A smart traffic monitoring, identification and control method and system

Abstract

The present invention relates to the field of traffic dispatch technology, and more specifically, to a method and system for intelligent traffic monitoring, identification and regulation. The scheme includes setting up sensors for information collection, dividing regions, forming remote areas and non-remote areas; continuously monitoring the remote areas to form a basic data group; online data collection for non-remote areas to form a driving estimate data group; estimating remote areas in combination with the basic data group to form form estimate data for remote areas, and updating the basic data group when the vehicle passes through; correcting other driving times that need to pass through corresponding remote areas; and completing the traffic dispatch plan based on the final vehicle driving time, so that the client using the corresponding software can display the route with the optimal time. The scheme performs online correction and analysis through the vehicle-mounted GPS and the pre-entered vehicle driving environment information, thereby completing the intelligent traffic dispatch for remote and non-remote areas.

Description

Technical Field

[0001] The present invention relates to the field of traffic dispatch technology, and more specifically, to an intelligent traffic monitoring, identification and control method and system. Background Art

[0002] Smart traffic dispatching is of great significance in optimizing resource allocation, improving dispatching efficiency, real-time monitoring and early warning, promoting information sharing, and improving emergency response speed. First, smart traffic dispatching can perceive traffic demand in real time, reasonably allocate resources, and reduce resource waste. Secondly, through big data analysis and artificial intelligence technology, smart traffic dispatching can greatly improve dispatching efficiency and effectively alleviate traffic congestion. In addition, the real-time monitoring and early warning system can detect traffic anomalies in time, give early warnings, and reduce the accident rate. At the same time, the information sharing mechanism helps inter-departmental collaboration and form a unified dispatch. Finally, smart traffic dispatching can quickly respond to emergencies and ensure traffic safety.

[0003] Prior to the present invention, in the existing technology, the data for intelligent traffic dispatching was mainly based on vehicle driving data and road test data. When vehicles were running in areas with fewer monitoring equipment, it was difficult to extract effective information, making it often impossible to effectively dispatch traffic information in these areas. Summary of the invention

[0004] In view of the above problems, the present invention proposes an intelligent traffic monitoring, identification and control method and system, which performs online correction and analysis through the on-board GPS and the pre-entered information of the vehicle driving environment, and then completes the intelligent traffic scheduling of remote and non-remote areas.

[0005] According to a first aspect of an embodiment of the present invention, a smart traffic monitoring, identification and control method is provided.

[0006] In one or more embodiments, preferably, the intelligent traffic monitoring, identification and control method includes:

[0007] Set up sensors for information collection and divide the regions into remote and non-remote areas;

[0008] Conducting continuous monitoring of the remote areas to form a basic data set;

[0009] Performing online data collection on the non-remote areas to form a driving estimation data group;

[0010] Estimate remote areas in combination with the basic data set to form estimated driving data for remote areas, and update the basic data set when the vehicle passes through;

[0011] Correction of other travel times required to pass through corresponding remote areas based on the updated basic data set;

[0012] According to the final vehicle travel time, the traffic dispatch plan is completed so that the clients using the corresponding software can display the route with the optimal time.

[0013] In one or more embodiments, preferably, the sensors for information collection are configured to divide regions into remote areas and non-remote areas, specifically including:

[0014] After the vehicle is running, the vehicle's passing situation will be formed on each road in the map;

[0015] Set a time length as the unit time length;

[0016] Determine the number of vehicles passing through the area with traffic lights in each road section within a unit time length;

[0017] Using the first calculation formula to judge, when the first calculation formula is satisfied, it is considered to be a non-remote area, otherwise it is a remote area;

[0018] The first calculation formula is:

[0019] S÷T>y

[0020] Among them, T is the unit time length, S is the number of passing vehicles, and y is the comparison margin.

[0021] In one or more embodiments, preferably, the continuous monitoring of the remote area to form a basic data set specifically includes:

[0022] For remote areas, drones are moved to the corresponding areas to record traffic lights during a preset time period, forming a record of traffic light flashing time as a basic data set;

[0023] It is performed according to the basic data set cycle as an estimate of traffic lights in remote areas.

