Regional traffic intelligent green wave control method and system based on cloud-edge-end collaboration

Abstract

The present invention provides a method and system for intelligent green wave control of regional traffic based on cloud-edge-end collaboration, the method comprising: a cloud server obtaining a green wave route that requires green wave operation, constructing a green wave collaborative control unit according to the green wave route, each green wave collaborative control unit corresponding to a green wave section of the green wave route; calculating the shortest green time of the applicant phase of all intersections in the green wave section, and calculating the phase opening time of the responder of all green wave sections, and calculating the shortest green time of the responder phase of all intersections in all green wave sections; constructing the time factor of the applicant phase and the time factor of the responder phase, constructing the periodic change of the green wave collaborative control unit, and sending the data of the periodic change to the edge end devices corresponding to all intersections of the green wave section; each edge end device adjusts the green time of the current phase according to the received periodic change data. The system implements the above method. The present invention can realize the dynamic adjustment of the green wave and improve the traffic efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic light control, and more specifically, to a method for intelligent green wave control of regional traffic based on cloud-edge-end collaboration and a system for implementing the method. Background Art

[0002] With socioeconomic development, urban populations and the number of vehicles are increasing, leading to increasingly severe urban traffic congestion. Currently, urban roads are home to numerous intersections, many of which are equipped with traffic lights. Vehicles and pedestrians follow these signals to navigate. Improper signal timing at multiple intersections can lead to even greater congestion. Therefore, effective urban traffic signal control can effectively alleviate congestion, but traditional control methods are difficult to apply to complex traffic control problems.

[0003] Some existing traffic coordination control systems use cloud servers, edge devices, and device-side device systems to coordinate traffic signal processing at various intersections, aiming to improve regional traffic efficiency. For example, patent application publication number CN116884248A discloses a traffic signal phase control method based on intelligent cycles. This scheme collects vehicle data at each intersection and phase, performs vehicle benchmarking and standardization operations, and then intelligently adjusts the phase sequence of each intersection and phase based on traffic flow conditions, thereby improving traffic efficiency at each intersection.

[0004] As traffic organization changes, implementing green wave control on some routes within the region, especially trunk routes, allows these routes to operate in green wave mode for a period of time. This can significantly improve traffic efficiency on these routes and significantly contribute to improving traffic efficiency across the entire area. However, existing traffic control systems often fix the operating time of the green wave when setting the green wave mode. That is, when certain routes are set to green wave mode, the phase sequence at the corresponding intersection and the travel time for each phase are fixed.

[0005] However, due to the high randomness and contingency of traffic operations, if the operation time of the green wave is fixed, it is easy for there to be fewer vehicles passing through the green wave route during a certain period of time, resulting in the green wave route being in a state of sparse vehicles for a long time, which in turn affects the traffic efficiency of other routes in the area and reduces the overall traffic efficiency of the area. Summary of the Invention

[0006] The first purpose of the present invention is to provide a regional traffic intelligent green wave control method based on cloud-edge-end collaboration that can dynamically control green wave routes.

[0007] The second object of the present invention is to provide a regional traffic intelligent green wave control system based on cloud-edge-end collaboration that can improve the traffic efficiency of regional routes.

[0008] To achieve the above-mentioned first purpose, the regional traffic intelligent green wave control method based on cloud-edge-end collaboration provided by the present invention includes a cloud server obtaining a green wave route that needs to run a green wave, constructing a green wave collaborative control unit according to the green wave route, and each green wave collaborative control unit corresponds to a green wave section of the green wave route; calculating the shortest green time of the applicant phase of all intersections in the green wave section, and calculating the responder phase opening time of all green wave sections, and calculating the shortest green time of the responder phase of all intersections in all green wave sections; constructing the time factor of the applicant phase and the time factor of the responder phase, constructing the periodic change of the green wave collaborative control unit according to the time factor of the applicant phase and the time factor of the responder phase, and sending the data of the periodic change to the edge end devices corresponding to all intersections of the green wave section; each edge end device adjusts the green time of the current phase according to the received periodic change data.

