CN110474803B

CN110474803B - Processing method of Internet of vehicles system and related equipment

Info

Publication number: CN110474803B
Application number: CN201910746145.9A
Authority: CN
Inventors: 侯琛
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2019-08-13
Filing date: 2019-08-13
Publication date: 2021-08-20
Anticipated expiration: 2039-08-13
Also published as: CN110474803A

Abstract

The disclosure provides a processing method of a vehicle networking system and related equipment. The method comprises the following steps: the server acquires a state vector, a system matrix, an input matrix, an output matrix and an incidence matrix of the Internet of vehicles system; the server carries out observability analysis on the state vector according to the system matrix, the input matrix, the output matrix and the incidence matrix to obtain an observable state and an unobservable state in the state vector; the server determining n expected poles; the server obtains a feedback vector of a first controller and a coefficient vector of a second controller according to the expected poles, the system matrix, and observable and unobservable states in the state vector; the server processes the state vector to obtain an output vector of the Internet of vehicles system; and the server sends the output vector to the terminal equipment so as to display the output vector on a display screen of the terminal equipment.

Description

Processing method of Internet of vehicles system and related equipment

Technical Field

The disclosure relates to the technical field of computers and communication, in particular to a processing method of a vehicle networking system and related equipment.

Background

The concept of the internet of vehicles is derived from the internet of things, namely the internet of vehicles, and the network connection between vehicles, people, roads, service platforms and the like is realized by taking the running vehicles as information perception objects and by means of a new generation of information communication technology, so that the overall intelligent driving level of the vehicles is improved, safe, comfortable, intelligent and efficient driving feeling and traffic service are provided for users, meanwhile, the traffic operation efficiency is improved, and the intelligent level of social traffic service is improved.

The stability of the car networking system has a great influence on the normal operation of the car networking. In the prior art, in order to improve the stability of the car networking system, the technical scheme of adoption is: firstly, judging whether a vehicle networking system is stable or not; when the Internet of vehicles system is judged to be unstable, further judging which component causes the instability of the Internet of vehicles system; after determining the component causing the instability of the internet of vehicles system, replacing the component causing the instability of the internet of vehicles system to stabilize the internet of vehicles system.

However, the solutions adopted by the above prior art have at least the following disadvantages: on one hand, when the vehicle networking system is judged to be unstable, the components in the system need to be checked one by one to determine which component causes the instability of the system, and the mode of checking one by one has low efficiency, wastes time and labor and has higher cost; on the other hand, the method of stabilizing the system by replacing the components causing the instability of the system wastes component resources, and meanwhile, the efficiency of replacing the components is low, and the cost is high.

Therefore, a new processing method of the car networking system and related equipment are needed.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

The embodiment of the disclosure provides a processing method and device of a vehicle networking system, electronic equipment and a computer readable storage medium, which can improve the stability of the vehicle networking system.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.

According to one aspect of the present disclosure, there is provided a processing method of a vehicle networking system, the method including: the method comprises the steps that a server obtains a state vector, a system matrix, an input matrix, an output matrix and an incidence matrix of the Internet of vehicles system, wherein the state vector comprises n states, and n is a positive integer greater than or equal to 1; the server carries out observability analysis on the state vector according to the system matrix, the input matrix, the output matrix and the incidence matrix to obtain an observable state and an unobservable state in the state vector; the server performs stability analysis on the Internet of vehicles system to determine n expected poles; the server obtains a feedback vector of a first controller and a coefficient vector of a second controller according to the expected poles, the system matrix, and observable and unobservable states in the state vector; the server processes the state vector by using the feedback vector of the first controller and the coefficient vector of the second controller to obtain an output vector of the Internet of vehicles system; and the server sends the output vector to the terminal equipment so as to display the output vector on a display screen of the terminal equipment.

According to an aspect of the present disclosure, there is provided a processing apparatus of a vehicle networking system, the apparatus comprising: the state information acquisition module is configured to acquire a state vector, a system matrix, an input matrix, an output matrix and an incidence matrix of the Internet of vehicles system, wherein the state vector comprises n states, and n is a positive integer greater than or equal to 1; the observability analysis module is configured to perform observability analysis on the state vector according to the system matrix, the input matrix, the output matrix and the incidence matrix to obtain observable states and unobservable states in the state vector; the expected pole determining module is configured to perform stability analysis on the Internet of vehicles system and determine n expected poles; a feedback coefficient obtaining module configured to obtain a feedback vector of a first controller and a coefficient vector of a second controller according to the expected poles, the system matrix, and observable and unobservable states in the state vector; the output vector obtaining module is configured to process the state vector by using the feedback vector and the coefficient vector to obtain an output vector of the Internet of vehicles system; and the output vector sending module is configured to send the output vector to the terminal equipment so as to display the output vector on a display screen of the terminal equipment.

According to an aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored, which when executed by a processor, implements the processing method of the car networking system as described in the above embodiments.

According to an aspect of the embodiments of the present disclosure, there is provided an electronic device including: one or more processors; a storage device configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the processing method of the internet of vehicles system as described in the above embodiments.

In the technical scheme provided by some embodiments of the present disclosure, a state vector, a system matrix, an input matrix, an output matrix and an association matrix of an internet of vehicles system are obtained by a server, wherein the state vector includes n states, and n is a positive integer greater than or equal to 1; the server carries out observability analysis on the state vector according to the system matrix, the input matrix, the output matrix and the incidence matrix to obtain an observable state and an unobservable state in the state vector; the server performs stability analysis on the Internet of vehicles system to determine n expected poles; the server obtains a feedback vector of a first controller and a coefficient vector of a second controller according to the expected poles, the system matrix, and observable and unobservable states in the state vector; the server processes the state vector by using the feedback vector of the first controller and the coefficient vector of the second controller to obtain an output vector of the Internet of vehicles system; and the server sends the output vector to the terminal equipment so as to display the output vector on a display screen of the terminal equipment. On one hand, when the Internet of vehicles system is unstable, parts causing the system instability do not need to be checked one by one, so that the processing efficiency can be improved, and the time can be saved; on the other hand, components causing system instability do not need to be replaced, and only observation and stability analysis of the state are needed, so that component resources can be saved, and cost is saved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty. In the drawings:

fig. 1 shows a schematic diagram of an exemplary system architecture to which a processing method of a vehicle networking system or a processing device of a vehicle networking system of an embodiment of the present disclosure may be applied;

FIG. 2 illustrates a schematic structural diagram of a computer system suitable for use with the electronic device used to implement embodiments of the present disclosure;

FIG. 3 schematically illustrates a flow chart of a processing method of a vehicle networking system, according to an embodiment of the present disclosure;

FIG. 4 is a diagram illustrating a processing procedure of step S310 shown in FIG. 3 in one embodiment;

FIG. 5 is a schematic diagram illustrating a scenario in which the method provided by the embodiment of the present disclosure is applied;

FIG. 6 schematically illustrates an exploded view of a state of a vehicle networking system, according to an embodiment of the present disclosure;

FIG. 7 is a diagram illustrating a processing procedure of step S330 shown in FIG. 3 in one embodiment;

FIG. 8 schematically illustrates a diagram of a complex plane and an expected pole according to an embodiment of the present disclosure;

FIG. 9 is a schematic diagram illustrating a processing procedure of step S330 shown in FIG. 3 in another embodiment;

FIG. 10 is a diagram illustrating a processing procedure of step S340 illustrated in FIG. 3 in one embodiment;

FIG. 11 is a diagram illustrating a processing procedure of step S350 shown in FIG. 3 in one embodiment;

FIG. 12 schematically illustrates a flow chart of a processing method of a vehicle networking system, according to another embodiment of the present disclosure;

FIG. 13 schematically illustrates an exploded view of a state of a vehicle networking system, according to another embodiment of the present disclosure;

FIG. 14 schematically illustrates a flow chart of a processing method of a vehicle networking system, according to yet another embodiment of the present disclosure;

FIG. 15 is a schematic diagram illustrating a processing procedure of step S350 shown in FIG. 3 in another embodiment;

FIG. 16 schematically shows a schematic diagram of an output vector according to an embodiment of the present disclosure;

fig. 17 schematically shows a block diagram of a processing device of a car networking system according to an embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.

