CPS multi-scale based urban road intersection traffic control method

Abstract

The application relates to a CPS multi-scale-based urban road intersection traffic control method and belongs to the field of traffic informatization. According to the space-time relationship of an urban traffic control system, the operation scale of an intelligent automobile information physical system is divided, and the intelligent automobile information physical system is divided into five scales, namely, a node level, a unit level, a subsystem level, a system level and a region level. A signal intersection and vehicle speed joint control method is constructed based on the multi-scale characteristics of the information physical system. The breadth and real-time requirements of the systems of different scales are different, meanwhile, the scales have a containing relationship, and multiple lower-level scales are coordinated by higher-level scales. For the proposed information physical system multi-scale urban traffic control problem, a basic model of the multi-scale urban traffic control problem is constructed. The application can more clearly divide the system boundary, understand the system composition and operation mechanism, and realize high-cohesion, low-coupling system decoupling.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of traffic informatization, and relates to a multi-scale urban road intersection traffic control method based on a cyber-physical system (CPS). BACKGROUND

[0002] An intersection is a key node of a city traffic system. From the perspective of traffic flow theory, an intersection, as a typical discontinuous flow facility, has a forced blocking control on traffic flow, and a signal lamp controls the operation order of the intersection by isolating traffic flows in different directions in time. As a key node of a city traffic system and a basic unit of traffic control, an intersection bears the functions of convergence and dispersion of traffic flow, and a reasonable and efficient intersection control system is of great significance to the efficient, safe and sustainable development of a city traffic system. A cyber-physical system can integrate sensing and control technologies, describe the dynamic evolution relationship between physical entities and information virtual entities in traffic, and provide a new idea for building a vehicle-road collaborative intelligent traffic system in a networked environment and solving city traffic management problems.

[0003] For the optimization control of a signal intersection, a large number of researches have been carried out at home and abroad and some important progress has been made, and the intersection passing efficiency and vehicle passing delay are improved compared with traditional control schemes. However, most of the current researches on intersection signal control separately optimize traffic signals and vehicle control, lack effective use of vehicle networking information, and few researches are directed to the coordinated optimization control of intersection signals and vehicle speed. In addition, although a few researches are directed to the joint optimization of signals and vehicle speed, the methods provided by these researches have complex models and require a large amount of computing resources, which limits the further development of these methods, and these methods are more suitable for planning and evaluation tasks rather than real-time applications. Therefore, it is necessary to simultaneously consider the control of signal lights and the optimization of vehicle speed from the system level, and further explore the signal intersection coordinated control strategy that meets the actual traffic application requirements.

[0004] An intelligent vehicle cyber-physical system is a new form of future intelligent traffic systems. As a new emerging system architecture, an intelligent vehicle cyber-physical system deeply integrates a physical system and an information system, depicts the mutual mapping, information interaction, feedback and execution relationship between various traffic objects in the physical system and the information system, and accurately describes the dynamic evolution process of the entire system over time. For a complex and strongly coupled city traffic system, an intelligent vehicle cyber-physical system helps to accurately grasp and understand the inherent mechanism and operation law of city traffic control, and can effectively promote the development of an intelligent traffic ecological system.

[0005] In the face of complex and strongly coupled urban traffic system, it is necessary to study the complex interaction and fusion mechanism of urban traffic information factors and physical factors from the perspective of information physical system, combined with intelligent networked vehicles, cloud computing and advanced information communication technology, and design efficient and environmentally friendly urban traffic management framework and control algorithm. From the perspective of information physical system, exploring the control problem of urban signal intersection can help to understand the internal coupling mechanism of vehicles, signal lights and intelligent control platform in urban intelligent transportation, and promote the systematic development of urban intelligent transportation.

[0006] Based on the above reasons, the urban traffic control framework based on the multi-scale characteristics of information physical system is designed, which has the advantages of clear hierarchy between scales, clear correlation, clear object and easy expansion, and can be widely applied to the cooperative control of road signal intersections to meet the actual traffic application requirements. SUMMARY

[0007] Therefore, the purpose of the present application is to provide a CPS multi-scale based urban road intersection traffic control method.

