Intelligent traffic flow statistical analysis method and system

Abstract

The invention relates to the technical field of traffic flow metering processing, and discloses an intelligent traffic flow statistical analysis method and system, and the system comprises a traffic flow measurement position management module, a traffic flow measurement data management module, and a traffic flow measurement module. Based on traffic flow measuring equipment, traffic flow measuring points can arrive autonomously, and traffic flow videos can be acquired accurately at the set time of the traffic flow measuring points; according to traffic flow video frame parameters of the traffic flow measurement point, combining an intelligent identification algorithm and road traffic vehicle license plate standard image information stored based on big data, carrying out dynamic intelligent statistics on the number of passing vehicles at a set time of the traffic flow measurement point, and realizing accurate measurement of the total number of passing vehicles at the set time of the traffic flow measurement point; and based on flow measurement setting time parameters of the traffic flow measurement points and the total number of passing vehicles at the traffic flow measurement points in the setting time, efficient statistics of traffic flow of the traffic flow measurement points is performed by combining numerical processing.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic flow measurement and processing, and in particular to an intelligent traffic flow statistical analysis method and system. Background Art

[0002] Intelligent traffic flow measurement is one of the core technologies in intelligent transportation systems. By collecting, analyzing, and processing traffic data in real time, it optimizes traffic management efficiency, reduces congestion, and improves road safety. Intelligent traffic flow measurement utilizes sensors, video surveillance, wireless communications, and other technologies to monitor the number, speed, and density of vehicles on the road in real time. Combined with data analysis models, it predicts traffic trends and dynamically adjusts traffic signals and routing to achieve efficient traffic management. Its importance lies in optimizing traffic scheduling: using real-time data to dynamically adjust traffic light timing to alleviate congestion; accident warning and handling: analyzing traffic anomalies to quickly locate accidents and coordinate rescue efforts; and energy conservation and emission reduction: reducing vehicle idling time and exhaust emissions. Current intelligent traffic flow measurement systems can only count vehicle flow at fixed traffic flow measurement points. Existing intelligent traffic flow measurement systems cannot dynamically set traffic flow measurement points or efficiently and dynamically measure traffic flow information, reducing the intelligence and convenience of intelligent traffic management.

[0003] Chinese invention patent publication number CN109785632B discloses a traffic flow statistics method and device. The method obtains point cloud data of vehicles at a target location on a road to be counted and converts it into at least one square wave signal. The amplitude of the square wave signal corresponds to the distance data included in the point cloud data, and the distance data corresponds to the distance between the solid-state lidar sensor and the reflector. For each square wave signal, the method identifies the fluctuation unit included in the square wave signal based on the amplitude change of the square wave signal. The fluctuation unit is a single concave waveform with an amplitude greater than a preset amplitude threshold. The method determines the traffic flow data corresponding to the target location based on the number of fluctuation units included in each square wave signal within a preset time period. The above technical solution can only measure traffic flow at a fixed location and cannot flexibly measure traffic flow at a traffic flow measurement point. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In order to solve the problem that the above-mentioned existing intelligent traffic flow statistics system cannot dynamically set traffic flow measurement points, cannot achieve efficient dynamic measurement of traffic flow information, and reduces the intelligence and convenience of intelligent traffic management, the above-mentioned dynamic setting of traffic flow measurement points, intelligent planning of flight path information to traffic flow measurement points, autonomous collection of traffic flow video information at the set time of traffic flow measurement points, scientific statistics of the number of vehicles passing through the traffic flow measurement points at the set time, and accurate measurement of traffic flow parameters at traffic flow measurement points are realized to achieve the purpose of dynamic intelligent traffic flow monitoring.

[0006] (2) Technical solution

[0007] The present invention is implemented through the following technical solution: an intelligent traffic flow statistical analysis method, the method comprising the following steps:

[0008] S1, collecting traffic flow measurement point coordinate data, traffic flow measurement setting time data and traffic flow measurement equipment position coordinate data;

[0009] S2, performing flight path planning processing from the location of the traffic flow measurement device to the traffic flow measurement point based on the traffic flow measurement device location coordinate data and the traffic flow measurement point coordinate data, and generating target traffic flow measurement point flight path data;

[0010] S3, executing a flight arrival operation at the destination of the traffic flow measurement point according to the flight path data of the target traffic flow measurement point;

[0011] S4, performing a vehicle flow video information collection operation at the set time of the traffic flow measurement point according to the flow measurement set time data of the traffic flow measurement point, and generating vehicle flow video data at the set time of the traffic flow measurement point;

[0012] S5, performing a vehicle flow video frame generation process at the traffic flow measurement point based on the vehicle flow video data at the set time of the traffic flow measurement point, and constructing vehicle flow video frame data at the traffic flow measurement point;

[0013] S6. Counting the number of vehicles passing through the traffic flow measurement point at the set time based on the traffic flow video frame data and the standard image data of the license plates of road traffic vehicles at the traffic flow measurement point, and generating the total number of vehicles passing through the traffic flow measurement point at the set time;

[0014] S7. Perform traffic flow statistics processing at the traffic flow measurement point based on the traffic flow measurement set time data of the traffic flow measurement point and the total number of vehicles passing through the traffic flow measurement point at the set time, generate traffic flow data at the traffic flow measurement point, and perform traffic flow statistics result feedback operation at the traffic flow measurement point.

