A data processing system for obtaining vehicle flow

Abstract

The present invention provides a data processing system for obtaining vehicle flow, comprising: a current vehicle information set, a historical vehicle information set corresponding to the current vehicle information set, a target road identification list, a processor, and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: obtaining a first vehicle information list based on the current vehicle information set; obtaining a predicted vehicle flow list based on the first vehicle information list and the historical vehicle information set; and determining a target vehicle flow set based on the predicted vehicle flow list. It can be seen that, on the one hand, the present invention uses a wireless access point to obtain the current vehicle information set. The cost of the wireless access point is low, which is conducive to saving resources. On the other hand, when obtaining road traffic flow, the data in the first vehicle information list is processed to obtain multiple data such as the vehicle position, first vehicle speed, parking time, etc., and then the traffic flow is obtained, which can improve the accuracy of the traffic flow.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a data processing system for obtaining vehicle flow. Background Art

[0002] With the development of intelligent transportation systems, the traffic volume on roads is increasing. Accurate analysis of road traffic flow can provide road traffic managers and travelers with the necessary traffic information, and can also provide useful information for road construction planning, solving road congestion, and traffic police attendance.

[0003] To obtain road traffic flow, it is necessary to use a GPS positioning system in combination with a device that can obtain vehicle identification. The unique vehicle identification is obtained through the device and combined with the GPS positioning system to obtain the vehicle's location. The location data obtained by multiple positioning of all vehicles at the intersection is obtained and input into the preset model to obtain the predicted road traffic flow.

[0004] However, the above method also has the following technical problems:

[0005] On the one hand, when obtaining vehicle-related data, the cost of GPS positioning systems and devices that can obtain vehicle identification is high. Obtaining traffic flow at intersections requires multiple GPS positioning systems and devices that can obtain vehicle identification, which will result in a waste of resources. On the other hand, when predicting road traffic flow, there are many factors that affect it, such as time and vehicle speed. Obtaining predicted traffic flow only through vehicle location data will result in low accuracy of the obtained traffic flow. Summary of the Invention

[0006] In view of the above technical problems, the technical solution adopted by the present invention is:

[0007] The present invention provides a data processing system for obtaining vehicle flow, comprising: a current vehicle information set, a historical vehicle information set corresponding to the current vehicle information set, a target road ID list A={A1, ..., A i ,……,A m}, a processor, a memory storing a computer program, wherein A i is the i-th target road ID, i=1...m, and m is the number of target road IDs. When the computer program is executed by the processor, the following steps are implemented:

[0008] S100, according to the current vehicle information set, obtain the first vehicle information list B = {B1, ..., B i ,……,B m}, B i ={B i1 ,……,B ij ,……,B in}, Bij A in the first time slice of j i The corresponding first vehicle information list, j=1...n, n is the number of the first time slices.

[0009] S200, according to B and the historical vehicle information set, obtain the predicted traffic flow list G = {G1, ..., G i ,……,G m}, G i ={G i1 ,……,G ij ,……,G in}, wherein, in step S200, G is obtained by the following steps ij :

[0010] S201, obtain A i Corresponding middle road ID list A 0 i ={A 0 i1 ,……,A 0 ik ,……,A 0 im-1}, A 0 ik A i The corresponding kth middle road ID, k=1...m-1, where the middle road ID is the ID of A except A i Any target road ID other than .

[0011] S203, according to B ij With A 0 i , get A 0 i Corresponding first vehicle quantity list C ij ={C 1 ij ,……,C k ij ,……,C m-1 ij}, C k ij For B ij A in the corresponding first time slice 0 ik The corresponding first vehicle number, wherein the first vehicle number is the number of vehicles turning from a road corresponding to a target road ID to a road corresponding to any intermediate road ID.

[0012] S205, according to the historical vehicle information set, obtain C k ij Corresponding first historical vehicle quantity list D kij ={D k1 ij ,……,D ke ij ,……,D kf ij} and the second historical vehicle quantity list E k ij ={E k1 ij ,……,E ke ij ,……,E kf ij}, D ke ij C is the second time point in the eth k ij The corresponding first historical vehicle number, E ke ij is C at the e-th second time point k ij The corresponding second historical vehicle number, e=1...f, f is the number at the second time point, the first historical vehicle number is the number of vehicles with on-board wifi and the number of vehicles without on-board wifi, and the second historical vehicle number is the number of vehicles with on-board wifi corresponding to the first historical vehicle number.