The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

Artificial Intelligence (AI) is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human Intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.

The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

With the research and progress of artificial intelligence technology, the artificial intelligence technology is developed and applied in a plurality of fields, such as common smart homes, smart wearable devices, virtual assistants, smart speakers, smart marketing, unmanned driving, automatic driving, unmanned aerial vehicles, robots, smart medical care, smart customer service, and the like.

The automatic driving technology generally comprises technologies such as high-precision maps, environment perception, behavior decision, path planning, motion control and the like, and the self-determined driving technology has wide application prospects.

The scheme provided by the embodiment of the application relates to technologies such as automatic driving of artificial intelligence, and the like, and is specifically explained by the following embodiment.

Fig. 1 shows a schematic diagram of an exemplary system architecture 100 to which a processing method of a vehicle networking system or a processing device of a vehicle networking system of an embodiment of the present disclosure may be applied.

As shown in fig. 1, the system architecture 100 may include one or more of

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, server 105 may be a server cluster comprised of multiple servers, or the like.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may be various electronic devices having display screens including, but not limited to, smart phones, tablets, portable and desktop computers, digital cinema projectors, and the like.

The server 105 may be a server that provides various services. For example, the terminal device 103 (or the terminal device 101 or 102) on the vehicle is used to upload various data (such as vehicle position, vehicle speed, vehicle direction, vehicle acceleration, etc.) to the server 105. The server 105 may obtain a state vector, a system matrix, an input matrix, an output matrix, and an association matrix of the internet of vehicles system based on the received various data, where the state vector includes n states, and n is a positive integer greater than or equal to 1; the server 105 may perform observability analysis on the state vector according to the system matrix, the input matrix, the output matrix, and the association matrix to obtain observable states and unobservable states in the state vector; the server 105 may also perform stability analysis on the car networking system to determine n expected poles; the server 105 may also obtain a feedback vector for the first controller and a coefficient vector for the second controller from the expected poles, the system matrix, observable states and unobservable states in the state vector; the server 105 may further process the state vector by using the feedback vector of the first controller and the coefficient vector of the second controller to obtain an output vector of the car networking system; the server 105 transmits the output vector (e.g., collision warning information, safe inter-vehicle distance prompt information, etc.) to the terminal device 103, so that the output vector is displayed on a display screen of the terminal device 103, and a passenger or a driver on the vehicle can view the displayed output vector on the terminal device 103; or for an autonomous capable vehicle, it may make a corresponding driving decision based on the received output vector.

Also, for example, the terminal device 103 (also may be the terminal device 101 or 102) may be a smart tv set installed on a vehicle, a VR (Virtual Reality)/AR (Augmented Reality) helmet display worn by a passenger or a driver on the vehicle, or a mobile terminal such as a smart phone, a tablet computer, etc. on which an instant messaging Application (APP) or the like is installed, and the user may send various data to the server 105 through the smart tv, the VR/AR helmet display or the instant messaging application, the video APP. Based on these data, the server 105 may obtain feedback information in response to these data and return the feedback information to the smart tv, the VR/AR head mounted display, or the instant messaging and video APP, and then display the returned feedback information through the smart tv, the VR/AR head mounted display, or the instant messaging and video APP.

FIG. 2 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present disclosure.

It should be noted that the computer system 200 of the electronic device shown in fig. 2 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments of the present disclosure.

As shown in fig. 2, the computer system 200 includes a Central Processing Unit (CPU)201 that can perform various appropriate actions and processes in accordance with a program stored in a Read-Only Memory (ROM) 202 or a program loaded from a storage section 208 into a Random Access Memory (RAM) 203. In the RAM 203, various programs and data necessary for system operation are also stored. The CPU 201, ROM 202, and RAM 203 are connected to each other via a bus 204. An input/output (I/O) interface 205 is also connected to bus 204.

The following components are connected to the I/O interface 205: an input portion 206 including a keyboard, a mouse, and the like; an output section 207 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 208 including a hard disk and the like; and a communication section 209 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 209 performs communication processing via a network such as the internet. A drive 210 is also connected to the I/O interface 205 as needed. A removable medium 211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 210 as necessary, so that a computer program read out therefrom is installed into the storage section 208 as necessary.

In particular, the processes described below with reference to the flowcharts may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 209 and/or installed from the removable medium 211. The computer program, when executed by a Central Processing Unit (CPU)201, performs various functions defined in the methods and/or apparatus of the present application.

It should be noted that the computer readable storage medium shown in the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM) or flash Memory), an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF (Radio Frequency), etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods, apparatus, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules and/or units described in the embodiments of the present disclosure may be implemented by software, or may be implemented by hardware, and the described modules and/or units may also be disposed in a processor. Wherein the names of such modules and/or units do not in some way constitute a limitation on the modules and/or units themselves.

As another aspect, the present application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer-readable storage medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method as described in the embodiments below. For example, the electronic device may implement the steps shown in fig. 3, 4, 7, 9, 10, 11, 12, 14, or 15.

FIG. 3 schematically shows a flow chart of a processing method of a vehicle networking system according to an embodiment of the present disclosure. The method provided by the embodiment of the present disclosure may be processed by any electronic device with computing processing capability, for example, the server 105 and/or the

terminal devices

102 and 103 in the embodiment of fig. 1 described above, and in the following embodiment, the server 105 is taken as an execution subject for example, but the present disclosure is not limited thereto. It can be understood that the server mentioned in the embodiment of the present disclosure may be a single server, may also be a server cluster, and may also be a cloud server, which is not limited in the present disclosure.

The vehicle networking system in the embodiment of the disclosure is characterized in that vehicle-mounted terminal equipment is arranged on a vehicle instrument desk, so that all working conditions and static and dynamic information of a vehicle are collected, stored and transmitted. The system is divided into three parts: the vehicle-mounted terminal, the cloud computing processing platform and the data analysis platform realize effective monitoring and management of the vehicle according to different functional requirements of different industries on the vehicle. The vehicle-mounted terminal collects the real-time running data of the vehicle, and collects, stores and sends all working information and static and dynamic information of the vehicle. The running of the vehicle usually involves a plurality of switching values, sensor analog quantity, signal data and the like, in the running process of the vehicle, the generated vehicle data are continuously sent back to a background database to form mass data, the mass data are filtered and cleaned through a cloud computing platform, and the data are processed through a data analysis platform.

As shown in fig. 3, a processing method of a car networking system provided by an embodiment of the present disclosure may include the following steps.

In step S310, the server obtains a state vector, a system matrix, an input matrix, an output matrix, and an association matrix of the car networking system, where the state vector includes n states, and n is a positive integer greater than or equal to 1.

