Intelligent road cone traffic early warning system

Abstract

The invention relates to the technical field of intelligent traffic, in particular to an intelligent road cone traffic early warning system. The problems that in the prior art, an intelligent road cone is single in function, and warning strategies cannot be intelligently adjusted according to real-time road conditions and construction environment changes are solved. The intelligent road cone is arranged and used for being deployed on a road construction or accident site to warn vehicles, collecting road environment information through a sensor and conducting risk assessment and early warning on the vehicles according to the detected vehicle information; the control center is used for integrating the monitoring information of the intelligent road cones and dynamically adjusting a placement scheme and an early warning strategy of the intelligent road cones; and the user terminal is used for receiving the early warning information from the intelligent road cone and the control center and sending an alarm to a user. Through cooperation of the intelligent road cone, the control center and the user terminal, effective safety early warning and guidance are ensured to be performed on vehicles passing through the construction area during construction, accident risks are reduced, and traffic management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent traffic technology, and in particular to an intelligent road cone traffic warning system. Background Art

[0002] Road cones are often used in road construction and accident scenes. Through their eye-catching structure and color, they remind vehicle drivers to pay attention to the construction or accident area ahead. However, since road cones can only provide passive visual warnings, they lead to low information transmission efficiency and difficulty in adapting to complex road conditions and work changes. Especially at night, in rainy and foggy weather or when the driver is not paying attention, traditional road cones are difficult to effectively ensure the safety of construction sections.

[0003] In recent years, with the rapid development of the Internet of Things, artificial intelligence and communication technology, intelligent road cones have gradually replaced traditional road cones for use in road construction and accident scenes; however, the existing intelligent road cones have relatively single functions and are unable to intelligently adjust warning strategies based on real-time road conditions and changes in the road construction environment.

[0004] Therefore, there is an urgent need to develop an intelligent road cone traffic warning system to overcome the shortcomings of the prior art. Summary of the invention

[0005] In order to solve the problem that the intelligent road cones in the prior art have relatively single functions and cannot intelligently adjust the warning strategy according to the real-time road conditions and construction environment changes, the present invention provides such an intelligent road cone traffic warning system, including intelligent road cones, a control center and a user terminal; The smart road cone is used to be deployed in road construction areas to warn vehicles, collect road environment information through sensors, and conduct risk assessment and early warning of vehicles based on detected vehicle information; The control center is used to integrate the monitoring information of the smart road cones and dynamically adjust the placement plan and early warning strategy of the smart road cones; The user terminal is used to receive warning information from the smart road cone and the control center, and issue a warning to the user.

[0006] Furthermore, the intelligent road cone includes a perception module, a data processing module, a communication module, a warning module and a mobile module; The perception module is used to obtain the real-time position information of the smart road cone and perceive the surrounding environment information in real time, wherein the surrounding environment information includes the position of the surrounding smart road cone, lane line information and traffic signs; The data processing module is used to process and analyze the original perception data collected by the perception module to determine potential risks; The communication module is used to communicate with the control center, upload perception data and risk information and receive control instructions, and interact with other smart road cones in real time to share vehicle information and its own status; The warning module is used to provide differentiated warnings to vehicles entering the warning range according to the data processing results; The moving module is used to move to a designated position on the road according to the control instruction obtained by the communication module.

[0007] Furthermore, the data processing module determines potential risks specifically including: S101: defining a fuzzy set for each key vehicle behavior, the key vehicle behavior including lane changing, acceleration, deceleration and normal driving, and setting a corresponding membership function for the fuzzy set; S102: The data processing module calculates the degree of membership of each vehicle behavior feature to the fuzzy set based on the vehicle information acquired in real time by the perception module; S103: Based on the fuzzy rule base, reasoning the comprehensive membership of the current state of the vehicle to various vehicle behaviors; S104: using a maximum membership method, converting the comprehensive membership of the current vehicle state into a clear behavior judgment; S105: According to the behavior recognition threshold, screening the vehicle behaviors whose comprehensive membership exceeds the behavior recognition threshold; S106: Prioritizing the vehicle behaviors corresponding to the comprehensive memberships according to the sizes of the comprehensive memberships, and generating a comprehensive behavior description.

