A road dangerous condition prompting system based on an edge computing node

Abstract

Embodiments of the present application disclose an edge computing node-based road danger condition prompting system, and relate to the technical field of edge computing. The system comprises: a plurality of camera modules arranged on the side of a road, which are used to collect road condition pictures and send the pictures to an edge computing node; the edge computing node is used to identify the danger condition of a target vehicle in the road condition pictures according to a preset danger condition identification algorithm, obtain danger condition information of the current target vehicle, and send the danger condition information to a preset vehicle-mounted terminal installed on the target vehicle; and the vehicle-mounted terminal is used to provide the danger condition information to a driver. The present application effectively reduces the time delay of data transmission and improves the efficiency of road danger condition perception.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of edge computing, and particularly relates to a road danger situation prompting system based on an edge computing node. BACKGROUND

[0002] Road danger situation perception refers to predicting the state development of objective entities in the road and analyzing potential dangers that may exist, and is an effective way to prevent or reduce vehicle danger losses. Human factors directly lead to most of the accidents caused by the failure of danger situation perception.

[0003] With the rapid development of Internet of Vehicles technology, a road danger situation perception scheme for assisting drivers has appeared, which first collects road condition information, then sends it to a cloud platform for danger situation identification, and finally the cloud platform sends the identified danger situation information to vehicles driving on the road for auxiliary decision-making. This scheme reduces the occurrence of traffic accidents to a certain extent. However, because all road information needs to be sent to a remote cloud platform, there is a large data transmission delay, and when a sudden event occurs on the road, it cannot be discovered in time, thereby making the danger situation perception effect poor. SUMMARY

[0004] Therefore, the present application provides a road danger situation prompting system based on an edge computing node, which solves the problem of poor road danger situation perception effect caused by a large data transmission delay in the existing road danger situation perception scheme. The present application can intelligently and automatically identify danger situation information in the picture collected by the road side camera and send it to the target vehicle through the edge computing node, effectively reducing the data transmission delay and improving the efficiency of road danger situation perception.

[0005] The present application provides a road danger situation prompting system based on an edge computing node, which includes:

[0006] A plurality of camera modules arranged on the side of the road are used to collect road condition pictures and send them to the edge computing node;

[0007] The edge computing node is used to identify the danger situation of the target vehicle in the road condition picture according to a preset danger situation identification algorithm, obtain the danger situation information of the current target vehicle, and send the danger situation information to a preset vehicle terminal installed on the target vehicle;

[0008] The vehicle terminal is used to provide the danger situation information to the driver.

[0009] In an optional embodiment, the camera module comprises a data transceiver unit, a camera, and a response unit.

[0010] The data transceiver unit is configured to receive self-information data of a target vehicle sent by a vehicle terminal of the target vehicle, and send a picture collection instruction to the camera upon receiving the self-information data of the target vehicle; the data transceiver unit is further configured to send response data generated by the response unit to the vehicle terminal of the target vehicle; and the data transceiver unit is further configured to send interaction data to an edge computing node; wherein the self-information data of the target vehicle at least comprises identification information of the target vehicle; and the interaction data at least comprises the self-information data of the target vehicle, an identifier of a current camera module responding to the self-information data of the target vehicle, and a road condition picture collected by the current camera module in response to the self-information data of the target vehicle.

[0011] The camera is configured to collect a road condition picture according to the picture collection instruction.

[0012] The response unit is configured to generate response data responding to the self-information data of the target vehicle; the response data comprises the self-information data of the target vehicle and has a number of data bits greater than that of the self-information data of the target vehicle.

[0013] The vehicle terminal of the target vehicle is specifically configured to determine a control value of a road danger condition prompt signal lamp according to the response data, and control a display color of a pre-set road danger condition prompt signal lamp on the vehicle terminal according to a pre-set corresponding relationship between the control value of the road danger condition prompt signal lamp and a color of the signal lamp.

[0014] In an optional embodiment, the vehicle terminal is further configured to send a user inquiry about whether to open a vehicle danger condition prompt when the target vehicle is started, and send the self-information data of the target vehicle to a specified range around the target vehicle at a pre-set period upon receiving an inquiry result confirming to open the vehicle danger condition prompt.

