A commuting hotspot path identification method, device, storage medium and terminal

Abstract

The present invention discloses a commuting hotspot path identification method, device, storage medium and terminal, the method includes: generating multiple standard vehicle passing data according to the original checkpoint passing data of the area to be identified, and determining the optimal checkpoint number in combination with the longest common string algorithm, establishing the continuous path chain data of each target vehicle according to the optimal checkpoint number, determining the vehicle traffic volume of each path, constructing a standard path set that conforms to the vehicle driving route according to the continuous path chain data of each target vehicle and the vehicle traffic volume of each path, and arranging the traffic volume values ​​in reverse order according to the constructed standard path set and the vehicle traffic volume of each path to obtain a hotspot driving path set; according to the hotspot driving path set, combining the k-means clustering algorithm for hierarchical division, obtaining a multi-level hotspot driving path, and performing visual display based on the multi-level hotspot driving path. This application can be used to study and judge the root causes of road congestion, and assist in business decisions such as road traffic control, road planning and construction.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a commuting hotspot path identification method, device, storage medium and terminal. Background Art

[0002] In recent years, the problem of urban road congestion has become increasingly serious, which is a major problem affecting the sustainable development of cities. Solving congestion has become an important task of comprehensive urban management and governance. Road congestion is a highly comprehensive business proposition, and the most critical link is to locate the temporal and spatial characteristics of congestion, that is, to finely judge the temporal and spatial distribution laws of congestion, determine the key locations and main causes of congestion problems, and provide a decision-making basis for road traffic control, road planning and construction and other businesses.

[0003] At present, the previous congestion analysis is mainly based on data such as road speed, vehicle flow, and floating vehicle trajectory. It analyzes urban road congestion from the perspectives of road congestion performance and small-proportion vehicle operation, and has strong decision-making support capabilities in realizing the spatiotemporal distribution of road congestion. Due to the current variety of data types such as road speed, vehicle flow, and floating vehicle trajectory, it is impossible to analyze congestion with multiple types of massive data in large cities, and thus it is impossible to provide a decision-making basis for road traffic control, road planning and construction, and other businesses. Summary of the invention

[0004] The embodiments of the present application provide a method, device, storage medium and terminal for identifying commuting hotspot paths. In order to have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0005] In a first aspect, an embodiment of the present application provides a commuting hotspot path identification method, the method comprising:

[0006] Generate multiple pieces of standard vehicle passing data according to the original vehicle passing data of the area to be identified;

[0007] Based on multiple pieces of standard vehicle passing data and combined with the longest common string algorithm, the optimal number of checkpoints is determined;

[0008] Establishing continuous path chain data for each target vehicle according to the optimal number of checkpoints, and determining the vehicle traffic volume of each path;

[0009] Constructing a standard path set that conforms to the vehicle driving route according to the continuous path chain data of each target vehicle and the vehicle traffic volume of each path;

[0010] According to the standard path set that meets the vehicle driving route and the vehicle traffic volume of each path, the traffic volume values ​​are arranged in reverse order to obtain the hotspot driving path set of commuting vehicles during peak hours in the morning and evening;

[0011] Based on the hotspot driving path set and combined with the k-means clustering algorithm, a multi-level hotspot driving path is obtained, and a visualization display is performed based on the multi-level hotspot driving path.

[0012] Optionally, multiple pieces of standard vehicle passing data are generated based on the original vehicle passing data of the area to be identified, including:

[0013] Obtain multiple pieces of original checkpoint vehicle passing data in the area to be identified;

[0014] Each piece of original checkpoint vehicle passing data is inspected according to the preset data quality inspection strategy to obtain the quality inspection result of each piece of original checkpoint vehicle passing data;

[0015] When the quality inspection result of each piece of original checkpoint vehicle passing data shows that the original checkpoint vehicle passing data is unqualified, the unqualified original checkpoint vehicle passing data is cleaned to obtain multiple pieces of standard vehicle passing data.

[0016] Optionally, the optimal number of checkpoints is determined based on multiple pieces of standard vehicle passing data and in combination with the longest common string algorithm, including:

[0017] According to the plurality of standard vehicle passing data, the travel trajectory data of each target vehicle in the order of the passing time is obtained;

[0018] Combining the travel trajectory data of each target vehicle to obtain a trajectory set of travel events;

[0019] The longest common string algorithm is used to calculate the longest continuous identical trajectory substring between any two target vehicles in the trajectory string set of the travel event to obtain multiple substring trajectories;

[0020] constructing a substring trajectory matrix according to the plurality of substring trajectories;

[0021] The number of occurrences of different substring trajectories in the substring trajectory matrix is ​​counted, and the number of checkpoints contained in the substring trajectory with the highest number of occurrences is determined as the optimal number of checkpoints.

[0022] Optionally, establishing continuous path chain data for each target vehicle according to the optimal number of checkpoints, and determining the vehicle traffic volume of each path, including:

[0023] Establishing a vehicle passing data subject database centered on vehicle license plates according to the plurality of standard vehicle passing data;

[0024] Establishing continuous path chain data for each target vehicle according to the vehicle passing data database and the optimal number of checkpoints;

[0025] Calculating the time intervals between the adjacent camera snapshots in the continuous path chain data of each target vehicle to obtain multiple time intervals for each target vehicle;

[0026] Calculating the stay duration value of each target vehicle according to the multiple time intervals of each target vehicle;

[0027] Comparing the stay duration value of each target vehicle with the multiple time intervals to determine the termination checkpoint of each target vehicle;

[0028] The vehicle traffic volume of each path is determined according to the terminal checkpoint of each target vehicle.

