An information interaction method, device, apparatus, and storage medium

Abstract

The application discloses an information interaction method and device, equipment and a storage medium, and relates to the technical field of computers, which comprises the following steps: receiving a picture uploaded by a machine learning platform and an analysis result corresponding to the picture; determining whether a matched component exists in a component assembly library based on the analysis result; if the matched component does not exist, generating a target component based on the analysis result and the picture by using a preset component generation method, so that rendered data obtained after rendering operation is performed on data obtained through information interaction is displayed on the target component. The application determines whether a matched component exists currently by using the analysis result received from the machine learning platform, creates a corresponding component if the matched component does not exist, generates an accurate target component according to the analysis result of a real hardware element picture, displays data obtained after interaction and rendered by using the component, improves interaction and user experience, and improves development efficiency.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to an information interaction method, apparatus, device, and storage medium. Background Technology

[0002] Arduino is a convenient, flexible, and easy-to-use open-source electronic prototyping platform. It includes hardware (various Arduino boards) and software (Arduino IDE). It is the world's most popular open-source hardware, an excellent hardware development platform, and a trendsetter in hardware development. Arduino development is simple and easy to learn, allowing developers to focus more on creativity and implementation, greatly reducing learning costs and shortening development cycles. Due to these advantages, it is used not only by professional hardware developers but also by an increasing number of software developers in hardware, IoT, and other development fields. In this context, controlling and interacting with Arduino components through a web browser is a novel development method. The ability to automatically simulate and draw graphics based on physical components on the webpage, allowing users to simulate realistic operating scenarios, thereby reducing learning costs and improving development efficiency, is a direction worth exploring. Current methods only connect the webpage and the Arduino development board; users cannot automatically generate simulated buttons and other information images based on physical components on the webpage to interact with the Arduino. This results in poor interactivity, a cumbersome development process, a lack of realistic scenario experience, high learning costs, low development efficiency, and a poor user experience. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide an information interaction method, apparatus, device, and storage medium that can enhance interactivity and user experience while improving development efficiency. The specific solution is as follows:

[0004] In a first aspect, this application discloses an information interaction method applied to a front-end, comprising:

[0005] Receive images uploaded by the machine learning platform and the corresponding analysis results;

[0006] Based on the analysis results, determine whether there are matching components in the parts and components library;

[0007] If no matching component exists, a target component is generated based on the analysis results and the image using a preset component generation method, so that the rendered data obtained from the information interaction is displayed on the target component.

[0008] Optionally, before receiving the image uploaded by the machine learning platform and the corresponding analysis results, the method further includes:

[0009] The machine learning platform acquires a preset image sample dataset and loads the machine learning platform's software development kit.

[0010] A first learning channel is created by establishing code through a preset channel, so as to analyze the image sample dataset using the first learning channel and obtain dataset information;

[0011] Create a target model based on the dataset information and the preset model code;

[0012] The target model is stored in the machine learning platform.

[0013] Optionally, before receiving the image uploaded by the machine learning platform and the corresponding analysis results, the method further includes:

[0014] The machine learning platform receives the images uploaded by the user;

[0015] A second learning channel is created by establishing code through the preset channel, so that the image can be analyzed using the second learning channel to obtain the current image information;

[0016] The target model is read, and the target model is used to analyze and judge the current image information to obtain the analysis results;

[0017] The analysis results and the image are sent from the machine learning platform to the front end.

[0018] Optionally, after determining whether a matching component exists in the parts and components library based on the analysis results, the method further includes:

[0019] If a matching component exists, the corresponding target component is directly obtained and data is received or sent through the state management mode module.

[0020] Optionally, after generating the target component based on the analysis results and the image, the method further includes:

[0021] All the target components are connected through the state management mode module so that data can be transferred between different target components through the state management mode module.

[0022] Optionally, the step of generating a target component based on the analysis results and the image, so as to display rendered data after rendering operations on the data obtained from information interaction on the target component, includes:

[0023] Based on the analysis results and the image, a target component is generated, and the component interacts with the Arduino through the state management mode module to receive the data sent from the Arduino's process module.

