Vehicle-mounted TBOX test method

Abstract

The invention discloses a vehicle-mounted TBOX test method, and relates to the technical field of intelligent network connection automobile communication test, and the method comprises the following steps: S1, carrying out state extraction through employing multi-scene signal data; s2, performing integrity analysis by using a signal intensity sequence; s3, performing channel quality calculation by using the data recovery capability sequence; s4, performing interference analysis by using the stability measurement sequence; s5, performing anti-interference analysis by using disturbance characteristic distribution; and S6, performing stability evaluation by using an interference response structure. By setting the anti-interference capability model based on the channel disturbance intensity, the anti-interference capability of the TBOX can be accurately quantified in a dynamic wireless communication environment, and compared with an existing fixed signal-to-noise ratio evaluation method, the method introduces a signal interference characteristic matrix and combines the real-time matching relation between the signal intensity and interference factors, so that the anti-interference capability of the TBOX can be accurately quantified. And finer interference influence evaluation is provided, so that the TBOX can maintain high communication stability in a changeable environment.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent connected vehicle communication testing, and specifically provides a vehicle-mounted TBOX testing method. Background Art

[0002] As an important communication unit of intelligent connected vehicles, the vehicle-mounted TBOX is responsible for functions such as remote data interaction between the vehicle and the cloud, OTA (Over-the-Air) upgrade, remote diagnosis, and emergency call. With the rapid development of intelligent connected technology, the TBOX needs to maintain a stable signal connection in a complex wireless communication environment and ensure the reliability of data transmission under various interference conditions. However, the existing TBOX testing methods have certain limitations in complex interference environments and are difficult to comprehensively evaluate the anti-interference ability and channel adaptability of the TBOX. 1. In the prior art, the anti-interference ability of the TBOX is usually evaluated by relying on fixed signal strength thresholds or bit error rate thresholds, which cannot accurately reflect the changes in signal quality in complex interference environments; 2. In the prior art, the TBOX testing methods usually adopt fixed sampling intervals and fixed test parameters and cannot adapt to the dynamic changes under different channel states. In a complex network environment, the channel quality may change over time and space, and the fixed testing methods are difficult to adapt to these fluctuations, resulting in insufficient representativeness and accuracy of the test results and being unable to truly reflect the adaptability of the TBOX in complex environments. Summary of the Invention

[0003] Aiming at the deficiencies of the prior art, the present invention provides a vehicle-mounted TBOX testing method to solve the problems proposed in the above background art.

[0004] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a vehicle-mounted TBOX testing method, including the following steps: S1. Use multi-scenario signal data for state extraction to obtain a time-varying signal strength sequence; S2. Use the signal strength sequence for integrity analysis to obtain a data recovery ability sequence; S3. Use the data recovery ability sequence for channel quality calculation to obtain a stability metric sequence; S4. Use the stability metric sequence for interference analysis to obtain a perturbation feature distribution; S5. Use the perturbation feature distribution for anti-interference analysis to obtain an interference response structure; S6. Use the interference response structure for stability evaluation to obtain a channel adaptation test result.

[0005] Further optimize this technical solution. In step S1, a signal strength distribution model is used to depict the change of the signal over time: ; where, is the received signal strength at time ; is the maximum transmission power of the signal source; is the distance between the TBOX and the base station at time ; is the signal attenuation coefficient; is the environmental noise interference component.

[0006] Further optimize this technical solution. In step S2, the data integrity of the TBOX is quantified by the data recovery ability factor : ; where, is the data recovery ability at time , with a range of ; is the number of lost data packets at time ; is the total number of data packets transmitted at time .

[0007] Further optimize this technical solution. In step S3, an anti-interference evaluation model based on dynamic channel stability is used to describe the influence of channel state fluctuations on the data transmission stability of the TBOX: ; where, : Channel stability, reflecting the anti-interference ability of the TBOX at different time periods; : Test duration; : During the test duration ; : At each time point the channel quality factor.

[0008] Further optimize this technical solution. The calculation method of the channel quality factor in step S3 is as follows: ; where, represents time Signal strength at a moment Indicates the data recovery ability of the TBOX, that is, the ratio of successful recovery after packet loss, and the value range ; Indicates the noise interference intensity.