[0015] In an optional embodiment, the vehicle terminal is specifically configured to calculate the control value of the road danger condition prompt signal lamp according to a first formula.

[0016] The first formula is as follows:

[0017]

[0018] In the first formula, G(D2) represents the control value of the road hazard warning signal light of the target vehicle's on-board terminal; D2 represents the target vehicle's own information data; E1(D2_a) represents the data length verification value of the a-th response data received by the target vehicle's on-board terminal within a preset period; E2(D2_a) represents the data type verification value of the a-th response data received by the target vehicle's on-board terminal within a preset period; P2(a) represents the a-th response data in binary form received by the target vehicle's on-board terminal within a preset period; len() represents calculating the number of bits in the data within the parentheses. Indicates a circular left shift; This represents the binary data from bit 1 to bit len(D2) in the new binary data obtained by left-shifting data P2(a) by i bits; i represents an integer variable, i = 1, 2, ..., len[P2(a)]; a = 1, 2, ..., n(T); n(T) represents the total number of response data received by the vehicle terminal of the target vehicle within a preset period; T represents the preset period. This means that if the value of a is taken from 1 to n(T) and substituted into the parentheses, the whole formula is true if there is one or more values ​​of a that satisfy the formula in the parentheses; otherwise, the whole formula is false. This means that if the value of a is taken from 1 to n(T) and substituted into the parentheses, the entire expression is true if all the values ​​of a satisfy the expression in the parentheses; otherwise, the entire expression is false.

[0019] In an optional embodiment, the correspondence between the control value of the road hazard warning signal light and the signal light color includes:

[0020] When the control value of the road hazard warning signal light is 1, the corresponding signal light color is the first preset color, indicating that the current target vehicle and the roadside equipment are transmitting signals smoothly.

[0021] When the control value of the road hazard warning signal light is -1, the corresponding signal light color is the second preset color, indicating that the current target vehicle and the roadside equipment are not communicating smoothly.

[0022] In one optional embodiment, the identification information of the target vehicle is the license plate data of the target vehicle;

[0023] The edge computing node includes:

[0024] The auxiliary decision-making unit is used to identify license plates in road condition images from the interactive data sent by the camera module, and to filter out a set of valid road condition images corresponding to the same target vehicle based on the identification results and interactive data.

[0025] The danger identification unit is configured to identify a danger condition in pictures in the effective picture set of the same target vehicle according to a preset danger condition identification algorithm, and obtain danger condition information of the corresponding target vehicle.

[0026] In an optional embodiment, the auxiliary decision unit is specifically configured to determine the effective camera module identification screening set corresponding to the same target vehicle based on a second formula, and obtain the road condition pictures corresponding to the target vehicle and the camera module identification in the screening set from the interaction data received by the edge computing node, to obtain the effective road condition picture set corresponding to the target vehicle.

[0027] The edge computing node further comprises:

[0028] The intelligent analysis data transmission unit is configured to determine the edge intelligent analysis data of the target vehicle according to a third formula, and send the edge intelligent analysis data of the target vehicle to the vehicle-mounted terminal of the target vehicle.

[0029] The second formula is:

[0030]

[0031] In the second formula, B(D2) represents the effective camera module identification screening set corresponding to the target vehicle screened from the interaction data received by the edge computing node; the target vehicle is a vehicle whose own information data is D2; C(k) represents the license plate data identified in the road condition picture collected by the kth camera module in the interaction data received by the edge computing node and including the own information data of the target vehicle, and the data format is binary; C(D2) represents the license plate data in the own information data D2 of the target vehicle, and the data format is binary; K(D2) represents the total number of camera modules involved in the interaction data received by the edge computing node and including the own information data of the target vehicle. represents that the value of k is taken from 1 to K(D2) to calculate in the parentheses, and a set composed of camera module identifications corresponding to all k values satisfying the formula in the parentheses is obtained.