[0013] S207, according to C k ij 、D ke ij 、E ke ij , get C k ij The corresponding predicted traffic flow G k ij , where G k ij Meet the following conditions:

[0014] Wherein, T is the length of the first time slice, and W is the preset weight corresponding to the predicted traffic flow.

[0015] S209, according to G k ij , get G ij ={G 1 ij ,……,G k ij ,……,G m-1 ij}.

[0016] S300, according to G, determine the target traffic flow set G 0 ={G0 1, ..., G 0 i ,……,G 0 m}, G 0 i ={G 0 i1 ,……,G 0 ij ,……,G 0 in}, G 0 ij G ij The corresponding target traffic flow list.

[0017] The present invention has at least the following beneficial effects:

[0018] The present invention provides a data processing system for obtaining vehicle flow, comprising: a current vehicle information set, a historical vehicle information set corresponding to the current vehicle information set, a target road identification list, a processor, and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: obtaining a first vehicle information list based on the current vehicle information set; obtaining a predicted vehicle flow list based on the first vehicle information list and the historical vehicle information set; and determining a target vehicle flow set based on the predicted vehicle flow list. It can be seen that, on the one hand, the present invention uses a wireless access point to obtain the current vehicle information set. The cost of the wireless access point is low, which is conducive to saving resources. On the other hand, when obtaining road traffic flow, the data in the first vehicle information list is processed to obtain multiple data such as the vehicle position, first vehicle speed, parking time, etc., and then the traffic flow is obtained, which can improve the accuracy of the traffic flow. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flowchart of a computer program executed by a data processing system for obtaining vehicle flow provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] The present invention provides a data processing system for obtaining vehicle flow, comprising: a current vehicle information set, a historical vehicle information set corresponding to the current vehicle information set, a target road ID list A={A1, ..., A i ,……,A m}, a processor, a memory storing a computer program, wherein A i is the i-th target road ID, i=1...m, m is the number of target road IDs, when the computer program is executed by the processor, the following steps are implemented, such as Figure 1 As shown:

[0024] S100, according to the current vehicle information set, obtain the first vehicle information list B = {B1, ..., B i ,……,B m}, B i ={B i1 ,……,B ij ,……,B in}, B ij A in the first time slice of j i The corresponding first vehicle information list, j=1...n, n is the number of the first time slices.

[0025] Specifically, the current vehicle information is the vehicle information at the current time point, where the value of the current time point is 1 day.

[0026] Specifically, the vehicle information includes the vehicle Wi-Fi name, vehicle Wi-Fi strength, and vehicle Wi-Fi time.

[0027] Furthermore, the vehicle-mounted WiFi strength is the wireless network signal strength sent by the wireless access point to the vehicle-mounted WiFi each time the wireless access point scans the wireless access point.

[0028] Furthermore, the in-vehicle WiFi time is the time it takes for the wireless access point to scan the in-vehicle WiFi each time.

[0029] Specifically, the first time slice is the time interval between obtaining adjacent current vehicle information. Those skilled in the art know that time intervals that meet actual needs in the prior art all fall within the protection scope of the present invention and will not be described in detail here.

[0030] Furthermore, the value range of the first time slice is 10 seconds to 20 seconds. The length of the time slice is relatively short, and the vehicle information on the road can be obtained accurately and timely.

[0031] Specifically, the system further includes: A corresponding wireless access point ID list F = {F1, ..., F i ,……,F m}, F i A i The corresponding wireless access point ID.

[0032] Furthermore, the wireless access point ID is a unique identifier of a wireless access point.

[0033] In a specific embodiment, when the computer program is executed by a processor, the following steps are further included before step S100 to obtain the current vehicle information set:

[0034] S1. According to F i , get F i Corresponding key equipment information list H i ={H i1 ,……,H ij ,……,H in}, H ij ={H 1 ij ,……,H x ij ,……,H p ij}, H x ij is F in the first time slice of j i The corresponding x-th key device information, x = 1 ... p, p is the first time slice of F in the j-th i The corresponding number of key equipment.

[0035] Specifically, the key equipment information includes vehicle information and / or non-vehicle information.

[0036] Furthermore, the non-vehicle information includes: non-vehicle WiFi name, non-vehicle WiFi strength and non-vehicle WiFi time.

[0037] Specifically, based on F i The geographical location coordinates meet the preset conditions to obtain the optimal F i Geographical location, where F i The geographic coordinates of meet the following preset conditions:

[0038] (UX i -UX i+1 ) 2 +(UY i -UY i+1 ) 2 =4a 2 And when i=m, UX m+1 =UX1,UY m+1 =UY1, where a is the radius of the scanning area of the wireless access point, UX i F in the preset coordinate system i The corresponding horizontal coordinate value, UY i F in the preset coordinate system i The corresponding vertical coordinate value.

