CN111862727A - Artificial intelligence graphical programming teaching platform and method - Google Patents
Artificial intelligence graphical programming teaching platform and method Download PDFInfo
- Publication number
- CN111862727A CN111862727A CN202010736896.5A CN202010736896A CN111862727A CN 111862727 A CN111862727 A CN 111862727A CN 202010736896 A CN202010736896 A CN 202010736896A CN 111862727 A CN111862727 A CN 111862727A
- Authority
- CN
- China
- Prior art keywords
- module
- artificial intelligence
- instruction
- automatic driving
- jetson nano
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 70
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000011161 development Methods 0.000 claims abstract description 53
- 238000003860 storage Methods 0.000 claims abstract description 16
- 238000010276 construction Methods 0.000 claims abstract description 7
- 238000004519 manufacturing process Methods 0.000 claims abstract description 6
- 230000018109 developmental process Effects 0.000 claims description 51
- 238000006243 chemical reaction Methods 0.000 claims description 12
- 238000012549 training Methods 0.000 claims description 12
- 238000004891 communication Methods 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 5
- 238000001514 detection method Methods 0.000 claims description 3
- 230000007246 mechanism Effects 0.000 claims description 3
- 238000003058 natural language processing Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 9
- 238000004364 calculation method Methods 0.000 abstract description 4
- 230000006870 function Effects 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 206010003805 Autism Diseases 0.000 description 1
- 208000020706 Autistic disease Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001149 cognitive effect Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B19/00—Teaching not covered by other main groups of this subclass
- G09B19/0053—Computers, e.g. programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/30—Creation or generation of source code
- G06F8/34—Graphical or visual programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention relates to an artificial intelligence graphical programming teaching platform and a method, wherein the teaching platform comprises: the Jetson Nano system operation module is used for operating a Jetson Nano system; the teaching storage module is used for storing a basic artificial intelligence algorithm; the teaching calling module is used for calling from the teaching storage module according to a first instruction based on a Jetson Nano system and assisting a user in mastering and learning the basic artificial intelligence algorithm; an automatic driving algorithm support library for storing an automatic driving algorithm; the automatic driving expansion module is used for calling an automatic driving algorithm from the automatic driving algorithm support library according to a second instruction based on a Jetson Nano system, and assisting a user to learn and finish lane identification, automatic obstacle avoidance, driving decision, instant positioning and map construction; the integrated development module is used for manufacturing the works based on the operated Jetson Nano system and the integrated development environment according to the third instruction; therefore, students can experience a complete calculation processing flow at the terminal.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence graphical programming teaching platform and method based on embedded high-performance computing.
Background
With the rapid development of artificial intelligence technology, artificial intelligence has been widely used in various industries and fields, and has become a new focus of global international competition and industrial revolution. The countries in the world propose that the artificial intelligence related courses are set at the stage of primary and middle schools, and an artificial intelligence education equipment system for primary and middle schools is gradually formed, wherein the artificial intelligence application software, the programming platform and the intelligent robot building module are main modules. The existing education equipment is lack of systematicness and usability, and therefore middle and primary school students need a systematical artificial intelligence teaching system which meets the cognitive level and has progressive difficulty gradient, so that the students can learn knowledge of all levels of artificial intelligence through guidance and autonomous learning.
A group of research institutions for educational robots have been introduced, and the research directions thereof relate to robot teaching, man-machine interaction, autism childhood education, and the like, and are applied to STEAM (Science, Technology, Engineering, Arts, and Mathematics, Science, Technology, Engineering, Arts, and maths) fusion education, children's entertainment education partners, and remote control robots, and the like. The teaching external member based on the Micro bit or Arduino open source hardware platform, the unmanned aerial vehicle, the intelligent sound equipment type intelligent home and other products become main channels for students to experience and practice artificial intelligence technology, and more education robots are applied to off-line education scenes in the future.
However, the existing artificial intelligence teaching equipment is mainly based on the classical sensing control function. Although some computer vision and intelligent voice programming products exist, the computing service of a remote server needs to be called, and students cannot experience complete computing processing flows of deep artificial intelligence algorithms such as data collection, feature extraction, deep neural network training, test verification and the like at a terminal.
Disclosure of Invention
In order to solve the above problems in the prior art, that is, in order to enable students to experience a complete calculation processing flow at a terminal, the present invention aims to provide an artificial intelligence graphical programming teaching platform and method based on embedded high performance calculation.