[0024] In one or more embodiments, preferably, the online data collection of the non-remote area to form a driving estimation data group specifically includes:

[0025] For non-remote areas, when each vehicle drives to the corresponding area, the time when the GPS position remains unchanged is determined, and the red light time of the vehicle in the non-remote area is automatically formed;

[0026] The time from when the GPS position does not change to when it starts to move is determined as the green light moment;

[0027] The current traffic light flashing situation is repeated for 5 cycles in a constant cycle as a driving prediction data set.

[0028] In one or more embodiments, preferably, the estimating the remote area in combination with the basic data set to form the estimated driving data of the remote area, and updating the basic data set when the vehicle passes through, specifically includes:

[0029] Obtain the basic data set at the current moment and extract the red light interval therein;

[0030] Determine whether the parking time of the current vehicle satisfies the second calculation formula, and if not, do not process;

[0031] If the second calculation formula is satisfied, the updated red light activation time is calculated using the third calculation formula;

[0032] Update the green light start time using a fourth calculation formula according to the updated red light start time;

[0033] The red light start time and the green light start time are run periodically as the updated basic data group;

[0034] The second calculation formula is:

[0035] T c >0

[0036] Among them, T c For parking time;

[0037] The third calculation formula is:

[0038] T 0 =tT c

[0039] Among them, t is the current time, T 0 is the updated red light start time;

[0040] The fourth calculation formula is:

[0041] T 1 =T 0 +z f

[0042] Among them, z f is the red light interval in the basic data set, T 1 It's green light start time.

[0043] In one or more embodiments, preferably, the correction of other travel times required to pass through corresponding remote areas based on the updated basic data set specifically includes:

[0044] Determine whether the top three alternative routes for each vehicle include the remote area corresponding to the updated basic data group, and if not, do not make any changes;

[0045] If included, the driving time of the corresponding vehicle is updated.

[0046] In one or more embodiments, preferably, the traffic scheduling plan is completed according to the final vehicle travel time so that the client using the corresponding software can display the route with the optimal time, specifically including:

[0047] Re-changing the corresponding alternative route according to the correction of the travel time;

[0048] A recommendation for a new time-optimal route for the current vehicle is generated online in the corresponding software.

[0049] According to a second aspect of an embodiment of the present invention, a smart traffic monitoring, identification and control system is provided.

[0050] In one or more embodiments, preferably, the intelligent traffic monitoring, identification and control system includes:

[0051] A collection module is used to set sensors for information collection and divide regions into remote areas and non-remote areas;

[0052] A data forming module, used for continuously monitoring the remote areas to form a basic data set;

[0053] A first driving planning module, used for collecting online data of the non-remote area to form a driving estimation data group;

[0054] A second driving planning module is used to estimate the remote areas in combination with the basic data set to form driving estimation data for the remote areas, and update the basic data set when the vehicle passes through;

[0055] An online correction module, used to correct other travel times that need to pass through corresponding remote areas based on the updated basic data set;

[0056] The scheduling planning module is used to complete the traffic scheduling plan based on the final vehicle travel time, so that the clients using the corresponding software can display the route with the optimal time.

[0057] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0058] According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the methods described in the first aspect of the embodiment of the present invention.

[0059] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0060] In the solution of the present invention, the vehicle driving unit time is used to classify whether it is a remote area, and intelligent evaluation and analysis are performed.

[0061] In the solution of the present invention, the corresponding vehicle driving conditions are formed by calibrating the vehicle in the remote area in combination with the preset road information.

[0062] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0063] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0065] Figure 1 It is a flow chart of an intelligent traffic monitoring, identification and control method according to an embodiment of the present invention.

[0066] Figure 2 It is a flow chart of setting information collection sensors in a smart traffic monitoring, identification and control method according to an embodiment of the present invention, dividing regions, and forming remote areas and non-remote areas.

[0067] Figure 3 It is a flowchart of continuously monitoring the remote area to form a basic data group in an intelligent traffic monitoring, identification and control method according to an embodiment of the present invention.

[0068] Figure 4 It is a flowchart of an intelligent traffic monitoring, identification and control method in an embodiment of the present invention for online data collection in the non-remote area to form a driving prediction data group.

[0069] Figure 5 It is a flowchart of an intelligent traffic monitoring, identification and control method in an embodiment of the present invention, which estimates remote areas in combination with basic data groups to form driving prediction data for remote areas, and updates the basic data group when a vehicle passes through.