[0015] Preferably, the operation steps of collecting traffic flow measurement point coordinate data, traffic flow measurement point flow measurement setting time data and traffic flow measurement device position coordinate data are as follows:

[0016] S11, collect the spatial coordinate information of the target road traffic measurement point online through the intelligent traffic management platform, and generate the traffic flow measurement point coordinate data P ， The traffic flow measurement point coordinate data includes the longitude, latitude and altitude of the target road traffic measurement point;

[0017] The traffic flow statistics of the target road traffic measurement point are collected online through the intelligent traffic management platform to set the time length parameters, and the traffic flow measurement point flow measurement setting time data I is generated. ， The unit of I is seconds;

[0018] The real-time spatial position coordinate information of the traffic flow measurement equipment at the target road traffic measurement point is collected online through the intelligent traffic management platform, and the traffic flow measurement equipment position coordinate data Y is generated. ， The traffic flow measurement device location coordinate data includes the longitude, latitude and altitude of the real-time spatial location of the traffic flow measurement device. The traffic flow measurement device includes a drone and a cloud camera.

[0019] Preferably, the steps of performing flight path planning from the location of the traffic flow measurement device to the traffic flow measurement point based on the traffic flow measurement device location coordinate data and the traffic flow measurement point coordinate data, and generating target traffic flow measurement point flight path data are as follows:

[0020] S21: Input the traffic flow measurement device location coordinate data Y and the traffic flow measurement point coordinate data P into the departure dialog box and the destination dialog box of the flight map navigation software respectively to perform flight path measurement processing from the traffic flow measurement device location to the traffic flow measurement point, and generate the target traffic flow measurement point flight path data U. ， The flight map navigation software includes any one of PlanIt, DronesMap, and Smart.

[0021] Preferably, the operation steps of performing the flight arrival operation at the traffic flow measurement point destination according to the flight path data of the target traffic flow measurement point are as follows:

[0022] S31. The traffic flow measurement device flies from the location of the traffic flow measurement device to the traffic flow measurement point according to the target traffic flow measurement point flight path data U, and executes a flight arrival operation at the traffic flow measurement point destination. If the traffic flow measurement device does not arrive at the traffic flow measurement point, the traffic flow measurement device continues to execute the flight arrival operation at the traffic flow measurement point destination.

[0023] Preferably, the operation steps of collecting vehicle flow video information at the set time of the traffic flow measurement point according to the flow measurement set time data of the traffic flow measurement point and generating vehicle flow video data at the set time of the traffic flow measurement point are as follows:

[0024] S41. When the traffic flow measurement device arrives at the traffic flow measurement point, the intelligent traffic management platform controls the traffic flow measurement device to collect the traffic flow video information of the traffic flow measurement point online according to the time length corresponding to the flow measurement setting time data I of the traffic flow measurement point, and generates the traffic flow video data Q of the traffic flow measurement point setting time.

[0025] Preferably, the steps of generating a traffic flow video frame at a traffic flow measurement point based on the traffic flow video data at the set time of the traffic flow measurement point and constructing the traffic flow video frame data at the traffic flow measurement point are as follows:

[0026] S51, obtaining the traffic flow video data Q of the set time at the traffic flow measurement point;

[0027] S52, using a video editing tool to perform traffic flow measurement point traffic flow video frame generation processing on the traffic flow measurement point set time traffic flow video data Q, and construct a traffic flow measurement point traffic flow video frame data set where h o represents the generated traffic flow video frame data of the oth traffic flow measurement point, Indicates the maximum number of vehicle flow video frames at the traffic flow measurement point, and the video editing tool includes any one of Adobe Premiere Pro, DaVinci Resolve, and Final Cut Pro.

[0028] Preferably, the steps of performing statistical processing on the number of vehicles passing through the traffic flow measurement point at the set time based on the traffic flow video frame data of the traffic flow measurement point and the standard image data of the license plate of the road traffic vehicle, and generating the total number of vehicles passing through the traffic flow measurement point at the set time are as follows:

[0029] S61, establish a road traffic vehicle license plate standard image data set K = (k1, ..., k x ,…,k τ ), x=1,2,3,…,τ; where k x represents the standard image data of road traffic vehicle license plates corresponding to the xth type of motor vehicle license plate, τ represents the maximum number of motor vehicle license plate types; motor vehicle license plate types include blue background with white characters, yellow background with black characters, green background with black characters, and white background with black characters, and the standard image data of road traffic vehicle license plates represents standard road traffic vehicle license plate image information set for different types of motor vehicle license plates;

[0030] S62: The traffic flow measurement point vehicle flow video frame data h in the traffic flow measurement point vehicle flow video frame data set H is converted into the traffic flow measurement point vehicle flow video frame data h o The traffic flow video frames are numbered in order according to the traffic flow measurement point and the road traffic vehicle license plate standard image data set K. x Perform image feature matching to search for all the traffic flow video frame data h of the traffic flow measurement points o The total number of vehicles in the corresponding video frame is used to generate the total number of vehicles passing through the traffic flow measurement point at the set time J through data identification. The specific steps for generating the total number of vehicles passing through the traffic flow measurement point at the set time J are as follows:

[0031] S621, initialization, defining the relevant structural parameters as vectors, in the road traffic vehicle license plate standard image data set K constitutes a τ-dimensional optimization problem, the vehicle search sand cat represents the 1×τ array of the problem solution, each variable value k1, k2, k3, ..., k τ are all floating point numbers, and each variable value is from k1 to k τ is located between the lower bound and the upper bound in the search space of the road traffic vehicle license plate standard image data set K;