In order to solve the technical problems, the invention provides the following scheme:
an artificial intelligence graphical programming teaching platform, the teaching platform comprising:
the Jetson Nano system operation module is used for operating a Jetson Nano system;
the teaching storage module is used for storing a basic artificial intelligence algorithm;
the teaching calling module is respectively connected with the Jetson Nano system operation module and the teaching storage module and is used for calling from the teaching storage module according to a first instruction of a user based on the Jetson Nano system and assisting the user in mastering and learning the basic artificial intelligence algorithm;
an automatic driving algorithm support library for storing an automatic driving algorithm;
the automatic driving expansion module is respectively connected with the Jetson Nano system operation module and the automatic driving algorithm support library, and is used for calling an automatic driving algorithm from the automatic driving algorithm support library according to a second instruction of a user based on the Jetson Nano system, and assisting the user to learn and complete lane identification, automatic obstacle avoidance, driving decision, instant positioning and map construction;
and the integrated development module is connected with the Jetson Nano system operation module and used for making the works based on the operated Jetson Nano system and the integrated development environment according to a third instruction of a user.
Optionally, the basic artificial intelligence algorithm comprises at least one of image classification, object detection, target tracking, semantic segmentation, speech recognition, text recognition, natural language processing.
Optionally, the integrated development module is divided into an offline development module and an online development module.
Optionally, the integrated development module includes: the system comprises a graphical interface submodule, a virtual machine submodule, a code conversion submodule and a communication management submodule; wherein the content of the first and second substances,
the graphical interface submodule is used for realizing a software interface of an integrated development environment;
the virtual machine submodule is used for realizing a bottom mechanism and a driver for code interpretation and compilation;
the code conversion submodule is used for realizing switching between graphical coding and python text codes according to a third instruction of a user;
the communication management submodule is respectively connected with the code conversion submodule and the Jetson Nano operation system module, and is used for sending the converted and executable python code to the Jetson Nano system and starting execution.
Optionally, the teaching platform further comprises:
and the external electronic equipment is respectively connected with the teaching calling module, the automatic driving extension module, the integrated development module and the Jetson Nano system operation module.
Optionally, the external electronic device comprises a plurality of sensors and actuators connected through GPIO,
each sensor is connected with a Jetson Nano operation system module and used for sending intelligently sensed information to a Jetson Nano system;
the actuator is respectively connected with the teaching calling module, the automatic driving extension module and the integrated development module and is used for sending a first instruction, a second instruction and a third instruction.
Optionally, the external electronic device further includes:
and the display equipment is connected with the Jetson Nano operation system and used for displaying the learning process or the production process of the work based on the basic artificial intelligence algorithm or the automatic driving algorithm in the operation of the Jetson Nano operation system.
Optionally, the display device is a display device that supports an HDMI interface or supports conversion to an HDMI interface.
In order to solve the technical problems, the invention also provides the following scheme:
an artificial intelligence graphical programming teaching method, the teaching method comprising:
burning a system image file containing a development tool in a memory card;
inserting the burned storage card into the artificial intelligent graphical programming teaching platform, and operating a Jetsonnano system; and completing any of the following operations:
receiving a first instruction of a user, calling the first instruction and assisting the user to master and learn the basic artificial intelligence algorithm;
receiving a second instruction of a user, calling an automatic driving algorithm according to the second instruction, and assisting the user to learn and complete lane identification, automatic obstacle avoidance, driving decision, instant positioning and map construction;
and receiving a third instruction of a user, and making the work based on the operated Jetson Nano system and the integrated development environment according to the third instruction.
Optionally, the manufacturing of the work based on the executed Jetson Nano system and the integrated development environment according to the third instruction specifically includes:
completing an automatic driving code program or opening an example program in a code editing area of the integrated development environment;
the programming platform issues the code program to a Jetson Nano system through WIFI network connection;
when the device end program service receives a code message sent by the programming platform through network connection, a python interpreter is called to interpret and execute the code;
determining debugging information to be output by looking up the debugging command output area;
and saving the automatic driving training model codes.
