Teenager science popularization robot control system based on artificial intelligence

Abstract

The invention discloses a teenager science popularization robot control system based on artificial intelligence, and belongs to the technical field of robots, and the system comprises a navigation obstacle avoidance module which is used for carrying out the autonomous navigation and obstacle avoidance control of a teenager science popularization robot; the science popularization acquisition module is used for acquiring a real-time image of an article to be subjected to science popularization based on computer vision; and the science popularization control terminal is used for processing, analyzing and identifying the real-time image of the article to be subjected to science popularization based on computer vision, and performing automatic science popularization control on the article to be subjected to science popularization according to the analysis and identification result of the article to be subjected to science popularization based on artificial intelligence. The problems that the existing teenager science popularization mode is single and depends on manpower, teenager science popularization cannot be effectively explained and intelligently controlled, the teenager science popularization effect is poor, and the interestingness and the sense of participation of science popularization education are reduced are solved. According to the invention, effective explanation and intelligent control can be carried out on teenager science popularization, the teenager science popularization effect can be improved, and the interestingness and participation sense of science popularization education are improved.

Description

Technical Field

[0001] The present invention relates to the field of robotics technology, in particular to a youth science popularization robot control system based on artificial intelligence. Background Art

[0002] A robot is an intelligent machine that can work semi-autonomously or fully autonomously. It can perform tasks such as operation or movement through programming and automatic control. With the development of artificial intelligence interaction technology, more and more robots have come into people's view.

[0003] The Chinese patent with publication number CN117666454A discloses a children's programming robot based on artificial intelligence and its control system, including a supporting base plate, a protective box is fixedly connected to the upper surface of the supporting base plate, a mainboard mask is fixedly connected to one side of the protective box, and a display panel is fixedly connected to the inside of the mainboard mask away from the protective box. By performing a combined evaluation analysis from the three perspectives of the power supply end, the drive end and the execution end, and combining the interference evaluation coefficient R for analysis, the control effect and operation safety of the robot are guaranteed, and at the same time, it helps to improve the accuracy of the analysis results, that is, the power supply data of the power supply end is analyzed for operation risk supervision to ensure the normal operation and control of the robot, and the execution feedback analysis is performed on the state data of the drive end to ensure the execution performance of the robot, and the out-of-control risk evaluation analysis is performed in combination with the execution data of the execution end to ensure the control effect of the robot; however, the patent has the following defects:

[0004] The existing technology has a single way of popularizing science for teenagers and relies on manual labor. It cannot effectively explain and intelligently control popular science for teenagers, resulting in poor results of popularizing science for teenagers and reducing the interest and sense of participation in popular science education. Summary of the invention

[0005] The purpose of the present invention is to provide a youth science popularization robot control system based on artificial intelligence, which can effectively explain and intelligently control youth science popularization, improve the effect of youth science popularization, increase the interest and sense of participation in science popularization education, and solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The youth science robot control system based on artificial intelligence includes:

[0008] Navigation and obstacle avoidance module, used for autonomous navigation and obstacle avoidance control of the youth science robot;

[0009] The science collection module is used to collect real-time images of objects to be popularized based on computer vision;

[0010] The science popularization control terminal is used to process, analyze and identify real-time images of science objects to be popularized based on computer vision, and to perform automated science popularization control on the science objects based on the analysis and identification results of the science objects to be popularized based on artificial intelligence.

[0011] Preferably, the navigation obstacle avoidance module comprises:

[0012] An environment collection unit is used to monitor and collect the surrounding environment of the youth science popularization robot in real time based on sensors, and obtain the surrounding environment data of the youth science popularization robot;

[0013] The autonomous navigation unit is used to perform autonomous navigation control on the youth science popularization robot according to the surrounding environment data of the youth science popularization robot, so that the youth science popularization robot can autonomously walk and avoid obstacles in various environments according to changes in the surrounding environment.

[0014] Preferably, the science popularization collection module includes:

[0015] The object collection unit is used to monitor and collect the objects to be popularized in the environment around the youth science robot in real time based on computer vision technology, and obtain real-time images of the objects to be popularized based on computer vision;

[0016] A wireless transmission unit, used to transmit the collected real-time images of the objects to be popularized based on computer vision to the popularization control terminal based on a wireless network;

[0017] Among them, a data transmission link is established between the item collection unit and the science popularization control terminal;

[0018] The item collection unit sends a command to the science popularization control terminal requesting to establish a data transmission link;

[0019] After the science popularization control terminal receives the instruction sent by the item collection unit to request to establish a data transmission link, the science popularization control terminal checks the data transmission port of the item collection unit to determine whether the item collection unit is qualified to perform data transmission with the science popularization control terminal;

[0020] When the item collection unit is qualified to perform data transmission with the popular science control terminal, the popular science control terminal sends an instruction to the item collection unit to agree to establish a data transmission link. After the item collection unit receives the instruction sent by the popular science control terminal to agree to establish a data transmission link, a data transmission link is established between the item collection unit and the popular science control terminal;

[0021] The data transmission link between the item collection unit and the science popularization control terminal is established. The item collection unit transmits the collected real-time images of the items to be popularized based on computer vision to the science popularization control terminal, so that the science popularization control terminal analyzes and performs science popularization control on the real-time images of the items to be popularized based on computer vision.