In the embodiment of the present disclosure, let x ═ { x ═ x₁,x₂,...,x_nIs the state vector of the vehicle networking system, x_iThe meaning of (i is a positive integer greater than or equal to 1 and less than or equal to n) depends on which physical quantity is selected as the state when the state equation of the Internet of vehicles is established, and x is_iIt may be a scalar quantity (i.e. it contains only one physical quantity), or it may be a vector quantity (i.e. it contains a vector quantity formed from several physical quantities), for example, it may contain barycentric speed, barycentric acceleration, barycentric displacement, network flow consumption rate and vehicle number of the car network system, and also may contain vehicle speed, vehicle acceleration, vehicle displacement, vehicle direction and network flow consumption rate of every vehicle in the car network system, where n is the state number of the car network system,

is the derivative of the state vector x of the internet of vehicles system over time (simply referred to as the state vector derivative).

For example, if a two-dimensional vector consisting of the vehicle speed and the vehicle direction of a vehicle in the internet of vehicles system is selected as a state, and it is assumed that the subscript i is the number of the vehicle, then x_iWhere n is the total number of vehicles in the vehicle networking system.

In the embodiment of the disclosure, the system matrix A is n x n-dimensional and represents the effect of an input vector y of the car networking system on a state vector x of the car networking system; the input matrix B of the vehicle networking system is n x m-dimensional, m is an integer greater than or equal to 1, and represents the effect of an input vector u of the vehicle networking system on a state vector x of the vehicle networking system; the output matrix C of the vehicle networking system is k x n-dimensional, k is an integer greater than or equal to 1, and represents the effect of a state vector x of the vehicle networking system on an output vector y of the vehicle networking system; the incidence matrix D of the vehicle networking system is k x m dimensional, and represents the effect of the input vector u of the vehicle networking system on the output vector y of the vehicle networking system. Where m is the dimension of the input vector u of the vehicle networking system and k is the dimension of the output vector y of the vehicle networking system.

In the embodiment of the disclosure, a motion equation or a network traffic consumption law equation of a vehicle in the internet of vehicles is written according to kinematics or a network traffic consumption law, and then the equations are combined according to a network structure to obtain a state equation of the internet of vehicles system shown in the following formula:

for example, taking a vehicle networking consisting of two vehicles as an example, the two vehicles are respectively labeled as vehicle 1 and vehicle 2. In practical applications, the network traffic consumption of two vehicles and the available network traffic together affect the network traffic consumption rate. Suppose with x₁And x₂Representing the network traffic consumption of vehicle 1 and vehicle 2, respectively, the state vector of the internet of vehicles system is then x ═ x (x)₁,x₂) And is and

and

respectively representing the network traffic consumption rates of the vehicle 1 and the vehicle 2, and representing the available network traffic by u, so that the system matrix A represents the influence of the network traffic consumption on the network traffic consumption rate; the input matrix B represents the influence of available network traffic on the consumption rate of the network traffic; assume that the output vector y is used to characterize the network traffic consumption bandThe economic cost is obtained, and the output matrix C represents the influence of the network traffic consumption on the economic cost; the correlation matrix D characterizes the impact of the available network traffic on the economic cost.

It is understood that the state vector x, the system matrix a, the input matrix B, the output matrix C and the correlation matrix D may be given different meanings according to actual requirements.

In step S320, the server performs observability analysis on the state vector according to the system matrix, the input matrix, the output matrix, and the association matrix to obtain observable states and unobservable states in the state vector.

In the embodiment of the disclosure, the observable matrix of the system can be constructed by using the system matrix a, the input matrix B, the output matrix C and the incidence matrix D in the state equation, and observability analysis can be performed through the observable matrix of the vehicle networking system. The observability analysis is performed to find out the observable state and the invisible state of the internet of vehicles system. Wherein, the observable state refers to a state which can be measured by a certain method in a specific scene, and the observable state can form an observable state set X_COWherein, in the step (A),

is X_CODerivative with respect to time. An unobservable state refers to a state that cannot be measured in the scene, and the unobservable states may constitute an unobservable state set

Wherein the content of the first and second substances,

is that

Derivative with respect to time.

For example, also taking the car networking composed of two cars in the above example as an example, the state vector of the car networking system is x ═ (x)₁,x₂) Suppose network traffic consumption x of vehicle 1₁Can measure, thenx₁Is an observable state; assume network traffic consumption x of vehicle 2₂Not measurable, then x₂Is in an invisible state.

In step S330, the server performs stability analysis on the car networking system to determine n expected poles.

In the embodiment of the present disclosure, stability analysis is performed on the car networking system, if the car networking system is unstable, n expected poles p are selected, and controller 1 (see the following embodiment as a first controller) and controller 2 (refer to the following embodiment as a second controller) are designed to configure the expected poles. If the vehicle networking system is stable, the expected pole does not need to be configured for the vehicle networking system.

In step S340, the server obtains a feedback vector k of the first controller and a coefficient vector f of the second controller according to the expected pole p, the system matrix a, and observable and unobservable states in the state vector x.

In step S350, the state vector is processed by using the feedback vector k of the first controller and the coefficient vector f of the second controller, so as to obtain an output vector of the vehicle networking system.

In step S360, the server sends the output vector to a terminal device, so as to display the output vector on a display screen of the terminal device.

According to the processing method of the Internet of vehicles system, a server is used for obtaining a state vector, a system matrix, an input matrix, an output matrix and an incidence matrix of the Internet of vehicles system, wherein the state vector comprises n states, and n is a positive integer greater than or equal to 1; the server carries out observability analysis on the state vector according to the system matrix, the input matrix, the output matrix and the incidence matrix to obtain an observable state and an unobservable state in the state vector; the server performs stability analysis on the Internet of vehicles system to determine n expected poles; the server obtains a feedback vector of a first controller and a coefficient vector of a second controller according to the expected poles, the system matrix, and observable and unobservable states in the state vector; the server processes the state vector by using the feedback vector of the first controller and the coefficient vector of the second controller to obtain an output vector of the Internet of vehicles system; and the server sends the output vector to the terminal equipment so as to display the output vector on a display screen of the terminal equipment. On one hand, when the Internet of vehicles system is unstable, parts causing the system instability do not need to be checked one by one, so that the processing efficiency can be improved, and the time can be saved; on the other hand, components causing system instability do not need to be replaced, and only observation and stability analysis of the state are needed, so that component resources can be saved, and cost is saved.

Fig. 4 is a schematic diagram illustrating a processing procedure of step S310 shown in fig. 3 in an embodiment. As shown in fig. 4, in the embodiment of the present disclosure, the step S310 may further include the following steps.

In step S311, the vehicle type, the number of vehicles, the vehicle position, the vehicle speed, the vehicle acceleration, and the vehicle direction information in the internet-of-vehicles system are extracted.

In the embodiment of the disclosure, the vehicle-mounted sensors on the vehicles in the vehicle networking system may acquire various information of the vehicles, such as vehicle types, vehicle positions, vehicle speeds, vehicle accelerations, vehicle direction information, vehicle mileage, engine speeds, in-vehicle temperatures, road environment parameters, and the like, and upload the information to the cloud computing processing platform or the backend server of the vehicle networking system, or may also acquire vehicle and/or road information via auxiliary devices such as cameras, positioning devices, and the like, and upload the information to the cloud computing processing platform or the backend server.

In step S312, a state vector of the vehicle networking system is obtained according to the vehicle type, the vehicle number, the vehicle position, the vehicle speed, the vehicle acceleration and the vehicle direction information in the vehicle networking system.

Wherein the state vector may include a center of mass velocity, a center of mass acceleration, a center of mass displacement, a network traffic consumption rate, and a number of vehicles of the internet of vehicles system, and a velocity, an acceleration, a displacement, and a network traffic consumption rate of each vehicle in the internet of vehicles system.

