An intelligent early warning system for urban road traffic based on big data

Abstract

The present invention belongs to the technical field of road traffic supervision, and specifically is an intelligent early warning system for urban road traffic based on big data, including a traffic supervision and early warning platform, a regional capture marking module, a forward smoothness detection and analysis module, a weather detection feedback module and a vehicle management reminder module; the present invention sets a tracing cycle and performs traffic capture analysis to determine the high-risk area, and obtains the forward analysis section of the corresponding vehicle, and performs smoothness analysis and weather detection analysis on the corresponding forward analysis section, so as to achieve effective detection and comprehensive judgment of vehicles traveling in the supervised road area, and the analysis and judgment results are more accurate, and based on the traffic risk symbol and the weather risk symbol, the corresponding speed upper limit value is reasonably allocated and sent to the corresponding vehicle, thereby helping to ensure the safe driving of the corresponding vehicle and urban road traffic safety.

Description

Technical Field

[0001] The present invention relates to the technical field of road traffic supervision, and in particular to an intelligent early warning system for urban road traffic based on big data. Background Art

[0002] Urban roads refer to roads that connect various areas of a city, are used for transportation and pedestrians within the city, are convenient for residents' lives, work, and cultural and entertainment activities, and are connected to roads outside the city to carry external traffic; urban roads are usually wider than highways. In order to adapt to complex traffic modes, more motor vehicle lanes, bus priority lanes, and non-motor vehicle lanes are divided. There are sidewalks and houses on both sides of the road. Urban roads are divided into expressways, trunk roads, secondary trunk roads, and branch roads; At present, when conducting urban road traffic control, it is impossible to reasonably determine the high-risk areas of the supervised road area, and it is impossible to effectively detect the vehicles in the supervised road area and comprehensively judge the safety status of the road ahead. It is difficult to timely warn and remind the corresponding vehicle drivers to make corresponding adjustments, which is not conducive to the safety of vehicle driving and cannot ensure the safety of urban road traffic; In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent early warning system for urban road traffic based on big data, which solves the problem that the prior art cannot reasonably determine the high-risk areas in the supervised road area, cannot effectively detect the vehicles traveling in the supervised road area and comprehensively judge the safety status of the road section ahead, is difficult to warn and remind the corresponding vehicle drivers in time, and cannot ensure the safety of urban road traffic.

[0004] To achieve the above object, the present invention provides the following technical solutions: An intelligent early warning system for urban road traffic based on big data includes a traffic supervision and early warning platform, a regional capture and marking module, a forward smoothness detection and analysis module, a weather detection feedback module, and a vehicle management reminder module; the traffic supervision and early warning platform obtains the urban road area that needs to be supervised, the regional capture and marking module traces back with the current date as the end date and sets a tracing cycle of T1 days, determines the high-risk area through traffic capture and analysis, and sends the high-risk area to the traffic supervision and early warning platform; the forward smoothness detection and analysis module obtains the forward analysis section of the corresponding vehicle, performs smoothness analysis on the corresponding forward analysis section to assign it a travel risk symbol TX-1 or TX-2, and sends the travel risk symbol of the forward analysis section of the corresponding vehicle to the traffic supervision and early warning platform; The weather detection feedback module will perform weather detection and analysis on the corresponding vehicle's forward analysis section, and assign the corresponding forward risk section a weather risk symbol YX-1 or YX-2 accordingly, and send the weather risk symbol of the corresponding vehicle's forward analysis section to the traffic supervision and early warning platform; the traffic supervision and early warning platform will send the corresponding vehicle's passage risk symbol and weather risk symbol to the vehicle management reminder module. When the vehicle management reminder module receives the passage risk symbol TX-1 and the weather risk symbol YX-1, it assigns a speed limit value ST1 to the corresponding vehicle. When it receives the passage risk symbol TX-2 and the weather risk symbol YX-2, it assigns a speed limit value ST3 to the corresponding vehicle. In other cases, the speed limit value ST2 is assigned to the corresponding vehicle, and the corresponding speed limit value is sent to the corresponding vehicle.