According to the embodiment of the invention, the invention discloses the following technical effects:
the invention supports on-line programming, does not need additional computer, can log in the remote integrated development environment through the browser, students only need to master basic artificial intelligence algorithm knowledge, and can use the algorithm model through the teaching platform of the invention, collect data, train, extract characteristics, adjust parameters, and realize machine vision, voice processing and automatic driving functions; and the related functions of automatic driving and the like require a user to manually assemble equipment, understand an algorithm, adjust parameters and train a model, so that the manual ability of students can be exercised.
Drawings
FIG. 1 is a schematic block diagram of an artificial intelligence graphical programming teaching platform according to the present invention;
FIG. 2 is a schematic diagram of an embodiment of an artificial intelligence graphical programming teaching platform of the present invention;
fig. 3 is a training flow for the automatic driving function.
Description of the symbols:
the system comprises a Jetson Nano system operation module-1, a teaching storage module-2, a teaching calling module-3, an automatic driving algorithm support library-4, an automatic driving extension module-5 and an integrated development module-6.
Detailed Description
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and are not intended to limit the scope of the present invention.
The invention aims to provide an artificial intelligence graphical programming teaching platform, which supports online programming, does not need an additional computer, can log in a remote integrated development environment through a browser, and students can collect data, train, extract characteristics and adjust parameters by using an algorithm model through the teaching platform only by mastering basic artificial intelligence algorithm knowledge, thereby realizing the functions of machine vision, voice processing and automatic driving; and the related functions of automatic driving and the like require a user to manually assemble equipment, understand an algorithm, adjust parameters and train a model, so that the manual ability of students can be exercised.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the artificial intelligence graphical programming teaching platform of the invention comprises a Jetson Nano system operation module 1, a teaching storage module 2, a teaching calling module 3, an automatic driving algorithm support library 4, an automatic driving extension module 5 and an integrated development module 6.
The Jetson Nano system operation module 1 is used for operating a Jetson Nano system;
the teaching storage module 2 is used for storing a basic artificial intelligence algorithm;
the teaching calling module 3 is respectively connected with the Jetson Nano system operation module 1 and the teaching storage module 2, and the teaching calling module 3 is used for calling from the teaching storage module according to a first instruction of a user based on the Jetson Nano system and assisting the user in mastering and learning the basic artificial intelligence algorithm;
the automatic driving algorithm support library 4 is used for storing an automatic driving algorithm;
the automatic driving expansion module 5 is respectively connected with the Jetson Nano system operation module 1 and the automatic driving algorithm support library 5, and the automatic driving expansion module 5 is used for calling an automatic driving algorithm from the automatic driving algorithm support library according to a second instruction of a user based on the Jetson Nano system, and assisting the user to learn and complete lane identification, automatic obstacle avoidance, driving decision, instant positioning and map construction;
the integrated development module 6 is connected with the Jetson Nano system operation module 1, and the integrated development module 6 is used for making works based on the operational Jetson Nano system and the integrated development environment according to a third instruction of a user.
The basic artificial intelligence algorithm comprises at least one of image classification, object detection, target tracking, semantic segmentation, voice recognition, text recognition and natural language processing.
Further, as shown in fig. 2, the integrated development modules of the two programming modes of the graph and Python code of Scratch include an offline development module (offline version) and an online development module (network version): the off-line version can run in an operating system of the JetbotNano module to realize a teaching system independent of a personal computer; the network version operates in the form of a cloud server, provides SAAS (Software As a Service) leasing Service of a development environment, logs in after a user account needs to be registered, saves user information and a deep neural network model needing to be trained, submits demonstration works created by the user, and performs technical communication in a virtual community.
Preferably, the integrated development environment can be operated off line and opened or logged in through a browser and consists of an instruction building block area, an instruction editing area, a debugging output area, a code editing area and a toolbar;
the instruction editing area is an area graphically programmed by a user; the user can drag an instruction building block from the instruction building block area to select different artificial intelligence algorithms and models to the instruction editing area, edit algorithm processing flows, adjust model parameters and form a graphical program; the code editing area is used for python code programming; the integrated development environment may automatically switch the graphical display and the python code text. The debugging output area is used for displaying debugging information of a user execution program; the toolbar includes: open, save, edit, connect hardware, run program, save program, graphics and code switch and code frame selection.