In the disclosed embodiment, as vehicles in the internet of vehicles move, the center of mass of the internet of vehicles consisting of the vehicles will transmit the movement, therefore, the information such as the centroid speed, the centroid acceleration, the centroid displacement, the vehicle quantity and the like of the internet of vehicles system can be calculated according to the information such as the vehicle types, the vehicle quantity, the vehicle positions, the vehicle speeds, the vehicle accelerations and the vehicle directions and the like in the internet of vehicles system, the network flow consumption rate of the internet of vehicles system can be calculated according to the network flow consumption rate of each vehicle in the internet of vehicles system, the barycentric speed, the barycentric acceleration, the barycentric displacement, the network traffic consumption rate and the number of vehicles of the vehicle networking system can be used as a part of a state vector x, and the state vector x can further comprise information such as the speed, the acceleration, the displacement and the network traffic consumption rate of each vehicle in the vehicle networking system.

Fig. 5 shows a scene schematic diagram to which the method provided by the embodiment of the present disclosure is applied. As shown in fig. 5, information of the type of vehicle, the number of vehicles, the position of the vehicle, the speed of the vehicle, the acceleration of the vehicle, the direction of the vehicle, and the like in the internet system can be extracted from the actual scene.

The application scenario of the embodiment of the present disclosure may satisfy the following conditions: 1) part of the internet of vehicles state can be observed; 2) the state of the Internet of vehicles does not change greatly in a short time; 3) inputs to the internet of vehicles system may be accessed.

The fact that the state of the internet of vehicles does not change greatly in a short time means that if the change of the state of the internet of vehicles is supported by facilities (including vehicles in the internet) in the internet of vehicles, the change is the change which is not changed greatly. For example, if the speed of a vehicle is ramped from 10km/h to 100km/h within 1 second, this is a "giant change". Namely, whether the device belongs to a huge change is judged according to whether the device in the Internet of vehicles can support the change of the Internet of vehicles state.

In a practical application scenario, if the vehicles in the car networking system are on a highway, the following conditions can be met: if the speed of the vehicle exceeds 100km/h, the safe distance between vehicles in the same lane should be kept above 100 m; if the vehicle speed is lower than 100 kilometers, the safe distance between the vehicles in the same lane is kept above 50 meters. Furthermore, it is common that the driver situation does not send a large change in a short time. For example, in actual situations, the physical and mental conditions of 90% or more of the drivers who can drive the vehicle on the road do not change greatly in a short time, and unexpected abnormalities do not occur.

Fig. 6 schematically illustrates an exploded view of a state of a car networking system, according to an embodiment of the present disclosure.

As shown in FIG. 6, according to the above embodiments, the state of the car networking system is observably analyzed to obtain an observable state set X_COAnd set of unobservable states

Wherein the controller 1 is responsible for selecting from an observable set of states X_COIs obtained by

Is a linear combination of observable states, where a feedback vector k ═ k is introduced₁,k₂,...,k_nThe controller 1 is in n states x₁,x₂,...,x_nAre respectively given feedback k₁,k₂,...,k_nThen the linear combination of the states is k₁x₁+k₂x₂+...+k_nx_nAnd if the ith state x_iIs in an invisible state, then k corresponds to_i0. It can be seen that if x_iNot considerable, then k_ix_i0, and thus does not appear at k₁x₁+k₂x₂+...+k_nx_nIn fact, therefore

Is to linearly combine the observable states, i.e. the controller 1 here is to set X the observable states_COAre linearly combined to generate

The controller 1 may comprise a multiplier and an adder, the multiplier being operative to calculate k_iAnd x_iThe adder is used for summing k₁x₁+k₂x₂+...+k_nx_nTo obtain

After integration, x is obtained_n+1Assume that the original Internet of vehicles system has n states, where x_n+1Is a newly introduced state, labeled as the n +1 th state,

is x_n+1Derivatives with respect to time, i.e.

And x_n+1The integration links between can use integrators. With continued reference to FIG. 6, the controller 2 is responsible for assigning x_n+1The input vector u introduced into the vehicle networking system, i.e. the role of the controller 2, is to set the coefficient vector f to { f ═ f₁,f₂,...,f_nAre multiplied to f respectively₁x_n+1,f₂x_n+1,...,f₂x_n+1And then added to the state vector derivatives, respectively. The controller 2 may also comprise multipliers and adders, the role of the multipliers being to calculate f_iAnd x_n+1The adder is used for adding f_ix_n+1Is added to

Fig. 7 is a schematic diagram illustrating a processing procedure of step S330 shown in fig. 3 in an embodiment. In the embodiment of fig. 7, the internet of vehicles system is assumed to be a continuous system. As shown in fig. 7, in the embodiment of the present disclosure, the step S330 may further include the following steps.

In step S331, a characteristic equation of the internet of vehicles system is obtained.

In the embodiment of the present disclosure, laplace transform may be performed on the output vector y and the input vector u, and then the transfer function of the car networking system is a ratio of the laplace transform of the output vector y to the laplace transform of the input vector u under an initial condition of zero. And (5) making the transfer function denominator equal to zero to obtain a characteristic equation of the Internet of vehicles system.

In step S332, poles are obtained according to the characteristic equation.

Wherein the roots of the characteristic equations are called poles. The poles determine the motion mode of the system and determine the stability of the system.

In step S333, if there are (n-r) poles located in the left half plane of the complex plane, r poles are selected from the poles located in the right half plane of the complex plane as r expected poles, where r is an integer greater than or equal to 0 and less than or equal to n.

In step S334, (n-r) poles located in the left half-plane of the complex plane are shifted to the right half-plane of the complex plane.

In step S335, the (n-r) poles shifted to the right half-plane of the complex plane are treated as (n-r) desired poles.

In the embodiment of the present disclosure, since the root of the characteristic equation is more than one, the motion of the system should be regarded as the composition of a plurality of motion components. As long as one motion component is divergent, the system is unstable. Therefore, the real parts of all roots of the eigen equation must be negative, i.e., all roots are in the left half plane of the complex plane. Specifically, if all poles are located in the left half plane of the complex plane, that is, the real parts of all poles are negative real partsAnd then, the vehicle networking system can be judged to be stable, and at the moment, n poles can be arbitrarily selected from all the poles to serve as expected poles. If at least one pole falls on the right half plane of the complex plane, that is, the real part of the at least one pole is a positive real part, it can be determined that the vehicle networking system is unstable, and the at least one pole (assumed here to be (n-r) poles) can be referred to as a pole causing instability of the vehicle networking system. If the car networking system is unstable, the positive real part of the pole causing the instability of the car networking system can be changed into the negative real part (the imaginary part can be arbitrarily modified or not modified, the controller 1 and the controller 2 cannot be influenced, but the difference between the imaginary part and the real part is generally not too large), namely, the unstable (n-r) poles are moved to the left half plane from the right half plane of the complex plane, and the moved (n-r) poles are taken as p of n expected poles_r+1,p_r+2,...,p_nThe original real part can arbitrarily select r poles from stable poles of the left half plane of the complex plane as p of n expected poles₁,p₂,...,p_rFinally, n expected poles p ═ p are obtained₁,p₂,...,p_nAnd the Internet of vehicles system can be stabilized.

It should be noted that the method for determining whether the vehicle internet system is stable is not limited to the above example, and the characteristic equation of the system may be directly obtained through the system matrix a. Or whether the vehicle networking system is stable or not can be judged by adopting a root track method.

Fig. 8 schematically illustrates a diagram of a complex plane and an expected pole according to an embodiment of the present disclosure. As shown in fig. 8, which is an example of a complex plane and stable expected poles, the abscissa is the real part, the ordinate is the imaginary part, and small circles represent the poles.

Fig. 9 is a schematic diagram illustrating a processing procedure of step S330 illustrated in fig. 3 in another embodiment. In the embodiment of fig. 9, the internet of vehicles system is assumed to be a discrete system. As shown in fig. 9, in the embodiment of the present disclosure, the step S330 may further include the following steps.