[0005] Furthermore, the specific analysis process of traffic capture analysis is as follows: The location where the traffic accident occurred in the urban road area that needs to be supervised during the tracing period is obtained, and a circle with a radius of R1 is drawn with the location where the traffic accident occurred as the center, and the area at the center of the circle is marked as the risk diagnosis area; the frequency of traffic accidents in the corresponding risk diagnosis area during the tracing period is collected, and the impact duration of each traffic accident is obtained, and the impact duration of all traffic accidents is summed up to obtain the sum of the impact durations; the frequency of traffic accidents and the sum of the impact durations are numerically calculated to obtain the regional capture analysis value, and the regional capture analysis value is numerically compared with the preset regional capture analysis threshold. If the regional capture analysis value exceeds the preset regional capture analysis threshold, the corresponding risk diagnosis area is marked as a high-risk area.

[0006] Furthermore, the specific analysis process of the forward smoothness detection and analysis module is as follows: Taking the current position of the corresponding vehicle as the initial analysis point, a forward analysis section with a length of L1 is set based on the navigation path of the corresponding vehicle, the number of high-risk areas in the forward analysis section is obtained, and the number of high-risk areas is numerically compared with a preset high-risk area number threshold. If the number of high-risk areas exceeds the preset high-risk area number threshold, a traffic risk symbol TX-1 is assigned; If the number of high-risk areas does not exceed the preset high-risk area number threshold, the road vehicle coverage rate, the average vehicle parking time and the average vehicle driving speed in the previous analysis section are obtained, and the road vehicle coverage rate, the average vehicle parking time and the average vehicle driving speed are normalized and calculated to obtain the traffic score value, and the traffic score value is numerically compared with the preset traffic score threshold. If the traffic score value exceeds the preset traffic score threshold, the traffic risk symbol TX-1 is assigned; if the traffic score value does not exceed the preset traffic score threshold, TX-2 is generated.

[0007] Furthermore, the specific operation process of the weather detection feedback module includes: Obtain the rainfall data and visibility data of the environment of the preceding analysis section, compare the rainfall data and visibility data with the preset rainfall data and visibility data, respectively, and assign a weather risk symbol YX-1 if the rainfall data exceeds the preset rainfall data threshold or the visibility data does not exceed the preset visibility data threshold; If the rainfall data does not exceed the preset rainfall data threshold and the visibility data does not exceed the preset visibility data threshold, the ambient temperature deviation value, ultraviolet intensity, light brightness deviation value and ambient wind speed of the environment in which the previous analysis section is located are obtained, and the ambient temperature deviation value, ultraviolet intensity, light brightness deviation value and ambient wind speed are normalized and calculated to obtain the adverse environment value; the adverse environment value is numerically compared with the preset adverse environment threshold. If the adverse environment value exceeds the preset adverse environment threshold, the weather risk symbol YX-1 is assigned; otherwise, the weather risk symbol YX-2 is assigned.

[0008] Furthermore, after generating the corresponding speed upper limit value for the vehicle, the vehicle management reminder module collects the current driving speed of the corresponding vehicle, and compares the current driving speed of the corresponding vehicle with the corresponding speed upper limit value. If the current driving speed exceeds the corresponding speed upper limit value, a speed warning reminder signal is generated and sent to the corresponding vehicle.

[0009] Furthermore, the specific operation process of the vehicle management reminder module also includes: Vehicles traveling in urban road areas and the navigation paths of the corresponding vehicles are obtained. When the corresponding vehicle needs to pass through a high-risk area, the distance between the corresponding vehicle and the corresponding high-risk area is obtained and marked as TG. When TG=TGmax, TGmax is the preset reminder distance value. Safety reminder information is generated and sent to the corresponding vehicle via the traffic supervision and early warning platform.