[0008] To achieve the above purpose, the present application provides the following technical solutions:

[0009] The multi-scale theory of information physical system proposed by the present application considers the traffic dynamics and interaction within and between scales for the problem of urban traffic control, and designs an urban traffic control framework based on the multi-scale characteristics of information physical system. The framework divides the entire traffic system into five different scales: node level, unit level, subsystem level, system level and regional level. The service range and real-time requirements of each scale system are different: the service range gradually expands from the node level to the regional level, and the real-time requirement gradually decreases. At the same time, there is a containing relationship between scales, and multiple lower level scales are coordinated by higher level scales. For example, a system level scale usually supervises and controls the activities of a group of subsystem level scales. In addition, the framework describes the intra-scale state, control target and interaction relationship between information layer and physical layer elements of each scale. According to different control requirements, the framework can also be flexibly extended.

[0010] The node level intelligent vehicle information physical system is the smallest scale of the entire complex large system, and this scale of information physical system takes a single intelligent vehicle as the perspective and only considers the internal components and system operation process of the vehicle. The node level intelligent vehicle information physical system must have the functions of perception, calculation, interaction, expansion and self-decision, and through the sensing device, it completes the perception of the state of the vehicle itself and the surrounding environment information, maps it to the information space, and through calculation, analysis and decision in the information space, controls the vehicle entity in the physical world, constructs the most basic data automatic flow closed loop, and forms the fusion and interaction of the physical world and the information world.

[0011] Unit-level intelligent CPS breaks through the single-vehicle intelligence, and establishes a communication connection with the surrounding vehicles in the way of VANETs. It further shares the independent information space through the network, so that the vehicles evolve into a collaborative and self-organizing function. The typical unit-level intelligent CPS is CAV platooning. The unit-level scale focuses on information interaction and collaborative control within the vehicle group. The communication frequency and timing of each vehicle in the system depends on the state of the physical space at the time. If the state is relatively stable, the communication frequency is reduced; if the state fluctuates greatly, the communication frequency is increased, and the collaborative task is completed together.

[0012] Subsystem-level intelligent CPS focuses on the coordination between the vehicle groups and the road sections beyond the line of sight. The system contains a certain scale of vehicles, which makes it impossible to meet the global optimization control by relying on local vehicle-to-vehicle communication and vehicle-to-vehicle coordination. The heavy perception and computing tasks within the vehicle group need to be connected with the edge cloud through V2I to support task offloading. In addition, the system also contains road sections and their attached infrastructure (the spatial range is usually 50 to 300 meters), typical road sections include intersections and traffic lights, merging areas, tunnels, etc. When analyzing the subsystem-level intelligent CPS, the interaction between vehicles and road sections needs to be fully considered. The subsystem-level scale mainly focuses on the closed-loop communication link and fusion control within the local range formed by the interconnection of vehicle groups, road sections, and edge clouds. In addition, the form of subsystem-level scale control also changes. In the node-level intelligent CPS, the system directly controls the vehicle chassis system, while in the subsystem-level intelligent CPS, the system's control changes to the specification of the final state, without directly specifying the specific action process of the vehicle.

[0013] System-level intelligent CPS is composed of multiple subsystem-level intelligent CPS and regional clouds. The spatial range of the system is expanded to 300 to 5000 meters, reaching the road network level, containing several signal intersections; the time range is expanded to 10 to 20 seconds, so this level of system provides longer computing and decision-making time. For the regional clouds and edge clouds within the system, they are connected through wired communication to transmit a large amount of information to share local traffic states and decision-making information.

[0014] The regional intelligent vehicle cyber-physical system can realize cross-system, cross-platform interconnection, intercommunication and interoperation through the central cloud and big data platform, promote the effective integration, exchange and sharing of multi-source heterogeneous data, and realize comprehensive information perception, deep analysis, scientific decision-making and precise execution in the city. This level system focuses on the macro-control of the traffic situation in the city and is responsible for the allocation and coordination of road resources to balance the traffic pressure of each region and road section in the city and ensure smooth traffic.