[0032] The third formula is:

[0033]

[0034] In the third formula, F(D2) represents the edge intelligent analysis data of the target vehicle determined by the intelligent analysis data transmission unit, with the self information data D2, and the data form is ASCII form; size[] represents the number of elements in the set in the parentheses; "Out of the shooting range" represents the ASCII form of the string "Out of the shooting range"; A[B(D2)] represents the dangerous condition information of the corresponding target vehicle obtained by the dangerous identification unit by identifying the pictures in the picture set corresponding to the effective road condition of the target vehicle corresponding to the set B(D2).

[0035] The application provides a road danger condition prompting system based on an edge computing node, which first collects road condition picture information through multiple cameras on the side of the road, then identifies the danger condition information in the image information according to a preset danger condition identification algorithm and sends the information to a vehicle terminal, and finally displays the danger condition information to a driver by the vehicle terminal to assist the driver in decision-making. The application can connect the roadside equipment through the edge computing node MEC, directly connect the roadside camera, make edge intelligent analysis, transmit the relevant results to the vehicle for auxiliary decision-making, effectively reduce the data transmission delay, and improve the efficiency of road danger state perception. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0037] Figure 1 A structure schematic diagram of a road danger condition prompting system based on an edge computing node is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0038] The embodiments of the present application will be described in detail below with reference to the drawings.

[0039] It should be clear that the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0040] Figure 1 A structure schematic diagram of a road danger condition prompting system based on an edge computing node is provided for the embodiments of the present application. Referring to Figure 1 The system comprises:

[0041] A plurality of camera modules 1 arranged on the side of the road are used to collect road condition pictures and send them to the edge computing node 2.

[0042] The edge computing node 2 is used to identify the dangerous condition of the target vehicle in the road condition picture according to a preset dangerous condition identification algorithm, obtain the dangerous condition information of the current target vehicle, and send the dangerous condition information to a preset vehicle terminal 3 installed on the target vehicle.

[0043] In this embodiment, the dangerous condition identification algorithm can be obtained by machine learning training on sample data of dangerous condition events that need to be paid attention to, such as identifying that a vehicle is pressing a solid line, the distance between the target vehicle and other vehicles is less than a preset distance threshold, there is an obstacle in front of the vehicle, and the like. Thus, the driver can be effectively assisted in decision-making, and the occurrence of traffic accidents can be reduced.

[0044] The vehicle terminal 3 is used to provide the dangerous condition information to the driver.

[0045] The above technical solution has the beneficial effects that the road dangerous condition prompting system based on the edge computing node provided in the embodiment of the present application first collects road condition picture information through a plurality of cameras 1 arranged on the side of the road, then the edge computing node 2 identifies the dangerous condition information in the image information according to a preset dangerous condition identification algorithm and sends the dangerous condition information to the vehicle terminal 3, and finally the vehicle terminal 3 displays the dangerous condition information to the driver to assist the driver in decision-making. The present application can connect the roadside equipment through the edge computing node 2 MEC, directly connect the roadside camera 1, and make edge intelligent analysis, so as to transmit the related results to the vehicle for assisting in decision-making, effectively reduce the data transmission delay, and improve the efficiency of road dangerous state perception.

[0046] As an optional embodiment, the camera module 1 comprises a data transceiver unit, a camera, and a response unit.

[0047] The data transceiver unit is used to receive the self-information data of the target vehicle sent by the vehicle terminal 3 of the target vehicle, and send a picture collection instruction to the camera when the self-information data of the target vehicle is received. The data transceiver unit is also used to send the response data generated by the response unit to the vehicle terminal 3 of the target vehicle. The data transceiver unit is also used to send the interaction data to the edge computing node 2. The self-information data of the target vehicle at least includes the identification information of the target vehicle. The interaction data at least includes the self-information data of the target vehicle, the identification of the current camera module 1 responding to the self-information data of the target vehicle, and the road condition picture collected by the current camera module 1 in response to the self-information data of the target vehicle.

[0048] The camera is configured to collect a road condition picture according to the picture collection instruction.

[0049] The response unit is configured to generate response data in response to the self-information data of the target vehicle, wherein the response data includes the self-information data of the target vehicle and has a number of data bits greater than that of the self-information data of the target vehicle.

