Method, device, electronic system and medium for forming a road data collection fleet

Abstract

The present invention relates to the technical field of traffic control systems, and in particular to a method, device, electronic system and medium for forming a road data collection fleet, wherein the method comprises: extracting the speed data of each vehicle in a target time period from the collected data in the target area, and establishing a speed database for each vehicle; obtaining the speed-acceleration distribution information corresponding to any number of fleets tested according to the speed database; calculating the average value and variation coefficient of the confidence results corresponding to each speed interval, and judging whether the average value and variation coefficient of the confidence results meet the preset convergence conditions, so as to determine the actual number of vehicles that meet the preset convergence conditions, so as to form a corresponding data collection fleet using the actual number. Thus, the technical problem that the method of collecting road data by setting a signal collection device in the related art is difficult to apply to complex road conditions, so that the accuracy of the collected data is low, the cost is high, and it is not conducive to promotion is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic control systems, and in particular to a method, device, electronic system and medium for forming a road data collection fleet. Background Art

[0002] In the related art, photoelectric sensors, electromagnetic wave sensors, infrared heat source sensors, etc. can be used as signal acquisition devices to collect actual data information of the road, thereby replacing the image information collected by the existing cameras installed at various intersections.

[0003] However, in the related technologies, road traffic conditions and related information are often very complex. For example, some main road intersections in cities generally have several or even dozens of lanes. During peak traffic hours, the traffic flow in each lane is very large. Since the actual data information available for collection in different lanes is prone to mutual interference, the monitoring principle of the above-mentioned sensors is difficult to complete the accurate collection of road data, especially during peak traffic hours at main road intersections in cities. In addition, the cost of setting up a signal collection device on the side of the road is high, which is not conducive to promotion and application and needs to be improved. Summary of the invention

[0004] The present invention provides a method, device, electronic system and medium for forming a road data collection fleet, so as to solve the technical problem in the related art that the method of collecting road data by setting a signal collection device is difficult to apply to complex road conditions, so that the accuracy of the collected data is low, the cost is high, and it is not conducive to promotion.

[0005] A first aspect of the present invention provides a method for forming a road data collection fleet, comprising the following steps: extracting the speed data of each vehicle in a target time period from the collected data in a target area, and establishing a speed database for multiple speed intervals of each vehicle based on the speed data; obtaining speed-acceleration distribution information corresponding to any number of fleets tested according to the speed database; calculating the average value and coefficient of variation of the confidence results corresponding to each speed interval based on the speed-acceleration distribution information, and determining whether the average value and the coefficient of variation of the confidence results meet preset convergence conditions, and when both the confidence results and the coefficient of variation meet the preset convergence conditions, determining the actual number of vehicles that meet the preset convergence conditions, so as to use the actual number to form a corresponding data collection fleet.

[0006] Optionally, in one embodiment of the present invention, the speed-acceleration distribution corresponding to a fleet of any inspection number is obtained based on the vehicle speed database, including: randomly selecting a plurality of vehicles whose inspection number is an initial inspection number of vehicles from any sample fleet, merging the vehicle speed data of the plurality of vehicles in each speed interval, and calculating the duration ratio of the speed-acceleration interval of the fleet composed of the plurality of vehicles in each preset span based on the vehicle speed data; re-randomly selecting a plurality of vehicles whose inspection number is the initial inspection number of vehicles to repeatedly calculate the duration ratio until the number of selections of the plurality of vehicles reaches a preset number, to obtain the speed-acceleration distribution of the fleet corresponding to the initial inspection number of vehicles; and increasing the inspection number by one vehicle in turn to obtain the speed-acceleration distribution of the increased fleet.

[0007] Optionally, in one embodiment of the present invention, the calculation of the average value and the coefficient of variation of the confidence results corresponding to each speed interval based on the speed-acceleration distribution includes: combining each fleet of any inspection quantity with each fleet of the next adjacent inspection quantity to generate multiple binary combination samples; in each speed interval, performing a chi-square test on the speed-acceleration distribution corresponding to the two fleets in each binary combination sample until the chi-square confidence results of all binary combination samples in each speed interval are obtained; calculating the average value of the chi-square confidence results of all binary combination samples in each speed interval, and obtaining the average value and the coefficient of variation of the confidence results of the two fleets in each speed interval based on the average value.

[0008] Optionally, in one embodiment of the present invention, the preset convergence condition includes: the average value of the confidence result is greater than a preset average value threshold; the coefficient of variation is less than a preset coefficient of variation threshold.

