Intelligent road full-time-space instant simulation system

Abstract

The invention discloses an intelligent road full-time-space instant simulation system, which comprises a data acquisition subsystem, a data transmission subsystem, an edge calculation subsystem, an interface display subsystem and a situation expansion subsystem, the data acquisition subsystem depends on data provision of a road camera, an unmanned aerial vehicle group and a large map database, the data transmission subsystem establishes a ground-ground and ground-air two-dimensional transmission network, and the edge calculation subsystem is connected with the data acquisition subsystem through the road camera serving a road and a built-in computer processor in the unmanned aerial vehicle group. The situation development subsystem provides real-time data processing, calculation and analysis services for road conditions, the interface display subsystem provides a simulation interface and can visually display the real-time road conditions, and the situation development subsystem provides graded and intelligent interface selection for the interface display subsystem. The method has the beneficial effects that road information can be analyzed in real time, and full-time-space, intelligent and visual intelligent road service is provided through deployment of road-air computing power.

Description

Technical Field

[0001] The invention relates to the field of intelligent services and data processing, and specifically to a full-time and space real-time simulation system for smart roads. Background Art

[0002] With the rapid development of society and the acceleration of urbanization, traffic congestion, road safety and environmental pollution have become common challenges faced by many cities around the world. In particular, with the continuous increase in the number of vehicles, traditional road traffic management methods have been unable to meet the increasingly complex traffic needs. In order to meet this challenge, intelligent transportation systems have come into being. Intelligent transportation systems use modern information technology, communication technology and control technology to achieve real-time monitoring, analysis and management of traffic to improve traffic efficiency, ensure traffic safety and reduce energy consumption. However, although intelligent transportation systems have been applied in many cities and regions, there are still some problems with the current traffic management system, especially when dealing with complex traffic scenarios, emergencies and extreme weather conditions, the intelligence, real-time and predictive capabilities are still insufficient. Most of the existing systems adopt a centralized processing method, and the monitoring and decision-making of traffic status rely on a single data source or a traditional sensor network, which is difficult to achieve comprehensive, accurate and real-time response in some environments and special circumstances.

[0003] At present, although the application scope of intelligent transportation systems is becoming wider and wider, there are still many limitations. Traditional traffic monitoring systems mainly rely on fixed cameras and ground sensors on the road. These devices can only capture part of the information on the road surface and it is difficult to fully perceive the complex conditions of the road. In addition, most of these systems are based on a centralized processing architecture, and the distribution of data collection and analysis is relatively fixed, which makes it difficult for the system to respond in time when facing changing traffic environments or special scenarios, and the processing capacity is limited. For example, the traffic monitoring and accident handling capabilities in specific environments such as mountain roads, tunnels, and bridges are relatively weak. In order to make up for these shortcomings, many systems have begun to introduce drone technology to provide dynamic monitoring through the aerial perspective of drones. However, traditional drone applications are mostly limited to local monitoring in a short period of time and lack systematic and continuous data collection capabilities. Summary of the invention

[0004] In view of the above problems, the present invention aims to provide a smart road full-time and space real-time simulation system to solve the problems raised in the above background technology.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] The present invention provides a full-time and space real-time simulation system for a smart road, including a data acquisition subsystem, a data transmission subsystem, an edge computing subsystem, an interface display subsystem, and a situation expansion subsystem; the data acquisition subsystem includes a camera array module, a drone group coordination module, and a large map module. The camera array module is a camera deployed on the road, which is the first way for the data acquisition subsystem to obtain online data. Cameras are deployed on each road to provide services for monitoring the real-time dynamics of the road. All roads contained in an area are monitored by the deployed cameras, forming a camera array, which provides basic guarantee for ground-to-ground monitoring of all roads in the area of ​​the data acquisition subsystem. In particular, the camera array is deployed along the road, at intersections, in tunnels, and under bridges to complete the data collection under obstructed conditions; the drone swarm collaboration module is the second way for the data collection subsystem to obtain online data. It is mainly responsible for real-time ground-to-air monitoring and can be deployed in the blind spots of the camera array to achieve mobile deployment. In particular, it is used for special road conditions, mountainous road conditions, and disaster area road conditions; the big map module is the way for the data collection subsystem to obtain offline data. It is the underlying database resource of the data collection subsystem. The big map data is open source data, and the data source is mainly KML files downloaded from BIGEMAP, and is based on the statistics of road conditions over the years, the statistics of the same period, and Based on the statistics of the same period, the load of a certain road and a certain period of time is arranged. According to the size of the load, the elevation level download is decided. For the elevation information of the map, the higher the level, the more detailed the map is and the more cache resources are required. The data transmission subsystem provides the interconnection of online data, including the gateway module and the link module. The gateway module transmits the camera array data to the edge computing subsystem through the Internet protocol in the form of optical fiber transmission. The link module transmits the road video data collected by the drone group to the edge computing subsystem through the L link in the form of downlink L link transmission. After the edge computing subsystem receives the data transmitted by the camera array and the drone group, it transmits the data to the edge computing subsystem through the L link. Through the IoT4Edge framework, intelligent data calculation and processing of remote edges between ground and ground and between ground and air are realized, and the numerical results calculated by the edge computing subsystem are visualized in the interface display subsystem. The situation expansion subsystem enhances the real-time performance, operability and interactivity on the basis of the interface display subsystem, including the map elevation module and the map grid module. The map elevation module provides elevation map information for the situation expansion subsystem, provides differentiated processing for key monitoring areas, and can reduce loading and unloading caches. The map grid module separates map areas in a rasterized manner, and provides users with small-area, dynamic, and high-precision display and interactive services in the form of selection, color blocks, and dynamic deduction.

[0007] Furthermore, the data acquisition subsystem includes a camera array module, a drone swarm coordination module and a large map module. The camera array module collects traffic dynamic information on the road in real time through multiple cameras deployed on the road, including multiple high-definition cameras. The cameras are arranged along the road, at intersections, in tunnels, and under bridges to form a complete camera array, providing all-round support for real-time monitoring of roads. In an area, all roads are covered by cameras, forming a complete ground-to-ground monitoring system. The cameras transmit road traffic status, road conditions, and weather conditions to the data transmission subsystem through real-time video stream transmission, providing basic data for subsequent data processing and decision-making.

[0008] Furthermore, the drone swarm collaboration module can achieve rapid deployment through drone swarms and flexibly respond to various emergencies, disaster road conditions and complex mountain terrain. Drones can quickly enter the blind spots of the camera array monitoring and flexibly adjust the flight path according to changes in road traffic conditions to achieve efficient collection of traffic flow, vehicle speed, and license plate number recognition information. The drone swarm collaboration module has multi-machine collaborative combat capabilities. Through coordinated flight, it can monitor over a wider area and transmit road information in real time. It is particularly suitable for handling large-scale traffic incidents and natural disaster emergencies.

[0009] Furthermore, the big map module relies on open source data resources, especially the KML files provided by the BIGEMAP platform, combined with big data analysis technology, to provide the system with accurate road basic data based on the historical statistics of the roads, the traffic flow data of the same period, and the road load conditions in different time periods. The big map module provides rich spatial information support for edge computing and data processing through a high-precision geographic information system (GIS). The degree of sophistication of the map data will be dynamically adjusted according to demand. In sections with heavy traffic loads or during peak hours, the system will automatically download high-precision map data to facilitate more accurate road status assessment in subsequent data processing and calculations. For areas with light loads and low traffic, the map data can appropriately reduce the accuracy to reduce system resource consumption; the big map module also supports the download and processing of elevation information. There may be large elevation differences in roads in different geographical areas, especially in mountainous areas or urban elevated roads. The big map module supports multi-level elevation data downloads and can dynamically select to load different levels of map data according to different road conditions, thereby achieving accurate tracking and intelligent analysis of elevation changes.