[0008] Furthermore, the control center includes a data receiving module, a traffic situation awareness module, a warning strategy optimization module and a command sending module; The data receiving module is used to receive the perception data, status information and risk information monitored by the intelligent road cone, and perform pre-processing operations on the perception data and the status information; The traffic situation awareness module is used to access the real-time traffic data of the traffic management department and, combined with the historical traffic data, to predict and analyze the traffic flow of the construction section; The warning strategy optimization module is used to dynamically adjust the placement plan and warning strategy of the smart road cones according to real-time road conditions, traffic prediction results and risk information; The instruction sending module is used to send the placement plan and warning strategy of the smart road cone to the smart road cone, and send warning information to the user terminal.

[0009] Furthermore, the traffic flow forecasting and analysis on the construction section includes: S201: The traffic situation awareness module receives real-time road condition information and historical traffic information acquired by the traffic management department; S202: performing data cleaning, standardization and time alignment operations on the received data; S203: extracting traffic characteristics from the data, the traffic characteristics including the number and width of lanes in the road construction area, traffic flow variation patterns and speed limits; S204: The traffic situation awareness module predicts the traffic flow change trend of the construction area in the future by using a machine learning algorithm and combining the real-time road condition information with the traffic features extracted from the historical traffic information; S205: According to the traffic flow change trend and the traffic characteristics of the road construction area, the congestion risk and accident risk of each lane are evaluated by setting risk factors.

[0010] Furthermore, the early warning strategy optimization module dynamically adjusts the early warning strategy specifically including: S301: Establish a multi-factor model based on the traffic flow prediction result of the traffic situation awareness module and the characteristics and safety requirements of the road construction area to calculate the risk coefficient of each road section; S302: standardizing the risk coefficients of different road sections, and using a clustering algorithm to divide the road sections in the road construction area into different groups according to the risk data, and determining the risk level; S303: The warning strategy optimization module calculates the warning strategy priority of each smart road cone in real time according to the risk information and vehicle location information uploaded by the smart road cone, the risk area division result, and the stored information in the warning rule library; S304: Selecting the warning strategy with the highest risk score as the execution strategy of the smart road cone at the current moment, and sending it to the corresponding smart road cone.

[0011] Furthermore, the real-time calculation of the warning strategy priority of each smart road cone is to calculate the risk score of all warning strategies corresponding to each smart road cone. The calculation is: ;

[0012] in, Provide a risk score for the early warning strategy corresponding to each smart road cone; Risk factors set for areas with different risk levels; is the rule matching degree, which is used to indicate the matching degree between the current traffic conditions and the warning rules; is the weight coefficient, which is used to adjust the importance of different warning rules.

[0013] Furthermore, the warning strategy optimization module also includes dynamically adjusting the placement of smart road cones: S401: Before the road construction begins, the initial layout of the intelligent road cones is preliminarily planned using a genetic algorithm according to the characteristics of the road construction area, safety requirements, and traffic flow change trends; S402: Based on the initial layout, an ant colony algorithm is used to calculate an optimal deployment path for each smart road cone, and a smart road cone deployment plan is generated; S403: deploying smart road cones based on the smart road cone deployment solution, monitoring the deployment process in real time, and dynamically adjusting the deployment strategy through a reinforcement learning algorithm until the smart road cones are deployed at corresponding positions; S404: After the construction is completed, the early warning strategy optimization module uses a deep reinforcement learning algorithm to simulate different recycling sequences and paths according to the construction completion status and real-time traffic conditions.

[0014] Further, the user terminal includes a data communication module and a user interface; The data communication module is used to receive warning information sent by the intelligent road cone and the control center; The user interface is used to display the warning information received by the data communication module.

