Vehicle congestion intelligent management and control method and device based on cloud computing space-time grid division

Abstract

The invention provides a vehicle congestion intelligent management and control method based on cloud computing space-time grid division, and the method comprises the steps: obtaining real-time traffic flow information under different grids, detecting the vehicle density of the real-time traffic flow information under different grids, and obtaining a current grid number; vehicle density detection based on the vehicle driving direction is carried out on the vehicle density of the expressway under the current grid number, and longitude and latitude information of the expressway under the current grid number is obtained; vehicle information under the longitude and latitude information of the highway with the current grid number is obtained, and when the vehicle type in the vehicle information does not meet the driving lane or the vehicle speed in the vehicle information does not meet the driving speed required by the driving lane, the vehicle is intelligently controlled; according to the method, the information of the vehicles causing the congestion under the specific longitude and latitude conditions is acquired, the vehicles causing the congestion are intelligently managed and controlled in time, the efficiency of intelligently solving the congestion problem of the expressway is improved, and the vehicle passing time of the expressway is saved.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, and specifically to an intelligent vehicle congestion control method and processing device based on cloud computing spatiotemporal grid division. Background Art

[0002] Expressways, also known as highways, are roads designed for high-speed motorway travel. The definition of expressways varies across different countries, regions, eras, and academic fields. According to China's "Highway Engineering Technical Standard" (JTGB01-2014), expressways are multi-lane roads designed for motorway travel in separate directions and lanes, with fully controlled access. The design speed for expressways is 80 to 120 kilometers per hour.

[0003] However, the slower vehicles travel on highways, the greater the impact on traffic flow. On highways, some drivers often drive slowly in the fast lane, while the slow lane is often occupied by medium-sized and large passenger and freight vehicles. This significantly reduces the efficiency of the entire highway. Although there are many solutions to solve highway traffic congestion, research on congestion caused by slow vehicles in the fast lane is relatively limited. Therefore, how to overcome the above-mentioned technical problems and shortcomings has become a key issue that needs to be addressed. Summary of the Invention

[0004] In order to overcome the traffic congestion problem caused by slow-moving vehicles in the fast lane of a highway in the prior art, this application provides a method and device for intelligent vehicle congestion control based on cloud computing spatiotemporal grid division, which adopts the following technical solutions:

[0005] In a first aspect, the present application provides a method for intelligently controlling vehicle congestion based on cloud computing spatiotemporal grid division, comprising:

[0006] Step S1: Based on the geographic information of the highway, a preset spatiotemporal grid division algorithm is used to divide the highway into a plurality of grids, each grid representing a spatial region, wherein the method of dividing the highway into the plurality of grids using the preset spatiotemporal grid division algorithm includes numbering the plurality of grids, wherein different grids have unique identification number information;

[0007] Step S2: obtaining grid attribute information of each grid based on cloud computing, wherein the grid attribute information includes vehicle density, vehicle speed, vehicle congestion, and vehicle type of each grid;

[0008] Step S3: detecting the vehicle density of the real-time traffic flow information under each grid, obtaining a first vehicle density, and when the first vehicle density is greater than a preset vehicle density threshold, marking the current grid as a vehicle congestion grid, and obtaining the current grid number;

[0009] Step S4: performing vehicle density detection based on the vehicle density of the highway under the current grid number and the direction of vehicle travel to obtain a second vehicle density; when the second vehicle density is less than the preset vehicle density threshold, obtaining a critical position between the first vehicle density and the second vehicle density; and obtaining dynamic latitude and longitude information of the critical position based on dynamic vehicle travel information at the critical position;

[0010] Step S5, obtaining vehicle information under the dynamic latitude and longitude information, wherein the vehicle information includes vehicle type, vehicle speed, and driving lane;

[0011] Step S6: When the vehicle type in the vehicle information does not meet the driving lane, or the vehicle speed in the vehicle information does not meet the driving speed required by the driving lane, intelligent management and control of the vehicle causing congestion is performed.

