Bayonet duplicate number checking method and device based on floating car positioning data

Abstract

The invention discloses a checkpoint duplicate number checking method and device based on floating car positioning data, and the method comprises the steps: obtaining a target checkpoint, obtaining the historical detection data of the target checkpoint, and obtaining floating car detection data from the historical detection data; then extracting positioning data of each floating car from the floating car detection data; then, according to the positioning data, carrying out clustering processing on the longitude and latitude of the floating car, and determining the number of types of clustering results; and finally, according to the type number of the clustering result, performing duplicate number judgment on the target gate, and determining the number of gates with duplicate numbers. According to the method, the huge workload of manually checking the checkpoint pictures one by one can be avoided, so that checkpoint duplicate number checking becomes simpler, more convenient, more efficient and more accurate, and the method can be widely applied to the technical field of computers.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular, to a method and device for checking duplicate numbers of bayonet devices based on floating car positioning data. Background Art

[0002] Bayonet devices are important facilities in the fields of modern transportation and security, and have been widely deployed in many cities at home and abroad, playing an indispensable role in urban management. First of all, in the field of traffic management, bayonet devices can be used to monitor and record the driving conditions of vehicles, including key information such as vehicle speed, vehicle type, license plate, etc. This helps traffic management departments better understand the road usage situation, improve traffic efficiency, and also provides an important basis for accident investigation and accountability. Secondly, in the field of public security prevention and control, bayonet devices can assist the police in monitoring and tracking criminal acts. In addition, bayonet devices can also be used for the construction of smart cities, providing data support for urban management and decision-making through the collection and analysis of urban traffic data.

[0003] As the number of bayonet devices in the city increases day by day, the management difficulty increases day by day, and it is inevitable that there will be a situation of duplicate bayonet numbers, that is, multiple completely independent bayonets located far apart share the same unique identification number. If the urban traffic management department uses this unique identification number to screen bayonets and obtains incorrect duplicate bayonet data for management and law enforcement, it will cause misjudgment and bring many adverse effects. Therefore, an effective method is needed to screen the problem of duplicate bayonet numbers and correct it in time.

[0004] Currently, the screening of the problem of duplicate bayonet numbers mainly relies on manual verification. Bayonet data is screened through the unique identification number, and then the backgrounds of the pictures taken by each bayonet record are compared one by one to see if they are basically the same. If there are two bayonet pictures with significantly inconsistent backgrounds, there is a problem of duplicate bayonet numbers. However, considering the huge volume and data volume of the bayonets built in current large and medium-sized cities, it is obviously unrealistic to manually check each bayonet and each captured picture, and the screening efficiency is extremely low. There is also a potential method, namely image comparison, which judges whether the backgrounds are the same based on the similarity of pixels between two bayonet pictures, and then judges whether there is a problem of duplicate numbers. This method is prone to misjudgment. During the day and at night, on sunny days and rainy or snowy days, and when the traffic flow is large and small, even if the two pictures are taken in the same background, the pixel differences are extremely large, and it is completely inapplicable to bayonets with freely rotatable shooting angles. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides an efficient and accurate method and device for checking duplicate numbers of bayonet devices based on floating car positioning data.

[0006] One aspect of the embodiment of the present invention provides a method for checking duplicate numbers of bayonet devices based on floating car positioning data, including:

[0007] Obtain a target checkpoint, and obtain the historical detection data of the target checkpoint, and obtain the floating vehicle detection data from the historical detection data;

[0008] Extract the positioning data of each floating vehicle from the floating vehicle detection data;

[0009] According to the positioning data, perform clustering processing on the longitude and latitude of the floating vehicles, and determine the number of types of the clustering results;

[0010] According to the number of types of the clustering results, perform duplicate number determination on the target checkpoint, and determine the number of checkpoints with duplicate numbers.

[0011] Optionally, the floating vehicle detection data includes a license plate number and a detection time.