[0009] Optionally, in one embodiment of the present invention, after determining whether the average value of the confidence result and the variance coefficient satisfy a preset convergence condition, it further includes: if the average value of the confidence result and the variance coefficient do not satisfy the preset convergence condition, then calculating the average value of the confidence result and the variance coefficient based on the next inspection number of fleets and the corresponding adjacent next inspection number of fleets, until the average value of the confidence result and the variance coefficient satisfy the preset convergence condition, and stopping the iteration, wherein the number of inspection vehicles in the adjacent next inspection number of fleets is the number of inspection vehicles in the next inspection number of fleets plus one.

[0010] Optionally, in one embodiment of the present invention, after forming a corresponding data collection fleet using the actual number, it also includes: using the data collection fleet to collect road data of the target area; and obtaining road condition information of the target area based on the road data.

[0011] A second aspect of the present invention provides a device for forming a road data collection fleet, including: a construction module, used to extract the speed data of each vehicle within a target time period from the collected data in the target area, and establish a speed database of multiple speed intervals for each vehicle based on the speed data; a first acquisition module, used to obtain speed-acceleration distribution information corresponding to any inspection number of fleets based on the speed database; a construction module, used to calculate the average value and variation coefficient of the confidence result corresponding to each speed interval based on the speed-acceleration distribution information, and determine whether the average value and the variation coefficient of the confidence result meet the preset convergence condition, and when the confidence result and the variation coefficient meet the preset convergence condition at the same time, determine the actual number of vehicles that meet the preset convergence condition, so as to use the actual number to form a corresponding data collection fleet.

[0012] Optionally, in one embodiment of the present invention, the first acquisition module includes: an acquisition unit, used to randomly select multiple vehicles whose inspection number is the initial inspection number of vehicles from any sample fleet, merge the speed data of the multiple vehicles in each speed interval, and calculate the time ratio of the speed-acceleration interval of the fleet composed of the multiple vehicles in each preset span based on the speed data; a first calculation unit, used to randomly re-select the multiple vehicles whose inspection number is the initial inspection number of vehicles to repeatedly calculate the time ratio until the number of selections of the multiple vehicles reaches a preset number, so as to obtain the speed-acceleration distribution of the fleet corresponding to the initial inspection number of vehicles; a second calculation unit, used to increase the inspection number by one vehicle in turn, so as to obtain the speed-acceleration distribution of the increased fleet.

[0013] Optionally, in one embodiment of the present invention, the assembly module includes: a combination unit, used to combine each fleet of any inspection quantity with each fleet of the next adjacent inspection quantity, respectively, to generate multiple binary combination samples; a test unit, used to perform a chi-square test on the speed-acceleration distribution corresponding to the two fleets in each binary combination sample in each speed interval, respectively, until the chi-square confidence results of all binary combination samples in each speed interval are obtained; a third calculation unit, used to calculate the average value of the chi-square confidence results of all binary combination samples in each speed interval, and obtain the average value and coefficient of variation of the confidence results of the two fleets in each speed interval based on the average value.

[0014] Optionally, in one embodiment of the present invention, the preset convergence condition includes: the average value of the confidence result is greater than a preset average value threshold; the coefficient of variation is less than a preset coefficient of variation threshold.

[0015] Optionally, in one embodiment of the present invention, it also includes: an iteration module, which is used to calculate the average value of the confidence result and the variation coefficient based on the next inspection number of fleets and the corresponding adjacent next inspection number of fleets when the average value of the confidence result and the variation coefficient do not meet the preset convergence condition, until the average value of the confidence result and the variation coefficient meet the preset convergence condition, and the iteration is stopped, wherein the number of inspection vehicles in the adjacent next inspection number of fleets is the number of inspection vehicles in the next inspection number of fleets plus one.

[0016] Optionally, in one embodiment of the present invention, it further includes: a collection module, used to use the data collection fleet to collect road data of the target area; and a second acquisition module, used to obtain road condition information of the target area based on the road data.

[0017] A third aspect of the present invention provides an electronic system, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for forming a road data collection fleet as described in the above embodiment.

[0018] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for forming a road data collection fleet as described in the above embodiment.

[0019] A fifth aspect of the present invention provides a computer program product, including a computer program, which, when executed, is used to implement the above method for forming a road data collection fleet.