[0032] S622, Searching for prey, the final and main parameters controlling the transition between exploration and exploitation phases are when When the vehicle search sand cat places the traffic flow measurement point vehicle flow video frame data h in the search space of the road traffic vehicle license plate standard image data set K o The traffic flow video frames are numbered in order according to the traffic flow measurement points and the road traffic vehicle license plate standard image data k x Perform image feature matching to search for all the traffic flow video frame data h of the traffic flow measurement points o The total number of vehicles in the corresponding video frame; the vehicle search sand cat relies on the release of low-frequency noise in the search space of the road traffic vehicle license plate standard image data set K to convert the traffic flow measurement point vehicle flow video frame data h o The traffic flow video frames are numbered in order according to the traffic flow measurement points and the road traffic vehicle license plate standard image data k x Perform image feature matching to search for all the traffic flow video frame data h of the traffic flow measurement points o Corresponding to the total number of vehicles in the video frame, assuming that the sensitivity range of vehicle search sand cat Φ is from 0 to 2kHz, ∫ represents the auditory characteristics inspired by vehicle search sand cat, assuming that the ∫ value is 2, t is the current iteration number, T is the maximum iteration number, and rand(0,1) represents a random number with a value between 0 and 1; in Represents the sensitivity vector; each vehicle search cat updates its own position in the search space of the road traffic vehicle license plate standard image data set K according to the best candidate position and the current position and its sensitivity range Φ, and searches for the vehicle flow video frame data h corresponding to the traffic flow measurement point in the search space of the road traffic vehicle license plate standard image data set K. o The most matching road traffic vehicle license plate standard image data k x The position is calculated as follows:

[0033] W(t+1)=Φ×(W(t)-rand(0,1)×W best (t)), where W(t+1) represents the current position of the vehicle searching for the sand cat individual in the search space of the road traffic vehicle license plate standard image dataset K after the t+1th iteration in the search for prey phase, W(t) represents the current position of the vehicle searching for the sand cat individual in the search space of the road traffic vehicle license plate standard image dataset K after the tth iteration in the search for prey phase, and W best (t) represents the best candidate position of the sand cat in the search space of the road traffic vehicle license plate standard image dataset K after the tth iteration in the prey search stage;

[0034] S623, attack prey, when When the vehicle search sand cat searches in the search space of the road traffic vehicle license plate standard image data set K, a random position is generated using the best candidate position and the current position, which is consistent with the traffic flow video frame data h of the traffic flow measurement point. o The matching road traffic vehicle license plate standard image data k x Assuming that the sensitivity range of the vehicle search sand cat is a circle, the roulette method is used to randomly select an angle Λ for each vehicle search sand cat, according to the following random position calculation formula:

[0035] Search at random positions in the search space of the road traffic vehicle license plate standard image dataset K, where W'(t+1) represents the random position of the sand cat individual in the search space of the road traffic vehicle license plate standard image dataset K at the t+1th iteration in the attack prey phase, represents the sensitivity vector, cos(Λ) represents the cosine value of the angle Λ, and W rand represents the random position of the vehicle searching sand cat in the search space of the road traffic vehicle license plate standard image dataset K in the attack prey phase. The random position enables the vehicle searching sand cat to approach and attack the traffic flow video frame data h of the traffic flow measurement point in the search space of the road traffic vehicle license plate standard image dataset K. oThe matching road traffic vehicle license plate standard image data k x of prey;

[0036] S624: When the algorithm meets the maximum number of iterations, all the traffic flow video frame data h of the traffic flow measurement points are output. o The total number of vehicles in the corresponding video frame, otherwise continue to iterate until the maximum number of iterations is met;

[0037] S625, all the traffic flow measurement point vehicle flow video frame data h output in step S624 o The total number of vehicles in the corresponding video frame is used to generate the total number of vehicles passing through the traffic flow measurement point at the set time J through data identification. The total number of vehicles passing through the traffic flow measurement point at the set time represents the total number of vehicles passing through the traffic flow measurement point counted within the set time length of the traffic flow measurement point. The unit of J is vehicle.

[0038] Preferably, the operation steps of performing traffic flow statistics processing at the traffic flow measurement point based on the traffic flow measurement setting time data of the traffic flow measurement point and the total number of vehicles passing through the traffic flow measurement point at the setting time, generating traffic flow data at the traffic flow measurement point and performing traffic flow statistics result feedback operation at the traffic flow measurement point are as follows:

[0039] S71, the total number of vehicles passing through the traffic flow measurement point at the set time J is numerically divided by the traffic flow measurement setting time data I of the traffic flow measurement point, and the traffic flow data G of the traffic flow measurement point is calculated, where The unit of G is vehicles per second;

[0040] S72: Transmit the traffic flow data G at the traffic flow measurement point to the intelligent traffic management platform through the Internet of Things communication network and execute the traffic flow statistical result feedback operation at the traffic flow measurement point in conjunction with the display screen output.

[0041] An intelligent traffic flow statistical analysis system is used to implement the intelligent traffic flow statistical analysis method, the system includes a traffic flow measurement location management module, a traffic flow measurement data management module, and a traffic flow measurement module;

[0042] The traffic flow measurement location management module includes a traffic flow measurement point coordinate acquisition unit, a traffic flow measurement point flow measurement setting time acquisition unit, a traffic flow measurement device location coordinate acquisition unit, and a traffic flow measurement point flight path planning unit;

[0043] The traffic flow measurement point coordinate acquisition unit acquires traffic flow measurement point coordinate data through the intelligent traffic management platform; the traffic flow measurement point flow measurement setting time acquisition unit acquires traffic flow measurement setting time data through the intelligent traffic management platform; the traffic flow measurement device position coordinate acquisition unit acquires traffic flow measurement device position coordinate data through the intelligent traffic management platform; the traffic flow measurement point flight path planning unit performs flight path planning from the traffic flow measurement device position to the traffic flow measurement point based on the traffic flow measurement device position coordinate data and the traffic flow measurement point coordinate data in combination with flight map navigation software, and generates target traffic flow measurement point flight path data;

[0044] The traffic flow measurement data management module includes a traffic flow measurement point destination arrival execution unit, a traffic flow measurement point set time vehicle flow video acquisition unit, a traffic flow measurement point set time vehicle flow video frame generation unit, a road traffic vehicle license plate standard image information storage unit, and a traffic flow measurement point set time vehicle number statistics unit;

