Electric bicycle traffic control early warning method and device and storage medium

Abstract

The invention provides an electric bicycle traffic control early warning method, and relates to the technical field of traffic early warning, and the method comprises the following steps: S1, data collection: receiving a plurality of driving data uploaded by an Internet of Things device installed on an electric bicycle in a certain time period S, and forming a driving track; s2, track segmentation: segmenting the driving track into a plurality of sub-tracks; s3, sub-track classification: classifying the sub-tracks into a municipal road track set and a park road track set; s4, track superposition dispersion processing: performing neighbor analysis on each track point in the municipal road track set and the park road track set and the corresponding limited curved surface road network layer to obtain a plurality of neighbor point locations, and calculating dispersion conditions of the neighbor point locations and the corresponding track points; and S5, carrying out traffic control early warning analysis, and finding out a place where violation occurs frequently according to the discrete situation. According to the invention, traffic police or public security can be rapidly assisted in screening risk road sections, and supervision of key accident hidden danger road sections is enhanced.

Description

Technical Field

[0001] The present application relates to the field of traffic warning technology, and in particular to an electric bicycle traffic management warning method, device and storage medium. Background Art

[0002] The existing traffic situation monitoring is mainly achieved through the Internet of Things and video. Vehicle violation identification is supervised through video monitoring at traffic checkpoints. The monitoring object of this method is motor vehicles, and the monitoring location is the traffic checkpoint where the camera is installed. The existing video analysis method for non-motor vehicle traffic management has the following technical problems: First, it is difficult to identify the owner and license plate of non-motor vehicles through photos; second, the monitoring location is limited, and the driving range of non-motor vehicles is more flexible, which cannot cover the key monitoring scenarios of non-motor vehicles; third, the recognition and analysis of unstructured data such as video streams or photos has complex algorithms, long tuning time, and is not universal enough for regional differences. Summary of the invention

[0004] The present application provides an electric bicycle traffic management warning method, device and storage medium, device and storage medium to at least solve the above technical problems existing in the prior art.

[0005] According to a first aspect of the present application, a method for early warning of electric bicycle traffic management is provided, comprising the following steps: S1, data collection, receiving multiple driving data uploaded by the IoT device installed on the electric bicycle within a certain time period S to form a driving trajectory; S2, track segmentation, segmenting the driving track into a number of sub-tracks; S3, sub-trajectory classification, classifying all sub-trajectories into a municipal road trajectory set and a park road trajectory set; S4, trajectory superposition discreteness processing, each trajectory point in the municipal road trajectory set and the park road trajectory set is subjected to a neighbor analysis with the corresponding finite surface road network layer to obtain a number of neighboring points, and the discreteness of each neighboring point and the corresponding trajectory point is calculated; S5, traffic management early warning analysis, find the places where violations are frequent based on the discrete situations.

[0006] In certain embodiments of the first aspect of the present application, the driving data includes: electric bicycle number, longitude, latitude, altitude, driving direction, instantaneous speed, and timestamp.

[0007] In certain embodiments of the first aspect of the present application, in S2, the method for dividing the driving trajectory into a plurality of sub-trajectories is as follows: extracting the upload time of all driving data in the driving trajectory, and if the time interval between any two adjacent driving data is greater than a threshold value t, the two adjacent driving data are separated in the middle to obtain multiple sub-trajectories, and the threshold value t is the time interval for uploading driving data by the Internet of Things device.

[0008] In some embodiments of the first aspect of the present application, in S2, noise processing is further performed on the sub-trajectory, and the method is as follows: Calculate the positions of the two measurements in time sequence , If the speed between two adjacent position information is greater than the set speed threshold, then calculate Then run at a constant speed to the point you reach ;

[0009] in, is the state transition matrix, according to and Joint decision making; Then the position obtained by the latter measurement is The calculated point , obtained by weighted average correction replace ,According to the time sequence, the adjacent points are processed pair by pair, and finally the denoised sub-trajectory is generated;

[0010] in, Takes 0 or 1.