[0012] Furthermore, the highway geographic information in step S1 includes the section length and intersection locations of the highway.

[0013] Furthermore, the highway geographic information is obtained through a geographic information data system.

[0014] Furthermore, the step S1 of dividing the highway into a plurality of grids by using a preset spatiotemporal grid division algorithm is specifically performed by dividing the highway into a plurality of spatial grids by using an equidistant division method.

[0015] Furthermore, the cloud computing-based acquisition of grid attribute information of each grid in step S2 includes acquiring real-time traffic flow information through traffic monitoring cameras, ETC systems, and vehicle GPS.

[0016] Furthermore, the detecting of the vehicle density of the real-time traffic flow information under each grid in step S3 includes dynamically adjusting the adjacent grids when it is detected that the vehicle density of adjacent grids is greater than a preset vehicle density, wherein the method of dynamically adjusting the adjacent grids is: merging the adjacent grids.

[0017] Furthermore, the intelligent control of vehicles causing congestion in step S6 includes:

[0018] (1) obtaining image information of the vehicle causing the congestion, locating and identifying the license plate area of the image information based on a preset image processing algorithm, and obtaining the license plate number of the vehicle causing the congestion;

[0019] (2) Associating the acquired license plate number with the reminder module of the current grid and the vehicle reminder module;

[0020] (3) Turning on the reminder module of the current grid and the vehicle reminder module, the reminder module of the current grid and the vehicle reminder module turn on early warning, and reminding the vehicle with the license plate number to change lanes.

[0021] In a second aspect, the present application also provides a vehicle congestion intelligent control device based on cloud computing time-space grid division, including:

[0022] a spatiotemporal grid division module, configured to divide the highway into a plurality of grids based on the geographic information of the highway and using a preset spatiotemporal grid division algorithm, wherein each grid represents a spatial region, wherein the step of dividing the highway into the plurality of grids using the preset spatiotemporal grid division algorithm includes numbering the plurality of grids, wherein different grids have unique identification number information;

[0023] A real-time traffic flow information acquisition module is used to acquire grid attribute information of each grid based on cloud computing, wherein the grid attribute information includes vehicle density, vehicle speed, vehicle congestion, and vehicle type of each grid;

[0024] a first vehicle density detection module, configured to detect the vehicle density of the real-time traffic flow information under each grid, obtain a first vehicle density, and when the first vehicle density is greater than a preset vehicle density threshold, mark the current grid as a vehicle congestion grid and obtain the current grid number;

[0025] a second vehicle density detection module, configured to perform vehicle density detection based on the vehicle density of the highway under the current grid number and the direction of vehicle travel, obtain a second vehicle density, and when the second vehicle density is less than the preset vehicle density threshold, obtain a critical position between the first vehicle density and the second vehicle density, and obtain dynamic latitude and longitude information of the critical position based on dynamic vehicle travel information at the critical position;

[0026] A vehicle information acquisition module is used to acquire vehicle information under the dynamic latitude and longitude information, wherein the vehicle information includes vehicle type, vehicle speed, and driving lane;

[0027] The congestion intelligent management and control module is used to intelligently manage vehicles that cause congestion when the vehicle type in the vehicle information does not meet the driving lane requirements, or when the vehicle speed in the vehicle information does not meet the driving speed requirements of the driving lane requirements.

[0028] In a third aspect, the present application provides an electronic device, comprising:

[0029] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, cause the device to perform the method as described in the first aspect.

[0030] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer-readable storage medium is run on a computer, the computer executes the method described in the first aspect.

[0031] In a fifth aspect, the present application provides a computer program, which, when executed by a computer, is used to execute the method described in the first aspect.

[0032] In one possible design, the program in the fifth aspect may be stored in whole or in part on a storage medium packaged with the processor, or may be stored in whole or in part on a memory not packaged with the processor.