[0045] The traffic flow measurement point destination arrival execution unit executes the traffic flow measurement point destination flight arrival operation based on the target traffic flow measurement point flight path data in combination with the traffic flow measurement device; the traffic flow measurement point set time vehicle flow video acquisition unit, the intelligent traffic management platform controls the traffic flow measurement device to perform the traffic flow video information acquisition operation at the traffic flow measurement point set time based on the traffic flow measurement set time data, and generates the traffic flow video data at the traffic flow measurement point set time; the traffic flow video frame generation unit at the traffic flow measurement point set time, performs vehicle flow video frame generation processing at the traffic flow measurement point based on the traffic flow video data at the traffic flow measurement point set time in combination with a video editing tool, and constructs the traffic flow video frame data at the traffic flow measurement point; the road traffic vehicle license plate standard image information storage unit is used to store the road traffic vehicle license plate standard image data; the traffic flow measurement point set time passing vehicle number counting unit performs vehicle flow video frame processing at the traffic flow measurement point set time based on the traffic flow video frame data and the road traffic vehicle license plate standard image data, and generates the total number of vehicles passing at the traffic flow measurement point set time;

[0046] The traffic flow measurement module includes a traffic flow metering unit at a traffic flow measurement point and a traffic flow statistics result feedback unit at a traffic flow measurement point;

[0047] The traffic flow metering unit at the traffic flow measurement point performs traffic flow statistics processing at the traffic flow measurement point based on the flow measurement setting time data of the traffic flow measurement point and the total number of vehicles passing through the traffic flow measurement point at the setting time, and generates traffic flow data at the traffic flow measurement point; the traffic flow statistics result feedback unit at the traffic flow measurement point transmits the traffic flow data at the traffic flow measurement point to the intelligent traffic management platform through the Internet of Things communication network and performs traffic flow statistics result feedback operation at the traffic flow measurement point in conjunction with the display screen output.

[0048] (3) Beneficial effects

[0049] The present invention provides an intelligent traffic flow statistical analysis method and system, which has the following beneficial effects:

[0050] 1. Accurately collect the location coordinate parameters of traffic flow measurement points, flow measurement setting time, and location coordinate parameters of traffic flow measurement equipment through the intelligent traffic management platform, providing reliable data support for the dynamic setting of intelligent traffic flow measurement points, and realizing intelligent monitoring of traffic flow information at any road traffic location; based on the location coordinate parameters of traffic flow measurement equipment and the coordinate parameters of traffic flow measurement points combined with flight map navigation software, efficient and intelligent planning of the flight path of traffic flow measurement points can be realized, thereby improving the reliability of the intelligent traffic flow statistics system.

[0051] 2. By using traffic flow measurement equipment to realize autonomous arrival of traffic flow measurement points and accurate collection of traffic flow videos at set times at traffic flow measurement points; based on the traffic flow video data at set times at traffic flow measurement points combined with video editing tools, real-time and efficient processing of traffic flow video frames at traffic flow measurement points is carried out, providing reliable data support for accurate statistics of traffic flow at traffic flow measurement points; based on the traffic flow video frame parameters of traffic flow measurement points combined with intelligent recognition algorithms and standard image information of road traffic vehicle license plates based on big data storage, dynamic intelligent statistics of the number of passing vehicles at the set time of traffic flow measurement points are carried out, realizing accurate measurement of the total number of passing vehicles at the traffic flow measurement points at the set time, and improving the applicability of the intelligent traffic flow statistics system.

[0052] 3. By combining numerical processing with flow measurement setting time parameters at traffic flow measurement points and the total number of vehicles passing through at the set time of traffic flow measurement points, efficient statistics of traffic flow at traffic flow measurement points are performed to realize digital management of traffic flow at traffic flow measurement points; traffic flow information at traffic flow measurement points is transmitted to the intelligent traffic management platform through the Internet of Things communication network and visualized on the display screen to realize intuitive real-time feedback of traffic flow monitoring results at traffic flow measurement points, thereby improving the traffic flow statistics effect of the intelligent traffic flow statistics system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic diagram of a module of an intelligent traffic flow statistical analysis system provided by the present invention;

[0054] Figure 2 The present invention provides a flow chart of an intelligent traffic flow statistical analysis method. DETAILED DESCRIPTION

[0055] 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 ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] The embodiments of the intelligent traffic flow statistical analysis method and system are as follows:

[0057] Example 1:

[0058] See also Figure 1 - Figure 2 , an intelligent traffic flow statistical analysis method, the method comprises the following steps:

[0059] S1, collecting traffic flow measurement point coordinate data, traffic flow measurement setting time data and traffic flow measurement equipment position coordinate data;

[0060] S2. performing flight path planning processing from the location of the traffic flow measurement device to the traffic flow measurement point based on the traffic flow measurement device location coordinate data and the traffic flow measurement point coordinate data, and generating target traffic flow measurement point flight path data;

[0061] S3, executing a flight arrival operation at the destination of the traffic flow measurement point according to the flight path data of the target traffic flow measurement point;

[0062] S4, performing a vehicle flow video information collection operation at the set time of the traffic flow measurement point according to the flow measurement set time data of the traffic flow measurement point, and generating vehicle flow video data at the set time of the traffic flow measurement point;

[0063] S5, performing a vehicle flow video frame generation process at the traffic flow measurement point based on the vehicle flow video data at the set time of the traffic flow measurement point, and constructing vehicle flow video frame data at the traffic flow measurement point;

[0064] S6. Counting the number of vehicles passing through the traffic flow measurement point at the set time based on the traffic flow video frame data and the standard image data of the license plates of road traffic vehicles at the traffic flow measurement point, and generating the total number of vehicles passing through the traffic flow measurement point at the set time;

[0065] S7. Perform traffic flow statistics processing at the traffic flow measurement point based on the traffic flow measurement setting time data of the traffic flow measurement point and the total number of vehicles passing through the traffic flow measurement point at the setting time, generate traffic flow data at the traffic flow measurement point, and perform traffic flow statistics result feedback operation at the traffic flow measurement point.

