Dynamic interaction method and system for practical training device of Internet of Vehicles

Abstract

The invention discloses a dynamic interaction method and system of an Internet of Vehicles practical training device, and the method and system define the practical training process of an intelligent vehicle according to a plurality of practical training spaces, the intelligent vehicle controlled by a user and corresponding vehicle data, thereby guaranteeing the precision of the practical training process of the intelligent vehicle. Further, according to each practical training subject of the intelligent vehicle and the corresponding vehicle data, a corresponding practical training picture is matched, and an abnormal operation part is defined based on the practical training picture, the operation data of the user and the interaction signal of the intelligent vehicle; the corresponding optimization event is matched according to the abnormal operation part, the model of the intelligent vehicle and the corresponding optimization logic, autonomous optimization of the intelligent vehicle is triggered according to the optimization event, and an interaction picture with indicating significance is output according to the autonomous optimization of the intelligent vehicle, so that the accuracy of the optimization event is ensured, and the user experience is improved. And an interaction picture with indicating significance is output, so that the user can be guided to further perform practical training through the interaction picture.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle networking training devices, and in particular to a dynamic interaction method and system for vehicle networking training devices. Background Art

[0002] With the development of science and technology, smart vehicles are gradually applied to people's lives, and the training of smart vehicles is gradually popularized. Internet of Vehicles training devices are applied to the training scenarios of smart vehicles. In the prior art, the Internet of Vehicles training device has multiple training spaces, and the multiple training spaces are arranged according to human experience. However, the smart vehicles controlled by users perform training of different courses in the Internet of Vehicles training device, and there are some abnormal operations during the training process, and the corresponding interactive screens cannot be presented. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art. The present invention provides a dynamic interaction method and system for a vehicle networking training device, which collects the location of the vehicle networking training device; collects corresponding training scenes based on the positioning detection of the location of the vehicle networking training device; defines multiple training spaces based on the traversal of the training scenes; defines the training history of the smart vehicle according to the multiple training spaces, the smart vehicles controlled by the user and the corresponding vehicle data, and is compatible with the overall consideration of multiple training spaces, the smart vehicles controlled by the user and the corresponding vehicle data, thereby realizing multi-dimensional control of multiple training spaces, the smart vehicles controlled by the user and the corresponding vehicle data, and ensuring the accuracy of the training history of the smart vehicle.

[0004] Furthermore, based on the tracing of the training history of the smart vehicle, various training subjects of the smart vehicle are collected, and corresponding training screens are matched according to various training subjects of the smart vehicle and the corresponding vehicle data. The abnormal operation part is defined based on the training screen, the user's operation data and the interactive signal of the smart vehicle; the corresponding optimization event is matched according to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, the autonomous optimization of the smart vehicle is triggered according to the optimization event, and an interactive screen with indicative significance is output according to the autonomous optimization of the smart vehicle. The abnormal operation part is introduced, and multi-dimensional control of the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic is realized, the accuracy of the optimization event is ensured, and then an interactive screen with indicative significance is output, so as to guide the user to further train through the interactive screen.

[0005] An embodiment of the present invention provides a dynamic interaction method of a vehicle networking training device, which is applied to a dynamic interaction scenario of a vehicle networking training device;

[0006] The dynamic interaction method of the vehicle networking training device includes:

[0007] Collect the location of the Internet of Vehicles training device;

[0008] Collect corresponding training scenes based on the location detection of the Internet of Vehicles training device;

[0009] Define multiple training spaces based on the traversal of training scenarios;

[0010] Defining a training process of an intelligent vehicle according to a plurality of training spaces, an intelligent vehicle controlled by a user, and corresponding vehicle data;

[0011] Based on the tracing of the training history of the smart vehicle, various training subjects of the smart vehicle are collected, corresponding training screens are matched according to various training subjects of the smart vehicle and corresponding vehicle data, and abnormal operation parts are defined based on the training screens, user operation data and interactive signals of the smart vehicle;

[0012] According to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic matching corresponding optimization events, the autonomous optimization of the smart vehicle is triggered according to the optimization event, and an interactive screen with indicative significance is output according to the autonomous optimization of the smart vehicle.

[0013] Optionally, collecting the location of the Internet of Vehicles training device includes:

[0014] Collect interactive signals of the Internet of Vehicles training device;

[0015] Defining corresponding position information according to the analysis of the interactive signal of the Internet of Vehicles training device;

[0016] Freeze the location information;

[0017] Based on the location information, the Internet of Vehicles training device defines the location of the Internet of Vehicles training device;

[0018] The surrounding cameras are triggered according to the location of the Internet of Vehicles training device, and the Internet of Vehicles training device is directionally photographed based on the cameras, and the corresponding images are output, and the Internet of Vehicles training device is verified according to the corresponding images.

[0019] Optionally, the collecting corresponding training scenes according to the positioning detection of the location of the Internet of Vehicles training device includes:

[0020] Freeze the location of the Internet of Vehicles training device;

[0021] Matching a corresponding detection mode based on the location of the Internet of Vehicles training device and the space occupied by the Internet of Vehicles training device;

[0022] Triggering positioning detection of the location of the Internet of Vehicles training device according to the Internet of Vehicles training device and the corresponding detection mode;

[0023] Real-time monitoring of the location of the Internet of Vehicles training device;

[0024] Based on the positioning detection of the location of the Internet of Vehicles training device, multiple scene parameters are collected, corresponding training scenes are defined according to the multiple scene parameters and the Internet of Vehicles training device, and the corresponding training scenes are collected.

[0025] Optionally, the multiple training spaces are defined based on the traversal of the training scenarios, including:

[0026] Freeze the training scene;

[0027] Traverse the training scenarios;

[0028] Collect multiple spatial parameters based on traversal of the training scene;

[0029] Associating multiple spatial parameters, IoV training devices, and corresponding spatial logic;

[0030] A plurality of training spaces are defined according to a plurality of space parameters, Internet of Vehicles training devices and corresponding space logics.