[0010] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, a tracing cycle is set and traffic capture analysis is performed to determine the high-risk area, and the forward analysis section of the corresponding vehicle is obtained. The corresponding forward analysis section is subjected to a smoothness analysis to assign it a traffic risk symbol TX-1 or TX-2, so as to accurately and reasonably feedback the smoothness of the forward analysis section of the corresponding vehicle, and the forward analysis section of the corresponding vehicle is subjected to a weather detection analysis, and accordingly, the weather risk symbol YX-1 or YX-2 is assigned to the corresponding forward risk section, so as to accurately and reasonably feedback the weather condition of the forward analysis section of the corresponding vehicle, so as to achieve effective detection and comprehensive judgment of vehicles traveling in the supervised road area, and the analysis and judgment results are more accurate; 2. In the present invention, the vehicle management reminder module reasonably allocates the corresponding speed limit value based on the traffic risk symbol and the weather risk symbol and sends it to the corresponding vehicle, so as to realize the reasonable division of the vehicle running speed. The driver adjusts the speed as needed, which helps to ensure the safe driving of the corresponding vehicle and the urban road traffic safety; and when the driving speed of the corresponding vehicle exceeds the corresponding speed limit value, or the distance between it and the corresponding high-risk area is close to the preset reminder distance value, an early warning is issued and the corresponding driver is reminded, so that the driver of the corresponding vehicle can adjust the speed in time and improve driving vigilance, so as to further ensure the urban road traffic safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 It is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0013] Embodiment 1: Figure 1 As shown, the present invention proposes an intelligent early warning system for urban road traffic based on big data, including a traffic supervision and early warning platform, a regional capture mark module, a forward smoothness detection and analysis module, a weather detection feedback module and a vehicle management reminder module, and the traffic supervision and early warning platform is in communication connection with the regional capture mark module, the forward smoothness detection and analysis module, the weather detection feedback module and the vehicle management reminder module; the traffic supervision and early warning platform obtains the urban road area to be supervised, and the regional capture mark module traces back with the current date as the end date and sets the tracing period of days to T1, preferably, T1 is 50 days; the high-risk area is determined by traffic capture analysis, and the high-risk area is sent to the traffic supervision and early warning platform; the specific analysis process of traffic capture analysis is as follows: The location of the traffic accident in the urban road area that needs to be supervised during the tracing period is obtained, and a circle with a radius of R1 is drawn with the location of the traffic accident as the center, and the center area is marked as the risk diagnosis area; the frequency of traffic accidents in the corresponding risk diagnosis area during the tracing period is collected, and the impact duration of each traffic accident is obtained, where the longer the impact duration of the traffic accident, the greater the adverse impact of the corresponding traffic accident; the impact duration of all traffic accidents is summed up to obtain the impact duration sum value; the traffic accident frequency JS and the impact duration sum value YH are numerically calculated by the formula QB=a1*JS+a2*YH to obtain the regional capture analysis value QB; Among them, a1 and a2 are preset weight coefficients, a1>a2>0; and, the larger the value of the regional capture analysis value QB, the greater the degree of traffic hazard in the corresponding risk diagnosis area; the regional capture analysis value QB is numerically compared with the preset regional capture analysis threshold, if the regional capture analysis value QB exceeds the preset regional capture analysis threshold, indicating that the degree of traffic hazard in the corresponding risk diagnosis area is large, then the corresponding risk diagnosis area is marked as a high-risk area.

[0014] The forward smoothness detection and analysis module obtains the forward analysis section of the corresponding vehicle, performs smoothness analysis on the corresponding forward analysis section to assign it a traffic risk symbol TX-1 or TX-2, and sends the traffic risk symbol of the forward analysis section of the corresponding vehicle to the traffic supervision and early warning platform, so as to accurately and reasonably feedback the smoothness of the forward analysis section of the corresponding vehicle; the specific analysis process of the forward smoothness detection and analysis module is as follows: Taking the current position of the corresponding vehicle as the initial analysis point, a forward analysis section with a length of L1 is set based on the navigation path of the corresponding vehicle, the number of high-risk areas in the forward analysis section is obtained, and the number of high-risk areas is numerically compared with a preset high-risk area number threshold. If the number of high-risk areas exceeds the preset high-risk area number threshold, indicating that the degree of traffic hazard in the forward section of the corresponding vehicle is relatively large, a traffic risk symbol TX-1 is assigned; if the number of high-risk areas does not exceed the preset high-risk area number threshold, the road vehicle coverage rate, the average vehicle parking time and the average vehicle driving speed in the forward analysis section are obtained; The road vehicle coverage rate FG, the average vehicle parking time PS and the average vehicle driving speed PD are normalized and calculated to obtain the smoothness score CF through the formula CF=b1*FG+b2*PS+b3 / PD, wherein b1, b2 and b3 are preset proportional coefficients, and b3>b1>b2>0; and the smaller the value of the smoothness score CF is, the smoother the current forward analysis section of the corresponding vehicle is; the smoothness score CF is numerically compared with the preset smoothness score threshold value, if the smoothness score CF exceeds the preset smoothness score threshold value, indicating that the road smoothness of the forward analysis section of the corresponding vehicle is poor, then the traffic risk symbol TX-1 is assigned; if the smoothness score CF does not exceed the preset smoothness score threshold value, indicating that the road smoothness of the forward analysis section of the corresponding vehicle is good, then TX-2 is generated.