[0029] Optionally, the vehicle traffic volume of each path is determined according to the terminal checkpoint of each target vehicle, including:

[0030] Preprocess the continuous path chain data of each target vehicle according to the target vehicle's terminal checkpoint and the preset morning and evening peak hours to filter out the peak hour vehicle path data in the area to be identified;

[0031] Based on the vehicle path data during peak hours, the number of passing vehicles is counted from the path dimension to obtain the vehicle traffic volume of each path.

[0032] Optionally, constructing a standard path set that conforms to the vehicle driving route according to the continuous path chain data of each target vehicle and the vehicle traffic volume of each path includes:

[0033] Obtaining the coordinate data of each bayonet device in the continuous path chain data of each target vehicle, and correcting the coordinate data of each bayonet device to obtain the standard coordinate data of each bayonet device;

[0034] Based on the route generation service in the geographic information system and the standard coordinate data of each checkpoint device, the longitude and latitude of the peak-hour vehicle route data in the identification area is converted to obtain a peak-hour route set in the form of a standard urban route;

[0035] The traffic volume of each path is combined and calculated according to the peak period path set of the standard urban path type and the preset path repetition calculation rules, so as to generate a standard path set that meets the vehicle driving route after repeated path deletion and / or path extension processing.

[0036] Optionally, the preset path duplication calculation rule includes a deduplication deletion rule and a path extension rule;

[0037] The method of merging and calculating the traffic volume of each path according to the peak period path set of the standard urban path type and the preset path repetition calculation rule includes:

[0038] Calculate the first parameter of the deduplication deletion rule; the first parameter n=q*0.6, q is the optimal number of checkpoints;

[0039] Inputting the peak-hour path set of the standard urban path formula and the first parameter into the function corresponding to the deduplication deletion rule to determine whether the first n checkpoints and the last n checkpoints in each path are the same, and if so, deleting the paths with the same first n checkpoints and the same last n checkpoints;

[0040] and,

[0041] Calculate the second parameter of the path extension rule; the second parameter k=q*0.8, q is the optimal number of bayonet holes;

[0042] The peak-hour path set of the standard urban path type and the second parameter are input into the function corresponding to the path extension rule to determine whether the first k checkpoints in each path are the same as the front and rear k checkpoints in other paths, or to determine whether the last k checkpoints in each path are the same as the front and rear k checkpoints in other paths. If so, the path with the largest traffic volume is taken as the trunk, and the difference points are used as extension points of the path for extension. This step is repeated for the extended paths until all paths are calculated.

[0043] In a second aspect, an embodiment of the present application provides a commuting hotspot path identification device, the device comprising:

[0044] A standard vehicle passing data generation module is used to generate multiple pieces of standard vehicle passing data based on the original vehicle passing data of the area to be identified;

[0045] The module for determining the optimal number of checkpoints is used to determine the optimal number of checkpoints based on multiple pieces of standard vehicle passing data and in combination with the longest common string algorithm;

[0046] A vehicle traffic volume determination module, used to establish continuous path chain data for each target vehicle according to the optimal number of checkpoints, and determine the vehicle traffic volume of each path;

[0047] A standard path set construction module, used to construct a standard path set that conforms to the vehicle driving route according to the continuous path chain data of each target vehicle and the vehicle traffic volume of each path;

[0048] A hotspot driving path set generation module is used to perform reverse sorting of traffic volume values ​​according to a standard path set that meets the vehicle driving route and the vehicle traffic volume of each path, and obtain a hotspot driving path set for commuting vehicles during peak hours in the morning and evening;

[0049] The hotspot path visualization display module is used to classify the hotspot driving path set according to the k-means clustering algorithm to obtain multi-level hotspot driving paths, and perform visualization based on the multi-level hotspot driving paths.

[0050] In a third aspect, an embodiment of the present application provides a computer storage medium, which stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the above-mentioned method steps.

[0051] In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0052] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0053] In an embodiment of the present application, the commuting hotspot path identification device first generates a plurality of standard vehicle passing data based on the original checkpoint vehicle passing data of the area to be identified, and determines the optimal checkpoint number in combination with the longest common string algorithm, and then establishes the continuous path chain data of each target vehicle based on the optimal checkpoint number, and determines the vehicle traffic volume of each path, and constructs a standard path set that conforms to the vehicle driving route based on the continuous path chain data of each target vehicle and the vehicle traffic volume of each path, and then arranges the traffic volume values ​​in reverse order based on the constructed standard path set and the vehicle traffic volume of each path to obtain a hotspot driving path set, and finally, based on the hotspot driving path set, and combined with the k-means clustering algorithm, it performs hierarchical division to obtain a multi-level hotspot driving path, and performs a visual display based on the multi-level hotspot driving path. Since the present application is based on the checkpoint vehicle passing data, constructs the vehicle traffic volume of each path, completes the analysis of the hotspot paths of urban vehicle travel, realizes the mining of the hotspot paths of public travel, further studies and judges the root causes of road congestion, and assists in business decisions such as road traffic control, road planning and construction.

[0054] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0056] Figure 1 It is a flowchart of a commuting hotspot path identification method provided in an embodiment of the present application;

[0057] Figure 2 It is a structural schematic diagram of a commuting hotspot path identification device provided in an embodiment of the present application;

[0058] Figure 3 It is a structural diagram of a terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.

[0060] It should be clear that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are only examples of devices and methods consistent with some aspects of the present invention as detailed in the attached claims.

[0062] In the description of the present invention, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. In addition, in the description of the present invention, unless otherwise specified, "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the previously associated objects are in an "or" relationship.

[0063] The present application provides a commuter hotspot path identification method, device, storage medium and terminal to solve the problems existing in the above-mentioned related technical problems. In the technical solution provided by the present application, since the present application is based on the checkpoint vehicle passing data, constructs the vehicle traffic volume of each path, completes the analysis of the hotspot paths of urban vehicle travel, realizes the mining of the hotspot paths of public travel, further analyzes the root causes of road congestion, and assists in business decisions such as road traffic control, road planning and construction, etc., the following uses an exemplary embodiment for detailed description.