[0009] As can be seen from the above scheme, the green wave route is split into multiple green wave sections, and a corresponding green wave collaborative control unit is constructed for each green wave section. When the green wave is running on the green wave section, the time factor of the applicant phase and the time factor of the responder phase are calculated, and the corresponding time factor of the applicant phase and the time factor of the responder phase are sent according to the actual situation of each intersection. This allows each edge device to adjust the green time of the current phase according to the received periodic data, thereby realizing dynamic adjustment of the green wave operation time. In this way, the green wave operation time of each intersection and phase is not fixed, but is dynamically adjusted according to the actual situation, thereby improving the traffic efficiency of the route in the entire area.

[0010] A preferred solution is that each edge device adjusts the green time of the current phase according to the received periodic change data, including: the edge device adjusts the green time of the current phase according to the received periodic change data based on the pre-calculated intelligent phase sequence or intelligent cycle.

[0011] Since each edge device has been operating the intelligent phase sequence or intelligent cycle according to the pre-set rules, after receiving the data of cycle changes, the green time of the current phase is adjusted according to the received data of cycle changes, which can avoid sudden changes in the operating phase sequence and reduce the impact on traffic operations.

[0012] A further solution is that the edge device of an intersection only receives the time factor of one applicant phase; the edge device of an intersection can receive the time factors of more than one responder phase.

[0013] It can be seen that an intersection can only receive the time factor of one applicant phase, that is, an intersection can only be set with one applicant phase, but can be set with multiple responder phases, which can avoid the problem of operating phase sequence conflict caused by running multiple applicant phases at the same intersection.

[0014] A further solution is that the time factor of the applicant phase includes the applicant phase and the shortest green time of the applicant phase.

[0015] A further solution is that the time factor of the responder phase includes the applicant intersection unique identifier, the applicant phase, the responder phase opening time, the responder phase and the shortest green time of the responder phase.

[0016] It can be seen from this that by setting multiple parameters of the applicant's phase time factor and the responder's phase time factor, the corresponding edge device can calculate the periodic change data based on the applicant's phase time factor and the responder's phase time factor, thereby making precise adjustments to the current phase sequence and green wave communication time.

[0017] A further solution is that the shortest green time of the applicant phase is calculated in the following way: the shortest green time of the applicant phase = the distance from the threshold-th traffic participant of the applicant phase to the entrance stop line - the distance from the first traffic participant of the applicant phase to the entrance stop line / the average speed of the green wave section + the shortest green time compensation value of the green wave phase; among which the shortest green time compensation value of the green wave phase is a preset value.

[0018] A further solution is that the responder phase opening time is calculated in the following way: responder phase opening time = (distance from the first traffic participant of the applicant phase to the entrance stop line + distance of the green wave section) / average vehicle speed of the green wave section.

[0019] A further solution is that the shortest green time of the responder phase is calculated in the following way: the shortest green time of the responder phase = (the distance from the threshold-th traffic participant of the applicant phase to the entrance stop line + the distance of the green wave section) / the average vehicle speed of the green wave section - the responder phase opening time + the green wave phase shortest green time compensation value.

[0020] It can be seen that the above formula can accurately calculate parameters such as the applicant's phase shortest green time, the responder's phase opening time and the responder's phase shortest green time, providing a basis for accurately adjusting the green wave operation time of each intersection.

[0021] To achieve the above-mentioned second purpose, the regional traffic intelligent green wave control system based on cloud-edge-end collaboration provided by the present invention includes a cloud server and one or more edge devices; wherein, the cloud server is provided with a dynamic green wave control engine for obtaining the green wave route where the green wave needs to be run, and constructing a green wave collaborative control unit according to the green wave route, each green wave collaborative control unit corresponds to a green wave section of the green wave route; calculating the shortest green time of the applicant phase of all intersections in the green wave section, and calculating the responder phase opening time of all green wave sections, and calculating the shortest green time of the responder phase of all intersections in all green wave sections; constructing the time factor of the applicant phase and the time factor of the responder phase, constructing the periodic change of the green wave collaborative control unit according to the time factor of the applicant phase and the time factor of the responder phase, and sending the data of the periodic change to the edge devices corresponding to all intersections of the green wave section; each edge device is used to adjust the green time of the current phase according to the received periodic change data.

[0022] A preferred solution is that the cloud server stores a green wave parameter model, a green wave scheme model and a green wave scheduling scheme model.