[0050] The target vehicle's on-board terminal is specifically configured to determine a control value of a road danger situation prompt signal lamp according to the response data, and control the display color of a pre-set road danger situation prompt signal lamp on the on-board terminal according to a pre-set corresponding relationship between the control value of the road danger situation prompt signal lamp and the color of the signal lamp.

[0051] The above technical solution has the following beneficial effects: the target vehicle sends the self-information data to the surrounding camera module 1, the camera module 1 sends the corresponding response data to the target vehicle after receiving the self-information data of the target vehicle, the on-board terminal 3 of the target vehicle controls the display color of the road danger situation prompt signal lamp thereon according to the received response data, and informs the target vehicle driver of the signal transmission state in the form of color, thereby facilitating subsequent decision-making.

[0052] As an optional embodiment, the on-board terminal 3 is further configured to send a user inquiry about whether to turn on the vehicle danger situation prompt when the target vehicle starts, and send the self-information data of the target vehicle to a specified range around the current target vehicle at a pre-set period when an inquiry result confirming the turning on of the vehicle danger situation prompt is received.

[0053] The above technical solution has the following beneficial effects: the driver and user of the vehicle can select whether to turn on the road danger situation prompt through the software of the on-board terminal 3 of the vehicle, if the road danger situation prompt is turned on, the vehicle will send the self-information data to the surrounding at a fixed time interval, so as to determine the signal transmission state around the vehicle, especially the signal transmission state between the vehicle and the camera, thereby effectively improving the use experience of the vehicle driver.

[0054] As an optional embodiment, the on-board terminal 3 is specifically configured to calculate the control value of the road danger situation prompt signal lamp according to a first formula.

[0055] The first formula is as follows:

[0056]

[0057] In the first formula, G(D2) represents the control value of the road danger condition prompt signal light of the vehicle terminal 3 of the target vehicle; D2 represents the self-information data of the target vehicle; E1(D2_a) represents the data length verification value of the a-th response data received by the vehicle terminal 3 of the target vehicle within a preset period; E2(D2_a) represents the data type verification value of the a-th response data received by the vehicle terminal 3 of the target vehicle within a preset period; P2(a) represents the a-th response data in binary form received by the vehicle terminal 3 of the target vehicle within a preset period; and len() represents the number of data bits in the parentheses. represents a cyclic left shift; represents the binary data from the 1st bit to the len(D2)th bit in the new binary data obtained by cyclically left shifting the data P2(a) by i bits; i represents an integer variable, i = 1, 2, …, len[P2(a)]; a = 1, 2, …, n(T); n(T) represents the total number of response data received by the vehicle terminal 3 of the target vehicle within a preset period; and T represents the preset period. represents that the value of a is taken from 1 to n(T) and substituted into the parentheses; if one or more values of a satisfy the formula in the parentheses, the overall formula is true, otherwise the overall formula is false. represents that the value of a is taken from 1 to n(T) and substituted into the parentheses; if all values of a satisfy the formula in the parentheses, the overall formula is true, otherwise the overall formula is false.

[0058] Preferably, the corresponding relationship between the control value of the road danger condition prompt signal light and the signal light color comprises:

[0059] When the control value of the road danger condition prompt signal light is 1, the corresponding signal light color is a first preset color, indicating that the current interaction signal transmission between the target vehicle and the roadside device is smooth.

[0060] When the control value of the road danger condition prompt signal light is -1, the corresponding signal light color is a second preset color, indicating that the current interaction signal transmission between the target vehicle and the roadside device is not smooth.

[0061] In this embodiment, the first preset color can be green and the second preset color can be red, so that the user and the vehicle driver can timely understand the surrounding signal transmission state. When it is green, it indicates that the interaction signal transmission between the target vehicle and the roadside camera is smooth, and the road danger condition of the vehicle can be detected; when it is red, it indicates that the target vehicle is located in a relatively remote place, and the interaction signal transmission between the target vehicle and the roadside camera is not smooth, and the road danger condition of the vehicle cannot be detected, having the advantages of intuitive and clear display.