[0015] Furthermore, there is also an intelligent road cone traffic warning method, comprising: S1: At the beginning of road construction, determine the placement plan of intelligent road cones according to the construction location and traffic flow; S2: The smart road cone moves to the target location along the route planned by the smart road cone placement plan according to the control command issued by the control center, and issues a deployment warning; S3: The intelligent road cone detects the distance between the front and rear vehicles in real time through the sensing module and the communication module, determines the risk factor between the vehicles, and takes early warning when the risk is too high; provides different warning information to the vehicles directly entering the warning area and the subsequent traffic flow to ensure safe driving; S4: When a vehicle enters the warning range, the warning measures are updated in real time. The control center continuously monitors traffic flow and road conditions and adjusts the warning strategy in real time. S5: After the road construction is completed, the control center updates the warning rules and controls the smart road cones to be gradually withdrawn and recovered. During the recovery process of the smart road cones, the warning operation for surrounding vehicles is also maintained. Beneficial Effects

[0016] 1. The intelligent road cones in the present invention help to accurately determine the risk of accidents by identifying vehicle behavior, avoiding false alarms or too frequent alarms. At the same time, by understanding the specific behavior of the vehicle, the control center can more accurately assess which behavior will lead to higher risks, thereby optimizing the timing and content of the alarm. 2. In the present invention, the control center integrates the risk assessment information, traffic situation awareness information and historical data from each smart road cone to more accurately determine the overall risk level of the current construction section and adjust the warning strategy accordingly; when the risk level in a certain area is high, the warning frequency and intensity of the smart road cones in the area can be increased to remind vehicle drivers to pay attention to safety; and when the risk level generally rises at a certain time, warning information can be issued in advance to guide vehicles to detour or slow down to avoid congestion and accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a system structure diagram of the present invention; Figure 2 is a flow chart of the method of the present invention; Figure 3 Schematic diagram of the intelligent road cone of the present invention.

[0018] The markings in the attached drawings are: 1. shell; 2. warning light; 3. base; 4. roller. DETAILED DESCRIPTION

[0019] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0020] As attached Figure 1 As shown, the present invention discloses an intelligent road cone traffic warning system, including an intelligent road cone, a control center and a user terminal.

[0021] In this embodiment, the intelligent road cone is used to be deployed at road construction or accident sites to warn vehicles, collect road environment information through sensors, and perform risk assessment and early warning on vehicles based on the detected vehicle information. Figure 3 As shown, the smart road cone is made of green environmentally friendly materials, including a shell 1, a warning light 2, a base 3 and a roller 4, wherein the shell 1 is in a cone shape as a whole, narrow at the top and wide at the bottom, which effectively improves the stability of the smart road cone. The warning light 2 is located around the shell 1 and is used to warn vehicles through bright light colors. The base 3 is located at the bottom of the shell 1, and the functional module attached to the smart road cone is deployed in the base 3. At the same time, the roller 4 is installed on the lower side of the base 3, so that the smart road cone can move to the designated position by itself after receiving the deployment and recovery plan of the control center.

[0022] The functional modules of the intelligent road cone include perception module, data processing module, communication module, warning module and mobile module.

[0023] The perception module is equipped with various sensors for obtaining the real-time position information of the smart road cone and real-time perception of the surrounding environment information. The surrounding environment information includes but is not limited to the positions of other smart road cones, lane line information and traffic signs in the surrounding area. It also includes obtaining vehicle information, which includes but is not limited to the vehicle position, speed, acceleration, driving trajectory and vehicle model.

[0024] The data processing module is used to process and analyze the original perception data collected by the perception module to determine potential risks.

[0025] Specifically, the data processing module determines the risk by identifying vehicle behavior, including: S101: Define a fuzzy set for each key vehicle behavior, wherein the key vehicle behavior includes but is not limited to lane changing, acceleration, deceleration, too close distance between vehicles and normal driving, and set a corresponding membership function for the fuzzy set; that is, when the key vehicle behavior is too close distance between vehicles, the judgment indicator is the distance between vehicles, so the fuzzy set can be set to {very close, close, safe, far}, and its membership function can be calculated using one of the following types including but not limited to Gaussian, triangular or trapezoidal types.

[0026] S102: The data processing module calculates the membership of each vehicle behavior feature to the fuzzy set based on the vehicle information obtained in real time by the perception module. For example, the key vehicle behavior of too close distance is set in S101, and its too close membership calculation can use a Gaussian membership function: ;

[0027] in, is the actual vehicle distance, For safe distance, To adjust the standard deviation.