[0012] Optionally, the positioning data includes a license plate number, a collection time, a longitude position, and a latitude position. The extracting the positioning data of each floating vehicle from the floating vehicle detection data includes:

[0013] Obtain the checkpoint detection time of each floating vehicle at each checkpoint;

[0014] When the license plate of each floating vehicle is consistent with the license plate detected by the checkpoint, extract the positioning data with the closest data collection time to the checkpoint detection time from the floating vehicle detection data.

[0015] Optionally, the performing clustering processing on the longitude and latitude of the floating vehicles according to the positioning data and determining the number of types of the clustering results includes:

[0016] Configure an input radius and a minimum number of clusters;

[0017] According to the input radius and the minimum number, perform clustering processing on the longitude and latitude of the positioning data of the floating vehicles through the DBSCAN algorithm, and determine the number of types of the clustering results.

[0018] Optionally, the performing clustering processing on the longitude and latitude of the positioning data of the floating vehicles according to the input radius and the minimum number through the DBSCAN algorithm and determining the number of types of the clustering results includes:

[0019] Within the area of a circle with a radius of the input radius around each data point, if the total number of data points included is greater than the minimum number, merge the data points within this range into the same class. If the number of points included does not meet the minimum number and this point does not belong to any class, then this point belongs to noise data.

[0020] Optionally, the performing duplicate number determination on the target checkpoint according to the number of types of the clustering results and determining the number of checkpoints with duplicate numbers includes:

[0021] After removing the noise data, if there is only one class in the clustering result, there is no problem of duplicate numbers at the bayonet.

[0022] If there is more than one class in the clustering result, there is a problem of duplicate numbers, and the number of bayonets with duplicate numbers is equal to the number of classes in the clustering. Each clustering center is the approximate distribution point of each bayonet with duplicate numbers.

[0023] Another aspect of the embodiments of the present invention also provides a device for checking duplicate numbers at bayonets based on floating car positioning data, including:

[0024] A first module, configured to obtain a target bayonet, obtain historical detection data of the target bayonet, and obtain floating car detection data from the historical detection data;

[0025] A second module, configured to extract positioning data of each floating car from the floating car detection data;

[0026] A third module, configured to perform clustering processing on the longitude and latitude of the floating cars according to the positioning data to determine the number of types of the clustering result;

[0027] A fourth module, configured to determine whether there are duplicate numbers at the target bayonet according to the number of types of the clustering result, and determine the number of bayonets with duplicate numbers.

[0028] Another aspect of the embodiments of the present invention also provides an electronic device, including a processor and a memory;

[0029] The memory is used to store a program;

[0030] The processor executes the program to implement the method as described above.

[0031] Another aspect of the embodiments of the present invention also provides a computer-readable storage medium, where the storage medium stores a program, and the program is executed by a processor to implement the method as described above.

[0032] The embodiments of the present invention also disclose a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method described above.

[0033] In an embodiment of the present invention, first, a target checkpoint is obtained, and historical detection data of the target checkpoint is acquired. Floating vehicle detection data is obtained from the historical detection data. Then, positioning data of each floating vehicle is extracted from the floating vehicle detection data. Next, based on the positioning data, clustering processing is performed on the longitude and latitude of the floating vehicles to determine the number of types of clustering results. Finally, based on the number of types of clustering results, duplicate number determination is performed on the target checkpoint, and the number of checkpoints with duplicate numbers is determined. The present invention can eliminate the huge workload of manually checking checkpoint pictures one by one, making the investigation of duplicate checkpoint numbers simpler, more efficient, and more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the following described drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 is the overall step flow chart of the embodiment of the present invention;

[0036] Figure 2 is a schematic diagram of the DBSCAN clustering result distribution. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0038] In view of the limitations of the existing technology for the method of investigating the problem of duplicate checkpoint numbers, the present invention proposes a method for investigating duplicate checkpoint numbers based on floating vehicle positioning data, which can eliminate the huge workload of manually checking checkpoint pictures one by one, making the investigation of duplicate checkpoint numbers simpler, more efficient, and more accurate.