[0031] Optionally, defining the training process of the intelligent vehicle according to the plurality of training spaces, the intelligent vehicle controlled by the user and the corresponding vehicle data includes:

[0032] Freeze multiple training spaces;

[0033] Collecting smart vehicles controlled by users;

[0034] Defining corresponding vehicle data according to the reverse tracing of the smart vehicle;

[0035] Associating multiple training spaces, intelligent vehicles controlled by users, and corresponding vehicle data;

[0036] Defining first course parameters based on multiple training spaces and intelligent vehicles controlled by users;

[0037] Defining the second course parameters based on multiple training spaces and vehicle data;

[0038] A training process of the intelligent vehicle is defined according to the first process parameter, the second process parameter and the intelligent vehicle.

[0039] Optionally, the method of collecting various training subjects of the smart vehicle based on tracing the training history of the smart vehicle, matching corresponding training screens according to various training subjects of the smart vehicle and corresponding vehicle data, and defining abnormal operation parts based on the training screens, user operation data and interactive signals of the smart vehicle includes:

[0040] Freeze the practical training process of intelligent vehicles;

[0041] Trace the practical training process of intelligent vehicles;

[0042] Collect various training subjects of smart vehicles based on the tracing of the training history of smart vehicles;

[0043] Associate various practical training subjects of the intelligent vehicle and the corresponding vehicle data, and match corresponding practical training screens based on the various practical training subjects of the intelligent vehicle and the corresponding vehicle data.

[0044] Optionally, the method of collecting various training subjects of the intelligent vehicle based on tracing the training history of the intelligent vehicle, matching corresponding training screens according to various training subjects of the intelligent vehicle and corresponding vehicle data, and defining abnormal operation parts based on the training screens, user operation data and interactive signals of the intelligent vehicle, further includes:

[0045] Associate each training subject of the intelligent vehicle and the corresponding vehicle data to match the corresponding training screen;

[0046] Define the abnormal operation part according to the training screen, the user's operation data and the interaction signal of the intelligent vehicle.

[0047] Optionally, matching a corresponding optimization event according to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, triggering the autonomous optimization of the smart vehicle according to the optimization event, and outputting an interactive screen with indicative significance according to the autonomous optimization of the smart vehicle, includes:

[0048] Freeze abnormal operation part;

[0049] Multiple matching of abnormal operation parts, smart vehicle models and corresponding optimization logic;

[0050] The corresponding optimization events are defined according to the multiple matching of the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic.

[0051] Optionally, the matching of the corresponding optimization event according to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, triggering the autonomous optimization of the smart vehicle according to the optimization event, and outputting an interactive screen with indicative significance according to the autonomous optimization of the smart vehicle, further includes:

[0052] triggering autonomous optimization of the intelligent vehicle according to the optimization event;

[0053] Real-time monitoring of autonomous optimization of smart vehicles;

[0054] Output interactive images with indicative significance based on the autonomous optimization of the intelligent vehicle.

[0055] In addition, an embodiment of the present invention further provides a dynamic interaction system of a vehicle networking training device, and the dynamic interaction system of the vehicle networking training device includes:

[0056] A collection module is used to collect the location of the Internet of Vehicles training device;

[0057] A training scenario module, used to collect corresponding training scenarios based on the location detection of the Internet of Vehicles training device;

[0058] A training space module is used to define multiple training spaces based on the traversal of training scenarios;

[0059] A training process module, used to define a training process of an intelligent vehicle according to multiple training spaces, an intelligent vehicle controlled by a user, and corresponding vehicle data;

[0060] The abnormal operation module is used to collect various training subjects of the intelligent vehicle based on the tracing of the training history of the intelligent vehicle, match the corresponding training screens according to the various training subjects of the intelligent vehicle and the corresponding vehicle data, and define the abnormal operation part based on the training screens, the user's operation data and the interactive signals of the intelligent vehicle;

[0061] The interactive screen module is used to match the corresponding optimization event according to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, trigger the autonomous optimization of the smart vehicle according to the optimization event, and output an interactive screen with indicative significance according to the autonomous optimization of the smart vehicle.

[0062] In an embodiment of the present invention, through the method in the embodiment of the present invention, the location of the Internet of Vehicles training device is collected; the corresponding training scene is collected based on the positioning detection of the location of the Internet of Vehicles training device; multiple training spaces are defined based on the traversal of the training scene; the training history of the smart vehicle is defined according to the multiple training spaces, the smart vehicles controlled by the user and the corresponding vehicle data, which is compatible with the overall consideration of multiple training spaces, the smart vehicles controlled by the user and the corresponding vehicle data, realizes multi-dimensional control of multiple training spaces, the smart vehicles controlled by the user and the corresponding vehicle data, and ensures the accuracy of the training history of the smart vehicle.