[0033] This application has the following beneficial effects:

[0034] 1. This application numbers multiple grids, with different grids having unique identification number information, and processes highway information in segments, thereby improving the efficiency of highway data processing and efficiently managing highway vehicle flow.

[0035] 2. This application dynamically adjusts adjacent grids when the vehicle density in adjacent grids is greater than the preset vehicle density, adapts to changes in traffic conditions in real time, and efficiently manages vehicle flow.

[0036] 3. This application improves road utilization by intelligently controlling vehicles that cause congestion, accurately identifying and resolving congestion problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is an exemplary system architecture diagram to which the embodiments of the present application can be applied;

[0038] Figure 2 is a flow chart of a method according to an embodiment of the present application;

[0039] Figure 3 This is a schematic diagram of obtaining the first vehicle density according to an embodiment of the present application;

[0040] Figure 4 This is a schematic diagram of obtaining the first vehicle speed according to an embodiment of the present application;

[0041] Figure 5 is a schematic diagram of a device according to an embodiment of the present application;

[0042] Figure 6 It is a schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0044] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0045] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0046] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0047] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0048] Terminal devices 101, 102, and 103 can be various electronic devices with display screens and support web browsing, including but not limited to smartphones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV), laptop computers, desktop computers, etc.

[0049] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0050] It should be noted that the intelligent vehicle congestion control method based on cloud computing space-time grid division provided in the embodiment of the present application is generally executed by a server / terminal device. Accordingly, the intelligent vehicle congestion control device based on cloud computing space-time grid division is generally set in the server / terminal device.

[0051] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0052] Continue to refer Figure 2 , the figure shows a flow chart of a method for intelligent vehicle congestion control based on cloud computing space-time grid division of the present application, the method comprising the following steps:

[0053] Step S1: Based on the geographic information of the highway, a preset spatiotemporal grid division algorithm is used to divide the highway into multiple grids, each grid representing a spatial area, wherein the use of the preset spatiotemporal grid division algorithm to divide the highway into multiple grids includes numbering the multiple grids, and different grids have unique identification number information.

[0054] In one possible implementation, highway geographic information is obtained through a geographic information data system, where the highway geographic information includes the length of highway sections and intersection locations. A preset spatiotemporal grid division algorithm is used to divide the highway into multiple grids, specifically: the highway is divided into multiple spatial grids using an equidistant division method.

[0055] Step S2: obtaining grid attribute information of each grid based on cloud computing, wherein the grid attribute information includes vehicle density, vehicle speed, vehicle congestion, and vehicle type of each grid;

[0056] In a possible implementation, the cloud computing-based acquisition of grid attribute information of each grid includes acquiring real-time traffic flow information through traffic monitoring cameras, ETC systems, and vehicle GPS.

[0057] Step S3: detecting the vehicle density of the real-time traffic flow information under each grid, obtaining a first vehicle density, and when the first vehicle density is greater than a preset vehicle density threshold, marking the current grid as a vehicle congestion grid, and obtaining the current grid number;

[0058] In a possible implementation, the method for judging the real-time traffic flow information in different grids can be found in Figure 3 , the specific steps are as follows:

[0059] Step 301a, obtaining first density information: detecting the vehicle density of the real-time traffic flow information under the different grids to obtain the first vehicle density.

[0060] Step 302a, determining first density information: when the first vehicle density is greater than a preset vehicle density threshold, marking the current grid as a vehicle congestion grid.

[0061] Step 303a, obtaining the current grid number: obtaining the current grid number.

[0062] In a possible implementation, the method for judging the real-time traffic flow information in different grids can also refer to Figure 4 , the specific steps are as follows:

[0063] Step 301b, obtaining first vehicle speed information: detecting the vehicle speeds of the real-time traffic flow information in the different grids to obtain the first vehicle speeds.

[0064] Step 302b, determining first vehicle speed information: when the first vehicle speed is greater than a preset vehicle congestion threshold, the current grid is marked as a vehicle congestion grid.