[0011] In certain embodiments of the first aspect of the present application, in S3, the sub-trajectory classification method is as follows: Obtain the municipal road network information vector map within the control range and convert it into a municipal road network information graphic. Compare the sub-trajectories with the municipal road network information graphic one by one to determine the classification of the sub-trajectory; if the municipal road network information graphic contains the graphic of the sub-trajectory, all the points in the sub-trajectory are included in the municipal road trajectory set; if the boundary of the municipal road network information graphic and the sub-trajectory have more than one intersection, and more than half of the points in the sub-trajectory are in the municipal road network information graphic, all the points in the sub-trajectory are included in the municipal road trajectory set; Obtain the park road network information vector map within the control range and convert it into a park road network information graphic. Compare the sub-trajectories with the park road network information graphic one by one to determine the classification of the sub-trajectory; if the park road network information graphic contains the graphic of the sub-trajectory, all the points in the sub-trajectory are included in the park road trajectory set; if the boundary of the park road network information graphic and the sub-trajectory have more than one intersection, and more than half of the points in the sub-trajectory are in the park road network information graphic, all the points in the sub-trajectory are included in the park road trajectory set; Other sub-trajectories that are not included in the municipal road trajectory set and the park road trajectory set will not be processed later.

[0012] In certain embodiments of the first aspect of the present application, in S4, the calculation method of the discrete case is as follows: The municipal road trajectory set and the park road trajectory set are matched with the corresponding road network GEOTIFF layer data respectively. Records are expressed as point data layers:

[0013] in, is the longitude, is the latitude, is the altitude, is the horizontal direction, is the vertical direction, is the data collection time; Perform neighbor analysis, the neighboring elements are the corresponding finite surface road network layer, and calculate the trajectory point position The shortest distance to the corresponding finite surface road network layer, and obtain Zhongyu The nearest point ; Analysis Get The horizontal collection , vertical collection ; calculate and The discrete case of , that is, calculating the horizontal deviation, vertical deviation, and straight-line distance deviation; All the trajectory points in the municipal road trajectory set and the park road trajectory set are processed, and the resulting data obtained after processing are divided into blocks to obtain each block Horizontal deviation dataset , vertical deviation dataset , straight-line distance deviation dataset ; Calculate each block The standard deviation of the horizontal deviation , vertical deviation standard deviation , distance deviation standard deviation ; Get the horizontal deviation standard deviation sets of the municipal road trajectory set and the park road trajectory set respectively: , vertical deviation standard deviation set , the standard deviation of the straight-line distance deviation .

[0014] In certain embodiments of the first aspect of the present application, the method for solving the horizontal deviation is as follows: when When there are 1 or more horizontal directions, if exist In the case of acute angles in multiple directions, the deviation value is 0; otherwise, Distance Collection The minimum angle in any direction is assigned to the horizontal deviation.

[0015] In certain embodiments of the first aspect of the present application, in S5, the method of finding a location with a high incidence of traffic violations according to the discrete situation is as follows: Randomly place the initial position at a certain position in the road network GEOTIFF layer; calculate other directions of the neighbors, divide them into blocks of fixed size, calculate the direction with a higher sum of the horizontal deviation standard deviation, vertical deviation standard deviation, and straight-line distance deviation standard deviation of the neighbor blocks, and update the blocks to move in that direction; repeat the above steps until the position of the block no longer changes, which is the place where violations are frequent.

[0016] According to a second aspect of the present application, an electronic device is provided, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present application.

[0017] According to a third aspect of the present application, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the present application.

[0018] Compared with the prior art, this application has the following beneficial effects: 1. This application calculates the neighboring points of each trajectory point and the finite surface road network layer in all sub-trajectories through trajectory superposition discreteness processing, and calculates the discreteness of each neighboring point and trajectory point, so as to find the places where violations are frequent through the discreteness, which can quickly assist traffic police or public security in screening risky sections, and then strengthen the supervision of key accident potential sections.

