A vehicle training examination system

Abstract

The present application relates to the technical field of training examination management, and more particularly to a vehicle training examination system, comprising the following modules: a panoramic digital twin module, a multi-level command hub module, an intelligent evaluation engine module, a collaborative scale module, an electronic fence early warning module and a data bus; the panoramic digital twin module is used to build a three-dimensional virtual training scene; the multi-level command hub module is used to monitor and playback according to the space-time data; the collaborative scale module outputs a space-time collaborative error index; the intelligent evaluation engine module is used to generate a multi-dimensional quantitative examination report; the electronic fence early warning module is used to detect boundary crossing in real time according to the space-time data and to alarm when boundary crossing occurs. The present application breaks down functional islands through a unified data bus, realizes a real-time closed loop of the whole process of "evaluation-collaboration-monitoring-early warning-command", and solves the fundamental problem of the fragmentation of data and processes in traditional systems.

Description

Technical Field

[0001] This invention relates to the field of training and assessment management technology, and in particular to a vehicle training and assessment system. Background Technology

[0002] Traditional training and assessment methods rely heavily on manual visual inspection, manual timing, and subjective evaluation, which results in low efficiency, inconsistent standards, and difficulty in quantification.

[0003] Currently, there are some training management systems on the market that use technologies such as satellite positioning and trajectory display, but they generally suffer from the following systemic defects: Functional silos: Subsystems such as monitoring, assessment, command, and security alerts are often built independently, with inconsistent data formats and interfaces, leading to data fragmentation and process interruptions. For example, reports generated by the assessment system cannot be fed back to the command center in real time, making it difficult for commanders to intervene based on real-time assessment data.

[0004] Coordination errors cannot be accurately quantified: Existing systems mostly focus on single-vehicle trajectory tracking and simple timing, lacking precise measurement methods for the spatiotemporal coordination relationships between vehicles within an opposing team. The inability to objectively quantify subtle errors in coordinated actions such as "alignment," "line straddling," and "equidistant positioning" makes it difficult to support top-level training standards.

[0005] Safety management is passive and lagging: Safety monitoring relies heavily on reviewing trajectory playback or manual observation after an incident, and cannot provide millisecond-level real-time warnings when a vehicle is about to or has just crossed the boundary or stopped abnormally, resulting in high safety risks.

[0006] Therefore, there is an urgent need for an intelligent training and assessment system that can break down functional silos, achieve end-to-end data connectivity, and perform ultra-high precision quantification and real-time closed-loop management of collaborative errors. Summary of the Invention

[0007] This invention aims to solve at least one of the technical problems existing in related technologies. To this end, this invention provides a vehicle training and assessment system that realizes a real-time closed loop throughout the entire process of "assessment-coordination-monitoring-early warning-command," solving the fundamental problem of data and process separation in traditional systems.

[0008] This invention provides a vehicle training and assessment system, comprising: a panoramic digital twin module, a multi-level command center module, an intelligent assessment engine module, a collaborative measurement module, an electronic fence early warning module, and a data bus. The panoramic digital twin module is used to construct a three-dimensional virtual training scene, providing spatiotemporal data for individual vehicles and clusters; The multi-level command center module is used to monitor and replay the spatiotemporal data, and supports personnel at different levels to obtain situation information and issue instructions according to their permissions; The collaborative measuring module is used to calculate the time when a vehicle passes through the virtual baseline and the real-time distance between vehicles by processing the spatiotemporal data of a single vehicle and a cluster, and outputs a spatiotemporal collaborative error index. The intelligent evaluation engine module is used to embed a quantitative assessment algorithm library, analyze spatiotemporal data and spatiotemporal coordination error indicators, and automatically generate multi-dimensional quantitative assessment reports. The electronic fence early warning module is used to set virtual boundaries in a three-dimensional virtual training scene and perform boundary crossing detection in real time based on spatiotemporal data, and issue an alarm when a boundary crossing occurs. The panoramic digital twin module, multi-level command center module, intelligent evaluation engine module, collaborative measuring module, and electronic fence early warning module form a tightly coupled closed-loop workflow through a data bus.