[0015] The weather detection feedback module performs weather detection and analysis on the forward analysis section of the corresponding vehicle, and assigns the weather risk symbol YX-1 or YX-2 to the corresponding forward risk section accordingly, and sends the weather risk symbol of the forward analysis section of the corresponding vehicle to the traffic supervision and early warning platform, so as to accurately and reasonably feedback the weather conditions of the forward analysis section of the corresponding vehicle; the specific analysis process of weather detection and analysis is as follows: The rainfall data and visibility data of the environment in which the forward analysis section is located are obtained, and the rainfall data and visibility data are numerically compared with the preset rainfall data and the preset visibility data respectively. If the rainfall data exceeds the preset rainfall data threshold or the visibility data does not exceed the preset visibility data threshold, a weather risk symbol YX-1 is assigned; if the rainfall data does not exceed the preset rainfall data threshold and the visibility data does not exceed the preset visibility data threshold, the ambient temperature deviation value, ultraviolet intensity, light brightness deviation value and ambient wind speed of the environment in which the forward analysis section is located are obtained; it should be noted that the ambient temperature deviation value is a data quantity value indicating the deviation of the ambient temperature from the preset suitable temperature, and the light brightness deviation value is a data quantity value indicating the deviation of the ambient brightness from the preset suitable light brightness, and the smaller the values ​​of the ambient temperature deviation value and the light brightness deviation value are, the more conducive to safe driving of the vehicle; The ambient temperature deviation value WP, ultraviolet intensity ZQ, light brightness deviation value GP and ambient wind speed HF are normalized and calculated to obtain the adverse environment value HB through the formula HB=c1*WP+c2*GP+c3*GP+c4*HF; wherein, c1, c2, c3, c4 are preset weight coefficients, and the values ​​of c1, c2, c3, c4 are all greater than zero; and, the larger the value of the adverse environment value HB is, the more unfavorable the current environment of the corresponding vehicle's forward analysis section is for safe driving; the adverse environment value HB is numerically compared with the preset adverse environment threshold value, if the adverse environment value HB exceeds the preset adverse environment threshold value, indicating that the environmental condition of the corresponding vehicle's forward analysis section is poor, then the weather risk symbol YX-1 is assigned; if the adverse environment value HB does not exceed the preset adverse environment threshold value, then the weather risk symbol YX-2 is assigned.

[0016] The traffic supervision and early warning platform sends the traffic risk symbol and weather risk symbol of the corresponding vehicle to the vehicle management reminder module. When the vehicle management reminder module receives the traffic risk symbol TX-1 and the weather risk symbol YX-1, it assigns the speed limit value ST1 to the corresponding vehicle. When receiving the traffic risk symbol TX-2 and the weather risk symbol YX-2, it assigns the speed limit value ST3 to the corresponding vehicle. In other cases, the speed limit value ST2 is assigned to the corresponding vehicle. The corresponding speed limit value is sent to the corresponding vehicle to achieve a reasonable division of the vehicle's operating speed. The driver adjusts the vehicle speed as needed, which helps to ensure the safe driving of the corresponding vehicle and help to ensure urban road traffic safety; among them, 0<ST1<ST2<ST3.