[0009] To further optimize this technical solution, in step S4, an anti-interference ability model based on the channel perturbation intensity is used to quantify the signal recovery ability of the TBOX under different interference conditions, and its formula model is as follows: ; Among them, Is the channel perturbation intensity factor, which measures the anti-interference ability of the TBOX; Is the test duration; Time Interference intensity at a moment.

[0010] To further optimize this technical solution, in step S4 Interference intensity at a moment The calculation method is as follows: ; Among them, Is the environmental noise interference obtained in step S3, calculated through Calculation, Is the total number of interference sources; Is the signal strength obtained in step S1, calculated through Calculation, Is the total number of signal sources.

[0011] To further optimize this technical solution, the anti-interference ability model in step S4 includes in the anti-interference ability evaluation application: Calculate the interference intensity : Use the data of steps S1 and S3 to calculate the interference influence degree in different environments; Calculate the channel perturbation intensity factor : Solve through integration To obtain the anti-interference ability of the TBOX in different scenarios; Judge the anti-interference level of the TBOX: Set the anti-interference threshold , Compare With The difference; Provide data support for subsequent steps: The calculated Transfer to step S5 for analyzing the system response ability of the TBOX in a complex environment.

[0012] To further optimize this technical solution, in step S5, a dynamic response time prediction model based on the channel perturbation intensity is adopted to characterize the system response characteristics of the TBOX in a complex network environment: ; Wherein, is the system response time of the TBOX; is the influence coefficient of the channel state on the system response time; is the dynamic data load factor: , where is the test duration is the number of concurrent requests within it, is the transmission time of each request; is the influence coefficient of the concurrent load on the system response time; is the basic delay of the TBOX.

[0013] To further optimize this technical solution, when the dynamic response time prediction model in step S5 is used, it includes: Calculating the system response time : Based on the obtained in step S4 and the current system load data , calculate the actual response time of the TBOX; Analyzing the real-time performance of the TBOX: Set a threshold and compare it with the response time ; Providing a test basis for step S6: Transmit the calculated to step S6 for optimizing the transmission mechanism of the TBOX during OTA upgrade.

[0014] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program instructions are executed by the processor, the steps of a vehicle-mounted TBOX testing method as described in the first aspect of the present invention are implemented.

[0015] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program instructions are executed by the processor, the steps of a vehicle-mounted TBOX testing method as described in the first aspect of the present invention are implemented.

[0016] Compared with the prior art, the present invention provides a vehicle-mounted TBOX testing method, belonging to the technical field of fault prediction and health management, and having the following beneficial effects: In this vehicle-mounted TBOX testing method, by setting an anti-interference ability model based on channel perturbation intensity, the anti-interference ability of the TBOX can be accurately quantified in a dynamic wireless communication environment. Compared with the existing fixed signal-to-noise ratio evaluation method, this method introduces a signal interference characteristic matrix, combines the real-time matching relationship between signal strength and interference factors, and provides a more refined interference impact evaluation. This system can dynamically adjust the test standard to maintain a high communication stability of the TBOX in a changing environment. At the same time, the method combines a dynamic response time prediction model of channel perturbation intensity, and by calculating channel state information and data load factor, dynamically optimizes test parameters and strategies. This innovative strategy overcomes the limitation in traditional testing methods that cannot adapt to channel changes, makes the testing closer to actual applications, and can effectively reflect the true performance of the TBOX in a complex interference environment. Through this method, the adaptability, stability, and response speed of the TBOX have been significantly improved, and it is applicable to various complex vehicle-mounted communication environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is a schematic structural diagram of a vehicle-mounted TBOX testing method proposed by the present invention; Figure 2 It is a schematic diagram of the anti-interference ability evaluation process of a vehicle-mounted TBOX testing method proposed by the present invention; Figure 3 It is a schematic diagram of the system response speed and delay testing process of a vehicle-mounted TBOX testing method proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0020] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0021] Secondly, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is separate or selectively exclusive of other embodiments.