The user can save the program edited and debugged in the instruction or code editing area through the function of saving the program. The network edition user can check the stored code through the online works library
Optionally, the integrated development module includes: the system comprises a graphical interface submodule, a virtual machine submodule, a code conversion submodule and a communication management submodule; wherein the content of the first and second substances,
the graphical interface submodule is used for realizing a software interface of an integrated development environment;
the virtual machine submodule is used for realizing a bottom mechanism and a driver for code interpretation and compilation;
the code conversion submodule is used for realizing switching between graphical coding and python text codes according to a third instruction of a user;
the communication management submodule is respectively connected with the code conversion submodule and the Jetson Nano operation system module, and is used for sending the converted and executable python code to the Jetson Nano system and starting execution.
In addition, the artificial intelligence graphical programming teaching platform further comprises: and the external electronic equipment is respectively connected with the teaching calling module, the automatic driving extension module, the integrated development module and the Jetson Nano system operation module.
Preferably, the external electronic device comprises a plurality of sensors and actuators connected through GPIO (General purpose input Output);
each sensor is connected with a Jetson Nano operation system module and used for sending intelligently sensed information to a Jetson Nano system;
the actuator is respectively connected with the teaching calling module, the automatic driving extension module and the integrated development module and is used for sending a first instruction, a second instruction and a third instruction.
Optionally, the external electronic device further includes:
and the display equipment is connected with the Jetson Nano operation system and used for displaying the learning process or the production process of the work based on the basic artificial intelligence algorithm or the automatic driving algorithm in the operation of the Jetson Nano operation system.
In this embodiment, the display device is a display device that supports an HDMI interface or supports conversion to an HDMI interface.
In addition, the external electronic device further includes: communication equipment and power module. The communication equipment is various network adapter modules supporting the WIFI function, and the power supply module is a 5V rechargeable power supply.
The invention discloses an artificial intelligence algorithm graphical programming teaching platform with an embedded CPU + GPU as an inner core. Because the invention adopts the high-performance computing platform with powerful functions, the developed educational equipment system does not depend on the support of the cloud artificial intelligence platform, and can also experience the processing flows of conventional high-end artificial intelligence applications such as complex data processing, model training and the like. Graphical programming has not only reduceed mr and student's threshold of entrying, and the teaching process does not need computer equipment moreover, and the hardware platform supports the graphical programming environment of localization, has simplified the application of complicated artificial intelligence algorithm, utilizes JetsonNano module can not rely on the artificial intelligence computational service in high in the clouds, greatly reduced with artificial intelligence technique popularization's threshold and cost, better practical value has.
In addition, the invention also provides an artificial intelligence graphical programming teaching method, which can enable students to experience a complete calculation processing flow at a terminal.
Specifically, the artificial intelligence graphical programming teaching method comprises the following steps:
burning a system image file containing a development tool in a memory card; in the present embodiment, the memory card is a micro SD.
Inserting the burned storage card into the artificial intelligent graphical programming teaching platform, and operating a Jetsonnano system; and completing any of the following operations:
receiving a first instruction of a user, calling the first instruction and assisting the user to master and learn the basic artificial intelligence algorithm;
receiving a second instruction of a user, calling an automatic driving algorithm according to the second instruction, and assisting the user to learn and complete lane identification, automatic obstacle avoidance, driving decision, instant positioning and map construction;
and receiving a third instruction of a user, and making the work based on the operated Jetson Nano system and the integrated development environment according to the third instruction.
Preferably, the manufacturing of the work based on the executed Jetson Nano system and the integrated development environment according to the third instruction specifically includes:
completing an automatic driving code program or opening an example program in a code editing area of the integrated development environment;
the programming platform issues the code program to a Jetson Nano system through WIFI network connection;
when the device end program service receives a code message sent by the programming platform through network connection, a python interpreter is called to interpret and execute the code;
determining debugging information to be output by looking up the debugging command output area;
and saving the automatic driving training model codes.
The user may save the model code built in the code editing area of the integrated development environment to the server under a custom name, and may subsequently reload the saved code and edit and run it (as shown in fig. 3).
Specifically, the use flow of the artificial intelligent graphical programming teaching method mainly comprises five stages: hardware assembly, system mirror image downloading of an integrated development tool, selection and creation of an artificial intelligence basic algorithm or an automatic driving model, collection of training data, training of the model, testing of the effect of the trained algorithm, and storage of model data.