In step S336, a characteristic equation of the internet of vehicles system is obtained.

In the embodiment of the present disclosure, Z transformation may be performed on the output vector y and the input vector u, and then the transfer function of the car networking system is the ratio of Z transformation of the output vector y to Z of the input vector u under the initial condition of zero. And (5) making the transfer function denominator equal to zero to obtain a characteristic equation of the Internet of vehicles system.

In step S337, poles are obtained according to the characteristic equation.

In step S338, if there are (n-r) poles located outside the unit circle of the complex plane, r poles are selected from the poles located inside the unit circle of the complex plane as r expected poles, where r is an integer greater than or equal to 0 and less than or equal to n.

In step S339, (n-r) poles located outside the unit circle of the complex plane are shifted into the unit circle of the complex plane.

In step S3310, (n-r) poles moved into the unit circle of the complex plane are treated as (n-r) expected poles.

In the embodiment of the present disclosure, if the car networking system is a discrete system, it is assumed that solving the characteristic equation results in a plurality of roots, i.e., a plurality of poles, and if at least one (here, it is assumed that (n-r) poles) is located outside a unit circle of the complex plane, it may be determined that the system is unstable, and at this time, r poles may be arbitrarily selected from the poles in the unit circle as p poles of n expected poles₁,p₂,...,p_rThe (n-r) poles located outside the unit circle may be shifted into the unit circle as p of the n desired poles_r+1,p_r+2,...,p_nFinally, n expected poles p ═ p are obtained₁,p₂,...,p_n}. If all poles are located within the unit circle, n poles can be arbitrarily selected from the unit circle as the expected poles.

Fig. 10 is a schematic diagram illustrating a processing procedure of step S340 illustrated in fig. 3 in an embodiment. As shown in fig. 10, in the embodiment of the present disclosure, the step S340 may further include the following steps.

In step S341, feedback of the feedback vector corresponding to an invisible state in the state vector is determined as a set value.

Here, the set value may be 0. For example, assume the ith state x_iIs in an invisible state, then k_i＝0。

In step S342, a feedback vector for the first controller and a coefficient vector for the second controller are determined based on the expected poles and the system matrix.

Specifically, a coefficient vector k ═ k with linear representation is introduced₁,k₂,...,k_nF and feedback vector f ═ f₁,f₂,...,f_n}, judging x_i∈X_COIf true, then k _i0. Based on p ═ p₁,p₂,...,p_n}、k＝{k₁,k₂,...,k_nF ═ f₁,f₂,...,f_nIntroduce equation set (2) as shown below. Wherein I_nIs an n-order identity matrix;

k and f can be solved from equation set (2).

Fig. 11 is a schematic diagram illustrating a processing procedure of step S350 shown in fig. 3 in an embodiment. As shown in fig. 11, in the embodiment of the present disclosure, the step S350 may further include the following steps.

In step S351, the observable states in the state vector are combined according to the feedback vector k of the first controller, obtaining a combined state.

In step S352, integration processing is performed on the combination state.

In step S353, the integrated combined state is processed by using the coefficient vector f of the second controller to obtain a state vector derivative of the vehicle networking system, so that the vehicle networking system is stabilized.

Specifically, k and f are solved from the above equation set (2), and k is considered_i0 (if x)_i∈X_CO) The following three steps are executed:

will f is_ix_n+1Is added to

The three steps are uniformly written into a general formula (k ^ xdt)^Tf is added to

The state equation in the above (1) becomes the following equation set (3). At this time, the desired pole of the system is p ═ { p ═ p₁,p₂,...,p_nThe Internet of vehicles system can be stabilized;

it should be noted that in other embodiments, other feedback forms may be used, and k and f may be varied. For example, the linear combination may be changed to a non-linear combination, and in order to eliminate the information distortion caused by the non-linear combination, a distortion signal filtering device may be further added to the controller 1 and the controller 2. The corresponding k and f can become the intervals (k-10% k, k + 10% k) and (f-10% f, f + 10% f), respectively.

In an actual application scenario, the car networking system usually receives interference, so that the accuracy of an output result is affected. In the related art, the adopted scheme is as follows: detecting which interferences exist in the system, and analyzing the nature of the interferences; and taking the interference negative signal based on the observation data to counteract the interference of the system. However, since the interference information is often dynamically changed and has strong randomness, the negative signal cancellation method cannot keep up with the rhythm of the interference change, which causes delay.

Fig. 12 schematically shows a flow chart of a processing method of the internet of vehicles system according to another embodiment of the present disclosure. As shown in fig. 12, the processing method of the car networking system provided by the embodiment of the present disclosure is different from the foregoing embodiments in that the following steps may be further included.

In step S1210, interference information of the car networking system is acquired.

In the embodiment of the present disclosure, the interference information W of the vehicle system may be acquired in the following manner_orig: the interference information may be characterized by an interference model. The interference information is characterized, for example, by a normal distribution model or a combination of normal distribution models. The statistical characteristics of the interference information can be obtained through historical data of the system interference information.

In step S1220, the interference information is low-pass filtered.

In the embodiment of the disclosure, the interference information W of the car networking system is acquired_origThen, the controller 1 designed in the above embodiment may be replaced with a low-pass filter, and the interference information W may be subjected to the low-pass filter_origFiltering to obtain interference information W after low-pass filtering_tran. The low-pass filter filters out high-frequency interference, so W_tranNo high frequency interference is contained.

In principle any low-pass filter can be used here. But in practice should be a low pass filter that the system can normally use. For example, in practice, some low-pass filters are of poor quality and suffer from strong interference. Such a low pass filter cannot be used normally in the present system.

In step S1230, the coefficient vector is adjusted based on the low-pass filtered interference information, the system matrix and the (n-r) expected poles.

For example, the adjusted coefficient vector f may be calculated based on the following equation set (4):

adjusting the controller 2 based on x ═ x₁,x₂,...,x_n}、p＝{p₁,p₂,...,p_n}、k＝{k₁,k₂,...,k_n}、f＝{f₁,f₂,...,f_nW and_tranthe system of equations (4) is introduced and f is solved from this system of equations to obtain the adjusted controller 2.

In addition, (2) and(4) solving for f may be the same or different, and solving for k may be the same or different. Whether the same depends on the distribution of original points of the system and the interference information W_origAnd the cut-off frequency of the low-pass filter.

In step S1240, a derivative of a state vector of the vehicle networking system is obtained according to the adjusted coefficient vector and the interference information after the low-pass filtering.

Fig. 13 schematically illustrates a state exploded view of a car networking system, according to another embodiment of the present disclosure.

As shown in fig. 13, when the interference information W of the car networking system is acquired_origThe controller 1 in FIG. 6 is replaced by a low-pass filter, disturbing the information W_origAfter passing through a low-pass filter, the signal becomes W_tran，W_tranIntegration to generate X_n+1The controller 2 will X_n+1An input vector u is introduced, others may be as described above with reference to fig. 6.

According to the processing method of the car networking system provided by the embodiment of the disclosure, the interference information of the car networking system can be acquired; low-pass filtering the interference information; adjusting the coefficient vector based on the low-pass filtered interference information, a system matrix, and (n-r) expected poles; and obtaining a state vector derivative of the Internet of vehicles system according to the adjusted coefficient vector and the interference information after low-pass filtering, so as to reduce the interference of the Internet of vehicles system. On one hand, interference signals do not need to be measured accurately, and the processing efficiency is improved; on the other hand, the negative signal of the interference signal is not needed to offset the interference, so that excessive time delay can be avoided.