[0010] Furthermore, the data transmission subsystem is mainly composed of two modules: a gateway module and a link module. The gateway module aggregates, processes and transmits the data collected by the camera array, and has data aggregation and forwarding functions. The camera array module transmits real-time monitoring data to the gateway module through the optical fiber network. The gateway module integrates and formats the data and then transmits it to the edge computing subsystem. It supports TCP / IP, HTTP, and MQTT communication protocols. The gateway module enables the system to process data from different devices to ensure its interoperability.

[0011] Furthermore, the link module is responsible for ground-to-air data transmission, mainly processing the return of road video data collected from the drone fleet, and using the L-band link with a frequency band of 960-1215MHz to receive telemetry data. The L-link supports high-bandwidth, low-latency transmission, which can ensure that the high-definition video stream collected by the drone is not distorted during the return process, while reducing delays and realizing real-time monitoring and analysis. The L-link can ensure the stability of the transmission link through optimized frequency selection and signal modulation. Through frequency hopping and intelligent signal adjustment, the L-link can adapt to complex environments and reduce transmission errors caused by signal interference.

[0012] Furthermore, the edge computing subsystem processes, calculates and analyzes the data collected by the data acquisition subsystem in real time. Due to the complexity and dynamics of the road traffic system, the edge computing subsystem needs to have high computing power, low latency, high real-time and reliability. Especially in traffic management and disaster warning scenarios, it is necessary to respond to system inputs in real time to generate efficient decision support. The edge computing subsystem is mainly composed of a camera array module and a drone swarm collaboration module. Combined with the IoT4Edge framework, it provides efficient and reliable data processing and service support through ground-to-ground and ground-to-air remote intelligent computing.

[0013] Furthermore, the IoT4Edge framework is as follows:

[0014] (1) IoT4Edge framework based on the whole space

[0015] The IoT4Edge framework is built on a complex road of R1*R2. The complex road includes terrain features along the road, intersections, tunnels, under bridges, and mountainous areas. The road along the road is mainly long straight lines, representing the high-speed road A1. The intersection is mainly characterized by turns and poor road features. The road monitoring deployment device is a camera, which represents the typical road features of the intersection A2. The road monitoring deployment device is a camera. The tunnel is mainly composed of long straight roads with road features covered by mountains on the outside, representing the feature that the road cannot be known from the outside A3. The road monitoring deployment device is a camera. The under bridge is mainly composed of spatially reused roads, representing the road features of the vertical space in the unified plane position A4. The road monitoring department The deployment device is a camera. The mountainous area is mainly rugged roads, which represents the road feature A5 that is tortuous and difficult to deploy cameras and network transmission equipment. The road monitoring deployment device is a drone. All roads contained in an area are monitored by deployed cameras, forming a camera array B1. The designated locations in the special spatial area are monitored by deployed drones, forming a drone fleet B2. Among them, the special spatial area refers to the mountainous area in the IoT4Edge framework based on the entire space. The designated location is the area that the user requires to survey. If on a complex road R1*R2, it contains |B1| cameras and |B2| drones, the deployment height of each camera is unified as h1, and the camera field of view is unified as θ 1 , where θ 1 ∈[0,360], the camera pitch angle is α 1 , the camera monitoring coverage is The drone deployment height is h2, and the drone's downward field of view angle is α 2 , the drone monitoring range is The horizontal distance that each camera can detect is In the IoT4Edge framework based on full space, cameras are evenly distributed and drones are deployed in a mobile manner. The IoT4Edge framework based on full space divides roads along the road, at intersections, in tunnels, under bridges, and in mountainous areas into grids, that is, each Perform a grid division, and the camera field of view angle is unified to θ 1 , where θ 1 ∈[0,360], the road width can be ignored, so we have Roads of a certain length can be monitored by cameras. The computing offloading strategy of the IoT4Edge framework based on the entire space is partial offloading, that is, according to the working conditions of occupied cameras and idle cameras, if the idle camera is in the neighborhood of the occupied camera, the occupied camera will offload part of the task to the idle camera to speed up the computing processing capability of the entire framework. The framework contains two evaluation criteria, namely cost and computing efficiency. The cost is reflected in the deployment cost, working time cost, and rental cost. The deployment cost is the cost incurred by deploying each camera, the working time cost is the product of the working cost per unit time of each camera and the working time, and the rental cost is the cost incurred by renting idle resources to speed up the task completion rate under the partial offloading strategy. The computing efficiency is reflected in that each camera can be regarded as an edge computing node that can calculate independently, and the energy consumption occupied by these edge computing nodes is the computing efficiency; if the cost of deploying each camera is c 11 , work unit time cost c 12 , working time is T 1 , the offloading strategy of the i-th occupied camera is y i ∈{0,1}, where y i =1 means that the i-th camera needs to unload data to an idle node, y i =0 means that the i-th camera does not need to unload data to the idle node. The idle cameras in the neighborhood are in a competitive relationship, that is, each camera wants to compete for the rental cost provided by camera i when it is idle. The equity incentive mechanism is used to conduct competitive screening of cameras in the neighborhood. If the TA neighborhood of camera i contains S idle cameras, where the TA neighborhood is the neighborhood where camera i can establish a communication range, the initial equity of each idle camera is c rent The first round of the equity incentive mechanism is to carry out the Euclidean distance screening competition, and the distance from the i-th occupied camera to the s-th idle camera is Then the equity update of the first round of Euclidean distance screening competition is: in is the distance matrix, d i1 is the distance from the i-th occupied camera to the first idle camera, d i2 is the distance from the i-th occupied camera to the second idle camera, d is is the distance from the ith occupied camera to the sth idle camera, x i is the horizontal coordinate of the i-th occupied camera, x 1 is the horizontal coordinate of the first idle camera, x 2 is the horizontal coordinate of the second idle camera, x s is the horizontal coordinate of the sth idle camera, y iis the ordinate of the i-th occupied camera, y 1 is the ordinate of the first idle camera, y 2 is the ordinate of the second idle camera, y s is the ordinate of the sth idle camera, SORT is the ascending sorting function, · is the dot product, and the second round of cost bidding competition is carried out. The principle is: idle cameras at a longer distance will sacrifice costs in exchange for tasks and strive for rental costs due to the influence of higher transmission delays. If the rental cost is c rent , when the idle node is far away from the occupied camera, it will have a cheaper cost according to probability, and the probability of unloading the sth idle camera is p 1 for Affected by the cost bidding competition, there is a discount factor γ, a decay factor of ε per round, and the bottom line cost of the sth idle camera is The initial cost is r 0 , then the cost after the g-th round of bidding is The lower the cost, the greater the probability that the framework will choose to offload. Then the probability that the sth idle camera is selected according to the cost is p 2 for:

[0017]

[0018] The comprehensive unloading probability of the sth idle camera is p 1 *p 2 , the equity obtained through the equity incentive mechanism is updated as follows:

[0019]

[0020] in<R→s> It means that under the equity incentive mechanism, the idle node s is selected to unload the camera, the corresponding equity is R, and the total cost of the camera is C 1 C 1 =|B1|(c 11 +c 12 T 1 )+∑ i y i <R→s> , the camera computing efficiency is Among them, k i is the constant of the camera chip architecture, f i is the CPU cycle of the ith camera, in revolutions per second, D i is the amount of data generated by the task of the i-th camera, a i The number of CPU cycles required to complete 1 bit of data offloading;