[0065] Step 303b, obtaining the current grid number: obtaining the current grid number.

[0066] In a possible embodiment, the detecting the vehicle density of the real-time traffic flow information under each grid also includes dynamically adjusting the adjacent grids when it is detected that the vehicle density of adjacent grids is greater than a preset vehicle density, wherein the method of dynamically adjusting the adjacent grids is: merging the adjacent grids.

[0067] Step S4: performing vehicle density detection based on the vehicle density of the highway under the current grid number and the direction of vehicle travel to obtain a second vehicle density; when the second vehicle density is less than the preset vehicle density threshold, obtaining a critical position between the first vehicle density and the second vehicle density; and obtaining dynamic latitude and longitude information of the critical position based on dynamic vehicle travel information at the critical position;

[0068] In a possible implementation, the dynamic latitude and longitude information of the critical position is obtained based on the dynamic driving information of the vehicle at the critical position. Figure 4 The corresponding method is: based on the vehicle speed of the highway under the current grid number, vehicle speed detection is performed according to the direction of vehicle travel to obtain a second vehicle speed; when the second vehicle speed is less than the preset vehicle speed threshold, a critical position between the first vehicle speed and the second vehicle speed is obtained; based on the vehicle dynamic driving information at the critical position, the dynamic latitude and longitude information of the critical position is obtained.

[0069] Step S5, obtaining vehicle information under the dynamic latitude and longitude information, wherein the vehicle information includes vehicle type, vehicle speed, and driving lane;

[0070] In a possible implementation, when a slow-moving vehicle is traveling on a highway, the latitude and longitude information of the slow-moving vehicle is changing dynamically, and the latitude and longitude information of the slow-moving vehicle needs to be acquired in real time in order to accurately locate the slow-moving vehicle.

[0071] In a possible implementation, the latitude and longitude information of a vehicle traveling in the wrong lane is dynamically changing, and the latitude and longitude information of the vehicle traveling in the wrong lane needs to be acquired in real time in order to accurately locate the vehicle traveling in the wrong lane.

[0072] Step S6: When the vehicle type in the vehicle information does not meet the driving lane, or the vehicle speed in the vehicle information does not meet the driving speed required by the driving lane, intelligent management and control of the vehicle causing congestion is performed.

[0073] In one possible implementation, the intelligent management and control of vehicles causing congestion includes:

[0074] (1) obtaining image information of the vehicle causing the congestion, locating and identifying the license plate area of the image information based on a preset image processing algorithm, and obtaining the license plate number of the vehicle causing the congestion;

[0075] (2) Associating the acquired license plate number with the reminder module of the current grid and the vehicle reminder module;

[0076] (3) Turning on the reminder module of the current grid and the vehicle reminder module, the reminder module of the current grid and the vehicle reminder module turn on early warning, and reminding the vehicle with the license plate number to change lanes.

[0077] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0078] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed 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 some of the steps in the flowcharts 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 portion of the sub-steps or stages of other steps.

[0079] Continue to refer Figure 5 The vehicle congestion intelligent control device based on cloud computing space-time grid division described in this embodiment includes:

[0080] The spatiotemporal grid division module 501 is configured to divide the highway into a plurality of grids based on the geographic information of the highway using a preset spatiotemporal grid division algorithm, where each grid represents a spatial region, wherein the dividing the highway into the plurality of grids using the preset spatiotemporal grid division algorithm includes numbering the plurality of grids, where each grid has unique identification number information;

[0081] A real-time traffic flow information acquisition module 502 is configured to acquire grid attribute information of each grid based on cloud computing, wherein the grid attribute information includes vehicle density, vehicle speed, vehicle congestion, and vehicle type of each grid;

[0082] A first vehicle density detection module 503 is configured to detect the vehicle density of the real-time traffic flow information under each grid, obtain a first vehicle density, and when the first vehicle density is greater than a preset vehicle density threshold, mark the current grid as a vehicle congestion grid and obtain the current grid number;

