A Json-based MPU calibration system and calibration method

Abstract

The present invention discloses a Json-based MPU calibration system, including a host computer module, an embedded controller, a power module and an Ethernet transmission medium. The host computer module is used to read the calibration parameters in the code and generate a Json file. The embedded controller is used to identify the Json file, parse and read the variables to be modified in the Json file, and achieve the effect of changing the calibration amount. The power module is used to power the host computer module and the embedded controller, and the Ethernet transmission medium is used to connect the host computer module and the embedded controller. The present invention also discloses a method for marking using the Json-based MPU calibration system. The present invention uses the Json file to realize the parameter calibration of the algorithm in the MPU chip with high computing power; at the same time, the parameter calibration in the method directly permanently modifies the calibration value in the software, which is very efficient and convenient.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a Json-based MPU calibration system and a calibration method thereof. Background Art

[0002] The automotive industry is in a period of rapid development, with an increasing number of intelligent and personalized functions. Especially as autonomous driving is gradually moving towards commercial applications, the requirements for independent algorithm development are getting higher and higher, and the computing power requirements for controllers are getting higher and higher. The controller architecture has also gradually evolved from the previous single MCU main chip architecture to multiple MCU main chips, MCU+MPU main chip architecture, etc., that is, a domain controller with "control chip + computing chip" as the main architecture.

[0003] In the process of algorithm development, there is a lot of calibration work. Traditional MCU chips are mostly based on A2L files and mapping files, and some automatic generation methods have also been formed. However, with the increasing demand for MPU calibration, since MPUs are generally based on operating systems such as Linux / QNX / Vxworks, their programs run in virtual space, and variables are implemented using virtual addresses, the general A2L+Map calibration method cannot be implemented.

[0004] Therefore, as the proportion of independent algorithm development increases, the application scenarios of MPU increase, and there is an urgent need to find a new system and method to realize the calibration of MPU. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is: how to provide a system that can identify and extract the parameters to be calibrated in the algorithm file, calibrate and modify the relevant data, and realize the calibration of the MPU.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A Json-based MPU calibration system comprises a host computer module, an embedded controller, a power module and an Ethernet transmission medium. The host computer module is used to read calibration parameters in a code and generate a Json file. The embedded controller is used to identify the Json file, parse and read the calibration parameter information in the Json file, so as to achieve the effect of changing the calibration amount. The power module is used to supply power to the host computer module and the embedded controller. The Ethernet transmission medium is used to connect the host computer module and the embedded controller.

[0008] The present invention also discloses a method for calibration using the above-mentioned Json-based MPU calibration system, which uses a host computer module to read the code, reflects the signal to be calibrated in the form of calibration parameters, generates a Json file, downloads it to an embedded controller via an Ethernet transmission medium, uses the embedded controller to identify the Json file, writes an interface code according to an interface table with a calibration quantity, reads the calibration parameter information in the Json, assigns the parameter position in the code via the interface code, installs the Json file, and restarts the Json file related process to realize the calibration process.

[0009] As an optimization, the calibration parameters include the calibration parameter name, the number of signals and distinguish between arrays, signed or unsigned numbers, signal type, signal length, and signal initial value.

[0010] As an optimization, parsing the Json file using the embedded controller includes the following steps:

[0011] (1) Initialize the Json file;

[0012] (2) Execute the FOR loop and query the parameter Name;

[0013] (3) If the parameter Name is found, search for the parameter Type; otherwise, return to step (2) and search for the parameter Name again.

[0014] (4) If the parameter Type is found, search for the parameter Signed; otherwise, return to step (2) and search for the parameter Name again.

[0015] (5) If the parameter Signed is found, search for the parameter Type-len; otherwise, return to step (2) and search for the parameter Name again.

[0016] (6) If the parameter Type-len is found, search for the parameter Value; otherwise, return to step (2) and search for the parameter Name again.

[0017] (7) If the parameter Value is found, the basic type is parsed;

[0018] (8) If the parsed basic type is consistent with the Json object value type, read the calibration parameter information, otherwise end.