[0064] The following will be combined with the attached Figure 1 , a commuting hotspot path identification method provided in an embodiment of the present application is introduced in detail. The method can be implemented by a computer program and can be run on a commuting hotspot path identification device based on a von Neumann system. The computer program can be integrated into an application or run as an independent tool application.

[0065] See also Figure 1 , which is a flow chart of a method for identifying a commuting hotspot path according to an embodiment of the present application. Figure 1 As shown, the method of the embodiment of the present application may include the following steps:

[0066] S101, generating a plurality of standard vehicle passing data according to the original vehicle passing data of the area to be identified;

[0067] The area to be identified is where the hotspot path identification is required, which can be a city, a county, or a district. The original checkpoint vehicle passing data is the vehicle driving data continuously collected by the checkpoint equipment deployed on the road. For example, an original checkpoint vehicle passing data is D = {serial number, vehicle passing serial number, checkpoint number, license plate number, license plate type, vehicle passing time}. The original checkpoint vehicle passing data of the area to be identified is shown in Table 1.

[0068] Table 1

[0069]

[0070] In an embodiment of the present application, when generating multiple standard vehicle passing data based on the original checkpoint vehicle passing data of the area to be identified, firstly obtain the multiple original checkpoint vehicle passing data of the area to be identified, and then inspect each original checkpoint vehicle passing data according to the preset data quality inspection strategy to obtain the quality inspection result of each original checkpoint vehicle passing data, and finally, when the quality inspection result of each original checkpoint vehicle passing data shows that the original checkpoint vehicle passing data is unqualified, the unqualified original checkpoint vehicle passing data is cleaned to obtain multiple standard vehicle passing data.

[0071] Specifically, the preset data quality inspection strategies include data field missing filtering, data field empty value filtering, license plate rule error filtering, data field length error filtering, etc.

[0072] Specifically, the rules for cleaning the unqualified original checkpoint vehicle passing data include:

[0073] Vehicle serial number: extract the first 6 digits, i.e. the administrative division code;

[0074] Checkpoint number: check according to field length and field format;

[0075] License plate number: Verify according to the license plate naming sorting rules and field length;

[0076] License plate type: Verify according to the national standard license plate type rules;

[0077] Passing time: Check according to the time range and time expression rules.

[0078] After cleaning is completed according to the above rules, multiple pieces of standard vehicle passing data are generated as shown in Table 1.

[0079] S102, determining the optimal number of checkpoints based on multiple pieces of standard vehicle passing data and in combination with the longest common string algorithm;

[0080] In an embodiment of the present application, when determining the optimal number of checkpoints based on multiple standard vehicle passing data and in combination with the longest common string algorithm, first, the travel trajectory data of each target vehicle in the order of the passing time is obtained based on the multiple standard vehicle passing data, and then the travel trajectory data of each target vehicle is combined to obtain a trajectory string set of the travel event, and then the longest common string algorithm is used to calculate the longest continuous substring of the same trajectory between any two target vehicles in the trajectory string set of the travel event to obtain multiple substring trajectories, and a substring trajectory matrix is ​​constructed based on the multiple substring trajectories, and finally, the number of occurrences of different substring trajectories in the substring trajectory matrix is ​​counted, and the number of checkpoints contained in the substring trajectory with the highest number of occurrences is determined as the optimal number of checkpoints.

[0081] Specifically, when obtaining the travel trajectory data of each target vehicle in the order of passing time based on multiple standard vehicle passing data, first obtain the license plate number of each standard vehicle passing data to obtain multiple license plate numbers, then merge the same license plate numbers in the multiple license plate numbers to obtain multiple target vehicles with different license plate numbers, and then map the standard vehicle passing data belonging to each target vehicle in the multiple standard vehicle passing data to obtain multiple vehicle passing data for each target vehicle, and then arrange the multiple vehicle passing data for each target vehicle in the order of passing time to obtain the travel trajectory data of each target vehicle.

[0082] In a possible implementation, it is first necessary to calculate multiple vehicle passing data of each target vehicle based on multiple standard vehicle passing data. For example, the trajectory data of a single vehicle i can be specifically expressed as: tr i ={44122500041321000123,44122500041321001123…,p t ,…,p m}, where p t is the trajectory point data of the vehicle passing the checkpoint at time t, and m is the total number of vehicles passing the checkpoint. Then, multiple passing data of each target vehicle are pieced together into a set to construct the trajectory string set Tr = {tr1, tr2, tr3, …, tr i ,…,tr j ,…,tr n}, n is the number of vehicles traveling; secondly, the longest common substring method is used to calculate any two trajectory strings tr in the trajectory string set of the travel event i With trj The longest continuous identical substrings between them can be combined into a substring matrix based on the calculated large number of substrings: Where S ij =s1,s2,…,s q},S ij is the longest continuous string of identical checkpoints in the travel trajectory q>2. Finally, count the different continuous substrings S in the substring matrix ij The number of occurrences, that is, the travel frequency, and the continuous substring S with the highest number of occurrences ij The number of checkpoints included is determined to be the optimal number of checkpoints. The optimal number of checkpoints q for commuter routes calculated based on this data is 5.