[0019] 2. Due to factors such as signal interference, there may be a large deviation between the location information (longitude, latitude, altitude) in the uploaded driving data and the actual position of the electric bicycle. Position information with excessive deviation affects the subsequent trajectory conversion and analysis. Such points with excessive deviation are called noise points. This application adopts a method that combines the predicted position and the measured position to calibrate and update the trajectory, which is conducive to providing more accurate data for subsequent discreteness analysis.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become readily understood. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-limiting manner, wherein: In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0022] Figure 1 A schematic diagram of the implementation process of the first embodiment of the present application is shown.

[0023] Figure 2 A schematic diagram of the structure of an electronic device according to the second embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] In order to make the purpose, features, and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0025] Embodiment 1: Please refer to Figure 1 The first embodiment provides an electric bicycle traffic management early warning method, comprising the following steps: S1, data collection, receiving multiple driving data uploaded by the IoT device installed on the electric bicycle within a certain time period S to form a driving trajectory.

[0026] For example, if there are C electric bicycles in total, within a certain time period S, the IoT devices installed on the electric bicycles upload driving data at intervals, that is, each electric bicycle has a period of driving data within this time period S. Preferably, the driving data includes: electric bicycle number, longitude, latitude, altitude, driving direction, instantaneous speed, and timestamp.

[0027] It is worth mentioning that the time interval for uploading the driving data is F, and F is preferably 5S or 10S.

[0028] Among them, longitude and latitude use the WGS84 coordinate system and are accurate to 8 decimal places; altitude is accurate to 1 decimal place, in meters; horizontal driving direction is accurate to one decimal place from 0-360 degrees in a horizontal clockwise direction (due north 0, due east 90, due south 180, due west 270); vertical direction is accurate to one decimal place from 0-360 degrees in a vertical clockwise direction.

[0029] S2, track segmentation, segmenting the driving track into a plurality of sub-tracks.

[0030] For each track L of the electric bicycle within the time period S, the track L may be a combination of track segments reported by the electric bicycle device in multiple times, and the track L is pre-grouped and divided into sub-tracks.

[0031] The specific method is: extract the upload time of all driving data in the driving trajectory. If the time interval between any two adjacent driving data is greater than the threshold t, it means that the two driving data do not belong to the same sub-trajectory. The two adjacent driving data are separated in the middle and split into two sub-trajectories. The above steps are performed from the front to the back of the trajectory L to obtain multiple sub-trajectories. The threshold t is the time interval for uploading driving data by the IoT device.

[0032] Then, noise processing is performed on each sub-track as follows: Calculate the positions of the two measurements in time sequence , If the speed between two adjacent position information is greater than the set speed threshold, then calculate Then run at a constant speed to the point you reach ;

[0033] in, is the state transition matrix, according to and Joint decision making; Computational Example: Prediction The point after the duration. The state of and speed To describe. Moment , Moment ,

[0034]

[0035] Then the position obtained by the latter measurement is The calculated point , obtained by weighted average correction replace ,According to the time sequence, the adjacent points are processed pair by pair, and finally the denoised sub-trajectory is generated;

[0036] in, Takes 0 or 1; When 0 is taken, = , that is, the calculated point; when When 1 is taken, the final = , that is, the actual measured position, The probability distribution of follows a normal distribution.

[0037] S3, sub-trajectory classification, classifies all sub-trajectories into a municipal road trajectory set and a park road trajectory set.

[0038] Specifically, the sub-trajectory classification method is as follows: Obtain the municipal road network information vector map within the control range, use the WGS84 coordinate system, and convert it into a municipal road network information GEOJSON graphic. Compare the sub-trajectories with the municipal road network information graphic one by one to determine the sub-trajectory classification; if the municipal road network information graphic contains the sub-trajectory graphic, all points in the sub-trajectory are included in the municipal road trajectory set; if the boundary of the municipal road network information graphic and the sub-trajectory have more than one intersection, and more than half of the points in the sub-trajectory are in the municipal road network information graphic, all points in the sub-trajectory are included in the municipal road trajectory set; Similarly, obtain the park road network information vector map within the control range, use the WGS84 coordinate system, and convert it into the park road network information GEOJSON graphic. Compare the sub-trajectories with the park road network information graphics one by one to determine the classification of the sub-trajectory; if the park road network information graphics contains the graphics of the sub-trajectory, all the points in the sub-trajectory are included in the park road trajectory set; if the boundary of the park road network information graphics has more than one intersection with the sub-trajectory, and more than half of the points in the sub-trajectory are in the park road network information graphics, all the points in the sub-trajectory are included in the park road trajectory set.