[0024] The data is rendered using a browser to obtain the rendered data.

[0025] The rendered data is displayed on the target component;

[0026] When a user clicks a target button on the browser, the current data information is sent to the process module of the Arduino in order to control the components in the Arduino.

[0027] Optionally, if no matching component exists, a target component is generated based on the analysis results and the image using a preset component generation method, including:

[0028] If no matching component is found, the corresponding target setting is obtained based on the analysis results and the preset setting matching instruction.

[0029] Based on the analysis results, the corresponding animation effects are matched from the preset animation script library to obtain the target animation effects;

[0030] Input the target settings into the preset file conversion plugin to obtain the target plugin;

[0031] The image is transformed using the target plugin to obtain the target file;

[0032] The target animation effect is applied to the target file to obtain the target component.

[0033] Secondly, this application discloses an information interaction device applied to a front end, comprising:

[0034] The receiving module is used to receive images uploaded by the machine learning platform and the corresponding analysis results of the images;

[0035] The component judgment module is used to determine whether a matching component exists in the accessory component library based on the analysis results;

[0036] The component generation module is used to generate a target component based on the analysis results and the image by using a preset component generation method if no matching component exists, so as to display the rendered data after rendering the data obtained from the information interaction on the target component.

[0037] Thirdly, this application discloses an electronic device, including:

[0038] Memory, used to store computer programs;

[0039] A processor is configured to execute the computer program to implement the steps of the information interaction method disclosed above.

[0040] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the information interaction method disclosed above.

[0041] As can be seen, this application provides an information interaction method, including: receiving an image uploaded by a machine learning platform and the corresponding analysis results of the image; determining whether a matching component exists in the accessory component library based on the analysis results; if no matching component exists, generating a target component based on the analysis results and the image using a preset component generation method, so as to display rendered data obtained from information interaction on the target component. Thus, this application determines whether a matching component exists based on the received analysis results from the machine learning platform; if not, it creates a corresponding component. That is, it generates an accurate target component based on the analysis results of a real hardware component image, and uses this component to display the rendered data obtained after interaction, improving the interactivity and user experience while increasing development efficiency. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0043] Figure 1 This is a flowchart of an information interaction method disclosed in this application;

[0044] Figure 2 This is a flowchart of a specific information interaction method disclosed in this application;

[0045] Figure 3 This is a flowchart of a specific information interaction method disclosed in this application;

[0046] Figure 4 This is a flowchart of a specific information interaction method disclosed in this application;

[0047] Figure 5 A schematic diagram of the information interaction device provided in this application;

[0048] Figure 6 This application provides a structural diagram of an electronic device. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Currently, controlling and interacting with Arduino components via a web browser is a novel development method. The ability to automatically simulate and draw graphics based on physical components on the webpage, allowing users to experience realistic operating scenarios, thereby reducing learning costs and improving development efficiency, is a promising area for exploration. Existing methods simply connect the webpage and the Arduino development board; users cannot automatically generate simulated buttons and other information images based on physical components for interaction with the Arduino. This results in poor interactivity, a cumbersome development process, a lack of realistic scenario experience, high learning costs, low development efficiency, and a poor user experience. Therefore, this application provides an information interaction method that enhances interactivity and user experience while improving development efficiency.

[0051] This invention discloses an information interaction method, see [link to relevant documentation]. Figure 1 As shown, when applied to the front end, the method includes:

[0052] Step S11: Receive the image uploaded by the machine learning platform and the corresponding analysis results.

[0053] In this embodiment, images uploaded by a machine learning platform and the corresponding analysis results are received. Currently, existing technologies simply connect a webpage (the general term for the internet) and an Arduino (an open-source electronic prototyping platform) development board for communication. This solution automatically categorizes and generates accurate SVG (Scalable Vector Graphics) files based on real hardware component images, adds animation effects, and creates a webpage infographic that simulates the effects of real hardware components and can interact with the Arduino. It is understood that the user uploads images to the machine learning platform, which then analyzes and judges the images to obtain corresponding analysis results. The front end then receives the analysis results obtained from the machine learning platform along with the user-uploaded images.