[0039] Furthermore, the scanning area of the wireless access point is a circular area centered on the wireless access point. Those skilled in the art will appreciate that any method of setting a preset coordinate system and obtaining geographic location coordinates in the prior art falls within the scope of protection of the present invention and will not be further described herein.

[0040] As described above, the cost of wireless access points is relatively low. Setting the geographic coordinates of wireless access points and using them to collect key device information can save resources and ensure that the scanning ranges of any two wireless access points do not intersect, so that the key device information obtained by any two wireless access points is not repeated, which is conducive to improving processor processing efficiency and increasing the accuracy of obtaining predicted traffic flow.

[0041] S3, according to H ij , get H ij Corresponding wifi name list K ij ={K 1 ij ,……,K x ij ,……,K p ij}, K x ij H x ijThe corresponding Wi-Fi name, where Wi-Fi name is the name of the vehicle-mounted Wi-Fi or the name of the non-vehicle Wi-Fi.

[0042] S5, K ij Process and obtain K ij The corresponding first keyword list K 0 ij ={K 01 ij ,……,K 0x ij ,……,K 0p ij}, K 0x ij K x ij The corresponding first keyword, wherein the first keyword is words such as BMW, Mercedes-Benz, and Audi, is known to those skilled in the art. Any method of obtaining the first keyword based on the name of the in-vehicle WiFi in the prior art falls within the scope of protection of the present invention and will not be described in detail here.

[0043] S7, when K 0x ij If it is the same as any preset keyword in the preset keyword list, get K 0x ij The corresponding key equipment information is inserted into the current vehicle information set. Those skilled in the art know that the preset keywords in the prior art that meet actual needs all fall within the protection scope of the present invention and will not be described in detail here.

[0044] In the above, the key device information obtained by the wireless access point is processed, and the key device information that matches the first keyword and the preset keyword is obtained as the current vehicle information. It can be understood that the vehicle information of the vehicle with on-board WiFi is obtained, which can reduce the error caused by non-vehicle information during the data processing process and improve the accuracy of obtaining the predicted traffic flow.

[0045] Specifically, in step S100, the following steps are also included:

[0046] S101. According to the current vehicle information set, obtain the current vehicle Wi-Fi strength list L corresponding to the current vehicle information set in the jth first time slice. ij ={L 1 ij ,……,L y ij ,……,L q ij}, L y ij ={L y1 i1 ,……,L yrij ,……,L ys in}, L yr ij is the rth F i The corresponding wireless access point scans the yth vehicle's in-vehicle Wi-Fi strength, r = 1 ... s, s is the number of times the wireless access point scans, y = 1 ... q, q is F i Regarding the number of vehicles scanned by the wireless access point, those skilled in the art know that any method of obtaining the vehicle Wi-Fi strength from the current vehicle information set in the prior art falls within the scope of protection of the present invention and will not be described in detail here.

[0047] S103, according to L y ij , get L y ij Corresponding vehicle distance list L ′y ij ={L ′y1 i1 , ..., L′ yr ij , ..., L′ ys in}, L′ yr ij For L yr ij The corresponding vehicle position coordinates to F i The distance to the corresponding wireless access point geographic coordinates, where L ′yr ij Meet the following conditions:

[0048] L′ yr ij =a×L yr ij / L 0 , where L 0 The value of the vehicle Wi-Fi strength threshold is preset. Those skilled in the art know that the value of the vehicle Wi-Fi strength threshold that meets actual needs in the prior art all falls within the protection scope of the present invention and will not be repeated here.

[0049] S105. According to L' y ij , get L y ij The corresponding first vehicle speed M y ij , M y ij Meet the following conditions:

[0050] t is F iThe time interval between the scanning of adjacent current vehicle information by the corresponding wireless access point.

[0051] Specifically, those skilled in the art know that any method in the prior art for obtaining the time interval between wireless access points scanning adjacent current vehicle information falls within the protection scope of the present invention and will not be described in detail herein.

[0052] S107, according to L' y ij , get L y ij The corresponding first parking time N y ij , where N y ij Meet the following conditions:

[0053] N y ij =N 0 ×t,N 0 L′ y ij Middle L′ yr ij and L′ y(r+1) ij Same quantity.