[0021] (2) IoT4Edge framework based on full time

[0022] The characteristics of the full-time IoT4Edge framework are as follows: based on the historical statistics of road traffic conditions, the road conditions are calculated based on the 24-hour standard, and the road conditions of the gridded roads are counted with an accuracy of 1 hour, recording each The length of the traffic flow through within 1 hour, and the traffic flow thresholds are set as δ 1 , δ 2 ,have: A smooth road refers to a road with a traffic flow rate lower than δ 1 , that is, in On roads with a length of δ, the camera remains idle within the 1h statistical duration. For general roads, the vehicle flow Through threshold is δ 1 and δ 2 between On roads with a length of δ, the amount of data collected by the camera within a one-hour statistical period does not exceed the data processing capacity of the camera within a one-hour statistical period. Congested roads refer to roads with a traffic flow rate higher than δ 2 , that is, in On a road with a length of 1 hour, the camera remains in an occupied state within the 1-hour statistical period. The amount of data from the camera within the 1-hour statistical period exceeds the data processing capacity of the camera within the 1-hour statistical period. The task needs to be offloaded to other idle cameras in the neighborhood to complete the task processing.

[0023] The full-time IoT4Edge framework stipulates that the road congestion situation is counted every other cycle. If a road is continuously counted as congested within a cycle, the full-time IoT4Edge framework will recommend adding cameras to speed up the task processing capacity of the road and reduce the rental cost.

[0024] The full-time IoT4Edge framework stipulates that if the variance of road congestion in the same period within a cycle exceeds the expected threshold, that is, irregular events or special events have occurred, causing the road conditions to change. It is necessary to record the road conditions at the statistical moment when the cycle begins (congestion = 1, general = 0.5, smooth = 0) and the road conditions at the statistical moment when the cycle ends (congestion = 1, general = 0.5, smooth = 0). If the value in the road condition is increasing, it means that the road condition is developing towards congestion, and it is necessary to dispatch additional drone fleets to assist the system in road monitoring.

[0025] Furthermore, the data visualization module converts the massive data obtained through cameras and drones into visual charts, maps, and dynamic animations to help users understand complex road information and make decisions quickly. The system displays the real-time traffic status of roads through dynamic maps. The system dynamically displays the real-time status of roads by refreshing maps in real time. The data displayed includes traffic flow, vehicle speed, road conditions, and weather warnings. On the map interface, users can choose different view levels to view real-time data for the entire city or a local area. They can predict the future traffic of a road based on historical data to help traffic managers make plans in advance.

[0026] Furthermore, the situation expansion subsystem is responsible for further processing and enhancing the real-time road information based on the data display, providing hierarchical and intelligent interface selection and operation to support more detailed and accurate decision-making and interactive operations. The situation expansion subsystem mainly includes the map elevation module and the map grid module, which respectively provide differentiated processing of elevation information and fine division of map areas, enhancing the hierarchy, intelligence and operability of road information. The map elevation module provides elevation information for the road simulation system to help users perform differentiated analysis in road environments with complex terrain. Through the processing of the elevation module, the system can accurately reflect the road elevations in different areas and provide more To provide more refined and dynamic traffic management support, the map grid module is responsible for dividing the road map area into multiple grids, further refining the display of road information, and providing users with high-precision and dynamic traffic information display. Rasterization is to convert continuous geographic information data into discrete grid units. Each grid represents a specific geographical area. Through the grid module, the system can dynamically select specific areas and information levels as needed to meet the needs of different users. The grid display can be updated according to real-time data and dynamically adjust the display area. When a traffic accident occurs, the grid data of the accident area will be updated in real time, changing its color or status mark, and immediately reflected on the map. The system can also automatically expand or reduce the scope of the grid area according to actual needs, so as to focus on displaying the traffic conditions of a hot spot area. When a large-scale traffic incident occurs, the grid module can zoom in on the specific area and provide more detailed information to facilitate managers to handle it urgently.

[0027] The present invention proposes an IoT4Edge framework based on full space and an IoT4Edge framework based on full time, which include terrain features along roads, intersections, tunnels, under bridges, and mountainous areas. In the IoT4Edge framework based on full space, the framework gridding can enhance the statistical granularity of the entire system, and considers the deployment scheme of cameras and the edge collaboration of idle nodes, and takes cost and computing performance as evaluation indicators. The equity incentive mechanism is used to conduct competitive screening of cameras in the neighborhood to increase the utilization rate of idle resources and mobilize the activity of spatial computing resources of the entire system. Two rounds of competition were carried out in the incentive mechanism. The first round was a Euclidean distance screening competition, that is, within the neighborhood of the communication range, the closer ones were given priority. The second round was a cost bidding competition. The principle was: idle cameras at a farther distance would sacrifice costs in exchange for tasks and strive for rental costs due to the impact of higher transmission delays, that is, cheaper ones were given priority. Finally, the equity incentive mechanism took both into consideration and selected nodes that were not far away and had reasonable prices as unloading nodes. On the one hand, it could reduce the load on occupied camera resources; on the other hand, it reduced the rental cost of the entire system, bringing cheaper and more reliable services. In the full-time IoT4Edge framework, the road traffic conditions are calculated based on the historical statistical time and the 24-hour standard. The road conditions are counted with an accuracy of 1 hour for the gridded roads, and it is stipulated that the road congestion conditions are counted every other cycle. If a road is continuously counted as congested during the cycle, the full-time IoT4Edge framework will recommend adding cameras to speed up the task processing capacity of the road and reduce the rental cost. If the variance of the road congestion conditions during the same period of a cycle exceeds the expected threshold, that is, an irregular event or special event has occurred, which has caused the road conditions to change, and the road conditions at the statistical time when the cycle starts need to be recorded.

[0028] The road conditions at the statistical moment of the end of the cycle (congestion = 1, general = 0.5, smooth = 0) are compared with the road conditions at the statistical moment of the end of the cycle (congestion = 1, general = 0.5, smooth = 0). If the value in the road condition is increasing, it means that the road condition is developing towards congestion, and it is necessary to increase the number of drones to assist the system in road monitoring. Based on the full-time IoT4Edge framework, by statistically analyzing historical data and the variance of statistics within the cycle, it intelligently evaluates whether the camera resources are reasonably deployed in the original deployment scenario, whether it is necessary to increase camera resources, or to dispatch drone resources for short-term road reconnaissance, which brings smart choices for flexible road deployment.

[0029] The innovation of the system in visual display is also very valuable. Through the integrated interface display subsystem, the system can display huge traffic information and data to users through an intuitive and easy-to-operate interface. Users can view the traffic flow, vehicle speed, and road status information of the entire city or a specific road in real time. At the same time, the interactivity of the system enables users to query, analyze, and operate the information as needed. This visual display not only enhances the user's intuitive perception of road conditions, but also enables traffic management personnel to make decisions and management more efficiently. Especially in emergency situations, operators can quickly understand and dispatch road conditions, avoid human omissions, and ensure the timeliness of emergency response. The situation expansion subsystem of the present invention provides users with a more accurate and dynamic display of traffic information by introducing a map elevation module and a map grid module. The elevation module combines the terrain undulation data of the road and can perform differentiated processing for road conditions in different geographical environments. Especially in complex mountainous areas, tunnels, and bridge areas, the system can provide more accurate road condition information to help traffic management personnel identify potential risk points and adjust traffic strategies in a timely manner. The map grid module can provide users with more detailed local area data display by dividing the map area into several small blocks, especially in key areas with dense traffic flow and frequent accidents. It can accurately display the status of the road through raster processing and provide dynamic analysis and prediction based on the data of the area. This hierarchical and detailed display method enhances the interactivity and intelligence of the system, allowing users to respond quickly when faced with complex road conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The invention is further illustrated by the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0031] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0032] 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.

