Traffic signal lamp control method and system based on star-ground cooperative perception

Abstract

The application relates to the technical field of intelligent traffic, and provides a traffic signal lamp control method and system based on star-ground cooperative perception. The method comprises the following steps: obtaining network interconnection of a space-based network and a ground-based network to generate a star-ground cooperative network; obtaining demand data and control data of ground nodes in the ground-based network, training the demand data and the control data through a network management system in the ground-based network to generate a demand control model; obtaining a target ground node of a target control area; calling the demand control model according to the star-ground cooperative network, generating a perception instruction according to a sensor on the traffic signal lamp in combination with the demand control model, and determining the perception control of the traffic signal lamp. The application solves the technical problems that traffic safety hidden dangers are relatively large and the timeliness of signal lamp control is relatively low in the prior art, reduces traffic conflicts and accidents, and improves the effective control and management of intersections.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and in particular to a traffic signal control method and system based on satellite-ground cooperative sensing. Background Technology

[0002] Space-Ground converged networks are characterized by multi-layered, three-dimensional, and dynamic time-varying features. The dynamic, complex, and scalable nature of their topology, the high-speed mobility of space nodes, and their limited storage and processing capabilities mean that existing space information transmission technologies cannot adequately meet the communication demands of this new space network architecture. Space-Ground collaboration is a future trend in 6G communication networks, and the application of edge computing technology in space-ground collaborative networks is one of the important research directions. With the continuous acceleration of urbanization, traffic congestion is gradually worsening, making traffic lights even more crucial. The rational design and operation of traffic light control can simultaneously improve traffic efficiency and enhance traffic safety.

[0003] In summary, existing technologies have significant traffic safety hazards and low timeliness in traffic light control. Summary of the Invention

[0004] Therefore, it is necessary to provide a traffic signal control method and system based on satellite-ground cooperative perception that can reduce traffic conflicts and accidents and improve the effective control and management of intersections, in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a traffic light control method based on space-ground cooperative sensing. The method includes: acquiring a space-based network and a ground-based network for network interconnection to generate a space-ground cooperative network; interacting with ground nodes in the ground-based network to acquire demand data and control data, and training the demand data and control data through a network management system in the ground-based network to generate a demand control model; acquiring target ground nodes in a target control area, wherein the target ground nodes include traffic lights; calling the demand control model according to the space-ground cooperative network, generating a sensing command based on sensors on the traffic lights and the demand control model, and determining the sensing control of the traffic lights.

[0006] Secondly, this application provides a traffic signal control system based on space-ground cooperative perception. The system includes: a space-ground cooperative network generation module, which acquires space-based and ground-based networks and interconnects them to generate a space-ground cooperative network; a demand control model generation module, which interacts with ground nodes in the ground-based network to acquire demand data and control data, and trains the demand data and control data through a network management system in the ground-based network to generate a demand control model; a target ground node acquisition module, which acquires target ground nodes in a target control area, wherein the target ground nodes include traffic signals; and a perception control determination module, which calls the demand control model according to the space-ground cooperative network, generates perception commands based on sensors on the traffic signals and the demand control model, and determines the perception control of the traffic signals.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] First, a space-based network and a ground-based network are interconnected to generate a space-ground cooperative network. Second, ground nodes in the ground-based network interact to obtain demand data and control data. The network management system in the ground-based network trains the demand data and control data to generate a demand control model. Then, target ground nodes in the target control area are acquired, including traffic lights. Finally, the demand control model is invoked based on the space-ground cooperative network. Based on the sensors on the traffic lights, perception commands are generated in conjunction with the demand control model, and the perception control of the traffic lights is determined. This application solves the technical problems of significant traffic safety hazards and low timeliness of traffic light control in existing technologies, achieving the technical effect of reducing traffic conflicts and accidents and improving the effective control and management of intersections.

[0009] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating a traffic light control method based on satellite-ground cooperative sensing in one embodiment.