[0019] In summary, the beneficial effects of the present invention are: the present invention utilizes Json files to realize parameter calibration of algorithms in MPU chips with high computing power; at the same time, the parameter calibration in this method directly and permanently modifies the calibration values ​​in the software, which is very efficient and convenient. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0021] Figure 1 is a system architecture diagram of the present invention;

[0022] Figure 2 is a flow chart of the calibration method of the present invention;

[0023] Figure 3 This is a flow chart of parsing the calibration file in the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0025] like Figure 1 As shown, a Json-based MPU calibration system in this specific implementation includes a host computer module, an embedded controller, a power module and an Ethernet transmission medium. The host computer module is used to read the calibration parameters in the code and generate a Json file. The embedded controller is used to identify the Json file, parse and read the calibration parameter information inside the Json file to achieve the effect of changing the calibration amount. The power module is used to power the host computer module and the embedded controller. The Ethernet transmission medium is used to connect the host computer module and the embedded controller.

[0026] like Figure 2 As shown, a method for calibration using the above-mentioned Json-based MPU calibration system is used. The upper computer module is used to read the code, the signal to be calibrated is reflected in the form of calibration parameters, a Json file is generated, and it is downloaded to the embedded controller through the Ethernet transmission medium. The Json file is identified by the embedded controller, and the interface code is written according to the interface table with the calibration quantity. The calibration parameter information in the Json is read and assigned to the parameter position in the code through the interface code, the Json file is installed, and the Json file related process is restarted to realize the calibration process.

[0027] In this specific implementation, the calibration parameters include the calibration parameter name, the number of signals and distinguish between arrays, signed numbers, unsigned numbers, signal types, signal lengths, and signal initial values.

[0028] like Figure 3 As shown, in this specific implementation, parsing the Json file using the embedded controller includes the following steps:

[0029] (1) Initialize the Json file;

[0030] (2) Execute the FOR loop and query the parameter Name;

[0031] (3) If the parameter Name is found, search for the parameter Type; otherwise, return to step (2) and search for the parameter Name again.

[0032] (4) If the parameter Type is found, search for the parameter Signed; otherwise, return to step (2) and search for the parameter Name again.

[0033] (5) If the parameter Signed is found, search for the parameter Type-len; otherwise, return to step (2) and search for the parameter Name again.

[0034] (6) If the parameter Type-len is found, search for the parameter Value; otherwise, return to step (2) and search for the parameter Name again.

[0035] (7) If the parameter Value is found, the basic type is parsed;

[0036] (8) If the parsed basic type is consistent with the Json object value type, read the calibration parameter information, otherwise end.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described with reference to the preferred embodiments of the present invention, it should be understood by those skilled in the art that various changes may be made in form and details without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. A method for calibrating an MPU calibration system based on Json, characterized in that: A Json-based MPU calibration system is used. The Json-based MPU calibration system includes a host computer module, an embedded controller, a power module and an Ethernet transmission medium. The host computer module is used to read the calibration parameters in the code and generate a Json file. The embedded controller is used to identify the Json file, parse and read the calibration parameter information in the Json file to achieve the effect of changing the calibration amount. The power module is used to power the host computer module and the embedded controller. The Ethernet transmission medium is used to connect the host computer module and the embedded controller. The upper computer module is used to read the code, and the signal to be calibrated is reflected in the form of calibration parameters, and a Json file is generated. The file is downloaded to the embedded controller through the Ethernet transmission medium, and the Json file is identified by the embedded controller. The interface code is written according to the interface table with the calibration quantity, and the calibration parameter information in the Json file is read and assigned to the parameter position in the code through the interface code. The Json file is installed, and the Json file-related process is restarted to realize the calibration process; The calibration parameters include the calibration parameter name, the number of signals and distinguish between arrays, signed or unsigned numbers, signal type, signal length, and signal initial value.

2. The method for calibrating the MPU calibration system based on Json according to claim 1, characterized in that: Parsing a Json file using an embedded controller includes the following steps: (1) Initialize the Json file; (2) Execute the FOR loop and query the parameter Name; (3) If the parameter Name is found, search for the parameter Type; otherwise, return to step (2) and search for the parameter Name again. (4) If the parameter Type is found, search for the parameter Signed; otherwise, return to step (2) and search for the parameter Name again. (5) If the parameter Signed is found, search for the parameter Type-len; otherwise, return to step (2) and search for the parameter Name again. (6) If the parameter Type-len is found, search for the parameter Value; otherwise, return to step (2) and search for the parameter Name again. (7) If the parameter Value is found, the basic type is parsed; (8) If the parsed basic type is consistent with the Json object value type, read the calibration parameter information, otherwise end.

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