[0083] A second vehicle density detection module 504 is configured to perform vehicle density detection based on the vehicle density of the highway under the current grid number and the direction of vehicle travel, obtain a second vehicle density, and when the second vehicle density is less than the preset vehicle density threshold, obtain a critical position between the first vehicle density and the second vehicle density, and obtain dynamic latitude and longitude information of the critical position based on dynamic vehicle travel information at the critical position;

[0084] The vehicle information acquisition module 505 is used to acquire the vehicle information under the dynamic latitude and longitude information, wherein the vehicle information includes the vehicle type, vehicle speed, and driving lane;

[0085] The congestion intelligent management and control module 506 is used to intelligently manage vehicles that cause congestion when the vehicle type in the vehicle information does not meet the driving lane requirements, or when the vehicle speed in the vehicle information does not meet the driving speed requirements of the driving lane requirements.

[0086] To solve the above technical problems, the present application also provides a computer device. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0087] The computer device 6 includes a memory 6a, a processor 6b, and a network interface 6c that are interconnected through a system bus. It should be noted that the figure only shows a computer device 6 having components 6a-6c, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art will 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 signal processor (DSP), an embedded device, etc.

[0088] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0089] The memory 6a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), 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, magnetic disk, optical disk, etc. In some embodiments, the memory 6a can be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 6a can also be an external storage device of the computer device 6, such as a plug-in hard disk equipped on the computer device 6, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. Of course, the memory 6a can also include both the internal storage unit of the computer device 6 and its external storage device. In this embodiment, the memory 6a is generally used to store the operating system and various application software installed on the computer device 6, such as the program code of the vehicle congestion intelligent control method and processing device based on cloud computing spatiotemporal grid division. In addition, the memory 6a can also be used to temporarily store various types of data that have been output or are to be output.

[0090] In some embodiments, the processor 6b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 6b is generally used to control the overall operation of the computer device 6. In this embodiment, the processor 6b is used to execute program code or process data stored in the memory 6a, such as executing the program code of the vehicle congestion intelligent control method and processing device based on cloud computing spatiotemporal grid division.

[0091] The network interface 6c may include a wireless network interface or a wired network interface. The network interface 6c is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0092] The present application also provides another embodiment, namely, providing a non-volatile computer-readable storage medium, which stores a program of a vehicle congestion intelligent control method and processing device based on cloud computing space-time grid division. The vehicle congestion intelligent control method and processing device based on cloud computing space-time grid division can be executed by at least one processor, so that the at least one processor performs the steps of the vehicle congestion intelligent control method and processing device based on cloud computing space-time grid division as described above.

[0093] 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 the 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 this understanding, the technical solution of the present application, 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, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0094] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application 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 application more thorough and comprehensive. Although the present application 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 described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present application specification and the accompanying drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present application.

Claims

1. A method for intelligent vehicle congestion control based on cloud computing space-time grid division, characterized in that: include: Based on the geographic information of the highway, a preset spatiotemporal grid division algorithm is used to divide the highway into a plurality of grids, each grid representing a spatial region, wherein the use of the preset spatiotemporal grid division algorithm to divide the highway into a plurality of grids includes numbering the plurality of grids, wherein different grids have unique identification number information; Obtaining grid attribute information of each grid based on cloud computing, wherein the grid attribute information includes vehicle density, vehicle speed, vehicle congestion, and vehicle type of each grid; detecting a vehicle density of the real-time traffic flow information under each grid, obtaining a first vehicle density, and when the first vehicle density is greater than a preset vehicle density threshold, marking the current grid as a vehicle congestion grid, and obtaining the current grid number; Performing vehicle density detection based on the vehicle density of the highway under the current grid number and the direction of vehicle travel to obtain a second vehicle density; when the second vehicle density is less than the preset vehicle density threshold, obtaining a critical position between the first vehicle density and the second vehicle density; and obtaining dynamic latitude and longitude information of the critical position based on dynamic vehicle travel information at the critical position; Acquire vehicle information under the dynamic latitude and longitude information, the vehicle information including vehicle type, vehicle speed, and driving lane; When the vehicle type in the vehicle information does not meet the driving lane, or the vehicle speed in the vehicle information does not meet the driving speed required by the driving lane, intelligent management and control are performed on the vehicle causing congestion.