[0054] Step S12: Based on the analysis results, determine whether there are matching components in the accessory component library.

[0055] In this embodiment, after receiving the image uploaded by the machine learning platform and the corresponding analysis results, the system determines whether a matching component exists in the accessory component library based on the analysis results. It can be understood that the front-end checks its own accessory component library to see if a component matches the received analysis results. If a matching component exists, the corresponding component is directly retrieved from the accessory component library, and the rendered data obtained from the information interaction is displayed on the target component.

[0056] Step S13: If no matching component exists, a target component is generated based on the analysis results and the image using a preset component generation method, so that the rendered data obtained from the information interaction is displayed on the target component.

[0057] In this embodiment, based on the analysis results, it is determined whether a matching component exists in the accessory component library. If no matching component exists, a target component is generated using a preset component generation method, based on the analysis results and the image. This target component displays the rendered data obtained from the information interaction. It is understood that when no component matches the received analysis results, a target component needs to be automatically generated based on the analysis results and the image using a preset component generation method. This creates a web page infographic that simulates the effects of real hardware components and interacts with the Arduino. Finally, based on the front-end state management mode, data is received and sent to the Arduino to achieve information interaction and create an Arduino interactive infographic.

[0058] Understandably, this solution utilizes the TensorFlow.js machine learning platform to automatically classify and analyze Arduino accessory images. Based on the Vue.js front-end framework, it incorporates primitive.js and the GSAP (GreenSock Animation Platform) animation library to automatically generate SVG components that highly match the Arduino accessory images. Data interaction with the Arduino is then achieved through Vuex. It should be noted that this technical solution can also be used in other IoT application scenarios.

[0059] This application discloses a method for constructing custom HTML components (i.e., SVG components) and interactive infographics that simulate hardware components on web pages, based on the machine learning framework Tensorflow.js, the web front-end framework Vue.js, and SVG technology. It can automatically generate SVG files on the web page based on Arduino accessory images, and then wrap them into virtual electronic components through Vue, assemble them, receive and send data, and interact with Arduino, thereby improving the user experience, simplifying the development process, and increasing development efficiency.

[0060] As can be seen, this application provides an information interaction method, including: receiving an image uploaded by a machine learning platform and the corresponding analysis results of the image; determining whether a matching component exists in the accessory component library based on the analysis results; if no matching component exists, generating a target component based on the analysis results and the image using a preset component generation method, so as to display rendered data obtained from information interaction on the target component. Thus, this application determines whether a matching component exists based on the received analysis results from the machine learning platform; if not, it creates a corresponding component. That is, it generates an accurate target component based on the analysis results of a real hardware component image, and uses this component to display the rendered data obtained after interaction, improving the interactivity and user experience while increasing development efficiency.

[0061] See Figure 2 As shown, this embodiment of the invention discloses an information interaction method. Compared with the previous embodiment, this embodiment further explains and optimizes the technical solution.

[0062] Step S21: The machine learning platform acquires a preset image sample dataset and loads the machine learning platform's software development kit.

[0063] In this embodiment, the machine learning platform acquires a preset image sample dataset and loads its software development kit (SDK). It is understood that the image sample dataset is pre-built to obtain a preset image sample dataset, such as an Arduino accessory image sample dataset. The image sample dataset can be created manually or using a preset tool, and it contains data information corresponding to each image. Then, the pre-acquired preset image sample dataset is imported into the machine learning platform, for example, into Tensorflow.js (TF, Google's open-source platform for machine learning). Next, the TensorFlow.js SDK is loaded, i.e., the TensorFlow.js SDK (Software Development Kit) is directly loaded using pre-written loading code to activate TensorFlow.js. After the TensorFlow.js SDK is loaded, TensorFlow.js is run to read data from the Arduino accessory image sample dataset.

[0064] Step S22: Create a first learning channel through a preset channel establishment code, so as to use the first learning channel to analyze the image sample dataset and obtain dataset information.

[0065] In this embodiment, after loading the software development kit (SDK) of the machine learning platform and reading the data from the image sample dataset, a first learning channel is created through a preset channel establishment code. This first learning channel is then used to analyze the image sample dataset and obtain dataset information. Specifically, creating the first learning channel through the preset channel establishment code ensures that all training images are of the same size and pixelated; the images are converted into matrices containing pixel data, and features are extracted. That is, the size of all images in the image sample dataset is first adjusted to ensure they are all the same size, and then pixelated; then the images obtained in the previous step are converted into matrices containing pixel data, and features are extracted. Essentially, the first learning channel converts the image information of all images in the image sample dataset into image data information that can be recognized by the code. Then, all the image data information obtained from the first channel is integrated to obtain the dataset information.

[0066] Step S23: Create the target model based on the dataset information and the preset model code.

[0067] In this embodiment, after analyzing the image sample dataset and obtaining dataset information using the first learning channel, a target model is created based on the dataset information and the preset model code in TensorFlow.js. It is understood that the model created based on the preset model code is trained using the dataset information to obtain the target model. It should be noted that after training the model, it is also necessary to evaluate the model.

[0068] Step S24: Store the target model on the machine learning platform.

[0069] In this embodiment, after creating the target model based on the dataset information and the preset model code, the target model is stored in the machine learning platform. It is understood that storing the target model in the machine learning platform allows the platform to directly retrieve the trained target model when it receives an image uploaded by a user.

[0070] Step S25: Receive the images uploaded by the machine learning platform and the corresponding analysis results.

[0071] In this embodiment, an image uploaded by a machine learning platform and the corresponding analysis results are received. It is understood that the machine learning platform receives the image uploaded by the user; creates a second learning channel through the preset channel establishment code to analyze the image and obtain current image information; reads the target model and uses the target model to judge and analyze the current image information to obtain analysis results; and sends the analysis results and the image from the machine learning platform to the front end. Specifically, as shown... Figure 3As shown, when a user uploads an image of an Arduino accessory to the TensorFlow.js platform, TensorFlow.js reads the image information contained in the uploaded image, then creates a second learning channel through the preset channel. This second learning channel is used to process and analyze the uploaded image to obtain the current image information. It should be noted that the first and second learning channels are not the same channel, but the operations performed in both channels are similar. Specifically, in the second learning channel, the size of the uploaded image is adjusted, pixelated, and then converted into a matrix containing pixel data, followed by feature extraction. After obtaining the current image information, a pre-stored trained target model is read from TensorFlow.js, and the target model is used to analyze and judge the current image to obtain the corresponding analysis result (which could also be a classification result). Then, TensorFlow.js sends the obtained analysis result along with the user-uploaded image to the front end. The front end, Vue (a library for building interactive web interfaces), receives the image uploaded by the machine learning platform and the corresponding analysis result.

[0072] Step S26: Based on the analysis results, determine whether there are matching components in the accessory component library.

[0073] In this embodiment, after receiving the image uploaded by the machine learning platform and the corresponding analysis results, it determines whether a matching component exists in the accessory component library based on the analysis results. It can be understood that, based on the received analysis results, a match is performed in Vue's Arduino accessory SVG (Scalable Vector Graphics) component library. If a matching component exists, the corresponding target component is directly obtained, and data is received or sent through the state management mode module.

[0074] Step S27: If no matching component exists, a target component is generated based on the analysis results and the image using a preset component generation method, so that the rendered data obtained from the information interaction is displayed on the target component.

[0075] Understandably, this solution is based on Tensorflow.js, training an Arduino accessory image model library by analyzing a dataset of Arduino accessory images. Then, it classifies the uploaded Arduino accessory images to determine their image types. Based on these classifications, it matches the images against the Arduino accessory SVG component library within the Vue framework to obtain the corresponding SVG files. Finally, using Vuex, it achieves information interaction by receiving and sending data to and from the Arduino's processing module, thus creating an interactive Arduino infographic.