[0020] The embodiment of the present invention can obtain the speed-acceleration distribution of a random fleet of a specific number of vehicles based on the vehicle speed database of a fixed time period of a single vehicle in a speed interval, and based on the chi-square confidence result, check the working condition convergence degree of the data, and finally determine the number of fleets required to collect data on the actual road, so as to form a corresponding data collection fleet using the actual number, and by forming a fleet, freely drive on the actual roads in a specific area, collect and establish a vehicle speed database, and study the working condition characteristics of the area, so as to obtain driving scenarios under different road traffic conditions, so as to determine the minimum number of vehicles that can be used for road data collection on the basis of ensuring the accuracy of data collection, taking into account both the cost and the accuracy of data collection, and facilitating promotion and application. Thus, the technical problem that the method of collecting road data by setting a signal collection device in the related art is difficult to apply to complex road conditions, so that the accuracy of the collected data is low, and the cost is high, which is not conducive to promotion is solved.

[0021] Additional aspects and advantages 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

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0023] Figure 1 A flowchart of a method for forming a road data collection fleet according to an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of the principle of a method for forming a road data collection fleet according to an embodiment of the present invention;

[0025] Figure 3 A schematic diagram of the short-stroke cutting principle according to an embodiment of the present invention;

[0026] Figure 4 Schematic diagram of VA distribution of random vehicle combinations in each section according to one embodiment of the present invention;

[0027] Figure 5 A schematic diagram of a binary combination process in a low-speed interval according to an embodiment of the present invention;

[0028] Figure 6 is a schematic diagram of the convergence degree of working conditions according to an embodiment of the present invention;

[0029] Figure 7 A schematic diagram of the structure of a device for forming a road data collection fleet according to an embodiment of the present invention;

[0030] Figure 8 It is a schematic diagram of the structure of an electronic system provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0032] The following describes the method, device, electronic system and medium for forming a road data collection fleet in an embodiment of the present invention with reference to the accompanying drawings. In view of the technical problem that the method for collecting road data by setting a signal collection device in the related art mentioned in the above background technology is difficult to apply to complex road conditions, so that the accuracy of the collected data is low, the cost is high, and it is not conducive to promotion, the present invention provides a method for forming a road data collection fleet, in which the speed-acceleration distribution of a random fleet of a specific number of vehicles can be obtained based on the vehicle speed database of a single vehicle in a fixed time period divided into speed intervals, and the working condition convergence degree of the data can be checked based on the chi-square confidence result, and finally the number of fleets required to be formed when collecting data on the actual road is determined, so as to form a corresponding data collection fleet using the actual number, and by forming a fleet, freely travel on the actual roads in a specific area, collect and establish a vehicle speed database and study the working condition characteristics of the area, so as to obtain driving scenarios under different road traffic conditions, so as to determine the minimum number of vehicles that can be used for road data collection on the basis of ensuring the accuracy of data collection, taking into account both the cost and the accuracy of data collection, and facilitating promotion and application. Thereby, the technical problem in the related art that the method of collecting road data by setting up a signal collection device is difficult to apply to complex road conditions, resulting in low accuracy of the collected data and high cost, which is not conducive to promotion is solved.

[0033] Specifically, Figure 1 A schematic flow chart of a method for forming a road data collection fleet provided in an embodiment of the present invention.

[0034] like Figure 1 As shown, the method for forming the road data collection fleet includes the following steps:

[0035] In step S101 , the vehicle speed data of each vehicle in the target time period is extracted from the collected data of the target area, and a vehicle speed database of multiple speed intervals of each vehicle is established based on the vehicle speed data.

[0036] Specifically, the embodiment of the present invention can distinguish traffic conditions according to the maximum speed of the vehicle during movement, by cutting the second-by-second vehicle speed into short trips, and establishing a vehicle speed database with three intervals of low speed, medium speed and high speed for each vehicle according to the vehicle speed. Among them, the division of low speed, medium speed and high speed intervals can be set accordingly by technical personnel in this field according to the actual situation of the target area, and no specific limitation is made here.

[0037] The embodiment of the present invention can implement speed-acceleration analysis corresponding to vehicle speeds in different intervals based on the vehicle speed database of each vehicle, so as to determine the number of vehicles in the subsequent fleet according to the analysis results.

[0038] In step S102, speed-acceleration distribution information corresponding to any number of vehicle fleets is obtained according to the vehicle speed database.

[0039] In actual implementation, the embodiment of the present invention can obtain the speed-acceleration distribution of the random vehicle fleet according to the vehicle speed database.