[0022] Embodiment 1: Referring to Figures 1 to 3 , this is the first embodiment of the present invention, which provides a vehicle-mounted TBOX test method, including the following steps: S1. Use multi-scenario signal data for state extraction to obtain a time-varying signal strength sequence; In step S1, a signal strength distribution model is used to characterize the change of the signal over time, so as to obtain a time-varying signal strength sequence: ; Wherein, is the received signal strength at time ; is the maximum transmit power of the signal source; is the distance between the TBOX and the base station at time ; is the signal attenuation coefficient, which is determined by the environment; is the environmental noise interference component;.

[0023] As the starting point of the entire test process, step S1 quantifies the communication quality in different environments by constructing a signal strength distribution model, providing data support for the subsequent calculation of the dynamic channel stability.

[0024] S2. Use the signal strength sequence for integrity analysis to obtain a data recovery ability sequence; In step S2, the data integrity of the TBOX is quantified by the data recovery ability factor : ; Wherein, is the data recovery ability at time , with a range of ; is the number of lost data packets at time ; is the total number of data packets sent at time .

[0025] Step S2 records under different channel states (good, medium, poor) and , calculates using the formula and plots a curve to analyze the data recovery ability of the TBOX in different environments, and transmits the calculated to step S3 for evaluating the channel quality.

[0026] S3. Use the data recovery ability sequence to calculate the channel quality and obtain the stability metric sequence; In step S3, the anti-interference evaluation model based on the dynamic channel stability expresses the influence of channel state fluctuations on the data transmission stability of the TBOX: ; where : Channel stability, reflecting the anti-interference ability of the TBOX at different time periods; : Test duration; : During the test duration the number of sampling times; : At each time point the channel quality factor; The calculation method of the channel quality factor in step S3 is as follows: ; where represents the signal strength at time ; represents the data recovery ability of the TBOX, that is, the ratio of successful recovery after packet loss, and the value range is ; represents the noise interference intensity.

[0027] The channel stability of the TBOX is determined by multiple factors, including the signal strength , the data recovery ability and the noise interference , and is calculated through the anti-interference evaluation model for analyzing the anti-interference performance of the TBOX in step S4.

[0028] S4. Use the stability metric sequence to perform interference analysis and obtain the perturbation characteristic distribution; In step S4, the anti-interference ability model based on the channel perturbation intensity is used to quantify the signal recovery ability of the TBOX under different interference conditions, and its formula model is as follows: ; Among them, is the channel disturbance intensity factor, which measures the anti-interference ability of the TBOX. The larger the value, the stronger the anti-interference ability. is the test duration. Time The interference intensity at the moment. In the said step S4 The interference intensity at the moment The calculation method is as follows: ; Among them, is the environmental noise interference obtained in step S3, which is calculated through Calculation, is the total number of interference sources; is the signal intensity obtained in step S1, which is calculated through Calculation, is the total number of signal sources; The anti-interference ability model in the said step S4 in the anti-interference ability evaluation application includes: Calculate the interference intensity : Use the data of steps S1 and S3 to calculate the interference influence degree in different environments; Calculate the channel disturbance intensity factor : Solve through integration to obtain the anti-interference ability of the TBOX in different scenarios; Judge the anti-interference level of the TBOX: Set the anti-interference threshold , compare with The difference; Provide data support for the subsequent steps: Transmit the calculated to step S5 for analyzing the system response ability of the TBOX in a complex environment; This step introduces an anti-interference ability model based on the channel disturbance intensity to quantify the adaptability of the TBOX in a complex interference environment. The existing technology mainly relies on a fixed interference threshold for anti-interference evaluation and is difficult to dynamically adapt to different environments. This method calculates the anti-interference ability of the TBOX under different channel conditions through the channel disturbance intensity factor , combines the dynamic matching relationship between the signal intensity and the interference characteristics to construct a mathematical model, so that the anti-interference evaluation is no longer limited to a static threshold, but can adjust the evaluation criteria according to the real-time signal environment, thereby improving the communication stability of the TBOX in a changing environment.