[0066] For further information, see Figure 1 - Figure 2 The steps for collecting traffic flow measurement point coordinate data, traffic flow measurement setting time data and traffic flow measurement equipment location coordinate data are as follows:

[0067] S11, collect the spatial coordinate information of the target road traffic measurement point online through the intelligent traffic management platform, and generate the traffic flow measurement point coordinate data P ， The traffic flow measurement point coordinate data includes the longitude, latitude and altitude of the target road traffic measurement point;

[0068] The traffic flow statistics of the target road traffic measurement point are collected online through the intelligent traffic management platform to set the time length parameters, and the traffic flow measurement point flow measurement setting time data I is generated. ， The unit of I is seconds;

[0069] The real-time spatial position coordinate information of the traffic flow measurement equipment at the target road traffic measurement point is collected online through the intelligent traffic management platform, and the traffic flow measurement equipment position coordinate data Y is generated. ， The traffic flow measurement device location coordinate data includes the longitude, latitude and altitude of the real-time spatial location of the traffic flow measurement device. The traffic flow measurement device includes a drone and a cloud camera.

[0070] The steps for planning a flight path from the location of the traffic flow measurement device to the traffic flow measurement point based on the location coordinate data of the traffic flow measurement device and the traffic flow measurement point coordinate data, and generating the flight path data of the target traffic flow measurement point are as follows:

[0071] S21, input the traffic flow measurement device location coordinate data Y and the traffic flow measurement point coordinate data P into the departure dialog box and the destination dialog box of the flight map navigation software respectively to perform flight path measurement processing from the traffic flow measurement device location to the traffic flow measurement point, and generate the target traffic flow measurement point flight path data U ， The flight map navigation software includes any one of PlanIt, DronesMap, and Smart.

[0072] Through the mutual cooperation between the traffic flow measurement point coordinate acquisition unit, the traffic flow measurement point flow measurement setting time acquisition unit and the traffic flow measurement point flight path planning unit, the intelligent traffic management platform is used to accurately collect the location coordinate parameters of the traffic flow measurement point, the flow measurement setting time and the location coordinate parameters of the traffic flow measurement equipment, so as to provide reliable data support for the dynamic setting of the intelligent traffic flow measurement point and realize the intelligent monitoring of the traffic flow information at any road traffic location; the traffic flow measurement point flight path planning unit realizes efficient and intelligent planning of the flight path of the traffic flow measurement point based on the location coordinate parameters of the traffic flow measurement equipment and the coordinate parameters of the traffic flow measurement point in combination with the flight map navigation software, thereby improving the reliability of the intelligent traffic flow statistics system.

[0073] For further information, see Figure 1 - Figure 2 The steps for executing the flight arrival operation at the traffic flow measurement point destination based on the flight path data of the target traffic flow measurement point are as follows:

[0074] S31. The traffic flow measurement device flies from the location of the traffic flow measurement device to the traffic flow measurement point according to the flight path data U of the target traffic flow measurement point, and executes the flight arrival operation at the traffic flow measurement point destination. If the traffic flow measurement device does not arrive at the traffic flow measurement point, the traffic flow measurement device continues to execute the flight arrival operation at the traffic flow measurement point destination.

[0075] The steps for collecting traffic flow video information at the set time of the traffic flow measurement point according to the traffic flow measurement set time data and generating traffic flow video data at the set time of the traffic flow measurement point are as follows:

[0076] S41. When the traffic flow measurement device arrives at the traffic flow measurement point, the intelligent traffic management platform controls the traffic flow measurement device to collect the traffic flow video information of the traffic flow measurement point online according to the time length corresponding to the traffic flow measurement setting time data I of the traffic flow measurement point, and generates the traffic flow video data Q of the traffic flow measurement point setting time.

[0077] The steps for generating and processing the traffic flow video frames at the traffic flow measurement points based on the traffic flow video data at the set time of the traffic flow measurement points and constructing the traffic flow video frame data at the traffic flow measurement points are as follows:

[0078] S51, obtaining traffic flow video data Q at a set time at a traffic flow measurement point;

[0079] S52, using a video editing tool to generate a traffic flow measurement point traffic flow video frame for the traffic flow video data Q at the set time of the traffic flow measurement point, and constructing a traffic flow video frame data set of the traffic flow measurement point where h orepresents the generated traffic flow video frame data of the oth traffic flow measurement point, Indicates the maximum number of vehicle flow video frames at the traffic flow measurement point. The video editing tools include any one of Adobe Premiere Pro, DaVinci Resolve, and Final Cut Pro.

[0080] The steps for performing statistical processing on the number of vehicles passing through the traffic flow measurement point at the set time based on the traffic flow video frame data and the standard image data of the license plate of the road traffic vehicle are as follows:

[0081] S61, establish a road traffic vehicle license plate standard image data set K = (k1, ..., k x ,…,k τ ), x=1,2,3,…,τ; where k x represents the standard image data of road traffic vehicle license plates corresponding to the x-th motor vehicle license plate type, τ represents the maximum number of motor vehicle license plate types; motor vehicle license plate types include blue background with white characters, yellow background with black characters, green background with black characters, and white background with black characters. The standard image data of road traffic vehicle license plates represents standard road traffic vehicle license plate image information set for different types of motor vehicle license plates;

[0082] S62, the traffic flow measurement point vehicle flow video frame data h in the traffic flow measurement point vehicle flow video frame data set H o According to the traffic flow measurement point traffic video frame numbering order and road traffic vehicle license plate standard image data set K of road traffic vehicle license plate standard image data k x Perform image feature matching to search for all traffic flow measurement point traffic video frame data h o The total number of vehicles in the corresponding video frame is used to generate the total number of vehicles passing through the traffic flow measurement point at the set time J through data identification. The specific steps for generating the total number of vehicles passing through the traffic flow measurement point at the set time J are as follows:

