Internet of vehicles management system and method based on block chain technology

Abstract

The invention discloses an Internet of Vehicles management system and method based on a block chain technology, and belongs to the technical field of Internet of Vehicles. Through user authorization, a vehicle-mounted terminal extracts a vehicle log, a vehicle information storage database is constructed, the vehicle information storage database is stored in a block mode and uploaded to an Internet of Vehicles cloud, and the vehicle information storage database comprises vehicle environment data and vehicle driving data; based on the vehicle driving data, when the GPS is judged to stop updating, the vehicle-mounted terminal automatically opens an offline updating mode; performing off-line updating on the navigation route based on the vehicle information storage database; on the basis of the marker information of the planned route, calibrating a real-time route of off-line navigation, and presenting a calibration result to a display end of the Internet of Vehicles; the problem that the route cannot be updated due to weak GPS signals is solved, the real-time route of the offline navigation is optimized, and the accuracy of the offline navigation is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Vehicles, and in particular to an Internet of Vehicles management system and method based on blockchain technology. Background Art

[0002] The Internet of Vehicles, or the Internet of Vehicles, takes moving vehicles as information perception objects, and uses new information and communication technologies to achieve network connections between vehicles and cloud platforms, vehicles and vehicles, vehicles and roads, etc., realizing the integration of in-vehicle networks, inter-vehicle networks, and on-board mobile Internet, while realizing intelligent management of traffic, intelligent decision-making of traffic information services, and intelligent control of vehicles.

[0003] In real life, due to the instability of communication signals in remote areas, the GPS signal of the vehicle connected to the Internet of Vehicles is often weak, making it impossible to update real-time route information; this not only affects people's travel, but also greatly reduces the safety of travel. Summary of the invention

[0004] The purpose of the present invention is to provide a vehicle networking management system and method based on blockchain technology to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A vehicle networking management method based on blockchain technology, the method comprising the following steps:

[0007] Step S100: With the authorization of the user, the vehicle terminal extracts the vehicle log, constructs a vehicle information storage database, and uploads it to the Internet of Vehicles cloud in block storage, wherein the vehicle information storage database includes vehicle environment data and vehicle driving data;

[0008] Step S200: Based on the vehicle driving data, when it is determined that the GPS stops updating, the vehicle terminal automatically turns on the offline update mode;

[0009] Step S300: updating the navigation route offline based on the vehicle information storage database;

[0010] Step S400: Based on the landmark information of the planned route, the real-time route of the offline navigation is calibrated, and the calibration result is presented on the Internet of Vehicles display terminal.

[0011] Furthermore, the specific implementation process of step S100 includes:

[0012] Step S101: constructing a vehicle information storage database, wherein the vehicle information storage database is used to store vehicle environment data and vehicle driving data, wherein the vehicle environment data includes a planned route of the vehicle and landmarks on the planned route, and the vehicle driving data includes a driving speed of the vehicle, an acceleration of the vehicle, a vehicle position data, and road surrounding image data;

[0013] Step S102: The radar sensor and the vehicle-mounted camera identify the image data of the road surrounding within the sensing range, the speed sensor collects the vehicle's driving speed, the wheel speed sensor collects the vehicle's driving acceleration, and the GPS obtains the vehicle's location data, the planned route, and the landmarks on the planned route.

[0014] Furthermore, the specific implementation process of step S200 includes:

[0015] Step S201: Extracting the position data S per unit time based on the vehicle information storage database t =(x1, y1), S t+T =(x2, y2), where S t Represents the vehicle location data at the current timestamp, S t+T Represents the vehicle position data after a unit of time, x represents latitude, y represents longitude;

[0016] Step S202: When the vehicle's speed per unit time v>0, compare S t and S t+T If x1=x2 and y1=y2, it is determined that the GPS has stopped updating, and the vehicle terminal automatically turns on the offline route update mode; otherwise, the GPS works normally and has not stopped updating.

[0017] Furthermore, the specific implementation process of step S300 includes:

[0018] Step S301: when the vehicle performs a steering operation, a vehicle turning angle model is constructed and trained based on the historical state of the vehicle, and the maximum angle of the vehicle steering wheel, the vehicle speed, the vehicle acceleration and the vehicle steering time are preprocessed;

[0019] When a vehicle merges at multiple intersections, the maximum steering wheel rotation angle and the torque of the vehicle's steering wheel determine the vehicle's steering angle, which in turn determines the intersection the vehicle will merge into. At the same time, the vehicle's speed, acceleration and steering time also affect the vehicle's steering angle. The four types of data are fused and analyzed, and the vehicle's steering angle is obtained through a large amount of training, making offline navigation more accurate and preventing vehicles from merging into non-target intersections.

[0020] Step S302: Formula of vehicle turning angle model: In the equation, α represents the angle between the direction of successful steering of the vehicle and the direction before steering, i.e., the steering angle of the vehicle, β represents the duration of vehicle steering, γ represents the maximum angle of the vehicle steering wheel, a represents the preprocessed vehicle acceleration, v represents the preprocessed vehicle speed, n1, n2, n3, and n4 represent regression terms, and n5 represents the random error after the independent variable affects the dependent variable;

[0021] Step S303: using the least square method to continuously update n1, n2, n3, n4, and n5 until the average sum of the errors between the predicted value of the steering angle of the vehicle and the actual value of the steering angle of the vehicle reaches a minimum;

[0022] Step S304: Based on the actual operating state of the vehicle, the processed maximum steering wheel rotation angle, vehicle speed, vehicle acceleration and vehicle steering duration are input, the actual steering angle of the vehicle is obtained from the vehicle turning angle model output, and the offline navigation route is updated.

[0023] Furthermore, the specific implementation process of step S400 includes:

[0024] Step S401: When a landmark on the planned route is identified by the road surrounding image, the distance between the vehicle and the landmark is obtained through the radar sensor and the vehicle-mounted camera, and the real-time route of the offline navigation is updated;

[0025] In real life, there are usually various landmark objects on both sides of the road where the GPS planned route is located, such as landmark buildings or landmark traffic equipment. The landmark objects can be used to determine the location of the vehicle. Therefore, the accuracy of the real-time route of offline navigation can be determined by obtaining the distance between the vehicle and the landmark.

[0026] Step S402: extract the distance between the vehicle and the landmark in the offline navigation route, and construct the navigation deviation set p={p n |n∈(1,N)},p n =l1 n -l0 n , where p n represents the navigation deviation of the nth offline navigation, N represents the total number of navigation deviations of offline navigation, l0 n Indicates the distance between the vehicle and the landmark, l1 n Indicates the distance between the vehicle and the landmark in the offline navigation route;

[0027] Step S403: Optimizing the position of the vehicle in the offline navigation route according to the navigation deviation of the offline navigation, and the vehicle predicts the distance between the current position of the vehicle and the next landmark:

[0028]

[0029] Where k represents the distance between the current position of the vehicle and the next landmark before the offline navigation route is optimized, and u n Indicates the distance traveled by the vehicle when the nth offline navigation has navigation deviation, u k Indicates the distance from the location where the navigation deviation was last updated to the current vehicle location.

[0030] A vehicle networking management system based on blockchain technology, the system comprising a data storage module, an offline determination module, an offline update module and a calibration module;

[0031] The data storage module includes a database construction unit and a data collection unit for collecting and storing vehicle environment data and vehicle driving data;

[0032] The offline determination module includes a location unit and a determination unit, which is used to determine whether the GPS stops updating;

[0033] The offline update module includes a vehicle turning angle model unit and an update unit, which are used to determine the actual turning angle of the vehicle and update the offline navigation route;

[0034] The calibration module includes a navigation deviation unit and an optimization unit, which calibrates the real-time route of the offline navigation based on the landmark information of the planned route, and presents the calibration result on the Internet of Vehicles display terminal.