[0023] As can be seen from the above scheme, the green wave parameter model, green wave scheme model and green wave scheduling scheme model are stored on the cloud server, and the cloud server can quickly calculate the appropriate green wave travel time under the current road conditions through the above models. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a structural block diagram of an embodiment of the regional traffic intelligent green wave control system based on cloud-edge-end collaboration of the present invention.

[0025] Figure 2 It is a structural block diagram of the dynamic green wave model in an embodiment of the regional traffic intelligent green wave control system based on cloud-edge-end collaboration of the present invention.

[0026] Figure 3 This is a schematic diagram of multiple green wave intersections.

[0027] Figure 4 It is a structural block diagram of the edge device in an embodiment of the regional traffic intelligent green wave control system based on cloud-edge-end collaboration of the present invention.

[0028] Figure 5 It is a flowchart of an embodiment of the regional traffic intelligent green wave control method based on cloud-edge-end collaboration of the present invention.

[0029] The present invention will be further described below with reference to the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0030] The present invention collects real-time images of road traffic participants through cameras installed at intersections, analyzes the traffic flow conditions at various intersections and routes, and calculates the green wave timing for each intersection on the route where green waves need to be operated based on a pre-set green wave model, thereby realizing dynamic green wave timing adjustment to improve the traffic efficiency of the area.

[0031] Example of a regional traffic intelligent green wave control system based on cloud-edge-end collaboration:

[0032] See also Figure 1 The regional traffic intelligent green wave control system based on cloud-edge-end collaboration of the present invention comprises a cloud server 100, an edge device 200, and a device-end device 300. An edge device 200 is set at a traffic intersection. A traffic intersection includes multiple directions, each of which has multiple lanes. Typically, different lanes have different traffic directions. For example, if there are four lanes in a direction, the leftmost lane is a left-turn lane, the two middle lanes are through lanes, and the rightmost lane is a right-turn lane.

[0033] At least one set of traffic lights is required for each direction. Multiple sets of traffic lights at an intersection are typically controlled by a single signal. Typically, the device-side device 300 includes the signal, a camera installed at the intersection, and so on. Furthermore, the edge device 200 can communicate with the cloud server 100 to obtain traffic configuration models from it. Furthermore, the edge device 200 can also communicate with the device-side device 300, for example, to obtain runtime data from the signal and send control instructions to the signal. Typically, a single cloud server 100 can communicate with multiple edge devices 200 and coordinate the operations of these multiple edge devices 200.

[0034] The cloud server 100 includes a dynamic green wave control engine 110 and a rule engine 180 , wherein the dynamic green wave control engine 110 includes a dynamic green wave evaluator 120 , a dynamic green wave lifecycle manager 130 , a dynamic green wave coordinator 140 , a dynamic green wave time factor 150 , a dynamic green wave operation monitor 160 , and a dynamic green wave model 170 .

[0035] The dynamic green wave evaluator 120 includes an intersection control mode evaluation module 121, an edge device monitoring status evaluation module 122, and a telemetry data real-time evaluation module 123. The intersection control mode evaluation module 121 is used to evaluate whether each intersection meets the conditions for entering dynamic green wave control based on its current actual conditions. The edge device monitoring status evaluation module 122 is used to obtain status data from each edge device 200 and, based on this status data, determine whether each edge device 200 is ready for dynamic green wave control. The telemetry data real-time evaluation module 123 is used to obtain telemetry data, such as data such as traffic flow on the road, through cameras, and use this telemetry data to evaluate whether the conditions for entering dynamic green wave control are met.

[0036] The dynamic green wave lifecycle manager 130 includes an intersection readiness status module 131, a road section readiness status module 132, a trunk line readiness status module 133 and a regional readiness status module 134. Among them, the intersection readiness status module 131 is used to determine whether each intersection is ready and can enter the dynamic green wave control mode, while the road section readiness status module 132 is used to determine whether each road section is ready, the trunk line readiness status module 133 is used to determine whether each trunk line is ready, and the regional readiness status module 134 is used to determine whether each intersection and road section in the entire area is ready. If the entire area is ready, the dynamic green wave control mode can be entered.

[0037] The dynamic green wave coordinator 140 includes a trunk coordinator 141 and a regional coordinator 142, wherein the trunk coordinator 141 is used to coordinate the control of the edge devices 200 set at multiple intersections on the trunk, and the regional coordinator 142 is used to coordinate the control of the edge devices 200 set at multiple intersections in the entire area.