[0083] S103, establishing continuous path chain data for each target vehicle according to the optimal number of checkpoints, and determining the vehicle traffic volume of each path;

[0084] In an embodiment of the present application, when establishing continuous path chain data for each target vehicle according to the optimal number of checkpoints and determining the vehicle traffic volume of each path, first, a vehicle passing data subject library centered on the vehicle license plate is established based on multiple standard vehicle passing data, and secondly, a continuous path chain data for each target vehicle is established based on the vehicle passing data subject library and the optimal number of checkpoints, and then the time intervals between adjacent checkpoint captures in the continuous path chain data of each target vehicle are calculated to obtain multiple time intervals for each target vehicle, and then the stay time value of each target vehicle is calculated based on the multiple time intervals for each target vehicle, and then the stay time value of each target vehicle is compared with the multiple time intervals to determine the terminal checkpoint for each target vehicle, and finally the vehicle traffic volume for each path is determined based on the terminal checkpoint for each target vehicle.

[0085] Specifically, when determining the vehicle traffic volume of each path according to the terminal checkpoint of each target vehicle, the continuous path chain data of each target vehicle is first preprocessed according to the terminal checkpoint of the target vehicle and the preset morning and evening peak hours to filter out the peak hour vehicle path data in the area to be identified, and then the number of vehicles is counted from the path dimension based on the peak hour vehicle path data to obtain the vehicle traffic volume of each path.

[0086] In a possible implementation, based on the license plate number and the time of passing, multiple pieces of standard vehicle passing data are sorted to establish a vehicle passing data subject database centered on the vehicle license plate. The subject database is shown in Table 2.

[0087] Table 2

[0088]

[0089] After obtaining the thematic library in Table 2, the continuous path chain data of each target vehicle is established based on the thematic library and the optimal number of checkpoints q. For example, when q is 5, the continuous path chain d′ of the vehicle's individual commuting path 5 is obtained, as shown in Table 3.

[0090] Table 3

[0091]

[0092]

[0093] After obtaining the continuous path chain data of each target vehicle in Table 3, the time intervals between adjacent checkpoints can be calculated for each vehicle license plate to obtain multiple time intervals T for each target vehicle. ij , as shown in Table 4.

[0094] Table 4

[0095] Serial number License plate number Path number Passing interval Interval duration (seconds) 1 Beijing A****F 1 1-2 120 2 Beijing A****F 1 2-3 180 3 Beijing A****F 1 3-4 360 4 Beijing A****F 1 4-5 1680 ……

[0096] After obtaining multiple time intervals for each target vehicle in Table 4, the stay duration value of each target vehicle can be calculated according to the multiple time intervals for each target vehicle. For example, when calculating the stay duration value of the license plate in Table 4, the calculation formula is: ρ=2*avg(120,180,360,1680)=1170.

[0097] After obtaining the license plate stay time value of 1170 in Table 4, it can be compared with the interval time in multiple serial numbers in Table 4. It can be seen that the interval time of serial number 4 is 1680 >> 1170, so it can be judged that this path should terminate at checkpoint 4. At this time, checkpoint 4 is determined as the termination checkpoint of the target vehicle.

[0098] After obtaining the terminal checkpoint of each target vehicle, the continuous path chain data of each target vehicle is preprocessed according to the terminal checkpoint of the target vehicle and the preset morning and evening peak hours, such as the morning peak hour 7:00-9:00 and the evening peak hour 17:00-19:00. The preprocessing includes marking and screening operations. After the preprocessing is completed, the peak period vehicle path data d″={d′ 早高峰 ,d′ 晚高峰}, as shown in Table 5.

[0099] Table 5

[0100]

[0101] After obtaining the data in Table 5, the number of vehicles can be counted from the path dimension based on the data in Table 5 to obtain the vehicle traffic volume of each path, as shown in Table 6.

[0102] Table 6

[0103] Serial number Path number Path Content Traffic volume 1 11 …… 3451 2 12 …… 3111 3 14 …… 5987 ……

[0104] S104, constructing a standard path set that meets the vehicle driving route based on the continuous path chain data of each target vehicle and the vehicle traffic volume of each path;

[0105] In one possible implementation, when constructing a standard path set that conforms to vehicle driving rules, first obtain the coordinate data of each checkpoint device in the continuous path chain data of each target vehicle, and correct each checkpoint device coordinate data to obtain the standard coordinate data of each checkpoint device. Then, based on the path generation service in the geographic information system and the standard coordinate data of each checkpoint device, the longitude and latitude of the peak-hour vehicle path data in the identified area are converted to obtain a peak-hour path set of a standard urban path type. Finally, according to the peak-hour path set of the standard urban path type and according to the preset path repetition calculation rules, the traffic volume of each path is merged and calculated to perform duplicate path deletion and / or path extension processing to generate a standard path set that conforms to the vehicle driving route.

[0106] Specifically, the preset path duplication calculation rule includes a deduplication deletion rule and a path extension rule. Specifically, when the traffic volume of each path is combined and calculated according to the peak period path set of the standard urban path type and the preset path repetition calculation rule, the first parameter of the deduplication deletion rule is first calculated; the first parameter n=q*0.6, q is the optimal number of checkpoints; the peak period path set of the standard urban path type and the first parameter are input into the function corresponding to the deduplication deletion rule to determine whether the first n checkpoints and the last n checkpoints in each path are the same, if so, the path with the same first n checkpoints and the same last n checkpoints is deleted; and, the second parameter of the path extension rule is calculated; the second parameter k=q*0.8, q is the optimal number of checkpoints; the peak period path set of the standard urban path type and the second parameter are input into the function corresponding to the path extension rule to determine whether the first k checkpoints in each path are the same as the front and back k checkpoints in other paths, or determine whether the back k checkpoints in each path are the same as the front and back k checkpoints in other paths, if so, the path with the largest traffic volume is taken as the trunk, and the difference point is used as the extension point of the path for extension, and this step is repeated for the extended path until all paths are calculated.

