Traffic flow analysis method and system, medium and electronic equipment

Abstract

The invention discloses a traffic flow analysis method and system, a medium and an electronic device, and the method comprises the steps: obtaining and preprocessing vehicle overrun information from an overrun dynamic detection device and vehicle passing information from an electronic gate device in a preset period, and obtaining a plurality of pieces of overrun detection data and a plurality of pieces of gate snapshot data, the over-limit detection data comprises a license plate number and a license plate color of the vehicle; performing matching analysis on each piece of overrun detection data according to the license plate number and the license plate color of each piece of checkpoint snapshot data to obtain a first vehicle set of each preset vehicle category; according to the license plate number and the license plate color of each piece of overrun detection data, performing matching analysis on each piece of checkpoint snapshot data to obtain a second vehicle set of each preset vehicle category; and fusing and summarizing the first vehicle set and the second vehicle set to generate the traffic flow of the target road section. Therefore, by adopting the embodiment of the invention, the accuracy of traffic flow calculation is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, and in particular to a traffic flow analysis method, system, medium and electronic equipment. Background Art

[0002] In modern urban traffic management, accurate understanding of traffic flow is crucial for planning road use, optimizing traffic flow, reducing congestion, and improving road safety. A typical application scenario is during peak hours when there are dense vehicles on the main traffic arteries in the city. The traffic management department needs to accurately calculate the number of vehicles passing through a specific section of the road to assess the road carrying capacity and make corresponding traffic diversion decisions.

[0003] In the prior art, the monitoring and calculation of traffic flow mainly relies on electronic checkpoint equipment. By counting the number of vehicles passing through the checkpoint within a certain period of time, the traffic management department can estimate the traffic flow. In addition, overload dynamic detection equipment is also used to detect whether a vehicle is overloaded.

[0004] Currently, there is no technology that can effectively integrate electronic checkpoint equipment and over-limit dynamic detection equipment to reflect traffic flow. Unilateral data cannot fully reflect the actual situation of traffic flow, thereby reducing the accuracy of traffic flow calculation. Summary of the invention

[0005] The embodiments of the present application provide a method, system, medium and electronic device for analyzing traffic flow. In order to have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0006] In a first aspect, an embodiment of the present application provides a traffic flow analysis method, the method comprising:

[0007] Acquire and pre-process vehicle overload information from overload dynamic detection equipment and vehicle traffic information from electronic checkpoint equipment within a preset period, and obtain multiple overload detection data and multiple checkpoint capture data. The overload detection data includes the vehicle's license plate number, license plate color, and vehicle specifications, and the checkpoint capture data includes the vehicle's license plate number, license plate color, and vehicle type;

[0008] According to the license plate number and license plate color of each piece of the card gate captured data, each piece of overload detection data is matched and analyzed to obtain the first vehicle set of each preset vehicle category;

[0009] According to the license plate number and license plate color of each overload detection data, each checkpoint captured data is matched and analyzed to obtain a second vehicle set for each preset vehicle category;

[0010] The first vehicle set and the second vehicle set are fused and aggregated to generate the traffic flow of the target road section.

[0011] In a second aspect, an embodiment of the present application provides a traffic flow analysis system, the system comprising:

[0012] A preprocessing module is used to obtain and preprocess vehicle overload information from an overload dynamic detection device and vehicle traffic information from an electronic checkpoint device within a preset period, and obtain a plurality of overload detection data and a plurality of checkpoint capture data. The overload detection data includes the vehicle's license plate number, license plate color, and vehicle specifications, and the checkpoint capture data includes the vehicle's license plate number, license plate color, and vehicle type;

[0013] A first matching analysis module is used to perform matching analysis on each overload detection data according to the license plate number and license plate color of each card port capture data, and obtain a first vehicle set for each preset vehicle category;

[0014] The second matching analysis module is used to perform matching analysis on each piece of the camera captured data according to the license plate number and license plate color of each piece of overload detection data, and obtain a second vehicle set for each preset vehicle category;

[0015] The fusion and aggregation module is used to fuse and aggregate the first vehicle set and the second vehicle set to generate the traffic flow of the target road section.

[0016] In a third aspect, an embodiment of the present application provides a computer storage medium, which stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the above-mentioned method steps.

[0017] In a fourth aspect, an embodiment of the present application provides an electronic device, which may include: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0018] The technical solutions provided by some embodiments of the present application may have the following beneficial effects:

[0019] In the embodiment of the present application, on the one hand, by preprocessing and matching analysis, the information of the over-limit dynamic detection device is combined with the information of the electronic bayonet device, and the data attributes in the obtained vehicle data are richer and the content is more comprehensive, that is, more comprehensive traffic flow data is obtained, so that vehicles can be identified and classified more accurately, thereby improving the accuracy of traffic flow calculation. On the other hand, by using the existing over-limit dynamic detection equipment and electronic bayonet equipment, additional hardware investment and construction costs are avoided, and resource reuse is achieved, thereby significantly reducing the overall cost of traffic flow monitoring.

[0020] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0022] Figure 1 It is a flow chart of a traffic flow analysis method provided in an embodiment of the present application;

[0023] Figure 2 is a schematic block diagram of a data preprocessing process provided by an embodiment of the present application;

[0024] Figure 3 is a schematic block diagram of a traffic flow analysis process provided by an embodiment of the present application;

[0025] Figure 4 It is a structural schematic diagram of a traffic flow analysis system provided in an embodiment of the present application;

[0026] Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following description and the accompanying drawings sufficiently illustrate specific embodiments of the present application to enable those skilled in the art to practice them.

[0028] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0029] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of systems and methods consistent with some aspects of the present application as detailed in the attached claims.