[0083] S621, initialization, defining the relevant structural parameters as vectors, in the τ-dimensional optimization problem of the road traffic vehicle license plate standard image data set K, the vehicle search sand cat represents the 1×τ array of the problem solution, each variable value k1, k2, k3, ..., k τ are all floating point numbers, and each variable value is from k1 to k τ It is between the lower bound and the upper bound in the search space of the road traffic vehicle license plate standard image data set K;

[0084] S622, Searching for prey, the final and main parameters controlling the transition between exploration and exploitation phases are when When the vehicle search sand cat places the traffic flow measurement point vehicle flow video frame data h in the search space of the road traffic vehicle license plate standard image data set K o According to the traffic flow measurement point traffic flow video frame numbering order and road traffic vehicle license plate standard image data k x Perform image feature matching to search for all traffic flow measurement point traffic video frame data h o The total number of vehicles in the corresponding video frame; the vehicle search sand cat relies on the release of low-frequency noise in the search space of the road traffic vehicle license plate standard image data set K to convert the traffic flow measurement point vehicle flow video frame data h o According to the traffic flow measurement point traffic flow video frame numbering order and road traffic vehicle license plate standard image data k x Perform image feature matching to search for all traffic flow measurement point traffic video frame data h o Corresponding to the total number of vehicles in the video frame, assuming that the sensitivity range of vehicle search sand cat Φ is from 0 to 2kHz, ∫ represents the auditory characteristics inspired by vehicle search sand cat, assuming that the ∫ value is 2, t is the current iteration number, T is the maximum iteration number, and rand(0,1) represents a random number with a value between 0 and 1; in Represents the sensitivity vector; each vehicle search sand cat updates its own position in the search space of the road traffic vehicle license plate standard image dataset K according to the best candidate position and current position and its sensitivity range Φ, and searches for the vehicle flow video frame data h corresponding to the traffic flow measurement point in the search space of the road traffic vehicle license plate standard image dataset K. o The most matching road traffic vehicle license plate standard image data k x The position is calculated as follows: W(t+1)=Φ×(W(t)-rand(0,1)×W best (t)), where W(t+1) represents the current position of the vehicle searching for the sand cat individual in the search space of the road traffic vehicle license plate standard image dataset K after the t+1th iteration in the search for prey phase, W(t) represents the current position of the vehicle searching for the sand cat individual in the search space of the road traffic vehicle license plate standard image dataset K after the tth iteration in the search for prey phase, and W best (t) represents the best candidate position of the vehicle searching for the sand cat in the search space of the road traffic vehicle license plate standard image dataset K after the tth iteration in the prey search phase;

[0085] S623, attack prey, when When the vehicle search sand cat searches in the search space of the road traffic vehicle license plate standard image data set K, it uses the best candidate position and the current position to randomly generate a traffic flow video frame data h corresponding to the traffic flow measurement point. o Matched road traffic vehicle license plate standard image data k x Assuming that the sensitivity range of the vehicle search sand cat is a circle, the roulette method is used to randomly select an angle Λ for each vehicle search sand cat, according to the following random position calculation formula: Search at random positions in the search space of the road traffic vehicle license plate standard image dataset K, where W'(t+1) represents the random position of the sand cat individual in the search space of the road traffic vehicle license plate standard image dataset K in the t+1th iteration of the attack prey phase, represents the sensitivity vector, cos(Λ) represents the cosine value of the angle Λ, and W rand represents the random position of the vehicle searching sand cat in the search space of the road traffic vehicle license plate standard image dataset K in the attack prey phase. The random position makes the vehicle searching sand cat approach and attack the traffic flow video frame data h of the traffic flow measurement point in the search space of the road traffic vehicle license plate standard image dataset K. o Matched road traffic vehicle license plate standard image data k x of prey;

[0086] S624: When the algorithm meets the maximum number of iterations, all traffic flow measurement points’ vehicle flow video frame data h are output. o The total number of vehicles in the corresponding video frame, otherwise continue to iterate until the maximum number of iterations is met;

[0087] S625, all the traffic flow measurement point vehicle flow video frame data h output in step S624 o The total number of vehicles in the corresponding video frame is used to generate the total number of vehicles passing through the traffic flow measurement point at the set time J after data identification. The total number of vehicles passing through the traffic flow measurement point at the set time represents the total number of vehicles passing through the traffic flow measurement point counted within the set time length of the traffic flow measurement point. The unit of J is vehicle.

[0088] Through the cooperation of the traffic flow measurement point destination arrival execution unit and the traffic flow video collection unit at the set time of the traffic flow measurement point, the autonomous arrival of the traffic flow measurement point and the accurate collection of the traffic flow video at the set time of the traffic flow measurement point are realized based on the traffic flow measurement equipment; the traffic flow video frame generation unit at the set time of the traffic flow measurement point, based on the traffic flow video data at the set time of the traffic flow measurement point combined with video editing tools, performs real-time and efficient processing of the traffic flow video frames of the traffic flow measurement point, providing reliable data support for the accurate statistics of the traffic flow at the traffic flow measurement point; the traffic flow measurement point set time passing vehicle number statistics unit, based on the traffic flow video frame parameters of the traffic flow measurement point combined with the intelligent recognition algorithm and the standard image information of the road traffic vehicle license plate based on big data storage, performs dynamic intelligent statistics of the number of passing vehicles at the set time of the traffic flow measurement point, realizes accurate measurement of the total number of passing vehicles at the traffic flow measurement point at the set time, and improves the applicability of the intelligent traffic flow statistics system.