1. Hardware preparation
In the stage, a system mirror image containing a development tool is firstly burnt in advance on a micro SD card, then the micro SD card is inserted into a card slot of a Jetson Nano, then a power supply module, a display device, a mouse, a keyboard, a communication module, a motor drive and a vehicle frame wheel are all connected to the Jetson Nano module, and after the machine is started up and normally networked, a Jetson Nano operating system is logged in.
2. Establishing device connections
And opening a web browser (a browser installed on the Jetson Nano or a browser on a computer in the same local area network with the Jetson Nano), inputting the website of the cloud server, and logging in by using a given user name and a given password. And then clicking a device connection button to connect the device, and connecting the Jetson Nano to the server.
3. Creating artificial intelligence algorithm training program
This example requires creating a correlation program of artificial intelligence algorithms and adjusting neural network parameters in the program.
4. Collecting training data
And opening a camera and a microphone connected to the Jetson Nano board, and acquiring video and audio information.
5. Training model
And importing the collected audio and video information into the selected artificial intelligence algorithm model, and training the model by combining the algorithm function.
6. Test-trained automatic driving model
The method is realized by graphical programming or python code programming. And (4) running an algorithm program, detecting to realize the effect, putting the result into practice if the result is acceptable, and continuing to iterate the data acquisition and training parameter adjusting process if the result is not acceptable.
In conclusion, the invention can convert the application of the artificial intelligence technology depending on the display experience in the traditional teaching into the realization of the artificial intelligence algorithm by students, and only needs one set of hardware teaching platform and necessary external input and output equipment without using an additional PC computer, thereby greatly reducing the threshold and the cost of the artificial intelligence teaching. The basic knowledge of the artificial intelligence algorithm realizes a teaching mode of 'learning during playing' through the practice of students, stimulates the learning interest, creativity and imagination of the students, and enables the students to learn and understand the artificial intelligence technology more vividly and profoundly.
Compared with the prior art, the artificial intelligent graphical programming teaching method has the same beneficial effects as the artificial intelligent graphical programming teaching system, and is not repeated herein.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (10)
1. The utility model provides an artificial intelligence graphical programming teaching platform which characterized in that, teaching platform includes:
the Jetson Nano system operation module is used for operating a Jetson Nano system;
the teaching storage module is used for storing a basic artificial intelligence algorithm;
the teaching calling module is respectively connected with the Jetson Nano system operation module and the teaching storage module and is used for calling from the teaching storage module according to a first instruction of a user based on the Jetson Nano system and assisting the user in mastering and learning the basic artificial intelligence algorithm;
an automatic driving algorithm support library for storing an automatic driving algorithm;
the automatic driving expansion module is respectively connected with the Jetson Nano system operation module and the automatic driving algorithm support library, and is used for calling an automatic driving algorithm from the automatic driving algorithm support library according to a second instruction of a user based on the Jetson Nano system, and assisting the user to learn and complete lane identification, automatic obstacle avoidance, driving decision, instant positioning and map construction;
and the integrated development module is connected with the Jetson Nano system operation module and used for making the works based on the operated Jetson Nano system and the integrated development environment according to a third instruction of a user.
2. The artificial intelligence graphical programming teaching platform of claim 1 wherein the basic artificial intelligence algorithm comprises at least one of image classification, object detection, object tracking, semantic segmentation, speech recognition, text recognition, natural language processing.
3. The artificial intelligence graphical programming teaching platform of claim 1 wherein said integrated development module is divided into an off-line development module and an on-line development module.
4. The artificial intelligence graphical programming teaching platform of claim 1 wherein said integrated development module comprises: the system comprises a graphical interface submodule, a virtual machine submodule, a code conversion submodule and a communication management submodule; wherein the content of the first and second substances,
the graphical interface submodule is used for realizing a software interface of an integrated development environment;
the virtual machine submodule is used for realizing a bottom mechanism and a driver for code interpretation and compilation;
the code conversion submodule is used for realizing switching between graphical coding and python text codes according to a third instruction of a user;
the communication management submodule is respectively connected with the code conversion submodule and the Jetson Nano operation system module, and is used for sending the converted and executable python code to the Jetson Nano system and starting execution.
5. The artificial intelligence graphical programming teaching platform of claim 1 wherein said teaching platform further comprises:
and the external electronic equipment is respectively connected with the teaching calling module, the automatic driving extension module, the integrated development module and the Jetson Nano system operation module.