[0009] According to the vehicle training and assessment system provided by the present invention, the panoramic digital twin module has a three-dimensional playback function, which allows users to freely select any historical time point as the starting point and play it at any rate, and allows users to freely switch between top-down, follow-up, and vehicle-mounted perspectives for observation and analysis during the playback process.

[0010] According to the vehicle training and assessment system provided by the present invention, the multi-level command center module adopts a role-based access control model. The predefined roles include the general commander, the squad commander, and the instructor. The general commander role has the authority to view global data and issue instructions, the squad commander role can only view and command the squad to which it belongs, and the instructor role can only view the detailed data and evaluation report of the designated vehicle.

[0011] According to the vehicle training and assessment system provided by the present invention, the assessment rule base built into the intelligent evaluation engine module includes: a single-vehicle route preservation algorithm and a formation alignment consistency algorithm, wherein the steps of the single-vehicle route preservation algorithm are as follows: S100: Set the preset ideal route as a smooth baseline composed of a series of ordered coordinate points; S200: For the actual trajectory point sequence of the vehicle during the assessment period, calculate the shortest Euclidean distance from each trajectory point to the baseline. ; S300: All within the statistical period Standard deviation With the maximum value ; S400: Route Preservation Score Calculated by the following formula: Where A is the benchmark full score, B is the first weighting coefficient, and C is the second weighting coefficient; The steps of the formation alignment and consensus algorithm are as follows: S110: During the assessment period, samples are taken at a fixed frequency. For vehicles in the formation, their position sequence perpendicular to the direction of travel is extracted. S120: Construct a baseline sequence, specifying the sequence of the baseline vehicle as the baseline sequence; S130: Calculate DTW distance ; S140: Calculate the consistency score : Among them, W j Let τ be the weight of vehicle j, and τ be the normalization parameter.

[0012] According to a vehicle training and assessment system provided by the present invention, the measurement process of the collaborative measuring module is as follows: Receive positioning data streams from BeiDou or GNSS high-precision positioning terminals with a time synchronization accuracy of less than or equal to 1 millisecond; Using geometric intersection detection and linear interpolation algorithms, the precise UTC time of intersection between the vehicle contour model and the virtual baseline is determined, with a time capture accuracy of less than or equal to 1 millisecond; Based on the vehicle coordinates under the same spatiotemporal reference, the Euclidean distance between vehicles is calculated in real time, and the Euclidean distance between vehicles is a spatiotemporal coordination error index.

[0013] According to a vehicle training and assessment system provided by the present invention, the electronic fence early warning module triggers the following actions after detecting a conflict between the vehicle's location and the virtual boundary: visually highlighting the vehicle in the command interface and digital twin scene, playing a preset warning audio, generating a structured log containing time-vehicle-event type, and pushing it to relevant personnel.

[0014] According to the vehicle training and assessment system provided by the present invention, the data bus is implemented based on gRPC or high-speed message middleware, and the data exchange latency between modules is less than 10 milliseconds.

[0015] The present invention also provides a vehicle training and assessment method, comprising: S1: Install position sensors on the training vehicle to collect its position information; S2: The location information is sent to the data bus. The data bus distributes the location information to the panoramic digital twin module, the multi-level command center module, the intelligent evaluation engine module, the collaborative measuring scale module, and the electronic fence early warning module. The panoramic digital twin module distributes the spatiotemporal data to the multi-level command center module, the collaborative measuring scale module, the intelligent evaluation engine module, and the electronic fence early warning module. The collaborative measuring scale module sends the spatiotemporal coordination error index to the intelligent evaluation engine module. The multi-level command center module issues instructions, the electronic fence early warning module provides real-time early warning, and the intelligent evaluation engine module generates the vehicle's training results.

[0016] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of a vehicle training and assessment method as described above.

[0017] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a vehicle training and assessment method as described above.