[0028] S103: Based on the fuzzy rule base, infer the comprehensive membership of the current state of the vehicle to various behaviors. The fuzzy rule base is used to describe the risk level corresponding to different behavior combinations, and the inference process can be: if the "vehicle speed" is "high speed" and the "vehicle distance" is "very close", then the risk level is "extremely high".

[0029] S104: using the maximum membership method, converting the comprehensive membership of the current vehicle state into a clear behavior judgment, that is, selecting the maximum value of the comprehensive membership as the judgment result.

[0030] S105: According to the behavior recognition threshold, the vehicle behaviors whose membership exceeds the behavior recognition threshold are screened. After selecting the maximum value of the comprehensive membership as the judgment result in step S104, the remaining comprehensive membership still needs to be calculated, and the behaviors whose comprehensive membership exceeds a certain standard are screened by setting the behavior recognition threshold.

[0031] S106: Prioritize the vehicle behaviors corresponding to the membership degrees according to the sizes of the membership degrees to generate a comprehensive behavior description, wherein behaviors with high membership degrees are more urgent or important in the current situation and therefore need to be responded to first, while other behaviors in the comprehensive behavior description can help the smart road cones and the control center decide on the next action.

[0032] Specifically, smart road cones can achieve differentiated warnings by identifying vehicle behavior, rather than just directly initiating an alarm after detecting that a vehicle has entered the monitoring range. This is because different vehicle behaviors, including but not limited to acceleration, deceleration, and lane changes, require different warning response times; for example, vehicles approaching a construction area quickly require more urgent and eye-catching alarms, while vehicles traveling slowly may require gentler reminders. At the same time, smart road cones identifying vehicle behavior can help accurately determine accident risks and avoid false alarms or too frequent alarms; by understanding the specific behavior of the vehicle, the control center can more accurately assess which behavior will lead to higher risks, thereby optimizing the timing and content of the alarm.

[0033] The communication module is used to exchange real-time data with other intelligent road cones, share vehicle information and its own status, and communicate with the control center to upload perception data and receive control instructions, as well as upload risk assessment information from the data processing module to the control center, so that the control center can have more comprehensive road condition information.

[0034] The warning module is used to provide differentiated warnings to vehicles entering the warning range based on data processing results through flashing lights, voice broadcasts, V2X communication and other methods.

[0035] The mobile module is used to move to a designated position on the road according to the optimal placement plan of the intelligent road cones obtained by the communication module.

[0036] Specifically, after the intelligent road cone detects a vehicle within the monitoring range through the perception module and obtains the vehicle information of the vehicle, the behavior of the vehicle is identified through the data processing module, and the risk level is evaluated. The warning module issues a preliminary warning to the vehicle through the data processing module; at the same time, the intelligent road cone sends the vehicle information to the control center in real time through the data communication module, and the control center further analyzes the risk of the vehicle and determines whether further warning strategies are needed; if yes, the intelligent road cone will take further warning reminders to the vehicle.

[0037] Therefore, in this embodiment, the control center is used to integrate the monitoring information of the smart road cones and dynamically adjust the placement plan and warning strategy of the smart road cones. The control center includes a data receiving module, a traffic situation perception module, a warning strategy optimization module and a command sending module.

[0038] Specifically, the data receiving module is used to receive the perception data, status information and risk information monitored by the intelligent road cone, and perform pre-processing operations such as cleaning, filtering and integration on the perception data and status information.