[0039] Other sub-trajectories that are not included in the municipal road trajectory set and the park road trajectory set will not be processed later.

[0040] S4, trajectory superposition discreteness processing, each trajectory point in the municipal road trajectory set and the park road trajectory set is subjected to neighbor analysis with the corresponding finite surface road network layer to obtain several neighboring points, and the discreteness of each neighboring point and the corresponding trajectory point is calculated.

[0041] Specifically, the calculation method for discrete cases is as follows: The municipal road trajectory set and the park road trajectory set are matched with the corresponding road network GEOTIFF layer data respectively, that is, the municipal road trajectory set is matched with the municipal road network GEOTIFF layer data, and the park road trajectory set is matched with the park road network GEOTIFF layer data. Records are expressed as point data layers:

[0042] in, is the longitude, is the latitude, is the altitude, is the horizontal direction, is the vertical direction, is the data collection time; The neighbor analysis is performed, and the neighboring elements are the corresponding finite surface road network layers. Similarly, for the trajectory points of the municipal road trajectory set, its neighboring elements are the finite surface municipal road network layers; and for the trajectory points of the park road trajectory set, its neighboring elements are the finite surface park road network layers.

[0043] Calculate trajectory points The shortest distance to the corresponding finite surface road network layer, and obtain Zhongyu The nearest point ;

[0044] Analysis Get The horizontal collection , vertical collection ; It should be noted here that different from Attribute Horizontal , vertical direction , a point has only one horizontal direction and one vertical direction, It may have one or more horizontal and vertical directions, so it is defined as a set.

[0045] calculate and The discrete case of , that is, calculating the horizontal deviation, vertical deviation, and straight-line distance deviation; Specifically, the method for solving the horizontal deviation is as follows: when When there are 1 or more horizontal directions, if exist In the case of acute angles in multiple directions, the deviation value is 0; otherwise, Distance Collection The minimum angle in any direction is assigned to the horizontal deviation.

[0046] Similarly, the method for solving the vertical deviation is as follows: when When there are 1 or more vertical directions, if exist In the case of acute angles in multiple directions, the deviation value is 0; otherwise, Distance Collection The minimum angle in any direction is assigned to the vertical deviation.

[0047] The solution method for straight line distance deviation is as follows:

[0048] All the trajectory points in the municipal road trajectory set and the park road trajectory set are processed, and the resulting data obtained after processing are divided into blocks to obtain each block Horizontal deviation dataset , vertical deviation dataset , straight-line distance deviation dataset ; The block segmentation methods include: segmenting the municipal road network TIF layer according to a fixed size of 10*10 meters (example size); or segmenting the municipal road network TIF layer according to road type, a specific classification or attribute (for example: segmenting all intersections).

[0049] Calculate each block The standard deviation of the horizontal deviation , vertical deviation standard deviation , distance deviation standard deviation ; Get the horizontal deviation standard deviation sets of the municipal road trajectory set and the park road trajectory set respectively: , vertical deviation standard deviation set , the standard deviation of the straight-line distance deviation .

[0050] S5, traffic management early warning analysis, mainly includes the following two scenarios: Scenario 1: Find the places where traffic violations are most frequent based on the discrete situations. The method is as follows: Randomly place the initial position at a certain position in the road network GEOTIFF layer; use the Mean Shift algorithm to calculate other directions of the neighbors, divide them into blocks of fixed size, calculate the direction with a higher sum of the horizontal deviation standard deviation, vertical deviation standard deviation, and straight-line distance deviation standard deviation of the neighbor blocks, and update the blocks to move in that direction; repeat the above steps until the position of the block no longer changes, which is the location where violations occur frequently.