[0054] S109, when M y ij ≥M0 and N y ij When ≤N′, obtain L y ij The corresponding vehicle information is inserted into B ij In, M 0 is a preset first vehicle speed threshold, and N′ is a preset first parking time threshold. Those skilled in the art know that the values of the vehicle speed threshold and the values of the first parking time threshold that meet actual needs in the prior art both fall within the scope of protection of the present invention and are not described in detail here.

[0055] Specifically, the value range of N′ is 5 seconds to 10 seconds, so as to prevent the first parking time threshold from being set too low and thus omitting the first vehicle information.

[0056] In the above, the wireless network signal strength is processed, the first vehicle speed and the first parking time of the current vehicle corresponding to the wireless access point are obtained, the information of the vehicle in motion is obtained, and the vehicle information in motion is processed. This reduces the amount of data that the system needs to process, is conducive to improving the processing efficiency of the processor, and improves the accuracy of obtaining the predicted traffic flow.

[0057] S200, according to B and the historical vehicle information set, obtain the predicted traffic flow list G = {G1, ..., G i ,……,G m},

[0058] G i ={G i1 ,……,G ij ,……,G in}.

[0059] Specifically, historical vehicle information is vehicle information within a historical time period, wherein the historical time period is a period with the current time point as the starting time point and a preset time length as the time span. Those skilled in the art know that the values of the preset time lengths in the prior art that meet actual needs all fall within the scope of protection of the present invention and will not be repeated here.

[0060] Furthermore, the preset time length ranges from 10 days to 30 days, which can prevent the preset time length from being too high, resulting in too much data being obtained and low system processing efficiency, or the preset time length from being too low, resulting in too little data being obtained, and thus resulting in low accuracy in obtaining predicted traffic flow.

[0061] Specifically, in step S200, G is obtained by the following steps: ij :

[0062] S201, obtain A i Corresponding middle road ID list A 0 i ={A 0 i1 ,……,A 0 ik ,……,A 0 im-1}, A 0 ik A i The corresponding kth intermediate road ID, k=1...m-1, wherein the intermediate road ID is the ID of A except A i Any target road ID other than .

[0063] S203, according to B ij With A 0 i , get A 0 i Corresponding first vehicle quantity list C ij ={C 1 ij ,……,C k ij ,……,C m-1 ij}, Ck ij For B ij A in the corresponding first time slice 0 ik The corresponding first vehicle number, wherein the first vehicle number is the number of vehicles turning from a road corresponding to a target road ID to a road corresponding to any intermediate road ID.

[0064] Specifically, in step S203, the following steps are also included:

[0065] S2031, according to B ij 、A i 、A 0 i , get A i With A 0 i Corresponding second car wifi name list P ij ={P 1 ij ,……,P k ij ,……,P m-1 ij}, P k ij ={P k1 ij ,……,P kg ij ,……,P kh ij}, P kg ij For B ij In the corresponding first time slice, in A i The corresponding target road and A 0 ik The name of the g-th vehicle’s onboard Wi-Fi network that appears on the corresponding target road, g = 1…h, where h is the name of the vehicle in A i The corresponding target road and A 0 ik The number of vehicles that appear on the corresponding target road.

[0066] S2033, according to A i With P k ij , get P k ij The corresponding first car wifi time list P′ k ij ={P′ k1 ij , ..., P′ kg ij , ..., P′ khij}, P′ kg ij P kg ij In A i The corresponding in-vehicle Wi-Fi time on the target road.

[0067] S2035, according to A 0 i With P k ij , get P k ij The corresponding second car wifi time list P 0k ij ={P 0k1 ij ,……,P 0kg ij ,……,P 0kh ij}, P 0kg ij P kg ij In A 0 ik The corresponding in-vehicle Wi-Fi time on the target road.

[0068] S2037, according to P ′k ij With P 0k ij , get P ′k ij P′ kg ij In P 0k ij Medium P 0kg ij The previous quantity is C k ij .

[0069] Above, for A i The corresponding target road and A 0 ik By comparing the in-vehicle Wi-Fi time of all vehicles appearing on the corresponding target road, the direction of travel of the first vehicle can be determined, and the A i The corresponding target road turns to A 0 ik The number of first vehicles corresponding to the target road is conducive to improving the accuracy of predicting traffic flow.

[0070] Specifically, the system also includes the number of vehicles corresponding to the historical vehicle information set.