The method provided by the embodiment of the disclosure can be used for the Internet of vehicles, vehicle and road coordination, safe driving assistance, automatic driving products and the like, particularly the Internet of vehicles, vehicle and road coordination, safe driving assistance, automatic driving products and the like which are influenced by interference.

Fig. 14 schematically shows a flow chart of a processing method of a car networking system according to yet another embodiment of the present disclosure. The method provided by the embodiment of the present disclosure may be processed by any electronic device with computing processing capability, for example, the server 105 and/or the

terminal devices

102 and 103 in the embodiment of fig. 1 described above, and in the following embodiment, the server 105 is taken as an execution subject for example, but the present disclosure is not limited thereto. As shown in fig. 14, a processing method of a car networking system provided by an embodiment of the present disclosure may include the following steps.

In step S1410, a system equation of the vehicle internet system is acquired.

The development platform is first set up, the development environment is configured, and auxiliary libraries and packages are installed, such as math, requests, numpy, and time (for example, a programming language python may be used, but the disclosure is not limited thereto, and any computer language including python may also be used).

Then, a state equation of the vehicle networking system is obtained. Specifically, a state vector of the Internet of vehicles system, the number of states of the Internet of vehicles system, an output vector of the Internet of vehicles system, a state matrix, an input matrix, an output matrix and an incidence matrix of the Internet of vehicles system are determined.

In step S1420, observability analysis is performed on the state of the internet of vehicles system and an observable state set is obtained.

Specifically, the state of the Internet of vehicles system is subjected to observability analysis to obtain an observable state set X_COAnd non-observable collection

In step S1430, a stability analysis is performed on the state of the internet of vehicles system and an expected pole is selected.

And carrying out stability analysis on the Internet of vehicles system, and selecting an expected pole. If the vehicle networking system is stable, the pole does not need to be configured for the vehicle networking system. If the internet of vehicles is unstable, the positive real part of the pole causing the instability of the internet of vehicles is changed into the negative real part, the imaginary part is randomly selected, and all expected poles p are obtained₁,p₂,...,p_n}。

In step S1440, design controller 1 and controller 2 configure the desired poles.

Coefficient vector k-k incorporating linear representation₁,k₂,...,k_nF and feedback vector f ═ f₁,f₂,...,f_nAnd k and f are calculated according to the equation set (2), and

controllers

1 and 2 are respectively designed according to k and f, wherein the pole at the moment is the expected node, and the system is stable.

In step S1450, when the internet of vehicles system has interference, the controller 1 is replaced with a low-pass filter and filtered.

The controller 1 is replaced by a low-pass filter, and the interference W of the low-pass filter to the car networking system_origFiltering to obtain interference W_tran. The low-pass filter filters out the high-frequency interference, so that the interference W_tranNo high frequency interference is contained.

In step S1460, the controller 2 is adjusted.

And (4) solving the adjusted f according to the equation set (4), and adjusting the designed controller 2 to realize the anti-interference of the system.

Fig. 15 is a schematic diagram illustrating a processing procedure of step S350 illustrated in fig. 3 in another embodiment.

The internet of vehicles is an important component for realizing automatic driving and even unmanned driving, is also a core component of a future intelligent transportation system, and will play more and more important roles in the following aspects.

Vehicle safety aspects: the Internet of vehicles can remind drivers through related means such as early warning, overspeed warning, retrograde motion warning, red light warning and pedestrian warning, and can effectively reduce the incidence rate of traffic accidents through measures such as emergency braking and fatigue driving prohibition, thereby ensuring the safety of personnel and vehicles.

And (3) traffic control aspect: the vehicle end and the traffic information are sent to the cloud in time to carry out intelligent traffic management, so that traffic and accident conditions are reported in real time, traffic jam is relieved, and the road utilization rate is improved.

And (3) information service aspect: the internet of vehicles provides convenient and fast information services for enterprises and individuals, such as high-precision electronic maps and accurate road navigation. The vehicle enterprises can also know the use condition and problems of the vehicle by collecting and analyzing the vehicle running information, thereby ensuring the driving safety of users. Other enterprises can also know the needs and interests of users through related specific information services and mine profit points.

In the aspect of smart city and intelligent transportation: the intelligent transportation can be realized by taking the Internet of vehicles as a communication management platform. For example, the aspects of traffic signal lamp intelligent control, intelligent parking lot management, traffic accident handling, intelligent bus scheduling and the like can be realized through the internet of vehicles. With the informatization and intellectualization of traffic, the method is inevitably helpful for the construction of smart cities.

As shown in fig. 15, in the embodiment of the present disclosure, the step S350 may further include the following steps.

In step S354, an input vector of the vehicle networking system is acquired.

In step S355, an output vector of the car networking system is obtained according to the input vector, the state vector derivative, the system matrix, the input matrix, the output matrix, and the correlation matrix of the car networking system.

Wherein the output vector may include collision risk, vehicle distance safety prompt, lane blind zone prompt, lane offset prompt, and the like of any two vehicles in the vehicle networking system.

For example, information such as vehicle types, vehicle numbers, vehicle positions, vehicle speeds, vehicle accelerations, vehicle directions, and the like in the car networking system in fig. 5 may be extracted, interference for determining the risk of vehicle collision is reduced, in the case that the car networking system is stable and interference is small, vehicle collision risk analysis, safe distance early warning, blind area scanning, and the like are performed based on the above information, for example, the collision risk of any two vehicles may be calculated through gravitational field theory, spring field theory, gravitational field theory with doppler effect, and vehicle distance safety prompt, lane blind area prompt, lane departure prompt information, and the like may be given.

Fig. 16 schematically shows a schematic diagram of an output vector according to an embodiment of the present disclosure.

As shown in fig. 16, the collision probability between vehicles may be output in the form of a matrix and a safety distance warning may be given, and the ith row and jth column elements of the matrix in the left diagram (a) represent the probability that the vehicle j collides with the vehicle i. For example, an element 0.08 in the first row below the letter in fig. (a) indicates that the probability that the vehicle 2 collides with the vehicle 1 is 0.08. The right panel (b) gives a blind lane danger/holding distance/lane departure warning. For example, for the vehicles 5, 3, 1, "blind zone of leading lane", "keeping of vehicle distance", and "lane offset" are prompted.

It should be noted that, selection of development environment, development language, information acquisition source, parameters in related formulas, and the like in the embodiments of the present disclosure may be varied, and on the basis of the technical solution of the present disclosure, any improvement and equivalent transformation performed on a certain part according to the principle of the present disclosure should not be excluded from the scope of protection of the present disclosure.

Fig. 17 schematically shows a block diagram of a processing device of a car networking system according to an embodiment of the present disclosure. In the embodiment of the present disclosure, the processing device of the car networking system may be disposed in the server 105, but the present disclosure is not limited thereto.

As shown in fig. 17, a processing device 1700 of a car networking system provided in an embodiment of the present disclosure may include: the system comprises a state information obtaining module 1710, an observability analyzing module 1720, an expected pole determining module 1730, a feedback coefficient obtaining module 1740, an output vector obtaining module 1750 and an output vector sending module 1760.

The state information obtaining module 1710 may be configured to obtain a state vector, a system matrix, an input matrix, an output matrix, and an association matrix of the car networking system, where the state vector includes n states, and n is a positive integer greater than or equal to 1. Observability analysis module 1720 may be configured to perform observability analysis on the state vector according to the system matrix, the input matrix, the output matrix, and the correlation matrix to obtain observable and unobservable states in the state vector. Expected pole determination module 1730 may be configured to perform a stability analysis on the vehicle networking system to determine n expected poles. The feedback coefficient obtaining module 1740 may be configured to obtain a feedback vector for the first controller and a coefficient vector for the second controller based on the expected poles, the system matrix, observable and unobservable states in the state vector. The output vector obtaining module 1750 may be configured to process the state vector using a feedback vector of the first controller and a coefficient vector of the second controller to obtain an output vector of the vehicle networking system. The output vector sending module 1760 may be configured to send the output vector to a terminal device to facilitate display of the output vector on a display screen of the terminal device.