[0040] Optionally, in one embodiment of the present invention, a speed-acceleration distribution corresponding to a fleet of any inspection number is obtained based on a vehicle speed database, including: randomly selecting multiple vehicles whose inspection number is the initial inspection number of vehicles from any sample fleet, merging the vehicle speed data of the multiple vehicles in each speed interval, and calculating the duration ratio of the speed-acceleration interval of the fleet composed of the multiple vehicles in each preset span based on the vehicle speed data; re-randomly selecting multiple vehicles whose inspection number is the initial inspection number of vehicles to repeatedly calculate the duration ratio until the number of selections of the multiple vehicles reaches a preset number, to obtain the speed-acceleration distribution of the fleet corresponding to the initial inspection number of vehicles; and increasing the number of inspections by one vehicle in turn to obtain the speed-acceleration distribution of the increased fleet.

[0041] As a possible implementation method, the embodiment of the present invention can determine the starting number of vehicles for inspection, that is, the initial number of vehicles for inspection is n, randomly select n vehicles from the sample fleet, form a new fleet of n vehicles, merge the interval databases corresponding to the n vehicles in the new fleet, and calculate the speed of each interval per second in each span of [5km / h-0.5m / s 2 ] is the time ratio of the velocity (v)-acceleration (a) interval, that is, VA n_低速(1) , VA n_中速(1) , VA n_高速(1) .

[0042] Furthermore, the embodiment of the present invention can repeat the above operation m times to obtain the speed-acceleration distribution of the sub-speed intervals corresponding to m random fleets of n vehicles: [VA n_低速(1) , VA n_中速(1) , VA n_高速(1) 】、

VA n_低速(2) , VA n_中速(2) , VA n_高速(2)

VA n_低速(m) , VA n_中速(m) , VA n_高速(m)

[0043] The embodiment of the present invention can also gradually increase the number of vehicles and repeat the above operation to obtain the speed-acceleration distribution of the speed intervals corresponding to m random (n+1) vehicles: [VA (n+1)_低速(1) , VA (n+1)_中速(1) , VA (n+1)_高速(1) 】、

VA (n+1)_低速(2), VA (n+1)_中速(2) , VA (n+1)_高速(2)

VA (n+1)_低速(m) , VA (n+1)_中速(m) and VA (n+1)_高速(m)

[0044] In step S103, the average value and the coefficient of variation of the confidence results corresponding to each speed interval are calculated based on the speed-acceleration distribution information, and it is determined whether the average value and the coefficient of variation of the confidence results meet the preset convergence conditions. When both the confidence results and the coefficient of variation meet the preset convergence conditions, the actual number of vehicles that meet the preset convergence conditions is determined, so as to use the actual number to form a corresponding data collection fleet.

[0045] In some embodiments, the average value and the coefficient of variation of the confidence results corresponding to each speed interval can be calculated based on the speed-acceleration distribution information obtained in the above steps, and it can be determined whether the average value and the coefficient of variation of the confidence results meet the preset convergence conditions. If the preset convergence conditions are not met, the above steps are repeated. If the preset conditions are met, the number of vehicles at this time is determined as the number of vehicles required to form a fleet when collecting data in the target area.

[0046] Optionally, in one embodiment of the present invention, the average value and the coefficient of variation of the confidence results corresponding to each speed interval are calculated based on the speed-acceleration distribution, including: combining each fleet of any inspection quantity with each fleet of the next adjacent inspection quantity to generate multiple binary combination samples; in each speed interval, performing a chi-square test on the speed-acceleration distribution corresponding to the two fleets in each binary combination sample, until the chi-square confidence results of all binary combination samples in each speed interval are obtained; and obtaining the average value and the coefficient of variation of the confidence results of the two fleets in each speed interval.

[0047] Furthermore, the embodiment of the present invention can combine the above m random n-vehicle convoys and m random (n+1)-vehicle convoys in pairs to generate m*m binary combination samples and divide them into speed intervals. For example, for each sample containing VA n_低速(i) and VA (n+1)_低速(j) Perform a chi-square test and obtain m*m chi-square confidence results P for the low-speed interval. ij_ Low speed (i = 1,2…m, j = 1,2…m).

[0048] The average value P of the above results is n_低速 , calculate P in the same way n_中速 and P n_高速 .

[0049] The coefficient of variation CV of the above results is calculated n_低速、CV n_中速 and CV n_高速 .