[0071] S205, according to the historical vehicle information set, obtain C k ij Corresponding first historical vehicle quantity list D k ij ={D k1 ij ,……,D ke ij ,……,D kf ij} and the second historical vehicle quantity list E k ij ={E k1 ij ,……,E ke ij ,……,E kf ij}, D ke ij C is the second time point in the eth k ij The corresponding first historical vehicle number, E ke ij is C at the e-th second time point k ij The corresponding second historical number of vehicles, e=1…f, f is the number at the second time point, where the first historical number of vehicles is the number of vehicles at any second time point in the historical time period, and the second historical number of vehicles is the number of vehicles with in-vehicle wifi at the same time point as the first historical number of vehicles. It can be understood as: D ke ij with C k ij For the same time slice at different time points, those skilled in the art know that any method in the prior art for obtaining the first historical vehicle number and the second historical vehicle number based on the historical vehicle information set falls within the scope of protection of the present invention and will not be described in detail here.

[0072] Specifically, the second time point is a single time point in the historical time period, wherein the value of the second time point is consistent with the value of the current time point.

[0073] S207, according to C k ij 、D ke ij 、E ke ij , get C k ij The corresponding predicted traffic flow G k ij , where G k ij Meet the following conditions:

[0074] Wherein, T is the length of the first time slice, and W is the preset weight corresponding to the predicted traffic flow.

[0075] Specifically, those skilled in the art know that the initial value of W is set by those skilled in the art according to actual needs.

[0076] S209, according to G k ij , get G ij ={G 1 ij ,……,G k ij ,……,G m-1 ij}.

[0077] As described above, by processing the historical vehicle information and combining the first historical vehicle number and the second historical vehicle number in the historical vehicle information, a more accurate predicted traffic flow can be obtained. Processing the predicted traffic flow is conducive to further obtaining the target traffic flow and improving the accuracy of obtaining the target traffic flow.

[0078] S300, according to G, determine the target traffic flow set G 0 ={G 0 1, ..., G 0 i ,……,G 0 m}, G 0 i ={G 0 i1 ,……,G 0 ij ,……,G 0 in}, G 0 ij G ij The corresponding target traffic flow list.

[0079] Specifically, in step S300, the following steps are also included:

[0080] Specifically, the system also includes actual traffic flow corresponding to the first time slice.

[0081] S301, obtain the actual traffic flow list Q corresponding to G = {Q1, ..., Q i ,……,Q m}, Q i ={Q i1 ,……,Q ij ,……,Q in}, Q ij ={Q1 ij ,……,Q k ij ,……,Q m-1 ij}, Q k ij G k ij The corresponding actual traffic volume.

[0082] S303, when |G k ij -Q k ij When |≤Δw, determine G k ij G 0k ij , where Δw is the preset traffic flow difference threshold.

[0083] Specifically, the value range of Δw is 0-0.6; preferably, Δw is 0.3 to prevent the threshold from being set too low and obtaining an erroneous target traffic flow.

[0084] S305, when |G k ij -Q k ij |>Δw and G k ij >Q k ij When W=W 0 , execute step S207, wherein W 0 Meet the following conditions: W 0 =W×(1+|1-G k ij / Q k ij |).

[0085] S307, when |G k ij -Q k ij |>Δw and G k ij <Q k ij When W=W 0 , execute step S207, wherein W 0 Meet the following conditions: W 0 =W×(1-G k ij / Q k ij ).

[0086] S309, according to G0k ij , get G 0 ij ={G 01 ij ,……,G 0k ij ,……,G 0(m-1) ij}, G 0k ij G k ij The corresponding target traffic flow.

[0087] As described above, the predicted traffic flow is compared with the actual traffic flow. When the traffic flow difference between the predicted traffic flow and the actual traffic flow is greater than the traffic flow difference threshold, the preset weight corresponding to the predicted traffic flow is adjusted, and the predicted traffic flow is obtained again, which is conducive to improving the accuracy of obtaining the target traffic flow.

[0088] In a specific embodiment, after step S300, the following steps are further included:

[0089] S400. Obtain a preset time slice in a preset time period. Those skilled in the art know that any optional value of the preset time period and any optional value of the preset time slice fall within the protection scope of the present invention. It can be understood that: the preset time period is the morning peak time period, the evening peak time period, and the off-peak time period; the length of the preset time slice is the duration of obtaining a traffic flow within the preset time period, and the length of the preset time slice can be ten minutes.

[0090] S500: According to the preset time slice in the preset time period, obtain the A corresponding to the preset time slice. i The corresponding target road turns to A 0 ik The corresponding traffic volume of the target road.