6. The artificial intelligence graphical programming teaching platform of claim 5 wherein said external electronic device comprises a plurality of sensors, actuators connected through GPIO,
each sensor is connected with a Jetson Nano operation system module and used for sending intelligently sensed information to a Jetson Nano system;
the actuator is respectively connected with the teaching calling module, the automatic driving extension module and the integrated development module and is used for sending a first instruction, a second instruction and a third instruction.
7. The artificial intelligence graphical programming teaching platform of claim 5 wherein said external electronic device further comprises:
and the display equipment is connected with the Jetson Nano operation system and used for displaying the learning process or the production process of the work based on the basic artificial intelligence algorithm or the automatic driving algorithm in the operation of the Jetson Nano operation system.
8. The artificial intelligence graphical programming teaching platform of claim 7, wherein the display device is a display device supporting an HDMI interface or a conversion to an HDMI interface.
9. An artificial intelligence graphical programming teaching method, characterized in that the teaching method comprises:
burning a system image file containing a development tool in a memory card;
inserting the burned memory card into the artificial intelligent graphical programming teaching platform of any one of claims 1-8, and operating a Jetson Nano system; and completing any of the following operations:
receiving a first instruction of a user, calling the first instruction and assisting the user to master and learn the basic artificial intelligence algorithm;
receiving a second instruction of a user, calling an automatic driving algorithm according to the second instruction, and assisting the user to learn and complete lane identification, automatic obstacle avoidance, driving decision, instant positioning and map construction;
and receiving a third instruction of a user, and making the work based on the operated Jetson Nano system and the integrated development environment according to the third instruction.
10. The artificial intelligence graphical programming teaching method according to claim 9, wherein the making of the work based on the executed Jetson Nano system and the integrated development environment according to the third instruction specifically includes:
completing an automatic driving code program or opening an example program in a code editing area of the integrated development environment;
the programming platform issues the code program to a Jetson Nano system through WIFI network connection;
when the device end program service receives a code message sent by the programming platform through network connection, a python interpreter is called to interpret and execute the code;
determining debugging information to be output by looking up the debugging command output area;
and saving the automatic driving training model codes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010736896.5A CN111862727B (en) | 2020-07-28 | 2020-07-28 | Artificial intelligence graphical programming teaching platform and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010736896.5A CN111862727B (en) | 2020-07-28 | 2020-07-28 | Artificial intelligence graphical programming teaching platform and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111862727A true CN111862727A (en) | 2020-10-30 |
| CN111862727B CN111862727B (en) | 2022-11-08 |
Family
ID=72948543
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010736896.5A Active CN111862727B (en) | 2020-07-28 | 2020-07-28 | Artificial intelligence graphical programming teaching platform and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111862727B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112463138A (en) * | 2020-11-18 | 2021-03-09 | 华南师范大学 | Software and hardware combined artificial intelligence education learning system |
| CN114035447A (en) * | 2021-10-20 | 2022-02-11 | 华南师范大学 | Hardware simulator for graphic programming software of primary and secondary school artificial intelligence education |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106504614A (en) * | 2016-12-01 | 2017-03-15 | 华南理工大学 | A kind of educational robot of building block system programming |
| CN109523848A (en) * | 2018-12-12 | 2019-03-26 | 珠海城市职业技术学院 | Artificial intelligence technology instructional device |
| CN109783256A (en) * | 2019-01-10 | 2019-05-21 | 上海商汤智能科技有限公司 | Artificial intelligence tutoring system and method, electronic equipment, storage medium |
| CN110316194A (en) * | 2019-07-08 | 2019-10-11 | 广州易流新能源汽车科技有限公司 | A kind of pilotless automobile drive system, drive control method and development approach |
| CN110362299A (en) * | 2019-06-14 | 2019-10-22 | 杭州古德微机器人有限公司 | A kind of inline graphics programing system and its application method based on blockly and raspberry pie |