[0107] For example, the coordinate data of each bayonet device in the continuous path chain data of each target vehicle is obtained as shown in Table 7:

[0108] Table 7

[0109] Serial number Mount number Installation location coordinate 1 44122500041321000133 …… …… 2 44122500041321000063 …… …… 3 44122500041321000133 …… …… ……

[0110] After obtaining each bayonet device coordinate data in Table 7, each bayonet device coordinate data is corrected to verify the validity of the bayonet coordinate data and avoid the bayonet device offset problem. The corrected bayonet device coordinate data is shown in Table 8.

[0111] Table 8

[0112]

[0113]

[0114] After obtaining the data in Table 8, the longitude and latitude of the vehicle path data during the peak period in the identification area are converted based on the path generation service in the geographic information system and the standard data in Table 8 to optimize the path data, and convert the path of the checkpoint coordinate type into actual road trajectory data to avoid the phenomenon that the path is directly connected to the checkpoint coordinates. After optimization, a peak period path set of standard urban path type is obtained, that is, the standard path trajectory d″′ of vehicle urban commuting, as shown in Table 9.

[0115] Table 9

[0116] Serial number Path number Path Content Path Contents (New) Traffic volume 1 11 …… …… 3451 2 12 …… …… 3111 3 14 …… …… 5987 ……

[0117] After obtaining the data content of Table 9, the vehicle traffic volume of each path is combined and calculated according to the peak period path set of the standard urban path type and the preset path repetition calculation rules, and then duplicate paths are deleted and / or paths are extended to generate a standard path set that meets the vehicle driving rules. For example, according to the rules of the path repetition function distinct_path(d"',n) and the path extension calculation function prolong_path(d"',k), the vehicle traffic volume of each path is combined and calculated to obtain the final path data table; n and k take the values ​​of: n=q*0.6=3, n=q*0.8=4; that is: according to the path repetition calculation rules for each pair, the paths are deduplicated and extended to complete the management of all paths, and finally a standard path set that meets the vehicle driving rules is obtained. The rules are shown in Table 10, and the standard path set that meets the vehicle driving rules is finally obtained, as shown in Table 11.

[0118] Table 10

[0119]

[0120]

[0121] Table 11

[0122] Serial number Path number Path Content Traffic volume 1 11 …… 3451 2 14 …… 5987 3 134 …… 5888 ……

[0123] S105, according to the standard path set that meets the vehicle driving route and the vehicle traffic volume of each path, the traffic volume values ​​are arranged in reverse order to obtain a set of hot driving paths for commuting vehicles during peak hours in the morning and evening;

[0124] In one possible implementation, based on a set of standard paths that conform to vehicle travel routes and the vehicle traffic volume of each path, the traffic volume values ​​are arranged in reverse order to obtain a set of hot travel paths for commuting vehicles during peak hours in the morning and evening, as shown in Table 12.

[0125] Table 12

[0126] Serial number Path number Path Content Path Contents (New) Traffic volume 1 31 …… …… 6451 2 59 …… …… 6111 3 14 …… …… 5987 ……

[0127] S106, performing hierarchical division based on the hotspot driving path set and in combination with a k-means clustering algorithm to obtain a multi-level hotspot driving path, and performing a visual display based on the multi-level hotspot driving path.

[0128] In one possible implementation, a set of hotspot driving paths is classified according to the k-means clustering algorithm to obtain a multi-level hotspot driving path. In view of the large amount of calculation, it is recommended to use SPSS software for auxiliary calculation. The multi-level hotspot driving paths can be divided into three categories: high, medium and low according to the morning and evening peak commuting paths, and visualized based on the multi-level hotspot driving paths. The visualized display can design three levels of line visualization legends from the line width dimension to display the commuting hotspot path levels.

[0129] In an embodiment of the present application, the commuting hotspot path identification device first generates a plurality of standard vehicle passing data based on the original checkpoint vehicle passing data of the area to be identified, and determines the optimal checkpoint number in combination with the longest common string algorithm, and then establishes the continuous path chain data of each target vehicle based on the optimal checkpoint number, and determines the vehicle traffic volume of each path, and constructs a standard path set that conforms to the vehicle driving route based on the continuous path chain data of each target vehicle and the vehicle traffic volume of each path, and then arranges the traffic volume values ​​in reverse order based on the constructed standard path set and the vehicle traffic volume of each path to obtain a hotspot driving path set, and finally, based on the hotspot driving path set, and combined with the k-means clustering algorithm, it performs hierarchical division to obtain a multi-level hotspot driving path, and performs a visual display based on the multi-level hotspot driving path. Since the present application is based on the checkpoint vehicle passing data, constructs the vehicle traffic volume of each path, completes the analysis of the hotspot paths of urban vehicle travel, realizes the mining of the hotspot paths of public travel, further studies and judges the root causes of road congestion, and assists in business decisions such as road traffic control, road planning and construction.

[0130] The following are embodiments of the device of the present invention, which can be used to implement the method embodiments of the present invention. For details not disclosed in the device embodiments of the present invention, please refer to the method embodiments of the present invention.

[0131] See also Figure 2 , which shows a schematic diagram of the structure of a commuting hotspot path identification device provided by an exemplary embodiment of the present invention. The commuting hotspot path identification device can be implemented as all or part of a terminal through software, hardware, or a combination of both. The device 1 includes a standard vehicle passing data generation module 10, an optimal checkpoint number determination module 20, a vehicle traffic volume determination module 30, a standard path set construction module 40, a hotspot driving path set generation module 50, and a hotspot path visualization display module 60.

[0132] A standard vehicle passing data generating module 10 is used to generate a plurality of standard vehicle passing data according to the original vehicle passing data of the area to be identified;

[0133] The optimal checkpoint number determination module 20 is used to determine the optimal checkpoint number based on multiple standard vehicle passing data and in combination with the longest common string algorithm;

[0134] A vehicle traffic volume determination module 30, for establishing continuous path chain data for each target vehicle according to the optimal number of checkpoints, and determining the vehicle traffic volume of each path;

[0135] A standard path set construction module 40 is used to construct a standard path set that conforms to the vehicle driving route according to the continuous path chain data of each target vehicle and the vehicle traffic volume of each path;

[0136] The hotspot driving path set generation module 50 is used to perform reverse sorting of the traffic volume values ​​according to the standard path set that meets the vehicle driving route and the vehicle traffic volume of each path, so as to obtain the hotspot driving path set of commuting vehicles during the morning and evening peak hours;

[0137] The hotspot path visualization display module 60 is used to perform hierarchical division based on the hotspot driving path set and in combination with the k-means clustering algorithm to obtain a multi-level hotspot driving path, and perform visualization based on the multi-level hotspot driving path.

[0138] It should be noted that the commuting hotspot path identification device provided in the above embodiment only uses the division of the above-mentioned functional modules as an example when executing the commuting hotspot path identification method. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the commuting hotspot path identification device provided in the above embodiment and the commuting hotspot path identification method embodiment belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0139] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0140] In an embodiment of the present application, the commuting hotspot path identification device first generates a plurality of standard vehicle passing data based on the original checkpoint vehicle passing data of the area to be identified, and determines the optimal checkpoint number in combination with the longest common string algorithm, and then establishes the continuous path chain data of each target vehicle based on the optimal checkpoint number, and determines the vehicle traffic volume of each path, and constructs a standard path set that conforms to the vehicle driving route based on the continuous path chain data of each target vehicle and the vehicle traffic volume of each path, and then arranges the traffic volume values ​​in reverse order based on the constructed standard path set and the vehicle traffic volume of each path to obtain a hotspot driving path set, and finally, based on the hotspot driving path set, and combined with the k-means clustering algorithm, it performs hierarchical division to obtain a multi-level hotspot driving path, and performs a visual display based on the multi-level hotspot driving path. Since the present application is based on the checkpoint vehicle passing data, constructs the vehicle traffic volume of each path, completes the analysis of the hotspot paths of urban vehicle travel, realizes the mining of the hotspot paths of public travel, further studies and judges the root causes of road congestion, and assists in business decisions such as road traffic control, road planning and construction.

[0141] The present invention also provides a computer-readable medium having program instructions stored thereon, and when the program instructions are executed by a processor, the commuting hotspot path identification method provided by the above-mentioned various method embodiments is implemented.

[0142] The present invention also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the commuting hotspot path identification method of each of the above method embodiments.

[0143] See also Figure 3 , which is a schematic diagram of the structure of a terminal provided in an embodiment of the present application. Figure 3 As shown, the terminal 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0144] The communication bus 1002 is used to realize the connection and communication between these components.

[0145] The user interface 1003 may include a display screen (Display) and a camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.

[0146] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0147] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts within the entire electronic device 1000, and executes various functions and processes data of the electronic device 1000 by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0148] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a commuting hotspot path identification application.

[0149] exist Figure 3In the terminal 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 1001 can be used to call the commuting hotspot path identification application stored in the memory 1005, and specifically perform the following operations:

[0150] Generate multiple pieces of standard vehicle passing data according to the original vehicle passing data of the area to be identified;

[0151] Based on multiple pieces of standard vehicle passing data and combined with the longest common string algorithm, the optimal number of checkpoints is determined;

[0152] Establishing continuous path chain data for each target vehicle according to the optimal number of checkpoints, and determining the vehicle traffic volume of each path;

[0153] Constructing a standard path set that conforms to the vehicle driving route according to the continuous path chain data of each target vehicle and the vehicle traffic volume of each path;

[0154] According to the standard path set that meets the vehicle driving route and the vehicle traffic volume of each path, the traffic volume values ​​are arranged in reverse order to obtain the hotspot driving path set of commuting vehicles during peak hours in the morning and evening;

[0155] Based on the hotspot driving path set and combined with the k-means clustering algorithm, a multi-level hotspot driving path is obtained, and a visualization display is performed based on the multi-level hotspot driving path.

[0156] In one embodiment, when the processor 1001 generates a plurality of pieces of standard vehicle passing data according to the original vehicle passing data of the area to be identified, the processor 1001 specifically performs the following operations:

[0157] Obtain multiple pieces of original checkpoint vehicle passing data in the area to be identified;

[0158] Each piece of original checkpoint vehicle passing data is inspected according to the preset data quality inspection strategy to obtain the quality inspection result of each piece of original checkpoint vehicle passing data;

[0159] When the quality inspection result of each piece of original checkpoint vehicle passing data shows that the original checkpoint vehicle passing data is unqualified, the unqualified original checkpoint vehicle passing data is cleaned to obtain multiple pieces of standard vehicle passing data.

[0160] In one embodiment, when the processor 1001 determines the optimal number of checkpoints based on multiple pieces of standard vehicle passing data and in combination with the longest common string algorithm, the following operations are specifically performed:

[0161] According to the plurality of standard vehicle passing data, the travel trajectory data of each target vehicle in the order of the passing time is obtained;

[0162] Combining the travel trajectory data of each target vehicle to obtain a trajectory set of travel events;

[0163] The longest common string algorithm is used to calculate the longest continuous identical trajectory substring between any two target vehicles in the trajectory string set of the travel event to obtain multiple substring trajectories;

[0164] constructing a substring trajectory matrix according to the plurality of substring trajectories;

[0165] The number of occurrences of different substring trajectories in the substring trajectory matrix is ​​counted, and the number of checkpoints contained in the substring trajectory with the highest number of occurrences is determined as the optimal number of checkpoints.