[0011] Figure 2 This is a schematic diagram illustrating the process of updating and acquiring the space-based network in a traffic signal control method based on space-ground cooperative sensing in one embodiment.

[0012] Figure 3 This is a block diagram of a traffic signal control system based on satellite-ground cooperative sensing in one embodiment.

[0013] Figure labeling: 11 Space-Ground Collaborative Network Generation Module, 12 Demand Control Model Generation Module, 13 Target Ground Node Acquisition Module, 14 Perception Control Determination Module. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0015] like Figure 1 As shown, this application provides a traffic signal control method based on satellite-ground cooperative sensing, characterized in that the method includes:

[0016] Acquire space-based and ground-based networks and interconnect them to generate a space-ground collaborative network;

[0017] Space-ground collaboration is a future trend in 6G communication networks. Applying edge computing technology in space-ground collaborative networks is a key research direction. Based on a review of the current state of research on edge computing technology in space-ground collaborative networks, this paper proposes a system architecture for space-ground collaborative networks integrating mobile edge computing technology, and summarizes application scenarios and logical functional architectures. Traffic lights, which direct traffic flow, typically consist of red, green, and yellow lights. By providing a traffic light control method based on space-ground collaborative perception, the paper achieves the technical effects of reducing traffic conflicts and accidents, enhancing traffic safety, improving intersection capacity, effectively controlling and managing intersections with high traffic volume, and making right-of-way allocation more equitable, reasonable, and efficient.

[0018] Space-based networks are wireless mobile communication systems centered on satellite communication nodes. They consist of a space-based backbone network and satellite access networks (high, medium, and low orbits). Utilizing technologies such as remote sensing, navigation, positioning, and communication, they enable real-time observation of natural landscapes and human activities on the Earth's surface. Ground-based networks refer to ground-based augmentation systems, which improve satellite navigation accuracy by providing differential correction signals. Optimized positioning accuracy can range from millimeter to sub-meter levels, representing a comprehensive and in-depth fusion of advanced technologies such as satellite positioning, computer network, and digital communication. Interconnection between space-based and ground-based networks involves linking two or more communication networks using various methods and network communication devices to form a larger network system, creating a space-ground collaborative network. This interconnection allows users in different networks to communicate with each other, share software and data, and provides data support for establishing demand control models.

[0019] like Figure 2 As shown, the high-orbit and low-orbit satellite nodes in the space-based network are obtained;

[0020] The satellite high-orbit nodes and the satellite medium- and low-orbit nodes deploy inter-satellite links according to their distribution distances;

[0021] The satellite node link connection is updated based on the inter-satellite link, and the space-based network is updated and acquired.

[0022] Artificial Earth satellite orbits are classified into low Earth orbit (LEO), medium Earth orbit (MEO), and high Earth orbit (HEO) based on their altitude above the Earth's surface. Generally, orbits with altitudes between 200 and 2000 kilometers are called LEO, those between 2000 and 20000 kilometers are called MEO, and those above 20000 kilometers are called HEO. High-Earth orbit (HEO) nodes and medium- and low-Earth orbit (MEO) nodes are used for subsequent satellite status assessment. LEO satellites, due to their advantages of low cost, low latency, high speed, and large capacity, have played a crucial role in building satellite internet. LEO satellite constellations can achieve seamless global coverage, enabling commercial use with relatively low launch costs and high practical value. High-Earth orbit (HEO) satellites... (The sentence is incomplete and requires further context to translate accurately.) The signal reception method is relatively simple, and because it involves far fewer satellites, it is also easier to manage. It can continuously receive signals from a single satellite for several hours without the problem of frequent replacements of low-Earth orbit satellites. Inter-satellite links are deployed based on the distribution distances of the high-Earth orbit nodes and the low-Earth orbit nodes. Inter-satellite links are used for communication between satellites, also known as inter-satellite links or cross-links. Information transmission and exchange between satellites can be achieved through inter-satellite links. Multiple satellites are interconnected through inter-satellite links to form a space communication network with satellites as exchange nodes. The link connections of satellite nodes are updated according to the inter-satellite links, thus updating the inter-satellite links to obtain the space-based network. Obtaining the space-based network provides the foundation for the subsequent generation of a space-ground collaborative network.

[0023] The satellite nodes in the space-based network and the ground nodes in the ground-based network deploy airspace links according to their distribution distances;

[0024] The space-based network and the ground-based network are connected by the aforementioned air link to generate the space-ground collaborative network.

[0025] The satellite nodes in the space-based network and the ground nodes in the ground-based network are connected by air-to-ground links based on their distribution distances. An air-to-ground link is a physical connection between a satellite node and a ground node, without any other switching nodes in between. The space-based network and the ground-based network are connected according to these air-to-ground links to form the space-ground cooperative network. This space-ground cooperative network provides equipment support for subsequent control of traffic lights.

[0026] The ground nodes in the ground network interact to obtain demand data and control data, and the network management system in the ground network trains the demand data and control data to generate a demand control model.

[0027] In this application, a ground node refers to a traffic light on a road, denoted as a ground node. The demand data and control data of the ground node include: demand data refers to the required red and green light durations based on the vehicle situation on the road where the traffic light is located; control data refers to the red and green light durations input by the satellite-ground cooperative network. The network management system is a system that combines software and hardware to adjust the network state, ensuring the normal and efficient operation of the network system and better utilizing its resources. The demand data and control data are transmitted to the ground network for training to obtain a demand control model. The demand control model is obtained by inputting the demand data into the demand control model. Training the demand data and control data through the network management system in the ground network to generate the demand control model lays the groundwork for subsequently obtaining the target data for the traffic light.

[0028] Extract the first ground node based on the ground node;

[0029] Based on the first ground node, read the first demand data and the first control data, and generate training data;

[0030] The demand control model is established in the network management system, a first demand control unit is constructed in the demand control model, the training data is input and trained, and the first demand control unit is sent to the demand control model.

[0031] A first ground node is extracted based on the aforementioned ground nodes, and any node is randomly selected from these ground nodes and designated as the first ground node for further study. A first demand control unit is constructed, which is obtained through supervised training using first demand data and first control data. The demand control unit is trained using the first demand data and first control data. When the model output results tend to converge, the accuracy of the demand control unit's output results is verified using the first control data to obtain a preset verification accuracy index. This preset verification accuracy index can be customized by those skilled in the art based on actual conditions, for example, 95%. When the accuracy of the first demand control unit's output results is greater than or equal to the preset verification accuracy index, the first demand control unit is obtained and sent to the demand control model. By constructing the first demand control unit and obtaining the demand control model, the efficiency and accuracy of the demand control model's output results can be improved.

[0032] Acquire target ground nodes within the target control area, wherein the target ground nodes include traffic lights;

[0033] The target control area refers to the traffic road area within the control range of the satellite-ground cooperative network. Selecting a target ground node from the target control area means selecting a traffic light from the satellite-ground cooperative network. By acquiring the traffic light, a target is provided for subsequent research.

[0034] The demand control model is invoked based on the satellite-ground collaborative network. Based on the sensors on the traffic lights, a perception command is generated in conjunction with the demand control model, and the perception control of the traffic lights is determined.

[0035] According to the demand control model invoked by the space-ground cooperative network, the sensors on the traffic lights are used to acquire specific road conditions, such as vehicle status. By acquiring vehicle information, a perception command is generated based on the demand control model, whereby the perception command is an instruction to the traffic lights to monitor the road conditions. The perception control of the traffic lights determines the perception status of the traffic lights, such as traffic flow in the traffic lanes. This perception status is then transmitted to the space-ground cooperative system, and the traffic lights are controlled based on the control data of the target ground node obtained by the space-ground cooperative system in conjunction with the demand control model. Through intelligent control of the traffic lights, the effects of reducing traffic conflicts and accidents, enhancing traffic safety, and improving intersection throughput are achieved.