[0038] Dynamic Green Wave Time Factor 150 stores the applicant phase 151, the applicant phase's shortest green time 152, the responder phase 153, and the responder phase's shortest green time 154. Green Wave Phases are manually set as green wave phases at designated intersections on cloud server 100. These green wave phases generate green wave routes. When an intersection enters a green wave phase, that phase becomes applicant phase 151. When a threshold number of traffic participants queue at an intersection and leave the applicant phase at that intersection, they can reach phases at other intersections, which are called responder phases 153. Dynamic Green Wave Time Factor 150 stores which phases at which intersections are in applicant phase 151 and responder phase 153.

[0039] The dynamic green wave operation monitor 160 is provided with a dynamic green wave monitor 161 for monitoring the operation of the dynamic green wave. Once an abnormality occurs, an abnormality report is issued to facilitate manual adjustment of the operation parameters of the dynamic green wave or exit from the dynamic green wave operation mode.

[0040] The dynamic green wave model 170 stores a plurality of different parameter models, see Figure 2 The dynamic green wave model 170 stores a green wave parameter model 171, a green wave scheme model 174 and a green wave scheduling scheme model 177, wherein the green wave parameter model 171 includes a static parameter model 172 and a dynamic parameter model 173, the green wave scheme model 174 includes a phase model 175 and a phase timing model 176, and the green wave scheduling scheme model 177 includes a scheduling method 178 and a traffic scheme worksheet model 179. The above parameters and models are all pre-set and stored in the cloud server 100.

[0041] The dynamic green wave control engine 110 communicates with the edge device 200 through the rule engine 180. A device reporting rule node 181 and a device sending rule node 182 are set in the rule engine 180. The data of the edge device 200 is sent to the dynamic green wave control engine 110 through the reporting rule node 181, and the data of the dynamic green wave control engine 110 is sent to the edge device 200 through the device sending rule node 182.

[0042] This embodiment implements dynamic green wave control for routes within a region, requiring the pre-setting of multiple parameters. Specifically, it requires the calculation of the regional average speed, which is the average speed of all vehicles traveling within the region. This embodiment establishes the concept of a "pass threshold percentage," which represents the percentage of traffic participants in a queue who can successfully pass through a particular intersection according to the dynamic green wave algorithm at each time of release. Specifically, the number of traffic participants who successfully pass through a particular intersection can be calculated by multiplying the total number of traffic participants in the queue by the pass threshold percentage.

[0043] For a certain green wave road section, there are a starting entrance and an ending entrance, such as Figure 3 As shown, it is assumed that traffic participants at a certain entrance of intersection 401 can reach a certain entrance of intersection 402. In this case, the entrance of intersection 401 is called the starting entrance, and the entrance of intersection 402 is called the ending entrance.

[0044] For a particular intersection, the concepts of the first traffic participant and the threshold-th traffic participant are established. The traffic participant closest to the entrance and currently queuing is called the first traffic participant, and the traffic participant at the end of the traffic participant queue threshold calculated based on the clearance threshold percentage is called the threshold-th traffic participant. For example, if the clearance threshold percentage indicates that 50 traffic participants should be allowed through the intersection at a given time, the 50th traffic participant is called the threshold-th traffic participant.

[0045] In addition, this embodiment introduces the concept of green wave route, which can be generated according to the green wave phase. A green wave route can include any number of intersections. Figure 3 As shown, assuming that the green wave phase of intersection 401 is north-south straight, and the green wave phase of intersection 402 is north-south straight, these two green wave phases will generate two green wave routes: the first green wave route is from the northbound straight road of intersection 401 to the southbound straight road of intersection 402, and the second green wave route is from the southbound straight road of intersection 403 to the northbound straight road of intersection 402.

[0046] In the green wave routes generated according to the green wave phase, any two interconnected intersections constitute a green wave collaborative control unit. For example, the green wave phase of intersection 401 is north-south straight driving, the green wave phase of intersection 402 is north-south straight driving, and the green wave phase of intersection 403 is north-south straight driving. These two green wave phases will generate two green wave routes: the first green wave route is from the northbound straight road of intersection 401 to the southbound straight road of intersection 402 + the northbound straight road of intersection 402 to the southbound straight road of intersection 403; the second green wave route is from the southbound straight road of intersection 403 to the northbound straight road of intersection 402 + the southbound straight road of intersection 402 to the northbound straight road of intersection 401.