[0022] Preferably, the popular science control terminal includes:

[0023] An image processing module is used to process the real-time image of the object to be popularized based on computer vision, and determine the characteristic image of the object to be popularized based on computer vision;

[0024] An analysis and recognition module is used to analyze and recognize characteristic images of objects to be popularized based on computer vision, and determine analysis and recognition results of objects to be popularized based on artificial intelligence;

[0025] The science popularization control module is used to provide automated science popularization explanations and human-computer interactive intelligent recommendations for science popularization items based on the analysis and recognition results of science popularization items based on artificial intelligence;

[0026] The user interface module is used to display the human-computer interaction process and science popularization learning content in a visual form, and to control the youth science popularization robot based on different functions and settings selected by the user.

[0027] Preferably, the image processing module includes:

[0028] An image denoising unit is used to perform denoising on the real-time image of the object to be popularized based on computer vision based on a median filter, divide the real-time image of the object to be popularized based on computer vision into blocks, find all pixels in its neighborhood for each pixel in each block, calculate the median of the pixels in the neighborhood, and use the median to replace the pixels on the original image, thereby reducing the noise in the real-time image of the object to be popularized based on computer vision;

[0029] An image adjustment unit is used to adjust the brightness and contrast of the real-time image of the object to be popularized based on computer vision based on image segmentation technology, to make the brightness of the real-time image of the object to be popularized based on computer vision consistent based on interpolation technology, and to automatically adjust the contrast of pixels in the real-time image of the object to be popularized based on computer vision based on an adaptive adjustment method based on global contrast and adjacent pixel relationship;

[0030] The feature extraction unit is used to extract features from the real-time images of the objects to be popularized based on computer vision based on the principal component analysis method, extract useful features from the real-time images of the objects to be popularized based on computer vision, and determine the feature images of the objects to be popularized based on computer vision.

[0031] Preferably, the image adjustment unit further includes:

[0032] A grayscale value acquisition module is used to extract the grayscale value corresponding to each pixel point of the real-time image after the contrast adjustment is completed based on the adaptive adjustment method;

[0033] A target pixel point set acquisition module is used to extract pixel points whose grayscale values ​​are not less than a preset grayscale threshold to form a target pixel point set;

[0034] A gray value coefficient acquisition module, used to acquire the gray value coefficient by using the gray value before contrast adjustment and the gray value after contrast adjustment of the pixel points included in the target pixel point set;

[0035] The gray value coefficient is obtained by the following formula:

[0036]

[0037] Where R represents the gray value coefficient; n represents the number of pixels contained in the target pixel set; J xi represents the grayscale value before contrast adjustment corresponding to the i-th pixel in the target pixel set; J hi represents the gray value after contrast adjustment corresponding to the i-th pixel in the target pixel set; J y Indicates the preset grayscale threshold; J hmax Indicates the maximum grayscale value after contrast adjustment corresponding to the pixel points contained in the target pixel point set; J m Indicates the maximum grayscale value before contrast adjustment corresponding to the pixel points included in the target pixel point set;

[0038] A coefficient comparison module, used for comparing the gray value coefficient with a preset coefficient threshold;

[0039] The secondary contrast adjustment module is used to perform secondary contrast adjustment on the real-time image when the gray value coefficient is lower than a preset coefficient threshold.

[0040] Preferably, the secondary contrast adjustment module comprises:

[0041] A weight intensity value calling module is used to call the weight intensity value of each pixel in the target pixel set in the adaptive adjustment method based on global contrast and adjacent pixel relationship;

[0042] A grayscale value retrieving module, used to retrieve the grayscale value before contrast adjustment and the grayscale value after contrast adjustment corresponding to each pixel point in the target pixel point set;

[0043] A grayscale value compensation coefficient acquisition module is used to obtain the grayscale value compensation coefficient corresponding to each pixel point by using the weight intensity value of each pixel point in the adaptive adjustment method based on global contrast and adjacent pixel relationship in combination with the grayscale value before contrast adjustment and the grayscale value after contrast adjustment corresponding to each pixel point;

[0044] The gray value compensation coefficient is obtained by the following formula:

[0045]

[0046] Among them, B i represents the gray value compensation coefficient corresponding to the i-th pixel in the target pixel set; J xi represents the grayscale value before contrast adjustment corresponding to the i-th pixel in the target pixel set; J hi represents the grayscale value after contrast adjustment corresponding to the i-th pixel in the target pixel set; w i represents the weight intensity value corresponding to the i-th pixel in the target pixel set; w p Represents the average value of the weight intensity corresponding to n pixels in the target pixel set;

[0047] A grayscale value adjustment module is used to adjust the grayscale value of each pixel point using the grayscale value compensation coefficient of each pixel point;

[0048] Among them, the gray value of each pixel after adjustment is obtained by the following formula:

[0049] J ti =(1+B i )·J hi

[0050] Among them, J ti represents the adjusted grayscale value corresponding to the i-th pixel in the target pixel set; B i represents the gray value compensation coefficient corresponding to the i-th pixel in the target pixel set; J hi Represents the grayscale value after contrast adjustment corresponding to the i-th pixel in the target pixel set.