[0033] See also Figure 1 The present invention will be further described in conjunction with the following examples.

[0034] See also Figure 1The present invention aims to provide a full-time and space real-time simulation system for smart roads, including a data acquisition subsystem, a data transmission subsystem, an edge computing subsystem, an interface display subsystem, and a situation expansion subsystem; the data acquisition subsystem includes a camera array module, a drone swarm collaboration module, and a large map module. The camera array module is a camera deployed on the road, which is the first way for the data acquisition subsystem to obtain online data. Cameras are deployed on each road to provide services for monitoring the real-time dynamics of the road. All roads contained in an area are monitored by the deployed cameras, forming a camera array, which provides basic protection for ground-to-ground monitoring of all roads in the area of ​​the data acquisition subsystem. Obstacles, in particular, camera arrays are deployed along roads, intersections, tunnels, and under bridges to complete data collection under obstructed conditions; the drone swarm collaboration module is the second way for the data collection subsystem to obtain online data. It is mainly responsible for real-time ground-to-air monitoring and can be deployed in blind spots of camera arrays to achieve mobile deployment. In particular, it is used for special road conditions, mountainous road conditions, and disaster area road conditions; the big map module is the way for the data collection subsystem to obtain offline data. It is the underlying database resource of the data collection subsystem. The big map data is open source data, and the data source is mainly from KML files downloaded by BIGEMAP, and according to the statistics of road conditions over the years and the statistics of the same period, The data transmission subsystem provides the interconnection and interoperability of online data, including the gateway module and the link module. The gateway module transmits the camera array data to the edge computing subsystem through the Internet protocol, and the transmission mode is optical fiber transmission. The link module transmits the road video data collected by the drone group to the edge computing subsystem through the L link in the downlink L link transmission mode. After the edge computing subsystem receives the data transmitted by the camera array and the drone group, Through the IoT4Edge framework, intelligent data calculation and processing at the remote edge of ground-ground and ground-air are realized, and the numerical results calculated by the edge computing subsystem are visualized in the interface display subsystem. The situation expansion subsystem enhances the real-time, operability and interactivity on the basis of the interface display subsystem, including the map elevation module and the map grid module. The map elevation module provides elevation map information for the situation expansion subsystem, provides differentiated processing for key monitoring areas, and can reduce loading and unloading caches. The map grid module separates map areas in a rasterized manner, and provides users with small-area, dynamic, and high-precision display and interactive services in the form of selection, color blocks, and dynamic deduction.

[0035] Specifically, the data acquisition subsystem includes a camera array module, a drone swarm coordination module and a large map module. The camera array module collects real-time traffic dynamic information on the road through multiple cameras deployed on the road, including multiple high-definition cameras. The cameras are arranged along the road, at intersections, in tunnels, and under bridges to form a complete camera array, providing all-round support for real-time monitoring of roads. In an area, all roads are covered by cameras, forming a complete ground-to-ground monitoring system. The cameras transmit road traffic status, road conditions, and weather conditions to the data transmission subsystem through real-time video stream transmission, providing basic data for subsequent data processing and decision-making.

[0036] Specifically, the drone swarm collaboration module can achieve rapid deployment through drone swarms and flexibly respond to various emergencies, disaster road conditions and complex mountain terrain. Drones can quickly enter the blind spots of the camera array monitoring and flexibly adjust the flight path according to changes in road traffic conditions to achieve efficient collection of traffic flow, vehicle speed, and license plate number recognition information. The drone swarm collaboration module has multi-machine collaborative combat capabilities. Through coordinated flight, it can monitor over a wider area and transmit road information in real time. It is particularly suitable for handling large-scale traffic incidents and natural disaster emergencies.

[0037] Specifically, the big map module relies on open source data resources, especially the KML files provided by the BIGEMAP platform, combined with big data analysis technology, to provide the system with accurate road basic data based on the historical statistics of the roads, the traffic flow data of the same period, and the road load conditions in different time periods. The big map module provides rich spatial information support for edge computing and data processing through a high-precision geographic information system (GIS). The degree of sophistication of the map data will be dynamically adjusted according to demand. In sections with heavy traffic loads or during peak hours, the system will automatically download high-precision map data to facilitate more accurate road status assessment in subsequent data processing and calculations. For areas with light loads and low traffic, the map data can appropriately reduce the accuracy to reduce system resource consumption. The big map module also supports the download and processing of elevation information. There may be large elevation differences in roads in different geographical areas, especially in mountainous areas or urban elevated roads. The big map module supports multi-level elevation data downloads and can dynamically select to load different levels of map data according to different road conditions, thereby achieving accurate tracking and intelligent analysis of elevation changes.

[0038] Specifically, the data transmission subsystem is mainly composed of two modules: the gateway module and the link module. The gateway module aggregates, processes and transmits the data collected by the camera array, and has data aggregation and forwarding functions. The camera array module transmits real-time monitoring data to the gateway module through the optical fiber network. The gateway module integrates and formats the data and then transmits it to the edge computing subsystem. It supports TCP / IP, HTTP, and MQTT communication protocols. The gateway module enables the system to process data from different devices to ensure its interoperability.

[0039] Specifically, the link module is responsible for ground-to-air data transmission, mainly processing the return of road video data collected from the drone fleet, and using the L-band link with a frequency band of 960-1215MHz to receive telemetry data. The L-link supports high-bandwidth, low-latency transmission, which can ensure that the high-definition video stream collected by the drone is not distorted during the return process, while reducing delays and realizing real-time monitoring and analysis. The L-link can ensure the stability of the transmission link through optimized frequency selection and signal modulation. Through frequency hopping and intelligent signal adjustment, the L-link can adapt to complex environments and reduce transmission errors caused by signal interference.

[0040] Specifically, the edge computing subsystem processes, calculates and analyzes the data collected by the data acquisition subsystem in real time. Due to the complexity and dynamics of the road traffic system, the edge computing subsystem needs to have high computing power, low latency, high real-time and reliability. Especially in traffic management and disaster warning scenarios, it is necessary to respond to system inputs in real time and generate efficient decision support. The edge computing subsystem is mainly composed of a camera array module and a drone swarm collaboration module. Combined with the IoT4Edge framework, it provides efficient and reliable data processing and service support through ground-to-ground and ground-to-air remote intelligent computing.

[0041] Specifically, the IoT4Edge framework is as follows:

[0042] (1) IoT4Edge framework based on the whole space

[0043] The IoT4Edge framework is built on a complex road of R1*R2. The complex road includes terrain features along the road, intersections, tunnels, under bridges, and mountainous areas. The road along the road is mainly long straight lines, representing the high-speed road A1. The intersection is mainly characterized by turns and poor road features. The road monitoring deployment device is a camera, which represents the typical road features of the intersection A2. The road monitoring deployment device is a camera. The tunnel is mainly composed of long straight roads with road features covered by mountains on the outside, representing the feature that the road cannot be known from the outside A3. The road monitoring deployment device is a camera. The under bridge is mainly composed of spatially reused roads, representing the road features of the vertical space in the unified plane position A4. The road monitoring department The deployment device is a camera. The mountainous area is mainly rugged roads, which represents the road feature A5 that is tortuous and difficult to deploy cameras and network transmission equipment. The road monitoring deployment device is a drone. All roads contained in an area are monitored by deployed cameras, forming a camera array B1. The designated locations in the special spatial area are monitored by deployed drones, forming a drone fleet B2. Among them, the special spatial area refers to the mountainous area in the IoT4Edge framework based on the entire space. The designated location is the area that the user requires to survey. If on a complex road R1*R2, it contains |B1| cameras and |B2| drones, the deployment height of each camera is unified as h1, and the camera field of view is unified as θ 1 , where θ 1 ∈[0,360], the camera pitch angle is α 1 , the camera monitoring coverage is The drone deployment height is h2, and the drone's downward field of view angle is α 2 , the drone monitoring range is The horizontal distance that each camera can detect is In the IoT4Edge framework based on full space, cameras are evenly distributed and drones are deployed in a mobile manner. The IoT4Edge framework based on full space divides roads along the road, at intersections, in tunnels, under bridges, and in mountainous areas into grids, that is, each Perform a grid division, and the camera field of view angle is unified to θ 1 , where θ 1 ∈[0,360], the road width can be ignored, so we have Roads of a certain length can be monitored by cameras. The computing offloading strategy of the IoT4Edge framework based on the entire space is partial offloading, that is, according to the working conditions of occupied cameras and idle cameras, if the idle camera is in the neighborhood of the occupied camera, the occupied camera will offload part of the task to the idle camera to speed up the computing processing capability of the entire framework. The framework contains two evaluation criteria, namely cost and computing efficiency. The cost is reflected in the deployment cost, working time cost, and rental cost. The deployment cost is the cost incurred by deploying each camera, the working time cost is the product of the working cost per unit time of each camera and the working time, and the rental cost is the cost incurred by renting idle resources to speed up the task completion rate under the partial offloading strategy. The computing efficiency is reflected in that each camera can be regarded as an edge computing node that can calculate independently, and the energy consumption occupied by these edge computing nodes is the computing efficiency; if the cost of deploying each camera is c 11 , work unit time cost c 12 , working time is T 1 , the offloading strategy of the i-th occupied camera is y i ∈{0,1}, where y i =1 means that the i-th camera needs to unload data to an idle node, y i =0 means that the i-th camera does not need to unload data to the idle node. The idle cameras in the neighborhood are in a competitive relationship, that is, each camera wants to compete for the rental cost provided by camera i when it is idle. The equity incentive mechanism is used to conduct competitive screening of cameras in the neighborhood. If the TA neighborhood of camera i contains S idle cameras, where the TA neighborhood is the neighborhood where camera i can establish a communication range, the initial equity of each idle camera is c rent The first round of the equity incentive mechanism is to carry out the Euclidean distance screening competition, and the distance from the i-th occupied camera to the s-th idle camera is Then the equity update of the first round of Euclidean distance screening competition is: in is the distance matrix, d i1 is the distance from the i-th occupied camera to the first idle camera, d i2 is the distance from the i-th occupied camera to the second idle camera, d is is the distance from the ith occupied camera to the sth idle camera, x i is the horizontal coordinate of the i-th occupied camera, x 1 is the horizontal coordinate of the first idle camera, x 2 is the horizontal coordinate of the second idle camera, x s is the horizontal coordinate of the sth idle camera, y iis the ordinate of the i-th occupied camera, y 1 is the ordinate of the first idle camera, y 2 is the ordinate of the second idle camera, y s is the ordinate of the sth idle camera, SORT is the ascending sorting function, · is the dot product, and the second round of cost bidding competition is carried out. The principle is: idle cameras at a longer distance will sacrifice costs in exchange for tasks and strive for rental costs due to the influence of higher transmission delays. If the rental cost is c rent , when the idle node is far away from the occupied camera, it will have a cheaper cost according to probability, and the probability of unloading the sth idle camera is p 1 for Affected by the cost bidding competition, there is a discount factor γ, a decay factor of ε per round, and the bottom line cost of the sth idle camera is The initial cost is r 0 , then the cost after the g-th round of bidding is The lower the cost, the greater the probability that the framework will choose to offload. Then the probability that the sth idle camera is selected according to the cost is p 2 for:

[0045] The comprehensive unloading probability of the sth idle camera is p 1 *p 2 , the equity obtained through the equity incentive mechanism is updated as follows:

[0046]

[0047] in<R→s> It means that under the equity incentive mechanism, the idle node s is selected to unload the camera, the corresponding equity is R, and the total cost of the camera is C 1 C 1 =|B1|(c 11 +c 12 T 1 )+∑ i y i <R→s> , the camera computing efficiency is Among them, k i is the constant of the camera chip architecture, f i is the CPU cycle of the ith camera, in revolutions per second, D i is the amount of data generated by the task of the i-th camera, a i The number of CPU cycles required to complete 1 bit of data offloading;

[0048] (2) IoT4Edge framework based on full time

[0049] The characteristics of the full-time IoT4Edge framework are as follows: based on the historical statistics of road traffic conditions, the road conditions are calculated based on the 24-hour standard, and the road conditions of the gridded roads are counted with an accuracy of 1 hour, recording each The length of the traffic flow through within 1 hour, and the traffic flow thresholds are set as δ 1 , δ 2 ,have: A smooth road refers to a road with a traffic flow rate lower than δ 1 , that is, in On roads with a length of δ, the camera remains idle within the 1h statistical duration. For general roads, the vehicle flow Through threshold is δ 1 and δ 2 between On roads with a length of δ, the amount of data collected by the camera within a one-hour statistical period does not exceed the data processing capacity of the camera within a one-hour statistical period. Congested roads refer to roads with a traffic flow rate higher than δ 2 , that is, in On a road with a length of 1 hour, the camera remains in an occupied state within the 1-hour statistical period. The amount of data from the camera within the 1-hour statistical period exceeds the data processing capacity of the camera within the 1-hour statistical period. The task needs to be offloaded to other idle cameras in the neighborhood to complete the task processing.

[0050] The full-time IoT4Edge framework stipulates that the road congestion situation is counted every other cycle. If a road is continuously counted as congested within a cycle, the full-time IoT4Edge framework will recommend adding cameras to speed up the task processing capacity of the road and reduce the rental cost.

[0051] The full-time IoT4Edge framework stipulates that if the variance of road congestion in the same period within a cycle exceeds the expected threshold, that is, irregular events or special events have occurred, causing the road conditions to change. It is necessary to record the road conditions at the statistical moment when the cycle begins (congestion = 1, general = 0.5, smooth = 0) and the road conditions at the statistical moment when the cycle ends (congestion = 1, general = 0.5, smooth = 0). If the value in the road condition is increasing, it means that the road condition is developing towards congestion, and it is necessary to dispatch additional drone fleets to assist the system in road monitoring.

[0052] Specifically, the data visualization module converts the massive data obtained by cameras and drones into visual charts, maps, and dynamic animations to help users understand complex road information and make decisions quickly. The system displays the real-time traffic status of roads through dynamic maps. The system refreshes the map in real time to dynamically display the real-time status of roads. The data displayed includes traffic flow, vehicle speed, road conditions, and weather warnings. On the map interface, users can choose different view levels to view real-time data for the entire city or a local area. They can predict the future traffic of a road based on historical data to help traffic managers make plans in advance.

