CN115762295A - Intelligent experiment teaching platform based on embedded core MCU and AI chip - Google Patents
Intelligent experiment teaching platform based on embedded core MCU and AI chip Download PDFInfo
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Abstract
The invention relates to an intelligent experiment teaching platform based on an embedded core MCU and an AI chip, which is characterized in that a hardware architecture arranged on an experiment board comprises a core MCU, a peripheral circuit, an expansion slot, a communication interface, an external storage and experiment simulation components for realizing onboard projects; the peripheral circuit comprises a PICKit debugger interface, an onboard power supply, a crystal oscillator, a reset circuit and a code downloading circuit; the expansion slots comprise a MicroBUS slot and an Xplain Pro expansion slot; the communication interface comprises a USB interface, an RS232 interface and an infrared transceiver; the external storage is a Flash storage chip; the onboard projects comprise industrial simulation projects and human-computer interaction projects; the teaching object completes an intelligent experiment project through software layer configuration and hardware layer deployment; the intelligent experiment project comprises an onboard project, an extended micro project and an AI image classification experiment.
Description
Technical Field
The invention relates to the field of intelligent device design and teaching instrument development, in particular to a novel intelligent experiment teaching platform based on an embedded core MCU and an AI chip.
Background
Compared with the embedded MCU which is different day by day, the MCU experiment platform for teaching is difficult to keep pace. At present, the embedded teaching experiment platforms of most schools mainly have two types: the teaching platform is a virtual experiment teaching platform mainly based on software simulation, and the fixed experiment table is mainly based on an experiment box. The virtual experiment teaching platform can improve the actual operation ability of students, accumulate practical experience and train the ability of students to analyze and solve problems [1]. However, the virtual experiment teaching platform is separated from manual practice and can only be used as an early-stage design auxiliary link in actual engineering application. Fixed test boxes are applied to many colleges, but at present, a large number of fixed test boxes still use 51-series single-chip microcomputers, and the problems of backward performance and poor expansion capability exist. These problems make existing test chambers poorly competent for comprehensive, innovative teaching and design experiments [2].
In addition, in the rapid development of science and technology, the demand of AI teaching is gradually increased [3], but with the increase of complexity of a deep learning model and the improvement of large data volume, the demand on AI computing capability of a chip is increased [4]. To support diverse AI computation tasks and performance requirements, ideal AI chips need to have highly parallel processing power capable of supporting bitwise, fixed, and floating point computations of various data lengths [5]. Therefore, the CPU and MCU which are used for experiment teaching in the past are difficult to meet the requirements, and the falling and popularization of AI teaching become difficult.
In summary, most of school embedded teaching experiment platforms are biased to be basic and popular, the problems that objects are single and difficult to replace, experiment design content is limited and the like are involved, and AI teaching experiment equipment is deficient. These problems limit the teaching object's exploration of modern embedded technologies, as well as the learning of AI technologies.
Reference to the literature
[1] The design of experimental teaching platform based on virtual simulation technique [ J ]. Laboratory research and exploration 2016,35 (04): 104-107.
[2] The construction of an embedded project teaching development system platform [ D ]. Shanghai: fudan university, 2010.
[3] Winghong, wujiayue, fan Li, qiui \38891, zuqian, juxiangting machine learning course practice teaching based on AI Studio platform [ J ] computer education, 2021 (06): 115-119.
[4]Momose H,Kaneko T,Asai T.Systems and circuits for ai chips and their trends[J].Japanese Journal of Applied Physics,2020,59(5):050502.
[5] Yi Xie Ying, guo honing, wei ShaoJun, the present situation and the trend of the development of artificial intelligent chips [ J ] science and technology guide, 2018,36 (17): 45-51.