2. The intelligent vehicle congestion control method based on cloud computing spatiotemporal grid division according to claim 1 is characterized in that: The expressway geographic information includes the length of the expressway section and the location of the intersection.

3. The intelligent vehicle congestion control method based on cloud computing spatiotemporal grid division according to claim 1 is characterized in that: The cloud computing-based acquisition of grid attribute information of each grid includes acquiring real-time traffic flow information through traffic monitoring cameras, ETC systems, and vehicle GPS.

4. The method for intelligent vehicle congestion control based on cloud computing spatiotemporal grid division according to claim 1 is characterized in that: The detecting of the vehicle density of the real-time traffic flow information under each grid includes dynamically adjusting the adjacent grids when it is detected that the vehicle density of adjacent grids is greater than a preset vehicle density, wherein the method of dynamically adjusting the adjacent grids is: merging the adjacent grids.

5. The intelligent vehicle congestion control method based on cloud computing spatiotemporal grid division according to claim 1 is characterized in that: The intelligent control of vehicles causing congestion includes: Obtaining image information of the vehicle causing the congestion, locating and identifying the license plate area of the image information based on a preset image processing algorithm, and obtaining the license plate number of the vehicle causing the congestion; Associating the acquired license plate number with the reminder module of the current grid and the vehicle reminder module; The reminder module and the vehicle reminder module of the current grid are turned on, and the reminder module and the vehicle reminder module of the current grid start early warning to remind the vehicle with the license plate number to change lanes.

6. A vehicle congestion intelligent control device based on cloud computing time-space grid division, characterized in that: include: a spatiotemporal grid division module, configured to divide the highway into a plurality of grids based on the geographic information of the highway and using a preset spatiotemporal grid division algorithm, wherein each grid represents a spatial region, wherein the step of dividing the highway into the plurality of grids using the preset spatiotemporal grid division algorithm includes numbering the plurality of grids, wherein different grids have unique identification number information; A real-time traffic flow information acquisition module is used to acquire grid attribute information of each grid based on cloud computing, wherein the grid attribute information includes vehicle density, vehicle speed, vehicle congestion, and vehicle type of each grid; a first vehicle density detection module, configured to detect the vehicle density of the real-time traffic flow information under each grid, obtain a first vehicle density, and when the first vehicle density is greater than a preset vehicle density threshold, mark the current grid as a vehicle congestion grid and obtain the current grid number; a second vehicle density detection module, configured to perform vehicle density detection based on the vehicle density of the highway under the current grid number and the direction of vehicle travel, obtain a second vehicle density, and when the second vehicle density is less than the preset vehicle density threshold, obtain a critical position between the first vehicle density and the second vehicle density, and obtain dynamic latitude and longitude information of the critical position based on dynamic vehicle travel information at the critical position; A vehicle information acquisition module is used to acquire vehicle information under the dynamic latitude and longitude information, wherein the vehicle information includes vehicle type, vehicle speed, and driving lane; The congestion intelligent management and control module is used to intelligently manage vehicles that cause congestion when the vehicle type in the vehicle information does not meet the driving lane requirements, or when the vehicle speed in the vehicle information does not meet the driving speed requirements of the driving lane requirements.

7. An electronic device, characterized in that: include: one or more processors; Memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions, which, when executed by the device, enable the device to execute the vehicle congestion intelligent management and control method based on cloud computing spatiotemporal grid division as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed on a computer, enables the computer to execute the vehicle congestion intelligent management and control method based on cloud computing spatiotemporal grid division as described in any one of claims 1 to 5.

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