[0053] Specifically, the situation expansion subsystem is responsible for further processing and enhancing the real-time road information based on the data display, and providing hierarchical and intelligent interface selection and operation to support more detailed and accurate decision-making and interactive operations. The situation expansion subsystem mainly includes the map elevation module and the map grid module, which respectively provide differentiated processing of elevation information and fine division of map areas, enhancing the hierarchy, intelligence and operability of road information. The map elevation module provides elevation information for the road simulation system to help users perform differentiated analysis in complex road environments. Through the processing of the elevation module, the system can accurately reflect the road elevations in different areas and provide more Fine and dynamic traffic management support. The map grid module is responsible for dividing the road map area into multiple grids, further refining the display of road information, and providing users with high-precision and dynamic traffic information display. Rasterization is to convert continuous geographic information data into discrete grid units. Each grid represents a specific geographical area. Through the grid module, the system can dynamically select specific areas and information levels as needed to meet the needs of different users. The grid display can be updated according to real-time data and dynamically adjust the display area. When a traffic accident occurs, the grid data of the accident area will be updated in real time, changing its color or status mark, and immediately reflected on the map. The system can also automatically expand or reduce the scope of the grid area according to actual needs, so as to focus on displaying the traffic conditions of a hot spot area. When a large-scale traffic incident occurs, the grid module can zoom in on the specific area and provide more detailed information to facilitate managers to handle it urgently.

[0054] Beneficial effects of the present invention: The present invention can provide full-time and space, intelligent traffic management. Unlike traditional intelligent transportation systems that mostly rely on fixed ground sensors and cameras, the present invention breaks through the limitations of traditional monitoring methods by combining ground camera arrays and aerial monitoring of drone swarms, and realizes full-time and space data collection and road condition monitoring. Especially in complex road environments, such as mountainous areas, bridges, and tunnels, traditional equipment often has blind spots and signal dead spots, while drone swarms can flexibly and maneuverably cover these hard-to-reach areas, ensuring real-time collection of comprehensive road information. This air-ground combined data collection method provides unprecedented accuracy and flexibility for traffic management, allowing the system to cope with more complex and changeable traffic conditions.

[0055] Secondly, the system's two-dimensional data transmission network design greatly improves the efficiency and real-time performance of data transmission. In traditional systems, data transmission usually relies on a single ground communication network. The present invention uses a combination of L-link and gateway modules to adopt a two-dimensional transmission method of ground-to-ground and ground-to-air, making the transmission of data from various sensors to edge computing nodes more stable and fast. Especially when the drone fleet transmits data back, the frequency range of the L-link (960-1215MHz) ensures strong anti-interference ability and greater bandwidth, and can provide stable signal transmission in different environments. This data transmission method not only ensures the real-time and high efficiency of the system, but also provides strong support for the network scalability of the system when it is deployed on a large scale.

[0056] The present invention proposes an IoT4Edge framework based on full space and an IoT4Edge framework based on full time, which include terrain features along roads, intersections, tunnels, under bridges, and mountainous areas. In the IoT4Edge framework based on full space, the framework gridding can enhance the statistical granularity of the entire system, and considers the deployment scheme of cameras and the edge collaboration of idle nodes, and takes cost and computing performance as evaluation indicators. The equity incentive mechanism is used to conduct competitive screening of cameras in the neighborhood to increase the utilization rate of idle resources and mobilize the activity of spatial computing resources of the entire system. Two rounds of competition were carried out in the incentive mechanism. The first round was a Euclidean distance screening competition, that is, within the neighborhood of the communication range, the closer ones were given priority. The second round was a cost bidding competition. The principle was: idle cameras at a farther distance would sacrifice costs in exchange for tasks and strive for rental costs due to the impact of higher transmission delays, that is, cheaper ones were given priority. Finally, the equity incentive mechanism took both into consideration and selected nodes that were not far away and had reasonable prices as unloading nodes. On the one hand, it could reduce the load on occupied camera resources; on the other hand, it reduced the rental cost of the entire system, bringing cheaper and more reliable services. In the full-time IoT4Edge framework, the road traffic conditions are calculated based on the historical statistical time and the 24-hour standard. The road conditions are counted with an accuracy of 1 hour for the gridded roads, and it is stipulated that the road congestion conditions are counted every other cycle. If a road is continuously counted as congested during the cycle, the full-time IoT4Edge framework will recommend adding cameras to speed up the task processing capacity of the road and reduce the rental cost. If the variance of the road congestion conditions during the same period of a cycle exceeds the expected threshold, that is, an irregular event or special event has occurred, which has caused the road conditions to change, and the road conditions at the statistical time when the cycle starts need to be recorded.

[0057] The road conditions at the statistical moment of the end of the cycle (congestion = 1, general = 0.5, smooth = 0) are compared with the road conditions at the statistical moment of the end of the cycle (congestion = 1, general = 0.5, smooth = 0). If the value in the road condition is increasing, it means that the road condition is developing towards congestion, and it is necessary to increase the number of drones to assist the system in road monitoring. Based on the full-time IoT4Edge framework, by statistically analyzing historical data and the variance of statistics within the cycle, it intelligently evaluates whether the camera resources are reasonably deployed in the original deployment scenario, whether it is necessary to increase camera resources, or to dispatch drone resources for short-term road reconnaissance, which brings smart choices for flexible road deployment.

[0058] The innovation of the system in visual display is also very valuable. Through the integrated interface display subsystem, the system can display huge traffic information and data to users through an intuitive and easy-to-operate interface. Users can view the traffic flow, vehicle speed, and road status information of the entire city or a specific road in real time. At the same time, the interactivity of the system enables users to query, analyze, and operate the information as needed. This visual display not only enhances the user's intuitive perception of road conditions, but also enables traffic management personnel to make decisions and management more efficiently. Especially in emergency situations, operators can quickly understand and dispatch road conditions, avoid human omissions, and ensure the timeliness of emergency response. The situation expansion subsystem of the present invention provides users with a more accurate and dynamic display of traffic information by introducing a map elevation module and a map grid module. The elevation module combines the terrain undulation data of the road and can perform differentiated processing for road conditions in different geographical environments. Especially in complex mountainous areas, tunnels, and bridge areas, the system can provide more accurate road condition information to help traffic management personnel identify potential risk points and adjust traffic strategies in a timely manner. The map grid module can provide users with more detailed local area data display by dividing the map area into several small blocks, especially in key areas with dense traffic flow and frequent accidents. It can accurately display the status of the road through raster processing and provide dynamic analysis and prediction based on the data of the area. This hierarchical and detailed display method enhances the interactivity and intelligence of the system, allowing users to respond quickly when faced with complex road conditions.

