Child Safety Travel Monitoring and Warning Method Based on Intelligent Network Connection and Scenario Recognition

Abstract

The present disclosure provides a method for monitoring and alarming children's safe travel based on intelligent networking and scenario recognition, which combines hardware collection with software applications and is associated with a navigation system at the same time, expanding the scope of application. It has a summary report function inside to assist in cultivating good traffic habits for children, so as to further realize the real social traffic safety concept and the cultivation of traffic behaviors. An innovative communication platform for parents, schools, and relevant traffic departments is established to realize the sharing of traffic safety data among the three parties, and to comprehensively guarantee children's safety through three aspects: parent reminder, school supervision, and unified adjustment by the control center. The present disclosure effectively uses the networking system to better connect people, vehicles, roads, and the environment, and solves the comprehensive problems of children's safe travel detection and assistance based on the intelligent networking system.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of traffic safety, and particularly relates to a method for monitoring and alarming children's safe travel based on intelligent networking and scene recognition. Background Art

[0002] In recent years, road safety traffic accidents have occurred frequently, causing many deaths every year. Statistical data from the China Traffic Management Bureau shows that children's traffic accidents exhibit some obvious characteristics, among which the number of deaths in traffic accidents while walking is the largest. In most of these accidents, it is caused by unsafe behaviors of children such as suddenly crossing the road, running wildly, suddenly accelerating or turning back midway; secondly, most of the children's traffic accidents are caused by inadequate care of parents or guardians. The above situation highlights an urgent problem, that is, an innovative solution is needed to improve the safety of primary and middle school students when traveling. Therefore, it is particularly important to develop an intelligent networking system that can monitor and assist children's safe travel.

[0003] An intelligent networking system that can collect and analyze children's travel traffic data will contribute to improving children's traffic safety levels. Such a system can utilize the latest technologies, such as artificial intelligence, the Internet of Things, and big data analysis, to monitor children's behaviors in real time, especially their traffic behaviors on the road. First, the system can timely detect dangerous behaviors of children when crossing the road, such as suddenly crossing or running wildly, so as to take timely measures to avoid potential traffic accidents. Second, through positioning technologies, sensors and other devices, it can track children's behaviors on the road in real time, record children's movement trajectories, and collect intersection information. When the system detects dangerous behaviors, it can remind them to take necessary preventive measures and subsequent long-term education measures by issuing an alarm or sending a notice to parents. At the same time, form a network for storing data in the cloud. Third, the system can also analyze traffic data, identify hot spots where children often have traffic accidents, so as to guide relevant departments to take more targeted traffic safety measures. In previous disclosures, the comprehensiveness and intelligence of the disclosure were often ignored, and the networking system was not well utilized to better connect people, vehicles, roads, and the environment. The present disclosure aims to design a comprehensive method for detecting and assisting children's safe travel using an intelligent networking system. Summary of the Invention

[0004] In order to overcome the deficiencies of the above-mentioned prior art, the present disclosure provides a method for monitoring and alarming children's safe travel based on intelligent networking and scene recognition to solve the problems existing in the above-mentioned prior art.

[0005] The present disclosure provides a dynamic traffic control method based on intelligent networking and scene recognition, which is characterized by including the following steps:

[0006] S1. Create a WeChat mini-program adapted to the navigation system. Obtain the real-time location information of children's terminals and the number of children at intersections through the mini-program. Specify the map center point, zoom level, and map type. Display the map on the mini-program interface and upload the obtained data to the cloud storage platform;

[0007] S2. Set the standard number of people for traffic congestion and the maximum adjustable number by humans. The standard number of people for traffic congestion refers to the defining value for judging whether an intersection is congested. The maximum adjustable number by humans means that when the number of children at an intersection reaches a certain value, the congestion at the current intersection cannot be handled manually;

[0008] S3. Input the number of children at the current intersection. If the number of children at the current intersection is less than the standard number of people for traffic congestion, then determine that the intersection is a non-congested intersection and execute S4; otherwise, determine that the intersection is a congested intersection and execute S5;

[0009] S4. Mark it as a recommended intersection, set the intersection as a green intersection, and display it in the mini-program;

[0010] S5. Mark it as prohibited from using autonomous driving and send a suggestion of staggering school dismissal times to the school through the mini-program;

[0011] S6. If it is determined that the intersection is a congested intersection, then judge whether the number of children at the intersection is greater than the maximum adjustable number by humans. If the number of children at the intersection is greater than the maximum adjustable number by humans, then determine that the congestion degree of the intersection cannot be adjusted manually and execute S7; otherwise, determine that the intersection can be adjusted manually and execute S8;

[0012] S7. Send a warning message to the traffic control center through the mini-program. The traffic control center controls the traffic lights, broadcasts a warning for surrounding vehicles to detour, and marks the intersection in red on the mini-program, and sets the intersection as a red intersection;

[0013] S8. Send instructions to the school security system and mobile traffic management personnel near the intersection through the mini-program for manual dredging;

[0014] It also includes summarizing the information of the green intersections and the red intersections, and using statistical methods and machine learning algorithms to analyze and predict the changing trends and patterns of children's routes to and from school. The statistical methods include descriptive statistics or inferential statistics, and the prediction methods include time series analysis or regression analysis.