[0070] Figure 6 It is a flowchart of a smart traffic monitoring, identification and control method according to an embodiment of the present invention for correcting other travel times that need to pass through corresponding remote areas based on an updated basic data set.

[0071] Figure 7 It is an intelligent traffic monitoring, identification and control method in one embodiment of the present invention, which completes the traffic scheduling plan according to the final vehicle travel time, so that the client using the corresponding software can display the flow chart of the route with the optimal time.

[0072] Figure 8 It is a structural diagram of an intelligent traffic monitoring, identification and control system according to an embodiment of the present invention.

[0073] Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0074] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0076] Smart traffic dispatching is of great significance in optimizing resource allocation, improving dispatching efficiency, real-time monitoring and early warning, promoting information sharing, and improving emergency response speed. First, smart traffic dispatching can perceive traffic demand in real time, reasonably allocate resources, and reduce resource waste. Secondly, through big data analysis and artificial intelligence technology, smart traffic dispatching can greatly improve dispatching efficiency and effectively alleviate traffic congestion. In addition, the real-time monitoring and early warning system can detect traffic anomalies in time, give early warnings, and reduce the accident rate. At the same time, the information sharing mechanism helps inter-departmental collaboration and form a unified dispatch. Finally, smart traffic dispatching can quickly respond to emergencies and ensure traffic safety.

[0077] Prior to the present invention, in the existing technology, the data for intelligent traffic dispatching was mainly based on vehicle driving data and road test data. When vehicles were running in areas with fewer monitoring equipment, it was difficult to extract effective information, making it often impossible to effectively dispatch traffic information in these areas.

[0078] In the embodiment of the present invention, a smart traffic monitoring, identification and control method and system are provided. The solution uses the vehicle-mounted GPS and the pre-entered vehicle driving environment information to perform online correction and analysis, thereby completing the smart traffic dispatch for remote and non-remote areas.

[0079] According to a first aspect of an embodiment of the present invention, a smart traffic monitoring, identification and control method is provided.

[0080] Figure 1 It is a flow chart of an intelligent traffic monitoring, identification and control method according to an embodiment of the present invention.

[0081] In one or more embodiments, preferably, the intelligent traffic monitoring, identification and control method includes:

[0082] S101, setting sensors for information collection, dividing regions into remote areas and non-remote areas;

[0083] S102, continuously monitoring the remote areas to form a basic data set;

[0084] S103, collecting data online in the non-remote area to form a driving estimation data group;

[0085] S104, estimating the remote areas in combination with the basic data set to form estimated driving data for the remote areas, and updating the basic data set when the vehicle passes through;

[0086] S105, correcting other travel times required to pass through corresponding remote areas according to the updated basic data set;

[0087] S106. Complete the traffic dispatch plan according to the final vehicle travel time, so that the client using the corresponding software can display the route with the optimal time.

[0088] In the embodiment of the present invention, during the intelligent traffic dispatching and monitoring process, the two most core influencing factors are whether the road conditions can be quickly known, and how the other is affected by the vehicle's driving. Among these two factors, the core is how to know and update the road conditions, and then continuously adjust the impact on the vehicle's driving according to the current vehicle's driving. For this reason, monitoring, identification and control are divided into two stages, the first stage of monitoring and the second stage of correction, and finally, real intelligent control is achieved.

[0089] Figure 2 It is a flow chart of setting information collection sensors in a smart traffic monitoring, identification and control method according to an embodiment of the present invention, dividing regions, and forming remote areas and non-remote areas.

[0090] like Figure 2 As shown, in one or more embodiments, preferably, the sensors for setting information collection are divided into regions to form remote areas and non-remote areas, specifically including:

[0091] S201, after the vehicle has traveled, a vehicle passing situation will be formed on each road on the map;

[0092] S202, setting a time length as a unit time length;

[0093] S203, determining the number of vehicles passing through the area with traffic lights in each road section within a unit time length;

[0094] S204, using the first calculation formula to determine that if the first calculation formula is satisfied, it is considered to be a non-remote area, otherwise it is a remote area;

[0095] The first calculation formula is:

[0096] S÷T>y

[0097] Among them, T is the unit time length, S is the number of passing vehicles, and y is the comparison margin.