[0039] As Figure 1 shown, one aspect of the embodiment of the present invention provides a method for investigating duplicate checkpoint numbers based on floating vehicle positioning data, including:

[0040] Obtain a target checkpoint, and obtain historical detection data of the target checkpoint. Obtain floating vehicle detection data from the historical detection data;

[0041] Extract positioning data of each floating vehicle from the floating vehicle detection data;

[0042] Based on the positioning data, perform clustering processing on the longitude and latitude of the floating vehicles to determine the number of types of clustering results;

[0043] Determine the duplicate number of the target checkpoint according to the number of types of the clustering result, and determine the number of checkpoints with duplicate numbers.

[0044] Optionally, the floating vehicle detection data includes the license plate number and the detection time.

[0045] Optionally, the positioning data includes the license plate number, the acquisition time, the longitude position, and the latitude position. Extracting the positioning data of each floating vehicle from the floating vehicle detection data includes:

[0046] Obtain the checkpoint detection time of each floating vehicle at each checkpoint;

[0047] When the license plate of each floating vehicle is consistent with the license plate detected by the checkpoint, extract the positioning data with the closest data acquisition time and checkpoint detection time from the floating vehicle detection data.

[0048] Optionally, clustering the longitude and latitude of the floating vehicle according to the positioning data to determine the number of types of the clustering result includes:

[0049] Configure the input radius and the minimum number of clusters;

[0050] According to the input radius and the minimum number, perform clustering processing on the longitude and latitude of the positioning data of the floating vehicle through the DBSCAN algorithm to determine the number of types of the clustering result.

[0051] Optionally, clustering the longitude and latitude of the positioning data of the floating vehicle through the DBSCAN algorithm according to the input radius and the minimum number to determine the number of types of the clustering result includes:

[0052] Within the area of the circle with the input radius around each data point, if the total number of data points included is greater than the minimum number, merge the data points within this range into the same class. If the number of points included does not meet the minimum number and the point does not belong to any class, then the point belongs to the noise data.

[0053] Optionally, determining the duplicate number of the target checkpoint according to the number of types of the clustering result and determining the number of checkpoints with duplicate numbers includes:

[0054] After removing the noise data, if there is only one class in the clustering result, there is no duplicate number problem for the checkpoint;

[0055] If the clustering result has more than one class, there is a duplicate number problem, and the number of checkpoints with duplicate numbers is equal to the number of classes of the clustering. Each clustering center is the approximate distribution point of each checkpoint with a duplicate number.

[0056] Another aspect of the embodiments of the present invention further provides a checkpoint duplicate number investigation device based on floating vehicle positioning data, including:

[0057] The first module is used to obtain a target checkpoint, obtain historical detection data of the target checkpoint, and obtain floating vehicle detection data from the historical detection data;

[0058] The second module is used to extract the positioning data of each floating vehicle from the floating vehicle detection data;

[0059] The third module is used to perform clustering processing on the longitude and latitude of the floating vehicle according to the positioning data, and determine the number of types of the clustering result;

[0060] The fourth module is used to determine duplicate numbers for the target checkpoint according to the number of types of the clustering result, and determine the number of checkpoints with duplicate numbers.

[0061] Another aspect of the embodiments of the present invention also provides an electronic device, including a processor and a memory;

[0062] The memory is used to store a program;

[0063] The processor executes the program to implement the method as described above.

[0064] Another aspect of the embodiments of the present invention also provides a computer-readable storage medium, where the storage medium stores a program, and the program is executed by a processor to implement the method as described above.

[0065] The embodiments of the present invention also disclose a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to enable the computer device to execute the method described above.

[0066] The following combines the accompanying drawings of the specification to describe the specific implementation process of the present invention in detail:

[0067] In the embodiments of the present invention, the preset implementation conditions are: 1. The checkpoint is in good condition, there is no obvious clock error, the license plate recognition is accurate, and the floating vehicle can be captured normally. 2. The quality of the floating vehicle positioning data is good, there is no obvious clock error, and the sampling frequency does not exceed 10 seconds.

[0068] Step 1: Select a target checkpoint, obtain its historical detection data for several days, and screen the data in which floating vehicles are detected. The checkpoint detection data includes: license plate number, detection time.

[0069] Take the detection data of a number of bayonet points with the unique identification number ******41 for several days, as well as the taxi positioning data within the same time range as an implementation case. The detection records of the bayonet points for floating vehicles are shown below, where "*" represents encrypted characters:

[0070] Table 1

[0071]

[0072]

[0073] Step 2: Extract the positioning data of each floating vehicle. According to each moment when a floating vehicle is detected by the bayonet point, only extract 1 piece of positioning data where the license plate of the floating vehicle positioning data is the same as the license plate detected by the bayonet point, and the positioning data collection time is the closest to the time when it is detected by the bayonet point. Generally speaking, the positioning data of floating vehicles operating in the city is collected once every 10 seconds. Therefore, it is required that the difference between the positioning data collection time and the time when it is detected by the bayonet point in the extracted data does not exceed 10 seconds. The content of the extracted positioning data of the floating vehicle includes: license plate number, collection time, longitude, and latitude. The example of the extracted bayonet detection data and its floating vehicle positioning data is shown in Table 2 below:

[0074] Table 2

[0075]

[0076] Step 3: Apply the DBSCAN algorithm to cluster the longitude and latitude of the extracted floating vehicle positioning data. DBSCAN is an algorithm for clustering based on density. The core of the algorithm is based on two parameters: the input radius e and the minimum number of points in a class MinPts. In the area of a circle with a radius of e around each data point, if the total number of points contained is greater than MinPts, then the points within this range are merged into the same class. If the number of points contained does not meet the minimum number and the point does not belong to any class, then this point belongs to the noise data.

[0077] Such as Figure 2 is the distribution diagram of the clustering result, where the minimum number of points in a class MinPts is taken as 100 and the radius e is taken as 50 meters.

[0078] Step 4: Based on the clustering result, determine whether there is a problem of duplicate numbers at the bayonet points. After removing the noise data, if there is only one class in the clustering result, then there is no problem of duplicate numbers at the bayonet points. Otherwise, there is a problem of duplicate numbers, and the number of bayonet points with duplicate numbers is equal to the number of clustering classes. The centers of each cluster are the approximate distribution points of each bayonet point with duplicate numbers.

[0079] Such as Figure 2The clustering results shown have two categories and noise data. Therefore, there is a problem of duplicate numbers for the checkpoint with the unique identification number ******41. The number of checkpoints with duplicate numbers is 2, and the approximate distribution points of the two checkpoints are the clustering centers of the two categories of data respectively.

[0080] In summary, the present invention has the following advantages:

[0081] 1. The present invention is applicable to the batch investigation of the problem of duplicate numbers of checkpoints at the urban level, completely eliminating the huge workload of manual verification one by one, which is simple and efficient;

[0082] 2. The present invention is widely applicable to various checkpoint devices, including road checkpoint devices with license plate recognition functions such as electronic police checkpoints, public security checkpoints, and video surveillance (video structured) multiplexed checkpoints;

[0083] 3. The present invention has low technical costs and does not require any additional detection equipment, giving full play to the advantages of floating vehicle positioning data.

[0084] In some alternative embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two consecutive blocks shown can actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowchart of the present invention are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical processes presented herein. Alternative embodiments are expected, in which the order of various operations is changed and the sub-operations described as part of a larger operation are executed independently.

[0085] In addition, although the present invention is described in the context of functional modules, it should be understood that unless otherwise stated to the contrary, one or more of the functions and / or features described can be integrated in a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It can also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More precisely, considering the attributes, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the ordinary skills of an engineer. Therefore, those skilled in the art can implement the present invention as set forth in the claims without undue experimentation using ordinary skills. It can also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0086] If the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0087] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a predefined list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0088] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or more wirings, a portable computer diskette case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0089] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0090] In the description of this specification, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0091] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0092] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for checking duplicate numbers at checkpoint based on floating car positioning data, characterized in that, it includes: Obtain the target checkpoint, and obtain the historical detection data of the target checkpoint, and obtain the floating car detection data from the historical detection data; Extract the positioning data of each floating car from the floating car detection data; According to the positioning data, perform clustering processing on the longitude and latitude of the floating cars, and determine the number of types of clustering results; According to the number of types of the clustering results, determine whether there are duplicate numbers at the target checkpoint, and determine the number of checkpoints with duplicate numbers.

2. The method for checking duplicate numbers at checkpoint based on floating car positioning data according to claim 1, characterized in that, the floating car detection data includes the license plate number and the detection time.

3. The method for checking duplicate numbers at checkpoint based on floating car positioning data according to claim 2, characterized in that, the positioning data includes the license plate number, the acquisition time, the longitude position, and the latitude position. The step of extracting the positioning data of each floating car from the floating car detection data includes: Obtain the checkpoint detection time of each floating car at each checkpoint; When the license plate of each floating car is the same as the license plate detected by the checkpoint, extract the positioning data with the closest data acquisition time to the checkpoint detection time from the floating car detection data.

4. The method for checking duplicate numbers at checkpoint based on floating car positioning data according to claim 3, characterized in that, the step of performing clustering processing on the longitude and latitude of the floating cars according to the positioning data and determining the number of types of clustering results includes: Configure the input radius and the minimum number of clusters; According to the input radius and the minimum number, perform clustering processing on the longitude and latitude of the positioning data of the floating cars through the DBSCAN algorithm, and determine the number of types of clustering results.

5. The method for checking duplicate numbers at checkpoint based on floating car positioning data according to claim 4, characterized in that, the step of performing clustering processing on the longitude and latitude of the positioning data of the floating cars through the DBSCAN algorithm according to the input radius and the minimum number and determining the number of types of clustering results includes: In the area of the circle within the range of the input radius for each data point, if the total number of data points included is greater than the minimum number, merge the data points within this range into the same category. If the number of points included does not meet the minimum number and this point does not belong to any category, then this point belongs to the noise data.

6. The method for checking duplicate numbers at checkpoint based on floating car positioning data according to claim 1, characterized in that, the step of determining whether there are duplicate numbers at the target checkpoint according to the number of types of the clustering results and determining the number of checkpoints with duplicate numbers includes: After removing the noise data, if there is only one category in the clustering result, there is no problem of duplicate numbers at the checkpoint; If the clustering result has more than one category, there is a problem of duplicate numbers, and the number of checkpoints with duplicate numbers is equal to the number of categories of the clustering. Each clustering center is the approximate distribution point of each checkpoint with duplicate numbers.

7. A device for checking duplicate numbers at checkpoint based on floating car positioning data, characterized in that, it includes: The first module is configured to obtain a target checkpoint and obtain historical detection data of the target checkpoint, and obtain floating vehicle detection data from the historical detection data; The second module is configured to extract positioning data of each floating vehicle from the floating vehicle detection data; The third module is configured to perform clustering processing on the longitude and latitude of the floating vehicles according to the positioning data and determine the number of types of the clustering result; The fourth module is configured to perform duplicate number determination on the target checkpoint according to the number of types of the clustering result and determine the number of checkpoints with duplicate numbers.

8. An electronic device, characterized in that, it includes a processor and a memory; the memory is used for storing programs; the processor executes the program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, the storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 6.

10. A computer program product, including a computer program, characterized in that, when the computer program is executed by a processor, it implements the method according to any one of claims 1 to 6.

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