[0029] S5. Perform anti-interference analysis using the perturbed feature distribution to obtain the interference response structure; In step S5, a dynamic response time prediction model based on the channel perturbation intensity is adopted to characterize the system response characteristics of the TBOX in a complex network environment: ; wherein, is the system response time of the TBOX; is the influence coefficient of the channel state on the system response time; is the dynamic data load factor: , where is the test duration is the number of concurrent requests within is the transmission time of each request; is the influence coefficient of the concurrent load on the system response time; is the base delay of the TBOX; When the dynamic response time prediction model in step S5 is used, it includes: Calculate the system response time : Based on the obtained in step S4 and the current system load data , calculate the actual response time of the TBOX; Analyze the real-time performance of the TBOX: Set a threshold and compare it with the response time ; Provide a test basis for step S6: Transmit the calculated to step S6 for optimizing the transmission mechanism of the TBOX during OTA upgrade; This step proposes a dynamic response time prediction model based on the channel perturbation intensity to optimize the system response ability of the TBOX. Existing methods usually use a fixed network delay threshold to measure the performance of the TBOX, but it is difficult to adapt to the dynamic changes in a complex environment. This method is based on the channel perturbation intensity factor obtained in step S4, combined with the data load factor , to establish a prediction model for the system response time , which can adjust the response parameters of the TBOX in real time to enable it to maintain a stable data transmission ability under different channel states. Compared with traditional methods, this method improves the adaptability of the TBOX in a dynamic environment and reduces the abnormal delay caused by network fluctuations.

[0030] S6. Use the interference response structure to conduct stability evaluation and obtain the channel adaptation test results; In step S6, an adaptive OTA upgrade model based on dynamic channel evaluation is adopted to improve the OTA upgrade success rate and efficiency of the TBOX: By setting the OTA adaptive rate factor Adjust the OTA upgrade rate of the TBOX: ; Among them, is the OTA adaptive rate factor; The OTA upgrade rate of the TBOX depends on : ; Among them, is the current OTA upgrade rate; is the maximum available transmission rate of the TBOX; The adaptive OTA upgrade model includes the following when in use: Calculate the OTA adaptive rate factor : Combine the data in steps S4 and S5 to calculate the adaptive adjustment factor in the current network environment; Adjust the OTA upgrade rate of the TBOX: According to the calculation result, dynamically adjust the OTA data transmission rate to ensure upgrade stability; Optimize the upgrade process: If is lower than the set threshold , strategies such as segmented transmission or data compression can be adopted to improve the upgrade efficiency; In step S6, an adaptive rate factor based on the perturbation intensity factor and the system response time is constructed to form an interference response structure, and the OTA transmission rate is calculated accordingly. This rate index is used as the core basis for evaluating the channel adaptation ability. Compared with the traditional test method that only makes judgments based on the static transmission rate, this step not only comprehensively considers channel perturbation and system response, but also quantifies and outputs the channel adaptation test results through comparison with the preset threshold , realizing a complete verification of the stability and reliability of the TBOX in a dynamic channel environment and forming a key closed loop of the test process; This step introduces an adaptive OTA upgrade model based on dynamic channel evaluation to replace the existing fixed-rate OTA transmission strategy. Traditional OTA upgrade methods usually transmit data at a fixed rate and cannot adaptively adjust according to different channel conditions, resulting in transmission failures or long times in high-interference or low-signal environments. This method uses the channel perturbation intensity factor obtained in step S4 and the system response time obtained in step S5 , calculate the OTA upgrade rate factor , and dynamically adjust the OTA upgrade rate based on this factor , so that the TBOX can optimize the upgrade strategy according to the real-time network environment, improve the upgrade success rate and efficiency, and at the same time reduce the waste of network resources.

[0031] Embodiment 2: This embodiment also provides a computer device, which is applicable to a situation of a vehicle-mounted TBOX test method, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a vehicle-mounted TBOX test method as proposed in the above embodiment.

[0032] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements a vehicle-mounted TBOX test method as proposed in the above embodiment.

[0033] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a button, a trackball or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad or a mouse, etc.