[0051] Scenario 2: Alarm judgment for illegal use of elevated structures or ladders. The method is as follows: Independent analysis of the distance deviation standard deviation can be applied to the analysis of illegal elevated road climbing scenarios in the municipal road network and illegal ladder climbing scenarios in the park road network. When the straight-line distance deviation standard deviation is higher than the surrounding area, combined with the terrain area, it can be judged whether there are elevated roads and elevators in the park, and an alarm judgment of illegal elevated road climbing or illegal ladder climbing can be made.

[0052] That is, when the difference between the standard deviation of the straight-line distance between the current block and the adjacent block is greater than the set difference threshold, and the road type of the current block includes an elevated road, it is judged as illegal to go on the elevated road.

[0053] Similarly, when the difference between the standard deviation of the straight-line distance between the current block and the adjacent block is greater than the set difference threshold, and the road type of the current block includes the elevator in the park, it is judged as illegal elevator climbing.

[0054] S6, expand technical solutions.

[0055] Plan in advance and focus on areas that need traffic management, such as intersections. Divide each intersection in advance, calculate the standard deviation of horizontal deviation and vertical deviation for each intersection area, and superimpose the traffic light time dimension at the same time to analyze areas where red lights are run or there are time-limited driving bans.

[0056] Specifically, before calculating the standard deviation in step S4, Data collection time Classify according to The time period, The traversable direction follows the change, originally The multiple horizontal and vertical directions are transformed into a single horizontal and vertical direction according to the time, and the horizontal deviation standard deviation and the vertical deviation standard deviation are calculated in the same manner as step S4. The locations with high incidence of red light running are calculated according to the sum of the two deviation values, which serves as the basis for subsequent key governance and priority governance.

[0057] Embodiment 2: According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

[0058] Figure 2 A schematic block diagram of an example electronic device that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0059] like Figure 2 As shown, the device includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM, and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0060] Multiple components in the device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0061] The computing unit may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of computing units include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit performs the various methods and processes described above, such as an electric bicycle traffic management early warning method described in Example 1. For example, in some embodiments, an electric bicycle traffic management early warning method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the computing unit, one or more steps of an electric bicycle traffic management early warning method described above may be executed. Alternatively, in other embodiments, the computing unit may be configured to execute an electric bicycle traffic management early warning method in any other appropriate manner (e.g., by means of firmware).

[0062] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0063] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0064] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0065] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0066] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0067] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0068] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0069] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0070] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An electric bicycle traffic management early warning method, characterized in that: The following steps are involved: S1, data collection, receiving multiple driving data uploaded by the IoT device installed on the electric bicycle within a certain time period S to form a driving trajectory; S2, track segmentation, segmenting the driving track into a number of sub-tracks; S3, sub-trajectory classification, classifying all sub-trajectories into a municipal road trajectory set and a park road trajectory set; S4, trajectory superposition discreteness processing, each trajectory point in the municipal road trajectory set and the park road trajectory set is subjected to neighbor analysis with the corresponding finite surface road network layer to obtain several neighboring points, and the discreteness of each neighboring point and the corresponding trajectory point is calculated; S5, traffic management early warning analysis, find the places where violations are frequent based on the discrete situations.

2. The electric bicycle traffic management early warning method according to claim 1 is characterized in that: The driving data includes: electric bicycle number, longitude, latitude, altitude, driving direction, instantaneous speed, and timestamp.

3. The electric bicycle traffic management early warning method according to claim 1 is characterized in that: In S2, the method for dividing the driving trajectory into a plurality of sub-trajectories is as follows: extracting the upload time of all driving data in the driving trajectory, and if the time interval between any two adjacent driving data is greater than a threshold value t, the two adjacent driving data are separated in the middle to obtain a plurality of sub-trajectories, and the threshold value t is the time interval for uploading driving data by the IoT device.