[0015] By dividing the intelligent vehicle cyber-physical system into different scales, the system boundary can be clearly divided, the composition and operation mechanism of the system can be deeply understood, and the system decoupling with high cohesion and low coupling can be realized, focusing on the research object.

[0016] In the designed urban traffic control framework, the information layers of each scale have different structures and provide different services.

[0017] The vehicle-mounted computing platform of the node-level system refers to the telematics and the real-time computing technology equipped with it, mainly running automatic driving or assisted driving application programs to provide strong real-time driving and safety services for vehicles, and is an important part of realizing single vehicle intelligence and autonomous decision-making.

[0018] The co-construction cloud of the unit-level system refers to breaking through single vehicle intelligence, and vehicles establishing stable communication connections with surrounding vehicles in a networked manner to share their independent information spaces through network communication, realizing fusion perception and cooperative control between vehicles, mainly running vehicle cooperative services with strong real-time requirements such as platooning, etc.

[0019] The edge cloud of the subsystem level includes mobile edge computing (MEC) and real-time caching technology, mainly running collaborative application programs with weak real-time requirements, and providing cloud-controlled application basic services for users.

[0020] The regional cloud of the system level provides parallel computing and wide-area communication, mainly running non-real-time collaborative applications in the regional range, providing traffic supervision, law enforcement and other cloud-controlled application basic services for transportation and traffic management departments, and providing macro coordination services for each edge cloud in the region.

[0021] The regional system benefits from the high-performance computing and big data storage of the central cloud, runs non-real-time application programs serving the entire city, and mainly faces traffic management departments, government departments, vehicle production and manufacturing enterprises, universities and scientific research institutions to provide macro traffic data analysis and basic data value-added services.

[0022] The present application establishes a general optimization model for the proposed cyber-physical system multi-scale urban traffic control problem. Let M represent all scales, m∈M and m=1 be the smallest scale in space-time range, and m=M be the largest scale in space-time range. Let k m be any discrete time under scale m, the time interval between any two times k m-1 (m≥1) and k m is ΔT m . Since different scales have different real-time requirements, the values of ΔT m are different. For the time interval ΔT m (m∈M) between different scales, there is ΔT1<ΔT2<…<ΔT M . Corresponding to the multi-scale problem proposed in the foregoing, scale m=1 represents the node-level intelligent vehicle cyber-physical system, and scale m=M represents the regional-level intelligent vehicle cyber-physical system. In addition, x m (k m ) represents the system state at the k m time under the m scale, such as the position, acceleration, signal light state, etc. of the vehicle. u m (k m ) represents the control variable at the k m time under the m scale, such as the acceleration of the vehicle, the phase switching of the signal light, etc. In summary, the general formula of the multi-scale cyber-physical system urban traffic control problem can be expressed in discrete form as Q0:

[0023]

[0024] subject to constraints:

[0025]

[0026] Equation (1) is the system objective function of the entire multi-scale problem, α m is the weight of the m scale objective in the objective function, and is subject to the constraint of equation (2). In equation (2), the first equation represents the transition of the system state within the scale, and the transition of the state complies with the kinematic laws within the scale, such as Newton's second law, the law of conservation of energy, etc. In addition, in this study, it can be considered that the interaction between adjacent scales is much greater than the interaction between other scales, so appropriate simplification is made, and the interaction between non-adjacent scales is ignored, that is, scale m is mainly affected by m-1(m≥0) and m+1 scales, and the interaction between adjacent scales is represented by the second equation in equation (2). In equation (2), the third equation x(k m=0) is a vector, which represents the initial state of each scale of the whole system. The fourth equation of formula (2) is a limit constraint of the system state and control, for example, the maximum speed and maximum acceleration of the vehicle are limited due to the engine power and other factors. The fifth equation of formula (2) is a constraint in the system, which is set to meet the actual scene requirements, for example, only one green light phase can be present at the same time at the traffic signal.

