Vehicle measurement method and system based on video detection and distributed optical fiber

Abstract

The present invention belongs to the technical field of vehicle overrun detection and discloses a vehicle measurement method and system based on video detection and distributed optical fiber. The method comprises acquiring target information identified by a camera and establishing a first database; acquiring target information identified by distributed optical fiber and establishing a second database; converting pixel coordinates in the first database into relative position coordinates; determining the similarity between the relative positions in the first database and the relative positions in the second database within the same detection time; and assuming that the data of the two vehicle trajectories originate from the same target vehicle if the similarity exceeds a certain threshold; and fusing the monitoring data in the first and second databases of the target vehicle to obtain an overrun detection database. The present invention combines the target detection function of deep learning with the advantages of distributed optical fiber to implement a vehicle measurement method based on multi-device fusion, achieving accurate vehicle measurement.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle overrun detection, and in particular to a vehicle measurement method and system based on video detection and distributed optical fiber. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Intelligent transportation systems utilize advanced sensor networks and data analysis technologies to monitor vehicle speed, size, and weight in real time. Upon detecting an overweight vehicle, the system immediately issues an alert, notifying law enforcement to take appropriate action, effectively reducing traffic violations. This intelligent detection not only improves traffic safety but also optimizes road use efficiency and reduces congestion. Furthermore, monitoring overweight vehicles protects infrastructure such as roads and bridges, reducing maintenance costs and extending their service life. In short, intelligent detection of overweight vehicles is key to achieving safe, efficient, and sustainable traffic management, providing strong support for modern urban transportation.

[0004] At present, the commonly used vehicle overload detection methods mainly include laser scanning methods based on laser scanners and video detection methods based on cameras. The above methods all use a single device to detect vehicles, and their detection accuracy is low and detection errors are prone to occur.

[0005] Video detection methods, using deep learning algorithms for object detection and recognition, can rapidly analyze traffic scenes and accurately identify key elements such as vehicles and license plates. Distributed fiber optic equipment, through weighing, can accurately measure each vehicle's gross weight and axle weight. Combining the advantages of these two approaches to achieve multi-device fusion vehicle measurement and improve detection accuracy has become a pressing challenge. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a vehicle measurement method and system based on video detection and distributed optical fiber. Combining the target detection function of deep learning and the advantages of distributed optical fiber, a vehicle measurement method based on multi-device fusion is realized, thereby improving the precision and accuracy of vehicle detection.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a vehicle detection method based on video detection and distributed optical fiber, comprising the following steps:

[0009] Acquire target information identified by the camera and establish a first database; acquire target information identified by the distributed optical fiber and establish a second database;

[0010] The pixel coordinates in the first database are converted into relative position coordinates. Within the same detection time, the similarity between the relative positions in the first database and the relative positions in the second database is determined. If the similarity exceeds a certain threshold, the data of the two vehicle trajectories are considered to come from the same target vehicle.

[0011] The monitoring data of the target vehicle in the first database and the second database are integrated to obtain an overload detection database.

[0012] As an optional implementation, the vehicle type information and target license plate information in the overload detection database are based on the detection values ​​of the camera, and the vehicle weight information and speed information are based on the measurement values ​​of the distributed optical fiber.

[0013] As an optional implementation, within the same detection time, the similarity between the relative position in the first database and the relative position in the second database is determined as follows:

[0014] Based on the relative position and detection time of each target, the distance and time between each target data point in the two targets are calculated and a matrix is ​​constructed;

[0015] Create two dynamic programming tables of the same size as the matrix, where the first table records the detection time data from the starting point to the current point, and the second table records the distance between the two targets at the current point;

[0016] Starting from the starting point, gradually update the data in the dynamic programming table, update the time data in the first table according to the detection time data, and update the distance data between the two targets when reaching each data point in combination with the time data in the first table;

[0017] Based on the data in the two tables, the time and distance interpolation between each data point between the two targets are calculated. If the calculated result is greater than the set threshold, it is considered that the two target position information comes from the same target vehicle.

[0018] As an optional implementation, the distance data may be updated by using the time data in the following manner: relative position data corresponding to a detection time point closest to the detection time in the first table may be selected, and the distance may be calculated.