[0039] The traffic situation awareness module is used to access the real-time traffic data of the traffic management department, grasp the overall traffic conditions of the current road, and combine historical traffic data and machine learning algorithms to predict and analyze the traffic flow of the construction section. The prediction and analysis of the traffic flow of the construction section includes: S201: The traffic situation awareness module receives real-time road condition information and historical traffic information acquired by the traffic management department; S202: performing data cleaning, standardization and time alignment operations on the received data; S203: extracting traffic features from the data, the traffic features including but not limited to identifying lane data and the width of each lane in the construction area based on the location information and lane line information of the intelligent road cone, analyzing the traffic flow variation pattern of the construction area in different time periods based on the historical traffic flow data, identifying the speed limit and the prohibited lane change area in the road construction area based on the traffic sign information and traffic rules, etc.; S204: The traffic situation awareness module predicts the traffic flow change trend of the construction area in the future by using a machine learning algorithm and combining the real-time road condition information with the traffic features extracted from the historical traffic information; S205: According to the traffic flow change trend and the traffic characteristics of the road construction area, risk factors are set and weights are assigned to each risk factor to calculate a comprehensive risk score. The comprehensive risk score is used to evaluate the congestion risk and accident risk of each lane according to a preset threshold range.

[0040] The control center combines risk assessment information, traffic situation awareness information and historical data from various smart road cones to more accurately determine the overall risk level of the current construction section and adjust the warning strategy accordingly. When the risk level in a certain area is high, the warning frequency and intensity of smart road cones in that area can be increased to remind vehicle drivers to pay attention to safety. When the risk level generally rises within a certain period of time, warning information can be issued in advance to guide vehicles to detour or slow down, thereby avoiding congestion and accidents.

[0041] Specifically, by adding a traffic situation awareness module to the control center, the control center can adjust the placement of smart road cones and early warning strategies for vehicles in advance by predicting traffic flow and congestion, and guide vehicles to change lanes or slow down in advance to avoid congestion and accidents, rather than passively responding when risks occur.

[0042] The early warning strategy optimization module is used to dynamically adjust the placement of smart road cones and early warning strategies based on real-time road conditions, traffic forecast results, and risk information. The early warning strategy optimization module dynamically adjusts the early warning strategy in the following ways: S301: Establish a multi-factor model based on the traffic flow prediction results of the traffic situation awareness module and the characteristics and safety requirements of the road construction area to calculate the risk coefficient of each road section.

[0043] The multi-factor model comprehensively considers factors including but not limited to traffic flow, vehicle speed, road geometry and weather conditions, assigns weights to each factor according to its impact on traffic safety, and then performs a weighted sum to calculate the risk score for each road section: ; in, For each road segment, is the weight coefficient of each factor, The quantified factors The numerical value of .

[0044] S302: Standardizing the risk coefficients of different road sections, and using a clustering algorithm to divide the road sections in the road construction area into different groups according to the risk data, and determining the risk levels.

[0045] S303: The warning strategy optimization module calculates the warning strategy priority of each smart road cone in real time according to the risk information and vehicle position information uploaded by the smart road cone, the risk area division result, and the stored information in the warning rule library.

[0046] Among them, the warning strategy priority of each smart road cone is calculated, that is, the risk score of all warning strategies corresponding to each smart road cone is calculated, and the risk score The calculation formula is: ;

[0047] in, Provide a risk score for the early warning strategy corresponding to each smart road cone; Risk factors set for areas with different risk levels; is the weight coefficient, which is used to adjust the importance of different warning rules; It is the rule matching degree, which is used to indicate the matching degree between the current traffic condition and the warning rule.

[0048] In risk scoring, rule matching It measures the degree of match between the current traffic conditions and a specific strategy stored in the warning rule library. Therefore, its calculation needs to consider multiple factors. First, the characteristic values ​​related to the warning strategy are extracted from the real-time data, including but not limited to the current speed of the vehicle. , Distance between vehicle and smart road cone , the traffic density of the current lane and lane current occupancy ; Then, the characteristic values ​​of different units and magnitudes are standardized to a unified range, that is, they can be unified to the range of [0, 1] to ensure that different indicators are comparable in calculation. For example, the current speed of the vehicle Unify to the range of [0,1]. The specific unification method can be ,in is the unified value of the vehicle’s current speed, The minimum speed value of the vehicle's current speed for setting the speed on the current road, The maximum speed value of the vehicle's current speed for the current road setting speed; Then the standardized eigenvalues ​​are weighted and summed to obtain the rule matching degree : ;

[0049] in, is a preset weight used to indicate the importance of the feature value; In order to match the speed characteristic value with the required degree in the warning rule base, the interpolation inverse or normal distribution can be used for calculation, and the calculation of other characteristic values ​​is similar; is the current speed alarm threshold of the vehicle specified in the warning rule base, It is the alarm threshold of the distance between the vehicle and the intelligent road cone in the early warning rule.