[0059] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to replace some of the technical features therein. Any modification, replacement, and improvement made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A full-time and space real-time simulation system for smart roads, including a data acquisition subsystem, a data transmission subsystem, an edge computing subsystem, an interface display subsystem, and a situation expansion subsystem; the data acquisition subsystem includes a camera array module, a drone swarm collaboration module, and a large map module. The camera array module is a camera deployed on the road, which is the first way for the data acquisition subsystem to obtain online data. Cameras are deployed on each road to provide services for monitoring the real-time dynamics of the road. All roads contained in an area are monitored by the deployed cameras, forming a camera array, which provides basic guarantees for ground-to-ground monitoring of all roads in the area of ​​the data acquisition subsystem. In particular, the camera array is deployed along the road, at intersections, tunnels, and under bridges to complete the collection of data under obstructed conditions; the drone swarm collaboration module is the second way for the data acquisition subsystem to obtain online data, which is mainly responsible for real-time ground-to-air monitoring and can be deployed in the blind spots of the camera array to achieve mobile deployment. In particular, it is applied to special road conditions, mountainous road conditions, and disaster area road conditions; The big map module is the way for the data collection subsystem to obtain offline data. It is the underlying database resource of the data collection subsystem. The big map data is open source data. The data source is mainly the KML file downloaded by BIGEMAP. According to the statistics of road conditions over the years, the statistics of the same period, and the statistics of the same period, the load of a certain road and a certain period of time is arranged. According to the size of the load, the download of the elevation level is decided. For the elevation information of the map, the higher the level, the more detailed the map is, and the more cache resources are required; The data transmission subsystem provides the interconnection and interoperability of online data, including the gateway module and the link module. The gateway module transmits the camera array data to the edge computing subsystem through the Internet protocol, and the transmission mode is optical fiber transmission. The link module transmits the road video data collected by the drone group to the edge computing subsystem through the L link in the downlink L link transmission mode. After receiving the data transmitted by the camera array and the drone group, the edge computing subsystem realizes the intelligent data calculation and processing of the ground-ground and ground-air remote edge through the IoT4Edge framework, and visualizes the numerical results calculated by the edge computing subsystem in the interface display subsystem. The situation expansion subsystem enhances the real-time, operability and interactivity on the basis of the interface display subsystem, including the map elevation module and the map grid module. The map elevation module provides elevation map information for the situation expansion subsystem, provides differentiated processing for key monitoring areas, and can reduce loading and unloading caches. The map grid module separates the map area in a rasterized manner, and provides users with small-area, dynamic, and high-precision display and interactive services in the form of selection, color blocks, and dynamic deduction.

2. According to claim 1, a smart road full-time and space real-time simulation system is characterized in that: The data acquisition subsystem includes a camera array module, a drone swarm coordination module and a large map module. The camera array module collects traffic dynamic information on the road in real time through multiple cameras deployed on the road, including multiple high-definition cameras. The cameras are arranged along the road, at intersections, in tunnels, and under bridges to form a complete camera array, providing all-round support for real-time monitoring of the road. In an area, all roads are covered by cameras, forming a complete ground-to-ground monitoring system. The cameras transmit road traffic status, road conditions, and weather conditions to the data transmission subsystem through real-time video stream transmission, providing basic data for subsequent data processing and decision-making.

3. According to claim 2, a smart road full-time and space real-time simulation system is characterized in that: The drone swarm collaboration module can be deployed quickly through drone swarms and flexibly respond to various emergencies, disaster road conditions and complex mountain terrain. Drones can quickly enter the blind spots of camera array monitoring and flexibly adjust flight paths according to changes in road traffic conditions to achieve efficient collection of traffic flow, vehicle speed and license plate number recognition information. The drone swarm collaboration module has multi-machine collaborative combat capabilities. Through coordinated flight, it can monitor over a wider area and transmit road information in real time. It is particularly suitable for handling large-scale traffic incidents and natural disaster emergencies.

4. According to claim 2, a smart road full-time and space real-time simulation system is characterized in that: The big map module relies on open source data resources, especially the KML files provided by the BIGEMAP platform, combined with big data analysis technology, to provide the system with accurate basic road data based on historical road statistics, traffic flow data of the same period, and road load conditions in different time periods. The big map module provides rich spatial information support for edge computing and data processing through a high-precision geographic information system (GIS). The precision of the map data will be dynamically adjusted according to demand. In sections with heavy traffic loads or during peak hours, the system will automatically download high-precision map data to facilitate more accurate road status assessment in subsequent data processing and calculations. For areas with light loads and low traffic, the map data can appropriately reduce the accuracy to reduce system resource consumption; the big map module also supports the download and processing of elevation information. There may be large elevation differences in roads in different geographical areas, especially in mountainous areas or urban elevated roads. The big map module supports multi-level elevation data downloads and can dynamically select to load different levels of map data according to different road conditions, thereby achieving accurate tracking and intelligent analysis of elevation changes.

5. According to claim 1, a smart road full-time and space real-time simulation system is characterized in that: The data transmission subsystem is mainly composed of two modules: the gateway module and the link module. The gateway module aggregates, processes and transmits the data collected by the camera array, and has data aggregation and forwarding functions. The camera array module transmits real-time monitoring data to the gateway module through the optical fiber network. The gateway module integrates and formats the data and then transmits it to the edge computing subsystem. It supports TCP / IP, HTTP, and MQTT communication protocols. The gateway module enables the system to process data from different devices to ensure its interoperability.

6. According to claim 1, a smart road full-time and space real-time simulation system is characterized in that: The link module is responsible for ground-to-air data transmission, mainly processing the return of road video data collected from the drone fleet, and using the L-band link with a frequency band of 960-1215MHz to receive telemetry data. The L-link supports high-bandwidth, low-latency transmission, which can ensure that the high-definition video stream collected by the drone is not distorted during the return process, while reducing delays and achieving real-time monitoring and analysis. The L-link can ensure the stability of the transmission link through optimized frequency selection and signal modulation. Through frequency hopping and intelligent signal adjustment, the L-link can adapt to complex environments and reduce transmission errors caused by signal interference.

7. According to claim 1, a smart road full-time and space real-time simulation system is characterized in that: The edge computing subsystem processes, calculates and analyzes the data collected by the data acquisition subsystem in real time. Due to the complexity and dynamics of the road traffic system, the edge computing subsystem needs to have high computing power, low latency, high real-time and reliability. Especially in traffic management and disaster warning scenarios, it is necessary to respond to system inputs in real time and generate efficient decision support. The edge computing subsystem is mainly composed of a camera array module and a drone swarm collaboration module. Combined with the IoT4Edge framework, it provides efficient and reliable data processing and service support through ground-to-ground and ground-to-air remote intelligent computing.