[0036] Target demand data is generated based on the sensors on the traffic lights, the target demand data is input into the demand control model, and target control data is output.

[0037] The perception command is generated based on the target control data, and the perception control of the traffic light is determined based on the perception command.

[0038] Target demand data refers to the data on the demand of the target ground nodes. This target demand data is input into the demand control model, and target control data is output. Based on the target control data, perception commands are generated, and then the perception control of the traffic lights is determined based on these commands. By acquiring this perception control, the technical effect of reducing traffic conflicts and accidents and enhancing traffic safety is achieved.

[0039] Determine whether the target control data meets the target expected perception standard, and obtain the determination result;

[0040] If not satisfied, calculate the target deviation between the target control data and the target expected perception standard;

[0041] The target deviation is compensated by combining the aforementioned demand control model;

[0042] The traffic lights are controlled based on the compensation data.

[0043] The target expectation perception standard refers to the target demand data of the traffic signal light, which is set by the staff. The system determines whether the target control data meets the target expectation perception standard and obtains a judgment result. If the judgment result is not met, the target deviation between the target control data and the target expectation perception standard is calculated. For example, if the target control data is a 30-second red light, and the target expectation perception standard is a 40-second red light, there is a 10-second deviation. This deviation is then used to compensate for the target control data in conjunction with the demand control model. Based on the compensated data, the traffic signal light is then controlled. This application solves the technical problems of significant traffic safety hazards and low timeliness of traffic signal light control in the prior art, achieving the technical effect of reducing traffic conflicts and accidents and improving the effective control and management of intersections.

[0044] like Figure 3 As shown, embodiments of this application also include a traffic signal control system based on satellite-ground cooperative sensing, the system comprising:

[0045] The satellite-ground collaborative network generation module 11 is used to acquire space-based networks and ground-based networks for network interconnection to generate a satellite-ground collaborative network.

[0046] Demand control model generation module 12 is used to interact with ground nodes in the ground network to obtain demand data and control data, and to train the demand data and control data through the network management system in the ground network to generate a demand control model.

[0047] The target ground node acquisition module 13 is used to acquire target ground nodes in the target control area, wherein the target ground nodes include traffic lights;

[0048] The perception control determination module 14 is used to call the demand control model according to the satellite-ground cooperative network, generate perception instructions based on the sensors on the traffic light and in combination with the demand control model, and determine the perception control of the traffic light.

[0049] Furthermore, embodiments of this application also include:

[0050] An orbit node acquisition module is used to acquire satellite high-orbit nodes and satellite medium- and low-orbit nodes in the space-based network.

[0051] An inter-satellite link deployment module is used to deploy inter-satellite links between the high-orbit nodes and the low-orbit nodes of the satellite according to their distribution distance.

[0052] The space-based network update module is used to update the link connections of satellite nodes according to the inter-satellite links and update the space-based network.

[0053] Furthermore, embodiments of this application also include:

[0054] An airspace link deployment module is used to deploy airspace links between satellite nodes in the space-based network and ground nodes in the ground-based network according to their distribution distance.

[0055] A space-ground collaborative network generation module is used to combine the space-based network and the ground-based network with the air-to-ground link to generate the space-ground collaborative network.

[0056] Furthermore, embodiments of this application also include:

[0057] A ground node extraction module, wherein the ground node extraction module is used to extract a first ground node based on the ground nodes;

[0058] The training data generation module is used to generate training data based on the first ground node, by reading the first demand data and the first control data.

[0059] The data model training module is used by the network management system to establish the demand control model, construct a first demand control unit in the demand control model, input and train the training data, and obtain the first demand control unit and send it to the demand control model.

[0060] Furthermore, embodiments of this application also include:

[0061] The target control data output module is used to generate target demand data based on the sensors on the traffic lights, input the target demand data into the demand control model, and output target control data.

[0062] A perception control determination module is used to generate the perception command based on the target control data, and determine the perception control of the traffic light based on the perception command.