[0050] S304: Selecting the warning strategy with the highest risk score as the execution strategy of the smart road cone at the current moment, and sending it to the corresponding smart road cone.

[0051] The early warning strategy optimization module dynamically adjusts the placement and playback plans of smart road cones before and after road construction, including: S401: Before the road construction begins, the initial layout of the smart road cones is preliminarily planned using a genetic algorithm according to the characteristics of the road construction area, safety requirements, and traffic flow change trends, wherein the initial layout includes but is not limited to the specific location coordinates, placement angles, and spacing of each smart road cone.

[0052] S402: The initial layout is then converted into specific coordinates and angles of each designated road cone, and an ant colony algorithm is used to calculate the optimal deployment path of each smart road cone, minimize the deployment time and impact on traffic, and determine a phased deployment strategy. The initial layout, the optimal deployment path, and the phased deployment strategy are combined to generate a smart road cone deployment plan.

[0053] S403: deploying smart road cones based on the smart road cone deployment solution, monitoring the deployment process in real time, and dynamically adjusting the deployment strategy through a reinforcement learning algorithm to cope with emergencies until the smart road cones are deployed at corresponding locations.

[0054] S404: After the road construction is completed, the early warning strategy optimization module uses a deep reinforcement learning algorithm to simulate different recovery orders and paths according to the construction completion status and real-time traffic conditions, formulates a safe and efficient intelligent road cone recovery plan, and generates a safe recovery route for each intelligent road cone.

[0055] Specifically, during the recycling process, a dynamic cluster of smart road cones is formed through a collaborative algorithm. The smart road cones detect the surrounding environment information in real time through the perception module and share the surrounding environment information between each module through the communication module. The data processing module calculates and updates the safety recycling index of each smart road cone, thereby coordinating the recycling action and ensuring the safety of the recycling process.

[0056] The warning strategy optimization module dynamically adjusts the warning strategy during the deployment and recovery of smart road cones. After receiving the deployment plan or playback plan from the control center, each smart road cone moves automatically according to the specified route and sequence. At the same time, during the process of self-movement, the smart road cones begin to monitor the vehicles and vehicle behaviors on the road in real time and transmit them to the control center, dynamically adjusting the warning strategy to prevent accidents caused by vehicles not noticing the smart road cones during the deployment or recovery process.

[0057] The instruction sending module is used to send warning strategies and control instructions to the intelligent road cone.

[0058] Specifically, when the control center determines that further warning reminders are needed for the vehicle, the smart road cone communicates with the infrastructure in the vehicle through V2X technology and sends the warning information of the control center to the user terminal.

[0059] The user terminal is used to receive warning information from the intelligent road cone and the control center, and send warning prompts to the user in the form of voice and images, and detour the road construction area according to the received warning information. The user terminal includes a data communication module and a user interface; The data communication module is used to receive warning information sent by the intelligent road cone and the control center.

[0060] The user interface is used to display the warning information received by the data communication module.

[0061] In this embodiment, there is also an intelligent road cone traffic warning method, as shown in the attached Figure 2 As shown, including: S1: At the beginning of road construction, the smart cone placement plan is determined based on the road construction location and traffic flow. The smart cone placement plan needs to ensure that the smart cones reasonably cover the lanes to provide accurate warnings.

[0062] S2: According to the control instructions issued by the control center, the smart road cone moves to the target position along the route planned by the smart road cone placement plan, and issues a deployment warning. When the smart road cone moves to the target position according to the planned route, the vehicle on the construction lane is reminded to slow down and change lanes through voice alarms and other measures. For vehicles in the normal lane, it is reminded that there is a vehicle changing lane on the side.