[0030] In the description of the present application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. In addition, in the description of the present application, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0031] The following will be combined with the attached Figure 1 -Attached Figure 3 , the traffic flow analysis method provided in the embodiment of the present application is introduced in detail. The method can be implemented by a computer program and can be run on a traffic flow analysis system based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application.

[0032] See also Figure 1 , which is a flow chart of a traffic flow analysis method provided in an embodiment of the present application.

[0033] like Figure 1 As shown, the method of the embodiment of the present application may include the following steps:

[0034] S101, obtaining and preprocessing vehicle overload information from an overload dynamic detection device and vehicle passage information from an electronic checkpoint device within a preset period, and obtaining a plurality of overload detection data and a plurality of checkpoint capture data, wherein the overload detection data includes a vehicle license plate number, a license plate color, and a vehicle specification, and the checkpoint capture data includes a vehicle license plate number, a license plate color, and a vehicle type;

[0035] Among them, the over-limit dynamic detection device is a device used to detect whether a vehicle exceeds the prescribed weight or size limit. It can monitor and record the weight, size and other information of passing vehicles in real time to determine whether the vehicle is over-limited. Electronic checkpoint equipment is a monitoring device installed on the road, mainly used to record vehicle traffic information, such as license plate number, vehicle type, passing time, etc. The preset period is a pre-set time period when calculating traffic flow. During this time period, the over-limit dynamic detection device and the electronic checkpoint device collect and report the stored vehicle history information. Vehicle over-limit information is data collected by the over-limit dynamic detection device, including information on whether the vehicle exceeds the prescribed weight or size limit. Vehicle passage information is data collected by the electronic checkpoint device, including information such as the time when the vehicle passes the checkpoint, license plate number, vehicle type, etc. The over-limit detection data is obtained after pre-processing the vehicle over-limit information. Checkpoint capture data refers to the vehicle passage information obtained from the electronic checkpoint device after pre-processing.

[0036] Among them, vehicle overload information and vehicle passage information come from vehicle data from overload dynamic detection equipment and electronic checkpoint monitoring equipment on the target road.

[0037] In some embodiments, the vehicle overload information and vehicle traffic information are derived from the vehicle data of the overload dynamic detection device and the electronic checkpoint monitoring device of the upstream section adjacent to the target road. It can be understood that in some embodiments, when the overload dynamic detection device B and the electronic checkpoint device C are installed on the road A adjacent to the target area (for example, a district in Beijing). The overload dynamic detection device B on road A monitors and records the overload information of passing vehicles, including the weight, size, license plate number, license plate color, number of axles, etc. of the vehicle. At the same time, the electronic checkpoint device C records the traffic information of the vehicle, including the license plate number, license plate color, vehicle type, passing time, etc. When analyzing the traffic flow of road A, a preset period is set, for example, from 7 am to 7 pm. Obtain the vehicle overload information and vehicle traffic information of the overload dynamic detection device B and the electronic checkpoint device C on road A from 7 am to 7 pm. Then, the acquired vehicle overload information is preprocessed to obtain a plurality of overload detection data, and the vehicle passage information is preprocessed to obtain a plurality of checkpoint capture data.

[0038] For example Figure 2 As shown, Figure 2This is a schematic diagram of a data preprocessing process provided by an embodiment of the present application. The information obtained by the over-limit dynamic detection device within a preset period is determined, and the license plate color in the information is processed, which may be converted into a unified coding format. The unit of vehicle length is converted to millimeters to unify the data format. The unit of vehicle height is converted to millimeters. The detection time is converted into a unified format, such as "year-month-day hour: minute: second". The total mass of the vehicle is converted from tons to kilograms. The information obtained by the electronic bayonet device within a preset period is determined, and the license plate color in the information obtained by the electronic bayonet device is processed and converted into a unified coding format. The capture time is converted into a unified format. The vehicle types are classified into preset categories, such as SUV, MPV, sedan, etc.

[0039] For example, when preprocessing the acquired vehicle overload information, the license plate color can be processed according to the Ministry of Transport standard (JT / T 697.7-2014), for example, "blue" is converted to "1" and "yellow" is converted to "2". The units of vehicle length and height are converted from meters to millimeters, for example, "6.92 meters" is converted to "6920". The detection time is uniformly converted to Beijing time and formatted as "year-month-day hour: minute: second", for example, "2024 / 10 / 01 12:30:30" is converted to "2024-10-0112:30:30". The gross vehicle mass is converted from tons to kilograms, for example, "32.5 tons" is converted to "32500". Finally, each piece of card-shot data includes: license plate number, license plate color, number of axles, vehicle length, vehicle height, gross vehicle mass, and detection time.

[0040] For example, when preprocessing vehicle traffic information, the license plate color can be processed according to the Ministry of Transport standard (JT / T697.7-2014), such as processing "blue" into "1" and "yellow" into "2"; convert the capture time into Beijing time and unify the format, such as converting "2024 / 10 / 01 12:30:30" into "2024-10-01 12:30:30"; summarize the vehicle types, including SUV, MPV, hatchback, sedan, coupe, van, mini car, small passenger car, small sedan, sedan, bus, small truck, pickup truck, mini truck / plate truck, muck truck, crane / engineering truck, tank truck, flatbed trailer, concrete mixer truck, container truck, two-wheeler, three-wheeler, unknown. Finally, each card port capture data includes license plate number, license plate color, vehicle type, and capture time.