[0050] The embodiments of the present invention can effectively reflect the working condition characteristics of a region by repeatedly and randomly merging the speed data of a specific number of vehicles, and further calculating their speed-acceleration distribution; random combinations of adjacent vehicle numbers are combined in pairs to form a large number of binary combinations, and further, the chi-square test results are statistically analyzed to accurately test the degree of convergence of the working conditions.

[0051] Optionally, in one embodiment of the present invention, the preset convergence condition includes: the average value of the confidence result is greater than a preset average value threshold; the coefficient of variation is less than a preset coefficient of variation threshold.

[0052] In the actual implementation process, the preset convergence condition of the embodiment of the present invention may be that the average value of the confidence result is greater than a preset average value threshold, and the coefficient of variation is less than a preset coefficient of variation threshold.

[0053] For example, if in the embodiment of the present invention, the average value P of the above confidence results n_低速 , P n_中速 and P n_高速 Both are greater than 90%, indicating that the probability that there is no significant difference between the operating characteristics of the data collected from a fleet of n vehicles and a fleet of n+1 vehicles in the same speed range for one month is above 90%, and the preset convergence condition is met;

[0054] If the above coefficient of variation CV n_低速 、CV n_中速 and CV n_高速 If both are less than 10%, it means that the fluctuation range of the confidence results caused by the random selection of vehicles does not exceed 10%, which meets the preset convergence conditions.

[0055] Optionally, in one embodiment of the present invention, after determining whether the average value and the coefficient of variation of the confidence result meet the preset convergence condition, it also includes: if the average value and the coefficient of variation of the confidence result do not meet the preset convergence condition, then calculating the average value and the coefficient of variation of the confidence result based on the next inspection number of fleets and the corresponding adjacent next inspection number of fleets, until the average value and the coefficient of variation of the confidence result meet the preset convergence condition, and stopping the iteration, wherein the number of inspection vehicles in the adjacent next inspection number of fleets is the number of inspection vehicles in the next inspection number of fleets plus one.

[0056] Furthermore, if P n ≤90% or CV n>10%, then the data convergence degree at this time does not meet the preset convergence condition. The embodiment of the present invention can increase the number of vehicles in sequence, repeat the above process until the preset convergence condition is met, and determine the number of vehicles at this time as the number of vehicles required to form a fleet when carrying out actual road data collection work in the area for one month.

[0057] Optionally, in one embodiment of the present invention, after forming a corresponding data collection fleet using the actual number, it also includes: using the data collection fleet to collect road data of the target area; and obtaining road condition information of the target area based on the road data.

[0058] As a possible implementation method, the embodiment of the present invention can distinguish road conditions according to the maximum vehicle speed in a short trip, use the distribution of vehicle speed-acceleration as the operating condition characteristic index, and check the convergence degree respectively. The obtained results are used to form a data collection fleet, and the data collection fleet is used to collect road data in the target area to obtain the road condition information of the target area, which can achieve the purpose of fully understanding the comprehensive driving scenarios in the region.

[0059] Combination Figures 2 to 6 As shown, the working principle of the method for forming a road data collection fleet according to an embodiment of the present invention is described in detail by taking an embodiment as an example.

[0060] like Figure 2 FIG. 1 is a schematic diagram of the principle of the method for forming a road data collection fleet according to an embodiment of the present invention. Based on this, the data of about 200,000 kilometers collected by 40 vehicles driving freely in Chongqing for one month is taken as an example to further illustrate the embodiment of the present invention.

[0061] It is understandable that the vehicle may experience multiple stops and starts due to road conditions. Figure 3 As shown, the speed points ≤1km / h in the second-by-second data are set to zero, and the motion process with a starting speed of 0 and a speed >1km / h in the first second is cut out to form a short-trip library. For the short-trip library of each vehicle, the embodiment of the present invention can divide the samples with a maximum speed of less than 60km / h, greater than 60km / h and less than 80km / h, and greater than 80km / h into three speed intervals: low speed, medium speed, and high speed.

[0062] The number of initial inspection vehicles in the embodiment of the present invention is set to 2. The second-by-second speed data of 2 vehicles are randomly extracted from 40 vehicles, and the corresponding VA distribution is calculated after merging the partitions. Figure 4 As shown in the three figures in the upper middle row; further, the embodiment of the present invention can gradually increase the number of inspection vehicles, randomly select three vehicles, and obtain their monthly VA distribution in the same way as above, such as Figure 4As shown in the three figures in the middle lower row. Finally, the embodiment of the present invention can repeat the above operation 10 times to form 10 VAs of 2 cars and 3 cars respectively.