[0063] Furthermore, based on the tracing of the training history of the smart vehicle, various training subjects of the smart vehicle are collected, and corresponding training screens are matched according to various training subjects of the smart vehicle and the corresponding vehicle data. The abnormal operation part is defined based on the training screen, the user's operation data and the interactive signal of the smart vehicle; the corresponding optimization event is matched according to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, the autonomous optimization of the smart vehicle is triggered according to the optimization event, and an interactive screen with indicative significance is output according to the autonomous optimization of the smart vehicle. The abnormal operation part is introduced, and multi-dimensional control of the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic is realized, the accuracy of the optimization event is ensured, and then an interactive screen with indicative significance is output, so as to guide the user to further train through the interactive screen. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0065] Figure 1 is a flowchart of a dynamic interaction method of a vehicle networking training device in an embodiment of the present invention;

[0066] Figure 2 is a flow chart of S11 in the dynamic interaction method of the vehicle networking training device in the embodiment of the present invention;

[0067] Figure 3 is a flow chart of S12 in the dynamic interaction method of the vehicle networking training device in the embodiment of the present invention;

[0068] Figure 4 is a flow chart of S13 in the dynamic interaction method of the vehicle networking training device in the embodiment of the present invention;

[0069] Figure 5 is a flow chart of S14 in the dynamic interaction method of the vehicle networking training device in the embodiment of the present invention;

[0070] Figure 6 is a flow chart of S15 in the dynamic interaction method of the vehicle networking training device in the embodiment of the present invention;

[0071] Figure 7 is a flow chart of S16 in the dynamic interaction method of the vehicle networking training device in the embodiment of the present invention;

[0072] Figure 8Schematic diagram of the structure of the dynamic interaction system of the vehicle networking training device in an embodiment of the present invention;

[0073] Fig. 9 The figure is a hardware diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0074] 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.

[0075] See also Figures 1 to 9 , a dynamic interaction method of a vehicle networking training device, applied to a dynamic interaction scenario of the vehicle networking training device; the dynamic interaction method of the vehicle networking training device includes:

[0076] Step S11: collecting the location of the Internet of Vehicles training device;

[0077] Step S12: collecting corresponding training scenes according to the positioning detection of the location of the Internet of Vehicles training device;

[0078] Step S13: defining multiple training spaces based on the traversal of the training scenarios;

[0079] Step S14: defining a training process of the smart vehicle according to the multiple training spaces, the smart vehicle controlled by the user and the corresponding vehicle data;

[0080] Step S15: collecting various training subjects of the smart vehicle based on the tracing of the training history of the smart vehicle, matching corresponding training screens according to various training subjects of the smart vehicle and corresponding vehicle data, and defining abnormal operation parts based on the training screens, user operation data and interactive signals of the smart vehicle;

[0081] Step S16: Match the corresponding optimization event according to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, trigger the autonomous optimization of the smart vehicle according to the optimization event, and output an interactive screen with indicative significance according to the autonomous optimization of the smart vehicle.

[0082] In an embodiment of the present invention, through the method in the embodiment of the present invention, the location of the Internet of Vehicles training device is collected; the corresponding training scene is collected based on the positioning detection of the location of the Internet of Vehicles training device; multiple training spaces are defined based on the traversal of the training scene; the training history of the smart vehicle is defined according to the multiple training spaces, the smart vehicles controlled by the user and the corresponding vehicle data, which is compatible with the overall consideration of multiple training spaces, the smart vehicles controlled by the user and the corresponding vehicle data, realizes multi-dimensional control of multiple training spaces, the smart vehicles controlled by the user and the corresponding vehicle data, and ensures the accuracy of the training history of the smart vehicle.

[0083] Furthermore, based on the tracing of the training history of the smart vehicle, various training subjects of the smart vehicle are collected, and corresponding training screens are matched according to various training subjects of the smart vehicle and the corresponding vehicle data. The abnormal operation part is defined based on the training screen, the user's operation data and the interactive signal of the smart vehicle; the corresponding optimization event is matched according to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, the autonomous optimization of the smart vehicle is triggered according to the optimization event, and an interactive screen with indicative significance is output according to the autonomous optimization of the smart vehicle. The abnormal operation part is introduced, and multi-dimensional control of the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic is realized, the accuracy of the optimization event is ensured, and then an interactive screen with indicative significance is output, so as to guide the user to further train through the interactive screen.

[0084] refer to Figure 2 , in step S11, the location of the Internet of Vehicles training device is collected;

[0085] In the specific implementation process of the present invention, the specific steps may be:

[0086] S111: collecting interactive signals of the Internet of Vehicles training device;

[0087] S112: defining corresponding position information according to the analysis of the interactive signal of the vehicle networking training device;

[0088] S113: freeze the position information;

[0089] S114: defining a location of the Internet of Vehicles training device based on the location information and the Internet of Vehicles training device;

[0090] S115: triggering the surrounding cameras according to the location of the Internet of Vehicles training device, taking a directional photo of the Internet of Vehicles training device based on the cameras, outputting a corresponding image, and verifying the Internet of Vehicles training device according to the corresponding image.

[0091] In an embodiment of the present application, the interaction signals of the Internet of Vehicles training device are collected, the interaction signals of the Internet of Vehicles training device are introduced, and the interaction signals of the Internet of Vehicles training device are analyzed accordingly to facilitate further processing of the interaction signals of the Internet of Vehicles training device, thereby realizing subsequent control based on the interaction signals of the Internet of Vehicles training device.

[0092] At this time, the corresponding position information is defined according to the analysis of the interactive signal of the vehicle networking training device, the analysis of the interactive signal of the vehicle networking training device is introduced, the analysis of the interactive signal of the vehicle networking training device is further controlled, and the corresponding position information is output, thereby realizing the control of the position information so as to make full use of the position information.

[0093] Therefore, the position information is frozen; based on the position information and the vehicle networking training device, the location of the vehicle networking training device is defined; according to the location of the vehicle networking training device, the surrounding cameras are triggered, and the vehicle networking training device is directionally photographed based on the cameras, and the corresponding images are output, and the vehicle networking training device is verified according to the corresponding images, thereby realizing online verification of the vehicle networking training device, so as to make full use of the surrounding cameras of the location of the vehicle networking training device, ensure the camera's directional verification of the location of the vehicle networking training device, and ensure the accuracy of the location of the vehicle networking training device.

[0094] refer to Figure 3 , in step S12, the corresponding training scene is collected according to the positioning detection of the location of the Internet of Vehicles training device;

[0095] In the specific implementation process of the present invention, the specific steps may be:

[0096] S121: freeze the location of the Internet of Vehicles training device;

[0097] S122: matching a corresponding detection mode based on the location of the Internet of Vehicles training device and the space occupied by the Internet of Vehicles training device;

[0098] S123: triggering positioning detection of the location of the vehicle networking training device according to the vehicle networking training device and the corresponding detection mode;

[0099] S124: real-time monitoring of the location of the Internet of Vehicles training device;

[0100] S125: Based on the positioning detection of the location of the Internet of Vehicles training device, multiple scene parameters are collected, corresponding training scenes are defined according to the multiple scene parameters and the Internet of Vehicles training device, and the corresponding training scenes are collected.

[0101] In an embodiment of the present application, the location of the Internet of Vehicles training device is fixed, the location of the Internet of Vehicles training device is introduced, and the location of the Internet of Vehicles training device is further processed, so as to match the corresponding detection mode based on the location of the Internet of Vehicles training device and the space occupied by the Internet of Vehicles training device, and be compatible with the overall consideration of the location of the Internet of Vehicles training device and the space occupied by the Internet of Vehicles training device, thereby realizing multi-dimensional control of the location of the Internet of Vehicles training device and the space occupied by the Internet of Vehicles training device, and ensuring the accuracy of the detection mode.

[0102] Therefore, the positioning detection of the location of the Internet of Vehicles training device is triggered according to the Internet of Vehicles training device and the corresponding detection mode, thereby realizing the dynamic interaction between the Internet of Vehicles training device and the corresponding detection mode and triggering the positioning detection of the location of the Internet of Vehicles training device.

[0103] Furthermore, the positioning detection of the location of the Internet of Vehicles training device is monitored in real time; multiple scene parameters are collected based on the positioning detection of the location of the Internet of Vehicles training device, corresponding training scenes are defined according to the multiple scene parameters and the Internet of Vehicles training device, and corresponding training scenes are collected, multiple scene parameters and the Internet of Vehicles training device are introduced, and the multiple scene parameters and the Internet of Vehicles training device are controlled in multiple dimensions to ensure the accuracy of the training scenes.

[0104] refer to Figure 4 , in step S13, a plurality of training spaces are defined based on the traversal of the training scenarios;

[0105] In the specific implementation process of the present invention, the specific steps may be:

[0106] S131: freeze-frame training scene;

[0107] S132: traversing the training scenarios;

[0108] S133: collecting multiple spatial parameters based on traversal of the training scene;

[0109] S134: Associating multiple spatial parameters, the Internet of Vehicles training device, and corresponding spatial logic;

[0110] S135: Define multiple training spaces according to multiple space parameters, the Internet of Vehicles training device, and corresponding space logic.

[0111] In the embodiments of the present application, the training scene is frozen, the training scene is introduced, and the training scene is further controlled to facilitate the traversal of the training scene, thereby realizing the traversal of the training scene and ensuring the subsequent processing of the training scene.

[0112] Therefore, based on the traversal of the training scene, multiple spatial parameters are collected and introduced, and multiple spatial parameters, the Internet of Vehicles training device and the corresponding spatial logic are controlled, thereby realizing the overall consideration of multiple spatial parameters, the Internet of Vehicles training device and the corresponding spatial logic, and ensuring the multi-dimensional control of multiple spatial parameters, the Internet of Vehicles training device and the corresponding spatial logic.

[0113] Furthermore, multiple spatial parameters, vehicle networking training devices and corresponding spatial logic are associated; multiple training spaces are defined according to the multiple spatial parameters, vehicle networking training devices and corresponding spatial logic, multiple spatial parameters, vehicle networking training devices and corresponding spatial logic are introduced, and multiple spatial parameters, vehicle networking training devices and corresponding spatial logic are multiple interacted, thereby ensuring the rationality and accuracy of the division of multiple training spaces.

[0114] refer to Figure 5 , S14: defining a training process of the intelligent vehicle according to a plurality of training spaces, the intelligent vehicle controlled by the user and the corresponding vehicle data;

[0115] In the specific implementation process of the present invention, the specific steps may be:

[0116] S141: Freeze multiple training spaces;

[0117] S142: Collecting the intelligent vehicle controlled by the user;

[0118] S143: defining corresponding vehicle data according to the reverse tracing of the smart vehicle;

[0119] S144: Associating multiple training spaces, smart vehicles controlled by users, and corresponding vehicle data;

[0120] S145: defining a first course parameter based on multiple training spaces and the intelligent vehicle controlled by the user;

[0121] S146: defining a second course parameter based on the plurality of training spaces and vehicle data;

[0122] S147: Define a training process of the intelligent vehicle according to the first process parameter, the second process parameter and the intelligent vehicle.

[0123] In an embodiment of the present application, the location of a vehicle networking training device is collected; corresponding training scenes are collected based on the positioning detection of the location of the vehicle networking training device; multiple training spaces are defined based on the traversal of the training scenes; and the training history of the smart vehicle is defined based on the multiple training spaces, the smart vehicles controlled by the user, and the corresponding vehicle data. This is compatible with the overall consideration of multiple training spaces, the smart vehicles controlled by the user, and the corresponding vehicle data, and realizes multi-dimensional control of multiple training spaces, the smart vehicles controlled by the user, and the corresponding vehicle data, thereby ensuring the accuracy of the training history of the smart vehicle.

[0124] At this time, multiple training spaces are frozen and controlled, so that multiple training spaces are managed based on overall thinking. At the same time, the smart vehicles controlled by users are collected; the corresponding vehicle data is defined based on the reverse tracing of the smart vehicle, and the smart vehicles and the corresponding vehicle data are introduced.

[0125] Therefore, multiple training spaces, user-controlled smart vehicles and corresponding vehicle data are associated, multiple training spaces, user-controlled smart vehicles and corresponding vehicle data are introduced, and multi-dimensional control of multiple training spaces, user-controlled smart vehicles and corresponding vehicle data is performed, ensuring the overall control of multiple training spaces, user-controlled smart vehicles and corresponding vehicle data.

[0126] Furthermore, a first process parameter is defined based on multiple training spaces and smart vehicles controlled by users; a second process parameter is defined based on multiple training spaces and vehicle data; a training process of the smart vehicle is defined according to the first process parameter, the second process parameter and the smart vehicle, the first process parameter, the second process parameter and the smart vehicle are introduced, and multiple interactions of the first process parameter, the second process parameter and the smart vehicle are realized, thereby ensuring the accuracy of the training process of the smart vehicle and performing subsequent control over the training process of the smart vehicle.

[0127] At this time, the training process of the intelligent vehicle is defined according to multiple training spaces, the intelligent vehicles controlled by the user and the corresponding vehicle data, which is compatible with the overall consideration of multiple training spaces, the intelligent vehicles controlled by the user and the corresponding vehicle data, and realizes multi-dimensional control of multiple training spaces, the intelligent vehicles controlled by the user and the corresponding vehicle data, ensuring the accuracy of the training process of the intelligent vehicle.

[0128] refer to Figure 6 , S15: collecting various training subjects of the intelligent vehicle based on the tracing of the training history of the intelligent vehicle, matching corresponding training screens according to various training subjects of the intelligent vehicle and corresponding vehicle data, and defining abnormal operation parts based on the training screens, user operation data and interactive signals of the intelligent vehicle;

[0129] In the specific implementation process of the present invention, the specific steps may be:

[0130] S151: Freeze the practical training process of intelligent vehicles;

[0131] S152: Tracing the training process of intelligent vehicles;

[0132] S153: collecting various training subjects of the intelligent vehicle according to the tracing back of the training history of the intelligent vehicle;

[0133] S154: associating various training subjects of the intelligent vehicle with corresponding vehicle data, and matching corresponding training screens based on various training subjects of the intelligent vehicle and corresponding vehicle data;

[0134] S155: Associating each training subject of the intelligent vehicle with the corresponding vehicle data and matching the corresponding training screen;

[0135] S156: Define abnormal operation parts according to the training screen, the user's operation data and the interaction signal of the intelligent vehicle.

[0136] In the embodiments of the present application, the training history of the intelligent vehicle is frozen, and the training history of the intelligent vehicle is introduced, so that the training history of the intelligent vehicle is traced, thereby realizing the tracing of the training history of the intelligent vehicle.

[0137] Furthermore, based on the tracing of the training history of the intelligent vehicle, various training subjects of the intelligent vehicle are collected, various training subjects of the intelligent vehicle are introduced, various training subjects of the intelligent vehicle are managed and controlled, and various training subjects of the intelligent vehicle and corresponding vehicle data are introduced.

[0138] Therefore, the various training subjects of the intelligent vehicle and the corresponding vehicle data are associated, the corresponding training screens are matched based on the various training subjects of the intelligent vehicle and the corresponding vehicle data, and the various training subjects of the intelligent vehicle and the corresponding vehicle data are considered as a whole, thereby achieving multi-dimensional control of the various training subjects of the intelligent vehicle and the corresponding vehicle data, and ensuring the accuracy of the training screens.

[0139] Furthermore, each training subject of the associated intelligent vehicle and the corresponding vehicle data are matched with the corresponding training screen; the abnormal operation part is defined according to the training screen, the user's operation data and the interaction signal of the intelligent vehicle, and the training screen, the user's operation data and the interaction signal of the intelligent vehicle are introduced. The training screen, the user's operation data and the interaction signal of the intelligent vehicle are controlled as a whole, realizing multiple interactions of the training screen, the user's operation data and the interaction signal of the intelligent vehicle, and ensuring the accuracy of the abnormal operation part.

[0140] refer to Figure 7, S16: matching a corresponding optimization event according to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, triggering the autonomous optimization of the smart vehicle according to the optimization event, and outputting an interactive screen with indicative significance according to the autonomous optimization of the smart vehicle;

[0141] In the specific implementation process of the present invention, the specific steps may be:

[0142] S161: freeze abnormal operation part;

[0143] S162: performing multiple matching of abnormal operation parts, models of smart vehicles, and corresponding optimization logics;

[0144] S163: defining corresponding optimization events according to multiple matches of the abnormal operation part, the model of the smart vehicle, and the corresponding optimization logic;

[0145] S164: triggering autonomous optimization of the intelligent vehicle according to the optimization event;

[0146] S165: Real-time monitoring of autonomous optimization of smart vehicles;

[0147] S166: Outputting an interactive screen with indicative significance based on the autonomous optimization of the intelligent vehicle.

[0148] In the specific implementation process of the present invention, various training subjects of the intelligent vehicle are collected based on the tracing of the training history of the intelligent vehicle, and the corresponding training screens are matched according to the various training subjects of the intelligent vehicle and the corresponding vehicle data. The abnormal operation part is defined based on the training screen, the user's operation data and the interactive signal of the intelligent vehicle; the corresponding optimization event is matched according to the abnormal operation part, the model of the intelligent vehicle and the corresponding optimization logic, the autonomous optimization of the intelligent vehicle is triggered according to the optimization event, and an interactive screen with indicative significance is output according to the autonomous optimization of the intelligent vehicle, and the abnormal operation part is introduced to realize multi-dimensional control of the abnormal operation part, the model of the intelligent vehicle and the corresponding optimization logic, thereby ensuring the accuracy of the optimization event, and then outputting an interactive screen with indicative significance, so as to guide the user to further train through the interactive screen.

[0149] At this time, the abnormal operation part is frozen and introduced to facilitate subsequent processing of the abnormal operation part. At the same time, multiple matching of the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic is performed to achieve multiple matching of the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic.

[0150] Therefore, corresponding optimization events are defined based on multiple matches of the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic; autonomous optimization of the smart vehicle is triggered based on the optimization event, which is compatible with multiple matches of the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, thereby ensuring the accuracy of the optimization event.

[0151] Furthermore, the autonomous optimization of the intelligent vehicle is monitored in real time; an interactive screen with indicative significance is output according to the autonomous optimization of the intelligent vehicle, an interactive screen with indicative significance is introduced, and is output through the autonomous optimization of the intelligent vehicle, so as to highly correlate the interactive screen with indicative significance with the abnormal operation part, thereby guiding the abnormal operation part and ensuring the accuracy of the interactive screen with indicative significance.

[0152] At the same time, the autonomous optimization of the intelligent vehicle is triggered according to the optimization event, and an interactive screen with indicative significance is output according to the autonomous optimization of the intelligent vehicle. The abnormal operation part is introduced, and multi-dimensional control of the abnormal operation part, the model of the intelligent vehicle and the corresponding optimization logic is realized. The accuracy of the optimization event is ensured, and then an interactive screen with indicative significance is output, so as to guide the user to further train through the interactive screen.

[0153] In another embodiment of the present invention, for the vehicle networking training device, the vehicle networking training device includes movable traffic lights, lane lines, student seats, smart vehicles, podiums, local area network controllers, etc. adapted for intelligent connected vehicles.

[0154] The present invention initially provides four lanes in both directions, but the number of lanes can be appropriately changed according to the specific site of the user, and can be as little as one lane in one direction. If the user needs two lanes in both directions, the number of lanes can be changed according to the actual situation of the user. In addition, the initial design top view of the present invention is a rectangular site. If the site provided by the user is an irregular non-structural site, it can also be changed according to the actual situation to meet the needs of the user.

[0155] The number of movable traffic lights in the initial state of two-way four-lane is 10, but it can be made according to actual conditions, and one movable traffic light can also realize the vehicle networking function; if it is changed to a one-way lane or a two-way two-lane, the number will also change appropriately, and it can also be customized according to user needs, or multiple movable traffic lights can be reserved according to user needs.

[0156] The initial state of the lane line is white, and no floor modification is required. It can be pasted on non-white floors. If the floor is white, change the lane line to non-white to facilitate camera recognition.

[0157] The LAN controller is initially arranged at a diagonal angle to ensure full coverage of the Internet of Vehicles training site without dead spots. If the site provided by the user is not large, consider installing a set of LAN controllers at the top of the center of the site to basically achieve full-scene coverage. If the site provided by the user is large, increase the number of LAN controllers appropriately to ensure full-scene coverage without dead spots.

[0158] The initial design quantity of smart vehicles is consistent with the number of student seats, that is, each set of seats is equipped with a smart vehicle, so that basically each student has a smart vehicle during class; if the user has other requirements, such as two people sharing a smart vehicle, adding a spare smart vehicle, etc., they can also be supplied on demand; the student seats are equipped with student-end controllers, and their number is actually adapted according to the size of the user's venue and user needs.

[0159] Smart vehicles include intelligent sensing systems, control systems, networking units and electronic control execution systems. The intelligent sensing systems mainly play the role of lane line recognition and obstacle recognition. The networking units are mainly linked to the local area network for real-time data transmission and sharing. The control unit is mainly the control brain of the smart vehicle and plays the role of decision-making and planning. The electronic control execution system mainly controls the driving, steering and braking actions of the smart vehicle.

[0160] With the LAN controller as the center, movable traffic lights, smart vehicles, and student-side controllers are linked together through a wireless LAN. Through the LAN controller, information can be transmitted and shared between systems in real time.

[0161] The movable traffic light is mainly composed of a movable traffic light controller, a traffic light networking unit, a red light, a green light, a yellow light, etc. The movable traffic light is centered on the movable traffic light controller, which controls the periodic flashing of the red light, the yellow light, and the green light. The movable traffic light controller transmits the control signal to the local area network controller through the traffic light networking unit.

[0162] The student-side controller is installed under the teaching table, and one end of it is connected to the display. Students can sit on a chair and observe the display interface. By receiving real-time data from the smart vehicle and real-number data of the movable traffic light from the LAN controller, they can observe and control the smart vehicle in real time. The student-side controller is connected to the smart vehicle through account binding, that is, the student only needs to enter the account and password of the smart vehicle to bind. After binding, the smart vehicle can be programmed, intelligently controlled and remotely controlled. In addition, students can perform independent programming control based on the smart vehicle's own feedback data and movable traffic light data. Teachers can also set corresponding assessment tasks based on this, allowing students to play and complete them independently, so as to truly master the essence of knowledge and skills of intelligent connected vehicles and the Internet of Vehicles.

[0163] Intelligent vehicles mainly include cameras, lidars, intelligent vehicle controllers, intelligent vehicle networking units, and intelligent vehicle electronic chassis; the intelligent vehicle electronic chassis mainly includes electronic drive systems, electronic steering systems, and electronic braking systems. The electronic drive system mainly controls the driving of the intelligent vehicle (forward or backward), the electronic steering system mainly controls the left and right steering of the intelligent vehicle, that is, lateral control, and the electronic braking system mainly realizes the deceleration or parking of the intelligent vehicle. The camera of an intelligent vehicle is mainly used to identify lane lines. During the actual driving of the intelligent vehicle, the camera collects lane line information on the road in real time and extracts the lane lines through image recognition technology, providing an information basis for the intelligent vehicle's driving path planning; the laser radar is mainly single-line or eight-line laser radar, which can be adapted to the relevant laser radar requirements according to user needs. The laser radar is mainly used to realize obstacle recognition and complete obstacle avoidance functions; the intelligent vehicle controller mainly collects real-time data information from cameras, laser radars, intelligent vehicle electronic control chassis, and networking units, and integrates all information to plan the next frame path, speed, speed change rate, turning angle and turning angle change rate of the intelligent vehicle, and then controls the normal and safe operation of the intelligent vehicle through the intelligent vehicle's electronic control drive system, electronic control steering system and electronic control braking system.

[0164] In an embodiment of the present invention, through the method in the embodiment of the present invention, the location of the Internet of Vehicles training device is collected; the corresponding training scene is collected based on the positioning detection of the location of the Internet of Vehicles training device; multiple training spaces are defined based on the traversal of the training scene; the training history of the smart vehicle is defined according to the multiple training spaces, the smart vehicles controlled by the user and the corresponding vehicle data, which is compatible with the overall consideration of multiple training spaces, the smart vehicles controlled by the user and the corresponding vehicle data, realizes multi-dimensional control of multiple training spaces, the smart vehicles controlled by the user and the corresponding vehicle data, and ensures the accuracy of the training history of the smart vehicle.

[0165] Furthermore, based on the tracing of the training history of the smart vehicle, various training subjects of the smart vehicle are collected, and corresponding training screens are matched according to various training subjects of the smart vehicle and the corresponding vehicle data. The abnormal operation part is defined based on the training screen, the user's operation data and the interactive signal of the smart vehicle; the corresponding optimization event is matched according to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, the autonomous optimization of the smart vehicle is triggered according to the optimization event, and an interactive screen with indicative significance is output according to the autonomous optimization of the smart vehicle. The abnormal operation part is introduced, and multi-dimensional control of the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic is realized, the accuracy of the optimization event is ensured, and then an interactive screen with indicative significance is output, so as to guide the user to further train through the interactive screen.

[0166] See also Figure 8 , Figure 8 It is a schematic diagram of the structural composition of the dynamic interactive system of the vehicle networking training device in an embodiment of the present invention.

[0167] like Figure 8 As shown, a dynamic interactive system of a vehicle networking training device, the dynamic interactive system of the vehicle networking training device includes:

[0168] A collection module 21 is used to collect the location of the Internet of Vehicles training device;

[0169] The training scenario module 22 is used to collect corresponding training scenarios according to the location detection of the Internet of Vehicles training device;

[0170] A training space module 23, used to define multiple training spaces based on the traversal of training scenarios;

[0171] A training process module 24, for defining a training process of an intelligent vehicle according to a plurality of training spaces, an intelligent vehicle controlled by a user, and corresponding vehicle data;

[0172] The abnormal operation module 25 is used to collect various training subjects of the intelligent vehicle based on the tracing of the training history of the intelligent vehicle, match the corresponding training screens according to the various training subjects of the intelligent vehicle and the corresponding vehicle data, and define the abnormal operation part based on the training screens, the user's operation data and the interaction signal of the intelligent vehicle;

[0173] The interactive screen module 26 is used to match the corresponding optimization event according to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, trigger the autonomous optimization of the smart vehicle according to the optimization event, and output an interactive screen with indicative significance according to the autonomous optimization of the smart vehicle.

[0174] See also Fig. 9 , refer to the following Fig. 9 The electronic device 40 according to this embodiment of the present invention will be described. Fig. 9 The electronic device 40 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0175] like Fig. 9 As shown, the electronic device 40 is in the form of a general computing device. The components of the electronic device 40 may include but are not limited to: at least one processing unit 41, at least one storage unit 42, and a bus 43 connecting different system components (including the storage unit 42 and the processing unit 41).

[0176] The storage unit stores program codes, which can be executed by the processing unit 41, so that the processing unit 41 executes the steps according to various exemplary embodiments of the present invention described in the above “Embodiment Method” section of this specification.

[0177] The storage unit 42 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 421 and / or a cache memory unit 422 , and may further include a read-only memory unit (ROM) 423 .

[0178] The storage unit 42 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0179] Bus 43 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0180] The electronic device 40 may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device 40, and / or any device that enables the electronic device 40 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 44. Furthermore, the electronic device 40 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 45. Fig. 9 As shown, the network adapter 45 communicates with other modules of the electronic device 40 via the bus 43. It should be understood that although Fig. 9 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 40, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup planning systems.

[0181] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0182] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium can include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc. In addition, it stores computer program instructions, and when the computer program instructions are executed by a computer, the computer executes the above method.

[0183] In addition, the dynamic interaction method and system of the vehicle networking training device provided in the embodiment of the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A dynamic interaction method for a vehicle networking training device, characterized in that: Dynamic interactive scenarios applied to Internet of Vehicles training devices; The dynamic interaction method of the vehicle networking training device includes: Collect the location of the Internet of Vehicles training device; Collect corresponding training scenes based on the location detection of the Internet of Vehicles training device; Define multiple training spaces based on the traversal of training scenarios; Defining a training process of an intelligent vehicle according to a plurality of training spaces, an intelligent vehicle controlled by a user, and corresponding vehicle data; Based on the tracing of the training history of the smart vehicle, various training subjects of the smart vehicle are collected, corresponding training screens are matched according to various training subjects of the smart vehicle and corresponding vehicle data, and abnormal operation parts are defined based on the training screens, user operation data and interactive signals of the smart vehicle; According to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic matching corresponding optimization events, the autonomous optimization of the smart vehicle is triggered according to the optimization event, and an interactive screen with indicative significance is output according to the autonomous optimization of the smart vehicle.

2. The dynamic interaction method of the vehicle networking training device according to claim 1 is characterized in that: The location of the collected vehicle networking training device includes: Collect interactive signals of the Internet of Vehicles training device; Defining corresponding position information according to the analysis of the interactive signal of the Internet of Vehicles training device; Freeze the location information; Based on the location information, the Internet of Vehicles training device defines the location of the Internet of Vehicles training device; The surrounding cameras are triggered according to the location of the Internet of Vehicles training device, and the Internet of Vehicles training device is directionally photographed based on the cameras, and the corresponding images are output, and the Internet of Vehicles training device is verified according to the corresponding images.

3. The dynamic interaction method of the vehicle networking training device according to claim 1 is characterized in that: The collecting of corresponding training scenes according to the location detection of the vehicle networking training device includes: Freeze the location of the Internet of Vehicles training device; Matching a corresponding detection mode based on the location of the Internet of Vehicles training device and the space occupied by the Internet of Vehicles training device; Triggering positioning detection of the location of the Internet of Vehicles training device according to the Internet of Vehicles training device and the corresponding detection mode; Real-time monitoring of the location of the Internet of Vehicles training device; Based on the positioning detection of the location of the Internet of Vehicles training device, multiple scene parameters are collected, corresponding training scenes are defined according to the multiple scene parameters and the Internet of Vehicles training device, and the corresponding training scenes are collected.

4. The dynamic interaction method of the vehicle networking training device according to claim 3 is characterized in that: The multiple training spaces are defined based on the traversal of the training scenarios, including: Freeze the training scene; Traverse the training scenarios; Collect multiple spatial parameters based on traversal of the training scene; Associating multiple spatial parameters, IoV training devices, and corresponding spatial logic; A plurality of training spaces are defined according to a plurality of space parameters, Internet of Vehicles training devices and corresponding space logics.

5. The dynamic interaction method of the vehicle networking training device according to claim 4 is characterized in that: Defining the training process of the intelligent vehicle according to the plurality of training spaces, the intelligent vehicle controlled by the user and the corresponding vehicle data includes: Freeze multiple training spaces; Collecting smart vehicles controlled by users; Defining corresponding vehicle data according to the reverse tracing of the smart vehicle; Associating multiple training spaces, intelligent vehicles controlled by users, and corresponding vehicle data; Defining first course parameters based on multiple training spaces and intelligent vehicles controlled by users; Defining the second course parameters based on multiple training spaces and vehicle data; A training process of the intelligent vehicle is defined according to the first process parameter, the second process parameter and the intelligent vehicle.

6. The dynamic interaction method of the vehicle networking training device according to claim 5 is characterized in that: The method collects various training subjects of the intelligent vehicle based on the tracing of the training history of the intelligent vehicle, matches corresponding training screens according to various training subjects of the intelligent vehicle and corresponding vehicle data, and defines abnormal operation parts based on the training screens, user operation data and interactive signals of the intelligent vehicle, including: Freeze the practical training process of intelligent vehicles; Trace the practical training process of intelligent vehicles; Collect various training subjects of smart vehicles based on the tracing of the training history of smart vehicles; Associate various practical training subjects of the intelligent vehicle and the corresponding vehicle data, and match corresponding practical training screens based on the various practical training subjects of the intelligent vehicle and the corresponding vehicle data.

7. The dynamic interaction method of the vehicle networking training device according to claim 6 is characterized in that: The method includes collecting various training subjects of the smart vehicle based on tracing the training history of the smart vehicle, matching corresponding training screens according to various training subjects of the smart vehicle and corresponding vehicle data, and defining abnormal operation parts based on the training screens, user operation data and interactive signals of the smart vehicle, and further comprising: Associate each training subject of the intelligent vehicle and the corresponding vehicle data to match the corresponding training screen; Define the abnormal operation part according to the training screen, the user's operation data and the interaction signal of the intelligent vehicle.

8. The dynamic interaction method of the vehicle networking training device according to claim 7 is characterized in that: The method of matching a corresponding optimization event according to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, triggering the autonomous optimization of the smart vehicle according to the optimization event, and outputting an interactive screen with indicative significance according to the autonomous optimization of the smart vehicle includes: Freeze abnormal operation part; Multiple matching of abnormal operation parts, smart vehicle models and corresponding optimization logic; The corresponding optimization events are defined according to the multiple matching of the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic.

9. The dynamic interaction method of the vehicle networking training device according to claim 8 is characterized in that: The method of matching a corresponding optimization event according to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, triggering the autonomous optimization of the smart vehicle according to the optimization event, and outputting an interactive screen with indicative significance according to the autonomous optimization of the smart vehicle, further includes: triggering autonomous optimization of the intelligent vehicle according to the optimization event; Real-time monitoring of autonomous optimization of smart vehicles; Output interactive images with indicative significance based on the autonomous optimization of the intelligent vehicle.

10. A dynamic interactive system for a vehicle networking training device, characterized in that: The dynamic interaction system of the vehicle networking training device is applied to the dynamic interaction method of the vehicle networking training device as claimed in any one of claims 1 to 9, and the dynamic interaction system of the vehicle networking training device includes: A collection module is used to collect the location of the Internet of Vehicles training device; A training scenario module, used to collect corresponding training scenarios based on the location detection of the Internet of Vehicles training device; A training space module is used to define multiple training spaces based on the traversal of training scenarios; A training process module, used to define a training process of an intelligent vehicle according to multiple training spaces, an intelligent vehicle controlled by a user, and corresponding vehicle data; The abnormal operation module is used to collect various training subjects of the intelligent vehicle based on the tracing of the training history of the intelligent vehicle, match the corresponding training screens according to the various training subjects of the intelligent vehicle and the corresponding vehicle data, and define the abnormal operation part based on the training screens, the user's operation data and the interactive signals of the intelligent vehicle; The interactive screen module is used to match the corresponding optimization event according to the abnormal operation part, the model of the smart vehicle and the corresponding optimization logic, trigger the autonomous optimization of the smart vehicle according to the optimization event, and output an interactive screen with indicative significance according to the autonomous optimization of the smart vehicle.

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