[0041] S102, performing matching analysis on each overload detection data according to the license plate number and license plate color of each checkpoint captured data, and obtaining a first vehicle set for each preset vehicle category;

[0042] In some embodiments of the present application, the specific process of matching and analyzing each over-limit detection data according to the license plate number and license plate color of each bayonet capture data to obtain the first vehicle set of each preset vehicle category includes: determining whether a preset character exists in the license plate number included in each bayonet capture data; determining the vehicle indicated by the bayonet capture data with the preset character as an extra-large truck, and taking the extra-large truck as the classification result of the vehicle indicated by the bayonet capture data with the preset character; or, taking the vehicle indicated by the bayonet capture data without the preset character as multiple first vehicles, where the preset character is used to indicate that the vehicle is an extra-large truck, and each bayonet capture data corresponds to one vehicle; determining whether the vehicle type of the first vehicle itself exists in the bayonet capture data of each first vehicle; if so, obtaining the vehicle category corresponding to the vehicle type of the first vehicle itself from the mapping relationship between the vehicle type of the first vehicle itself and the preset vehicle category as the classification result of each first vehicle; if not, associating and matching the first license plate number and the first license plate color included in the bayonet capture data of each first vehicle with the second license plate number and the second license plate color included in each over-limit detection data to obtain a matching result, and each over-limit detection data corresponds to one vehicle; classifying each first vehicle according to the matching result to obtain a classification result; classifying the vehicles belonging to the same classification result to obtain the first vehicle set of each preset vehicle category.

[0043] Among them, the preset character refers to a specific character or string used to specifically identify the vehicle type in the license plate number. For example, the "trailer" existing in the license plate number of an extra-large truck. The first vehicle refers to the vehicle captured by the electronic bayonet device. The vehicle type of the first vehicle itself refers to the type information of the vehicle captured by the electronic bayonet device, such as SUV, sedan, truck, etc. The preset vehicle category is a vehicle classification predefined according to traffic management needs, such as small passenger car, large passenger car, small truck, etc. The mapping relationship is the corresponding relationship between the vehicle type of the first vehicle itself and the preset vehicle category, and is used to convert the captured vehicle type into the vehicle category required for traffic flow calculation. The first license plate number and the first license plate color refer to the license plate number and color of the vehicle captured by the electronic bayonet device. The second license plate number and the second license plate color refer to the license plate number and color of the vehicle detected by the over-limit dynamic detection device. The classification result refers to classifying the vehicle into the preset vehicle category in the mapping relationship according to information such as the license plate number, color, and type of the vehicle.

[0044] For example, if the license plate number included in the bayonet capture data has a "trailer", it can be known through the mapping relationship that the vehicle indicated by the bayonet capture data is an extra-large truck, and taking the extra-large truck as the classification result of the vehicle. Taking the vehicles indicated by the bayonet capture data without a "trailer" as multiple first vehicles, and determining whether the vehicle type of the first vehicle itself exists in the bayonet capture data of each first vehicle.

[0045] If there is a vehicle whose type is SUV, MPV, hatchback, sedan, coupe, van, microcar, minibus, compact car, sedan, or the vehicle type is a bus and the license plate color is blue or green, then the mapping relationship shows that the vehicle belongs to a compact car, and the compact car is classified as the first vehicle.

[0046] If there is a vehicle whose type is a passenger car and whose license plate color is blue and green, it can be known from the mapping relationship that the vehicle belongs to a small passenger car, and the small passenger car is taken as the classification result of the first vehicle.

[0047] If there is a vehicle whose type is a passenger car and whose license plate color is not blue or green, it can be known from the mapping relationship that the vehicle belongs to a large passenger car, and the large passenger car is taken as the classification result of the first vehicle.

[0048] If there is a vehicle whose type is a small truck, a pickup truck, a mini truck / flatbed truck with a blue or green license plate color, a dump truck with a blue or green license plate color, or a crane / construction vehicle with a blue or green license plate color, then the mapping relationship shows that the vehicle belongs to a small truck, and the small truck is classified as the first vehicle.

[0049] If there is a vehicle whose type is a mini truck / flatbed truck and the license plate color is not blue or green, a dump truck and the license plate color is not blue or green, a tank truck, a flatbed trailer and the license plate color is not blue or green, a crane / construction vehicle and the license plate color is yellow, then the mapping relationship shows that the vehicle belongs to a large truck, and the large truck is classified as the first vehicle.

[0050] If there is a vehicle whose own type is a concrete mixer truck, it can be known from the mapping relationship that the vehicle belongs to an extra-large truck, and the extra-large truck is used as the classification result of the first vehicle.

[0051] If there is a vehicle whose own type is a container truck, it can be known from the mapping relationship that the vehicle belongs to a container truck, and the container truck is taken as the classification result of the first vehicle.

[0052] If there is a vehicle type of two-wheeled vehicle, three-wheeled vehicle, or unknown, it can be known from the mapping relationship that the vehicle belongs to other types, and the other types are taken as the classification result of the first vehicle.

[0053] If the vehicle type does not exist in the checkpoint capture data of each first vehicle, the first license plate number and the first license plate color contained in the checkpoint capture data of each first vehicle are associated and matched with the second license plate number and the second license plate color contained in each over-limit detection data to obtain a matching result.

[0054] In some embodiments of the present application, each first vehicle is classified according to the matching results, and the specific process of obtaining the classification results includes: classifying the first vehicles indicated in the matching results as not matching the second license plate number and the second license plate color into other categories; or, treating the first vehicles matched to the second license plate number and the second license plate color as multiple second vehicles; obtaining the vehicle axle number of each second vehicle from the over-limit detection data belonging to the matched second license plate number and the second license plate color; according to the vehicle axle number, obtaining the corresponding vehicle category as the classification result of each second vehicle from the mapping relationship between the pre-configured vehicle axle number and the preset vehicle category.