[0063] like Figure 5 As shown, the above 10 VAs of 2 cars and 3 cars are combined in pairs to form 100 binary combinations.

[0064] For the binary combination ij containing the i-th VA of 2 cars and the j-th VA of 3 cars, calculate the VA chi-square test value of its low-speed interval , the formula is as follows:

[0065] ,

[0066] Among them, i = 1,2,3…10, j = 1,2,3…10, k = 1,2,3…26, l = 1,2,3…18.

[0067] Where VA(k,l) is the combined low-speed second-by-second data falling within each span of [5km / h-0.5m / s 2 ] is the ratio of the number of points in the velocity (v)-acceleration (a) interval. The corresponding 100 chi-square confidence The average value P and the coefficient of variation CV are calculated as the convergence degree of the low-speed range working condition of the binary combination. Among them, the calculation formula of the coefficient of variation is as follows:

[0068]

[0069] Similarly, the convergence degree of the medium speed and high speed ranges can be calculated as shown in Table 1, where Table 1 is a result table of the working condition convergence degree of the fleet.

[0070] Table 1

[0071]

[0072] The average value of the confidence results in each interval is less than 90%, indicating that there are significant differences between the operating characteristics of the speed data collected by the fleet of 2 vehicles and the fleet of 3 vehicles in the area for one month; the fluctuation range is greater than 10%, indicating that the random selection of the fleet has a greater impact on the confidence results. In summary, the fleet of 2 vehicles cannot meet the convergence requirements, and the number of fleets needs to be increased in turn. Similarly, the operating characteristics and convergence results can be calculated as follows Figure 6 shown.

[0073] according to Figure 6As shown, in this embodiment of the present invention, when the number of vehicles in the fleet increases from 11 to 12, each speed interval satisfies the data convergence degree verification, and it is believed that the data collected from a fleet of 11 vehicles in Chongqing for one month can fully and truly reflect the regional driving scenario.

[0074] According to the method for forming a road data collection fleet proposed in an embodiment of the present invention, the speed-acceleration distribution of a random fleet of a specific number of vehicles can be obtained based on the vehicle speed database of a single vehicle in a fixed time period divided into speed intervals, and the working condition convergence degree of the data can be checked based on the chi-square confidence result, and finally the number of fleets required to be formed when collecting data on the actual road can be determined, so as to form a corresponding data collection fleet using the actual number, and by forming a fleet, freely drive on the actual roads in a specific area, collect and establish a vehicle speed database, and study the working condition characteristics of the area, so as to obtain driving scenarios under different road traffic conditions, so as to determine the minimum number of vehicles that can be used for road data collection on the basis of ensuring the accuracy of data collection, taking into account both the cost and the accuracy of data collection, and facilitating promotion and application. Thus, the technical problem that the method of collecting road data by setting up a signal collection device in the related art is difficult to apply to complex road conditions, so that the accuracy of the collected data is low, and the cost is high, which is not conducive to promotion, is solved.

[0075] Next, a device for forming a road data collection fleet according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0076] Figure 7 It is a block diagram of a device for forming a road data collection fleet according to an embodiment of the present invention.

[0077] like Figure 7 As shown, the road data collection fleet assembly device 10 includes: a construction module 100, a first acquisition module 200 and an assembly module 300.

[0078] Specifically, the construction module 100 is used to extract the vehicle speed data of each vehicle within the target time period from the collected data in the target area, and to establish a vehicle speed database of multiple speed intervals of each vehicle based on the vehicle speed data.

[0079] The first acquisition module 200 is used to obtain speed-acceleration distribution information corresponding to any number of vehicle fleets according to the vehicle speed database.

[0080] The module 300 is formed to calculate the average value and the coefficient of variation of the confidence result corresponding to each speed interval based on the speed-acceleration distribution information, and to determine whether the average value and the coefficient of variation of the confidence result meet the preset convergence condition, and when both the confidence result and the coefficient of variation meet the preset convergence condition, determine the actual number of vehicles that meet the preset convergence condition, so as to form a corresponding data collection fleet using the actual number.

[0081] Optionally, in one embodiment of the present invention, the first acquisition module 200 includes: an acquisition unit, a first calculation unit and a second calculation unit.

[0082] Among them, the acquisition unit is used to randomly select multiple vehicles with the inspection number equal to the initial inspection number from any sample fleet, merge the speed data of the multiple vehicles in each speed interval, and calculate the duration ratio of the speed-acceleration interval of the fleet composed of multiple vehicles in each preset span based on the speed data.