[0047] According to the above green wave route, four green wave coordinated control units can be constructed, as follows: the first green wave coordinated control unit includes: the northbound straight lane of intersection 401 to the southbound straight lane of intersection 402; the second green wave coordinated control unit includes: the northbound straight lane of intersection 402 to the southbound straight lane of intersection 403; the third green wave coordinated control unit: from the southbound straight lane of intersection 403 to the northbound straight lane of intersection 402; the fourth green wave coordinated control unit: from the southbound straight lane of intersection 402 to the northbound straight lane of intersection 401.

[0048] In addition, to prevent the calculated phase running green time from being too short, the system configures a minimum green time compensation mechanism, which sets a pre-set minimum green time compensation value for the green wave phase. Of course, the minimum green time compensation value set for each intersection can be different according to the actual situation of each intersection.

[0049] The shortest green time for the threshold-th traffic participant at an intersection to successfully exit the applicant phase after entering the applicant phase is called the applicant phase shortest green time. The time required for the first traffic participant at an intersection to travel from the applicant phase to its corresponding responder phase is called the responder phase opening time. After the first traffic participant at an intersection reaches the responder phase after experiencing the responder phase opening time, the shortest time required for the threshold-th traffic participant to successfully exit the responder phase is called the responder phase shortest green time.

[0050] See also Figure 4 The edge device 200 includes a green wave plan scheduler 210, a visual acquisition and AI inference engine 220, and a dynamic green wave phase control unit 230. The green wave plan scheduler 210 includes system configuration parameters 211, system context 212, a traffic plan worksheet 213, a traffic plan time table 214, and a scheduling engine 215. The visual acquisition and AI inference engine 220 includes a visual acquisition module 221 for intersection traffic cameras, a visual AI inference task module 222, and structured vehicle and pedestrian data 223.

[0051] The dynamic green wave phase control unit 230 is equipped with a real-time traffic condition collector 240, a phase timer 250, a dynamic green wave optimizer 260, and a traffic phase executor 270. The real-time traffic condition collector 240 is equipped with a real-time intersection vehicle data collector 241, a signal operation information collector 242, and a real-time traffic condition model synthesizer 243. The real-time intersection vehicle data collector 241 obtains vehicle data at each intersection by acquiring data from the visual acquisition and AI inference engine 220. The signal operation information collector 242 is used to obtain data from the signal lights of each device-end device 300. The real-time traffic condition model synthesizer 243 synthesizes real-time traffic models for each intersection based on the vehicle data and signal light data at each intersection.

[0052] Phase scheduler 250 includes a vehicle acquisition unit 251, a vehicle data benchmarking unit 252, a phase vehicle normalization unit 253, and a phase schedule table 254. Vehicle acquisition unit 251 is used to acquire vehicle data from each entrance to each intersection. Vehicle data benchmarking unit 252 and phase vehicle normalization unit 253 respectively perform benchmarking and normalization calculations on the vehicle data at each entrance. The vehicle normalization and benchmarking calculations are as disclosed in CN116884248A and will not be further described. Phase schedule table 254 is used to record the execution order and running time of each phase at the intersection.

[0053] The dynamic green wave optimizer 260 includes a dynamic green wave application / response manager 261, a phase cycle adjuster 262, and a phase sequence / phase time adjuster 263. The dynamic green wave application / response manager 261 is used to accept dynamic green wave application requests and respond to them, such as performing response management. The phase cycle adjuster 262 adjusts the cycle of each phase based on the dynamic green wave application, such as increasing or decreasing the travel time. The phase sequence / phase time adjuster 263 adjusts the phase sequence and phase time.

[0054] The traffic phase executor 270 includes a current operation context 271, a phase ready waiting queue 27, a run phase queue 273, a phase ready waiting queue 274, a phase execution instruction mapper 275, and a signal instruction executor 276. The current operation context 271 contains the preceding phase information and following phase data of the phase currently being run by the edge device 200. The phase ready waiting queue 27 contains the phase queue that is about to be run. The run phase queue 273 contains the phase data that has been run before the current phase. The phase ready waiting queue 274 contains the phase queue information that is ready to run. The phase execution instruction mapper 275 is used to implement the mapping of phase execution instructions. The signal instruction executor 276 is used to control the execution instructions of the signal.