[0015] The technical effects of the present disclosure are:

[0016] The present disclosure combines hardware collection with software applications, making it more comprehensive. At the same time, it is associated with the navigation system, expanding the scope of application. It has a summary report function built-in to assist in cultivating children's good traffic habits, so as to further realize the true concept of socialized traffic safety and the cultivation of traffic behaviors. An innovative communication platform for parents, schools, and relevant traffic departments is established to achieve the sharing of traffic safety data among the three parties. Through parental reminders, school supervision, and unified adjustment by the control center, children's safety is comprehensively guaranteed in three aspects. A route recommendation function is also set up to better ensure the safety of children going to and from school. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] With reference to the accompanying drawings and the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. The drawings are used to better understand the solution and do not limit the present disclosure. In the drawings, the same or similar reference numerals represent the same or similar elements, where:

[0018] Figure 1 is a flowchart of the dynamic traffic control method based on intelligent network connection and scenario recognition provided by the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0020] As Figure 1 shown, in one embodiment, a dynamic traffic control method based on intelligent network connection and scenario recognition is provided, including:

[0021] S1. Create a WeChat mini-program adapted to the navigation system, obtain the real-time location information of children's terminals and the number of children at intersections through the mini-program, specify the map center point, zoom level, and map type, and display the map on the mini-program interface;

[0022] S2. Set the standard number of people for traffic congestion and the maximum adjustable number by humans. The standard number of people for traffic congestion refers to the defining value for judging whether an intersection is congested, and the maximum adjustable number by humans refers to the situation where when the number of children at an intersection reaches a certain value, the congestion at the current intersection cannot be handled manually;

[0023] S3. If the number of children at the current intersection is less than the standard number of people for traffic congestion, it is determined that the intersection is a non-congested intersection, and S4 is executed; otherwise, it is determined that the intersection is a congested intersection, and S5 is executed;

[0024] S4. Mark it as a recommended intersection, set this intersection as a green intersection, and display it in the mini-program.

[0025] S5. Mark it as prohibited from using autonomous driving, and send a suggestion of staggering school dismissal to the school through the mini-program.

[0026] S6. If it is determined that this intersection is a congested intersection, then judge whether the number of children at this intersection is greater than the maximum number that can be adjusted manually. If the number of children at this intersection is greater than the maximum number that can be adjusted manually, then determine that the congestion level of this intersection cannot be adjusted manually, and execute S7; otherwise, determine that this intersection can be adjusted manually, and execute S8.

[0027] S7. Send a warning message to the traffic control center through the mini-program. The traffic control center controls the traffic lights, broadcasts a warning to the surrounding vehicles to detour, and marks the intersection in red on the map in the mini-program, and set this intersection as a red section.

[0028] S8. Send instructions to the school security system and the mobile traffic management personnel near this section through the mini-program to conduct manual dredging.

[0029] It also includes summarizing the information of the green intersections and the red intersections, and using statistical methods and machine learning algorithms to analyze and predict the changing trends and patterns of the children's routes to and from school. The statistical methods include descriptive statistics or inferential statistics, and the prediction methods include time series analysis or regression analysis.

[0030] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification. The above is only the preferred specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by 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 dynamic traffic control method based on intelligent networking and scene recognition, characterized in that: The following steps are involved: S1. Create a WeChat applet compatible with the navigation system, obtain the real-time location information of the child's terminal and the number of children at the intersection through the applet, specify the center point of the map, the zoom level, and the map type, display the map in the applet interface, and upload the obtained data to the cloud storage platform; S2. Setting the standard number of people for traffic congestion and the maximum number of people who can be manually adjusted. The standard number of people for traffic congestion refers to the threshold for judging whether an intersection is congested. The maximum number of people who can be manually adjusted refers to the situation where the congestion at the current intersection cannot be manually handled when the number of children at the intersection reaches a certain value. S3, input the number of children at the current intersection. If the number of children at the current intersection is less than the traffic congestion standard number, the intersection is determined to be a non-congested intersection, and S4 is executed; Otherwise, the intersection is determined to be a congested intersection, and S5 is executed; S4, marking the intersection as a recommended intersection, setting the intersection as a green intersection, and displaying it in the mini program; S5. Mark as prohibiting the use of autonomous driving, and make suggestions to the school through the mini program on staggered school dismissal; S6, if it is determined that the intersection is a congested intersection, then determining whether the number of children at the intersection is greater than the maximum number that can be adjusted manually; if the number of children at the intersection is greater than the maximum number that can be adjusted manually, then determining that the congestion level of the intersection cannot be adjusted manually, and executing S7; Otherwise, it is determined that the intersection can be manually adjusted, and S8 is executed; S7. Sending warning information to the traffic control center through the mini program, and the traffic control center controls the traffic lights, broadcasts warnings to surrounding vehicles to detour, and marks the intersection on the map in red in the mini program to remind that the intersection is a red intersection; S8. Sending instructions to the school security system and mobile traffic management personnel near the intersection through the mini program to manually clear the road; It also includes summarizing the information of the green intersection and the red intersection, and using statistical methods and machine learning algorithms to analyze and predict the changing trends and patterns of children's commuting routes to and from school, the statistical methods including descriptive statistics or inferential statistics, and the prediction methods including time series analysis or regression analysis.

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