Disclosure of Invention
The invention provides a novel modularized experiment platform which is modularized, has the core MCU capable of being upgraded and replaced, has strong expandability and supports partial industrial objects. The invention adopts the modularized design, has expandability, can not only complete a series of representative basic experiments on an experiment platform through the combination of the experiment board and the expansion board, but also design micro-projects aiming at real objects, realizes hardware interconnection, and can deploy related software programs to complete AI picture classification experiments. In order to achieve the technical purpose, the invention provides the following technical scheme:
an intelligent experiment teaching platform based on an embedded core MCU and an AI chip is characterized in that,
software architecture, including MCU software architecture and Shengteng 200DK development board software architecture, in which the MCU software architecture includes basic routine, experimental board driver and extension board API interface program; the software architecture of the Shengteng 200DK development board comprises a cloud ModelArt platform, a PC Mind Studio development environment and an image classification model;
the hardware framework arranged on the experiment board comprises a core MCU, a peripheral circuit, an expansion slot, a communication interface, an external storage and experiment simulation components for realizing onboard projects; the peripheral circuit comprises a PICKit debugger interface, an onboard power supply, a crystal oscillator, a reset circuit and a code downloading circuit; the expansion slots comprise a MicroBUS slot and an Xplain Pro expansion slot; the communication interface comprises a USB interface, an RS232 interface and an infrared transceiver; the external storage is a Flash storage chip; the onboard projects comprise industrial simulation projects and human-computer interaction projects;
the teaching object completes the intelligent experiment project through software layer configuration and hardware layer deployment; the intelligent experiment project comprises onboard projects, extended micro-projects and AI image classification experiments
On-board items including, but not limited to, buzzer items, charactron display items, LCD display screen items, motors, and independent key items; the implementation process of each onboard project is as follows: designing a circuit diagram for a teaching object, and connecting a circuit on an experimental board by referring to the circuit diagram to realize hardware circuit connection; a teaching object designs a nixie tube control program in an IDE (integrated drive electronics) end of a PC (personal computer) to realize functions of countdown display and counter display. The PICKit 4 downloader is used for burning codes to the MCU of the experimental board for execution through an onboard downloading program interface to complete corresponding onboard projects,
the expansion micro-project is realized by combining an experimental board and an expansion board, and comprises but is not limited to entrance guard card simulation, drunk driving test, air temperature measurement, an 8 multiplied by 8RGB color board and atmospheric pressure test projects; the implementation process of each expansion micro item is as follows: a teaching object selects a corresponding expansion module and is deployed on the experiment board through a MicroBUS slot; a control program is designed on a teaching object in an IDE (integrated development environment) of a PC (personal computer) end, a PICKit 4 downloader is utilized, and a code is burnt to an MCU (microprogrammed control unit) of an experimental board for execution through an onboard downloading program interface to complete a corresponding expansion micro-project;
an AI image classification experiment, which is deployed on a hardware level and comprises the steps of connecting a reserved Xplain Pro expansion slot with a 40pin expansion interface of a rising Altas200DK development board to realize the hardware joint deployment of an experiment board and the rising Altas200DK development board; the main functions of the expansion interface are power supply and communication; the teaching object selects different peripheral equipment and hardware connection interfaces according to experiment requirements;
the AI image classification experiment implementation process comprises three processes of image acquisition and processing, model training deployment and AI image classification;
in the image acquisition and processing process, a teaching object is externally connected with a camera or a camera through a USB interface of an experimental teaching platform to acquire images, and the images are transmitted to a PC (personal computer) end through a serial port to wait for pretreatment through a PIC (peripheral interface controller) 16/18 series single chip microcomputer; the teaching object makes the image data collected by the experiment board and the peripheral equipment into a data set at the PC end, and the data alignment and labeling pretreatment processes are included; uploading a cloud server training model;
in the model training and deploying process, a teaching object processes a data set, and the data set processing comprises importing data, labeling the data set and releasing the data set; creating training operation on a cloud ModelArt platform to finish the training of the model; after training, the model is converted into a model architecture required by a local rising Altas200DK development board and is exported; the teaching object uses the derived model to compile and process by using the local PC terminal Mind Studio development environment and deploys to the local promotion Altas200DK development board;
in the AI image classification process, a teaching object utilizes an experimental teaching platform USB external camera to acquire image data; then, the software configuration and hardware of the teaching object are connected with an I2C, SPI or serial port communication interface in the experimental teaching platform, and the image data is sent to a rising Altas200DK development board; the related software programs on the Shengteng Atlas 200DK development board are compiled, and initialization, loading of an inference model, assignment of inference pictures, preprocessing of the inference model and inference circulation are sequentially completed, and finally classification and identification of the images are realized.