[0051] Preferably, the analysis and identification module includes:

[0052] Model training unit, used to train AI-based analysis and recognition models for popular science items based on deep learning technology;

[0053] The historical images of the objects to be popularized are collected, and the collected historical images of the objects to be popularized are divided into a training set and a test set;

[0054] Based on the training set, the deep learning model is trained to enable the deep learning model to autonomously learn the process of analyzing and identifying objects to be popularized, identify the content of the objects to be popularized, and determine the analysis and identification model of objects to be popularized based on artificial intelligence;

[0055] Based on the test set, the performance test of the artificial intelligence-based analysis and recognition model of items to be popularized is conducted to determine whether the artificial intelligence-based analysis and recognition model of items to be popularized can achieve the expected effect of identifying the content of items to be popularized, and according to the performance test results, the parameters of the artificial intelligence-based analysis and recognition model of items to be popularized are adjusted and the structure is optimized to determine the optimal artificial intelligence-based analysis and recognition model of items to be popularized;

[0056] An analysis and recognition unit, used to analyze and recognize the characteristic images of the objects to be popularized based on computer vision according to the optimal analysis and recognition model of the objects to be popularized based on artificial intelligence;

[0057] Among them, the optimal artificial intelligence-based analysis and recognition model of objects to be popularized science is deployed in the actual analysis and recognition environment of objects to be popularized science, and the characteristic images of objects to be popularized science based on computer vision are input into the optimal artificial intelligence-based analysis and recognition model of objects to be popularized science, and the characteristic images of objects to be popularized science based on computer vision are analyzed and recognized according to the optimal artificial intelligence-based analysis and recognition model of objects to be popularized science, to determine the artificial intelligence-based analysis and recognition results of objects to be popularized science.

[0058] Preferably, the popular science control module includes:

[0059] The science explanation unit is used to automatically explain the science items to be popularized based on the analysis and recognition results of the science items to be popularized based on artificial intelligence, so that young people can understand science knowledge;

[0060] The human-computer interaction unit is used to enable the youth science robot to understand the user's voice commands based on natural language processing technology, and interact with the user through voice feedback during the science popularization process;

[0061] The intelligent recommendation unit is used to intelligently recommend subsequent popular science learning content to users based on human-computer interaction feedback, including executing preset scientific experiments and providing science and technology exhibitions to users through demonstrations and explanations.

[0062] Preferably, the user interface module includes:

[0063] The interface display unit is used to display the human-computer interaction process and popular science learning content in a visual form. Brightly colored icons and animations are used to attract the attention of young people, while providing a variety of interactive methods to enhance user participation.

[0064] The function setting unit is used for users to select different functions and settings, which contains buttons and menus to control the youth science robot based on the different functions and settings selected by the user.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] 1. The present invention monitors and collects the surrounding environment of the youth science popularization robot in real time based on sensors, obtains the surrounding environment data of the youth science popularization robot, and performs autonomous navigation control on the youth science popularization robot according to the surrounding environment data of the youth science popularization robot, so that the youth science popularization robot can autonomously walk and avoid obstacles in various environments according to changes in the surrounding environment.

[0067] 2. The present invention conducts real-time monitoring and collection of science objects to be popularized in the surrounding environment of the youth science popularization robot based on computer vision technology, obtains real-time images of science objects to be popularized based on computer vision, determines feature images of science objects to be popularized based on computer vision by processing the real-time images of science objects to be popularized based on computer vision, and analyzes and identifies the feature images of science objects to be popularized based on computer vision according to the optimal artificial intelligence-based analysis and recognition model for science objects to be popularized, determines the artificial intelligence-based analysis and recognition results of science objects to be popularized, and conducts automated science popularization explanations and intelligent human-computer interaction recommendations for science objects according to the artificial intelligence-based analysis and recognition results of science objects to be popularized, which can effectively explain and intelligently control science popularization for teenagers, improve the science popularization effect for teenagers, and enhance the interest and participation of science popularization education. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 The module block diagram of the control system of the youth science popularization robot based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] In order to solve the problem that the existing popular science methods for teenagers are single and rely on manual labor, which cannot effectively explain and intelligently control popular science for teenagers, resulting in poor popular science effects for teenagers and reducing the fun and sense of participation in popular science education, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0071] The youth science popularization robot control system based on artificial intelligence includes: a navigation obstacle avoidance module, a science popularization collection module and a science popularization control terminal.

[0072] It should be noted that the youth science popularization robot is autonomously navigated and controlled to avoid obstacles through the navigation and obstacle avoidance module; the real-time images of the objects to be popularized based on computer vision are collected through the science popularization acquisition module; the real-time images of the objects to be popularized based on computer vision are processed, analyzed and identified through the science popularization control terminal, and the science popularization objects are automatically controlled according to the analysis and identification results of the objects to be popularized based on artificial intelligence. This can effectively explain and intelligently control youth science popularization, improve the science popularization effect for young people, and increase the fun and participation of science popularization education.

[0073] In this embodiment, the navigation obstacle avoidance module includes:

[0074] An environment collection unit is used to monitor and collect the surrounding environment of the youth science popularization robot in real time based on sensors, and obtain the surrounding environment data of the youth science popularization robot;

[0075] The autonomous navigation unit is used to perform autonomous navigation control on the youth science popularization robot according to the surrounding environment data of the youth science popularization robot, so that the youth science popularization robot can autonomously walk and avoid obstacles in various environments according to changes in the surrounding environment.

[0076] In this embodiment, the science collection module includes:

[0077] The object collection unit is used to monitor and collect the objects to be popularized in the environment around the youth science robot in real time based on computer vision technology, and obtain real-time images of the objects to be popularized based on computer vision;

[0078] A wireless transmission unit, used to transmit the collected real-time images of the objects to be popularized based on computer vision to the popularization control terminal based on a wireless network;

[0079] Among them, a data transmission link is established between the item collection unit and the science popularization control terminal;

[0080] The item collection unit sends a command to the science popularization control terminal requesting to establish a data transmission link;

[0081] After the science popularization control terminal receives the instruction sent by the item collection unit to request to establish a data transmission link, the science popularization control terminal checks the data transmission port of the item collection unit to determine whether the item collection unit is qualified to perform data transmission with the science popularization control terminal;

[0082] When the item collection unit is qualified to perform data transmission with the popular science control terminal, the popular science control terminal sends an instruction to the item collection unit to agree to establish a data transmission link. After the item collection unit receives the instruction sent by the popular science control terminal to agree to establish a data transmission link, a data transmission link is established between the item collection unit and the popular science control terminal;

[0083] The data transmission link between the item collection unit and the science popularization control terminal is established. The item collection unit transmits the collected real-time images of the items to be popularized based on computer vision to the science popularization control terminal, so that the science popularization control terminal analyzes and performs science popularization control on the real-time images of the items to be popularized based on computer vision.

[0084] In this embodiment, the science popularization control terminal includes: an image processing module, an analysis and recognition module, a science popularization control module and a user interface module.

[0085] It should be noted that the real-time image of the items to be popularized based on computer vision is processed by the image processing module to determine the characteristic image of the items to be popularized based on computer vision; the characteristic image of the items to be popularized based on computer vision is analyzed and identified by the analysis and identification module to determine the analysis and identification results of the items to be popularized based on artificial intelligence; the science popularization control module performs automated science popularization explanations and human-computer interaction intelligent recommendations on the items to be popularized according to the analysis and identification results of the items to be popularized based on artificial intelligence; the user interface module displays the human-computer interaction process and science popularization learning content in a visual form, and controls the youth science popularization robot based on different functions and settings selected by the user.

[0086] In this embodiment, the image processing module includes:

[0087] An image denoising unit is used to perform denoising on the real-time image of the object to be popularized based on computer vision based on a median filter, divide the real-time image of the object to be popularized based on computer vision into blocks, find all pixels in its neighborhood for each pixel in each block, calculate the median of the pixels in the neighborhood, and use the median to replace the pixels on the original image, thereby reducing the noise in the real-time image of the object to be popularized based on computer vision;

[0088] An image adjustment unit is used to adjust the brightness and contrast of the real-time image of the object to be popularized based on computer vision based on image segmentation technology, to make the brightness of the real-time image of the object to be popularized based on computer vision consistent based on interpolation technology, and to automatically adjust the contrast of pixels in the real-time image of the object to be popularized based on computer vision based on an adaptive adjustment method based on global contrast and adjacent pixel relationship;

[0089] The feature extraction unit is used to extract features from the real-time images of the objects to be popularized based on computer vision based on the principal component analysis method, extract useful features from the real-time images of the objects to be popularized based on computer vision, and determine the feature images of the objects to be popularized based on computer vision.

[0090] Specifically, the image adjustment unit further includes:

[0091] A grayscale value acquisition module is used to extract the grayscale value corresponding to each pixel point of the real-time image after the contrast adjustment is completed based on the adaptive adjustment method;

[0092] A target pixel point set acquisition module is used to extract pixel points whose grayscale values ​​are not less than a preset grayscale threshold to form a target pixel point set;

[0093] A gray value coefficient acquisition module, used to acquire the gray value coefficient by using the gray value before contrast adjustment and the gray value after contrast adjustment of the pixel points included in the target pixel point set;

[0094] The gray value coefficient is obtained by the following formula:

[0095]

[0096] Where R represents the gray value coefficient; n represents the number of pixels contained in the target pixel set; J xi represents the grayscale value before contrast adjustment corresponding to the i-th pixel in the target pixel set; J hi represents the gray value after contrast adjustment corresponding to the i-th pixel in the target pixel set; J y Indicates the preset grayscale threshold; J hmax Indicates the maximum grayscale value after contrast adjustment corresponding to the pixel points contained in the target pixel point set; J m Indicates the maximum grayscale value before contrast adjustment corresponding to the pixel points included in the target pixel point set;

[0097] A coefficient comparison module, used for comparing the gray value coefficient with a preset coefficient threshold;

[0098] The secondary contrast adjustment module is used to perform secondary contrast adjustment on the real-time image when the gray value coefficient is lower than a preset coefficient threshold.

[0099] The technical effect of the above technical solution is: through the grayscale value acquisition module, the grayscale value of each pixel in the real-time image after contrast adjustment can be accurately extracted to provide basic data for subsequent processing. The target pixel set acquisition module can screen out pixels with higher grayscale values. These pixels often contain important information or features in the image, which helps to more accurately evaluate the effect of contrast adjustment. The grayscale value coefficient acquisition module uses the grayscale values ​​before and after contrast adjustment to calculate the grayscale value coefficient through a specific formula. This coefficient reflects the degree of influence of contrast adjustment on the grayscale distribution of the image. This coefficient not only takes into account the grayscale value change of the pixel points in the target pixel set, but also introduces the preset grayscale threshold, the maximum grayscale value after contrast adjustment, and the maximum grayscale value before adjustment, so as to more comprehensively evaluate the effect of contrast adjustment. The coefficient comparison module compares the calculated grayscale value coefficient with the preset coefficient threshold. This step realizes the automatic evaluation of the contrast adjustment effect. When the grayscale value coefficient is lower than the preset coefficient threshold, the secondary contrast adjustment module will perform secondary adjustment on the real-time image to ensure that the contrast of the image reaches the desired effect. This adaptive adjustment mechanism helps to improve the flexibility and accuracy of image processing. The above technical solution can achieve precise control and optimization of image contrast, thereby improving the overall visual effect of the image. Especially when processing low-contrast images, this technical solution can significantly improve the clarity and detail of the image, making the information in the image easier to identify and interpret.

[0100] In summary, this technical solution achieves precise control and optimization of image contrast through accurate extraction of grayscale values, quantitative analysis of grayscale value changes, and an adaptive contrast adjustment mechanism, thereby improving the accuracy and flexibility of image processing.

[0101] Specifically, the secondary contrast adjustment module includes:

[0102] A weight intensity value calling module is used to call the weight intensity value of each pixel in the target pixel set in the adaptive adjustment method based on global contrast and adjacent pixel relationship;

[0103] A grayscale value retrieving module, used to retrieve the grayscale value before contrast adjustment and the grayscale value after contrast adjustment corresponding to each pixel point in the target pixel point set;

[0104] A grayscale value compensation coefficient acquisition module is used to obtain the grayscale value compensation coefficient corresponding to each pixel point by using the weight intensity value of each pixel point in the adaptive adjustment method based on global contrast and adjacent pixel relationship in combination with the grayscale value before contrast adjustment and the grayscale value after contrast adjustment corresponding to each pixel point;

[0105] The gray value compensation coefficient is obtained by the following formula:

[0106]

[0107] Among them, B i represents the gray value compensation coefficient corresponding to the i-th pixel in the target pixel set; J xi represents the grayscale value before contrast adjustment corresponding to the i-th pixel in the target pixel set; J hi represents the grayscale value after contrast adjustment corresponding to the i-th pixel in the target pixel set; w i represents the weight intensity value corresponding to the i-th pixel in the target pixel set; w p Represents the average value of the weight intensity corresponding to n pixels in the target pixel set;

[0108] A grayscale value adjustment module is used to adjust the grayscale value of each pixel point using the grayscale value compensation coefficient of each pixel point;

[0109] Among them, the gray value of each pixel after adjustment is obtained by the following formula:

[0110] J ti =(1+B i )·J hi

[0111] Among them, J ti represents the adjusted grayscale value corresponding to the i-th pixel in the target pixel set; B i represents the gray value compensation coefficient corresponding to the i-th pixel in the target pixel set; J hi Represents the grayscale value after contrast adjustment corresponding to the i-th pixel in the target pixel set.

[0112] The technical effect of the above technical solution is: through the weight intensity value acquisition module, the weight intensity value of each pixel in the target pixel set in the adaptive adjustment method based on global contrast and adjacent pixel relationship can be obtained. These weight intensity values ​​reflect the importance and influence range of each pixel in the image, and provide a basis for subsequent gray value compensation. The gray value compensation coefficient acquisition module uses these weight intensity values ​​and the gray values ​​before and after contrast adjustment to calculate the gray value compensation coefficient corresponding to each pixel. This step realizes the fine adjustment of different areas and different pixels in the image, which helps to improve the visual effect of the image. The calculation of the gray value compensation coefficient takes into account the global contrast and the relationship between adjacent pixels, so that the adjusted image not only has better contrast, but also maintains the details and features of the image. This adaptive optimization mechanism makes image processing more flexible and accurate. Through the gray value adjustment module, the gray value of each pixel is adjusted using the calculated gray value compensation coefficient, so as to achieve further optimization of the image contrast. This adjustment method can be adjusted according to the specific situation of the image, avoiding the problem of image distortion or detail loss that may be caused by the traditional contrast adjustment method. The above technical solution can significantly improve the quality of the image through refined contrast adjustment and adaptive optimization. The adjusted image has better contrast, clarity and detail, making the information in the image easier to identify and interpret. Especially when processing low-contrast or complex background images, the technical solution can significantly improve the visual effect of the image and provide a more favorable basis for subsequent image analysis and processing. Since the technical solution adopts an adaptive adjustment method based on global contrast and adjacent pixel relationships, and can compensate for grayscale values ​​according to the weight intensity value of each pixel, it has high flexibility. This allows the technical solution to adapt to different types of images and different application scenarios to meet the diverse needs of users.

[0113] In summary, the above technical solution realizes precise control and optimization of image contrast through refined contrast adjustment and adaptive optimization mechanism, improves image quality and visual effect, and enhances the flexibility of image processing.

[0114] In this embodiment, the analysis and identification module includes:

[0115] Model training unit, used to train AI-based analysis and recognition models for popular science items based on deep learning technology;

[0116] The historical images of the objects to be popularized are collected, and the collected historical images of the objects to be popularized are divided into a training set and a test set;

[0117] Based on the training set, the deep learning model is trained to enable the deep learning model to autonomously learn the process of analyzing and identifying objects to be popularized, identify the content of the objects to be popularized, and determine the analysis and identification model of objects to be popularized based on artificial intelligence;

[0118] Based on the test set, the performance test of the artificial intelligence-based analysis and recognition model of items to be popularized is conducted to determine whether the artificial intelligence-based analysis and recognition model of items to be popularized can achieve the expected effect of identifying the content of items to be popularized, and according to the performance test results, the parameters of the artificial intelligence-based analysis and recognition model of items to be popularized are adjusted and the structure is optimized to determine the optimal artificial intelligence-based analysis and recognition model of items to be popularized;

[0119] An analysis and recognition unit, used to analyze and recognize the characteristic images of the objects to be popularized based on computer vision according to the optimal analysis and recognition model of the objects to be popularized based on artificial intelligence;

[0120] Among them, the optimal artificial intelligence-based analysis and recognition model of objects to be popularized science is deployed in the actual analysis and recognition environment of objects to be popularized science, and the characteristic images of objects to be popularized science based on computer vision are input into the optimal artificial intelligence-based analysis and recognition model of objects to be popularized science, and the characteristic images of objects to be popularized science based on computer vision are analyzed and recognized according to the optimal artificial intelligence-based analysis and recognition model of objects to be popularized science, to determine the artificial intelligence-based analysis and recognition results of objects to be popularized science.

[0121] In this embodiment, the popular science control module includes:

[0122] The science explanation unit is used to automatically explain the science items to be popularized based on the analysis and recognition results of the science items to be popularized based on artificial intelligence, so that young people can understand science knowledge;

[0123] The human-computer interaction unit is used to enable the youth science robot to understand the user's voice commands based on natural language processing technology, and interact with the user through voice feedback during the science explanation process to enhance the fun of learning;

[0124] The intelligent recommendation unit is used to intelligently recommend subsequent popular science learning content to users based on human-computer interaction feedback, including executing preset scientific experiments and providing science and technology exhibitions to users through demonstrations and explanations.

[0125] In this embodiment, the user interface module includes:

[0126] The interface display unit is used to display the human-computer interaction process and popular science learning content in a visual form. Brightly colored icons and animations are used to attract the attention of young people, while providing a variety of interactive methods to enhance user participation.

[0127] The function setting unit is used for users to select different functions and settings, which contains buttons and menus to control the youth science robot based on the different functions and settings selected by the user.

[0128] In summary, based on the analysis and recognition results of the objects to be popularized based on artificial intelligence, automated popular science explanations and intelligent recommendations for human-computer interaction can be carried out, which can effectively explain and intelligently control popular science for teenagers, improve the effect of popular science for teenagers, and enhance the interest and sense of participation of popular science education.

[0129] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0130] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A youth science robot control system based on artificial intelligence, characterized by: include: Navigation and obstacle avoidance module, used for autonomous navigation and obstacle avoidance control of the youth science robot; The science collection module is used to collect real-time images of objects to be popularized based on computer vision; The science popularization control terminal is used to process, analyze and identify real-time images of science objects to be popularized based on computer vision, and to perform automated science popularization control on the science objects based on the analysis and identification results of the science objects to be popularized based on artificial intelligence.

2. The youth science popularization robot control system based on artificial intelligence as claimed in claim 1, characterized in that: The navigation obstacle avoidance module comprises: An environment collection unit is used to monitor and collect the surrounding environment of the youth science popularization robot in real time based on sensors, and obtain the surrounding environment data of the youth science popularization robot; The autonomous navigation unit is used to perform autonomous navigation control on the youth science popularization robot according to the surrounding environment data of the youth science popularization robot, so that the youth science popularization robot can autonomously walk and avoid obstacles in various environments according to changes in the surrounding environment.

3. The youth science popularization robot control system based on artificial intelligence as claimed in claim 2 is characterized in that: The science popularization collection module includes: The object collection unit is used to monitor and collect the objects to be popularized in the environment around the youth science robot in real time based on computer vision technology, and obtain real-time images of the objects to be popularized based on computer vision; A wireless transmission unit, used to transmit the collected real-time images of the objects to be popularized based on computer vision to the popularization control terminal based on a wireless network; Among them, a data transmission link is established between the item collection unit and the science popularization control terminal; The item collection unit sends a command to the science popularization control terminal requesting to establish a data transmission link; After the science popularization control terminal receives the instruction sent by the item collection unit to request to establish a data transmission link, the science popularization control terminal checks the data transmission port of the item collection unit to determine whether the item collection unit is qualified to perform data transmission with the science popularization control terminal; When the item collection unit is qualified to perform data transmission with the popular science control terminal, the popular science control terminal sends an instruction to the item collection unit to agree to establish a data transmission link. After the item collection unit receives the instruction sent by the popular science control terminal to agree to establish a data transmission link, a data transmission link is established between the item collection unit and the popular science control terminal; The data transmission link between the item collection unit and the science popularization control terminal is established. The item collection unit transmits the collected real-time images of the items to be popularized based on computer vision to the science popularization control terminal, so that the science popularization control terminal analyzes and performs science popularization control on the real-time images of the items to be popularized based on computer vision.

4. The youth science popularization robot control system based on artificial intelligence as claimed in claim 3 is characterized in that: The popular science control terminal comprises: An image processing module is used to process the real-time image of the object to be popularized based on computer vision, and determine the characteristic image of the object to be popularized based on computer vision; An analysis and recognition module is used to analyze and recognize characteristic images of objects to be popularized based on computer vision, and determine analysis and recognition results of objects to be popularized based on artificial intelligence; The science popularization control module is used to provide automated science popularization explanations and human-computer interactive intelligent recommendations for science popularization items based on the analysis and recognition results of science popularization items based on artificial intelligence; The user interface module is used to display the human-computer interaction process and science popularization learning content in a visual form, and to control the youth science popularization robot based on different functions and settings selected by the user.

5. The youth science popularization robot control system based on artificial intelligence as claimed in claim 4 is characterized in that: The image processing module comprises: An image denoising unit is used to perform denoising on the real-time image of the object to be popularized based on computer vision based on a median filter, divide the real-time image of the object to be popularized based on computer vision into blocks, find all pixels in its neighborhood for each pixel in each block, calculate the median of the pixels in the neighborhood, and use the median to replace the pixels on the original image, thereby reducing the noise in the real-time image of the object to be popularized based on computer vision; An image adjustment unit is used to adjust the brightness and contrast of the real-time image of the object to be popularized based on computer vision based on image segmentation technology, to make the brightness of the real-time image of the object to be popularized based on computer vision consistent based on interpolation technology, and to automatically adjust the contrast of pixels in the real-time image of the object to be popularized based on computer vision based on an adaptive adjustment method based on global contrast and adjacent pixel relationship; The feature extraction unit is used to extract features from the real-time images of the objects to be popularized based on computer vision based on the principal component analysis method, extract useful features from the real-time images of the objects to be popularized based on computer vision, and determine the feature images of the objects to be popularized based on computer vision.

6. The youth science popularization robot control system based on artificial intelligence as claimed in claim 5, characterized in that: The image adjustment unit comprises: A grayscale value acquisition module is used to extract the grayscale value corresponding to each pixel point of the real-time image after the contrast adjustment is completed based on the adaptive adjustment method; A target pixel point set acquisition module is used to extract pixel points whose grayscale values ​​are not less than a preset grayscale threshold to form a target pixel point set; A gray value coefficient acquisition module, used to acquire the gray value coefficient by using the gray value before contrast adjustment and the gray value after contrast adjustment of the pixel points included in the target pixel point set; The gray value coefficient is obtained by the following formula: Where R represents the gray value coefficient; n represents the number of pixels contained in the target pixel set; J xi represents the grayscale value before contrast adjustment corresponding to the i-th pixel in the target pixel set; J hi represents the gray value after contrast adjustment corresponding to the i-th pixel in the target pixel set; J y Indicates the preset grayscale threshold; J hmax Indicates the maximum grayscale value after contrast adjustment corresponding to the pixel points contained in the target pixel point set; J m Indicates the maximum grayscale value before contrast adjustment corresponding to the pixels included in the target pixel set; A coefficient comparison module, used for comparing the gray value coefficient with a preset coefficient threshold; The secondary contrast adjustment module is used to perform secondary contrast adjustment on the real-time image when the gray value coefficient is lower than a preset coefficient threshold.

7. The youth science popularization robot control system based on artificial intelligence as claimed in claim 6, characterized in that: The secondary contrast adjustment module comprises: A weight intensity value calling module is used to call the weight intensity value of each pixel in the target pixel set in the adaptive adjustment method based on global contrast and adjacent pixel relationship; A grayscale value retrieving module, used to retrieve the grayscale value before contrast adjustment and the grayscale value after contrast adjustment corresponding to each pixel point in the target pixel point set; A grayscale value compensation coefficient acquisition module is used to obtain the grayscale value compensation coefficient corresponding to each pixel point by using the weight intensity value of each pixel point in the adaptive adjustment method based on global contrast and adjacent pixel relationship in combination with the grayscale value before contrast adjustment and the grayscale value after contrast adjustment corresponding to each pixel point; The gray value compensation coefficient is obtained by the following formula: Among them, B i represents the gray value compensation coefficient corresponding to the i-th pixel in the target pixel set; J xi represents the grayscale value before contrast adjustment corresponding to the i-th pixel in the target pixel set; J hi represents the grayscale value after contrast adjustment corresponding to the i-th pixel in the target pixel set; w i represents the weight intensity value corresponding to the i-th pixel in the target pixel set; w p Represents the average value of the weight intensity corresponding to n pixels in the target pixel set; A grayscale value adjustment module is used to adjust the grayscale value of each pixel point using the grayscale value compensation coefficient of each pixel point; Among them, the gray value of each pixel after adjustment is obtained by the following formula: J ti =(1+B i )·J hi Among them, J ti represents the adjusted grayscale value corresponding to the i-th pixel in the target pixel set; B i represents the gray value compensation coefficient corresponding to the i-th pixel in the target pixel set; J hi Represents the grayscale value after contrast adjustment corresponding to the i-th pixel in the target pixel set.

8. The youth science popularization robot control system based on artificial intelligence as claimed in claim 5 is characterized in that: The analysis and identification module comprises: Model training unit, used to train AI-based analysis and recognition models for popular science items based on deep learning technology; The historical images of the objects to be popularized are collected, and the collected historical images of the objects to be popularized are divided into a training set and a test set; Based on the training set, the deep learning model is trained to enable the deep learning model to autonomously learn the process of analyzing and identifying objects to be popularized, identify the content of the objects to be popularized, and determine the analysis and identification model of objects to be popularized based on artificial intelligence; Based on the test set, the performance test of the artificial intelligence-based analysis and recognition model of items to be popularized is conducted to determine whether the artificial intelligence-based analysis and recognition model of items to be popularized can achieve the expected effect of identifying the content of items to be popularized, and according to the performance test results, the parameters of the artificial intelligence-based analysis and recognition model of items to be popularized are adjusted and the structure is optimized to determine the optimal artificial intelligence-based analysis and recognition model of items to be popularized; An analysis and recognition unit, used to analyze and recognize the characteristic images of the objects to be popularized based on computer vision according to the optimal analysis and recognition model of the objects to be popularized based on artificial intelligence; Among them, the optimal artificial intelligence-based analysis and recognition model of objects to be popularized science is deployed in the actual analysis and recognition environment of objects to be popularized science, and the characteristic images of objects to be popularized science based on computer vision are input into the optimal artificial intelligence-based analysis and recognition model of objects to be popularized science, and the characteristic images of objects to be popularized science based on computer vision are analyzed and recognized according to the optimal artificial intelligence-based analysis and recognition model of objects to be popularized science, to determine the artificial intelligence-based analysis and recognition results of objects to be popularized science.

9. The youth science popularization robot control system based on artificial intelligence as claimed in claim 8, characterized in that: The popular science control module comprises: The science explanation unit is used to automatically explain the science items to be popularized based on the analysis and recognition results of the science items to be popularized based on artificial intelligence, so that young people can understand science knowledge; The human-computer interaction unit is used to enable the youth science robot to understand the user's voice commands based on natural language processing technology, and interact with the user through voice feedback during the science popularization process; The intelligent recommendation unit is used to intelligently recommend subsequent popular science learning content to users based on human-computer interaction feedback, including executing preset scientific experiments and providing science and technology exhibitions to users through demonstrations and explanations.

10. The youth science popularization robot control system based on artificial intelligence as claimed in claim 9, characterized in that: The user interface module comprises: The interface display unit is used to display the human-computer interaction process and popular science learning content in a visual form. Brightly colored icons and animations are used to attract the attention of young people, while providing a variety of interactive methods to enhance user participation. The function setting unit is used for users to select different functions and settings, which contains buttons and menus to control the youth science robot based on the different functions and settings selected by the user.

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