[0063] S3: Intelligent road cones detect the distance between the front and rear vehicles in real time through the perception module and communication module, judge the risk factor between vehicles, and take early warning when the risk is too high; provide different warning information for vehicles directly entering the warning area and subsequent traffic to ensure safe driving.

[0064] S4: When a vehicle enters the warning range, the warning measures are updated in real time. The control center continuously monitors traffic flow and road conditions and adjusts the warning strategy in real time.

[0065] S5: After the road construction is completed, the control center updates the warning rules and controls the smart road cones to be gradually withdrawn and recovered. During the recovery process of the smart road cones, the warning operation for surrounding vehicles is also maintained to prevent the sudden appearance of the recovery vehicles and the sudden movement of the smart road cones from affecting traffic.

[0066] Through the above-mentioned traffic warning method, effective safety warning and guidance are ensured for vehicles passing through the road construction area during the highway construction period, reducing the risk of accidents and improving traffic management efficiency.

[0067] The above-mentioned embodiments only express the preferred implementation modes of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person skilled in the art, several modifications, improvements and substitutions can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the attached claims.

Claims

1. An intelligent traffic cone warning system, characterized in that: Includes intelligent road cones, control center and user terminals; The smart road cone is used to be deployed in road construction areas to warn vehicles, collect road environment information through sensors, and conduct risk assessment and early warning of vehicles based on detected vehicle information; The control center is used to integrate the monitoring information of the smart road cones and dynamically adjust the placement plan and early warning strategy of the smart road cones; The user terminal is used to receive warning information from the smart road cone and the control center, and issue a warning to the user.

2. The intelligent road cone traffic warning system according to claim 1, characterized in that: The intelligent road cone includes a perception module, a data processing module, a communication module, an early warning module and a mobile module; The perception module is used to obtain the real-time position information of the smart road cone and perceive the surrounding environment information in real time, wherein the surrounding environment information includes the position of the surrounding smart road cone, lane line information and traffic signs; The data processing module is used to process and analyze the original perception data collected by the perception module to determine potential risks; The communication module is used to communicate with the control center, upload perception data and risk information and receive control instructions, and interact with other smart road cones in real time to share vehicle information and its own status; The warning module is used to provide differentiated warnings to vehicles entering the warning range according to the data processing results; The moving module is used to move to a designated position on the road according to the control instruction obtained by the communication module.

3. The intelligent traffic cone warning system according to claim 2, characterized in that: The data processing module determines potential risks specifically including: S101: defining a fuzzy set for each key vehicle behavior, the key vehicle behavior including lane changing, acceleration, deceleration and normal driving, and setting a corresponding membership function for the fuzzy set; S102: The data processing module calculates the membership degree of each vehicle behavior feature to the fuzzy set based on the vehicle information acquired in real time by the perception module; S103: Based on the fuzzy rule base, reasoning the comprehensive membership of the current state of the vehicle to various vehicle behaviors; S104: using a maximum membership method, converting the comprehensive membership of the current vehicle state into a clear behavior judgment; S105: According to the behavior recognition threshold, screening the vehicle behaviors whose comprehensive membership exceeds the behavior recognition threshold; S106: Prioritizing the vehicle behaviors corresponding to the comprehensive memberships according to the sizes of the comprehensive memberships, and generating a comprehensive behavior description.

4. The intelligent traffic cone traffic warning system according to claim 3, characterized in that: The control center includes a data receiving module, a traffic situation perception module, a warning strategy optimization module and a command sending module; The data receiving module is used to receive the perception data, status information and risk information monitored by the intelligent road cone, and perform preprocessing operations on the perception data and the status information; The traffic situation awareness module is used to access the real-time traffic data of the traffic management department and, combined with the historical traffic data, to predict and analyze the traffic flow of the construction section; The warning strategy optimization module is used to dynamically adjust the placement plan and warning strategy of the smart road cones according to real-time road conditions, traffic prediction results and risk information; The instruction sending module is used to send the placement plan and warning strategy of the smart road cone to the smart road cone, and send warning information to the user terminal.