[0018] The above-described one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects: (1) By breaking down functional silos through a unified data bus, a real-time closed loop of the entire process of evaluation-coordination-monitoring-early warning-command was realized, solving the fundamental problem of data and process separation in traditional systems.

[0019] (2) Through the millisecond-level collaborative measuring module, the objective quantification of collaborative error from centimeters to milliseconds was realized for the first time at the training software level, providing a reliable and traceable technical basis for the training requirement of "not a difference between meters and seconds".

[0020] (3) The system has achieved full automation from data collection and analysis to report generation, greatly reducing manual intervention. It also makes the entire evaluation process intuitive and visible through three-dimensional digital twin technology, improving the objectivity and efficiency of the assessment.

[0021] (4) Proactive safety warning: Through electronic fences and real-time detection technology, safety management is upgraded from passive response to proactive prevention, which can intervene at the moment a risk occurs and significantly reduce training safety risks.

[0022] (5) Save costs and improve efficiency: The system greatly reduces the reliance on manpower such as timers, recorders, and safety officers, shortens the training and assessment cycle, and helps to improve training quality quickly through precise quantitative evaluation.

[0023] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the structure of a vehicle training and assessment system provided by the present invention.

[0026] Figure 2 This is a flowchart illustrating a vehicle training and assessment method provided by the present invention.

[0027] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0028] Figure label: 101. Panoramic Digital Twin Module; 102. Multi-level Command Center Module; 103. Intelligent Evaluation Engine Module; 104. Collaborative Measuring Module; 105. Electronic Fence Early Warning Module; 106. Data Bus; 810. Processor; 820. Communication Interface; 830. Memory; 840. Communication Bus. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but cannot be used to limit the scope of this invention.

[0030] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0031] The following is combined with Figures 1 to 3 This invention is described.

[0032] like Figure 1 As shown, the present invention includes the following modules: a panoramic digital twin module 101, a multi-level command center module 102, an intelligent evaluation engine module 103, a collaborative measuring scale module 104, an electronic fence early warning module 105, and a data bus 106. The panoramic digital twin module 101 is used to construct a three-dimensional virtual training scene and provide spatiotemporal data for individual vehicles and clusters; The multi-level command center module 102 is used to monitor and replay the spatiotemporal data, and supports commanders at different levels to obtain situation information and issue instructions according to their permissions. The collaborative measuring module 104 processes the spatiotemporal data of a single vehicle and a cluster to calculate the time when a vehicle passes through the virtual baseline and the real-time distance between vehicles, and outputs a spatiotemporal collaborative error index. The intelligent evaluation engine module 103 is used to embed a quantitative assessment algorithm library, analyze spatiotemporal data and spatiotemporal collaborative error indicators, and automatically generate multi-dimensional quantitative assessment reports. The electronic fence early warning module 105 is used to set virtual boundaries in the three-dimensional virtual training scene and perform boundary crossing detection in real time based on spatiotemporal data, and issue an alarm when a boundary crossing occurs. The panoramic digital twin module 101, the multi-level command center module 102, the intelligent evaluation engine module 103, the collaborative measuring module 104, and the electronic fence early warning module 105 form a tightly coupled closed-loop workflow through the data bus 106.

[0033] Specifically, the panoramic digital twin module 101 has a 3D playback function, allowing users to freely select any historical time point as the starting point and play the video at any speed. During playback, users can freely switch between top-down, follow-up, and vehicle-mounted perspectives for observation and analysis. The panoramic digital twin module 101 is developed based on the Unity3D engine. It imports oblique photogrammetry real-world 3D models and high-precision maps to construct a virtual environment consistent with the actual training ground. This module subscribes to the positioning stream on the data bus 106 in real time, parses it, and drives the corresponding vehicle model in the virtual scene to perform synchronized and smooth movement. Commanders can observe the entire scene from any angle via WebGL or a native client. All data is stored in a time series. When a user initiates a playback request by selecting a historical time period via the timeline, the module reads the data sequence for that time period from storage and re-drives the 3D scene to reconstruct it, supporting pause, speed adjustment, and free switching of perspectives, realizing "spatiotemporal retracing" of the training process.

[0034] Specifically, the multi-level command center module 102 adopts a role-based access control model, with predefined roles including commander-in-chief, squad commander, and instructor. The commander-in-chief role has global data viewing and command issuance permissions, the squad commander role can only view and command the squad to which they belong, and the instructor role can only view detailed data and evaluation reports of a specific vehicle.

[0035] Specifically, the built-in assessment rule library of the intelligent evaluation engine module 103 includes: single-vehicle route preservation algorithm and formation alignment consistency algorithm.

[0036] The algorithm for single-vehicle route preservation is as follows: S100: The preset ideal route is a smooth baseline composed of a series of ordered coordinate points.

[0037] S200: For the actual trajectory point sequence of the vehicle during the assessment period, calculate the shortest Euclidean distance to the baseline for each trajectory point. .

[0038] S300: All within the statistical period Standard deviation With the maximum value .

[0039] S400: Route Preservation Score Calculated by the following formula: Where A is the benchmark full score, B is the first weighting coefficient, and C is the second weighting coefficient.

[0040] Specifically, the process of the square formation alignment consensus algorithm is as follows: S110: During the assessment period, samples are taken at a fixed frequency, and the position sequence of the vehicles in the formation is extracted in the direction perpendicular to the direction of travel.

[0041] S120: Construct a baseline sequence, specifying the sequence of the baseline vehicle as the baseline sequence; S130: DTW distance calculation aims to quantify the consistency of the motion trajectories of two vehicles perpendicular to the direction of travel. It uses the Dynamic Time Warping (DTW) algorithm to eliminate the impact of slight time asynchrony caused by operations such as starting and changing speed on spatial position comparison.

[0042] Specifically, the DTW distance calculation process is as follows: S131: Input data, consisting of two vehicle lateral position sequences to be compared: The sequence of the benchmark vehicle The sequence of vehicle j to be evaluated ; in, Where N is the length of the baseline vehicle sequence, and N is the length of the vehicle sequence to be evaluated. for The sequence elements of the time reference vehicle, for The sequence elements of the vehicle to be evaluated at any given time. and They may be equal, or they may differ slightly due to minor differences in the start and end points of data collection.

[0043] S132: Calculate the local distance (usually Euclidean distance) between all point pairs in two sequences, forming a... Local distance matrix The element d(m, n) in the m-th row and n-th column of matrix D is calculated as follows: in, As an absolute value, d(m,n) reflects the absolute difference in lateral position between the reference vehicle at time m and vehicle j at time n, without considering the time correspondence.

[0044] S133: Find regular paths and calculate cumulative distances using dynamic programming; The core of the DTW algorithm is to find a regular path from the bottom left corner (1,1) to the top right corner (M,N) of the matrix. This minimizes the sum of all local distances along this path. This path defines the optimal nonlinear alignment of the two sequences on the time axis.

[0045] The minimum-cost path is efficiently found by calculating the cumulative distance matrix γ using dynamic programming. The cumulative distance matrix represents the minimum cumulative distance among all possible paths from the starting point (1,1) to the point (m,n). Its recursive calculation formula is as follows: in, This is the cumulative distance matrix from above. This is the cumulative distance matrix from the left. This is the cumulative distance matrix from the diagonal. Let be the element in the m-th row and n-th column of matrix γ.

[0046] Boundary conditions are Furthermore, for the first row and the first column, accumulation can only be performed from the only possible direction.

[0047] S134: Determine DTW distance After the dynamic programming calculation is completed, the cumulative distance matrix is ​​obtained. The value of the bottom right element is the sequence. and DTW distance between .

[0048] S140: Calculate the consistency score, specifically the alignment consistency score for the formation. Calculated by the following formula: Among them, W j Let τ be the weight of vehicle j, and τ be the normalization parameter. The closer the score is to 1, the better the homogeneity.

[0049] The collaborative measuring module 104 is the core algorithm microservice of this invention. Its workflow is as follows: First, a virtual baseline is established in the digital twin space. The module polls the latest positioning point of each vehicle on the data bus 106 at an extremely high frequency. Each positioning point contains a precise location and a UTC timestamp. When the leading edge contour of a vehicle is detected to intersect the line, the module uses linear interpolation to accurately calculate the moment of intersection between two adjacent sampling points, achieving sub-millisecond accuracy. The time when each vehicle in the formation passes the line is recorded, and the range of these times is the "time coordination error" of the formation passing the line at that moment. Simultaneously, at any given time, the module calculates the distance between adjacent vehicles based on their real-time coordinates and compares it with the standard vehicle distance to obtain the "distance coordination error."

[0050] The core of this module lies in using high-frequency, high-precision spatiotemporal data to achieve sub-millisecond-level event capture and centimeter-level spatial measurement.

[0051] The algorithm for capturing the precise moment when the vehicle "passes" the virtual baseline is as follows: S201: Obtain a series of high-frequency positioning points for vehicle k, sorted by timestamps with a timestamp precision in the microsecond range; obtain another sequence of positioning points starting from the starting point. and end point Defined virtual baseline segment L.

[0052] S202: Contour Modeling and Interpolation. Linear interpolation is performed between adjacent positioning points of a high-frequency positioning point to construct a continuous predicted trajectory segment. .

[0053] S203: Judgment Does it intersect line segment L? By solving the parametric equations of the two line segments, if an intersection is detected, let the parameter of the intersection point on the trajectory segment be λ (0 < λ < 1), then the precise time when vehicle k actually passes line L is determined. Calculated using linear interpolation: in, The moment before the intersection, The time after the intersection.

[0054] This calculation improves the accuracy of determining the "pass" time from the positioning sampling interval (e.g., 10 milliseconds) to the sub-millisecond level.

[0055] The algorithm flow for real-time distance calculation and collaborative error between vehicles is as follows: S210: At any time The planar coordinates of vehicle p are obtained by filtering the coordinates of vehicle p within the formation. and Its positioning error is no more than 2 cm within the 95% confidence interval.

[0056] S220: Calculation time Euclidean distance : .

[0057] S230: Distance Coordination Error : in, This is the standard vehicle distance.

[0058] S240: Timing Coordination Error : in, It is a function with maximum value. It is a minimum value function.

[0059] The electronic fence early warning module 105 provides a graphical tool for administrators to draw polygonal fences F on the digital twin map. This module subscribes to vehicle location streams in real time and uses geometric library functions such as ray casting to quickly determine the spatial relationship between V and F for each newly arrived location point V. Once it is determined that V has entered the "restricted area" or left the "restricted area," the module immediately (response time < 50 milliseconds) issues an alarm event on the data bus 106. This event is simultaneously consumed by the digital twin module and the command center module: the former turns the corresponding vehicle model red and flashes it; the latter pops up a warning box on the corresponding commander's screen and plays a prompt sound; simultaneously, an alarm record is written to the database and pushed to the relevant personnel's mobile terminals.

[0060] The detection algorithm of the electronic fence early warning module 105 is based on the ray-mapping method to determine the positional relationship between points and polygons. S31: Treat the user-defined electronic fence as a system with U vertices. A closed polygon formed by connecting them sequentially.

[0061] S32: Draw a horizontal ray R from the vehicle positioning point V to the right (or left).

[0062] S33: Calculate the number of intersections between ray R and all edges of the polygon. Traverse each edge and determine whether the ray intersects that edge.

[0063] S34: If the number of intersection points is odd, then point V is inside the polygon (no-entry zone alarm); if it is even, then it is outside. The logic is reversed for the "no-exit zone".

[0064] The algorithm has a time complexity of O(U), typically less than 10, and a single calculation can be completed in microseconds. Combined with data stream processing, the system guarantees that the total latency from detecting an out-of-bounds error to triggering an alarm is less than 50 milliseconds.

[0065] like Figure 2 As shown, the present invention also provides a vehicle training and assessment method, comprising: S1: Install position sensors on the training vehicle to collect its position information; S2: The location information is sent to the data bus. The data bus distributes the location information to the panoramic digital twin module, the multi-level command center module, the intelligent evaluation engine module, the collaborative measuring scale module, and the electronic fence early warning module. The panoramic digital twin module distributes the spatiotemporal data to the multi-level command center module, the collaborative measuring scale module, the intelligent evaluation engine module, and the electronic fence early warning module. The collaborative measuring scale module sends the spatiotemporal coordination error index to the intelligent evaluation engine module. The multi-level command center module issues instructions, the electronic fence early warning module provides real-time early warning, and the intelligent evaluation engine module generates the vehicle's training results.

[0066] The multi-dimensional quantitative assessment report refers to the training results of the vehicle.

[0067] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a vehicle training and assessment method, which includes: S1: Install position sensors on the training vehicle to collect its position information; S2: The location information is sent to the data bus. The data bus distributes the location information to the panoramic digital twin module, the multi-level command center module, the intelligent evaluation engine module, the collaborative measuring scale module, and the electronic fence early warning module. The panoramic digital twin module distributes the spatiotemporal data to the multi-level command center module, the collaborative measuring scale module, the intelligent evaluation engine module, and the electronic fence early warning module. The collaborative measuring scale module sends the spatiotemporal coordination error index to the intelligent evaluation engine module. The multi-level command center module issues instructions, the electronic fence early warning module provides real-time early warning, and the intelligent evaluation engine module generates the vehicle's training results.

[0068] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0069] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform one of the vehicle training and assessment methods provided above, the method comprising: S1: Install position sensors on the training vehicle to collect its position information; S2: The location information is sent to the data bus. The data bus distributes the location information to the panoramic digital twin module, the multi-level command center module, the intelligent evaluation engine module, the collaborative measuring scale module, and the electronic fence early warning module. The panoramic digital twin module distributes the spatiotemporal data to the multi-level command center module, the collaborative measuring scale module, the intelligent evaluation engine module, and the electronic fence early warning module. The collaborative measuring scale module sends the spatiotemporal coordination error index to the intelligent evaluation engine module. The multi-level command center module issues instructions, the electronic fence early warning module provides real-time early warning, and the intelligent evaluation engine module generates the vehicle's training results.

[0070] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0071] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0073] It should be noted that the embodiments of this disclosure can be implemented using hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a programmable memory or a data carrier such as an optical or electronic signal carrier.

[0074] Furthermore, although the operation of the methods of this disclosure is described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Rather, the steps depicted in the flowcharts may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps. It should also be noted that the features and functions of two or more devices according to this disclosure may be embodied in one device. Conversely, the features and functions of one device described above may be further divided and embodied by multiple devices.

[0075] While this disclosure has been described with reference to several specific embodiments, it should be understood that this disclosure is not limited to the specific embodiments disclosed. This disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A vehicle training and assessment system, characterized in that, It includes the following modules: panoramic digital twin module, multi-level command center module, intelligent assessment engine module, collaborative measurement module, electronic fence early warning module, and data bus: The panoramic digital twin module is used to construct a three-dimensional virtual training scene, providing spatiotemporal data for individual vehicles and clusters; The multi-level command center module is used to monitor and replay the spatiotemporal data, and supports personnel at different levels to obtain situation information and issue instructions according to their permissions; The collaborative measuring module is used to calculate the time when a vehicle passes through the virtual baseline and the real-time distance between vehicles by processing the spatiotemporal data of a single vehicle and a cluster, and outputs a spatiotemporal collaborative error index. The intelligent evaluation engine module is used to embed a quantitative assessment algorithm library, analyze spatiotemporal data and spatiotemporal coordination error indicators, and automatically generate multi-dimensional quantitative assessment reports. The electronic fence early warning module is used to set virtual boundaries in a three-dimensional virtual training scene and perform boundary crossing detection in real time based on spatiotemporal data, and issue an alarm when a boundary crossing occurs. The panoramic digital twin module, multi-level command center module, intelligent evaluation engine module, collaborative measuring module, and electronic fence early warning module form a tightly coupled closed-loop workflow through a data bus.

2. The vehicle training and assessment system according to claim 1, characterized in that, The panoramic digital twin module has a three-dimensional playback function, which allows users to freely select any historical time point as the starting point and play the video at any speed. It also allows users to freely switch between top-down, follow-up, and vehicle-mounted perspectives for observation and analysis during playback.

3. The vehicle training and assessment system according to claim 1, characterized in that, The multi-level command center module adopts a role-based access control model. Predefined roles include the overall commander role, the squad commander role, and the instructor role. The overall commander role has global data viewing and command issuance permissions, the squad commander role can only view and command the squad under its command, and the instructor role can only view detailed data and evaluation reports of the designated individual vehicle.

4. The vehicle training and assessment system according to claim 1, characterized in that, The intelligent evaluation engine module has a built-in assessment rule library including: a single-vehicle route preservation algorithm and a formation alignment consistency algorithm. The steps of the single-vehicle route preservation algorithm are as follows: S100: Set the preset ideal route as a smooth baseline composed of a series of ordered coordinate points; S200: For the actual trajectory point sequence of the vehicle during the assessment period, calculate the shortest Euclidean distance from each trajectory point to the baseline. ; S300: All within the statistical period Standard deviation With the maximum value ; S400: Route Preservation Score Calculated by the following formula: Where A is the benchmark full score, B is the first weighting coefficient, and C is the second weighting coefficient; The steps of the formation alignment and consensus algorithm are as follows: S110: During the assessment period, samples are taken at a fixed frequency. For vehicles in the formation, their position sequence perpendicular to the direction of travel is extracted. S120: Construct a baseline sequence, specifying the sequence of the baseline vehicle as the baseline sequence; S130: Calculate DTW distance ; S140: Calculate the consistency score : Among them, W j Let τ be the weight of vehicle j, and τ be the normalization parameter.

5. A vehicle training and assessment system according to claim 1, characterized in that, The measurement process of the collaborative measuring scale module is as follows: Receive positioning data streams from BeiDou or GNSS high-precision positioning terminals with a time synchronization accuracy of less than or equal to 1 millisecond; A geometric intersection detection and linear interpolation algorithm is used to determine the precise UTC time when the vehicle contour model intersects with the virtual baseline; Based on the vehicle coordinates under the same spatiotemporal reference, the Euclidean distance between vehicles is calculated in real time, and the Euclidean distance between vehicles is a spatiotemporal coordination error index.

6. The vehicle training and assessment system according to claim 1, characterized in that, After detecting a conflict between a vehicle's location and the virtual boundary, the electronic fence early warning module triggers the following actions: visually highlighting the vehicle in the command interface and digital twin scenario, playing a preset warning audio, generating a structured log containing time-vehicle-event type information, and pushing it to relevant personnel.

7. The vehicle training and assessment system according to claim 1, characterized in that, The data bus is based on either gRPC or high-speed messaging, and the data exchange latency between modules is less than 10 milliseconds.

8. A vehicle training and assessment method for implementing a vehicle training and assessment system as described in any one of claims 1 to 7, characterized in that, include: S1: Install position sensors on the training vehicle to collect its position information; S2: The location information is sent to the data bus. The data bus distributes the location information to the panoramic digital twin module, the multi-level command center module, the intelligent evaluation engine module, the collaborative measuring scale module, and the electronic fence early warning module. The panoramic digital twin module distributes the spatiotemporal data to the multi-level command center module, the collaborative measuring scale module, the intelligent evaluation engine module, and the electronic fence early warning module. The collaborative measuring scale module sends the spatiotemporal coordination error index to the intelligent evaluation engine module. The multi-level command center module issues instructions, the electronic fence early warning module provides real-time early warning, and the intelligent evaluation engine module generates the vehicle's training results.

9. An electronic device comprising a processor, a communication interface, a memory, and a communication bus, characterized in that, When the processor executes the computer program, it implements the steps of the vehicle training and assessment method as described in claim 8.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the vehicle training and assessment method as described in claim 8.

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