[0091] Specifically, step S500 includes the following steps:

[0092] S501: When the preset time slice is the first preset time slice in the preset time period, obtain the G in the preset time slice. 0k ij The average value of the preset time slice is used as the A i The corresponding target road turns to A 0 ik The corresponding traffic volume of the target road.

[0093] S503. When the preset time slice is not the first preset time slice in the preset time period, obtain the actual traffic flow of several key time slices corresponding to the preset time slice, wherein the key time slice is the preset time slice before the preset time slice in the preset time period. Those skilled in the art know that any method of obtaining the actual traffic flow in the prior art falls within the protection scope of the present invention and will not be described in detail here.

[0094] S505: Obtain the number of key vehicles corresponding to the key time slice based on the actual traffic volume of the key time slice and the length of the preset time slice, wherein the number of key vehicles meets the following conditions:

[0095] The number of critical vehicles = the actual traffic volume in the critical time slice × the length of the preset time slice.

[0096] S507. Obtain the first intermediate key vehicle number corresponding to the key time slice, wherein the first intermediate key vehicle number is the number of vehicles with on-board wifi. Those skilled in the art know that the method for obtaining the first intermediate key vehicle refers to the method for obtaining the current information set, and will not be repeated here.

[0097] S509: Obtain the A corresponding to the preset time slice according to the number of key vehicles corresponding to the key time slices corresponding to the preset time slice and the number of first intermediate key vehicles. i The corresponding target road turns to A 0 ik The traffic volume of the corresponding target road, wherein those skilled in the art know that obtaining A in the preset time slice i The corresponding target road turns to A 0 ik The formula for the traffic flow of the corresponding target road refers to the formula for obtaining the predicted traffic flow, wherein the preset weights in the formula for obtaining the traffic flow are fixed values and will not be repeated here.

[0098] As described above, the actual traffic flow of a known time slice in a time period is used to obtain the traffic flow of an unknown time slice. The time span is small and the timeliness is high. The real-time traffic flow can be obtained and the error caused by the large time span is reduced, which can improve the accuracy of obtaining the actual traffic flow.

[0099] S600. When the number of traffic flows corresponding to all preset time slices in the preset time period that is greater than the traffic flow threshold is greater than the preset first quantity threshold, adjust the duration and green-to-signal ratio of one phase of the traffic light corresponding to the preset time period. It is known to those skilled in the art that any optional traffic flow threshold and first quantity threshold in the prior art fall within the scope of protection of the present invention and will not be elaborated herein.

[0100] As mentioned above, when the turning traffic volume in the preset time slice is large and the number is too large, the duration of one phase of the traffic light and the green-to-signal ratio can be adjusted according to the value of the traffic volume, which is beneficial to improving the traffic efficiency of the road.

[0101] The present invention provides a data processing system for obtaining vehicle flow, the system comprising: a current vehicle information set, a historical vehicle information set corresponding to the current vehicle information set, a target road identification list, a processor, and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: obtaining a first vehicle information list based on the current vehicle information set; obtaining a predicted vehicle flow list based on the first vehicle information list and the historical vehicle information set; and determining a target vehicle flow set based on the predicted vehicle flow list. It can be seen that, on the one hand, the present invention uses a wireless access point to obtain the current vehicle information set. The cost of the wireless access point is low, which is conducive to saving resources. On the other hand, when obtaining road traffic flow, the data in the first vehicle information list is processed to obtain multiple data such as the vehicle position, first vehicle speed, parking time, etc., and then the traffic flow is obtained, which can improve the accuracy of the traffic flow.