5. The intelligent traffic cone traffic warning system according to claim 4, characterized in that: The traffic flow forecasting and analysis on the construction section includes: S201: The traffic situation awareness module receives real-time road condition information and historical traffic information acquired by the traffic management department; S202: performing data cleaning, standardization and time alignment operations on the received data; S203: extracting traffic characteristics from the data, the traffic characteristics including the number and width of lanes in the road construction area, traffic flow variation patterns and speed limits; S204: The traffic situation awareness module predicts the traffic flow change trend of the construction area in the future by using a machine learning algorithm and combining the real-time road condition information with the traffic features extracted from the historical traffic information; S205: According to the traffic flow change trend and the traffic characteristics of the road construction area, the congestion risk and accident risk of each lane are evaluated by setting risk factors.

6. The intelligent traffic cone traffic warning system according to claim 5, characterized in that: The early warning strategy optimization module dynamically adjusts the early warning strategy specifically including: S301: Establish a multi-factor model based on the traffic flow prediction result of the traffic situation awareness module and the characteristics and safety requirements of the road construction area to calculate the risk coefficient of each road section; S302: standardizing the risk coefficients of different road sections, and using a clustering algorithm to divide the road sections in the road construction area into different groups according to the risk data, and determining the risk level; S303: The warning strategy optimization module calculates the warning strategy priority of each smart road cone in real time according to the risk information and vehicle location information uploaded by the smart road cone, the risk area division result, and the stored information in the warning rule library; S304: Selecting the warning strategy with the highest risk score as the execution strategy of the smart road cone at the current moment, and sending it to the corresponding smart road cone.

7. The intelligent traffic cone warning system according to claim 6, characterized in that: The real-time calculation of the warning strategy priority of each smart road cone is to calculate the risk score of all warning strategies corresponding to each smart road cone. The calculation is: ； in, Provide a risk score for the early warning strategy corresponding to each smart road cone; Risk factors set for areas with different risk levels; is the rule matching degree, which is used to indicate the matching degree between the current traffic conditions and the warning rules; is the weight coefficient, which is used to adjust the importance of different warning rules.

8. The intelligent traffic cone warning system according to claim 7, characterized in that: The warning strategy optimization module also includes dynamically adjusting the placement of smart road cones: S401: Before the road construction begins, the initial layout of the intelligent road cones is preliminarily planned using a genetic algorithm according to the characteristics of the road construction area, safety requirements, and traffic flow change trends; S402: Based on the initial layout, an ant colony algorithm is used to calculate an optimal deployment path for each smart road cone, and a smart road cone deployment plan is generated; S403: deploying smart road cones based on the smart road cone deployment solution, monitoring the deployment process in real time, and dynamically adjusting the deployment strategy through a reinforcement learning algorithm until the smart road cones are deployed at corresponding positions; S404: After the construction is completed, the early warning strategy optimization module uses a deep reinforcement learning algorithm to simulate different recycling sequences and paths according to the construction completion status and real-time traffic conditions.

9. The intelligent traffic cone traffic warning system according to claim 8, characterized in that: The user terminal includes a data communication module and a user interface; The data communication module is used to receive warning information sent by the intelligent road cone and the control center; The user interface is used to display the warning information received by the data communication module.

10. The intelligent road cone traffic warning system according to claim 9, further comprising an intelligent road cone traffic warning method, characterized in that: Included are: S1: At the beginning of road construction, determine the placement plan of intelligent road cones according to the construction location and traffic flow; S2: The smart road cone moves to the target location along the route planned by the smart road cone placement plan according to the control command issued by the control center, and issues a deployment warning; S3: The intelligent road cone detects the distance between the front and rear vehicles in real time through the sensing module and the communication module, determines the risk factor between the vehicles, and takes early warning when the risk is too high; Provide different warning information to vehicles directly entering the warning area and subsequent traffic to ensure safe driving; S4: When a vehicle enters the warning range, the warning measures are updated in real time. The control center continuously monitors traffic flow and road conditions and adjusts the warning strategy in real time. S5: After the road construction is completed, the control center updates the warning rules and controls the smart road cones to be gradually withdrawn and recovered. During the recovery process of the smart road cones, the warning operation for surrounding vehicles is also maintained.

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