4. The electric bicycle traffic management early warning method according to claim 1 is characterized in that: In S2, the sub-trajectory is also subjected to noise processing, and the method is as follows: Calculate the positions of the two measurements in time sequence , If the speed between two adjacent position information is greater than the set speed threshold, then calculate Then run at a constant speed to the point you reach ; in, is the state transition matrix, according to and Joint decision making; Then the position obtained by the latter measurement is The calculated point , obtained by weighted average correction replace ,According to the time sequence, the adjacent points are processed pair by pair, and finally the denoised sub-trajectory is generated; in, Takes 0 or 1.

5. The electric bicycle traffic management early warning method according to claim 1 is characterized in that: In S3, the sub-trajectory classification method is as follows: Obtain the municipal road network information vector map within the control range and convert it into a municipal road network information graphic. Compare the sub-trajectories with the municipal road network information graphic one by one to determine the classification of the sub-trajectory; if the municipal road network information graphic contains the graphic of the sub-trajectory, all the points in the sub-trajectory are included in the municipal road trajectory set; if the boundary of the municipal road network information graphic and the sub-trajectory have more than one intersection, and more than half of the points in the sub-trajectory are in the municipal road network information graphic, all the points in the sub-trajectory are included in the municipal road trajectory set; Obtain the park road network information vector map within the control range and convert it into a park road network information graphic. Compare the sub-trajectories with the park road network information graphic one by one to determine the classification of the sub-trajectory; if the park road network information graphic contains the graphic of the sub-trajectory, all the points in the sub-trajectory are included in the park road trajectory set; if the boundary of the park road network information graphic and the sub-trajectory have more than one intersection, and more than half of the points in the sub-trajectory are in the park road network information graphic, all the points in the sub-trajectory are included in the park road trajectory set; Other sub-trajectories that are not included in the municipal road trajectory set and the park road trajectory set will not be processed later.

6. The electric bicycle traffic management early warning method according to claim 1 is characterized in that: In S4, the calculation method for the discrete case is as follows: The municipal road trajectory set and the park road trajectory set are matched with the corresponding road network GEOTIFF layer data respectively. Records are expressed as point data layers: in, is the longitude, is the latitude, is the altitude, is the horizontal direction, is the vertical direction, is the data collection time; Perform neighbor analysis, the neighboring elements are the corresponding finite surface road network layer, and calculate the trajectory point position The shortest distance to the corresponding finite surface road network layer, and obtain Zhongyu The nearest point ; Analysis Get The horizontal collection , vertical collection ; calculate and The discrete case of , that is, calculating the horizontal deviation, vertical deviation, and straight-line distance deviation; All the trajectory points in the municipal road trajectory set and the park road trajectory set are processed, and the resulting data obtained after processing are divided into blocks to obtain each block Horizontal deviation dataset , vertical deviation dataset , straight-line distance deviation dataset ; Calculate each block The standard deviation of the horizontal deviation , vertical deviation standard deviation , distance deviation standard deviation ; Get the horizontal deviation standard deviation sets of the municipal road trajectory set and the park road trajectory set respectively: , vertical deviation standard deviation set , the standard deviation of the straight-line distance deviation .

7. The electric bicycle traffic control early warning method according to claim 6 is characterized in that: The method for solving the horizontal deviation is as follows: when When there are 1 or more horizontal directions, if exist In the case of acute angles in multiple directions, the deviation value is 0; otherwise, Distance Collection The minimum angle in any direction is assigned to the horizontal deviation.

8. The electric bicycle traffic management early warning method according to claim 1 is characterized in that: In S5, the method of finding the location where traffic violations occur frequently according to the discrete situation is as follows: Randomly place the initial position at a certain position in the road network GEOTIFF layer; calculate other directions of the neighbors, divide them into blocks of fixed size, calculate the direction with a higher sum of the horizontal deviation standard deviation, vertical deviation standard deviation, and straight-line distance deviation standard deviation of the neighbor blocks, and update the blocks to move in that direction; repeat the above steps until the position of the block no longer changes, which is the place where violations occur frequently.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 8.

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