| CN110534009A (en) * | 2019-09-05 | 2019-12-03 | 北京青橙创客教育科技有限公司 | A kind of unmanned course teaching aid of artificial intelligence |
| CN110727219A (en) * | 2019-09-27 | 2020-01-24 | 芜湖市春正农业科技有限公司 | Agricultural machinery autopilot navigation |
| CN110908648A (en) * | 2018-09-17 | 2020-03-24 | 北京龙码科技有限公司 | Artificial intelligence aided graphical programming teaching method and system |
-
2020
- 2020-07-28 CN CN202010736896.5A patent/CN111862727B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106504614A (en) * | 2016-12-01 | 2017-03-15 | 华南理工大学 | A kind of educational robot of building block system programming |
| CN110908648A (en) * | 2018-09-17 | 2020-03-24 | 北京龙码科技有限公司 | Artificial intelligence aided graphical programming teaching method and system |
| CN109523848A (en) * | 2018-12-12 | 2019-03-26 | 珠海城市职业技术学院 | Artificial intelligence technology instructional device |
| CN109783256A (en) * | 2019-01-10 | 2019-05-21 | 上海商汤智能科技有限公司 | Artificial intelligence tutoring system and method, electronic equipment, storage medium |
| CN110362299A (en) * | 2019-06-14 | 2019-10-22 | 杭州古德微机器人有限公司 | A kind of inline graphics programing system and its application method based on blockly and raspberry pie |
| CN110316194A (en) * | 2019-07-08 | 2019-10-11 | 广州易流新能源汽车科技有限公司 | A kind of pilotless automobile drive system, drive control method and development approach |
| CN110534009A (en) * | 2019-09-05 | 2019-12-03 | 北京青橙创客教育科技有限公司 | A kind of unmanned course teaching aid of artificial intelligence |
| CN110727219A (en) * | 2019-09-27 | 2020-01-24 | 芜湖市春正农业科技有限公司 | Agricultural machinery autopilot navigation |
Non-Patent Citations (1)
| Title |
|---|
| DCEXPERT: "PythonEditor 图形化编程中文网站即将正式启动", 《电子工程世界-论坛》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112463138A (en) * | 2020-11-18 | 2021-03-09 | 华南师范大学 | Software and hardware combined artificial intelligence education learning system |
| CN114035447A (en) * | 2021-10-20 | 2022-02-11 | 华南师范大学 | Hardware simulator for graphic programming software of primary and secondary school artificial intelligence education |
| CN114035447B (en) * | 2021-10-20 | 2022-05-31 | 华南师范大学 | Hardware simulator for graphic programming software of primary and secondary school artificial intelligence education |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111862727B (en) | 2022-11-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hitron et al. | Can children understand machine learning concepts? The effect of uncovering black boxes | |
| Green et al. | Applying the Wizard-of-Oz framework to cooperative service discovery and configuration | |
| US10163365B2 (en) | System and method for teaching programming of devices | |
| Wächter et al. | Action sequence reproduction based on automatic segmentation and object-action complexes | |
| CN203300127U (en) | Children teaching and monitoring robot | |
| CN111862727B (en) | Artificial intelligence graphical programming teaching platform and method | |
| KR20180124310A (en) | Modular Robot System for Coding Education Using Artificial Intelligence | |
| CN112463138B (en) | Software and hardware combined artificial intelligence education learning system | |
| Bertacchini et al. | A project based learning approach for improving students’ computational thinking skills | |
| Clunne-Kiely et al. | Modelling and implementation of humanoid robot behaviour | |
| CN110908648A (en) | Artificial intelligence aided graphical programming teaching method and system | |
| Moshayedi et al. | Integrating virtual reality and robotic operation system (ROS) for AGV navigation | |
| KR20200063602A (en) | Modular System for Coding Education Using Artificial Intelligence | |
| Bugmann et al. | Using verbal instructions for route learning: Instruction analysis | |
| CN110070869B (en) | Voice teaching interaction generation method, device, equipment and medium | |
| CN115798033A (en) | Piano training method, system, equipment and storage medium based on gesture recognition | |
| Xiao et al. | A structure design of virtual and real fusion intelligent equipment and multimodal navigational interaction algorithm | |
| Lauria et al. | Instruction based learning: how to instruct a personal robot to find hal | |
| RU2718513C1 (en) | Small anthropomorphic robot educational and research complex | |
| CN112233208A (en) | Robot state processing method and device, computing equipment and storage medium | |
| Bugmann | Effective Spoken interfaces to service robots: Open problems. | |
| Haputhanthri et al. | Computer vision based navigation robot | |
| Borde et al. | Object Recognition based on Deep Learning Algorithms using Embedded IoT with Interactive Interface | |
| Benjamin et al. | Using cognitive semantics to integrate perception and motion in a behavior-based robot | |
| Zhang et al. | A ros-based universal robotic platform for artificial intelligence education |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |