Container trailer fee evasion identification method, system and device and storage medium

Abstract

The application discloses a container trailer fee evasion identification method, system and device and a storage medium, and relates to the technical field of highway electronic toll collection. The container trailer fee evasion identification method comprises the following steps: acquiring the current number of used axles of a container trailer in this passage; when the current number of used axles is less than the normal number of axles of the container trailer, acquiring the passage data of the container trailer in multiple continuous passages before this passage; constructing a Markov chain according to multiple passage data and determining a probability transition matrix of the Markov chain, wherein the elements in the probability transition matrix are used to represent the state transition probability of the transition from the axle number i to the axle number j; and determining whether there is a fee evasion behavior in the next passage according to the probability transition matrix and a probability threshold. The application can improve the accuracy of identifying the fee evasion behavior of the container trailer.

Description

Technical Field

[0001] This invention relates to the field of electronic toll collection technology for highways, and in particular to a method, system, device, and storage medium for identifying toll evasion by container trailers. Background Technology

[0002] With the implementation of a nationwide expressway network, provinces have abolished provincial toll stations. To improve the travel experience, ETC (Electronic Toll Collection) is being promoted. However, as ETC becomes more widespread, some container trailer drivers are registering their OBU (On-Board Unit) using the tractor unit of their container trailer, then connecting the trailer to the vehicle on the road, resulting in actual traffic exceeding the number of axles used during registration.

[0003] When entering the highway, due to factors such as visibility or equipment at toll booths, it is easy to miss axles, resulting in under-collection of tolls. At the same time, because container trailers are relatively flexible, the actual number of axles can be changed by changing trailers. If the driver changes the number of axles by changing the trailer, the method of judging container trailers based on statistical frequency is prone to being wrongly identified as "oversized vehicle with undersized label" to evade tolls because the number of axles in this trip is less than the usual number. The accuracy of toll evasion is low. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a method, system, device, and storage medium for identifying toll evasion by container trailers, which can improve the accuracy of identifying toll evasion behavior by container trailers.

[0005] On one hand, embodiments of the present invention provide a method for identifying toll evasion by container trailers, comprising the following steps:

[0006] Get the number of axles currently in use for this passage of the container trailer;

[0007] If the current number of axles used is less than the usual number of axles of the container trailer, then obtain the passage data of the container trailer for multiple consecutive trips before this trip.

[0008] A Markov chain is constructed based on multiple sets of data, and the probability transition matrix of the Markov chain is determined, wherein the elements in the probability transition matrix are used to characterize the state transition probability from axis number i to axis number j.

[0009] The probability transition matrix and probability threshold are used to determine whether toll evasion will occur on the next trip.

[0010] According to some embodiments of the present invention, the common number of axles of the container trailer is obtained through the following steps:

[0011] Obtain the historical number of axles used by the container trailer during its historical passages;

[0012] Calculate the ratio of the number of each of the multiple historical usage axes to the total number of historical usage axes obtained;

[0013] The number of commonly used axes is determined based on the aforementioned axis ratio.

[0014] According to some embodiments of the present invention, constructing a Markov chain based on a plurality of the access data and determining the probability transition matrix of the Markov chain includes the following steps:

[0015] A Markov chain is obtained by arranging the number of axes in the access data in ascending order based on the access entry time in the access data;

[0016] Calculate the state transition probability in the Markov chain where the current node is axis number i and the next node is axis number j.

[0017] Construct a probability transition matrix with the axis number of the current node as the column, the axis number of the next node as the row, and the state transition probability as the corresponding element value.

[0018] According to some embodiments of the present invention, determining whether toll evasion will occur on the next trip based on the probability transition matrix and the probability threshold includes the following steps:

[0019] The probability of the actual number of axles used in the next trip is determined based on the current number of axles in use and the probability transition matrix.

[0020] The probability of actual axle usage and the probability threshold are used to determine whether toll evasion will occur on the next trip.

[0021] According to some embodiments of the present invention, determining the probability of the actual number of axles used in the next trip based on the currently used number of axles and the probability transition matrix includes the following steps:

[0022] Get the actual number of axles used for the next trip;

[0023] The column of the probability transition matrix is ​​queried based on the currently used axis number, and the row of the probability transition matrix is ​​queried based on the actual used axis number to obtain the state transition probability;

[0024] The state transition probability obtained from the query is used as the probability of the actual number of axles used in the next trip.

[0025] According to some embodiments of the present invention, determining whether there is toll evasion on the next trip based on the probability of actual axle usage and the probability threshold includes the following steps:

[0026] If the probability of the actual number of axles used is greater than the probability threshold, it is determined that there will be no toll evasion on the next trip.

[0027] If the probability of the actual number of axles used is less than or equal to the probability threshold, it is determined that there is toll evasion in the next trip.

[0028] According to some embodiments of the present invention, the probability threshold is obtained through the following steps:

[0029] Obtain the ratio of the number of commonly used axes to the total number of historically used axes to get the ratio of commonly used axes;

[0030] The probability threshold is obtained by taking the complement of the commonly used axis ratio.

[0031] On the other hand, embodiments of the present invention also provide a container trailer toll evasion identification system, comprising:

[0032] The first module is used to obtain the number of axles currently in use for the container trailer's current passage;

[0033] The second module is used to obtain the passage data of the container trailer for multiple consecutive trips before the current trip when the number of axles currently in use is less than the number of axles commonly used by the container trailer.

[0034] The third module is used to construct a Markov chain based on multiple access data and determine the probability transition matrix of the Markov chain, wherein the elements in the probability transition matrix are used to characterize the state transition probability from axis number i to axis number j.

[0035] The fourth module is used to determine whether there is any toll evasion behavior in the next trip based on the probability transition matrix and the probability threshold.

[0036] On the other hand, embodiments of the present invention also provide a container trailer fare evasion identification device, comprising:

[0037] At least one processor;

[0038] At least one memory for storing at least one program;

[0039] When the at least one program is executed by the at least one processor, the at least one processor implements the container trailer toll evasion identification method as described above.

[0040] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the container trailer toll evasion identification method described above.

[0041] The technical solution described above has at least one of the following advantages or beneficial effects: First, the current number of axles used by the container trailer in this trip is obtained. If the current number of axles used is greater than the commonly used number of axles of the container trailer, the container trailer is initially considered a suspected toll evasion vehicle. At this time, the passage data of the container trailer for multiple consecutive trips prior to this trip is obtained. A Markov chain is constructed based on the obtained passage data, and the probability transition matrix of the Markov chain is determined. Then, based on the probability transition matrix and probability threshold, it is determined whether there will be toll evasion behavior in the next trip. This application combines the state transition probability in the state transition matrix to determine the toll evasion behavior of the container trailer, reducing the situation where the container trailer has recently replaced fewer axles than the commonly used number of axles but does not actually owe any fees, thus improving the accuracy of identifying toll evasion behavior of container trailers. Attached Figure Description

[0042] Figure 1 This is a flowchart of the container trailer toll evasion identification method provided in the embodiments of the present invention;

[0043] Figure 2 This is a schematic diagram of a Markov chain representation provided in an embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram of a Markov chain provided in an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of a Markov chain provided in another embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of the container trailer toll evasion identification device provided in an embodiment of the present invention. Detailed Implementation

[0047] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar originals or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0048] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0049] In the description of this invention, the use of terms such as "first," "second," etc., is merely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of technical features indicated, or implicitly indicating the order of the technical features indicated.

[0050] This invention provides a method for identifying toll evasion by container trailers. This method can be applied to a terminal, a server, or software running on either a terminal or server. The terminal can be a tablet, laptop, desktop computer, etc., but is not limited to these. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0051] Reference Figure 1 The container trailer toll evasion identification method of the present invention includes, but is not limited to, steps S110, S120, S130 and S140.

[0052] Step S110: Obtain the current number of axles used for this passage of the container trailer;

[0053] Step S120: If the current number of axles used is less than the usual number of axles of the container trailer, then obtain the passage data of the container trailer for multiple consecutive trips before this trip.

[0054] Step S130: Construct a Markov chain based on multiple passing data and determine the probability transition matrix of the Markov chain, wherein the elements in the probability transition matrix are used to characterize the state transition probability from axis number i to axis number j.

[0055] Step S140: Determine whether there is any toll evasion behavior in the next trip based on the probability transition matrix and probability threshold.

[0056] In some embodiments, the number of axles used by a container trailer per trip can be recorded by visual inspection equipment at highway entrances or manually and stored in a server to enable subsequent identification of toll evasion behavior by container trailers based on traffic data.