[0062] The beneficial effects of the above technical solutions are: the first formula (1) is used to control the road danger situation prompt signal lamp of the vehicle display end (i.e. the vehicle terminal) according to the information data received by the vehicle in the preset time period, and then the current signal transmission state (similar to the mobile phone signal) can be known through the signal lamp after the road danger situation prompt is opened, thereby assisting the driver to make further decisions.

[0063] As an optional embodiment, the identification information of the target vehicle is license plate data of the target vehicle.

[0064] The edge computing node 2 comprises:

[0065] An auxiliary decision unit is configured to identify the license plate in the road condition picture in the interaction data sent by the camera module 1, and filter out an effective road condition picture set corresponding to the same target vehicle according to the identification result and the interaction data.

[0066] A danger identification unit is configured to identify the danger situation of the picture in the effective road condition picture set corresponding to the same target vehicle filtered out by the auxiliary decision unit according to a preset danger situation identification algorithm, to obtain the danger situation information of the corresponding target vehicle.

[0067] The beneficial effects of the above technical solutions are: there is often more than one roadside equipment (i.e. camera) on the road, and the road condition pictures corresponding to the target vehicle are filtered out before the danger situation is identified, so that it is not necessary to analyze each road condition picture, thereby effectively improving the system execution efficiency.

[0068] As an optional embodiment, the auxiliary decision unit is specifically configured to determine the effective camera module identification filtering set corresponding to the same target vehicle based on a second formula, and obtain the road condition pictures corresponding to the target vehicle and the camera module identification in the filtering set from the interaction data received by the edge computing node, to obtain the effective road condition picture set corresponding to the target vehicle.

[0069] The edge computing node 2 further comprises:

[0070] An intelligent analysis data transmission unit is configured to determine the edge intelligent analysis data of the target vehicle according to a third formula, and send the edge intelligent analysis data of the target vehicle to the vehicle terminal 3 of the target vehicle.

[0071] The second formula is:

[0072]

[0073] In the second formula, B(D2) represents the valid camera module identification filtering set corresponding to the target vehicle filtered from the interaction data received from the edge computing node, that is, the road condition images taken by the road side device corresponding to all elements in the set B(D2) are the filtered images; the target vehicle is the vehicle whose own information data is D2; C(k) represents the license plate data identified in the road condition picture collected by the kth camera module in the interaction data received from the edge computing node and including the own information data of the target vehicle, and the data format is binary; C(D2) represents the license plate data in the own information data D2 of the target vehicle, and the data format is binary; K(D2) represents the total number of camera modules involved in the interaction data received by the edge computing node and including the own information data of the target vehicle; represents that the value of k is taken from 1 to K(D2) and substituted into the parentheses, and the set composed of the camera module identifiers corresponding to all k values satisfying the algorithm in the parentheses is obtained;

[0074] The third formula is:

[0075]

[0076] In the third formula, F(D2) represents the edge intelligent analysis data of the target vehicle whose own information data is D2 determined by the intelligent analysis data transmission unit, and the data format is ASCII; size[] represents the number of elements of the set in the parentheses; “Out of the shooting range” represents the ASCII data of the string “Out of the shooting range”; A[B(D2)] represents the dangerous condition information of the corresponding target vehicle obtained by the dangerous identification unit identifying the pictures in the set of valid road condition pictures corresponding to the target vehicle corresponding to the set B(D2). If F(D2) = “Out of the shooting range”, F(D2) will be displayed on the display end (i.e. the vehicle terminal) of the vehicle whose own information data is D2, indicating that the vehicle whose own information data is D2 is not within the shooting range of the road side device and cannot perform intelligent analysis; if F(D2) ≠ “Out of the shooting range”, F(D2) will be displayed on the display end of the vehicle whose own information data is D2, so that the driver will make decisions according to the intelligent analysis results, achieving the purpose of assisting decision-making.