[0017] Embodiment 2: The difference between this embodiment and embodiment 1 is that after the vehicle management reminder module generates the corresponding speed upper limit value of the vehicle, it collects the current driving speed of the corresponding vehicle, and compares the current driving speed of the corresponding vehicle with the corresponding speed upper limit value. If the current driving speed exceeds the corresponding speed upper limit value, it indicates that the current vehicle speed is relatively fast and the current driving is more dangerous. Then, a speed warning reminder signal is generated and sent to the corresponding vehicle to provide a timely reminder to the driver, ensure the timeliness of the driver's adjustment operation, and further ensure the safety of vehicle driving.

[0018] Embodiment 3: The difference between this embodiment and Embodiment 1 and Embodiment 2 is that the vehicle management reminder module is also used to obtain the navigation path of vehicles traveling in the urban road area and the corresponding vehicle, and obtain the position of the corresponding vehicle in real time when the corresponding vehicle needs to pass through the high-risk area, and calculate the distance between the position of the corresponding vehicle and the corresponding high-risk area to realize real-time update of the distance between the two, that is, the distance between the corresponding vehicle and the corresponding high-risk area is obtained and marked as TG. When TG=TGmax, TGmax is the preset reminder distance value, and a safety reminder message is generated and sent to the corresponding vehicle via the traffic supervision and early warning platform to remind the driver of the corresponding vehicle to adjust the vehicle speed in time and improve driving vigilance, thereby helping the corresponding vehicle to safely pass through the corresponding high-risk area and further ensure urban road traffic safety.

[0019] The working principle of the present invention is as follows: when in use, the urban road area that needs to be supervised is obtained through the traffic supervision and early warning platform, the area capture marking module sets the tracing cycle and determines the high-risk area through traffic capture analysis, the forward smoothness detection and analysis module obtains the forward analysis section of the corresponding vehicle, and performs smoothness analysis on the corresponding forward analysis section to assign it a traffic risk symbol TX-1 or TX-2, so as to accurately and reasonably feedback the smooth condition of the forward analysis section of the corresponding vehicle; the weather detection feedback module performs weather detection and analysis on the forward analysis section of the corresponding vehicle, and accordingly assigns the corresponding forward risk section a weather risk symbol YX-1 or YX-2, so as to accurately and reasonably feedback the weather condition of the forward analysis section of the corresponding vehicle; the vehicle management reminder module reasonably allocates the corresponding speed upper limit value based on the traffic risk symbol and the weather risk symbol and sends it to the corresponding vehicle, so as to realize the reasonable division of the vehicle running speed, and the driver adjusts the vehicle speed as needed, thereby helping to ensure the safe driving of the corresponding vehicle and the urban road traffic safety.

[0020] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An intelligent early warning system for urban road traffic based on big data, characterized in that: It includes a traffic supervision and early warning platform, a regional capture and marking module, a forward smoothness detection and analysis module, a weather detection feedback module, and a vehicle management reminder module. The traffic supervision and early warning platform obtains the urban road area that needs to be supervised. The regional capture and marking module traces back from the current date as the end date and sets the number of days to be T1 for the tracing cycle. It determines the high-risk area through traffic capture analysis and sends the high-risk area to the traffic supervision and early warning platform. The forward smoothness detection and analysis module obtains the forward analysis section of the corresponding vehicle, performs smoothness analysis on the corresponding forward analysis section to assign it a traffic risk symbol TX-1 or TX-2, and sends the traffic risk symbol of the forward analysis section of the corresponding vehicle to the traffic supervision and early warning platform; The weather detection feedback module performs weather detection and analysis on the forward analysis section of the corresponding vehicle, and accordingly assigns a weather risk symbol YX-1 or YX-2 to the corresponding forward risk section, and sends the weather risk symbol of the forward analysis section of the corresponding vehicle to the traffic supervision and early warning platform; The traffic supervision and early warning platform sends the traffic risk symbol and weather risk symbol of the corresponding vehicle to the vehicle management reminder module. When the vehicle management reminder module receives the traffic risk symbol TX-1 and the weather risk symbol YX-1, it assigns the speed limit value ST1 to the corresponding vehicle. When it receives the traffic risk symbol TX-2 and the weather risk symbol YX-2, it assigns the speed limit value ST3 to the corresponding vehicle. In other cases, the speed limit value ST2 is assigned to the corresponding vehicle and the corresponding speed limit value is sent to the corresponding vehicle.