[0089] For further information, see Figure 1 - Figure 2 The following are the steps for performing traffic flow statistics processing at a traffic flow measurement point based on the traffic flow measurement setting time data and the total number of vehicles passing through the traffic flow measurement point at the setting time, generating traffic flow data at the traffic flow measurement point, and performing traffic flow statistics result feedback at the traffic flow measurement point:

[0090] S71, the total number of vehicles passing through the traffic flow measurement point at the set time J is numerically divided by the traffic flow measurement point flow measurement set time data I, and the traffic flow data G of the traffic flow measurement point is calculated, where The unit of G is vehicles per second;

[0091] S72. Transmit the traffic flow data G at the traffic flow measurement point to the intelligent traffic management platform through the Internet of Things communication network and execute the traffic flow statistical result feedback operation at the traffic flow measurement point in conjunction with the display screen output.

[0092] Through the traffic flow metering unit at the traffic flow measurement point, the traffic flow at the traffic flow measurement point is efficiently counted based on the flow measurement setting time parameters at the traffic flow measurement point, the total number of vehicles passing through the traffic flow measurement point at the setting time, and numerical processing, thereby realizing digital management of the traffic flow at the traffic flow measurement point; the traffic flow statistical result feedback unit at the traffic flow measurement point transmits the traffic flow information of the traffic flow measurement point to the intelligent traffic management platform through the Internet of Things communication network and cooperates with the display screen for visual output, thereby realizing intuitive and real-time feedback of the traffic flow monitoring results at the traffic flow measurement point, and improving the traffic flow statistical effect of the intelligent traffic flow statistics system.

[0093] Example 2:

[0094] See also Figure 1 - Figure 2 , an intelligent traffic flow statistical analysis system, used to implement an intelligent traffic flow statistical analysis method, the system includes a traffic flow measurement location management module, a traffic flow measurement data management module, and a traffic flow measurement module;

[0095] The traffic flow measurement location management module includes a traffic flow measurement point coordinate acquisition unit, a traffic flow measurement point flow measurement setting time acquisition unit, a traffic flow measurement equipment location coordinate acquisition unit, and a traffic flow measurement point flight path planning unit;

[0096] A traffic flow measurement point coordinate acquisition unit collects traffic flow measurement point coordinate data through the intelligent traffic management platform; a traffic flow measurement point flow measurement setting time acquisition unit collects traffic flow measurement setting time data through the intelligent traffic management platform; a traffic flow measurement device position coordinate acquisition unit collects traffic flow measurement device position coordinate data through the intelligent traffic management platform; a traffic flow measurement point flight path planning unit plans and processes the flight path from the traffic flow measurement device position to the traffic flow measurement point based on the traffic flow measurement device position coordinate data and traffic flow measurement point coordinate data in combination with flight map navigation software, and generates target traffic flow measurement point flight path data;

[0097] The traffic flow measurement data management module includes a traffic flow measurement point destination arrival execution unit, a traffic flow measurement point set time vehicle flow video acquisition unit, a traffic flow measurement point set time vehicle flow video frame generation unit, a road traffic vehicle license plate standard image information storage unit, and a traffic flow measurement point set time vehicle number statistics unit;

[0098] A traffic flow measurement point destination arrival execution unit executes the traffic flow measurement point destination flight arrival operation based on the target traffic flow measurement point flight path data in combination with the traffic flow measurement equipment; a traffic flow measurement point set time vehicle flow video acquisition unit, the intelligent traffic management platform controls the traffic flow measurement equipment to perform the traffic flow video information acquisition operation at the traffic flow measurement point set time based on the traffic flow measurement set time data, and generates the traffic flow video data at the traffic flow measurement point set time; a traffic flow video frame generation unit at the traffic flow measurement point set time, performs vehicle flow video frame generation processing at the traffic flow measurement point based on the traffic flow video data at the traffic flow measurement point set time in combination with a video editing tool, and constructs the traffic flow video frame data at the traffic flow measurement point; a road traffic vehicle license plate standard image information storage unit is used to store the road traffic vehicle license plate standard image data; a traffic flow measurement point set time passing vehicle number statistics unit performs traffic flow video frame data and road traffic vehicle license plate standard image data passing the traffic flow measurement point set time statistics processing, and generates the total number of vehicles passing the traffic flow measurement point set time based on the traffic flow video frame data and the road traffic vehicle license plate standard image data;

[0099] The traffic flow measurement module includes a traffic flow metering unit at a traffic flow measurement point and a traffic flow statistics result feedback unit at a traffic flow measurement point;

[0100] The traffic flow metering unit at the traffic flow measurement point performs traffic flow statistics processing at the traffic flow measurement point based on the flow measurement setting time data and the total number of vehicles passing through the traffic flow measurement point at the setting time, and generates traffic flow data at the traffic flow measurement point; the traffic flow statistics result feedback unit at the traffic flow measurement point transmits the traffic flow data at the traffic flow measurement point to the intelligent traffic management platform through the Internet of Things communication network and executes the traffic flow statistics result feedback operation at the traffic flow measurement point in conjunction with the display screen output.

[0101] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent traffic flow statistical analysis method, characterized in that: The method comprises the following steps: S1, collecting traffic flow measurement point coordinate data, traffic flow measurement setting time data and traffic flow measurement equipment position coordinate data; S2. Perform flight path planning from the traffic flow measurement device to the traffic flow measurement point, and generate flight path data for the target traffic flow measurement point; S3, executing a flight arrival operation at the traffic flow measurement point destination according to the flight path data of the target traffic flow measurement point; S4, performing a vehicle flow video information collection operation at the set time of the traffic flow measurement point according to the flow measurement set time data of the traffic flow measurement point, and generating vehicle flow video data at the set time of the traffic flow measurement point; S5, performing vehicle flow video frame generation processing at the traffic flow measurement point, and constructing vehicle flow video frame data at the traffic flow measurement point; S6. Statistical processing is performed on the number of vehicles passing through the traffic flow measurement point at the set time, and the total number of vehicles passing through the traffic flow measurement point at the set time is generated; S7. Perform traffic flow statistics processing at the traffic flow measurement point, generate traffic flow data at the traffic flow measurement point, and perform traffic flow statistics result feedback operation at the traffic flow measurement point.