[0098] In an embodiment of the present invention, during the specific information collection process, the first thing that needs to be distinguished is whether the area is a remote area. The method of distinction is mainly based on the number of vehicle passes obtained during the collection phase. When the number of vehicle passes does not meet the standard, the area is considered to be a remote area, otherwise it is considered to be a non-remote area.

[0099] Figure 3It is a flowchart of continuously monitoring the remote area to form a basic data group in an intelligent traffic monitoring, identification and control method according to an embodiment of the present invention.

[0100] like Figure 3 As shown, in one or more embodiments, preferably, the continuous monitoring of the remote area to form a basic data set specifically includes:

[0101] S301. For remote areas, the drone is moved to the corresponding area to record the traffic lights in a preset time period, forming a record of the flashing time of the traffic lights as a basic data set;

[0102] S302, performing estimation of traffic lights in remote areas according to the basic data set period.

[0103] In an embodiment of the present invention, continuous monitoring is required for remote areas, and a basic data group will be generated during this monitoring process. The monitoring process is to move the drone to the corresponding area to record the traffic lights for a preset time period, thereby forming a record of the flashing time of the traffic lights. Later, the basic data group period is executed as an estimate of the traffic lights in remote areas.

[0104] Figure 4 It is a flowchart of an intelligent traffic monitoring, identification and control method in an embodiment of the present invention for online data collection in the non-remote area to form a driving prediction data group.

[0105] like Figure 4 As shown, in one or more embodiments, preferably, the online data collection of the non-remote area to form a driving prediction data group specifically includes:

[0106] S401, for non-remote areas, when each vehicle drives to a corresponding area, determine the time when the GPS position remains unchanged, and automatically form the red light time of the non-remote area corresponding to the vehicle;

[0107] S402, determining the time from when the GPS position remains unchanged to when the movement starts, as the green light time;

[0108] S403, repeating the current traffic light flashing situation for 5 cycles in a constant cycle as a driving prediction data set.

[0109] In an embodiment of the present invention, for non-remote areas, when each vehicle drives to the corresponding area, the flashing conditions of the traffic lights in the non-remote areas corresponding to the vehicle are automatically formed, and the current flashing conditions of the traffic lights are repeated for 5 cycles in a constant cycle as a driving prediction data group.

[0110] Figure 5It is a flowchart of an intelligent traffic monitoring, identification and control method in an embodiment of the present invention, which estimates remote areas in combination with basic data groups to form driving prediction data for remote areas, and updates the basic data group when a vehicle passes through.

[0111] like Figure 5 As shown, in one or more embodiments, preferably, the remote area is estimated in combination with the basic data set to form the driving estimation data of the remote area, and the basic data set is updated when the vehicle passes through, specifically including:

[0112] S501, obtaining a basic data set at the current moment, and extracting the red light interval therein;

[0113] S502, determining whether the current parking time of the vehicle satisfies the second calculation formula, and if not, no processing is performed;

[0114] S503, if the second calculation formula is satisfied, then using the third calculation formula to calculate the updated red light activation time;

[0115] S504, updating the green light start time using a fourth calculation formula according to the updated red light start time;

[0116] S505, using the red light start time and the green light start time to run periodically as an updated basic data set;

[0117] The second calculation formula is:

[0118] T c >0

[0119] Among them, T c For parking time;

[0120] The third calculation formula is:

[0121] T 0 =tT c

[0122] Among them, t is the current time, T 0 is the updated red light start time;

[0123] The fourth calculation formula is:

[0124] T 1 =T 0 +z f

[0125] Among them, z f is the red light interval in the basic data set, T 1 It's green light start time.

[0126] In an embodiment of the present invention, when a vehicle travels to a remote area, the vehicle driving data at the current moment will be automatically updated. The updating process mainly includes two parts, the first part is the red light start time, and the second part is the green light start time.

[0127] Figure 6 It is a flowchart of a smart traffic monitoring, identification and control method according to an embodiment of the present invention for correcting other travel times that need to pass through corresponding remote areas based on an updated basic data set.

[0128] like Figure 6 As shown, in one or more embodiments, preferably, the correction of other travel time required to pass through corresponding remote areas based on the updated basic data set specifically includes:

[0129] S601, determining whether the top three candidate routes for each vehicle include the remote area corresponding to the updated basic data group, and if not, making no modification;

[0130] S602: If included, update the driving time of the corresponding vehicle.

[0131] In an embodiment of the present invention, after obtaining an updated basic data set, if other vehicles need to pass through the corresponding remote areas, the corresponding travel time may change, so it is necessary to perform an online update at this stage to form a new corrected vehicle travel time.

[0132] Figure 7 It is an intelligent traffic monitoring, identification and control method in one embodiment of the present invention, which completes the traffic scheduling plan according to the final vehicle travel time, so that the client using the corresponding software can display the flow chart of the route with the optimal time.

[0133] like Figure 7 As shown, in one or more embodiments, preferably, the traffic scheduling plan is completed according to the final vehicle travel time, so that the client using the corresponding software can display the route of the optimal time, specifically including:

[0134] S701, re-changing the corresponding alternative route according to the correction of the travel time;

[0135] S702: Generate a recommendation for a new optimal time route for the current vehicle online in the corresponding software.

[0136] In the embodiment of the present invention, the corresponding alternative route can be changed according to the correction of the driving time. Therefore, during the traffic scheduling process, the traffic route in the remote area may be changed in real time with the situation of passing vehicles, forming a new recommendation of the optimal time route.

[0137] According to a second aspect of an embodiment of the present invention, a smart traffic monitoring, identification and control system is provided.

[0138] Figure 8 It is a structural diagram of an intelligent traffic monitoring, identification and control system according to an embodiment of the present invention.

[0139] In one or more embodiments, preferably, the intelligent traffic monitoring, identification and control system includes:

[0140] The collection module 801 is used to set sensors for information collection and divide regions into remote areas and non-remote areas;

[0141] A data forming module 802 is used to continuously monitor the remote area to form a basic data set;

[0142] A first driving planning module 803, used for collecting online data of the non-remote area to form a driving estimation data set;

[0143] The second driving planning module 804 is used to estimate the remote areas in combination with the basic data set to form driving estimation data for the remote areas, and update the basic data set when the vehicle passes through;

[0144] An online correction module 805 is used to correct other travel times that need to pass through corresponding remote areas according to the updated basic data set;

[0145] The scheduling planning module 806 is used to complete the traffic scheduling plan according to the final vehicle travel time, so that the client using the corresponding software can display the route with the optimal time.

[0146] In the embodiment of the present invention, a system suitable for different structures is realized through a series of modular designs. The system can achieve closed-loop, reliable and efficient execution through collection, analysis and control.

[0147] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0148] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided. Fig. 9 It is a structural diagram of an electronic device in one embodiment of the present invention. Fig. 9The electronic device shown is a general intelligent traffic monitoring, identification and control device. The electronic device can be a smart phone, a tablet computer and other devices. As shown, the electronic device 900 includes a processor 901 and a memory 902. Among them, the processor 901 is electrically connected to the memory 902. The processor 901 is the control center of the electronic device 900, which uses various interfaces and lines to connect the various parts of the entire electronic device, and executes various functions of the electronic device and processes data by running or calling the computer program stored in the memory 902, and calling the data stored in the memory 902, so as to monitor the electronic device as a whole.

[0149] In this embodiment, the processor 901 in the electronic device 900 will load the instructions corresponding to the processes of one or more computer programs into the memory 902 according to the following steps, and the processor 901 will run the computer program stored in the memory 902 to realize various functions: set sensors for information collection, divide the regions, and form remote areas and non-remote areas; continuously monitor the remote areas to form a basic data group; collect data online for the non-remote areas to form a driving estimation data group; estimate the remote areas in combination with the basic data group to form driving estimation data for the remote areas, and update the basic data group when the vehicle passes through; make corrections to other driving times that need to pass through the corresponding remote areas based on the updated basic data group; complete the traffic scheduling plan based on the final vehicle driving time, so that the client using the corresponding software can display the route with the optimal time.

[0150] The memory 902 may be used to store computer programs and data. The computer programs stored in the memory 902 include instructions that can be executed in the processor. The computer programs may constitute various functional modules. The processor 901 executes various functional applications and data processing by calling the computer programs stored in the memory 902.

[0151] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0152] In the solution of the present invention, the vehicle driving unit time is used to classify whether it is a remote area, and intelligent evaluation and analysis are performed.

[0153] In the solution of the present invention, the corresponding vehicle driving conditions are formed by calibrating the vehicle in the remote area in combination with the preset road information.

[0154] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0155] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0156] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0157] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0158] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A smart traffic monitoring, identification and control method, characterized in that: The method includes: Set up sensors for information collection and divide the regions into remote and non-remote areas; Conducting continuous monitoring of the remote areas to form a basic data set; Performing online data collection on the non-remote areas to form a driving estimation data set; Estimate remote areas in combination with the basic data set to form estimated driving data for remote areas, and update the basic data set when the vehicle passes through; Correction of other travel times required to pass through corresponding remote areas based on the updated basic data set; Complete the traffic dispatch plan based on the final vehicle travel time, so that all clients using the corresponding software can display the route with the best time; The online data collection of the non-remote areas to form a driving prediction data set specifically includes: For non-remote areas, when each vehicle drives to the corresponding area, the time when the GPS position remains unchanged is determined, and the red light time of the vehicle in the non-remote area is automatically formed; The time from when the GPS position does not change to when it starts to move is determined as the green light moment; The current traffic light flashing situation is repeated for 5 cycles in a constant cycle as a driving prediction data set; The estimating of remote areas in combination with the basic data set to form estimated driving data for remote areas, and updating the basic data set when the vehicle passes through, specifically includes: Obtain the basic data set at the current moment and extract the red light interval therein; Determine whether the parking time of the current vehicle satisfies the second calculation formula, and if not, do not process; If the second calculation formula is satisfied, the updated red light activation time is calculated using the third calculation formula; Update the green light start time using a fourth calculation formula according to the updated red light start time; The red light start time and the green light start time are run periodically as the updated basic data group; The second calculation formula is: T c >0 Among them, T c For parking time; The third calculation formula is: T0=t-T c Wherein, t is the current time, and T0 is the updated red light start time; The fourth calculation formula is: T1=T0+z f Among them, z f is the red light interval in the basic data group, and T1 is the green light start time.

2. The intelligent traffic monitoring, identification and control method according to claim 1, characterized in that: The sensors for information collection are arranged to divide regions into remote areas and non-remote areas, specifically including: After the vehicle is running, the vehicle's passing situation will be formed on each road in the map; Set a time length as the unit time length; Determine the number of vehicles passing through the area with traffic lights in each road section within a unit time length; Using the first calculation formula to judge, when the first calculation formula is satisfied, it is considered to be a non-remote area, otherwise it is a remote area; The first calculation formula is: S÷T>y Among them, T is the unit time length, S is the number of passing vehicles, and y is the comparison margin.

3. The intelligent traffic monitoring, identification and control method according to claim 1, characterized in that: The continuous monitoring of the remote areas to form a basic data set specifically includes: For remote areas, drones are moved to the corresponding areas to record traffic lights during a preset time period, forming a record of traffic light flashing time as a basic data set; It is performed according to the basic data set cycle as an estimate of traffic lights in remote areas.

4. The intelligent traffic monitoring, identification and control method according to claim 1, characterized in that: The correction of other travel time required to pass through corresponding remote areas based on the updated basic data set specifically includes: Determine whether the top three alternative routes for each vehicle include the remote area corresponding to the updated basic data group, and if not, do not make any changes; If included, the driving time of the corresponding vehicle is updated.

5. The intelligent traffic monitoring, identification and control method according to claim 1, characterized in that: The traffic dispatching plan is completed according to the final vehicle travel time, so that the client using the corresponding software can display the route with the optimal time, which specifically includes: Re-changing the corresponding alternative route according to the correction of the travel time; A recommendation for a new time-optimal route for the current vehicle is generated online in the corresponding software.

6. An intelligent traffic monitoring, identification and control system, characterized in that: The system is used to implement the method according to any one of claims 1 to 5, and the system comprises: A collection module is used to set sensors for information collection and divide regions into remote areas and non-remote areas; A data forming module, used for continuously monitoring the remote areas to form a basic data set; A first driving planning module, used for collecting online data of the non-remote area to form a driving estimation data group; A second driving planning module is used to estimate the remote areas in combination with the basic data set to form driving estimation data for the remote areas, and update the basic data set when the vehicle passes through; An online correction module, used to correct other travel times that need to pass through corresponding remote areas based on the updated basic data set; The scheduling planning module is used to complete the traffic scheduling plan based on the final vehicle travel time, so that the clients using the corresponding software can display the route with the optimal time.

7. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 5 when executed by a processor.

8. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1-5.

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