[0027] The general optimization model Q0 established by the present application is obviously multi-scale: first, at different scales m, ΔT m represents a time interval ranging from milliseconds to minutes, so it is multi-scale in the time domain; secondly, for scale m, the system state x m (k m ) and the control variable u m (k m ) are limited within a certain geographical area, such as vehicles, signal intersections, road networks, etc., so it is implicitly multi-scale in the spatial range.

[0028] The present application has the beneficial effect that the present application can well describe the process of information flow within and between scales. Within a single scale, by fusing the perceived real physical entities such as road environment, vehicle state, signal light phase and transmitting them to the basic platform through the automobile bus or V2X communication technology (the communication method is different depending on the scale), after preliminary processing in the basic platform, the final digital information is mapped and reconstructed in real time in the information layer, completing the process of physical layer to information layer (Physical to Cyber, P2C) in the CPS data flow closed loop, which is the embodiment of the correct reflection of the information space to the physical space.

[0029] Other advantages, objects, and features of the present application will be in part apparent and in part pointed out hereinafter in the specification, and it is the intention, therefore, to be limited only as indicated by the scope of the claims. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to make the purposes, technical solutions and advantages of the present application clearer, the preferred detailed description of the present application will be made below in combination with the drawings, in which:

[0031] Figure 1 It is a city traffic control framework based on the multi-scale characteristics of information physical system;

[0032] Figure 2 It is a schematic diagram of intelligent vehicle information physical system characteristics;

[0033] Figure 3The present application discloses a scale division method for intelligent vehicle cyber-physical system. DETAILED DESCRIPTION

[0034] Other advantages and benefits of the present application will become apparent to those skilled in the art upon consideration of the disclosure or can be learned by practice of the application. The present application can be realized and achieved by means of different specific embodiments and applications, and various modifications and changes thereto can be made based on different views and applications without departing from the spirit of the present application. It should be noted that the drawings provided in the following examples only schematically illustrate the basic concept of the present application, and the following examples and features in the examples can be combined with each other without conflict.

[0035] The drawings are only used for exemplary illustration, and the representation is only a schematic diagram, not a physical diagram, and should not be understood as a limitation of the present application. In order to better illustrate the embodiments of the present application, some components in the drawings may be omitted, enlarged or reduced, and do not represent the actual size of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions in the drawings may be omitted.

[0036] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it should be understood that if the terms "upper", "lower", "left", "right", "front", "back" and the like indicate the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, therefore the terms describing the positional relationship in the drawings are only used for exemplary illustration, and should not be understood as a limitation of the present application. For those skilled in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0037] The implementation steps of the present application are as follows:

[0038] The urban traffic system is a complex system, and the elements in the system have a strong coupling relationship. The traditional traffic management method is difficult to adapt to the traffic demand under the new background. In order to solve these limitations, based on the multi-scale theory of cyber-physical system proposed in the previous section, for the problem of urban traffic control, the traffic dynamics and interaction within and between scales are considered, and a city traffic control framework based on the multi-scale characteristics of cyber-physical system is designed as shown in the figure. Figure 1

[0039] ​The framework divides the whole transportation system into five different scales, i.e., node level, unit level, subsystem level, system level, and region level. The service range and real-time requirements of each scale are different: the service range expands gradually from node level to region level, while the real-time requirements decrease gradually. Meanwhile, the scales have a containing relationship, i.e., multiple lower-level scales are coordinated by a higher-level scale.

[0040] For example, a system level scale usually supervises and controls the activities of a group of subsystem level scales. In addition, the framework describes the intra-scale state, control objectives, and the interaction between the information layer and the physical layer elements of each scale. According to the different control requirements, the framework can also be flexibly extended.

[0041] Figure 2 The figure shows the characteristics of the intelligent vehicle CPS; Figure 3 The figure shows the scale division of the intelligent vehicle CPS.