[0102] Although some specific embodiments of the present invention have been described in detail by way of example, it will be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It will also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A data processing system for obtaining vehicle flow, characterized in that: The system includes: a current vehicle information set, a historical vehicle information set corresponding to the current vehicle information set, a target road ID list A={A1, ..., A i ,……,A m }, a processor, a memory storing a computer program, wherein A i is the i-th target road ID, i=1...m, and m is the number of target road IDs. When the computer program is executed by a processor, the following steps are implemented: S100, according to the current vehicle information set, obtain the first vehicle information list B = {B1, ..., B i ,……,B m }, B i ={B i1 ,……,B ij ,……,B in }, B ij A in the first time slice of j i The corresponding first vehicle information list, j=1...n, n is the number of first time slices; S200, according to B and the historical vehicle information set, obtain the predicted traffic flow list G = {G1, ..., G i ,……,G m }, G i ={G i1 ,……,G ij ,……,G in }, wherein, in step S200, G is obtained by the following steps ij : S201, obtain A i Corresponding middle road ID list A 0 i ={A 0 i1 ,……,A 0 ik ,……,A 0 im-1 }, A 0 ik A i The corresponding kth intermediate road ID, k=1...m-1, wherein the intermediate road ID is the ID of A except A i Any target road ID other than ; S203, according to B ij With A 0 i , get A 0 i Corresponding first vehicle quantity list C ij ={C 1 ij ,……,C k ij ,……,C m -1 ij }, C k ij For B ij A in the corresponding first time slice 0 ik The corresponding first vehicle number, wherein the first vehicle number is the number of vehicles that turn from a road corresponding to a target road ID to a road corresponding to any intermediate road ID; S205, according to the historical vehicle information set, obtain C in the historical time period k ij Corresponding first historical vehicle quantity list D k ij ={D k1 ij ,……,D ke ij ,……,D kf ij } and the second historical vehicle quantity list E k ij ={E k1 ij ,……,E ke ij ,……,E kf ij }, D ke ij is the C at the e-th second time point in the historical time period k ij The corresponding first historical vehicle number, E ke ij D ke ij The corresponding second historical vehicle number, e=1…f, where f is the number at the second time point, the first historical vehicle number is the number of vehicles at any second time point in the historical time period, and the second historical vehicle number is the number of vehicles with in-vehicle Wi-Fi at the same time point as the first historical vehicle number; S207, according to C k ij 、D ke ij 、E ke ij , get C k ij The corresponding predicted traffic flow G k ij , where G k ij Meet the following conditions: Where T is the length of the first time slice, and W is the preset weight corresponding to the predicted traffic flow; S209, according to G k ij , get G ij ={G 1 ij ,……,G k ij ,……,G m-1 ij }; S300, according to G, determine the target traffic flow set G 0 ={G 0 1, ..., G 0 i ,……,G 0 m }, G 0 i ={G 0 i1 ,……,G 0 ij ,……,G 0 in }, G 0 ij G ij The corresponding target traffic flow list.

2. The data processing system for obtaining vehicle flow according to claim 1, characterized in that: The current vehicle information is the vehicle information at the current time point, where the value of the current time point is 1 day.

3. The data processing system for obtaining vehicle flow according to claim 2, characterized in that: The historical vehicle information is vehicle information within a historical time period, wherein the historical time period is a period starting at the current time point and spanning a preset time length.

4. The data processing system for obtaining vehicle flow according to claim 2, characterized in that: The vehicle information includes the vehicle Wi-Fi name, vehicle Wi-Fi strength and vehicle Wi-Fi time.

5. The data processing system for obtaining vehicle flow according to claim 4, characterized in that: The system also includes: A corresponding wireless access point ID list F = {F1, ..., F i ,……,F m }, F i A i When the computer program is executed by the processor, the following steps are further performed before step S100 to obtain the current vehicle information set: S1. According to F i , get F i Corresponding key equipment information list H i ={H i1 ,……,H ij ,……,H in }, H ij ={H 1 ij ,……,H x ij ,……,H p ij }, H x ij is F in the first time slice of j i The corresponding x-th key device information, x = 1 ... p, p is the first time slice of F in the j-th i The number of corresponding key devices, where key device information includes vehicle information and / or non-vehicle information. Non-vehicle information includes: non-vehicle Wi-Fi name, non-vehicle Wi-Fi strength, and non-vehicle Wi-Fi time; S3, according to H ij , get H ij Corresponding wifi name list K ij ={K 1 ij ,……,K x ij ,……,K p ij }, K x ij H x ij The corresponding Wi-Fi name, where Wi-Fi name is the name of the vehicle Wi-Fi or the name of the non-vehicle Wi-Fi; S5, K ij Input into the preset feature extraction model to obtain K ij The corresponding first keyword list K 0 ij ={K 01 ij ,……,K 0x ij ,……,K 0p ij }, K 0x ij K x ij The corresponding first keyword; S7, when K 0x ij If it is the same as any preset keyword in the preset keyword list, get K 0x ij The corresponding key equipment information is inserted into the current vehicle information set.