[0166] In one embodiment, when the processor 1001 establishes continuous path chain data for each target vehicle according to the optimal number of checkpoints and determines the vehicle traffic volume of each path, the processor 1001 specifically performs the following operations:

[0167] Establish a vehicle passing data database centered on vehicle license plates based on multiple standard vehicle passing data;

[0168] Establish continuous path chain data for each target vehicle based on the vehicle passing data database and the optimal number of checkpoints;

[0169] Calculate the time intervals between adjacent camera snapshots in the continuous path chain data of each target vehicle to obtain multiple time intervals for each target vehicle;

[0170] Calculate the dwell time value of each target vehicle according to multiple time intervals of each target vehicle;

[0171] Compare the dwell time value of each target vehicle with multiple time intervals to determine the termination checkpoint of each target vehicle;

[0172] The vehicle traffic volume of each path is determined based on the terminal checkpoint of each target vehicle.

[0173] In one embodiment, when the processor 1001 determines the vehicle traffic volume of each path according to the terminal checkpoint of each target vehicle, the processor 1001 specifically performs the following operations:

[0174] Preprocess the continuous path chain data of each target vehicle according to the target vehicle's terminal checkpoint and the preset morning and evening peak hours to filter out the peak hour vehicle path data in the area to be identified;

[0175] Based on the vehicle path data during peak hours, the number of vehicles is counted from the path dimension to obtain the vehicle traffic volume of each path.

[0176] In one embodiment, when the processor 1001 constructs a standard path set that conforms to the vehicle driving route according to the continuous path chain data of each target vehicle and the vehicle traffic volume of each path, the processor 1001 specifically performs the following operations:

[0177] Acquire the coordinate data of each bayonet device in the continuous path chain data of each target vehicle, and correct each bayonet device coordinate data to obtain the standard coordinate data of each bayonet device;

[0178] Based on the path generation service in the geographic information system and the standard coordinate data of each checkpoint device, the longitude and latitude of the peak-hour vehicle path data in the to-be-identified area is converted to obtain a peak-hour path set in the form of a standard urban path;

[0179] The traffic volume of each path is combined and calculated according to the peak period path set of the standard urban path type and the preset path repetition calculation rules, so as to generate a standard path set that meets the vehicle driving route after repeated path deletion and / or path extension processing.

[0180] In one embodiment, when the processor 1001 performs the combined calculation of the traffic volume of each path according to the peak period path set of the standard urban path type and the preset path repetition calculation rule, the processor 1001 specifically performs the following operations:

[0181] Calculate the first parameter of the deduplication deletion rule; the first parameter n=q*0.6, q is the optimal number of checkpoints;

[0182] Inputting the peak-hour path set of the standard urban path formula and the first parameter into the function corresponding to the deduplication deletion rule to determine whether the first n checkpoints and the last n checkpoints in each path are the same, and if so, deleting the paths with the same first n checkpoints and the same last n checkpoints;

[0183] and,

[0184] Calculate the second parameter of the path extension rule; the second parameter k=q*0.8, q is the optimal number of bayonet holes;

[0185] The peak-hour path set of the standard urban path type and the second parameter are input into the function corresponding to the path extension rule to determine whether the first k checkpoints in each path are the same as the front and rear k checkpoints in other paths, or to determine whether the last k checkpoints in each path are the same as the front and rear k checkpoints in other paths. If so, the path with the largest traffic volume is taken as the trunk, and the difference points are used as extension points of the path for extension. This step is repeated for the extended paths until all paths are calculated.

[0186] In an embodiment of the present application, the commuting hotspot path identification device first generates a plurality of standard vehicle passing data based on the original checkpoint vehicle passing data of the area to be identified, and determines the optimal number of checkpoints in combination with the longest common string algorithm; establishes continuous path chain data for each target vehicle based on the optimal number of checkpoints, and determines the vehicle traffic volume of each path, and constructs a standard path set that conforms to the vehicle driving route based on the continuous path chain data of each target vehicle and the vehicle traffic volume of each path; arranges the traffic volume values ​​in reverse order based on the constructed standard path set and the vehicle traffic volume of each path to obtain a hotspot driving path set; and divides the hotspot driving path set into levels based on the k-means clustering algorithm to obtain a multi-level hotspot driving path, and performs a visual display based on the multi-level hotspot driving path. Since the present application is based on the checkpoint vehicle passing data, constructs the vehicle traffic volume of each path, completes the analysis of the hotspot paths of urban vehicle travel, realizes the mining of the hotspot paths of public travel, further studies and judges the root causes of road congestion, and assists in business decisions such as road traffic control, road planning and construction.

[0187] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program for identifying commuting hotspot paths can be stored in a computer-readable storage medium, which, when executed, can include the processes in the above-mentioned embodiments of the methods. The storage medium can be a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0188] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A commuting hotspot path identification method, characterized in that: The method comprises: Generate multiple pieces of standard vehicle passing data according to the original vehicle passing data of the area to be identified; Determine the optimal number of checkpoints based on the plurality of standard vehicle passing data and in combination with the longest common string algorithm; According to the optimal number of checkpoints, continuous path chain data for each target vehicle is established, and the vehicle traffic volume of each path is determined; wherein, The step of establishing continuous path chain data for each target vehicle according to the optimal number of checkpoints and determining the vehicle traffic volume of each path includes: Establishing a vehicle passing data subject database centered on vehicle license plates according to the plurality of standard vehicle passing data; Establishing continuous path chain data for each target vehicle according to the vehicle passing data database and the optimal number of checkpoints; Calculating the time intervals between the adjacent camera snapshots in the continuous path chain data of each target vehicle to obtain multiple time intervals for each target vehicle; Calculating the stay duration value of each target vehicle according to the multiple time intervals of each target vehicle; Comparing the stay duration value of each target vehicle with the multiple time intervals to determine the termination checkpoint of each target vehicle; Determine the vehicle traffic volume of each path according to the terminal checkpoint of each target vehicle; Constructing a standard path set that conforms to the vehicle driving route according to the continuous path chain data of each target vehicle and the vehicle traffic volume of each path; According to the standard path set that meets the vehicle driving route and the vehicle traffic volume of each path, the traffic volume values ​​are arranged in reverse order to obtain a set of hotspot driving paths for commuting vehicles during peak hours in the morning and evening; According to the hotspot driving path set, hierarchical division is performed in combination with the k-means clustering algorithm to obtain a multi-level hotspot driving path, and a visual display is performed based on the multi-level hotspot driving path.

