traffic flow, which not only significantly improves the road capacity, but also effectively alleviates urban traffic pressure and improves the overall operating efficiency of urban traffic.

[0160] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the various embodiment methods of the present invention.

[0161] Obviously, the embodiments described above are only some embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are given in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific embodiments, or to perform equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of the present invention, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. A radar-based intelligent traffic method, characterized in that: The steps include: Scan traffic conditions in real time through radar to obtain real-time data of vehicles and pedestrians; Processing and analyzing the real-time data to extract traffic flow information data; Analyzing the traffic status according to the traffic flow information data; Formulate a traffic control strategy based on the traffic status and in combination with traffic rules; Adjust signal lights and traffic signs according to traffic control strategies.

2. The radar-based intelligent traffic method according to claim 1, characterized in that: The step of scanning the traffic situation in real time by radar to obtain real-time data of vehicles and pedestrians specifically includes: According to the application scenario, radars are set up at different locations to build a radar network; Real-time data on vehicles and pedestrians is collected by a radar network.

3. The radar-based intelligent transportation method according to claim 1, characterized in that: The step of processing and analyzing the real-time data to extract traffic flow information data specifically includes: Cleaning the real-time data; Deduplication of the cleaned real-time data; Performing format conversion on the real-time data after deduplication; The data after format conversion is archived, and the real-time data after archiving is the traffic flow information data.

4. The radar-based intelligent transportation method according to claim 1, characterized in that: The step of analyzing the traffic status according to the traffic flow information data specifically includes: Classifying vehicles and pedestrians according to the traffic flow information data; Statistics on the number, density, and flow speed of vehicles and pedestrians.

5. The radar-based intelligent transportation method according to claim 1, characterized in that: The step of formulating a traffic control strategy according to the traffic status and in combination with traffic rules specifically includes: According to the traffic conditions and in combination with traffic rules, formulate traffic light control rules and pedestrian and vehicle traffic rules; Dynamically adjust and optimize traffic rules and control strategies based on real-time traffic data.

6. The radar-based intelligent transportation method according to claim 1, characterized in that: The steps of regulating the signal lights and traffic signs according to the traffic control strategy specifically include: Set the parameters of the signal light controller; When traffic flow changes, the traffic light timing scheme is dynamically adjusted through real-time detection of traffic data to achieve adaptive control; Establish a signal light fault monitoring and alarm system to promptly detect and handle signal light faults and ensure the normal operation of signal lights; Set and manage traffic signs.

7. The radar-based intelligent transportation method according to claim 6, characterized in that: The steps of setting and managing traffic signs specifically include: Set up traffic signs, including instruction signs, warning signs, and prohibition signs, based on road grade, traffic volume, and traffic direction; Setting up variable electronic display screens on the road to provide traffic information to drivers based on real-time traffic conditions; Maintain and update traffic signs regularly to ensure their clarity and accuracy.

8. A radar-based intelligent traffic device, characterized in that: include: The acquisition module is used to scan the traffic conditions in real time through radar and obtain the real-time data of vehicles and pedestrians; A processing module, used to process and analyze the real-time data and extract traffic flow information data; An analysis module, used for analyzing the traffic status according to the traffic flow information data; A formulation module, used for formulating a traffic control strategy according to the traffic status and in combination with traffic rules; The control module is used to control signal lights and traffic signs according to traffic control strategies.

9. A computer device, comprising a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the radar-based intelligent traffic method according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the radar-based intelligent traffic method according to any one of claims 1 to 7 are implemented.

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