[0057] In some embodiments, when the number of axles currently used in this trip is greater than the number of axles commonly used by the container trailer, it indicates that the container trailer is suspected of evading tolls by using a "large vehicle with a small label" method. In this case, the container trailer is marked and the passage data of the container trailer for multiple consecutive trips prior to this trip is obtained, so as to further analyze the toll evasion behavior of the container trailer based on the passage data.

[0058] In some embodiments, the common number of axles for a container trailer is obtained through the following steps:

[0059] Step S210: Obtain the historical number of axles used by the container trailer during its historical passage.

[0060] Step S220: Calculate the ratio of the number of each historical usage axis among the plurality of historical usage axis counts to the total number of historical usage axis counts obtained;

[0061] Step S230: Determine the number of commonly used axes based on the axis ratio.

[0062] Specifically, the historical axle usage data of container trailers (over the past year or six months) is obtained, and the proportion of each axle usage data to the total number of historical axle usage data is calculated. For example, if a total of 100 historical axle usage data are obtained, the proportion of historical axle usage data with 3 axles is 0.1, and the proportion of historical axle usage data with 6 axles is 0.9. 0.1 and 0.9 are the axle usage proportions for type 3 and type 6 trailers, respectively.

[0063] Based on audit experience, thresholds can be designed for screening to preliminarily determine the number of axles commonly used by the container trailer. For example, if the proportion of type 6 vehicles in all passages is greater than 0.8, it means that the container trailer uses type 6 vehicles more than 80% of the time in all historical passages. In this case, type 6 vehicles can be regarded as the commonly used vehicle type of the container trailer, that is, the commonly used number of axles of the container trailer is 6.

[0064] In some embodiments of step S130, the step of constructing a Markov chain based on multiple passing data and determining the probability transition matrix of the Markov chain includes, but is not limited to, the following steps:

[0065] Step S310: Arrange the number of axes in the passage data in ascending order according to the passage entry time in the passage data to obtain the Markov chain;

[0066] Step S320: Calculate the state transition probability in the Markov chain where the current node is axis number i and the next node is axis number j;

[0067] Step S330: Construct a probability transition matrix with the axis number of the current node as the column, the axis number of the next node as the row, and the state transition probability as the corresponding element value.

[0068] Specifically, the server records each passage data including the passage entry time and the number of axles. After obtaining the passage data of several previous passages of the container trailer before this passage, a Markov chain is constructed by sequentially sorting the number of axles in the passage data according to the passage entry time. The passage time for each trip is denoted as T. iWhere i = 1, 2, 3, ..., n, and the corresponding number of axles used for passage is S. i Where i = 1, 2, 3, ..., n, the Markov chain is as follows: Figure 2 As shown.

[0069] After obtaining the Markov chain, calculate the state nodes S in the Markov chain. i The state transition probabilities between nodes are used to obtain the probability transition matrix, where the list of the probability transition matrix represents the number of axes of the current node, the row of the probability transition matrix represents the number of axes of the next node, and the element value of the probability transition matrix represents the state transition probability of the number of axes of the current node to the number of axes of the next node.

[0070] In some embodiments of step S140, the step of determining whether toll evasion will occur on the next trip based on the probability transition matrix and the probability threshold includes, but is not limited to, the following steps:

[0071] Step S410: Determine the probability of the actual number of axles used in the next trip based on the current number of axles used and the probability transition matrix;

[0072] Step S420: Determine whether there is any toll evasion behavior in the next trip based on the probability of the actual number of axles used and the probability threshold.

[0073] In this embodiment, the probability threshold is obtained through the following steps:

[0074] Obtain the ratio of the number of frequently used axes to the total number of historically used axes to get the ratio of frequently used axes;

[0075] The probability threshold is obtained by taking the complement of the commonly used axis ratio.

[0076] For example, if the number of axes used in the past month is 100, and the number of axes that are frequently used (6 axes) is 90, then the proportion of frequently used axes is 0.9. The complement of 0.9, 0.1, is used as the probability threshold, which represents the proportion of infrequently used axes.

[0077] In some embodiments of step S410, determining the probability of the actual number of axles used for the next trip based on the current number of axles used and the probability transition matrix includes, but is not limited to, the following steps:

[0078] Step S510: Obtain the actual number of axles used in the next trip;

[0079] Step S520: Query the columns of the probability transition matrix based on the current number of axes used, and query the rows of the probability transition matrix based on the actual number of axes used to obtain the state transition probability;

[0080] Step S530: Use the obtained state transition probability as the probability of the actual number of axles used in the next trip.

[0081] In some embodiments of step S420, determining whether toll evasion will occur on the next trip based on the probability of actual axle usage and a probability threshold includes, but is not limited to, the following steps:

[0082] Step S510: If the probability of the actual number of axles used is greater than the probability threshold, then it is determined that there will be no toll evasion behavior in the next trip.

[0083] Step S520: If the probability of the actual number of axles used is less than or equal to the probability threshold, it is determined that there is toll evasion behavior in the next trip.

[0084] In some exemplary embodiments, taking a container trailer with 6 axles selected from those with a common axle count ratio of 0.7 as an example, a Markov chain is constructed based on its nearly 10 historical trips. Figure 3 As shown.

[0085] At this point, assuming the actual number of axes used in the next trip (represented by "?" in the diagram) is 5, and the current number of axes used in this trip is 6, the probability transition matrix is:

[0086] In the probability transition matrix, the first row indicates that the current trip uses 5 axes, the second row indicates that the current trip uses 6 axes, the first column indicates that the next trip will use 5 axes, and the second column indicates that the next trip will use 6 axes.

[0087] According to the columns of the probability transition matrix based on the current number of axles used, and the rows of the probability transition matrix based on the actual number of axles used, the probability of the actual number of axles used is 3 / 5. Since 3 / 5 is greater than the probability of the number of axles not used (0.3), we accept the hypothesis that the next trip will use 5 axles and there will be no missed toll payments.

[0088] In some exemplary embodiments, taking another container trailer with 6 axles selected from those with a common axle ratio of 0.7 as an example, a Markov chain of its nearly 10 historical trips is constructed as follows: Figure 4 As shown.

[0089] At this point, assuming the actual number of axes used in the next trip (represented by "?" in the diagram) is 5, and the current number of axes used in this trip is 6, the probability transition matrix is:

[0090] In the probability transition matrix, the first row indicates that the current trip uses 5 axes, the second row indicates that the current trip uses 6 axes, the first column indicates that the next trip will use 5 axes, and the second column indicates that the next trip will use 6 axes.

[0091] According to the columns of the probability transition matrix based on the current number of axles used, and the rows of the probability transition matrix based on the actual number of axles used, the probability of the actual number of axles used is 2 / 7. Since 2 / 7 is less than the probability of the number of axles not used (0.3), we reject the hypothesis. The next trip will use 5 axles, indicating that there is a toll fee that has been missed.

[0092] This invention uses statistical methods to calculate the number of axles that are likely to be used in traffic as the number of commonly used axles of the container trailer. It combines the state transition probability of the number of axles used in recent traffic with the improved single measurement method of statistical frequency, effectively reducing the situation of false "large vehicle small label" caused by the container trailer changing trailers in recent traffic.

[0093] On the other hand, embodiments of the present invention also provide a container trailer toll evasion identification system, comprising:

[0094] The first module is used to obtain the number of axles currently in use for the container trailer's current passage;

[0095] The second module is used to obtain the passage data of the container trailer for multiple consecutive trips before the current trip when the number of axles currently in use is greater than the number of axles commonly used by the container trailer.

[0096] The third module is used to construct a Markov chain based on multiple access data and determine the probability transition matrix of the Markov chain, wherein the elements in the probability transition matrix are used to characterize the state transition probability from axis number i to axis number j.

[0097] The fourth module is used to determine whether there is any toll evasion behavior in the next trip based on the probability transition matrix and the probability threshold.

[0098] It is understood that the content of the above-described container trailer toll evasion identification method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above-described container trailer toll evasion identification method embodiments, and the beneficial effects achieved are also the same as those achieved in the above-described container trailer toll evasion identification method embodiments.

[0099] Reference Figure 5 , Figure 5 This is a schematic diagram of a container trailer toll evasion detection device according to an embodiment of the present invention. The container trailer toll evasion detection device of this embodiment includes one or more control processors and a memory. Figure 5 The example consists of a control processor and a memory.

[0100] The control processor and memory can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0101] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the control processor, and these remote memories can be connected to the container trailer fare evasion detection device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0102] Those skilled in the art will understand that Figure 5 The device structure shown does not constitute a limitation on the container trailer toll evasion detection device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0103] The non-transitory software program and instructions required to implement the container trailer toll evasion identification method applied to the container trailer toll evasion identification device in the above embodiments are stored in the memory. When executed by the controlled processor, the container trailer toll evasion identification method applied to the container trailer toll evasion identification device in the above embodiments is executed.