[0083] The first calculation unit is used to randomly re-select multiple vehicles whose inspection number is the initial inspection number of vehicles to repeatedly calculate the duration ratio until the number of selections of the multiple vehicles reaches a preset number, thereby obtaining the speed-acceleration distribution of the fleet corresponding to the initial inspection number of vehicles.

[0084] The second calculation unit is used to increase the number of inspections by one vehicle in sequence to obtain the speed-acceleration distribution of the fleet after each increase.

[0085] Optionally, in one embodiment of the present invention, the assembly module 300 includes: a combination unit, a verification unit and a third calculation unit.

[0086] The combination unit is used to combine each fleet of any inspection quantity with each fleet of the next adjacent inspection quantity to generate a plurality of binary combination samples.

[0087] The testing unit is used to perform a chi-square test on the speed-acceleration distribution corresponding to the two fleets in each binary combination sample in each speed interval, until the chi-square confidence results of all binary combination samples in each speed interval are obtained.

[0088] The third calculation unit is used to calculate the chi-square confidence results of all binary combination samples in each speed range, and obtain the average value and coefficient of variation of the confidence results of the two fleets in each speed range.

[0089] Optionally, in one embodiment of the present invention, the preset convergence condition includes: the average value of the confidence result is greater than a preset average value threshold; the coefficient of variation is less than a preset coefficient of variation threshold.

[0090] Optionally, in one embodiment of the present invention, the road data collection fleet formation device 10 further includes: an iteration module.

[0091] Among them, the iteration module is used to calculate the average value and the variation coefficient of the confidence result based on the next inspection number of fleets and the corresponding adjacent next inspection number of fleets when the average value and the variation coefficient of the confidence result do not meet the preset convergence conditions, until the average value and the variation coefficient of the confidence result meet the preset convergence conditions, and stop the iteration, wherein the number of inspection vehicles in the adjacent next inspection number of fleets is the number of inspection vehicles in the next inspection number of fleets plus one.

[0092] Optionally, in one embodiment of the present invention, the road data collection fleet formation device 10 further includes: a collection module and a second acquisition module.

[0093] Among them, the collection module is used to collect road data of the target area using the data collection fleet.

[0094] The second acquisition module is used to obtain the road condition information of the target area based on the road data.

[0095] It should be noted that the above explanation of the embodiment of the method for forming a road data collection fleet is also applicable to the device for forming a road data collection fleet of this embodiment, and will not be repeated here.

[0096] According to the road data collection fleet formation device proposed in the embodiment of the present invention, the speed-acceleration distribution of a random fleet of a specific number of vehicles can be obtained based on the vehicle speed database of a single vehicle in a fixed time period divided into speed intervals, and the working condition convergence degree of the data can be checked based on the chi-square confidence result, and finally the number of fleets required to be formed when collecting data on the actual road can be determined, so as to form a corresponding data collection fleet using the actual number, and by forming a fleet, it can freely drive on the actual roads in a specific area, collect and establish a vehicle speed database, and study the working condition characteristics of the area, so as to obtain driving scenarios under different road traffic conditions, so as to determine the minimum number of vehicles that can be used for road data collection on the basis of ensuring the accuracy of data collection, taking into account both the cost and the accuracy of data collection, and facilitating promotion and application. Thus, the technical problem that the method of collecting road data by setting up a signal collection device in the related art is difficult to apply to complex road conditions, so that the accuracy of the collected data is low, and the cost is high, which is not conducive to promotion, is solved.

[0097] Figure 8 A schematic diagram of the structure of an electronic system provided in an embodiment of the present invention. The electronic system may include:

[0098] A memory 801 , a processor 802 , and a computer program stored in the memory 801 and executable on the processor 802 .

[0099] When the processor 802 executes the program, the method for forming a road data collection fleet provided in the above embodiment is implemented.

[0100] Furthermore, the electronic system further comprises:

[0101] The communication interface 803 is used for communication between the memory 801 and the processor 802 .

[0102] The memory 801 is used to store computer programs that can be executed on the processor 802 .

[0103] The memory 801 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0104] If the memory 801, the processor 802 and the communication interface 803 are implemented independently, the communication interface 803, the memory 801 and the processor 802 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0105] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can communicate with each other through an internal interface.

[0106] The processor 802 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0107] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for forming a road data collection fleet as described above is implemented.

[0108] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the method for forming a road data collection fleet provided in an embodiment of the present invention.