[0042] The designed urban traffic control framework can well describe the process of intra-scale and inter-scale information flow. In a single scale, the real physical entities such as road environment, vehicle state, and signal phase are fused and transmitted to the underlying platform through the vehicle bus or V2X communication technology (the communication method is different depending on the scale). After preliminary processing in the underlying platform, the final digital information is mapped and reconstructed in real time in the information layer, completing the process of P2C (Physical to Cyber) in the CPS data flow loop, which is the embodiment of the correct reflection of the physical space by the information space.

[0043] The information layer combines pre-designed and deployed algorithms and rules to make calculations and decisions, forming direct (such as node level control of vehicle throttle opening) or indirect (such as subsystem level specification of vehicle final state) control commands, implementing control on CAV and roadside controllable infrastructure, achieving vehicle and traffic regulation, and helping to improve the safety and efficiency of vehicle and traffic operation, completing the process of C2P (Cyber to Physical) in the CPS data flow loop, which is the embodiment of the control effect of the information space on the physical space. Between different scales, the formatted system state information is transmitted through the network to realize the coordination and control of systems between different scales, i.e., to complete the process of C2C (Cyber to Cyber), which is the embodiment of the extensive interconnection and logical coordination of the CPS information space.

[0044] In the designed urban traffic control framework, the information layers of each scale have different structures and provide different services. Specifically:

[0045] The vehicle-mounted computing platform of the node-level system refers to a vehicle machine and real-time computing technology equipped therewith, mainly running an automatic driving or assisted driving application program, providing a strong real-time driving and safety service for a vehicle, and being an important link for realizing single-vehicle intelligence and autonomous decision-making.

[0046] The co-construction cloud of the unit-level system refers to breaking through single-vehicle intelligence at this scale, and vehicles establishing stable communication connections with surrounding vehicles in a networked manner, sharing respective independent information spaces through network communication, realizing fusion perception and cooperative control between vehicles, mainly running a vehicle cooperative service with strong real-time requirements, such as platooning.

[0047] The edge cloud of the subsystem level includes mobile edge computing (MEC) and real-time caching technology, mainly running a collaborative application program with weak real-time requirements, and providing a cloud-controlled application basic service for users.

[0048] The regional cloud of the system level provides parallel computing and wide-area communication, mainly running a non-real-time collaborative application in a regional range, providing a traffic supervision, law enforcement and other cloud-controlled application basic service for a transportation and traffic management department, and providing a macro coordination service for each edge cloud in a region.

[0049] The regional-level system benefits from high-performance computing and large data storage of the center cloud, runs a non-real-time application program serving the entire city, and mainly faces a traffic management department, a government department, a vehicle production and manufacturing enterprise, a university and a scientific research unit, and provides a macro traffic data analysis and basic data value-added service.

[0050] For the above-mentioned urban traffic control framework, there are two problems to be further solved:

[0051] 1. How to model the traffic dynamics within the scale and the interaction between different scales;

[0052] 2. How to analyze and solve the multi-scale problem for practical application.

[0053] The present application establishes a general optimization model suitable for the proposed framework for the multi-scale urban traffic control problem of the proposed cyber-physical system.

[0054] Let M represent all scales, m is an element of M, and m = 1 is the smallest scale in terms of space and time, and m = M is the largest scale in terms of space and time.

[0055] Let k m be any discrete time at scale m, and the time interval between any two times k m-1 (m >= 1) and k m is ΔT m .

[0056] Since different scales have different real-time requirements, the values of ΔT m are different.

[0057] For the time interval ΔT m (m∈M) between different scales, there are ΔT1<ΔT2<…<ΔT M , and corresponding to the multi-scale problem proposed in the present application, scale m=1 represents the node-level intelligent vehicle cyber-physical system, and scale m=M represents the regional-level intelligent vehicle cyber-physical system.

[0058] In addition, x m (k m ) represents the system state at the k m th moment at the m scale, such as the position, acceleration, signal light state, etc. of the vehicle. u m (k m ) represents the control variable at the k m th moment at the m scale, such as the acceleration of the vehicle, the phase switching of the signal light, etc. In summary, the general formula of the multi-scale cyber-physical system urban traffic control problem can be represented in discrete form as Q0:

[0059]

[0060]

[0061] Equation (1) is the system objective function of the entire multi-scale problem, α m is the weight of the m scale objective in the objective function, and is subject to the constraint of equation (2).