[0055] Example of a method for intelligent green wave control of regional traffic based on cloud-edge-end collaboration:

[0056] The following combination Figure 5 This paper introduces a method for intelligent green wave control of regional traffic based on cloud-edge-end collaboration. In step S11, the cloud server obtains a green wave route on which green wave operation is required. Preferably, the green wave route is divided into multiple green wave sections, for example, based on a certain distance or a certain number of intersections. Each green wave section can correspond to a green wave collaborative control unit. Therefore, a green wave collaborative control unit involves at least two edge devices at the intersection.

[0057] Then, step S12 is executed to calculate the applicant phase's minimum green time for all intersections in all green wave sections. Specifically, the applicant phase's minimum green time for an intersection = the distance from the threshold-th traffic participant in the applicant phase to the entrance stop line - the distance from the first traffic participant in the applicant phase to the entrance stop line / the average speed of the green wave section + the green wave phase's minimum green time compensation value. The above formula is used to calculate the applicant phase's minimum green time for each intersection. By setting the green wave phase's minimum green time compensation value, the problem of excessively short green wave travel time at a particular intersection can be avoided.

[0058] Next, step S13 is executed to calculate the responder phase opening time for all green wave sections. Specifically, the responder phase opening time for a green wave section is calculated using the following formula: responder phase opening time = (distance from the first traffic participant in the applicant phase to the entrance stop line + distance of the green wave section) / average vehicle speed of the green wave section.

[0059] Then, execute step S14 to calculate the shortest green time of the responder phase for all intersections in all green wave sections. Specifically, the shortest green time of the responder phase at the intersection is obtained using the following calculation: Shortest green time of the responder phase = (the distance from the threshold-th traffic participant of the applicant phase to the entrance stop line + the distance of the green wave section) / average vehicle speed of the green wave section - the responder phase opening time + the shortest green time compensation value of the green wave phase.

[0060] Then, execute step S15 to construct the applicant's phase time factor. In this embodiment, the applicant's phase time factor includes the applicant's phase and the applicant's phase shortest green time. After obtaining the above-mentioned applicant's phase time factor, the cloud server needs to send the applicant's phase time factor to the intersection that currently belongs to the application stage.

[0061] Next, step S16 is executed to construct the responder phase time factor. The responder phase time factor includes the applicant's unique identifier, the applicant's phase, the responder phase start time, the responder phase, and the responder phase's shortest green time. After constructing the responder phase time factor, the cloud server also needs to send the constructed responder phase time factor to the intersection that is currently in the response phase. It should be noted that an intersection can be in the application phase and the response phase at the same time, and at any time, only one applicant phase time factor will be received, but multiple responder phase time factors can be received. In other words, the edge device of an intersection only receives the time factor of one applicant phase, but can receive more than one responder phase time factor.

[0062] Then, step S17 is executed, and the cloud server constructs the periodic change data of all green wave collaborative control units according to the applicant phase time factor and the responder phase time factor, and sends the periodic change data to the edge devices of all green wave intersections.

[0063] Finally, the edge devices at each intersection execute step S18. Based on the existing smart cycle / smart phase sequence data, upon receiving the cycle change data, they adjust the green time of the current phase to adapt to the dynamic green wave control mode. For example, based on the previously calculated smart cycle or smart phase sequence data, the edge devices adjust the phase sequence and phase cycle according to the cycle change data.

[0064] Since the present invention can dynamically set the green wave according to the actual situation of the green wave route, for example, according to the vehicle conditions at each intersection and the main line, for example, various parameters of the applicant phase time factor and the responder phase time factor at each intersection can be calculated, the phase of each intersection can be dynamically adjusted, thereby improving the traffic efficiency of the green wave section, thereby improving the traffic efficiency of the entire area.