[0109] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0110] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0111] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0112] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0113] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0114] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0115] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0116] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for forming a road data collection fleet, characterized in that: The following steps are involved: Extracting the vehicle speed data of each vehicle in the target time period from the collected data in the target area, and establishing a vehicle speed database of multiple speed intervals of each vehicle based on the vehicle speed data; Obtaining speed-acceleration distribution information corresponding to any number of vehicle fleets tested according to the vehicle speed database; Based on the speed-acceleration distribution information, the average value and the coefficient of variation of the confidence result corresponding to each speed interval are calculated, and it is determined whether the average value and the coefficient of variation of the confidence result meet the preset convergence condition, and when the confidence result and the coefficient of variation both meet the preset convergence condition, the actual number of vehicles that meet the preset convergence condition is determined, so as to form a corresponding data collection fleet using the actual number.

2. The method for forming a road data collection fleet according to claim 1, characterized in that: The obtaining, according to the vehicle speed database, speed-acceleration distribution corresponding to any number of vehicle fleets to be tested includes: Randomly select a plurality of vehicles from any sample fleet, the number of which is equal to the initial number of vehicles to be inspected, merge the speed data of the plurality of vehicles in each speed interval, and calculate the duration ratio of the speed-acceleration interval of the fleet consisting of the plurality of vehicles in each preset span based on the speed data; Re-randomly selecting a plurality of vehicles whose inspection number is the initial number of inspection vehicles to repeatedly calculate the duration ratio until the number of selections of the plurality of vehicles reaches a preset number, and obtaining a speed-acceleration distribution of the fleet corresponding to the initial number of inspection vehicles; The number of inspections is increased by one vehicle at a time to obtain a speed-acceleration distribution of the increased fleet.

3. The method for forming a road data collection fleet according to claim 2, characterized in that: The calculating the average value and the coefficient of variation of the confidence result corresponding to each speed interval based on the speed-acceleration distribution includes: Combine each fleet of any inspection quantity with each fleet of the next adjacent inspection quantity to generate multiple binary combination samples; In each speed range, the chi-square test is performed on the speed-acceleration distribution corresponding to the two fleets in each binary combination sample, until the chi-square confidence results of all binary combination samples in each speed range are obtained; Calculate the average value and coefficient of variation of the chi-square confidence results of all binary combination samples in each speed interval.

4. The method for forming a road data collection fleet according to claim 2, characterized in that: The preset convergence conditions include: The average value of the confidence results is greater than a preset average value threshold; The variance coefficient is less than a preset variance coefficient threshold.

5. The method for forming a road data collection fleet according to claim 3, characterized in that: After determining whether the average value of the confidence result and the coefficient of variation meet a preset convergence condition, the method further includes: If the average value of the confidence result and the variance coefficient do not satisfy the preset convergence condition, the average value of the confidence result and the variance coefficient are calculated based on the next inspection number of fleets and the corresponding adjacent next inspection number of fleets until the average value of the confidence result and the variance coefficient satisfy the preset convergence condition, and the iteration is stopped, wherein the number of inspection vehicles in the adjacent next inspection number of fleets is the number of inspection vehicles in the next inspection number of fleets plus one.

6. The method for forming a road data collection fleet according to claim 1, characterized in that: After the actual number is used to form a corresponding data collection fleet, it also includes: Using the data collection fleet to collect road data of the target area; The road condition information of the target area is obtained based on the road data.

7. A device for forming a road data collection fleet, characterized in that: include: A construction module, used to extract the vehicle speed data of each vehicle within the target time period from the collected data of the target area, and establish a vehicle speed database of multiple speed intervals of each vehicle based on the vehicle speed data; An acquisition module, used for obtaining speed-acceleration distribution information corresponding to any number of vehicle fleets tested according to the vehicle speed database; A building module is used to calculate the average value and the variation coefficient of the confidence result corresponding to each speed interval based on the speed-acceleration distribution information, and to determine whether the average value and the variation coefficient of the confidence result meet the preset convergence condition, and when the confidence result and the variation coefficient meet the preset convergence condition at the same time, determine the actual number of vehicles that meet the preset convergence condition, so as to use the actual number to build a corresponding data collection fleet.

8. An electronic system, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for forming a road data collection fleet as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for forming a road data collection fleet as described in any one of claims 1-6.

10. A computer program product, characterized in that It comprises a computer program, which, when executed, is used to implement the method for forming a road data collection fleet as described in any one of claims 1-6.

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