6. The data processing system for obtaining vehicle flow according to claim 4, characterized in that: The following steps are included in step S100: S101. According to the current vehicle information set, obtain the current vehicle Wi-Fi strength list L corresponding to the current vehicle information set in the jth first time slice. ij ={L 1 ij ,……,L y ij ,……,L q ij }, L y ij ={L y1 i1 ,……,L yr ij ,……,L ys in }, L yr ij is the rth F i The corresponding wireless access point scans the yth vehicle's in-vehicle Wi-Fi strength, r = 1 ... s, s is the number of times the wireless access point scans, y = 1 ... q, q is F i The number of vehicles scanned by the corresponding wireless access point; S103, according to L y ij , get L y ij Corresponding vehicle distance list L′ y ij ={L′ y1 i1 , ..., L′ yr ij , ..., L′ ys in }, L′ yr ij For L yr ij The corresponding vehicle geographic location coordinates to F i The distance to the corresponding wireless access point geographic coordinates, where L′ yr ij Meet the following conditions: L′ yr ij =a×L yr ij / L 0 , where a is the radius of the scanning area of the wireless access point, L 0 is the preset vehicle Wi-Fi strength threshold; S105. According to L' y ij , get L y ij The corresponding first vehicle speed M y ij , M y ij Meet the following conditions: t is F i The time interval between the corresponding wireless access point scanning adjacent current vehicle information; S107, according to L' y ij , get L y ij The corresponding first parking time N y ij , where N y ij Meet the following conditions: N y ij =N 0 ×t,N 0 L′ y ij Middle L′ yr ij and L′ y(r+1) ij the same quantity; S109, when M y ij ≥M0 and N y ij When ≤N′, obtain L y ij The corresponding vehicle information is inserted into B ij In the figure, M0 is a preset first vehicle speed threshold, and N′ is a preset first parking time threshold.

7. The data processing system for obtaining vehicle flow according to claim 4, characterized in that: The following steps are included in step S203: S2031, according to B ij 、A i 、A 0 i , get A i With A 0 i Corresponding second car wifi name list P ij ={P 1 ij ,……,P k ij ,……,P m-1 ij }, P k ij ={P k1 ij ,……,P kg ij ,……,P kh ij }, P kg ij For B ij In the corresponding first time slice, in A i The corresponding target road and A 0 ik The name of the g-th vehicle’s onboard Wi-Fi network that appears on the corresponding target road, g = 1…h, where h is the name of the vehicle in A i The corresponding target road and A 0 ik The number of vehicles appearing on the corresponding target road; S2033, according to A i With P k ij , get P k ij The corresponding first car wifi time list P′ k ij ={P′ k1 ij , ..., P′ kg ij , ..., P′ kh ij }, P′ kg ij P kg ij In A i The corresponding in-vehicle Wi-Fi time on the target road; S2035, according to A 0 i With P k ij , get P k ij The corresponding second car wifi time list P 0k ij ={P 0k1 ij ,……,P 0kg ij ,……,P 0kh ij }, P 0kg ij P kg ij In A 0 ik The corresponding in-vehicle Wi-Fi time on the target road; S2037, according to P' k ij With P 0k ij , obtain P′ k ij P′ kg ij In P 0k ij Medium P 0kg ij The previous quantity is C k ij .

8. The data processing system for obtaining vehicle flow according to claim 4, characterized in that: The system further includes: the actual traffic flow corresponding to the first time slice, and step S300 includes the following steps: S301, obtain the actual traffic flow list Q corresponding to G = {Q1, ..., Q i ,……,Q m }, Q i ={Q i1 ,……,Q ij ,……,Q in }, Q ij ={Q 1 ij ,……,Q k ij ,……,Q m-1 ij }, Q k ij G k ij The corresponding actual traffic volume; S303, when |G k ij -Q k ij When |≤Δw, determine G k ij G 0k ij , where Δw is the preset traffic flow difference threshold; S305, when |G k ij -Q k ij |>Δw and G k ij >Q k ij When W=W 0 , execute step S207, wherein W 0 Meet the following conditions: W 0 =W×(1+|1-G k ij / Q k ij |); S307, when |G k ij -Q k ij |>Δw and G k ij <Q k ij When W=W 0 , execute step S207, wherein W 0 Meet the following conditions: W 0 =W×(1-G k ij / Q k ij ); S309, according to G 0k ij , get G 0 ij ={G 01 ij ,……,G 0k ij ,……,G 0(m-1) ij }, G 0k ij G k ij The corresponding target traffic flow.

9. The data processing system for obtaining vehicle flow according to claim 1, characterized in that: The first time slice is the time interval between obtaining adjacent current vehicle information.

10. The data processing system for obtaining vehicle flow according to claim 2, characterized in that: The second time point is a single time point in the historical time period, wherein the value of the second time point is consistent with the value of the current time point.

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