[0035] Further, the data storage module includes a database construction unit and a data acquisition unit;

[0036] The database construction unit constructs a vehicle information storage database, wherein the vehicle information storage database is used to store vehicle environment data and vehicle driving data, wherein the vehicle environment data includes a planned route of the vehicle and landmarks on the planned route, and the vehicle driving data includes a driving speed of the vehicle, an acceleration of the vehicle, a vehicle position data, and road surrounding image data;

[0037] The data acquisition unit identifies the image data of the road surrounding within the sensing range through the radar sensor and the vehicle-mounted camera, the speed sensor collects the vehicle's driving speed, the wheel speed sensor collects the vehicle's driving acceleration, and the GPS obtains the vehicle's location data, planned route and landmarks on the planned route.

[0038] Further, the offline determination module includes a location unit and a determination unit;

[0039] The location unit extracts the location data S per unit time based on the vehicle information storage database. t =(x1, y1), S t+T =(x2, y2), where S t Represents the vehicle location data at the current timestamp, St+T Represents the vehicle position data after a unit of time, x represents latitude, y represents longitude;

[0040] The determination unit compares S when the vehicle's travel speed v per unit time>0. t and S t+T If x1=x2 and y1=y2, it is determined that the GPS has stopped updating, and the vehicle terminal automatically turns on the offline route update mode; otherwise, the GPS works normally and has not stopped updating.

[0041] Further, the offline updating module includes a vehicle corner model unit and an updating unit;

[0042] The vehicle turning angle model unit constructs and trains a vehicle turning angle model based on the historical state of the vehicle when the vehicle performs a steering operation, and preprocesses the maximum angle of the vehicle steering wheel, the vehicle speed, the vehicle acceleration and the vehicle steering time; the formula of the vehicle turning angle model is: α=n1β+n2γ+n3∫0 β afdf+n4vβ+n5, α represents the angle between the direction of successful steering of the vehicle and the direction before steering, i.e., the steering angle of the vehicle, β represents the duration of vehicle steering, γ represents the maximum angle of the vehicle steering wheel, a represents the preprocessed vehicle acceleration, v represents the preprocessed vehicle speed, n1, n2, n3, n4 represent regression terms, and n5 represents the random error after the independent variable affects the dependent variable; n1, n2, n3, n4, n5 are continuously updated using the least squares method until the average sum of the errors between the predicted value of the vehicle's steering angle and the actual value of the vehicle's steering angle is minimized;

[0043] The updating unit, based on the actual running state of the vehicle, inputs the processed maximum angle of vehicle steering wheel rotation, vehicle speed, vehicle acceleration and vehicle steering duration, obtains the actual steering angle of the vehicle from the vehicle steering angle model output, and updates the offline navigation route.

[0044] Further, the calibration module includes a navigation deviation unit and an optimization unit;

[0045] The navigation deviation unit, when the road surrounding image recognizes a landmark on the planned route, obtains the distance between the vehicle and the landmark through the radar sensor and the vehicle-mounted camera, and updates the real-time route of the offline navigation; extracts the distance between the vehicle and the landmark in the offline navigation route, and constructs a navigation deviation set p = {p n |n∈(1,N)},p n =l1 n -l0 n , where p n represents the navigation deviation of the nth offline navigation, N represents the total number of navigation deviations of offline navigation, l0n Indicates the distance between the vehicle and the landmark, l1 n Indicates the distance between the vehicle and the landmark in the offline navigation route;

[0046] The optimization unit optimizes the position of the vehicle in the offline navigation route according to the navigation deviation of the offline navigation, and the vehicle predicts the distance between the current position of the vehicle and the next landmark:

[0047]

[0048] Where k represents the distance between the current position of the vehicle and the next landmark before the offline navigation route is optimized, and u n Indicates the distance traveled by the vehicle when the nth offline navigation has navigation deviation, u k Indicates the distance from the location where the navigation deviation was last updated to the current vehicle location.

[0049] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in a vehicle networking management system and method based on blockchain technology provided by the present invention, through user authorization, the on-board terminal extracts the vehicle log, builds a vehicle information storage database, and uploads it to the vehicle networking cloud in block storage, wherein the vehicle information storage database includes vehicle environmental data and vehicle driving data; based on the vehicle driving data, when it is determined that the GPS stops updating, the on-board terminal automatically turns on the offline update mode; based on the vehicle information storage database, the navigation route is updated offline; based on the landmark information of the planned route, the real-time route of the offline navigation is calibrated, and the calibration result is presented on the vehicle networking display terminal; not only the situation that the route cannot be updated due to weak GPS signal is solved, but also the real-time route of the offline navigation is optimized, which greatly improves the accuracy of the offline navigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0051] Figure 1 It is a structural schematic diagram of a vehicle networking management system based on blockchain technology of the present invention;

[0052] Figure 2 It is a schematic diagram of the steps of a vehicle networking management method based on blockchain technology of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1 , the present invention provides a technical solution: a vehicle networking management system based on blockchain technology, the system includes a data storage module, an offline determination module, an offline update module and a calibration module;

[0055] The data storage module includes a database construction unit and a data acquisition unit, which are used to collect and store vehicle environment data and vehicle driving data;

[0056] The offline determination module includes a location unit and a determination unit, which is used to determine whether the GPS stops updating;

[0057] The offline update module includes a vehicle turning angle model unit and an update unit, which are used to determine the actual turning angle of the vehicle and update the offline navigation route;

[0058] The calibration module includes a navigation deviation unit and an optimization unit. It calibrates the real-time route of offline navigation based on the landmark information of the planned route and presents the calibration result on the Internet of Vehicles display terminal.

[0059] Preferably, the data storage module includes a database construction unit and a data acquisition unit;

[0060] A database construction unit, constructing a vehicle information storage database, the vehicle information storage database is used to store vehicle environment data and vehicle driving data, the vehicle environment data includes the planned route of the vehicle and the landmarks on the planned route, the vehicle driving data includes the vehicle's driving speed, the vehicle's acceleration, the vehicle's position data and road surrounding image data;

[0061] The data acquisition unit uses radar sensors and on-board cameras to identify image data of the road surrounding within the sensing range. The speed sensor collects the vehicle's driving speed, the wheel speed sensor collects the vehicle's driving acceleration, and the GPS obtains the vehicle's location data, planned route, and landmarks on the planned route.

[0062] Preferably, the offline determination module includes a location unit and a determination unit;

[0063] The location unit extracts the location data S per unit time based on the vehicle information storage database t =(x1, y1), S t+T =(x2, y2), where S tRepresents the vehicle location data at the current timestamp, S t+T Represents the vehicle position data after a unit of time, x represents latitude, y represents longitude;

[0064] The judgment unit compares S when the vehicle's speed v>0 per unit time. t and S t+T If x1=x2 and y1=y2, it is determined that the GPS has stopped updating, and the vehicle terminal automatically turns on the offline route update mode; otherwise, the GPS works normally and has not stopped updating.

[0065] Preferably, the offline updating module comprises a vehicle corner model unit and an updating unit;

[0066] The vehicle turning angle model unit builds and trains the vehicle turning angle model based on the historical state of the vehicle when the vehicle performs a steering operation, and pre-processes the maximum angle of the vehicle steering wheel, vehicle speed, vehicle acceleration and vehicle steering time; the formula of the vehicle turning angle model is: α=n1β+n2γ+n3∫0 β afdf+n4vβ+n5, α represents the angle between the direction of successful steering of the vehicle and the direction before steering, i.e., the steering angle of the vehicle, β represents the duration of vehicle steering, γ represents the maximum angle of the vehicle steering wheel, a represents the preprocessed vehicle acceleration, v represents the preprocessed vehicle speed, n1, n2, n3, n4 represent regression terms, and n5 represents the random error after the independent variable affects the dependent variable; n1, n2, n3, n4, n5 are continuously updated using the least squares method until the average sum of the errors between the predicted value of the vehicle's steering angle and the actual value of the vehicle's steering angle is minimized;

[0067] The updating unit, based on the actual running state of the vehicle, inputs the processed maximum steering wheel rotation angle of the vehicle, the vehicle speed, the vehicle acceleration and the vehicle steering time, obtains the actual steering angle of the vehicle from the vehicle steering angle model output, and updates the offline navigation route.

[0068] Preferably, the calibration module includes a navigation deviation unit and an optimization unit;

[0069] The navigation deviation unit, when the road surrounding image recognizes the landmarks on the planned route, obtains the distance between the vehicle and the landmark through the radar sensor and the on-board camera, and updates the real-time route of the offline navigation; extracts the distance between the vehicle and the landmark in the offline navigation route, and constructs the navigation deviation set p = {p n |n∈(1,N)},p n =l1 n -l0 n , where p n represents the navigation deviation of the nth offline navigation, N represents the total number of navigation deviations of offline navigation, l0n Indicates the distance between the vehicle and the landmark, l1 n Indicates the distance between the vehicle and the landmark in the offline navigation route;

[0070] The optimization unit optimizes the position of the vehicle in the offline navigation route according to the navigation deviation of the offline navigation, and the vehicle predicts the distance between the current position of the vehicle and the next landmark:

[0071]

[0072] Where k represents the distance between the current position of the vehicle and the next landmark before the offline navigation route is optimized, and u n Indicates the distance traveled by the vehicle when the nth offline navigation has navigation deviation, u k Indicates the distance from the location where the navigation deviation was last updated to the current vehicle location.

[0073] like Figure 2 As shown, a vehicle networking management method based on blockchain technology includes the following steps:

[0074] Step S100: With the authorization of the user, the vehicle terminal extracts the vehicle log, constructs a vehicle information storage database, and uploads it to the Internet of Vehicles cloud in block storage. The vehicle information storage database includes vehicle environment data and vehicle driving data;

[0075] The specific implementation process of step S100 includes:

[0076] Step S101: constructing a vehicle information storage database, the vehicle information storage database is used to store vehicle environment data and vehicle driving data, the vehicle environment data includes the planned route of the vehicle and the landmarks on the planned route, the vehicle driving data includes the vehicle's driving speed, the vehicle's acceleration, the vehicle's position data and road surrounding image data;

[0077] Step S102: The radar sensor and the vehicle-mounted camera identify the image data of the road surrounding within the sensing range, the speed sensor collects the vehicle's driving speed, the wheel speed sensor collects the vehicle's driving acceleration, and the GPS obtains the vehicle's location data, the planned route, and the landmarks on the planned route.

[0078] Step S200: Based on the vehicle driving data, when it is determined that the GPS stops updating, the vehicle terminal automatically turns on the offline update mode;

[0079] The specific implementation process of step S200 includes:

[0080] Step S201: Extracting the position data S per unit time based on the vehicle information storage database t =(x1, y1), S t+T=(x2, y2), where S t represents the vehicle position data of the current timestamp, S t+T represents the vehicle position data after a unit of time, x represents latitude, and y represents longitude;

[0081] Step S202: When the driving speed v of the vehicle within a unit of time > 0, compare S t and S t+T in terms of latitude and longitude. If x1 = x2 and y1 = y2, it is determined that the GPS has stopped updating, and the in-vehicle terminal automatically turns on the offline update route mode; otherwise, the GPS is working properly and has not stopped updating.

[0082] For example: Assuming the preset unit time T = 0.1s, v > 0, and t = 8h, S t＝8h =(29°, 114°), S t+T =(29°, 114°), then x1 = x2 and y1 = y2, and it is determined that the GPS has stopped updating, and the in-vehicle terminal automatically turns on the offline update route mode;

[0083] When v > 0 and t = 10h, S t＝10h =(29°, 114°), S t+T =(39°, 114°), then x1 < x2, y1 = y2, then

[0084] the GPS is working properly and has not stopped updating;

[0085] When v > 0 and t = 10h, S t＝10h =(29°, 120°), S t+T =(29°, 114°), then x1 = x2, y1 > y2, then

[0086] the GPS is working properly and has not stopped updating;

[0087] When v > 0 and t = 10h, S t＝10h =(29°, 120°), S t+T =(39°, 114°), then x1 < x2, y1 > y2, then

[0088] the GPS is working properly and has not stopped updating.

[0089] Step S300: Based on the vehicle information storage database, perform offline update on the navigation route;

[0090] The specific implementation process of Step S300 includes:

[0091] Step S301: when the vehicle performs a steering operation, a vehicle turning angle model is constructed and trained based on the historical state of the vehicle, and the maximum angle of the vehicle steering wheel, the vehicle speed, the vehicle acceleration and the vehicle steering time are preprocessed;

[0092] Step S302: Formula of vehicle turning angle model: In the equation, α represents the angle between the direction of successful steering of the vehicle and the direction before steering, i.e., the steering angle of the vehicle, β represents the duration of vehicle steering, γ represents the maximum angle of the vehicle steering wheel, a represents the preprocessed vehicle acceleration, v represents the preprocessed vehicle speed, n1, n2, n3, and n4 represent regression terms, and n5 represents the random error after the independent variable affects the dependent variable;

[0093] Step S303: using the least square method to continuously update n1, n2, n3, n4, and n5 until the average sum of the errors between the predicted value of the steering angle of the vehicle and the actual value of the steering angle of the vehicle reaches a minimum;

[0094] Step S304: Based on the actual running state of the vehicle, the actual steering angle of the vehicle is obtained and the offline navigation route is updated.

[0095] Step S400: Calibrate the real-time route of the offline navigation based on the landmark information of the planned route, and present the calibration result on the Internet of Vehicles display terminal;

[0096] The specific implementation process of step S400 includes:

[0097] Step S401: When a landmark on the planned route is identified by the road surrounding image, the distance between the vehicle and the landmark is obtained through the radar sensor and the vehicle-mounted camera, and the real-time route of the offline navigation is updated;

[0098] Step S402: extract the distance between the vehicle and the landmark in the offline navigation route, and construct the navigation deviation set p={p n |n∈(1,N)},p n =l1 n -l0 n , where p n represents the navigation deviation of the nth offline navigation, N represents the total number of navigation deviations of offline navigation, l0 n Indicates the distance between the vehicle and the landmark, l1 n Indicates the distance between the vehicle and the landmark in the offline navigation route;

[0099] Step S403: According to the navigation deviation of the offline navigation, the position of the vehicle in the offline navigation route is optimized, and the vehicle predicts the distance between the current position of the vehicle and the next landmark:

[0100]

[0101] Where k represents the distance between the current position of the vehicle and the next landmark before the offline navigation route is optimized, and u n Indicates the distance traveled by the vehicle when the nth offline navigation has navigation deviation, u k Indicates the distance from the location where the navigation deviation was last updated to the current vehicle location.

[0102] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0103] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A vehicle networking management method based on blockchain technology, characterized in that: The method comprises the following steps: Step S100: With the authorization of the user, the vehicle terminal extracts the vehicle log, constructs a vehicle information storage database, and uploads it to the Internet of Vehicles cloud in block storage, wherein the vehicle information storage database includes vehicle environment data and vehicle driving data; Step S200: Based on the vehicle driving data, when it is determined that the GPS stops updating, the vehicle terminal automatically turns on the offline update mode; Step S300: updating the navigation route offline based on the vehicle information storage database; Step S400: Based on the landmark information of the planned route, the real-time route of the offline navigation is calibrated, and the calibration result is presented on the Internet of Vehicles display terminal.

2. According to the method of claim 1, the method is characterized in that: The specific implementation process of step S100 includes: Step S101: constructing a vehicle information storage database, wherein the vehicle information storage database is used to store vehicle environment data and vehicle driving data, wherein the vehicle environment data includes a planned route of the vehicle and landmarks on the planned route, and the vehicle driving data includes a driving speed of the vehicle, an acceleration of the vehicle, a vehicle position data, and road surrounding image data; Step S102: The radar sensor and the vehicle-mounted camera identify the image data of the road surrounding within the sensing range, the speed sensor collects the vehicle's driving speed, the wheel speed sensor collects the vehicle's driving acceleration, and the GPS obtains the vehicle's location data, the planned route, and the landmarks on the planned route.

3. According to claim 2, a vehicle networking management method based on blockchain technology is characterized in that: The specific implementation process of step S200 includes: Step S201: Extracting the position data S per unit time based on the vehicle information storage database t =(x1, y1), S t+T =(x2, y2), where S t Represents the vehicle location data at the current timestamp, S t+T Represents the vehicle position data after a unit of time, x represents latitude, y represents longitude; Step S202: When the vehicle's speed per unit time v>0, compare S t and S t+T If x1=x2 and y1=y2, it is determined that the GPS has stopped updating, and the vehicle terminal automatically turns on the offline route update mode; otherwise, the GPS works normally and has not stopped updating.

4. According to claim 3, a vehicle networking management method based on blockchain technology is characterized in that: The specific implementation process of step S300 includes: Step S301: when the vehicle performs a steering operation, a vehicle turning angle model is constructed and trained based on the historical state of the vehicle, and the maximum angle of the vehicle steering wheel, the vehicle speed, the vehicle acceleration and the vehicle steering time are preprocessed; Step S302: Formula of vehicle turning angle model: In the equation, α represents the angle between the direction of successful steering of the vehicle and the direction before steering, i.e., the steering angle of the vehicle, β represents the duration of vehicle steering, γ represents the maximum angle of the vehicle steering wheel, a represents the preprocessed vehicle acceleration, v represents the preprocessed vehicle speed, n1, n2, n3, and n4 represent regression terms, and n5 represents the random error after the independent variable affects the dependent variable; Step S303: using the least square method to continuously update n1, n2, n3, n4, and n5 until the average sum of the errors between the predicted value of the steering angle of the vehicle and the actual value of the steering angle of the vehicle reaches a minimum; Step S304: Based on the actual running state of the vehicle, the actual steering angle of the vehicle is obtained and the offline navigation route is updated.

5. According to the method of claim 4, the method is characterized in that: The specific implementation process of step S400 includes: Step S401: When a landmark on the planned route is identified by the road surrounding image, the distance between the vehicle and the landmark is obtained through the radar sensor and the vehicle-mounted camera, and the real-time route of the offline navigation is updated; Step S402: extract the distance between the vehicle and the landmark in the offline navigation route, and construct the navigation deviation set p={p n |n∈(1,N)},p n =l1 n -l0 n , where p n represents the navigation deviation of the nth offline navigation, N represents the total number of navigation deviations of offline navigation, l0 n Indicates the distance between the vehicle and the landmark, l1 n Indicates the distance between the vehicle and the landmark in the offline navigation route; Step S403: According to the navigation deviation of the offline navigation, the position of the vehicle in the offline navigation route is optimized, and the vehicle predicts the distance between the current position of the vehicle and the next landmark: Where k represents the distance between the current position of the vehicle and the next landmark before the offline navigation route is optimized, and u n Indicates the distance traveled by the vehicle when the nth offline navigation has navigation deviation, u k Indicates the distance from the location where the navigation deviation was last updated to the current vehicle location.

6. A vehicle networking management system based on blockchain technology, characterized in that: The system includes a data storage module, an offline determination module, an offline update module and a calibration module; The data storage module includes a database construction unit and a data collection unit for collecting and storing vehicle environment data and vehicle driving data; The offline determination module includes a location unit and a determination unit, which is used to determine whether the GPS stops updating; The offline update module includes a vehicle turning angle model unit and an update unit, which are used to determine the actual turning angle of the vehicle and update the offline navigation route; The calibration module includes a navigation deviation unit and an optimization unit, which calibrates the real-time route of the offline navigation based on the landmark information of the planned route, and presents the calibration result on the Internet of Vehicles display terminal.

7. The vehicle networking management system based on blockchain technology according to claim 6 is characterized by: The data storage module includes a database construction unit and a data acquisition unit; The database construction unit constructs a vehicle information storage database, wherein the vehicle information storage database is used to store vehicle environment data and vehicle driving data, wherein the vehicle environment data includes a planned route of the vehicle and landmarks on the planned route, and the vehicle driving data includes a driving speed of the vehicle, an acceleration of the vehicle, a vehicle position data, and road surrounding image data; The data acquisition unit identifies the image data of the road surrounding within the sensing range through the radar sensor and the vehicle-mounted camera, the speed sensor collects the vehicle's driving speed, the wheel speed sensor collects the vehicle's driving acceleration, and the GPS obtains the vehicle's location data, planned route and landmarks on the planned route.

8. The vehicle networking management system based on blockchain technology according to claim 7 is characterized by: The offline determination module includes a location unit and a determination unit; The location unit extracts the location data S per unit time based on the vehicle information storage database. t =(x1, y1), S t+T =(x2, y2), where S t Represents the vehicle location data at the current timestamp, S t+T Represents the vehicle position data after a unit of time, x represents latitude, y represents longitude; The determination unit compares S when the vehicle's travel speed v per unit time>0. t and S t+T If x1=x2 and y1=y2, it is determined that the GPS has stopped updating, and the vehicle terminal automatically turns on the offline route update mode; otherwise, the GPS works normally and has not stopped updating.

9. The vehicle networking management system based on blockchain technology according to claim 8 is characterized by: The offline updating module includes a vehicle corner model unit and an updating unit; The vehicle turning angle model unit constructs and trains a vehicle turning angle model based on the historical state of the vehicle when the vehicle performs a steering operation, and preprocesses the maximum angle of the vehicle steering wheel, the vehicle speed, the vehicle acceleration and the vehicle steering time; the formula of the vehicle turning angle model is: In the formula, α represents the angle between the direction of successful steering of the vehicle and the direction before steering, i.e., the steering angle of the vehicle, β represents the duration of vehicle steering, γ represents the maximum angle of the vehicle steering wheel, a represents the preprocessed vehicle acceleration, v represents the preprocessed vehicle speed, n1, n2, n3, n4 represent regression terms, and n5 represents the random error after the independent variable affects the dependent variable; n1, n2, n3, n4, n5 are continuously updated using the least squares method until the average sum of the errors between the predicted value of the vehicle's steering angle and the actual value of the vehicle's steering angle reaches the minimum; The updating unit, based on the actual running state of the vehicle, inputs the processed maximum angle of vehicle steering wheel rotation, vehicle speed, vehicle acceleration and vehicle steering duration, obtains the actual steering angle of the vehicle from the vehicle steering angle model output, and updates the offline navigation route.

10. The vehicle networking management system based on blockchain technology according to claim 9 is characterized in that: The calibration module includes a navigation deviation unit and an optimization unit; The navigation deviation unit, when the road surrounding image recognizes a landmark on the planned route, obtains the distance between the vehicle and the landmark through the radar sensor and the vehicle-mounted camera, and updates the real-time route of the offline navigation; extracts the distance between the vehicle and the landmark in the offline navigation route, and constructs a navigation deviation set p = {p n |n∈(1,N)},p n =l1 n -l0 n , where p n represents the navigation deviation of the nth offline navigation, N represents the total number of navigation deviations of offline navigation, l0 n Indicates the distance between the vehicle and the landmark, l1 n Indicates the distance between the vehicle and the landmark in the offline navigation route; The optimization unit optimizes the position of the vehicle in the offline navigation route according to the navigation deviation of the offline navigation, and the vehicle predicts the distance between the current position of the vehicle and the next landmark: Where k represents the distance between the current position of the vehicle and the next landmark before the offline navigation route is optimized, and u n Indicates the distance traveled by the vehicle when the nth offline navigation has navigation deviation, u k Indicates the distance from the location where the navigation deviation was last updated to the current vehicle location.

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