BMS embedded device data monitoring method based on low code

Abstract

The invention discloses a BMS (Battery Management System) embedded equipment data monitoring method based on low codes, which comprises the following operation steps that: embedded equipment defines data variables needing to be monitored; the embedded device compiles the defined data variables needing to be monitored to generate an ELF format file; importing the ELF format file into a low-code platform upper computer from the embedded device to generate EXCEL and CSV table files which can be edited by a common user; an operator carries out customized monitoring requirements; an operator adjusts and edits the EXCEL and CSV table files; the edited and modified table file is imported into the low-code platform upper computer again, and the upper computer generates a monitoring control interface and a data transmission protocol of the low-code upper computer; and the low-code platform upper computer monitors the data of the embedded equipment. The method has the advantages that the development efficiency can be improved, the technical threshold can be reduced, the monitoring precision and the real-time performance can be improved, the battery management can be optimized, and the data monitoring can be quickly and stably implemented in each operation link.

Description

Technical Field

[0001] The present invention relates to the field of data monitoring of embedded devices, and particularly to a method for monitoring data of BMS embedded devices based on low-code. Background Art

[0002] In the field of data monitoring of embedded devices, the conventional approach is to define the variables to be monitored according to the product requirements, then define the data transmission protocol and data packaging protocol for data monitoring according to the characteristic attributes of the monitored variables, and finally develop the display interface and data transmission software for the monitored variables at the host computer end according to the requirements.

[0003] When the requirements for the variables to be monitored change, it is necessary to modify the software at the embedded device end, the data transmission protocol, the data packaging protocol, and the code of the data monitoring interface at the host computer end.

[0004] Although the above-mentioned existing technologies have solved the problem of data monitoring of embedded devices and met the requirements of traditional data visualization, with the research and development iteration of embedded products, the following problems are still exposed:

[0005] The software R & D cost is high. At the beginning, the hardware and software of the BMS product were small in volume and the system functions were relatively simple. The data that needed to be monitored by the host computer was only a small amount of information such as voltage and capacity. With the development of energy technology, BMS has become more and more complex. Currently, most BMSs include complex functions such as small current charging and discharging, health monitoring, and cell balancing. The data that needs to be monitored includes health status, capacity information, single-cell voltage, charging and discharging current, etc. Developing according to the traditional data monitoring method is difficult and has a long development cycle.

[0006] The iteration and change of monitoring functions are difficult. With the continuous iteration of products, the monitoring function requirements are continuously changing from the user end, the service end to the R & D end. The traditional data monitoring function development method cannot respond agilely, resulting in a long development cycle for function changes and a high risk of defects.

[0007] It is impossible to flexibly customize the monitoring effect. The roles using the monitoring function include R & D, testing, production, after-sales, users, etc. Each role focuses on different aspects of monitoring. For example, R & D is more concerned about the internal state and logical data of the software, production workers are more in need of paying attention to production-related calibration parameter data, and after-sales is more concerned about log information such as fault codes. Traditional data monitoring development cannot meet the monitoring needs of different roles. Summary of the Invention

[0008] The object of the present invention is to provide a low-code-based data monitoring method for BMS embedded devices, which has the advantages that the development and deployment processes of the monitoring platform are greatly simplified, the monitoring functions can more flexibly adapt to different application scenarios and demand changes, the monitoring accuracy and real-time performance are improved, not only the development efficiency can be improved, the technical threshold can be reduced, the system flexibility can be enhanced, but also the monitoring accuracy and real-time performance can be improved, the battery management can be optimized, and the data monitoring can be quickly and stably implemented in each operation link.

[0009] The above technical object of the present invention is achieved through the following technical solutions:

[0010] A low-code-based data monitoring method for BMS embedded devices, including a low-code platform host computer and an embedded device, comprises the following operation steps.

[0011] Step 1: Define the data variables to be monitored in the software code of the embedded device on the computer side;

[0012] After distinguishing the variable attributes of different types of data variables by name and specified mapping addresses, compile the firmware;

[0013] Store it in the code memory of the embedded device;

[0014] Step 2: The software code of the embedded device compiles the defined data variables to be monitored to generate an ELF format file;

[0015] The ELF format file contains executable firmware content, debugging information, and also contains the names, sizes, and mapping addresses of the software-defined monitoring variables;

[0016] Step 3: Import the ELF format file from the embedded device to the low-code platform host computer, and the ELF file conversion module in the low-code platform host computer converts the data variables to be monitored in the ELF format file into EXCEL and CSV table files that can be directly edited and modified by users;

[0017] Step 4: The operator performs customized monitoring requirements;

[0018] An ordinary operator adjusts and edits the EXCEL and CSV table files in Step 3 to obtain the edited and modified table files;

[0019] Step 5: Import the edited and modified table files in Step 4 into the low-code platform host computer again, and the host computer generates a monitoring control interface and a data transmission protocol for the low-code host computer according to the data variables to be monitored;

[0020] Step 6: The low-code platform host computer monitors the data of the embedded device through the generated monitoring controls and data transmission protocols.

[0021] The preferred solutions are as follows:

[0022] Preferably: The embedded device defines the data variables to be monitored, that is, the voltage data, current data, temperature data, and status code data to be monitored in the BMS are defined as global variables of the embedded device software in the BMS embedded device;

[0023] It distinguishes and identifies the read-write attributes of the data variables according to the characteristics of the data variables, and marks them through the names of the data variables and the specified mapping addresses.

[0024] Preferably: In step three, the ELF format file is imported into the low-code host computer, and the low-code host computer parses the ELF format file, generates CSV and EXCEL table files with the names of all data variables marked as to be monitored and the information on whether monitoring is required, and imports them into the CSV and EXCEL table files;

[0025] The CSV and EXCEL table files are divided according to the data variables to be monitored in step one, and the specific functions and usage of the data variables can be intuitively grasped.

[0026] Preferably: In step four, the operator performs a manual enabling operation to monitor the variables that the operator needs to pay attention to according to his own needs, and modifies the monitoring period and alarm threshold of the data variables according to the monitoring requirements, so as to realize the customized monitoring requirements of low code.

[0027] Preferably: In step five, the host computer generates a monitoring interface according to the data variables to be monitored;

[0028] The monitoring interface is any one of the QLineEdit, QLabel, and QTextBrowser data display controls of the QT interface;

[0029] Generate a specified data transmission protocol according to the monitoring, control attributes, variable mapping addresses, and monitoring period attributes of the monitored data variables, and generate alarm threshold logic and control codes according to the alarm thresholds of the data variables.

[0030] In summary, the present invention has the following beneficial effects:

[0031] 1. The BMS embedded data monitoring method based on low code and its application actual operation process in the BMS can realize the monitoring of specified data variables without modifying the code in the actual scenario according to steps one to six;

[0032] 2. Through a low-code approach, the development and deployment process of the host computer on the low-code platform is greatly simplified. Without the need for monitoring personnel to have profound programming skills, they can quickly complete the configuration of monitoring functions through a user-friendly interface, thus significantly shortening the development cycle and improving development efficiency. The low-code feature of the present invention enables more non-professional technical personnel to participate in the development and maintenance of the monitoring system, reduces the technical threshold, and expands the scope of the participating population;

[0033] 3. By configuring monitoring data variables through CSV and EXCEL tables, the monitoring function can more flexibly adapt to different application scenarios and changing requirements. Monitoring personnel can easily add, delete, or modify monitoring variables according to actual needs without complex modifications and redeployments to the system;

[0034] 4. The present invention improves monitoring accuracy and real-time performance. The application of the method of the present invention in the BMS can achieve real-time monitoring of key parameters of the battery pack, ensuring the accuracy and real-time performance of data. Through preset monitoring thresholds and alarm conditions, the system can promptly detect abnormal situations of the battery pack, providing a strong guarantee for fault prevention and the avoidance of safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is the step flow chart of the embodiment;

[0036] Figure 2 is the software code for defining the data variables to be monitored of the embedded device on the computer side in the embodiment;

[0037] Figure 3 is the monitoring variable data end in the ELF format file of the embodiment;

[0038] Figure 4 is the variable in the monitoring variable data end in the ELF format file of the embodiment;

[0039] Figure 5 is the tabular file generated from the ELF format file of the embodiment;

[0040] Figure 6 is the host computer interface of the host computer on the low-code platform in the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The present invention will be further described in detail below with reference to the accompanying drawings.

[0042] The method for monitoring data of the BMS embedded device based on low-code, as Figures 1-6 shown, includes a host computer on the low-code platform and an embedded device.

[0043] An embedded device software development unit is provided on the computer side. The software development unit includes the definition of data variables in the embedded device software and the compilation of the software. The definition of data variables includes specifying the mapping address according to the attribute and defining the variable name. During the compilation process, an ELF format file is generated. The ELF format file includes a compilation linker unit specified by the embedded device kernel. The linker can uniformly link the object files generated by each code.c file, and extract the variables to be monitored in the specified format content of the ELF;

[0044] That is, data variables are defined in the code editor of the computer and saved for use in the code memory of the embedded device.

[0045] A low-code configuration unit and an upper computer parsing configuration framework code development unit are provided in the upper computer of the low-code platform;

[0046] The low-code configuration unit is to configure the table editing configuration generated by the ELF format file. The operator configures to enable the monitoring of the variables that need to be concerned, and performs customized modification operations on attributes such as the monitoring period;

[0047] The upper computer parsing configuration framework code development unit is that the upper computer requires a code framework to implement functions including converting the ELF format file into a table file, parsing the table file into interface code and data transmission protocol code, specifically including the recognition and parsing of the ELF file format, the recognition and parsing of the table file, and the development of code for data communication with the embedded device.

[0048] The data monitoring method for the BMS embedded device based on low code includes the following operation steps,

[0049] Step 1, define the data variables to be monitored in the software code of the embedded device on the computer side, and compile the firmware after distinguishing the variable attributes of different types of data variables by name and specified mapping address;

[0050] That is, the voltage data, current data, temperature data, and status code data to be monitored in the BMS are defined as global variables of the embedded device software in the BMS embedded device; their read and write attributes are distinguished according to the characteristics of the data variables, and are marked by the name and specified mapping address of the data variables, and are stored in the code memory of the embedded device;

[0051] Step 2, the software code of the embedded device compiles the defined data variables to be monitored to generate an ELF format file;

[0052] The ELF format file contains executable firmware content, debugging information, and also contains the name, size, and mapping address of the monitoring variables defined by the above software;

[0053] Step 3: Import the ELF format file from the embedded device to the host computer of the low-code platform. The ELF file conversion module in the host computer of the low-code platform converts the data variables to be monitored in the ELF format file into EXCEL and CSV table files that can be directly edited and modified by users.

[0054] That is, the host computer of the low-code platform parses the ELF format file, generates CSV and EXCEL table files with the names of all data variables marked for monitoring and information on whether monitoring is required, and imports them into the CSV and EXCEL table files. The CSV and EXCEL table files are divided according to the data variables to be monitored in Step 1, so as to intuitively understand the specific functions and usage of the data variables.

[0055] Step 4: The operator makes customized monitoring requirements.

[0056] Ordinary operators adjust and edit the EXCEL and CSV table files in Step 3 to obtain the edited and modified table files.

[0057] The operator performs manual enabling operations for monitoring the variables that need to be concerned according to their own needs, and modifies the monitoring period and alarm threshold of the data variables according to the monitoring requirements, so as to realize the customized monitoring requirements of the low-code.

[0058] Step 5: Import the edited and modified table files in Step 4 into the host computer of the low-code platform again. The host computer generates a monitoring control interface and a data transmission protocol for the host computer of the low-code platform according to the data variables to be monitored.

[0059] The monitoring interface is any one of the QLineEdit, QLabel, and QTextBrowser data display controls of the QT interface.

[0060] Generate a specified data transmission protocol according to the monitoring, control attributes, variable mapping addresses, and monitoring period attributes of the monitored data variables, and generate alarm threshold logic and control codes according to the alarm thresholds of the data variables.

[0061] The data transmission protocol includes, but is not limited to, communication protocols such as CAN, USART, and ETH.

[0062] Step 6: The host computer of the low-code platform monitors the data of the embedded device through the generated monitoring control and data transmission protocol.

[0063] The present invention is in the known technical, product application fields and their application methods:

[0064] In the BMS fields such as new energy inverters and energy storage:

[0065] Use a low-code platform to quickly build a data monitoring interface for inverters and energy storage systems, real-time monitor key parameters such as voltage, current, and temperature. Through graphical dragging and parameterized configuration, it can quickly respond to changes in market or customer demands, optimize control algorithms, and improve the conversion efficiency and stability of inverters.

[0066] Fault diagnosis and early warning: The fault diagnosis technology in the embedded system combined with the data analysis ability of the low-code platform can quickly locate problems during the operation of the inverter. Through the data visualization BI function, it helps enterprises more intuitively understand and analyze application data, reduce data silos, discover potential faults in a timely manner, and take corresponding measures.

[0067] New energy vehicle and intelligent vehicle networking fields:

[0068] In the fields of new energy vehicles and intelligent vehicle networking, the low-code-based embedded data monitoring method not only supports real-time status monitoring of vehicles but also provides rich data support for intelligent vehicle networking.

[0069] Vehicle status monitoring: Through the ELF format file generated by embedded compilation, the system can obtain key information such as vehicle battery power, motor status, vehicle speed, and driving mileage in real time and visually display it on the upper computer interface, providing real-time vehicle status feedback to the driver. Combining with vehicle networking data, the low-code-based monitoring platform can also realize intelligent navigation and vehicle dispatching functions, improve traffic efficiency, and reduce congestion and emissions. Using the remote control function of the low-code platform, vehicle manufacturers can achieve remote maintenance and software upgrades for new energy vehicles to ensure that the vehicles are always in the best performance state.

[0070] Industrial production automation field:

[0071] Production line monitoring: In industrial automated production, the low-code-based embedded data monitoring method can monitor the working status of production lines and production equipment in real time, realizing automation and unmanned operation during the production process. Through graphical dragging and parameterized configuration, a production line monitoring interface can be quickly built to improve production efficiency and quality.

[0072] Fault early warning and maintenance: The embedded system combined with the data analysis ability of the low-code platform can monitor the operating status of production equipment in real time for fault early warning and maintenance. Through the data visualization BI function, equipment status data can be visually displayed to help enterprises make decisions quickly, reducing downtime and maintenance costs.

[0073] Medical device field:

[0074] Real-time monitoring and alarm: In medical device equipment, the low-code based embedded data monitoring method can monitor the operating status of the device and patient data in real time. Through graphical drag-and-drop and parameterized configuration, a monitoring interface can be quickly built to achieve real-time alarm and recording of abnormal data, improving medical safety and quality.

[0075] Remote monitoring and maintenance: Taking advantage of the flexibility of the low-code platform, remote monitoring and maintenance of medical device equipment can be realized. Through real-time data transmission and analysis, equipment failures can be detected in a timely manner and corresponding measures can be taken to reduce equipment downtime and maintenance costs.

[0076] Data analysis and optimization: Through the data analysis ability of the low-code platform, the usage data of medical device equipment can be deeply mined and analyzed to provide data support for the optimization and improvement of the equipment. At the same time, it can also provide valuable reference information for the improvement of medical services.

[0077] In summary, the low-code based embedded data monitoring method has broad application prospects and important application values in various fields. By quickly building and deploying a monitoring interface, achieving real-time monitoring and optimization, fault diagnosis and early warning, intelligent management and other functions, it can provide strong support for enterprises to improve production efficiency, reduce costs, and enhance safety and quality.

[0078] This specific embodiment is only an explanation of the present invention and is not a limitation thereof. After reading this specification, those skilled in the art can make modifications to this embodiment without creative contributions as needed, but as long as it is within the scope of the claims of the present invention, it is protected by the patent law.

Claims

1. A low-code based BMS embedded device data monitoring method, including a low-code platform host computer and an embedded device, characterized in that: The following steps are included: Step 1: Define the data variables to be monitored in the software code of the embedded device on the computer; For different types of data variables, distinguish the variable attributes by name and specified mapping address, and then compile the firmware; It is stored in the code memory of the embedded device; Step 2: The software code of the embedded device compiles the defined data variables to be monitored to generate an ELF format file; The ELF format file contains executable firmware content, debugging information, and the name, size, and mapping address of the above software-defined monitoring variables; Step 3: Import the ELF format file from the embedded device to the low-code platform host computer. The ELF file conversion module in the low-code platform host computer converts the data variables to be monitored in the ELF format file into EXCEL and CSV table files that can be directly edited and modified by the user; Step 4: Operators customize monitoring requirements; The general operator adjusts and edits the EXCEL and CSV table files in step 3 to obtain the edited and modified table files; Step 5: Import the table file edited in step 4 into the low-code platform host computer again. The host computer generates the monitoring control interface and data transmission protocol of the low-code host computer according to the data variables to be monitored; Step six: The low-code platform host computer monitors the data of the embedded device through the generated monitoring controls and data transmission protocols.

2. The low-code based BMS embedded device data monitoring method according to claim 1 is characterized in that: The embedded device defines the data variables that need to be monitored, that is, the voltage data, current data, temperature data, and status code data that need to be monitored in the BMS are defined as global variables of the embedded device software in the BMS embedded device; It distinguishes and identifies the read and write properties of data variables according to their characteristics, and marks them by the name of the data variable and the specified mapping address.

3. The low-code based BMS embedded device data monitoring method according to claim 2 is characterized in that: In step 3, the ELF format file is imported into the low-code host computer, and the low-code host computer parses the ELF format file, generates CSV and EXCEL table files with the names of all data variables marked as to be monitored and the information on whether they need to be monitored, and imports them into the CSV and EXCEL table files; The CSV and EXCEL spreadsheet files are divided according to the data variables that need to be monitored in step 1, so that the specific functions and usage of the data variables can be intuitively understood.

4. The low-code based BMS embedded device data monitoring method according to claim 3 is characterized in that: In step four, the operator manually enables monitoring of the variables that he needs to pay attention to according to his own needs, and modifies the monitoring cycle and alarm threshold of the data variables according to the monitoring requirements to achieve low-code customized monitoring requirements.

5. The low-code based BMS embedded device data monitoring method according to claim 4 is characterized in that: In step 5, the host computer generates a monitoring interface according to the data variables to be monitored; The monitoring interface is any one of the QLineEdit, QLabel, and QTextBrower data display controls of the QT interface; Generate the specified data transmission protocol according to the monitoring and control properties, variable mapping address, and monitoring cycle properties of the monitored data variables, and generate the alarm threshold logic and control code according to the alarm threshold of the data variables.

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