[0019] As an optional implementation, the pixel coordinates in the first database are converted into relative position coordinates, specifically:

[0020] The pixel coordinates of the reflective mark and the target are obtained through target recognition, the distance between the target and the reflective mark, as well as the distance between each reflective mark, are calculated, and the relative position coordinates of the target are obtained based on the above distances.

[0021] As an optional implementation, the camera recognizes target information based on deep learning technology.

[0022] In a second aspect, the present invention provides a vehicle measurement system based on video detection and distributed optical fiber, comprising:

[0023] The data acquisition module is configured to: acquire target information identified by the camera and establish a first database; acquire target information identified by the distributed optical fiber and establish a second database;

[0024] a similarity determination module configured to: convert pixel coordinates in the first database into relative position coordinates, and determine the similarity between the relative positions in the first database and the relative positions in the second database within the same detection time; if the similarity exceeds a certain threshold, it is considered that the data of the two vehicle trajectories come from the same target vehicle;

[0025] The data fusion module is configured to fuse the monitoring data in the first database and the second database of the target vehicle to obtain an overload detection database.

[0026] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0027] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0028] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which implements the method described in the first aspect when executed by a processor.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] This paper proposes a vehicle measurement method based on video detection and distributed optical fiber. This method performs coordinate transformation based on the position of reflective markers in the coordinate system, achieving joint calibration between the coordinate systems of multiple devices. Furthermore, a vehicle measurement method based on multi-device fusion is implemented. This method leverages the advantages of deep learning-based object detection for precise vehicle identification and distributed optical fiber for accurate vehicle weight detection, improving the precision and accuracy of vehicle detection.

[0031] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0033] Figure 1 A framework flow chart of a vehicle determination method based on video detection and distributed optical fiber provided in Example 1 of the present invention;

[0034] Figure 2 Schematic diagram of the equipment layout of the coordinate system joint calibration method provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0037] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments of the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but includes other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0039] Example 1

[0040] like Figure 1 As shown, this embodiment provides a vehicle detection method based on video detection and distributed optical fiber, including the following steps:

[0041] Acquire target information identified by the camera and establish a first database; acquire target information identified by the distributed optical fiber and establish a second database;

[0042] The pixel coordinates in the first database are converted into relative position coordinates. Within the same detection time, the similarity between the relative positions in the first database and the relative positions in the second database is determined. If the similarity exceeds a certain threshold, the data of the two vehicle trajectories are considered to come from the same target vehicle.

[0043] The monitoring data of the target vehicle in the first database and the second database are integrated to obtain an overload detection database.

[0044] Among them, the vehicle type information and target license plate information in the overload detection database are based on the detection values ​​of the camera, and the vehicle weight information and speed information are based on the measurement values ​​of the distributed optical fiber.

[0045] The solution of this application is described in detail below with reference to the accompanying drawings.

[0046] like Figure 2 As shown, the camera is fixedly installed directly above the road, encompassing all lanes within the camera's target area. Distributed fiber optic equipment is deployed between lanes to detect all passing vehicles. The real-time location and status data of vehicles detected by the camera and distributed fiber optic equipment is obtained.

[0047] Specifically, the target information identified by the camera device is obtained, and the overload vehicle information database (i.e., the first database) of the camera target vehicle is updated. The information includes the vehicle number, license plate number, target type, pixel coordinates, target detection frame, and detection time;

[0048] For each target vehicle detected by the camera, a unique identification number is assigned in the database as a piece of data. The fields of the piece of data include:

[0049] (1) Target number: This field indicates the identification number of the target vehicle detected by the camera.

[0050] (2) Target license plate: This field indicates the license plate number of the target vehicle detected by the camera.

[0051] (3) Target type: This field indicates the detection and recognition of different vehicle models by existing deep learning algorithm technology.

[0052] (4) Pixel coordinates: This field represents the image pixel coordinates .

[0053] (5) Relative position: This field indicates the coordinates (x, y) of the target in the XOY coordinate system.

[0054] Specifically, after the camera device detects and identifies the target, it will obtain the pixel coordinates of each target. First, the camera device can obtain the pixel coordinates (x1, y1) (x2, y2) (x3, y3) of the reflective marks 1, 2 and 3, as well as the pixel position coordinates (x4, y4) of the target through target recognition.

[0055] Convert the pixel coordinates of each target into relative position coordinates in the XOY coordinate system. The specific calculation process is as follows:

[0056] (1)

[0057] Where a is the distance between the target and the reflective mark 1.

[0058] (2)

[0059] Where b is the distance between the target and the reflective mark 2.

[0060] (3)

[0061] Where c is the distance between reflective mark 1 and reflective mark 2.

[0062] (4)

[0063] Where d is the distance between the target and the reflective mark 3.

[0064] (5)

[0065] Wherein, e is the distance between reflective mark 1 and reflective mark 3.

[0066] (6)

[0067] (7)

[0068] Where, is the distance between the target position and the Y axis of the XOY coordinate system; The distance between the target position and the X axis of the XOY coordinate system.

[0069] (8)

[0070] (9)

[0071] Where (X, Y) is the relative position coordinate of the target (i.e., the coordinates (x, y) of the target in the XOY coordinate system).

[0072] (6) Target detection box: This field indicates the detection box information of the real-time tracking of the target based on deep learning technology.

[0073] (7) Detection time: This field indicates the time when the target was detected.

[0074] The target information identified by the distributed optical fiber device is obtained, and the distributed optical fiber target vehicle overload information database (ie, the second database) is updated. The information includes the vehicle number, relative position, vehicle weight data, and detection time.

[0075] (1) Target number: This field indicates the number of the target vehicle detected by the distributed fiber optic device.

[0076] (2) Relative position: This field indicates the coordinates (x, y) of the target in the XOY coordinate system.

[0077] Specifically, after detecting the target, the distributed optical fiber device will obtain the coordinates (0, 0) of the reflective mark 1 and the coordinates (x, y) of the target in the XOY coordinate system.

[0078] (3) Target weight: This field indicates the weight of the target vehicle detected by the device.

[0079] (4) Detection time: This field indicates the time when the target was detected T .

[0080] (5) Target speed: This field indicates the speed v of the target vehicle detected by the device.

[0081] The calculation process of the target speed is:

[0082] After the distributed fiber optic system tracks the target, it can provide the coordinates (x, y) in the XOY coordinate system and the corresponding detection time. T ;

[0083] (10)

[0084] Where t is the time interval for the target to move from position 1 to position 2. T 1 is the detection time of the target at position 1, T 2 is the detection time of the target at position 2.

[0085] (11)

[0086] Where, l is the distance the target moves from position 1 to position 2, (x1, y1) and (x2, y2) are the coordinates of the target in the XOY coordinate system when it is at position 1 and position 2 respectively.

[0087] (12)

[0088] Where v is the velocity of the target.

[0089] The coordinate systems of the devices are jointly calibrated. A similarity threshold of 1 is set to judge the similarity between the relative positions of the targets recognized by the camera device and the distributed optical fiber device. If the similarity exceeds the threshold, the two targets are considered to come from the same target vehicle.

[0090] Specifically, after detecting a target, the distributed fiber optic device obtains the coordinates (0,0) of reflective marker 1 and the target position coordinates (x,y). The coordinates of reflective marker 1 obtained by the distributed fiber optic device after target detection are matched with the coordinates of reflective marker 1 obtained by the camera device after target recognition. Based on this, the similarity between the relative position of the target recognized by the camera device and the relative position of the target recognized by the distributed fiber optic device is determined.

[0091] Similarity detection and fusion identification of target vehicles.

[0092] Camera equipment and distributed fiber optic equipment in time Get the relative position coordinate data of the vehicle and ， The data values ​​of the camera device are the target position coordinates after the coordinate conversion in the previous step. By determining the similarity of the two relative positions, if the similarity exceeds a certain threshold, the data of the two vehicle trajectories are considered to come from the same target, and the fusion identification is completed. The details are as follows:

[0093] Create a matrix: First, based on the relative position and detection time of each target, calculate the distance and time between each target coordinate point in the two targets and construct a matrix.

[0094] Construct dynamic programming tables: Create two dynamic programming tables (the first table and the second table) of the same size as the matrix. The first table records the detection time data from the starting point to the current point. The second table records the distance between the two targets at the current point.

[0095] Update the dynamic programming table: Starting from the starting point, gradually update the data in the dynamic programming table, updating the time data in the first table based on the detection time data. Combined with the time data in the first table, update the distance data between the two targets at each data point. This update method selects the relative position data corresponding to the detection time point closest to the detection time in the first table and calculates the distance.

[0096] Calculate similarity: Based on the data in the two tables, interpolate the time and distance between each data point between the two targets. Set a similarity threshold. If the calculated result is greater than the set threshold, the two target locations are considered to come from the same target vehicle.

[0097] The monitoring data of this target vehicle is merged and the target vehicle real-time database (i.e., overload detection database) is updated. The unique number of the target vehicle is retained as the value when the camera device is monitoring; the length, width, and height data of the vehicle are based on the detection values ​​of the camera device; the weight data and speed data of the vehicle are based on the measurement values ​​of the distributed fiber optic device.

[0098] Data transmission and sharing are achieved between distributed fiber optic devices and camera devices through wireless bridges. If the detection time and relative position in the database are different due to network delay, the detection time and relative position are determined by the arithmetic mean of the monitoring values ​​of the distributed fiber optic devices and the camera devices.

[0099] Example 2

[0100] This embodiment provides a vehicle measurement system based on video detection and distributed optical fiber, including:

[0101] The data acquisition module is configured to: acquire target information identified by the camera and establish a first database; acquire target information identified by the distributed optical fiber and establish a second database;

[0102] a similarity determination module configured to: convert pixel coordinates in the first database into relative position coordinates, and determine the similarity between the relative positions in the first database and the relative positions in the second database within the same detection time; if the similarity exceeds a certain threshold, it is considered that the data of the two vehicle trajectories come from the same target vehicle;

[0103] The data fusion module is configured to fuse the monitoring data in the first database and the second database of the target vehicle to obtain an overload detection database.

[0104] It should be noted that the above modules correspond to the steps in Example 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above Example 1. It should be noted that the above modules can be executed in a computer system as part of the system.

[0105] In further embodiments, there is also provided:

[0106] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed by the processor, wherein when the computer instructions are executed by the processor, the method in embodiment 1 is performed. For the sake of brevity, no further details are given here.

[0107] It should be understood that in this embodiment, the processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0108] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method in embodiment 1 is completed.

[0109] The method in Example 1 can be directly executed by a hardware processor, or by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, it will not be described in detail here.

[0110] A computer program product includes a computer program, wherein the computer program implements the method in embodiment 1 when executed by a processor.

[0111] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions contained in program modules, which are executed in a device on a real or virtual processor of a target to perform the process / method described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided between program modules as needed. The machine-executable instructions for the program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0112] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the computer or other programmable data processing device, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0113] In the context of the present invention, computer program code or related data can be carried by any appropriate carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals include electrical, optical, radio, acoustic, or other forms of propagated signals, such as carrier waves, infrared signals, and the like.

[0114] Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0115] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A vehicle measurement method based on video detection and distributed optical fiber, characterized in that: The following steps are involved: Obtain target information identified by the camera, including vehicle number, license plate number, target type, pixel coordinates, target detection frame, and detection time, and establish a first database; obtain target information identified by the distributed optical fiber, including vehicle number, relative position, vehicle weight data, and detection time, and establish a second database; The pixel coordinates in the first database are converted into relative position coordinates. Within the same detection time, the similarity between the relative positions in the first database and the relative positions in the second database is determined. If the similarity exceeds a certain threshold, the data of the two vehicle trajectories are considered to come from the same target vehicle. Pixel coordinates of each target in the first database , the pixel coordinates of reflective marks 1, 2 and 3 (x1, y1) (x2, y2) (x3, y3), and the pixel position coordinates of the target (x4, y4), convert the pixel coordinates of each target into relative position coordinates in the coordinate system XOY, specifically: , a is the distance between the target and the reflective mark 1; , b is the distance between the target and the reflective mark 2; , c is the distance between reflective mark 1 and reflective mark 2; ; d is the distance between the target and the reflective mark 3; , e is the distance between reflective mark 1 and reflective mark 3; ； ； ； ； Where, is the distance between the target position and the Y axis of the XOY coordinate system; is the distance between the target position and the X axis of the XOY coordinate system; (X, Y) is the relative position coordinate of the target; The monitoring data in the first database and the second database of the target vehicle are integrated to obtain an over-limit detection database; the vehicle type information and target license plate information in the over-limit detection database are based on the detection values ​​of the camera, and the vehicle weight information and speed information are based on the measurement values ​​of the distributed optical fiber.

2. The vehicle measurement method based on video detection and distributed optical fiber according to claim 1, characterized in that: In the same detection time, the similarity between the relative position in the first database and the relative position in the second database is determined as follows: Based on the relative position and detection time of each target, the distance and time between each target data point in the two targets are calculated and a matrix is ​​constructed; Create two dynamic programming tables of the same size as the matrix, where the first table records the detection time data from the starting point to the current point, and the second table records the distance between the two targets at the current point; Starting from the starting point, gradually update the data in the dynamic programming table, update the time data in the first table according to the detection time data, and update the distance data between the two targets when reaching each data point in combination with the time data in the first table; Based on the data in the two tables, the time and distance interpolation between each data point between the two targets are calculated. If the calculated result is greater than the set threshold, it is considered that the two target position information comes from the same target vehicle.

3. The vehicle measurement method based on video detection and distributed optical fiber according to claim 2, characterized in that: The updating method of updating the distance data by using the time data is: selecting the relative position data corresponding to the detection time point closest to the detection time in the first table, and calculating the distance.

4. The vehicle measurement method based on video detection and distributed optical fiber according to claim 1, characterized in that: Convert the pixel coordinates in the first database into relative position coordinates, specifically: The pixel coordinates of the reflective mark and the target are obtained through target recognition, the distance between the target and the reflective mark, as well as the distance between each reflective mark, are calculated, and the relative position coordinates of the target are obtained based on the above distances.

5. The vehicle measurement method based on video detection and distributed optical fiber according to claim 1, characterized in that: The camera recognizes target information based on deep learning technology.

6. Vehicle measurement system based on video detection and distributed optical fiber, characterized in that: include: The data acquisition module is configured to: acquire target information recognized by the camera, including vehicle number, license plate number, target type, pixel coordinates, target detection frame, and detection time, and establish a first database; acquire target information recognized by the distributed optical fiber, including vehicle number, relative position, vehicle weight data, and detection time, and establish a second database; a similarity determination module configured to: convert pixel coordinates in the first database into relative position coordinates, and determine the similarity between the relative positions in the first database and the relative positions in the second database within the same detection time; if the similarity exceeds a certain threshold, it is considered that the data of the two vehicle trajectories come from the same target vehicle; Pixel coordinates of each target in the first database , the pixel coordinates of reflective marks 1, 2 and 3 (x1, y1) (x2, y2) (x3, y3), and the pixel position coordinates of the target (x4, y4), convert the pixel coordinates of each target into relative position coordinates in the coordinate system XOY, specifically: , a is the distance between the target and the reflective mark 1; , b is the distance between the target and the reflective mark 2; , c is the distance between reflective mark 1 and reflective mark 2; ; d is the distance between the target and the reflective mark 3; , e is the distance between reflective mark 1 and reflective mark 3; ； ； ； ； Where, is the distance between the target position and the Y axis of the XOY coordinate system; is the distance between the target position and the X axis of the XOY coordinate system; (X, Y) is the relative position coordinate of the target; The data fusion module is configured to: fuse the monitoring data in the first database and the second database of the target vehicle to obtain an over-limit detection database; the vehicle type information and target license plate information in the over-limit detection database are based on the detection values ​​of the camera, and the vehicle weight information and speed information are based on the measurement values ​​of the distributed optical fiber.

7. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 5 is completed.

8. A computer-readable storage medium, characterized in that Used to store computer instructions, which, when executed by a processor, complete the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that The invention comprises a computer program, which is used to implement the method according to any one of claims 1 to 5 when the computer program is executed by a processor.

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