[0063] Furthermore, embodiments of this application also include:

[0064] A target control data judgment module is used to judge whether the target control data meets the target expectation perception standard and obtain a judgment result.

[0065] The target deviation calculation module is used to calculate the target deviation between the target control data and the target expected perception standard if the target deviation does not meet the requirement.

[0066] A target deviation compensation module is used to compensate for the target deviation in conjunction with the demand control model.

[0067] A traffic signal light perception and control module is used to perform perception and control of the traffic signal light based on compensation data.

[0068] For specific embodiments of the traffic signal control system based on satellite-ground cooperative sensing, please refer to the embodiments of the traffic signal control method based on satellite-ground cooperative sensing described above, which will not be repeated here. The above modules can be embedded in hardware or independent of the processor in the computer device, or stored in software in the memory of the computer device, so that the processor can call and execute the operations corresponding to each module.

[0069] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0070] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A traffic signal control method based on satellite-ground cooperative sensing, characterized in that, The method includes: Acquire space-based and ground-based networks and interconnect them to generate a space-ground collaborative network; The ground nodes in the ground network interact to obtain demand data and control data, and the network management system in the ground network trains the demand data and control data to generate a demand control model. Acquire target ground nodes within the target control area, wherein the target ground nodes include traffic lights; The demand control model is invoked based on the satellite-ground cooperative network. Based on the sensors on the traffic lights, a perception command is generated in conjunction with the demand control model, and the perception control of the traffic lights is determined. The method involves obtaining demand and control data from ground nodes in the ground-based network, and training the demand and control data using the network management system within the ground-based network to generate a demand control model. The method includes: Extract the first ground node based on the ground node; Based on the first ground node, read the first demand data and the first control data, and generate training data; Establish the demand control model in the network management system, construct a first demand control unit in the demand control model, input and train the training data, and obtain the first demand control unit and send it to the demand control model. The method of invoking the demand control model based on the satellite-ground cooperative network, generating perception commands based on the sensors on the traffic light and in conjunction with the demand control model, and determining the perception control of the traffic light includes: Target demand data is generated based on the sensors on the traffic lights, the target demand data is input into the demand control model, and target control data is output. The perception command is generated based on the target control data, and the perception control of the traffic light is determined based on the perception command. The method further includes: Determine whether the target control data meets the target expected perception standard, and obtain the determination result; If not satisfied, calculate the target deviation between the target control data and the target expected perception standard; The target deviation is compensated by combining the aforementioned demand control model; The traffic lights are controlled based on the compensation data.

2. The method as described in claim 1, characterized in that, The method for obtaining the space-based network includes: Obtain the high-orbit and low-orbit satellite nodes in the space-based network; The satellite high-orbit nodes and the satellite medium- and low-orbit nodes deploy inter-satellite links according to their distribution distances; The satellite node link connection is updated based on the inter-satellite link, and the space-based network is updated and acquired.

3. The method as described in claim 2, characterized in that, The method for acquiring and interconnecting space-based and ground-based networks to generate a space-ground collaborative network includes: The satellite nodes in the space-based network and the ground nodes in the ground-based network deploy airspace links according to their distribution distances; The space-based network and the ground-based network are connected by the aforementioned air link to generate the space-ground collaborative network.

4. A traffic signal control system based on satellite-ground cooperative sensing, used to execute the method of claim 1, characterized in that, The system includes: A space-ground collaborative network generation module is used to acquire space-based networks and ground-based networks for interconnection and generate a space-ground collaborative network. A demand control model generation module is used to interact with ground nodes in the ground network to obtain demand data and control data, and to train the demand data and control data through the network management system in the ground network to generate a demand control model. A target ground node acquisition module is used to acquire target ground nodes in a target control area, wherein the target ground nodes include traffic lights; The perception and control determination module is used to call the demand control model according to the satellite-ground cooperative network, generate perception instructions based on the sensors on the traffic light and the demand control model, and determine the perception and control of the traffic light.

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