2. The method according to claim 1, characterized in that The method of generating a plurality of standard vehicle passing data according to the original vehicle passing data of the area to be identified includes: Obtain multiple pieces of original checkpoint vehicle passing data in the area to be identified; Each piece of original checkpoint vehicle passing data is inspected according to the preset data quality inspection strategy to obtain the quality inspection result of each piece of original checkpoint vehicle passing data; When the quality inspection result of each piece of original checkpoint vehicle passing data shows that the original checkpoint vehicle passing data is unqualified, the unqualified original checkpoint vehicle passing data is cleaned to obtain multiple pieces of standard vehicle passing data.

3. The method according to claim 1, characterized in that The method of determining the optimal number of checkpoints based on the plurality of standard vehicle passing data and in combination with the longest common string algorithm includes: According to the plurality of standard vehicle passing data, the travel trajectory data of each target vehicle in the order of the passing time is obtained; Combining the travel trajectory data of each target vehicle to obtain a trajectory set of travel events; The longest common string algorithm is used to calculate the longest continuous identical trajectory substring between any two target vehicles in the trajectory string set of the travel event to obtain multiple substring trajectories; constructing a substring trajectory matrix according to the plurality of substring trajectories; The number of occurrences of different substring trajectories in the substring trajectory matrix is ​​counted, and the number of checkpoints contained in the substring trajectory with the highest number of occurrences is determined as the optimal number of checkpoints.

4. The method according to claim 1, characterized in that: Determining the vehicle traffic volume of each path according to the terminal checkpoint of each target vehicle includes: Preprocess the continuous path chain data of each target vehicle according to the target vehicle's terminal checkpoint and the preset morning and evening peak hours, so as to filter out the peak hour vehicle path data in the area to be identified; Based on the vehicle path data during the peak period, the number of passing vehicles is counted from the path dimension to obtain the vehicle traffic volume of each path.

5. The method according to claim 4, characterized in that The step of constructing a standard path set that conforms to the vehicle driving route according to the continuous path chain data of each target vehicle and the vehicle traffic volume of each path includes: Acquire the coordinate data of each bayonet device in the continuous path chain data of each target vehicle, and correct each bayonet device coordinate data to obtain the standard coordinate data of each bayonet device; Based on the path generation service in the geographic information system and the standard coordinate data of each checkpoint device, the longitude and latitude of the peak-hour vehicle path data in the to-be-identified area is converted to obtain a peak-hour path set in the form of a standard urban path; The traffic volume of each path is combined and calculated according to the peak period path set of the standard urban path type and the preset path repetition calculation rules, so as to generate a standard path set that meets the vehicle driving route after repeated path deletion and / or path extension processing.

6. The method according to claim 5, characterized in that The preset path duplication calculation rules include deduplication deletion rules and path extension rules; The method of merging and calculating the traffic volume of each path according to the peak period path set of the standard urban path type and the preset path repetition calculation rule includes: Calculate the first parameter of the deduplication deletion rule; the first parameter n=q*0.6, q is the optimal number of checkpoints; Inputting the peak-hour path set of the standard urban path formula and the first parameter into the function corresponding to the deduplication deletion rule to determine whether the first n checkpoints and the last n checkpoints in each path are the same, and if so, deleting the paths with the same first n checkpoints and the same last n checkpoints; and, Calculate the second parameter of the path extension rule; the second parameter k=q*0.8, q is the optimal number of bayonet holes; The peak-hour path set of the standard urban path type and the second parameter are input into the function corresponding to the path extension rule to determine whether the first k checkpoints in each path are the same as the front and rear k checkpoints in other paths, or to determine whether the last k checkpoints in each path are the same as the front and rear k checkpoints in other paths. If so, the path with the largest traffic volume is taken as the trunk, and the difference points are used as extension points of the path for extension. This step is repeated for the extended paths until all paths are calculated.

7. A commuting hotspot path identification device implemented by the method described in any one of claims 1 to 6, characterized in that: The device comprises: A standard vehicle passing data generation module is used to generate multiple pieces of standard vehicle passing data based on the original vehicle passing data of the area to be identified; An optimal checkpoint number determination module, used to determine the optimal checkpoint number based on the plurality of standard vehicle passing data and in combination with the longest common string algorithm; A vehicle traffic volume determination module, used to establish continuous path chain data for each target vehicle according to the optimal number of checkpoints, and determine the vehicle traffic volume of each path; A standard path set construction module, used to construct a standard path set that conforms to the vehicle driving route according to the continuous path chain data of each target vehicle and the vehicle traffic volume of each path; A hotspot driving path set generation module is used to perform reverse sorting of traffic volume values ​​according to the standard path set that meets the vehicle driving route and the vehicle traffic volume of each path, so as to obtain a hotspot driving path set for commuting vehicles during peak hours in the morning and evening; The hotspot path visualization display module is used to perform hierarchical division based on the hotspot driving path set and in combination with the k-means clustering algorithm to obtain a multi-level hotspot driving path, and perform visualization based on the multi-level hotspot driving path.

8. A computer storage medium, characterized in that: The computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 6.

9. A terminal, characterized in that: include: A processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the method as claimed in any one of claims 1 to 6.

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