In an exemplary embodiment, the output vector obtaining module 1750 may include: a state combining unit, which may be configured to combine observable states in the state vector according to a feedback vector of the first controller, to obtain a combined state; a combination state integration unit that may be configured to integrate the combination state; the state vector derivative obtaining unit may be configured to process the integrated combined state by using the coefficient vector of the second controller to obtain a state vector derivative of the internet of vehicles system.

In an exemplary embodiment, the internet of vehicles system may be a continuous system. The expected pole determination module 1730 may include: a first characteristic equation obtaining unit which can be configured to obtain a characteristic equation of the Internet of vehicles system; a first pole obtaining unit, which can be configured to obtain a pole according to the characteristic equation; a first expected pole obtaining unit, configured to select r poles as r expected poles from the poles located in the right half plane of the complex plane if (n-r) poles are located in the left half plane of the complex plane, where r is an integer greater than or equal to 0 and less than or equal to n; moving (n-r) poles located in the left half-plane of the complex plane to the right half-plane of the complex plane; (n-r) poles shifted to the right half-plane of the complex plane are treated as (n-r) expected poles.

In an exemplary embodiment, the internet of vehicles system may be a discrete system. The expected pole determination module 1730 may include: a second characteristic equation obtaining unit which can be configured to obtain a characteristic equation of the Internet of vehicles system; a second pole obtaining unit, which can be configured to obtain poles according to the characteristic equation; a second expected pole obtaining unit, which may be configured to select r poles as r expected poles from the poles located in the unit circle of the complex plane if (n-r) poles are located outside the unit circle of the complex plane, where r is an integer greater than or equal to 0 and less than or equal to n; moving (n-r) poles located outside a unit circle of the complex plane into the unit circle of the complex plane; (n-r) poles within a unit circle shifted to the complex plane are taken as (n-r) expected poles.

In an exemplary embodiment, the processing device 1700 of the vehicle networking system may further include: the interference acquisition module can be configured to acquire interference information of the Internet of vehicles system; an interference filtering module configured to low-pass filter the interference information; a second controller adjustment module that may be configured to adjust a coefficient vector of the second controller based on the low-pass filtered interference information, the system matrix, and the (n-r) expected poles; and the system interference removing module can be configured to obtain a state vector derivative of the Internet of vehicles system according to the adjusted coefficient vector of the second controller and the interference information after low-pass filtering.

In an exemplary embodiment, the output vector obtaining module 1750 may include: an input vector acquisition unit that may be configured to acquire an input vector of the internet of vehicles system; and the output vector obtaining unit can be configured to obtain the output vector of the Internet of vehicles system according to the input vector, the state vector, the derivative of the state vector, the system matrix, the input matrix, the output matrix and the incidence matrix of the Internet of vehicles system. Wherein the output vector may include collision risk, vehicle distance safety cue, lane blind zone cue and lane offset cue information for any two vehicles in the networked-vehicles system.

In an exemplary embodiment, the feedback coefficient obtaining module 1740 may include: a feedback setting unit configured to determine feedback of the feedback vector corresponding to an unobservable state in the state vector as a set value; a feedback coefficient determination unit may be configured to determine a feedback vector for the first controller and a coefficient vector for the second controller based on the expected poles and the system matrix.

In an exemplary embodiment, the status information obtaining module 1710 may include: a vehicle information extraction unit that may be configured to extract a vehicle type, a vehicle number, a vehicle position, a vehicle speed, a vehicle acceleration, and vehicle direction information in the internet of vehicles system; a state vector obtaining unit, which may be configured to obtain a state vector of the internet of vehicles system according to the vehicle type, the vehicle number, the vehicle position, the vehicle speed, the vehicle acceleration and the vehicle direction information in the internet of vehicles system. Wherein the state vector may include a center of mass velocity, a center of mass acceleration, a center of mass displacement, a network traffic consumption rate, and a number of vehicles of the internet of vehicles system, and a velocity, an acceleration, a displacement, and a network traffic consumption rate of each vehicle in the internet of vehicles system.

The specific implementation of each module and unit in the processing apparatus of the car networking system provided in the embodiment of the present disclosure may refer to the content in the processing method of the car networking system, and is not described herein again.

It should be noted that although in the above detailed description several modules and units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more of the modules and units described above may be embodied in one module and unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module and unit described above may be further divided into embodiments by a plurality of modules and units.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A processing method of a vehicle networking system is characterized by comprising the following steps:

the method comprises the steps that a server obtains a state vector, a system matrix, an input matrix, an output matrix and an incidence matrix of the Internet of vehicles system, wherein the state vector comprises n states, and n is a positive integer greater than or equal to 1;

the server carries out observability analysis on the state vector according to the system matrix, the input matrix, the output matrix and the incidence matrix to obtain an observable state and an unobservable state in the state vector;

the server performs stability analysis on the Internet of vehicles system to determine n expected poles;

the server obtains a feedback vector of a first controller and a coefficient vector of a second controller according to the expected poles, the system matrix, and observable and unobservable states in the state vector;

the server processes the state vector by using the feedback vector of the first controller and the coefficient vector of the second controller to obtain an output vector of the Internet of vehicles system;

the server sends the output vector to a terminal device so as to display the output vector on a display screen of the terminal device;

the Internet of vehicles system is a continuous system; wherein, the server carries out stability analysis on the car networking system, determines n expected poles, and comprises:

obtaining a characteristic equation of the Internet of vehicles system;

obtaining a pole according to the characteristic equation;

if (n-r) poles exist in the poles are positioned on the left half plane of the complex plane, selecting r poles from the poles positioned on the right half plane of the complex plane as r expected poles, wherein r is an integer which is more than or equal to 0 and less than or equal to n;

moving (n-r) poles located in the left half-plane of the complex plane to the right half-plane of the complex plane;

(n-r) poles shifted to the right half-plane of the complex plane are treated as (n-r) expected poles.

2. The method of claim 1, wherein the server processes the state vector using a feedback vector of the first controller and a coefficient vector of the second controller, comprising:

combining the observable states in the state vector according to the feedback vector of the first controller to obtain a combined state;

performing integration processing on the combined state;

and processing the integrated combined state by using the coefficient vector of the second controller to obtain a state vector derivative of the Internet of vehicles system.

3. The method of claim 1, further comprising:

acquiring interference information of the Internet of vehicles system;

low-pass filtering the interference information;

adjusting a coefficient vector of the second controller based on the low-pass filtered interference information, the system matrix, and the (n-r) expected poles;

and acquiring a state vector derivative of the Internet of vehicles system according to the adjusted coefficient vector of the second controller and the interference information after low-pass filtering.

4. The method of claim 3, wherein the server processes the state vector using the feedback vector of the first controller and the coefficient vector of the second controller to obtain an output vector of the Internet of vehicles system, comprising:

acquiring an input vector of the Internet of vehicles system;

obtaining an output vector of the vehicle networking system according to the input vector, the state vector derivative, the system matrix, the input matrix, the output matrix and the incidence matrix of the vehicle networking system;

wherein the output vector comprises collision risk, vehicle distance safety prompt, lane blind zone prompt and lane offset prompt information of any two vehicles in the vehicle networking system.