[0034] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0035] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0036] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROMs). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0037] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0038] 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 preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A vehicle-mounted TBOX testing method, characterized in that, Including the following steps: S1. Use multi-scenario signal data for state extraction to obtain a time-varying signal intensity sequence; S2. Use the signal intensity sequence for integrity analysis to obtain a data recovery ability sequence; S3. Use the data recovery ability sequence for channel quality calculation to obtain a stability metric sequence; S4. Use the stability metric sequence for interference profiling to obtain a perturbation feature distribution; S5. Use the perturbation feature distribution for anti-interference analysis to obtain an interference response structure; S6. Use the interference response structure for stability evaluation to obtain the channel adaptation test result.

2. The vehicle-mounted TBOX test method according to claim 1, characterized in that, In step S1, a signal intensity distribution model is used to describe the change of the signal over time: ； Among them, is the time The received signal strength at the moment; is the maximum transmission power of the signal source; is the time distance between the TBOX and the base station at time T is the signal attenuation coefficient; is the environmental noise interference component.

3. The vehicle-mounted TBOX test method according to claim 1, characterized in that, The data integrity of the TBOX in step S2 is quantified by the data recovery ability factor as follows: ； wherein, is the data recovery ability at time with a range ; is the time the number of data packets lost at the moment; is the time The total number of data packets sent at the moment 4. A vehicle-mounted TBOX test method according to claim 1, characterized in that In step S3, through an anti-interference evaluation model based on dynamic channel stability, the data transmission stability of the TBOX due to channel state fluctuations is expressed: ； Among them, : Channel stability, which reflects the anti-interference ability of the TBOX in different time periods; : Test duration; : The number of samplings within the test duration ; : Channel quality factor at each time point underneath.

5. A vehicle-mounted TBOX test method according to claim 4, characterized in that, The calculation method of the channel quality factor in step S3 is as follows: ； Among them, represents the signal strength at a moment in time; Indicates the data recovery ability of the TBOX, that is, the ratio of successful recovery after packet loss, with a value range ; Indicates the noise interference intensity.

6. A vehicle-mounted TBOX test method according to claim 1, characterized in that, In step S4, an anti-interference ability model based on channel perturbation intensity is used to quantify the signal recovery ability of the TBOX under different interference conditions, and its formula model is as follows: ； Among them, is the channel disturbance intensity factor, which measures the anti-interference ability of the TBOX; For the test duration; Time Interference intensity at a moment.

7. The vehicle-mounted TBOX test method according to claim 6, wherein In the said step S4 interference intensity at a moment The calculation method is as follows: ； Among them, is the environmental noise interference obtained in step S3, and through calculation, is the total number of interference sources; is the signal strength obtained in step S1, and is calculated by calculation is the total number of signal sources.

8. The vehicle-mounted TBOX test method according to claim 6, characterized in that, The anti-interference ability model in step S4 includes in the anti-interference ability evaluation application: Calculate the interference intensity : Use the data from steps S1 and S3 to calculate the degree of interference influence in different environments; Calculate the channel disturbance intensity factor : Solve by integration , and obtain the anti-interference ability of the TBOX in different scenarios; Judge the anti-interference level of the TBOX: Set the anti-interference threshold , and compare with for the difference; Provide data support for subsequent steps: Transfer the calculated to step S5 for analyzing the system response ability of TBOX in complex environments.

9. A vehicle-mounted TBOX testing method according to claim 5, characterized in that In step S5, a dynamic response time prediction model based on channel perturbation intensity is used to describe the system response characteristics of the TBOX in a complex network environment: ； Among them, is the system response time of the TBOX; is the influence coefficient of the channel state on the system response time; is the dynamic data load factor: , where is the test duration is the number of concurrent requests within it, is the transmission time for each request; is the influence coefficient of concurrent load on the system response time; It is the basic delay of the TBOX.

10. A vehicle-mounted TBOX testing method according to claim 5, characterized in that, The dynamic response time prediction model in step S5 includes when in use: Calculation of the system response time : Based on what is obtained in step S4 and the current system load data , calculate the actual response time of the TBOX; Analyze the real-time performance of the TBOX: Set the threshold and compare it with the response time for comparison; Provide a test basis for step S6: Transmit the calculated to step S6 for optimizing the transmission mechanism of the TBOX in OTA upgrade.

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