8. According to claim 7, a smart road full-time and space real-time simulation system is characterized in that: The IoT4Edge framework is as follows: (1) IoT4Edge framework based on the whole space The IoT4Edge framework is built on a complex road of R1*R2. The complex road includes terrain features along the road, intersections, tunnels, under bridges, and mountainous areas. The road along the road is mainly long straight lines, representing the high-speed road A1. The intersection is mainly characterized by turns and poor road features. The road monitoring deployment device is a camera, which represents the typical road features of the intersection A2. The road monitoring deployment device is a camera. The tunnel is mainly composed of long straight roads with road features covered by mountains on the outside, representing the road features that cannot be known from the outside A3. The road monitoring deployment device is a camera. The under bridge is mainly composed of spatially reused roads, representing the road features of the vertical space in the unified plane position A4. The road monitoring deployment device is a camera. The mountainous area is mainly composed of rugged roads. , characterizing the road feature A5 where the road is tortuous and it is difficult to deploy cameras and network transmission equipment. The road monitoring deployment device is a drone. All roads contained in an area are monitored by deployed cameras, forming a camera array B1. The designated locations in the special spatial area are monitored by deployed drones, forming a drone swarm B2. Among them, the special spatial area refers to the mountainous area in the IoT4Edge framework based on the entire space, and the designated location is the area that the user requires to survey. If on a complex road of R1*R2, it contains |B1| cameras and |B2| drones, the deployment height of each camera is unified as h1, and the camera field of view is unified as θ1, where θ1∈[0,360], the camera pitch angle is α1, and the camera monitoring coverage is The drone deployment height is h2, the drone downward viewing angle is α2, and the drone monitoring range is The horizontal distance that each camera can detect is In the IoT4Edge framework based on full space, cameras are evenly distributed and drones are deployed in a mobile manner. The IoT4Edge framework based on full space divides roads along the road, at intersections, in tunnels, under bridges, and in mountainous areas into grids, that is, each Perform a grid division, the camera field of view is unified to θ1, where θ1∈[0,360], then the road width can be ignored, then we have Roads of a certain length can be monitored by cameras. The computing offloading strategy of the IoT4Edge framework based on the entire space is partial offloading, that is, according to the working conditions of occupied cameras and idle cameras, if the idle camera is in the neighborhood of the occupied camera, the occupied camera will offload part of the task to the idle camera to speed up the computing processing capability of the entire framework. The framework contains two evaluation criteria, namely cost and computing efficiency. The cost is reflected in the deployment cost, working time cost, and rental cost. The deployment cost is the cost incurred by deploying each camera, the working time cost is the product of the working cost per unit time of each camera and the working time, and the rental cost is the cost incurred by renting idle resources to speed up the task completion rate under the partial offloading strategy. The computing efficiency is reflected in that each camera can be regarded as an edge computing node that can calculate independently, and the energy consumption occupied by these edge computing nodes is the computing efficiency; if the cost of deploying each camera is c 11 , work unit time cost c 12 , the working time is T1, and the unloading strategy of the i-th occupied camera is y i ∈{0,1}, where y i =1 means that the i-th camera needs to unload data to an idle node, y i =0 means that the i-th camera does not need to unload data to the idle node. The idle cameras in the neighborhood are in a competitive relationship, that is, each camera wants to compete for the rental cost provided by camera i when it is idle. The equity incentive mechanism is used to conduct competitive screening of cameras in the neighborhood. If the TA neighborhood of camera i contains S idle cameras, where the TA neighborhood is the neighborhood where camera i can establish a communication range, the initial equity of each idle camera is c rent The first round of the equity incentive mechanism is to carry out the Euclidean distance screening competition, and the distance from the i-th occupied camera to the s-th idle camera is Then the equity update of the first round of Euclidean distance screening competition is: in is the distance matrix, d i1 is the distance from the i-th occupied camera to the first idle camera, d i2 is the distance from the i-th occupied camera to the second idle camera, d is is the distance from the ith occupied camera to the sth idle camera, x i is the horizontal coordinate of the i-th occupied camera, x1 is the horizontal coordinate of the first idle camera, x2 is the horizontal coordinate of the second idle camera, and x s is the horizontal coordinate of the sth idle camera, y i is the ordinate of the i-th occupied camera, y1 is the ordinate of the first idle camera, y2 is the ordinate of the second idle camera, and y s is the ordinate of the sth idle camera, SORT is the ascending sorting function, · is the dot product, and the second round of cost bidding competition is carried out. The principle is: idle cameras at a longer distance will sacrifice costs in exchange for tasks and strive for rental costs due to the influence of higher transmission delays. If the rental cost is c rent When the idle node is far away from the occupied camera, it will have a cheaper cost according to probability. The probability p1 of unloading the sth idle camera is Affected by the cost bidding competition, there is a discount factor γ, a decay factor of ε per round, and the bottom line cost of the sth idle camera is The initial cost is r0, so the cost after the g-th round of bidding is A lower cost will have a greater probability of being selected by the framework for unloading, so the probability p2 of the sth idle camera being selected according to the cost is: The comprehensive uninstall probability of the sth idle camera is p1*p2, and the equity obtained by the equity incentive mechanism is updated as follows: in<R→s> It means that under the equity incentive mechanism, the idle node s is selected to unload the camera, the corresponding equity is R, and the total cost of the camera C1 is C1=|B1|(c 11 +c 12 T1)+∑ i y i <R→s> , the camera computing efficiency is Where k is a constant of the camera chip architecture, f i is the CPU cycle of the ith camera, in revolutions per second, D i is the amount of data generated by the task of the i-th camera, a i The number of CPU cycles required to complete 1 bit of data offloading; (2) IoT4Edge framework based on full time The characteristics of the full-time IoT4Edge framework are as follows: based on the historical statistics of road traffic conditions, the road conditions are calculated based on the 24-hour standard, and the road conditions of the gridded roads are counted with an accuracy of 1 hour, recording each The length of the traffic flow Through within 1 hour, and the traffic flow thresholds are set to δ1 and δ2 respectively, and we have: A smooth road refers to a road with a traffic flow rate lower than δ1. On roads with a length of 1 hour, the camera remains idle within the 1 hour statistical duration. For general roads, the traffic flow Through threshold is between δ1 and δ2, that is, On roads with a length of δ2, the amount of data collected by the camera within a one-hour statistical period does not exceed the data processing capacity of the camera within a one-hour statistical period. Congested roads refer to roads with a traffic flow rate higher than δ2, that is, On a road with a length of 1 hour, the camera remains in an occupied state within the 1-hour statistical period. The amount of data from the camera within the 1-hour statistical period exceeds the data processing capacity of the camera within the 1-hour statistical period. The task needs to be offloaded to other idle cameras in the neighborhood to complete the task processing. The full-time IoT4Edge framework stipulates that the road congestion situation is counted every other cycle. If a road is continuously counted as congested within a cycle, the full-time IoT4Edge framework will recommend adding cameras to speed up the task processing capacity of the road and reduce the rental cost. The full-time IoT4Edge framework stipulates that if the variance of road congestion in the same period within a cycle exceeds the expected threshold, that is, irregular events or special events have occurred, causing the road conditions to change. It is necessary to record the road conditions at the statistical moment when the cycle begins (congestion = 1, general = 0.5, smooth = 0) and the road conditions at the statistical moment when the cycle ends (congestion = 1, general = 0.5, smooth = 0). If the value in the road condition is increasing, it means that the road condition is developing towards congestion, and it is necessary to dispatch additional drone fleets to assist the system in road monitoring.

9. According to claim 1, a smart road full-time and space real-time simulation system is characterized in that: The data visualization module converts the massive data obtained by cameras and drones into visual charts, maps, and dynamic animations to help users understand complex road information and make decisions quickly. The system displays the real-time traffic status of roads through dynamic maps. The system refreshes the map in real time to dynamically display the real-time status of roads. The data displayed includes traffic flow, vehicle speed, road conditions, and weather warnings. On the map interface, users can choose different view levels to view real-time data for the entire city or a local area. They can predict the future traffic of a road based on historical data to help traffic managers make plans in advance.

10. The intelligent road full-time and space real-time simulation system according to claim 1, characterized in that: The situation expansion subsystem is responsible for further processing and enhancing the real-time road information based on the data display, and provides hierarchical and intelligent interface selection and operation to support more detailed and accurate decision-making and interactive operations. The situation expansion subsystem mainly includes the map elevation module and the map grid module, which respectively provide differentiated processing of elevation information and fine division of map areas, enhancing the hierarchy, intelligence and operability of road information. The map elevation module provides elevation information for the road simulation system to help users perform differentiated analysis in road environments with complex terrain. Through the processing of the elevation module, the system can accurately reflect the road elevations in different areas and provide more refined and dynamic traffic management support. The map grid module is responsible for dividing the road map area into multiple grids to further refine the display of road information. , and provide users with high-precision and dynamic traffic information display. Raster processing is to convert continuous geographic information data into discrete grid units. Each grid represents a specific geographical area. Through the grid module, the system can dynamically select specific areas and information levels as needed to meet the needs of different users. The raster display can be updated according to real-time data and dynamically adjust the display area. When a traffic accident occurs, the raster data of the accident area will be updated in real time, changing its color or status mark, and immediately reflected on the map; the system can also automatically expand or reduce the scope of the grid area according to actual needs, so as to focus on displaying the traffic conditions of a hot spot area. When a large-scale traffic incident occurs, the grid module can zoom in on the specific area and provide more detailed information to facilitate managers to handle emergencies.

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