2. The intelligent traffic flow statistical analysis method according to claim 1, characterized in that: Said S1 comprises the following steps: S11. Collecting the spatial coordinate information of the target road traffic measurement point online through the intelligent traffic management platform and generating traffic flow measurement point coordinate data P; The traffic flow statistics of the target road traffic measurement point are collected online through the intelligent traffic management platform to set the time length parameters, and the traffic flow measurement point flow measurement setting time data I is generated. ， The unit of I is seconds; The real-time spatial position coordinate information of the traffic flow measurement equipment at the target road traffic measurement point is collected online through the intelligent traffic management platform, and the traffic flow measurement equipment position coordinate data Y is generated. ， Traffic flow measurement equipment includes drones and cloud cameras.

3. The intelligent traffic flow statistical analysis method according to claim 2, characterized in that: The S2 comprises the following steps: S21. Input Y and P into the departure dialog box and destination dialog box of the flight map navigation software respectively to perform flight path measurement processing from the location of the traffic flow measurement device to the traffic flow measurement point, and generate target traffic flow measurement point flight path data U.

4. The intelligent traffic flow statistical analysis method according to claim 3, characterized in that: The S3 includes the following steps: S31. The traffic flow measurement device flies from the position of the traffic flow measurement device to the traffic flow measurement point according to U, and executes the operation of flying to the destination of the traffic flow measurement point. If the traffic flow measurement device does not arrive at the traffic flow measurement point, the operation of flying to the destination of the traffic flow measurement point is continued.

5. The intelligent traffic flow statistical analysis method according to claim 4, characterized in that: The S4 comprises the following steps: S41. When the traffic flow measurement device arrives at the traffic flow measurement point, the intelligent traffic management platform controls the traffic flow measurement device to collect vehicle flow video information of the traffic flow measurement point online according to the time length corresponding to I, and generates vehicle flow video data Q of the set time of the traffic flow measurement point.

6. The intelligent traffic flow statistical analysis method according to claim 5, characterized in that: The S5 comprises the following steps: S51, obtaining the Q; S52, using a video editing tool to generate a traffic flow measurement point vehicle flow video frame for the Q, and construct a traffic flow measurement point vehicle flow video frame data set where h o represents the generated traffic flow video frame data of the oth traffic flow measurement point, Indicates the maximum number of traffic flow video frames at the traffic flow measurement point.

7. The intelligent traffic flow statistical analysis method according to claim 6, characterized in that: The S6 comprises the following steps: S61, establish a road traffic vehicle license plate standard image data set K = (k1, ..., k x ,…,k τ ), x=1,2,3,…,τ; where k x represents the standard image data of the road traffic vehicle license plate corresponding to the x-th motor vehicle license plate type, and τ represents the maximum number of motor vehicle license plate types; S62, the h in the H o The vehicle flow video frames are numbered in order according to the traffic flow measurement points and the k in K x Perform image feature matching and search for all the h o The total number of vehicles in the corresponding video frame is used to generate the total number of vehicles passing through the traffic flow measurement point at the set time J through data identification. The specific steps for generating the total number of vehicles passing through the traffic flow measurement point at the set time J are as follows: S621, initialization, defining the relevant structural parameters as vectors, in the K-structured τ-dimensional optimization problem, the vehicle search sand cat represents the 1×τ array of the problem solution, each variable value k1, k2, k3, ..., k τ are all floating point numbers, and each variable value is from k1 to k τ is between the lower bound and the upper bound in the search space of K; S622, Searching for prey, the final and main parameters controlling the transition between exploration and exploitation phases are when When the vehicle searches for the sand cat, the h o The vehicle flow video frames are numbered in order according to the traffic flow measurement points and the k x Perform image feature matching and search for all the h o The total number of vehicles in the corresponding video frame; the vehicle search sand cat relies on the release of low-frequency noise in the search space of K to release the h o The vehicle flow video frames are numbered in order according to the traffic flow measurement points and the k x Perform image feature matching and search for all the h o Corresponding to the total number of vehicles in the video frame, assuming that the sensitivity range of vehicle search sand cat Φ is from 0 to 2kHz, ∫ represents the auditory characteristics inspired by vehicle search sand cat, assuming that the ∫ value is 2, t is the current iteration number, T is the maximum iteration number, and rand(0,1) represents a random number with a value between 0 and 1; S623, attack prey, when When the vehicle searches for the sand cat in the search space of K, it uses the best candidate position and the current position to randomly generate a position that is consistent with the h o The k that matches x Assuming that the sensitivity range of the vehicle search sand cat is a circle, the roulette method is used to randomly select an angle Λ for each vehicle search sand cat, and search at a random position in the search space of K; S624, when the algorithm meets the maximum number of iterations, output h o The total number of vehicles in the corresponding video frame, otherwise continue to iterate until the maximum number of iterations is met; S625, all the h output in step S624 o The total number of vehicles in the corresponding video frame is used to generate the total number of vehicles passing through the traffic flow measurement point at the set time through data identification, where the unit of J is vehicle.

8. The intelligent traffic flow statistical analysis method according to claim 7, characterized in that: The S7 comprises the following steps: S71, performing numerical quotient processing on J and I to calculate traffic flow data G at the traffic flow measurement point, where the unit of G is vehicles per second; S72. Transmit the G to the intelligent traffic management platform through the Internet of Things communication network and execute the traffic flow statistical result feedback operation of the traffic flow measurement point in conjunction with the display screen output.

9. An intelligent traffic flow statistical analysis system, used to implement the intelligent traffic flow statistical analysis method according to any one of claims 1 to 8, characterized in that: The system includes a traffic flow measurement location management module, a traffic flow measurement data management module, and a traffic flow measurement module.

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