Tractor chassis load data acquisition system and method based on ZynqMP

Abstract

The invention discloses a ZynqMP-based tractor chassis load data acquisition system and method, and belongs to the technical field of agricultural machinery, and the system comprises a strain sensor module, a signal acquisition board card, a wifi router, programmable logic, a processing system, a data storage and transmission module, and a cloud synchronization system. Through cooperative work of an innovative memory mapping technology and a ZynqMP programmable logic and processing system, strain data of a tractor chassis can be accurately acquired in real time, and especially under dynamic load and complex working conditions, the problems that a traditional data acquisition system is complex in operation, insufficient in precision and unstable in data transmission are solved, and the reliability of the system is improved. And the real-time performance, the stability and the processing efficiency of data acquisition are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural machinery, and in particular to a tractor chassis load data acquisition system and method based on ZynqMP (Zynq UltraScale+MPSoC). Background Art

[0002] Tractors, as a key piece of power equipment in agricultural machinery, play a crucial role in performance and load testing, ensuring stable operation, improving operational efficiency, and reducing failure rates. Traditional tractor chassis load testing typically utilizes a combination of mechanical sensors and data acquisition systems to assess operating conditions by monitoring physical parameters such as chassis strain and vibration. In tractor performance testing and load analysis, the core challenge of the data acquisition system is how to quickly and accurately acquire load data from key tractor chassis components. Especially under dynamic operating conditions, the technical challenge is how to acquire data from each sensor in real time and stably, and transmit it to a computing system for further analysis via efficient and reliable communication methods. In traditional systems, the coordination between multiple devices increases system complexity, requiring complex maintenance and debugging, further increasing system costs and uptime. Field-programmable gate arrays (FPGAs), through their parallel processing architecture, can significantly improve the real-time performance and accuracy of data acquisition. Especially for high-frequency data acquisition tasks requiring large-scale parallel processing, FPGAs offer superior parallel processing capabilities compared to traditional central processing unit (CPU) systems. Therefore, embedded data acquisition systems based on FPGA technology have gradually demonstrated their advantages in high-speed data processing and high-concurrency applications. While existing FPGA-based data acquisition systems offer significant real-time advantages, they still face technical challenges, particularly in data transmission stability, computing resource utilization, and system scalability. While FPGA processing modules are powerful, in practical applications, avoiding packet loss and latency during data transmission and utilizing computing resources more efficiently remain key unresolved issues.

[0003] The Zynq UltraScale+ MPSoC (ZynqMP) series of chips, as a next-generation heterogeneous multi-processor platform, combines the parallel computing capabilities of FPGAs with the processing power of ARM Cortex-A series CPUs, providing a new system architecture. The ZynqMP's programmable logic (PL) inherits the parallel computing advantages of traditional FPGAs, efficiently processing large-scale parallel data and collaborating closely with other hardware modules (such as sensors and analog-to-digital converters) to achieve highly accurate data acquisition. The processing system (PS), integrating an ARM processor, boasts powerful computing and control capabilities, capable of running operating systems (such as Linux) for complex data analysis, intelligent task scheduling, and real-time cloud synchronization, thereby enhancing the system's overall collaborative capabilities and intelligence.

[0004] Compared to traditional solutions, ZynqMP fully combines the hardware parallel computing advantages of FPGAs with the efficient processing capabilities of CPUs. It can not only handle high-concurrency tasks in data acquisition tasks, but also analyze, schedule, and optimize data flows in real time, thereby improving overall system performance. However, traditional PL-PS data interaction methods require complex driver development, device tree configuration, and system adaptation, increasing the complexity of system design and deployment. Summary of the Invention

[0005] The purpose of the present invention is to provide a ZynqMP-based tractor chassis load data acquisition system and method. By leveraging innovative memory mapping technology and the collaborative work of ZynqMP's programmable logic and processing system, this system can accurately and real-timely acquire tractor chassis strain data. This system addresses the complex operation, insufficient accuracy, and unstable data transmission issues of traditional data acquisition systems, particularly under dynamic loads and complex operating conditions, and effectively improves the real-time performance, stability, and processing efficiency of data acquisition.

[0006] To achieve the above objectives, the present invention provides a tractor chassis load data acquisition system based on ZynqMP, which is characterized by comprising a strain sensor module, a signal acquisition board, a wifi router, a programmable logic, a processing system, a data storage and transmission module, and a cloud synchronization system;

[0007] The strain sensor module is installed on the key components of the tractor chassis and is connected to the signal acquisition board to collect chassis strain data;

[0008] The signal acquisition board integrates a bridge module and an analog-to-digital converter, wherein the bridge module converts the analog signal representing the resistance change collected by the strain sensor into an analog signal representing the voltage change, and the analog-to-digital converter converts the voltage analog signal into a digital signal;

[0009] The programmable logic includes an FPGA processing module and block random access memory (BRAM). The FPGA processing module is equipped with a dedicated IP core that controls the analog-to-digital converter for data acquisition. The dedicated IP core configures the acquisition board control signal for data acquisition and conversion, processes the data using custom parameters, and stores the processed data in the BRAM.

[0010] The processing system uses address mapping technology to extract the processed data in BRAM and perform further processing;

[0011] The data storage and transmission module stores the processed data into the SD card and uploads the corresponding data to the cloud synchronization system via the local area network constructed by the WiFi router;

[0012] The cloud synchronization system is deployed on the cloud server for remote viewing and management.

[0013] Preferably, the dedicated IP core includes several configurable data processing modules. Each module is expanded through hardware description language according to application requirements to customize new data processing functions. The same data processing IP core module supports multiplexing.

[0014] Based on the above-mentioned data acquisition method of the tractor chassis load data acquisition system based on ZynqMP, the specific steps include:

[0015] Step S1, pre-configuring the operating system of the processing system;

[0016] Step S2: The strain sensor collects analog signals of key components of the tractor chassis and transmits the data to the signal acquisition board;

[0017] Step S3: The signal acquisition board converts the signal. The bridge module converts the analog signal representing the resistance change collected by the strain sensor into an analog signal representing the voltage change. The analog-to-digital converter converts the voltage analog signal into a digital signal. The digital signal is then transmitted to the dedicated IP core.

[0018] Step S4: The dedicated IP core processes the data using the custom parameters, stores the processed data in the BRAM, and sends an interrupt signal indicating that the data processing is complete through the GPIO after the processing is completed;

[0019] Step S5: When the processing system detects an interrupt signal through the GPIO interrupt mechanism, the processing system responds to the interrupt event through the interrupt event processing function. The processing system uses the address mapping technology to read data from the BRAM using the memory mapping interface, and resets the GPIO to clear the interrupt signal and prepare for the next data read;

[0020] Step S6: The processing system performs further data analysis and processing according to actual needs;

[0021] Step S7: The processing system performs time synchronization and device synchronization on the processed data, and classifies and time-tags the data of multiple devices according to the devices.

[0022] Step S8: Upload the data to the cloud server via the network for further storage and analysis for subsequent remote control and automated testing. The uploaded data supports remote login, viewing, and operation.

[0023] Preferably, in step S1, an adapted Linux image file is generated by the Petalinux tool, and the operating system configuration steps include:

[0024] S11. U-Boot configuration: Modify the U-Boot configuration file to correctly identify and initialize the system's hardware resources, including the configuration of the boot storage device and the setting of the boot command adapted to the Zyn qMP platform. After the configuration is complete, compile and generate the adapted U-Boot image u-boot.bin file and burn it to the boot device.

[0025] S12, kernel configuration, configure the Linux kernel according to the hardware platform requirements, and select supported kernel drivers, file systems, and network protocol stack modules;

[0026] S13, root file system configuration, generating all basic files of the system, or generating a file system image suitable for the ZynqMP platform through cross-compilation;

[0027] S14. Build the entire Petalinux project and generate a complete Linux image file.

[0028] Preferably, in step S5, the address mapping technology is implemented as follows: the processing system is connected to the BRAM core through the AXI bus, the AXI_BRAM_Controller IP core maps the storage space of BRAM to the address space of the AXI bus, the AXI_BRAM_Controller IP core is responsible for managing the read and write operations between the BRAM, and completes the communication between the processing system and the programmable logic through the AXI protocol. The processing system accesses the data in the BRAM by controlling the relevant signals of the AXI bus to complete the storage, reading and updating of the data.

[0029] Preferably, in step S8, data is transmitted to the cloud, a network interface is configured, the processing system communicates with the cloud via an Ethernet or Wi-Fi module, and Python's socket library is used to achieve communication with the cloud. The read data is encapsulated as a network data packet and sent to the cloud server via the TCP / IP protocol. After receiving the data, the cloud server processes and stores it and displays it to the user through an interface. The user views the data and performs further analysis. Automatic startup of the service is achieved by writing a Python script, and the automatic startup service automatically runs through systemd when the system starts. Through the Python script, the cloud sends a remote command, and after receiving the command, the processing system executes the corresponding test operation.

[0030] Therefore, the present invention adopts the above-mentioned ZynqMP-based tractor chassis load data acquisition system and method. Through the innovative memory mapping technology and the collaborative work of ZynqMP's programmable logic and processing system, it can accurately collect tractor chassis strain data in real time. Especially under dynamic loads and complex working conditions, it solves the problems of complex operation, insufficient accuracy, and unstable data transmission of traditional data acquisition systems, and effectively improves the real-time performance, stability and processing efficiency of data acquisition.

[0031] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a schematic diagram of the structure of the tractor chassis load data acquisition system based on ZynqMP of the present invention;

[0033] Figure 2 Functional diagram of the dedicated IP core provided for the ZynqMP-based tractor chassis load data acquisition system of the present invention;

[0034] Figure 3 This is a flow chart of the tractor chassis load data acquisition method based on ZynqMP of the present invention;

[0035] Figure 4 CONVST timing diagram provided for an embodiment of the tractor chassis load data acquisition method based on ZynqMP of the present invention;

[0036] Figure 5 A schematic diagram of a serial read operation provided by an embodiment of a ZynqMP-based tractor chassis load data acquisition method of the present invention;

[0037] Figure 6 Schematic diagram of the address mapping method provided for the ZynqMP-based tractor chassis load data acquisition method of the present invention;

[0038] Figure 7Schematic diagram of the IP core parallel processing mode provided by the ZynqMP-based tractor chassis load data acquisition method of the present invention;

[0039] Figure 8 This is a flowchart of the processing system configuration and SD boot card production provided for the ZynqMP-based tractor chassis load data acquisition method of the present invention. DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0041] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," and "connected" should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0042] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0043] Example

[0044] like Figure 1 As shown, the ZynqMP-based tractor chassis load data acquisition system of the present invention uses the ZynqMP series chip. Its core consists of a programmable logic part and a processing system part, which can efficiently collect, process and transmit tractor chassis strain data. The tractor chassis load data acquisition system includes: a strain sensor module, a signal acquisition board, a Wi-Fi router, a programmable logic, a processing system, a data storage and transmission module, and a cloud synchronization system;

[0045] The strain sensor module is installed on the key components of the tractor chassis and is connected to the signal acquisition board. It is responsible for collecting chassis strain data and transmitting the collected analog signals to the signal acquisition board. Figure 2As shown, the signal acquisition board integrates a bridge module and an AD7606 analog-to-digital converter. The bridge module converts the analog signal representing resistance change collected by the strain sensor into an analog signal representing voltage change. The AD7606 analog-to-digital converter converts the voltage analog signal into a digital signal, which is then transmitted to a dedicated IP core. The programmable logic includes an FPGA processing module and a block random access memory (BRAM). The FPGA processing module is equipped with a dedicated IP core, which is responsible for controlling the analog-to-digital converter for data acquisition. The dedicated IP core configures the acquisition board control signals for data acquisition and conversion. The control signals include a reset signal (RES ET), a clock signal (CLK), data read signals (DA, DB), a chip select signal (CS), conversion signals (ConvstA, ConvstB), and a busy wait signal (BUSY). Its timing logic is as follows: Figure 4 and Figure 5 As shown in the figure, data processing (such as filtering and encoding) is performed using custom parameters, and the processed data is finally stored in BRAM. The processing system uses address mapping technology to extract the processed data from BRAM and perform further processing, such as model training. The data storage and transmission module stores the processed data on an SD card and uploads the corresponding data to the cloud synchronization system via the local area network constructed by the Wi-Fi router. The cloud synchronization system is deployed on a cloud server for remote viewing and management.

[0046] In addition to controlling the AD7606 analog-to-digital converter for data acquisition, the dedicated IP core also includes multiple functional modules for data processing to meet the needs of different application scenarios. The design of this dedicated IP core supports flexible data processing flows, which can be customized according to the requirements of the application scenario. The main data processing functional modules include but are not limited to the following modules:

[0047] (1) Filtering module: This module is used to remove noise and smooth the signal of the collected raw data. By designing filters (such as low-pass filters and band-pass filters), it is possible to effectively remove unnecessary high-frequency noise and retain the valid signal. This is crucial for applications that require high-quality, noise-free signals, such as precision measurement or control systems.

[0048] (2) Rainflow Counting Module: In applications involving fatigue analysis or vibration testing, the Rainflow Counting Module can count rainflows based on the collected signals. Rainflow counting technology is suitable for analyzing fatigue loads and can effectively evaluate the lifespan and performance of materials or mechanical structures under repeated loads. This module is suitable for applications such as structural health monitoring and vehicle vibration monitoring.

[0049] like Figure 7As shown, the same data processing IP core module supports multiplexing to achieve parallel processing of multi-channel data. For example, when data from n channels (Channel 1, Channel 2, Channel 3, ..., Channel n) is input, N dedicated IP cores can be reused, or different IP cores can be used for parallel processing. Each reused dedicated IP core IPx (where x is the corresponding channel number) processes the data for the corresponding channel. The data synchronization and control module then maps the data to the corresponding address area ADDRx. It is important to note that the IP cores for the corresponding processing channels can reuse the same processing IP core or use different IP cores, depending on the specific situation.

[0050] The modular design of this specialized IP core enables flexible configuration. Users can select and enable different functional modules based on specific application requirements. For example, in some applications, users may only need the filtering module, while in others, multiple modules, such as filtering and rainflow counting, may be required. Furthermore, these modules can be extended using hardware description languages (such as Verilog or VHDL) to customize new data processing functions.

[0051] like Figure 3 , a data acquisition method based on the above-mentioned ZynqMP-based tractor chassis load data acquisition system, specifically comprising the following steps:

[0052] Step S1: pre-configure the operating system of the processing system. The specific process is as follows:

[0053] The processing system generates an adapted Linux image file through the Petalinux tool, such as Figure 8 As shown, the process includes but is not limited to the following: U-Boot configuration, kernel configuration, and root file system configuration;

[0054] U-Boot configuration: U-Boot is the system's boot program, responsible for loading the operating system kernel when the device is powered on. First, you need to configure U-Boot to adapt to the hardware environment of the ZynqMP platform. Run the petalinux-config-cu-boot command to enter the U-Boot configuration interface and modify the U-Boot configuration file so that it can correctly identify and initialize the system's hardware resources. This includes configuring the boot storage device (such as SD card, eMMC, SPI Flash, etc.) and setting the boot command suitable for this platform. After the configuration is complete, compile and generate the adapted U-Boot image file (u-boot.bin) and burn it to the boot device.

[0055] Kernel configuration is the core of the operating system function. It involves configuring the Linux kernel according to the hardware platform requirements. By running the petalinux-config-c kernel command, you can select supported kernel drivers, file systems, network protocol stacks and other modules.

[0056] Root file system configuration. The root file system (Root FS) contains all basic files of the system, such as user programs, library files, configuration files, etc. Configure the root file system by running the petalinux-config rootfs command, or generate a file system image suitable for the Zynq MP platform through cross-compilation;

[0057] Build the entire Petalinux project through the petalinux-build command to generate a complete Linux image file.

[0058] Step S2: The strain sensor collects analog signals from the tractor chassis and transmits the data to the signal acquisition board;

[0059] Step S3: The signal acquisition board converts the signal. The bridge module converts the analog signal representing the resistance change collected by the strain sensor into an analog signal representing the voltage change. After receiving the control signal sent by the dedicated IP core through the logic state machine control module, the AD7606 analog-to-digital converter performs signal acquisition and conversion, converts the collected analog signal into a digital signal, and transfers the data to data buffer area 1.

[0060] Step S4: The dedicated IP core performs IIR filtering on the data. The filtered data is transferred to the data receiving buffer 2 in the BRAM. After the processing is completed, an interrupt signal representing the completion of data processing is sent through GPI 0.

[0061] Step S5: When the processing system detects an interrupt signal through the GPIO interrupt mechanism, the processing system responds to the interrupt event through the interrupt event processing function. The processing system uses the address mapping technology to read data from the BRAM using the memory mapping interface, and resets the GPIO to clear the interrupt signal and prepare for the next data read;

[0062] Address mapping method is as follows Figure 6 As shown in the figure, by mapping the BRAM core to the memory address space of the processing system, the memory access capability of the processing system is utilized to achieve efficient data read and write operations on the BRAM core of the programmable logic part. The core of this technical solution is to ensure higher performance and flexibility of the system by optimizing the data transmission mechanism between the processing system and the programmable logic. The specific solution is described as follows:

[0063] Data exchange between the BRAM and the dedicated IP core occurs via the Data_in and Data_out lines. The dedicated IP core's processing module sends raw signals collected from the external environment to the BRAM for storage. The required data is transferred from the BRAM to the dedicated IP core via the Data_in line. After processing the collected signal, the dedicated IP core transfers the resulting data to the BRAM for caching via the Data_out line. The processing system connects to the BRAM core via the AXI bus. The AXI_BRAM_Controller IP core maps the BRAM's storage space to the AXI bus's address space. The AXI_BRAM_Controller IP core manages read and write operations with the BRAM and communicates between the processing system and the programmable logic via the AXI protocol. The processing system accesses data in the BRAM by controlling the relevant signals on the AXI bus, completing data storage, reading, and updating.

[0064] Step S6: The processing system performs further data analysis and processing according to actual needs, such as data correction and corresponding formatting;

[0065] Step S7: The processing system performs time synchronization and device synchronization on the processed data, and classifies and time-tags the data of multiple devices according to the devices.

[0066] Step S8: Upload the data to the cloud server via the network for further storage and analysis for subsequent remote control and automated testing. The uploaded data supports remote login, viewing, and operation.

[0067] To achieve remote data viewing and operation, use Python scripts to transmit the read data through the network. The specific implementation steps are as follows:

[0068] (1) Configure the network interface to ensure that the processing system can communicate with the cloud through Ethernet or Wi-Fi module.

[0069] (2) Use Python’s socket library to achieve communication with the cloud. The read data is encapsulated as network data packets and sent to the cloud server via the TCP / IP protocol.

[0070] (3) After receiving the data, the cloud server processes and stores it, and displays it to the user through an interface to view the data and conduct further analysis.

[0071] (4) When processing the system configuration self-starting service, it can automatically run when the operating system starts and listen to remote commands from the cloud. Write a Python script to automatically start the service. The service will automatically run through systemd or other mechanisms when the system starts.

[0072] Through Python scripts, remote commands are sent from the cloud. Upon receiving the commands, the processing system executes the corresponding test operations. For example, new tests can be started, test parameters can be modified, or tests can be stopped. This mechanism allows testers to more conveniently control equipment remotely without manual intervention.

[0073] Therefore, the present invention adopts the above-mentioned ZynqMP-based tractor chassis load data acquisition system and method. Through the innovative memory mapping technology and the collaborative work of ZynqMP's programmable logic and processing system, it can accurately collect tractor chassis strain data in real time. Especially under dynamic loads and complex working conditions, it solves the problems of complex operation, insufficient accuracy, and unstable data transmission of traditional data acquisition systems, and effectively improves the real-time performance, stability and processing efficiency of data acquisition.

[0074] 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 the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A tractor chassis load data acquisition system based on ZynqMP, characterized by: Including strain sensor module, signal acquisition board, wifi router, programmable logic, processing system, data storage and transmission module, cloud synchronization system; The strain sensor module is installed on the key components of the tractor chassis and is connected to the signal acquisition board to collect chassis strain data; The signal acquisition board integrates a bridge module and an analog-to-digital converter, wherein the bridge module converts the analog signal representing the resistance change collected by the strain sensor into an analog signal representing the voltage change, and the analog-to-digital converter converts the voltage analog signal into a digital signal; The programmable logic includes an FPGA processing module and a block random access memory (BRAM). The FPGA processing module is equipped with a dedicated IP core that controls the analog-to-digital converter for data acquisition. The dedicated IP core configures the acquisition board control signal for data acquisition and conversion, processes the data using custom parameters, and stores the processed data in the BRAM. The processing system uses address mapping technology to extract the processed data in BRAM and perform further processing; The data storage and transmission module stores the processed data into the SD card and uploads the corresponding data to the cloud synchronization system via the local area network constructed by the WiFi router; The cloud synchronization system is deployed on the cloud server for remote viewing and management.

2. The ZynqMP-based tractor chassis load data acquisition system according to claim 1, characterized in that: The dedicated IP core includes several configurable data processing modules. Each module can be expanded through hardware description language according to application requirements to customize new data processing functions. The same data processing IP core module supports multiplexing.

3. A data acquisition method for a tractor chassis load data acquisition system based on ZynqMP according to any one of claims 1-2, characterized in that: The specific steps include: Step S1, pre-configuring the operating system of the processing system; Step S2: The strain sensor collects analog signals of key components of the tractor chassis and transmits the data to the signal acquisition board; Step S3: The signal acquisition board converts the signal. The bridge module converts the analog signal representing the resistance change collected by the strain sensor into an analog signal representing the voltage change. The analog-to-digital converter converts the voltage analog signal into a digital signal. The digital signal is then transmitted to the dedicated IP core. Step S4: The dedicated IP core processes the data using the custom parameters, stores the processed data in the BRAM, and sends an interrupt signal indicating that the data processing is complete through the GPIO after the processing is completed; Step S5: When the processing system detects an interrupt signal through the GPIO interrupt mechanism, the processing system responds to the interrupt event through the interrupt event processing function. The processing system uses the address mapping technology to read data from the BRAM using the memory mapping interface, and resets the GPIO to clear the interrupt signal and prepare for the next data read; Step S6: The processing system performs further data analysis and processing according to actual needs; Step S7: The processing system performs time synchronization and device synchronization on the processed data, and classifies and time-tags the data of multiple devices according to the devices. Step S8: Upload the data to the cloud server via the network for further storage and analysis for subsequent remote control and automated testing. The uploaded data supports remote login, viewing, and operation.

4. The ZynqMP-based tractor chassis load data acquisition method according to claim 3, characterized in that: In step S1, the adapted Linux image file is generated by the Petalinux tool. The steps of operating system configuration include: S11. U-Boot configuration: modify the U-Boot configuration file to correctly identify and initialize the system's hardware resources, including the configuration of the boot storage device and the setting of the startup command adapted to the ZynqMP platform. After the configuration is completed, generate the adapted U-Boot image u-boot.bin file by compiling and burn it to the boot device; S12, kernel configuration, configure the Linux kernel according to the hardware platform requirements, and select supported kernel drivers, file systems, and network protocol stack modules; S13, root file system configuration, generating all basic files of the system, or generating a file system image suitable for the ZynqMP platform through cross-compilation; S14. Build the entire Petalinux project and generate a complete Linux image file.

5. The ZynqMP-based tractor chassis load data acquisition method according to claim 3, characterized in that: In step S5, the address mapping technology is implemented as follows: the processing system is connected to the BRAM core through the AXI bus, the AXI_BRAM_Controller IP core maps the storage space of the BRAM to the address space of the AXI bus, the AXI_BRAM_Controller IP core is responsible for managing the read and write operations between the BRAM, and completing the communication between the processing system and the programmable logic through the AXI protocol. The processing system accesses the data in the BRAM by controlling the relevant signals of the AXI bus to complete the storage, reading and updating of the data.

6. The ZynqMP-based tractor chassis load data acquisition method according to claim 3, characterized in that: In step S8, data is transmitted to the cloud, the network interface is configured, and the processing system communicates with the cloud through the Ethernet or Wi-Fi module. The Python socket library is used to realize communication with the cloud. The read data is encapsulated as a network data packet and sent to the cloud server through the TCP / IP protocol. After the cloud server receives the data, it processes and stores it and displays it to the user through the interface. The user views the data and performs further analysis. The automatic startup service is realized by writing a Python script. The automatic startup service runs automatically through systemd when the system starts. Through the Python script, the cloud sends a remote command. After receiving the command, the processing system performs the corresponding test operation.

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