Drawings
FIG. 1 is a schematic diagram of the overall architecture of the experimental platform of the present invention;
FIG. 2 is a schematic diagram of the hardware layout structure of the experiment platform of the present invention;
FIG. 3 is an explanatory view of the kind of teaching experiment of the present invention;
FIG. 4 is a schematic diagram of an AI image classification experiment according to the present invention;
fig. 5 is a schematic diagram of an AI image classification experiment hardware deployment architecture according to the present invention.
Detailed Description
For the purpose of clearly presenting the technical solutions, infrastructures and advantages of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and embodiments:
the invention belongs to the field of intelligent experiment devices, and relates to an intelligent experiment teaching platform based on an embedded MCU and an AI chip, which comprises a software architecture and a hardware architecture, wherein the specific embodiment architecture is shown in figure 1. In the embodiment, PIC16/PIC18 series MCU and Shengteng Altas200DK development board are selected to realize the invention, the software and hardware adopted by the experimental teaching platform are the prior art, the patent is an application innovation, and the details of the technical implementation are not described in detail here.
The software architecture mainly includes MCU software architecture and Shengteng 200DK development board software architecture. The MCU software architecture mainly comprises a basic routine, an experiment board driving program and an expansion board API interface program. The software architecture of the Shengteng 200DK development board mainly comprises a cloud ModelArt platform, a PC-side Cold Studio development environment and an image classification model.
The hardware architecture is arranged on an experimental board, and a schematic structural diagram of the experimental board is shown in fig. 2. The experiment board mainly comprises a core MCU, a peripheral circuit, an expansion slot, a communication interface, an external storage, an onboard project and the like. The peripheral circuit comprises a PICKit debugger interface, an onboard power supply, a crystal oscillator, a reset circuit, a code downloading circuit and the like. The expansion slot is mainly a MicroBUS slot and an Xplain Pro expansion slot. The communication interface comprises a USB interface, an RS232 interface, an infrared transceiver and the like. The external storage is a Flash memory chip. The onboard projects mainly comprise industrial simulation projects and human-computer interaction projects.
The software architecture and the hardware architecture jointly realize the functions of the intelligent experimental system, and the teaching object completes the intelligent experimental project through software layer configuration and hardware layer deployment. The architecture, functionality, and principles of the present invention are described below in conjunction with specific items. Specific items including on-board items, extended micro-items, and AI image classification experiments are shown in fig. 3.
The onboard items include but are not limited to basic experiment items such as a buzzer item, a nixie tube display item, an LCD display screen item, a motor and an independent key item. Wherein, the items are displayed by nixie tubes to explain the concrete implementation process of the onboard items. The teaching object designs a circuit diagram, and the circuit is connected on the experiment board by referring to the circuit diagram to realize the connection of a hardware circuit. Then, the teaching object designs a nixie tube control program in the IDE of the PC end to realize the functions of countdown display, counter display and the like. Utilize PICKit 4 downloader, through board-mounted download program interface, burn the code and record to the execution of laboratory glassware MCU, accomplish the charactron and show the experiment. The deployment flow of hardware and software of the rest onboard projects is similar to that of the onboard projects, and is not described one by one.