[0104] Furthermore, one embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions that are executed by one or more control processors, causing the one or more control processors to perform the container trailer toll evasion identification method in the above method embodiment.

[0105] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0106] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for identifying toll evasion by container trailers, characterized in that, Includes the following steps: Get the number of axles currently in use for this passage of the container trailer; If the current number of axles used is less than the usual number of axles of the container trailer, then obtain the passage data of the container trailer for multiple consecutive trips before this trip. A Markov chain is constructed based on multiple sets of data, and the probability transition matrix of the Markov chain is determined, wherein the elements in the probability transition matrix are used to characterize the number of axes. i Transfer to axis number j The state transition probability; Determine the probability of the actual number of axles used in the next trip based on the current number of axles used and the probability transition matrix; specifically, this includes: querying the probability transition matrix based on the current number of axles used to obtain the state transition probability, and using the obtained state transition probability as the probability of the actual number of axles used in the next trip. Determining whether toll evasion occurs on the next trip based on the probability of actual axle usage and a probability threshold includes: if the probability of actual axle usage is greater than the probability threshold, then it is determined that there is no toll evasion on the next trip; if the probability of actual axle usage is less than or equal to the probability threshold, then it is determined that there is toll evasion on the next trip.

2. The method for identifying toll evasion by container trailers according to claim 1, characterized in that, The common number of axles for the container trailer is obtained through the following steps: Obtain the historical number of axles used by the container trailer during its historical passages; Calculate the ratio of the number of each of the multiple historical usage axes to the total number of historical usage axes obtained; The number of commonly used axes is determined based on the aforementioned axis ratio.

3. The method for identifying toll evasion by container trailers according to claim 1, characterized in that, The step of constructing a Markov chain based on multiple sets of access data and determining the probability transition matrix of the Markov chain includes the following steps: A Markov chain is obtained by arranging the number of axes in the access data in ascending order based on the access entry time in the access data; Calculate the state transition probability in the Markov chain where the current node is axis number i and the next node is axis number j. Construct a probability transition matrix with the axis number of the current node as the column, the axis number of the next node as the row, and the state transition probability as the corresponding element value.

4. The method for identifying toll evasion by container trailers according to claim 3, characterized in that, Determining the probability of the actual number of axles used for the next trip based on the current number of axles in use and the probability transition matrix includes the following steps: Get the actual number of axles used for the next trip; The column of the probability transition matrix is ​​queried based on the currently used axis number, and the row of the probability transition matrix is ​​queried based on the actual used axis number to obtain the state transition probability; The state transition probability obtained from the query is used as the probability of the actual number of axles used in the next trip.

5. The method for identifying toll evasion by container trailers according to claim 1, characterized in that, The probability threshold is obtained through the following steps: Obtain the ratio of the number of commonly used axes to the total number of historically used axes to get the ratio of commonly used axes; The probability threshold is obtained by taking the complement of the commonly used axis ratio.

6. A container trailer toll evasion detection system, characterized in that, include: The first module is used to obtain the number of axles currently in use for the container trailer's current passage; The second module is used to obtain the passage data of the container trailer for multiple consecutive trips before the current trip when the number of axles currently in use is less than the number of axles commonly used by the container trailer. The third module is used to construct a Markov chain based on multiple sets of access data and determine the probability transition matrix of the Markov chain, wherein the elements in the probability transition matrix are used to characterize the number of axes. i Transfer to axis number j The state transition probability; The fourth module is used to perform the following steps: Determine the probability of the actual number of axles used in the next trip based on the current number of axles used and the probability transition matrix; specifically, this includes: querying the probability transition matrix based on the current number of axles used to obtain the state transition probability, and using the obtained state transition probability as the probability of the actual number of axles used in the next trip. Determining whether toll evasion occurs on the next trip based on the probability of actual axle usage and a probability threshold includes: if the probability of actual axle usage is greater than the probability threshold, then it is determined that there is no toll evasion on the next trip; if the probability of actual axle usage is less than or equal to the probability threshold, then it is determined that there is toll evasion on the next trip.

7. A container trailer toll evasion detection device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the container trailer toll evasion identification method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a processor-executable program, characterized in that, When the processor executes the program, it is used to implement the container trailer toll evasion identification method as described in any one of claims 1 to 5.

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