[0062] In equation (2), the first equation represents the transition of the system state within the scale, and the transition of the state complies with the kinematic laws within the scale, such as Newton's second law, the law of conservation of energy, etc.

[0063] In addition, in the present research, it can be considered that the interaction between adjacent scales is much greater than the interaction between other scales, so appropriate simplification is made, and the interaction between non-adjacent scales is ignored, that is, the scale m is mainly affected by the m-1 (m≥0) and m+1 scales, and the interaction between adjacent scales is represented by the second equation in equation (2).

[0064] In the third equation of equation (2), x(k m =0) is a vector, representing the initial state of each scale of the entire system. The fourth equation of equation (2) is the limit constraint of the system state and control, such as the maximum speed and maximum acceleration of the vehicle being limited due to factors such as engine power. The fifth equation of equation (2) is a constraint set in the system to meet the actual scene requirements, such as a traffic signal light having only one green phase at the same time.

[0065] The established general optimization model Q0 is obviously multi-scale: first, ΔT m The time interval represented is different from millisecond to minute, so it is multi-scale in time domain; second, for scale m, the system state x m (k m ) and control variable u m (k m ) are limited in a certain geographical area, such as vehicle, signal intersection, road network, etc., so it is implicitly multi-scale in spatial range.

[0066] After the model is constructed, it needs to be solved. For the proposed multi-scale problem Q0, there is interaction between each scale in the system, which cannot be directly decomposed into multiple sub-problems for solving, so how to correctly handle these coupling relationships becomes a key challenge to solve the problem.

[0067] A simple solution is to unify the resolution of time, and solve the whole model with the smallest time interval ΔT1 in all scales. In this way, the details of the original model can be maintained, but there are two problems: first, the spatial range of the large-scale information physical system is large, and due to the limitation of communication delay and communication bandwidth, it is difficult to maintain high-frequency, super-quantity stable communication; second, the difficulty of problem solving increases, which requires a large amount of computing resources, and it is difficult to meet the real-time requirements. Therefore, this simple solution is difficult to implement.

[0068] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should be covered in the scope of the claims of the present application.

Claims

1. A multi-scale urban road intersection traffic control method based on CPS, characterized in that: The method specifically includes the following steps: S1: Based on the spatiotemporal relationship of the urban traffic control system, the operational scale of the intelligent vehicle cyber-physical system is divided into five scales: node level, unit level, subsystem level, system level, and regional level. S2: A joint control method for signalized intersections and vehicle speeds is constructed based on the multi-scale characteristics of cyber-physical systems. The breadth and real-time requirements of the services provided by each scale system are different. S3: For the proposed multi-scale signalized intersection and vehicle speed joint control method in cyber-physical systems, a basic model for the multi-scale urban traffic control problem is constructed; in S3, the basic model for the multi-scale urban traffic control problem is constructed in the following way: Let M represent all scales, m∈M and m=1 is the smallest scale in terms of spatiotemporal extent, and m=M is the largest scale in terms of spatiotemporal extent; let k m For any discrete time at scale m, any two times k m-1 With k m The time interval between them is ΔT m m≥1; for time intervals ΔT between different scales m , m∈M, there exists ΔT1<ΔT2<…<ΔT M ;x m (k m ) represents the k-th digit at the m-scale. m The system state at a given time; u m (k m ) represents the k-th digit at the m-scale. m The control variable at time; the formula for urban traffic problems in multi-scale cyber-physical systems is expressed as Q0 in discrete time: Satisfy constraints: Where, α m Let x be the weight of the m-th scale objective in the objective function, and let x be the weight of the objective function. m (k m +1)=x m (k m )+f m (x m (k m ),u m (k m ),ΔT M ) represents the state transition of the system within a scale; equation g m (x m-1 (k m ),x m (k m ),x m+1 (k m ),u m (k m ),k m )≤0 indicates the interaction between adjacent scales, ignoring the interaction between non-adjacent scales. That is, scale m is affected by scales m-1 and m+1, where m in m-1 satisfies m≥1; when m=1, i.e., when the spatiotemporal range is the smallest scale, it only interacts with the scale one level higher than m=2; when m=M, i.e., when the spatiotemporal range is the largest scale, it only interacts with the scale one level lower than m=m-1; the equation x(k m =0)=[x1(k1=0),x2(k2=0),…,x M (k M =0)] T Denotes the initial state of the system at various scales, x(k) m =0) is a vector; equation These are the limit constraints for system state and control; the limit constraints include: the maximum speed and maximum acceleration of the vehicle are limited due to engine power; the equation h(x) m (k m ),u m (k m ),k m )≤0 represents other constraints set in the system to meet the requirements; the constraints include: a traffic light can only have one green light phase at a time.