[0055] For example, when the matching result indicates that each first vehicle is not successfully matched, other types are used as the classification result of the first vehicle. When the matching result indicates that each first vehicle is successfully matched, if the number of axles of the vehicle is 2, it can be known that the vehicle belongs to a medium-sized truck through the mapping relationship between the pre-configured number of axles of the vehicle and the preset vehicle category, and the medium-sized truck is used as the classification result of the vehicle. If the number of axles of the vehicle is 3 or 4, it can be known that the vehicle belongs to a large truck through the mapping relationship between the pre-configured number of axles of the vehicle and the preset vehicle category, and the large truck is used as the classification result of the vehicle. If the number of axles of the vehicle is 5 or 6, it can be known that the vehicle belongs to an extra-large truck through the mapping relationship between the pre-configured number of axles of the vehicle and the preset vehicle category, and the extra-large truck is used as the classification result of the vehicle. If the number of axles of the vehicle is less than 2, or the number of axles is greater than 6, or there is no axle number, it can be known that the vehicle belongs to other categories through the mapping relationship between the pre-configured number of axles of the vehicle and the preset vehicle category, and the other categories are used as the classification result of the vehicle.

[0056] Among them, the preset vehicle categories include small passenger cars, large passenger cars, small trucks, medium-sized trucks, large trucks, extra-large trucks, container trucks, and other categories.

[0057] S103, performing matching analysis on each piece of the camera captured data according to the license plate number and the license plate color of each piece of overload detection data, and obtaining a second vehicle set for each preset vehicle category;

[0058] In an embodiment of the present application, each bayonet captured data is matched and analyzed according to the license plate number and license plate color of each over-limit detection data, and the specific process of obtaining the second vehicle set of each preset vehicle category includes: when the number of vehicle axles contained in each over-limit detection data is two axles, the third license plate number and the third license plate color contained in each over-limit detection data are associated and matched with the fourth license plate number and the fourth license plate color contained in each bayonet captured data; when all matches are successful, the fourth license plate number and the fourth license plate color matched to each over-limit detection data are obtained from the bayonet captured data. The third vehicle indicated by each over-limit detection data corresponds to the second vehicle own type; judging whether there is a vehicle category corresponding to each second vehicle own type in the mapping relationship between the pre-configured vehicle own type and the preset vehicle category; if so, taking the vehicle category corresponding to each second vehicle own type as the classification result of each third vehicle; if not, obtaining the total vehicle mass contained in each over-limit detection data; determining the classification result of each third vehicle according to the third license plate color and the total vehicle mass; classifying vehicles belonging to the same classification result to obtain a second vehicle set of each preset vehicle category.

[0059] The vehicle axle number is the number of wheel axles on the vehicle.

[0060] For example, determine whether the number of axles of the third vehicle contained in each over-limit detection data is two axles; if it is 2 axles, associate and match the third license plate number and the third license plate color contained in each over-limit detection data with the fourth license plate number and the fourth license plate color contained in each checkpoint capture data. If all matches are successful, the vehicle type contained in the matched checkpoint capture data is used as the vehicle type corresponding to the third vehicle indicated by each over-limit detection data.

[0061] If the vehicle type corresponding to the third vehicle indicated by each over-limit detection data is SUV, MPV, hatchback, sedan, coupe, van, microcar, small passenger car, small sedan, or sedan, or the vehicle type is a passenger car and the license plate color is blue or green, then the mapping relationship shows that the vehicle belongs to a small passenger car, and the small passenger car is used as the classification result of each third vehicle.

[0062] If the vehicle type corresponding to the third vehicle indicated by each overload detection data is a passenger car and the license plate color is blue and green, it can be known from the mapping relationship that the vehicle belongs to a small passenger car, and the small passenger car is used as the classification result of each third vehicle.

[0063] If the vehicle type corresponding to the third vehicle indicated by each overload detection data is a passenger car and the license plate color is not blue or green, it can be known from the mapping relationship that the vehicle belongs to a large passenger car, and the large passenger car is used as the classification result of each third vehicle.

[0064] If the vehicle type corresponding to the third vehicle indicated by each overload detection data is a small truck, a pickup truck, a micro truck / flatbed truck with a blue or green license plate color, a dump truck with a blue or green license plate color, or a crane / construction vehicle with a blue or green license plate color, then the mapping relationship shows that the vehicle belongs to a small truck, and the small truck is used as the classification result for each third vehicle.

[0065] If the vehicle type corresponding to the third vehicle indicated by each over-limit detection data cannot find a corresponding vehicle category in the mapping relationship, the total vehicle mass contained in each over-limit detection data is obtained, and the classification result of each third vehicle is determined according to the third license plate color and the total vehicle mass.

[0066] In some embodiments of the present application, the specific process of determining the classification result of each third vehicle based on the third license plate color and the gross vehicle mass includes: when the third license plate color is blue or green, according to the gross vehicle mass, obtaining the corresponding vehicle category from the pre-configured mapping relationship between the gross vehicle mass and the preset vehicle category as the classification result of each third vehicle; or, when the third license plate color is not blue or green, matching the third license plate number and the third license plate color with the vehicle data of the preset transport administration system; when the match is successful and the matched vehicle category in the preset transport administration system is a large passenger car, taking the large passenger car as the classification result of each third vehicle; when the match is not successful or the matched vehicle category in the preset transport administration system is not a large passenger car, taking the medium-sized truck as the classification result of each third vehicle.

[0067] For example, if the color of the third license plate is blue or green and the gross vehicle mass is less than 3 tons, it can be seen from the mapping relationship between the pre-configured gross vehicle mass and the preset vehicle category that each third vehicle belongs to a small passenger car, and the small passenger car is used as the classification result of each third vehicle; otherwise, the vehicle is classified as a small truck, and the small truck is used as the classification result of the third vehicle.

[0068] If the color of the third license plate is not blue or green, the third license plate number and the third license plate color are used to match the vehicle data in the preset transportation administration system. If the match is successful, it is judged according to the vehicle type in the transportation administration system. If the vehicle type in the transportation administration system is a large passenger bus, each third vehicle is classified as a large passenger bus, and the large passenger bus is used as the classification result of each third vehicle.

[0069] If the vehicle type preset in the transport administration system is not a large passenger car, or is not successfully matched with the vehicle in the transport administration system, each third vehicle will be classified as a medium-sized truck, and the medium-sized truck will be used as the classification result of each third vehicle.

[0070] In some embodiments of the present application, when the number of vehicle axles contained in each over-limit detection data is not two, it is determined whether the number of vehicle axles contained in the over-limit detection data is three-axles, four-axles, five-axles or six-axles; when the number of vehicle axles contained in each over-limit detection data is three-axles or four-axles, a large truck is used as the classification result of each third vehicle; when the number of vehicle axles contained in each over-limit detection data is five-axles or six-axles, the vehicle height contained in each over-limit detection data is obtained; when the vehicle height is within a preset height range, a container truck is used as the classification result of each third vehicle; or, when the vehicle height is not within the preset height range, an extra-large truck is used as the classification result of each third vehicle; when the number of vehicle axles contained in each over-limit detection data is not within the preset axle number range, other categories are used as the classification result of each third vehicle.

[0071] For example, if the number of vehicle axles contained in each over-limit detection data is not 2, determine whether the third vehicle indicated by each over-limit detection data is a 3-axle or 4-axle vehicle. If it is a 3-axle or 4-axle vehicle, each third vehicle is classified as a large truck, and a large truck is used as the classification result of each third vehicle.

[0072] If the third vehicle indicated by each overload detection data is 5-axle or 6-axle, it is necessary to continue to judge the vehicle height of each third vehicle. If the vehicle height is greater than or equal to 3.8 meters, or the vehicle height is less than or equal to 4.2 meters, each third vehicle is classified as a container truck, and the container truck is used as the classification result of each third vehicle; otherwise, each third vehicle is classified as an extra-large truck; if the number of vehicle axles is not in the range of 2 to 6 axles, each third vehicle is classified into other categories, and other categories are used as the classification result of each third vehicle.

[0073] S104, integrating and aggregating the first vehicle set and the second vehicle set to generate the traffic flow of the target road section.

[0074] In some embodiments of the present application, the specific process of fusing and aggregating the first vehicle set and the second vehicle set to generate the traffic flow of the target road section includes: adding the first vehicle set of each preset vehicle category to the second vehicle set of each preset vehicle category to obtain a fused vehicle set of each preset vehicle category; merging the same vehicles existing in the fused vehicle set of each preset vehicle category; aggregating the number of vehicles in the fused vehicle set of each preset vehicle category after the merger; and using the aggregated number of vehicles as the traffic flow of the target road section.

[0075] Specifically, the traffic flow related information includes multiple attributes related to the vehicle, including: vehicle license plate number and license plate color, vehicle specifications, vehicle type, vehicle gross mass, vehicle length and height, vehicle travel time, vehicle axle number, and preset vehicle category.

[0076] Among them, these attributes together provide more comprehensive data support for traffic management, making the data richer and providing more comprehensive data support for traffic management.

[0077] Among them, in the mapping relationship between the pre-configured vehicle type and the preset vehicle category, the preset vehicle category includes small passenger cars, large passenger cars, small trucks, medium trucks, large trucks, extra-large trucks, container trucks, and other categories.

[0078] Through step S102, a first vehicle set including small passenger cars, large passenger cars, small trucks, medium trucks, large trucks, extra-large trucks, container trucks, and other categories can be obtained; through step S103, a second vehicle set including small passenger cars, large passenger cars, small trucks, medium trucks, large trucks, extra-large trucks, container trucks, and other categories can be obtained.

[0079] For small passenger cars, the first vehicle set under small passenger cars obtained in step S102 and the second vehicle set under small passenger cars obtained in step S103 may contain the same vehicle, so it is necessary to merge the repeated vehicle marks into one vehicle mark to achieve the fusion between the first vehicle set and the second vehicle set, and obtain the fused vehicle set of each preset vehicle category. The fusion method of other preset vehicle categories can refer to the process of small passenger cars, which will not be repeated here.

[0080] For example Figure 3 As shown, Figure 3 This is a schematic diagram of a process of traffic flow analysis provided by the present application. A data source is obtained, and the data source includes information obtained by an overload dynamic detection device and information captured by an electronic checkpoint. The two data sources are preprocessed separately, and the preprocessed data are sent to a fusion calculation stage. In this stage, the preprocessed data of the overload dynamic detection device and the preprocessed data captured by the electronic checkpoint are combined for fusion calculation. The fusion calculation involves data matching and association analysis, and ultimately a fused vehicle set for each preset vehicle category can be obtained, that is, the vehicles are classified into different preset vehicle categories, and the preset vehicle categories include small passenger cars, large passenger cars, small trucks, medium trucks, large trucks, extra-large trucks, container trucks, and other categories. The vehicle set summarized under each preset vehicle category can characterize the traffic flow.

[0081] In the embodiment of the present application, on the one hand, through preprocessing and matching analysis, the information of the over-limit dynamic detection device is combined with the information of the electronic bayonet device to obtain more comprehensive traffic flow data, so that vehicles can be more accurately identified and classified, thereby improving the accuracy of traffic flow calculation. On the other hand, by using the existing over-limit dynamic detection equipment and electronic bayonet equipment, additional hardware investment and construction costs are avoided, and resource reuse is achieved, thereby significantly reducing the overall cost of traffic flow monitoring.

[0082] The following is a system embodiment of the present application, which can be used to execute the method embodiment of the present application. For details not disclosed in the system embodiment of the present application, please refer to the method embodiment of the present application.

[0083] See also Figure 4 , which shows a schematic diagram of the structure of a traffic flow analysis system provided by an exemplary embodiment of the present application. The traffic flow analysis system can be implemented as all or part of an electronic device through software, hardware, or a combination of both. The system 1 includes a preprocessing module 10, a first matching analysis module 20, a second matching analysis module 30, and a fusion summary module 40.

[0084] The preprocessing module 10 is used to obtain and preprocess the vehicle overload information from the overload dynamic detection device and the vehicle passage information from the electronic checkpoint device within a preset period, and obtain a plurality of overload detection data and a plurality of checkpoint capture data, wherein the overload detection data includes the vehicle license plate number, license plate color, and vehicle specifications, and the checkpoint capture data includes the vehicle license plate number, license plate color, and vehicle type;

[0085] A first matching analysis module 20 is used to perform matching analysis on each overload detection data according to the license plate number and license plate color of each card port capture data, and obtain a first vehicle set for each preset vehicle category;

[0086] The second matching analysis module 30 is used to match and analyze each piece of the camera captured data according to the license plate number and license plate color of each piece of overload detection data to obtain a second vehicle set for each preset vehicle category;

[0087] The fusion and aggregation module 40 is used to fuse and aggregate the first vehicle set and the second vehicle set to generate the traffic flow of the target road section.

[0088] It should be noted that the traffic flow analysis system provided in the above embodiment only uses the division of the above functional modules as an example when executing the traffic flow analysis method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the traffic flow analysis system provided in the above embodiment and the traffic flow analysis method embodiment belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0089] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0090] In the embodiment of the present application, on the one hand, through preprocessing and matching analysis, the information of the over-limit dynamic detection device is combined with the information of the electronic bayonet device to obtain more comprehensive traffic flow data, so that vehicles can be more accurately identified and classified, thereby improving the accuracy of traffic flow calculation. On the other hand, by using the existing over-limit dynamic detection equipment and electronic bayonet equipment, additional hardware investment and construction costs are avoided, and resource reuse is achieved, thereby significantly reducing the overall cost of traffic flow monitoring.

[0091] The present application also provides a computer-readable medium having program instructions stored thereon, and when the program instructions are executed by a processor, the traffic flow analysis methods provided by the above-mentioned various method embodiments are implemented.

[0092] The present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the traffic flow analysis methods of the above-mentioned various method embodiments.

[0093] See also Figure 5 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 5 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0094] The communication bus 1002 is used to realize the connection and communication between these components.

[0095] The user interface 1003 may include a display screen (Display) and a camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.

[0096] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0097] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts within the entire electronic device 1000, and executes various functions and processes data of the electronic device 1000 by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0098] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may also be optionally at least one storage system located away from the aforementioned processor 1001. As Figure 5 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a traffic flow analysis application.

[0099] exist Figure 5In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 1001 can be used to call the traffic flow analysis application stored in the memory 1005 and specifically perform the following operations:

[0100] Acquire and pre-process vehicle overload information from overload dynamic detection equipment and vehicle traffic information from electronic checkpoint equipment within a preset period, and obtain multiple overload detection data and multiple checkpoint capture data. The overload detection data includes the vehicle's license plate number, license plate color, and vehicle specifications, and the checkpoint capture data includes the vehicle's license plate number, license plate color, and vehicle type;

[0101] According to the license plate number and license plate color of each piece of the card gate captured data, each piece of overload detection data is matched and analyzed to obtain the first vehicle set of each preset vehicle category;

[0102] According to the license plate number and license plate color of each overload detection data, each checkpoint captured data is matched and analyzed to obtain a second vehicle set for each preset vehicle category;

[0103] The first vehicle set and the second vehicle set are fused and aggregated to generate the traffic flow of the target road section.

[0104] In one embodiment, when the processor 1001 performs matching analysis on each overload detection data according to the license plate number and license plate color of each bayonet capture data to obtain the first vehicle set of each preset vehicle category, the processor 1001 specifically performs the following operations:

[0105] Determine the vehicle indicated by the camera-shot data whose license plate number contained in each camera-shot data has preset characters as a classification result of an extra-large truck; or, the vehicles indicated by the camera-shot data without the preset characters are taken as multiple first vehicles, the preset characters are used to indicate that the vehicle is an extra-large truck, and each camera-shot data corresponds to one vehicle;

[0106] Determine whether the first vehicle type exists in the camera capture data of each first vehicle;

[0107] If yes, obtaining the vehicle category corresponding to the first vehicle's own type from a preconfigured mapping relationship between the vehicle's own type and the preset vehicle category as a classification result for each first vehicle;

[0108] If not, the first license plate number and the first license plate color contained in the camera capture data of each first vehicle are associated and matched with the second license plate number and the second license plate color contained in each overload detection data to obtain a matching result, where each overload detection data corresponds to one vehicle;

[0109] Classifying each first vehicle according to the matching result to obtain a classification result;

[0110] The vehicles belonging to the same classification result are classified to obtain a first vehicle set for each preset vehicle category.

[0111] In one embodiment, when the processor 1001 classifies each first vehicle according to the matching result to obtain the classification result, the processor 1001 specifically performs the following operations:

[0112] Classifying the first vehicle that is not matched with the second license plate number and the second license plate color in the matching result into other categories; or, treating the first vehicle that is matched with the second license plate number and the second license plate color as a plurality of second vehicles;

[0113] Obtaining the number of vehicle axles of each second vehicle from the matched second license plate number and the over-limit detection data to which the second license plate color belongs;

[0114] According to the number of vehicle axles, a corresponding vehicle category is obtained from a mapping relationship between a preconfigured number of vehicle axles and a preset vehicle category as a classification result of each second vehicle.

[0115] In one embodiment, the processor 1001 performs matching analysis on each piece of the camera captured data according to the license plate number and license plate color of each piece of overload detection data, and obtains the second vehicle set of each preset vehicle category, and specifically performs the following operations:

[0116] In the case that the number of vehicle axles contained in each piece of overload detection data is two, the third license plate number and the third license plate color contained in each piece of overload detection data are associated and matched with the fourth license plate number and the fourth license plate color contained in each piece of camera capture data;

[0117] In the case where all matches are successful, the second vehicle type corresponding to the third vehicle indicated by each piece of overload detection data is obtained from the checkpoint captured data to which the fourth license plate number and the fourth license plate color matched to each piece of overload detection data belong;

[0118] Determining whether there is a vehicle category corresponding to each second vehicle type in the mapping relationship between the pre-configured vehicle type and the preset vehicle category;

[0119] If so, taking the vehicle category corresponding to the own type of each second vehicle as the classification result of each third vehicle;

[0120] If it does not exist, obtain the total vehicle mass contained in each overload detection data; determine the classification result of each third vehicle according to the third license plate color and the total vehicle mass;

[0121] The vehicles belonging to the same classification result are classified to obtain a second vehicle set for each preset vehicle category.

[0122] In one embodiment, when the processor 1001 determines the classification result of each third vehicle according to the third license plate color and the total vehicle mass, the following operations are specifically performed:

[0123] When the color of the third license plate is blue or green, according to the total vehicle mass, the corresponding vehicle class is obtained from the pre-configured mapping relationship between the total vehicle mass and the preset vehicle class as the classification result of each third vehicle; or

[0124] When the third license plate color is not blue or green, the third license plate number and the third license plate color are matched with the preset transportation administration system vehicle data;

[0125] In the case where the matching is successful and the vehicle category matched in the preset transport administration system is a large passenger car, the large passenger car is used as the classification result of each third vehicle;

[0126] In the event that the matching is unsuccessful or the vehicle category matched in the preset transport administration system is not a large passenger car, a medium-sized truck is used as the classification result for each third vehicle.

[0127] In one embodiment, the processor 1001 further performs the following operations:

[0128] In the case that the number of vehicle axles contained in each piece of overload detection data is not two axles, determining whether the number of vehicle axles contained in each piece of overload detection data is three axles, four axles, five axles, or six axles;

[0129] In the case that the number of axles of the vehicle contained in each overload detection data is three or four, a large truck is used as the classification result of each third vehicle;

[0130] When the number of vehicle axles included in each piece of over-limit detection data is five or six, obtaining the vehicle height included in each piece of over-limit detection data;

[0131] When the height of the vehicle is within the preset height range, a container truck is used as the classification result of each third vehicle; or, when the height of the vehicle is not within the preset height range, an oversized truck is used as the classification result of each third vehicle;

[0132] In the case that the number of vehicle axles included in each piece of over-limit detection data is not within a preset axle number range, other categories are used as classification results for each third vehicle.

[0133] In one embodiment, when the processor 1001 performs the fusion aggregation of the first vehicle set and the second vehicle set to generate the traffic flow of the target road section, the following operations are specifically performed:

[0134] Adding the first vehicle set of each preset vehicle category to the second vehicle set of each preset vehicle category to obtain a fused vehicle set of each preset vehicle category;

[0135] Merge the same vehicles existing in the fused vehicle set of each preset vehicle category;

[0136] Summarize the number of vehicles in the fused vehicle set for each preset vehicle category after merging;

[0137] The summarized number of vehicles is taken as the traffic flow of the target road section.

[0138] In the embodiment of the present application, on the one hand, through preprocessing and matching analysis, the information of the over-limit dynamic detection device is combined with the information of the electronic bayonet device to obtain more comprehensive traffic flow data, so that vehicles can be more accurately identified and classified, thereby improving the accuracy of traffic flow calculation. On the other hand, by using the existing over-limit dynamic detection equipment and electronic bayonet equipment, additional hardware investment and construction costs are avoided, and resource reuse is achieved, thereby significantly reducing the overall cost of traffic flow monitoring.

[0139] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program for traffic flow analysis can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium of the program for traffic flow analysis can be a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0140] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A traffic flow analysis method, characterized in that: The method comprises: Acquire and pre-process vehicle overload information from an overload dynamic detection device and vehicle traffic information from an electronic checkpoint device within a preset period to obtain a plurality of overload detection data and a plurality of checkpoint capture data, wherein the overload detection data includes the vehicle license plate number, license plate color, and vehicle specifications, and the checkpoint capture data includes the vehicle license plate number, license plate color, and vehicle type; According to the license plate number and license plate color of each piece of the card gate captured data, each piece of overload detection data is matched and analyzed to obtain the first vehicle set of each preset vehicle category; According to the license plate number and license plate color of each overload detection data, matching analysis is performed on each of the checkpoint captured data to obtain a second vehicle set for each preset vehicle category; The first vehicle set and the second vehicle set are integrated and summarized to generate the traffic flow of the target road section.

2. The method according to claim 1, characterized in that The matching analysis is performed on each overload detection data according to the license plate number and license plate color of each bayonet capture data to obtain a first vehicle set for each preset vehicle category, including: Determine whether the license plate number included in each piece of the camera capture data contains preset characters; Determine the vehicle indicated by the camera-shot data with preset characters as an extra-large truck, and use the extra-large truck as the classification result of the vehicle indicated by the camera-shot data with preset characters; or use the vehicles indicated by the camera-shot data without preset characters as multiple first vehicles, the preset characters are used to indicate that the vehicle is an extra-large truck, and each camera-shot data corresponds to one vehicle; Determine whether the first vehicle type exists in the camera capture data of each first vehicle; If yes, obtaining the vehicle category corresponding to the first vehicle type from a preconfigured mapping relationship between the vehicle type and the preset vehicle category as the classification result of each first vehicle; If not, the first license plate number and the first license plate color contained in the bayonet capture data of each first vehicle are associated and matched with the second license plate number and the second license plate color contained in each over-limit detection data to obtain a matching result, where each over-limit detection data corresponds to one vehicle; Classifying each first vehicle according to the matching result to obtain a classification result; The vehicles belonging to the same classification result are classified to obtain a first vehicle set for each preset vehicle category.

3. The method according to claim 2, characterized in that The step of classifying each first vehicle according to the matching result to obtain a classification result includes: Classify the first vehicles that are not matched to the second license plate number and the second license plate color in the matching result into other categories; or, classify the first vehicles that are matched to the second license plate number and the second license plate color as multiple second vehicles; Acquire the number of vehicle axles of each second vehicle from the over-limit detection data to which the matched second license plate number and second license plate color belong; According to the vehicle axle number, a corresponding vehicle category is obtained from a mapping relationship between a preconfigured vehicle axle number and a preset vehicle category as a classification result of each second vehicle.

4. The method according to claim 1, characterized in that: The matching analysis of each of the bayonet captured data is performed according to the license plate number and the license plate color of each of the overload detection data to obtain a second vehicle set of each preset vehicle category, including: In the case where the number of vehicle axles contained in each piece of overload detection data is two axles, the third license plate number and the third license plate color contained in each piece of overload detection data are associated and matched with the fourth license plate number and the fourth license plate color contained in each piece of bayonet capture data; In the case where all matches are successful, obtaining the second vehicle type corresponding to the third vehicle indicated by each piece of overload detection data from the camera capture data to which the fourth license plate number and the fourth license plate color matched by each piece of overload detection data belong; Determining whether there is a vehicle category corresponding to each second vehicle type in the mapping relationship between the pre-configured vehicle type and the preset vehicle category; If so, taking the vehicle category corresponding to the own type of each second vehicle as the classification result of each third vehicle; If not present, obtaining the total vehicle mass contained in each overload detection data; determining the classification result of each third vehicle according to the third license plate color and the total vehicle mass; The vehicles belonging to the same classification result are classified to obtain a second vehicle set for each preset vehicle category.

5. The method according to claim 4, characterized in that Determining the classification result of each third vehicle according to the third license plate color and the total vehicle mass includes: When the color of the third license plate is blue or green, according to the total vehicle mass, a corresponding vehicle class is obtained from a preconfigured mapping relationship between the total vehicle mass and the preset vehicle class as the classification result of each third vehicle; or When the third license plate color is not blue or green, matching the third license plate number and the third license plate color with the preset transport administration system vehicle data; In the case where the matching is successful and the vehicle category matched in the preset transport administration system is a large passenger car, the large passenger car is used as the classification result of each of the third vehicles; In the event that the matching is unsuccessful or the vehicle category matched in the preset transport administration system is not a large passenger car, the medium-sized truck is used as the classification result of each third vehicle.

6. The method according to claim 4, characterized in that The method further comprises: In the case where the number of vehicle axles contained in each piece of over-limit detection data is not two axles, determining whether the number of vehicle axles contained in the piece of over-limit detection data is three axles, four axles, five axles, or six axles; In the case where the number of vehicle axles included in each piece of overload detection data is three axles or four axles, taking the large truck as the classification result of each third vehicle; When the number of vehicle axles included in each piece of over-limit detection data is five or six, obtaining the vehicle height included in each piece of over-limit detection data; When the height of the vehicle is within the preset height range, a container truck is used as the classification result of each third vehicle; or, when the height of the vehicle is not within the preset height range, an extra-large truck is used as the classification result of each third vehicle; When the number of vehicle axles included in each piece of over-limit detection data is not within a preset axle number range, other categories are used as classification results of each third vehicle.

7. The method according to claim 1, characterized in that The fusing and aggregating the first vehicle set and the second vehicle set to generate the traffic flow of the target road section includes: Adding the first vehicle set of each preset vehicle category to the second vehicle set of each preset vehicle category to obtain a fused vehicle set of each preset vehicle category; Merge the same vehicles existing in the fused vehicle set of each preset vehicle category; Summarize the number of vehicles in the fused vehicle set for each preset vehicle category after merging; The summarized number of vehicles is taken as the traffic flow of the target road section.

8. A traffic flow analysis device, characterized in that: The device comprises: A preprocessing module is used to obtain and preprocess vehicle overload information from an overload dynamic detection device and vehicle passage information from an electronic checkpoint device within a preset period, and obtain a plurality of overload detection data and a plurality of checkpoint capture data, wherein the overload detection data includes a vehicle license plate number, a license plate color, and a vehicle specification, and the checkpoint capture data includes a vehicle license plate number, a license plate color, and a vehicle type; A first matching analysis module is used to perform matching analysis on each overload detection data according to the license plate number and license plate color of each card port capture data, and obtain a first vehicle set for each preset vehicle category; A second matching analysis module is used to perform matching analysis on each of the checkpoint captured data according to the license plate number and license plate color of each of the overload detection data to obtain a second vehicle set for each preset vehicle category; The fusion and aggregation module is used to fuse and aggregate the first vehicle set and the second vehicle set to generate the traffic flow of the target road section.

9. A computer storage medium, characterized in that The computer storage medium stores a plurality of instructions, which are suitable for being loaded by a processor and executing the method steps according to any one of claims 1 to 7.

10. A terminal, characterized in that: include: A processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the method steps as claimed in any one of claims 1 to 7.

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