The extended micro-project specifically comprises but is not limited to novel projects such as entrance guard card simulation, drunk driving test, air temperature measurement, 8 × 8RGB color board and atmospheric pressure test. Wherein, the drunk driving test is used for explaining the specific implementation process of the extension project. The teaching object firstly selects a proper expansion module and is deployed on an experimental version through a MicroBUS slot. And then, designing a control program for a teaching object at a PC (personal computer) side IDE (integrated development environment), and burning a code to an MCU (microprogrammed control unit) of the experiment board for execution through an onboard downloading program interface by utilizing a PICKit 4 downloader to complete the drunk driving test experiment. The other expanding micro-project hardware and software deployment processes are similar to the expanding micro-project hardware and software deployment processes, and are not described one by one.
The core of the present invention is an AI image classification experiment, and a specific AI image classification experiment flow is shown in fig. 4. The invention provides a combined deployment of an experimental board and a rising Altas200DK development board, and a teaching object utilizes rich and flexible peripheral interfaces reserved on a hardware layer to complete three steps of image acquisition and processing, model training deployment and classification experiments, thereby realizing primary AI image classification experiments.
The specific AI image classification experiment is deployed on a hardware level as shown in FIG. 5, and the Xplain Pro expansion slot reserved in the experiment platform is connected with the 40pin expansion interface of the rising Altas200DK development board, so as to realize the hardware combined deployment of the experiment board and the rising Altas200DK development board. The main functions of the expansion interface are power supply and communication. And the teaching object selects different peripheral equipment and hardware connection interfaces according to the experiment requirements. For example, when the teaching object collects images, the teaching object is connected with a camera or a camera through a USB interface of the experiment board; the communication between the chips is completed by selecting protocols such as SPI, serial port and the like and interfaces.
Specifically, the AI image classification experiment implementation process comprises three processes of image acquisition and processing, model training deployment and AI image classification.
The teaching object collects images through an external camera or a camera of an experiment platform USB interface, and sends the images to a PC (personal computer) end through a serial port to wait for pretreatment through a PIC (peripheral interface controller) 16/18 series single chip microcomputer. The teaching object makes the image data collected by the experiment board and the peripheral equipment into a data set at the PC end, and the data preprocessing comprises the data alignment, labeling and the like. And then uploading the training model to a cloud server.
Specifically, model training deployment is performed, wherein a teaching object firstly processes a data set, and the process comprises the processes of data importing, data set labeling, data set publishing and the like. And then, creating a training operation on the cloud ModelArt platform to finish the training of the model. After training, the model is converted into the model architecture required by the local promotion Altas200DK development board and is exported. Then the teaching object uses the local PC side Mini Studio development environment to compile the exported model, and deploys the compiled model to the local Shengteng Altas200DK development board.
The specific AI image classification, the teaching object utilizes the external camera of experiment platform USB to gather image data. Then the teaching object software configuration and hardware are connected with an I2C, SPI or serial port communication interface in the experimental platform, and the image data is sent to the soar Altas200DK development board. The related software programs on the Shengteng Atlas 200DK development board are compiled, and initialization, loading of an inference model, assignment of inference pictures, preprocessing of the inference model and inference circulation are sequentially completed, and finally classification and identification of the images are realized.
The AI image classification experiment includes, but is not limited to, primary AI image classification tasks such as cat and dog classification, handwritten digit recognition, iris and the like.
The above description is only a preferred example of the present invention and is not intended to limit the present invention in other forms, and any modifications or equivalent variations which may be made by persons skilled in the art using the above teachings may be made in other fields, and all such modifications and variations are within the scope of the present invention.