[0077] The beneficial effects of the above technical scheme are: the second formula (2) is used to identify and screen the image according to the received vehicle self information data and the road condition images captured by the plurality of roadside devices, and then the image with the vehicle to be analyzed is screened out, thereby reducing the burden of analysis and improving the efficiency of the system; then the third formula (3) is used to generate edge intelligent analysis data according to the screening result of the image and transmit the edge intelligent analysis data to the corresponding vehicle, so that the driver can make a decision according to the intelligent analysis result, thereby achieving the purpose of assisting the decision.

[0078] From the above embodiment, it can be known that the edge computing node 2 MEC is connected with the roadside device, directly connected with the roadside camera, and performs edge intelligent analysis, and transmits the related result to the vehicle for assisting the decision; specifically, the user of the vehicle can select whether to open the road danger situation prompt through the software of the vehicle, if the road danger situation prompt is selected to be opened, the vehicle will send the vehicle self information data to the surrounding at a fixed time interval, the data transceiver is arranged in the roadside device, after receiving the vehicle self information data, first, the received data is replied, the replied data contains the vehicle self information data and is longer than the vehicle self information data, then the corresponding roadside camera is used to capture the current road condition image, and the received vehicle self information data and the captured road condition image are transmitted to the edge computing node 2, since there are a plurality of roadside devices transmitting the captured road condition image to the edge computing node 2, the edge computing node 2 first performs a screening on the image, then performs edge intelligent analysis on the screened image, and transmits the related result after the analysis to the corresponding vehicle for assisting the decision, thereby effectively improving the efficiency of system execution and road danger situation perception.

[0079] The present application is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The system specified in one flow or multiple flows and / or blocks Figure 1 The system specified in one flow or multiple flows and / or blocks

[0080] These computer program instructions can also be stored in a computer-readable storage medium that can guide the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable storage medium produce a product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams.Figure 1 one or more processes and / or blocks Figure 1 the system specified in one or more blocks.

[0081] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, so that the instructions executed on the computer or other programmable data processing devices provide processes for implementing the flow Figure 1 one or more processes and / or blocks Figure 1 the steps of the system specified in one or more blocks.