[0065] Finally, it should be emphasized that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and modifications. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A regional traffic intelligent green wave control method based on cloud-edge-end collaboration is characterized by: include: The cloud server obtains a green wave route on which a green wave is required to be operated, and constructs a green wave collaborative control unit according to the green wave route, wherein each green wave collaborative control unit corresponds to a green wave section of the green wave route; Calculate the shortest green time of the applicant phase at all intersections in the green wave section, calculate the phase opening time of the responder phase at all green wave sections, and calculate the shortest green time of the responder phase at all intersections in all green wave sections; Constructing a time factor of the applicant phase and a time factor of the responder phase, constructing a periodic change of the green wave collaborative control unit according to the time factor of the applicant phase and the time factor of the responder phase, and sending data of the periodic change to edge devices corresponding to all intersections of the green wave section; Each of the edge devices adjusts the green time of the current phase according to the received periodic change data; The time factor of the applicant phase includes the applicant phase and the shortest green time of the applicant phase. The shortest green time of the applicant phase is calculated in the following manner: the shortest green time of the applicant phase = the distance from the threshold-th traffic participant of the applicant phase to the entrance stop line - the distance from the first traffic participant of the applicant phase to the entrance stop line / the average speed of the green wave section + the shortest green time compensation value of the green wave phase; The time factor of the responder phase includes the applicant's intersection unique identifier, the applicant's phase, the responder phase opening time, the responder phase and the responder phase's shortest green time. The responder phase's shortest green time is calculated in the following manner: the responder phase's shortest green time = (the distance from the threshold-th traffic participant of the applicant's phase to the entrance stop line + the distance of the green wave section) / the average vehicle speed of the green wave section - the responder phase opening time + the green wave phase's shortest green time compensation value.

2. The method for intelligent green wave control of regional traffic based on cloud-edge-end collaboration according to claim 1 is characterized by: Each of the edge devices adjusts the green time of the current phase according to the received periodic change data, including: The edge device adjusts the green time of the current phase according to the received periodic change data based on the pre-calculated intelligent phase sequence or intelligent cycle.

3. The method for intelligent green wave control of regional traffic based on cloud-edge-end collaboration according to claim 1 or 2 is characterized by: The edge device of an intersection only receives the time factor of the phase of the applicant; An edge device at an intersection can receive the time factors of more than one responder phase.

4. The method for intelligent green wave control of regional traffic based on cloud-edge-end collaboration according to claim 1 or 2 is characterized by: The shortest green time compensation value of the green wave phase is a preset value.

5. The method for intelligent green wave control of regional traffic based on cloud-edge-end collaboration according to claim 1 or 2 is characterized by: The responder phase opening time is calculated as follows: The responder phase opening time=(the distance from the first traffic participant in the applicant phase to the entrance stop line+the distance of the green wave section) / the average vehicle speed of the green wave section.

6. Regional traffic intelligent green wave control system based on cloud-edge-end collaboration, including cloud servers and one or more edge devices; Its characteristics are: The cloud server is provided with a dynamic green wave control engine for obtaining a green wave route on which a green wave is required to be operated, and constructing a green wave collaborative control unit according to the green wave route, wherein each green wave collaborative control unit corresponds to a green wave section of the green wave route; Calculate the shortest green time of the applicant phase at all intersections in the green wave section, calculate the phase opening time of the responder phase at all green wave sections, and calculate the shortest green time of the responder phase at all intersections in all green wave sections; Constructing a time factor of the applicant phase and a time factor of the responder phase, constructing a periodic change of the green wave collaborative control unit according to the time factor of the applicant phase and the time factor of the responder phase, and sending data of the periodic change to edge devices corresponding to all intersections of the green wave section; Each of the edge devices is used to adjust the green time of the current phase according to the received periodic change data; The time factor of the applicant phase includes the applicant phase and the shortest green time of the applicant phase. The shortest green time of the applicant phase is calculated in the following manner: the shortest green time of the applicant phase = the distance from the threshold-th traffic participant of the applicant phase to the entrance stop line - the distance from the first traffic participant of the applicant phase to the entrance stop line / the average speed of the green wave section + the shortest green time compensation value of the green wave phase; The time factor of the responder phase includes the applicant's intersection unique identifier, the applicant's phase, the responder phase opening time, the responder phase and the responder phase's shortest green time. The responder phase's shortest green time is calculated in the following manner: the responder phase's shortest green time = (the distance from the threshold-th traffic participant of the applicant's phase to the entrance stop line + the distance of the green wave section) / the average vehicle speed of the green wave section - the responder phase opening time + the green wave phase's shortest green time compensation value.

7. The regional traffic intelligent green wave control system based on cloud-edge-end collaboration according to claim 6 is characterized by ； The cloud server stores a green wave parameter model, a green wave scheme model and a green wave scheduling scheme model.

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