5. The method of claim 1, wherein the server obtains a feedback vector for a first controller and a coefficient vector for a second controller based on the expected poles, the system matrix, observable states and unobservable states in the state vector, comprising:

determining feedback of the feedback vector corresponding to an invisible state in the state vector as a set value;

determining a feedback vector for the first controller and a coefficient vector for the second controller based on the expected poles and the system matrix.

6. The method of claim 1, wherein obtaining the state vector of the vehicle networking system by a server comprises:

extracting vehicle type, vehicle number, vehicle position, vehicle speed, vehicle acceleration and vehicle direction information in the Internet of vehicles system;

obtaining a state vector of the Internet of vehicles system according to the types, the number, the positions, the speeds, the accelerations and the direction information of the vehicles in the Internet of vehicles system;

wherein the state vector comprises a centroid speed, a centroid acceleration, a centroid displacement, a network traffic consumption rate and a vehicle number of the vehicle networking system, and a speed, an acceleration, a displacement and a network traffic consumption rate of each vehicle in the vehicle networking system.

7. A processing method of a vehicle networking system is characterized by comprising the following steps:

the Internet of vehicles system is a discrete system; wherein, the server carries out stability analysis on the car networking system, determines n expected poles, and comprises:

obtaining a characteristic equation of the Internet of vehicles system;

obtaining a pole according to the characteristic equation;

if (n-r) poles exist in the poles are positioned outside a unit circle of the complex plane, selecting r poles from the poles positioned in the unit circle of the complex plane as r expected poles, wherein r is an integer which is more than or equal to 0 and less than or equal to n;

moving (n-r) poles located outside a unit circle of the complex plane into the unit circle of the complex plane;

(n-r) poles within a unit circle shifted to the complex plane are taken as (n-r) expected poles.

8. The method of claim 7, wherein the server processes the state vector using the feedback vector of the first controller and the coefficient vector of the second controller, comprising:

performing integration processing on the combined state;

9. The method of claim 7, further comprising:

acquiring interference information of the Internet of vehicles system;

low-pass filtering the interference information;

10. The method of claim 9, wherein the server processes the state vector using the feedback vector of the first controller and the coefficient vector of the second controller to obtain an output vector of the internet of vehicles system, comprising:

acquiring an input vector of the Internet of vehicles system;

11. The method of claim 7, wherein the server obtains a feedback vector for a first controller and a coefficient vector for a second controller based on the expected poles, the system matrix, observable states and unobservable states in the state vector, comprising:

12. The method of claim 7, wherein obtaining the state vector of the vehicle networking system by a server comprises:

13. A processing apparatus of a car networking system, the apparatus comprising:

the state information acquisition module is configured to acquire a state vector, a system matrix, an input matrix, an output matrix and an incidence matrix of the Internet of vehicles system, wherein the state vector comprises n states, and n is a positive integer greater than or equal to 1;

the observability analysis module is configured to perform observability analysis on the state vector according to the system matrix, the input matrix, the output matrix and the incidence matrix to obtain observable states and unobservable states in the state vector;

the expected pole determining module is configured to perform stability analysis on the Internet of vehicles system and determine n expected poles;

a feedback coefficient obtaining module configured to obtain a feedback vector of a first controller and a coefficient vector of a second controller according to the expected poles, the system matrix, and observable and unobservable states in the state vector;

the output vector obtaining module is configured to process the state vector by using a feedback vector of the first controller and a coefficient vector of the second controller to obtain an output vector of the Internet of vehicles system;

the output vector sending module is configured to send the output vector to a terminal device so as to display the output vector on a display screen of the terminal device;

the Internet of vehicles system is a continuous system; wherein the expected pole determination module comprises:

a first characteristic equation obtaining unit configured to obtain a characteristic equation of the internet of vehicles system;

a first pole obtaining unit configured to obtain a pole according to the characteristic equation;

a first expected pole obtaining unit configured to select r poles as r expected poles from poles located in a right half plane of a complex plane if (n-r) poles are located in a left half plane of the complex plane, where r is an integer greater than or equal to 0 and less than or equal to n; moving (n-r) poles located in the left half-plane of the complex plane to the right half-plane of the complex plane; (n-r) poles shifted to the right half-plane of the complex plane are treated as (n-r) expected poles.

14. The apparatus of claim 13, wherein the output vector obtaining module comprises:

a state combination unit configured to combine observable states in the state vector according to a feedback vector of the first controller to obtain a combined state;

a combination state integration unit configured to integrate the combination state;

a state vector derivative obtaining unit configured to process the integrated combined state by using the coefficient vector of the second controller to obtain a state vector derivative of the vehicle networking system.

15. The apparatus of claim 13, further comprising:

the interference acquisition module is configured to acquire interference information of the Internet of vehicles system;

an interference filtering module configured to low-pass filter the interference information;

a second controller adjustment module configured to adjust a coefficient vector of the second controller based on the low-pass filtered interference information, the system matrix, and the (n-r) expected poles;

and the system interference removing module is configured to obtain a state vector derivative of the Internet of vehicles system according to the adjusted coefficient vector of the second controller and the interference information after low-pass filtering.

16. The apparatus of claim 15, wherein the output vector obtaining module comprises:

an input vector acquisition unit configured to acquire an input vector of the internet of vehicles system;

the output vector obtaining unit is configured to obtain an output vector of the Internet of vehicles system according to an input vector, a state vector derivative, a system matrix, an input matrix, an output matrix and an association matrix of the Internet of vehicles system;

17. The apparatus of claim 13, wherein the feedback coefficient obtaining module comprises:

a feedback setting unit configured to determine feedback of the feedback vector corresponding to an unobservable state in the state vector as a set value;

a feedback coefficient determination unit configured to determine a feedback vector for the first controller and a coefficient vector for the second controller based on the expected poles and the system matrix.

18. The apparatus of claim 13, wherein the status information obtaining module comprises:

a vehicle information extraction unit configured to extract a vehicle type, a vehicle number, a vehicle position, a vehicle speed, a vehicle acceleration, and vehicle direction information in the internet of vehicles system;

a state vector obtaining unit configured to obtain a state vector of the internet of vehicles system according to vehicle types, vehicle numbers, vehicle positions, vehicle speeds, vehicle accelerations, and vehicle direction information in the internet of vehicles system;

19. A processing apparatus of a car networking system, the apparatus comprising:

the Internet of vehicles system is a discrete system; wherein the expected pole determination module comprises:

a second characteristic equation obtaining unit configured to obtain a characteristic equation of the internet of vehicles system;

a second pole obtaining unit configured to obtain a pole according to the characteristic equation;

a second expected pole obtaining unit configured to select r poles as r expected poles from poles located in a unit circle of a complex plane if (n-r) poles are located outside the unit circle of the complex plane, where r is an integer greater than or equal to 0 and less than or equal to n; moving (n-r) poles located outside a unit circle of the complex plane into the unit circle of the complex plane; (n-r) poles within a unit circle shifted to the complex plane are taken as (n-r) expected poles.

20. The apparatus of claim 19, wherein the output vector obtaining module comprises:

21. The apparatus of claim 19, further comprising:

22. The apparatus of claim 21, wherein the output vector obtaining module comprises:

23. The apparatus of claim 19, wherein the feedback coefficient obtaining module comprises:

24. The apparatus of claim 19, wherein the status information obtaining module comprises:

25. An electronic device, comprising:

one or more processors;

a storage device configured to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the processing method of the internet of vehicles system of any one of claims 1 to 6 or the processing method of the internet of vehicles system of any one of claims 7 to 12.

26. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the method of processing of the car networking system according to any one of claims 1 to 6 or the method of processing of the car networking system according to any one of claims 7 to 12.