Claims (1)
1. An intelligent experiment teaching platform based on an embedded core MCU and an AI chip is characterized in that,
software architecture, including MCU software architecture and Shengteng 200DK development board software architecture, in which the MCU software architecture includes basic routine, experimental board driver and expansion board API interface program; the software architecture of the Shengteng 200DK development board comprises a cloud ModelArt platform, a PC Mind Studio development environment and an image classification model;
the hardware framework arranged on the experiment board comprises a core MCU, a peripheral circuit, an expansion slot, a communication interface, an external storage and experiment simulation components for realizing onboard projects; the peripheral circuit comprises a PICKit debugger interface, an onboard power supply, a crystal oscillator, a reset circuit and a code downloading circuit; the expansion slots comprise a MicroBUS slot and an Xplain Pro expansion slot; the communication interface comprises a USB interface, an RS232 interface and an infrared transceiver; the external storage is a Flash storage chip; the onboard projects comprise industrial simulation projects and human-computer interaction projects;
the teaching object completes the intelligent experiment project through software layer configuration and hardware layer deployment; the intelligent experiment project comprises onboard projects, extended micro-projects and AI image classification experiments
On-board items including, but not limited to, buzzer items, digital tube display items, LCD display screen items, motors, and independent key items; the implementation process of each on-board item is as follows: designing a circuit diagram for a teaching object, and connecting a circuit on an experimental board by referring to the circuit diagram to realize hardware circuit connection; a teaching object designs a nixie tube control program in an IDE (integrated drive electronics) end of a PC (personal computer) to realize functions of countdown display and counter display. The PICKit 4 downloader is utilized to burn the code into the MCU of the experimental board for execution through an onboard download program interface to complete the corresponding onboard project,
the expansion micro-project is realized by combining an experimental board and an expansion board, and comprises but is not limited to entrance guard card simulation, drunk driving test, air temperature measurement, 8 multiplied by 8RGB colored board and atmospheric pressure test projects; the implementation process of each expansion micro item is as follows: a teaching object selects a corresponding expansion module and is deployed on the experiment board through a MicroBUS slot; a control program is designed on a teaching object in an IDE (integrated development environment) of a PC (personal computer) end, a PICKit 4 downloader is utilized, and a code is burnt to an MCU (microprogrammed control unit) of an experimental board for execution through an onboard downloading program interface to complete a corresponding expansion micro-project;
an AI image classification experiment, which is deployed on a hardware level and comprises the steps of connecting a reserved Xplain Pro expansion slot with a 40pin expansion interface of a rising Altas200DK development board to realize the hardware joint deployment of an experiment board and the rising Altas200DK development board; the main functions of the expansion interface are power supply and communication; the teaching object selects different peripheral equipment and hardware connection interfaces according to experiment requirements;
the AI image classification experiment implementation process comprises three processes of image acquisition and processing, model training deployment and AI image classification;
in the image acquisition and processing process, a teaching object is externally connected with a camera or a camera through a USB interface of an experimental teaching platform to acquire images, and the images are transmitted to a PC (personal computer) end through a serial port to wait for pretreatment through a PIC (peripheral interface controller) 16/18 series single chip microcomputer; the teaching object makes image data collected by an experimental board and matched with peripheral equipment into a data set at a PC (personal computer) end, and the data alignment and labeling pretreatment processes are included; uploading a cloud server training model;
the model training and deploying process comprises the steps that a teaching object processes a data set, and the data set processing comprises data importing, data set labeling and data set publishing; creating training operation on a cloud ModelArt platform to finish the training of the model; after training, the model is converted into a model architecture required by a local soar Altas200DK development board and is exported; the teaching object uses the derived model to perform compiling processing by using the local PC terminal Mini Studio development environment and deploys the compiled model to a local Shengteng Altas200DK development board;
in the AI image classification process, a teaching object utilizes an experimental teaching platform USB external camera to acquire image data; then, the software configuration and hardware of the teaching object are connected with an I2C, SPI or serial port communication interface in the experimental teaching platform, and the image data is sent to a rising Altas200DK development board; the related software programs on the Shengteng Atlas 200DK development board are compiled, and initialization, loading of an inference model, assignment of inference pictures, preprocessing of the inference model and inference circulation are sequentially completed, and finally classification and identification of the images are realized.
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