[0082] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations. The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A road hazard condition alerting system based on edge computing nodes, characterized by, The method comprises the following steps: a plurality of camera modules arranged on the side of the road are used to collect road condition pictures and send them to an edge computing node; the edge computing node is used to identify the dangerous condition of a target vehicle in the road condition pictures according to a preset dangerous condition identification algorithm, obtain the dangerous condition information of the current target vehicle, and send the dangerous condition information to a preset vehicle terminal installed on the target vehicle; the vehicle terminal is used to provide the dangerous condition information to the driver; wherein the vehicle terminal is specifically used to calculate the control value of the road dangerous condition prompt signal lamp according to a first formula; wherein the first formula is: In the first formula, The control value of the road hazard warning light on the vehicle's onboard terminal indicates the target vehicle's road hazard warning signal. This represents the target vehicle's own information data; This indicates that the on-board terminal of the target vehicle received the first [number] message within a preset period. The data length verification value for each response data; This indicates that the on-board terminal of the target vehicle received the first [number] message within a preset period. Data type validation value for each response data; This indicates the binary form of the first number received by the target vehicle's onboard terminal within a preset period. One response data; This indicates the number of digits in the data within the parentheses; Indicates a circular left shift; Indicates data Circular left shift The first to the second bits of the new binary data obtained after the bit is... Binary data in bits; Represents integer variables, ; ; This indicates the total number of response data received by the vehicle's onboard terminal within a preset period; Indicates the preset period; Indicates will The value ranges from 1 to If one or more exist when substituted into the parentheses If the value satisfies the expression within the parentheses, then the entire expression is true; otherwise, the entire expression is false. Indicates will The value ranges from 1 to Substitute into the parentheses if all If all values ​​satisfy the expression within the parentheses, the entire expression is true; otherwise, the entire expression is false. wherein the self information data of the target vehicle at least includes the identification information of the target vehicle; the identification information of the target vehicle is the license plate data of the target vehicle; the edge computing node comprises: an auxiliary decision unit is used to identify the license plate in the road condition pictures in the interaction data sent by the camera module, and according to the identification result and the interaction data, the effective road condition picture set corresponding to the same target vehicle is screened out; a dangerous identification unit is used to identify the dangerous condition of the pictures in the effective road condition picture set corresponding to the same target vehicle screened out by the auxiliary decision unit according to a preset dangerous condition identification algorithm, and obtain the dangerous condition information of the corresponding target vehicle; wherein the auxiliary decision unit is specifically used to determine the effective camera module identification screening set corresponding to the same target vehicle based on a second formula, and obtain the effective road condition picture set corresponding to the target vehicle by acquiring the road condition pictures corresponding to the target vehicle and the camera module identification in the screening set in the interaction data received by the edge computing node; the edge computing node further comprises: an intelligent analysis data transmission unit is used to determine the edge intelligent analysis data of the target vehicle according to a third formula, and send the edge intelligent analysis data of the target vehicle to the vehicle terminal of the target vehicle; wherein the second formula is: In the second formula, represents the valid camera module identification screening set corresponding to the target vehicle screened from the interaction data received by the edge computing node; the target vehicle is a vehicle whose own information data is ; represents the license plate data identified in the road condition picture collected by the th camera module in the interaction data received by the edge computing node and including the own information data of the target vehicle, and the data format is binary form; represents the license plate data in the own information data of the target vehicle , and the data format is binary form; represents the total number of camera modules involved in the interaction data received by the edge computing node and including the own information data of the target vehicle; represents that the value of is taken from 1 to is substituted into the bracket to calculate, and a set composed of the camera module identifications corresponding to all values satisfying the algorithm in the bracket; the third formula is: In the third formula, represents the edge intelligent analysis data of the target vehicle determined by the intelligent analysis data transmission unit, and the data form is ASCII form; represents the number of elements in the set in the parentheses; represents the data in ASCII form of the string ; represents the dangerous situation information of the corresponding target vehicle obtained by the dangerous identification unit identifying the dangerous situation of the picture in the picture set corresponding to the target vehicle corresponding to the set .​ 2. The edge-computing node-based road hazard condition alerting system of claim 1, wherein, the camera module comprises a data transceiver unit, a camera and a response unit; the data transceiver unit is used to receive the self information data of the target vehicle sent by the vehicle terminal of the target vehicle, and send a picture collection instruction to the camera when the self information data of the target vehicle is received; the data transceiver unit is also used to send the response data generated by the response unit to the vehicle terminal of the target vehicle; the data transceiver unit is also used to send the interaction data to the edge computing node; the interaction data at least includes the self information data of the target vehicle, the current camera module identification responding to the self information data of the target vehicle and the road condition picture collected by the current camera module responding to the self information data of the target vehicle; the camera is used to collect road condition pictures according to the picture collection instruction; the response unit is used to generate response data responding to the self information data of the target vehicle; the response data includes the self information data of the target vehicle and the data bit number is more than the self information data of the target vehicle. The vehicle-mounted terminal of the target vehicle is specifically configured to determine a control value of a road danger situation prompt signal lamp according to the response data, and control a display color of a preset road danger situation prompt signal lamp on the vehicle-mounted terminal according to a preset corresponding relationship between the control value of the road danger situation prompt signal lamp and a signal lamp color.

3. The edge-computing node-based road hazard condition alerting system of claim 2, wherein, The vehicle-mounted terminal is further configured to send a user inquiry about whether to open a vehicle danger situation prompt when the target vehicle is started, and send self information data of the target vehicle to a specified range around the target vehicle at a preset period according to an inquiry result of confirming to open the vehicle danger situation prompt.

4. The edge-computing node-based road hazard condition alerting system of claim 1, wherein, The corresponding relationship between the control value of the road danger situation prompt signal lamp and the signal lamp color includes: When the control value of the road danger situation prompt signal lamp is 1, a first preset color corresponds to the signal lamp color, indicating that the current target vehicle and the roadside equipment interact with smooth signal transmission; When the control value of the road danger situation prompt signal lamp is -1, a second preset color corresponds to the signal lamp color, indicating that the current target vehicle and the roadside equipment interact with unsmooth signal transmission.

Images