2. According to the big data-based urban road traffic intelligent early warning system of claim 1, it is characterized in that: The specific analysis process of traffic capture analysis is as follows: Obtain the location of the traffic accident in the urban road area that needs to be supervised during the tracing period, draw a circle with a radius of R1 with the location of the traffic accident as the center, and mark the center area as the risk diagnosis area; collect the frequency of traffic accidents in the corresponding risk diagnosis area during the tracing period, and obtain the impact duration of each traffic accident, and sum up the impact duration of all traffic accidents to obtain the sum of the impact durations; The frequency and impact duration of traffic accidents are numerically calculated to obtain the regional capture analysis value, and the regional capture analysis value is numerically compared with the preset regional capture analysis threshold. If the regional capture analysis value exceeds the preset regional capture analysis threshold, the corresponding risk diagnosis area is marked as a high-risk area.

3. According to the big data-based urban road traffic intelligent early warning system of claim 1, it is characterized in that: The specific analysis process of the forward smoothness detection and analysis module is as follows: Taking the current position of the corresponding vehicle as the initial analysis point, a forward analysis section with a length of L1 is set based on the navigation path of the corresponding vehicle, the number of high-risk areas in the forward analysis section is obtained, and the number of high-risk areas is numerically compared with a preset high-risk area number threshold. If the number of high-risk areas exceeds the preset high-risk area number threshold, a traffic risk symbol TX-1 is assigned; If the number of high-risk areas does not exceed the preset high-risk area number threshold, the road vehicle coverage rate, the average vehicle parking time and the average vehicle driving speed in the previous analysis section are obtained, and the road vehicle coverage rate, the average vehicle parking time and the average vehicle driving speed are normalized and calculated to obtain the traffic score value, and the traffic score value is numerically compared with the preset traffic score threshold. If the traffic score value exceeds the preset traffic score threshold, the traffic risk symbol TX-1 is assigned; if the traffic score value does not exceed the preset traffic score threshold, TX-2 is generated.

4. According to the big data-based urban road traffic intelligent early warning system of claim 1, it is characterized in that: The specific operation process of the weather detection feedback module includes: Obtain the rainfall data and visibility data of the environment of the preceding analysis section, compare the rainfall data and visibility data with the preset rainfall data and visibility data, respectively, and assign a weather risk symbol YX-1 if the rainfall data exceeds the preset rainfall data threshold or the visibility data does not exceed the preset visibility data threshold; If the rainfall data does not exceed the preset rainfall data threshold and the visibility data does not exceed the preset visibility data threshold, the ambient temperature deviation value, ultraviolet intensity, light brightness deviation value and ambient wind speed of the environment in which the previous analysis section is located are obtained, and the ambient temperature deviation value, ultraviolet intensity, light brightness deviation value and ambient wind speed are normalized and calculated to obtain the adverse environment value; the adverse environment value is numerically compared with the preset adverse environment threshold. If the adverse environment value exceeds the preset adverse environment threshold, the weather risk symbol YX-1 is assigned; otherwise, the weather risk symbol YX-2 is assigned.

5. According to the big data-based urban road traffic intelligent early warning system of claim 1, it is characterized in that: After generating the corresponding speed upper limit value of the vehicle, the vehicle management reminder module collects the current driving speed of the corresponding vehicle, and compares the current driving speed of the corresponding vehicle with the corresponding speed upper limit value. If the current driving speed exceeds the corresponding speed upper limit value, a speed warning reminder signal is generated and sent to the corresponding vehicle.

6. The urban road traffic intelligent early warning system based on big data according to claim 5 is characterized in that: The specific operation process of the vehicle management reminder module also includes: Vehicles traveling in urban road areas and the navigation paths of the corresponding vehicles are obtained. When the corresponding vehicle needs to pass through a high-risk area, the distance between the corresponding vehicle and the corresponding high-risk area is obtained and marked as TG. When TG=TGmax, TGmax is the preset reminder distance value. Safety reminder information is generated and sent to the corresponding vehicle via the traffic supervision and early warning platform.

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