2. The CPS-based multi-scale urban road intersection traffic control method according to claim 1, characterized in that: S1 specifically refers to: deconstructing and reducing the dimensions of the intelligent vehicle cyber-physical system in a hierarchical manner, expanding the intelligent vehicle cyber-physical system into five spatiotemporal scales from the perspectives of time and space, so that the hierarchical structure is more in line with actual traffic scenarios. The system operates at five scales, from smaller to larger: node level, unit level, subsystem level, system level, and region level. When analyzing the system, each scale has its corresponding main focus, which ultimately achieves separation of concerns and facilitates a clear understanding of the operating mechanism of the intelligent vehicle cyber-physical system. The cyber-physical systems of intelligent vehicles involve different system components in different scenarios, and the division of scale reflects adaptability. The same subject or group in different scenarios may not be classified into the same scale.

3. The CPS-based multi-scale urban road intersection traffic control method according to claim 1, characterized in that: Specifically, S2 means that the service scope expands step by step from the node level to the regional level, while the real-time requirements decrease step by step; at the same time, there is an inclusion relationship between scales, and multiple lower-level scales are coordinated by higher-level scales. Within a single scale, by fusing perceptions of the real physical world, data is transmitted to the base platform via vehicle bus or V2X communication technology. After initial processing on the base platform, it is mapped and reconstructed in real-time at the information layer in the form of digital information. The information layer, combined with designed and deployed algorithms and rules, performs calculations and decisions to generate direct or indirect control commands to control CAVs and roadside controllable infrastructure. Between different scales, formatted system information is transmitted via the network to achieve coordination and control of systems at different scales, thus completing the information-to-information C2C process. Among these: The in-vehicle computing platform of the node-level system refers to the vehicle's infotainment system and its equipped real-time computing technology, which runs autonomous driving or assisted driving applications and provides vehicles with highly real-time driving and safety services. The co-construction cloud of the unit-level system refers to the breakthrough of single-vehicle intelligence at this scale, where multiple vehicles establish communication connections with surrounding vehicles through networking, share their independent information spaces through network communication, realize vehicle collaboration, and run real-time vehicle collaboration services. The subsystem-level edge cloud includes mobile edge computing (MEC) and real-time caching technology, runs collaborative applications with weak real-time requirements, and provides users with basic cloud-controlled application services. The system-level regional cloud provides parallel computing and wide-area communication, runs regional-scale non-real-time collaborative applications, provides basic services for cloud control applications for traffic supervision and law enforcement to transportation and traffic management departments, and provides macro-level collaborative services to various edge clouds within the region. The central cloud of the regional system benefits from high-performance computing and big data storage, running non-real-time applications that serve the entire city, providing macro-level traffic data analysis and basic data value-added services to traffic decision-making departments, vehicle design and manufacturing companies, traffic-related enterprises and research institutions.

4. An electronic device, characterized in that: The electronic device includes: Processor and memory; The processor executes the steps of the CPS-based multi-scale urban road intersection traffic control method as described in any one of claims 1 to 3 by calling the program or instructions stored in the memory.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or instructions that cause a computer to perform the steps of the CPS-based multi-scale urban road intersection traffic control method as described in any one of claims 1 to 3.

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