[0076] The specific details of step S27 can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0077] As can be seen, this embodiment of the application obtains a preset image sample dataset through the machine learning platform and loads the software development kit of the machine learning platform; it creates a first learning channel through a preset channel establishment code to analyze the image sample dataset and obtain dataset information; it creates a target model based on the dataset information and preset model code; it stores the target model on the machine learning platform; it receives images uploaded by the machine learning platform and the analysis results corresponding to the images; it determines whether there is a matching component in the accessory component library based on the analysis results; if there is no matching component, it generates a target component through a preset component generation method, based on the analysis results and the image, so as to display the rendered data after rendering the data obtained from information interaction on the target component, thereby improving the sense of interaction and user experience, and improving development efficiency.

[0078] See Figure 4 As shown, this embodiment of the invention discloses an information interaction method. Compared with the previous embodiment, this embodiment further explains and optimizes the technical solution.

[0079] Step S31: Receive the image uploaded by the machine learning platform and the corresponding analysis results.

[0080] Step S32: Based on the analysis results, determine whether there are matching components in the accessory component library.

[0081] In this embodiment, the analysis results are used to determine whether a matching component exists in the accessory component library. If a matching SVG component exists, the corresponding SVG component is directly obtained, and data is received and sent via Vuex.

[0082] Step S33: If no matching component exists, obtain the corresponding target setting based on the analysis result and the preset setting matching instruction.

[0083] In this embodiment, after determining whether a matching component exists in the accessory component library based on the analysis results, if no matching component exists, the corresponding target setting is obtained based on the analysis results and the preset setting matching instruction. It can be understood that if no matching SVG component exists, the corresponding target setting is obtained based on the analysis results and the preset setting matching instruction.

[0084] It should be noted that if this analysis result is obtained for the first time, it indicates that there is no matching component in the parts and components library at this time. If this analysis result is not obtained for the first time, it indicates that there may be a matching component in the parts and components library at this time.

[0085] Step S34: Based on the analysis results, match the corresponding animation effects from the preset animation script library to obtain the target animation effects.

[0086] In this embodiment, when obtaining the corresponding target setting based on the analysis results and the preset setting matching instruction, the corresponding animation effect is simultaneously matched from the preset animation script library based on the analysis results to obtain the target animation effect. It can be understood that the corresponding GreenSock animation effect is retrieved from the preset animation script library mixin.js according to the classification results and the preset animation matching code to obtain the target animation effect.

[0087] Step S35: Input the target settings into the preset file conversion plugin to obtain the target plugin.

[0088] In this embodiment, the target settings are input into a preset file conversion plugin to obtain the target plugin. It can be understood that the obtained target settings are used as input to an existing plugin to obtain the target plugin corresponding to this image; that is, the target settings are input into the primitive.js plugin to obtain the target plugin.

[0089] Step S36: Use the target plugin to convert the image to obtain the target file.

[0090] In this embodiment, the target settings are input into a preset file conversion plugin. After obtaining the target plugin, the image is converted using the target plugin to obtain the target file. Specifically, based on the target settings, the uploaded image is converted into an SVG file using the primitive.js plugin to obtain the target file.

[0091] Step S37: Apply the target animation effect to the target file to obtain the target component.

[0092] In this embodiment, after transforming the image using the target plugin to obtain the target file, the target animation effect is applied to the target file to obtain the target component. It can be understood that the corresponding GreenSock animation effect, retrieved from the preset animation script library mixin.js based on the classification result, is applied to the target SVG file to ultimately generate the SVG component.

[0093] It should be noted that after obtaining the target component, all the target components are connected through the state management mode (Vuex) module so that data can be transmitted between different target components through the state management mode module, thereby realizing data sharing between components.

[0094] Step S38: Display the rendered data after rendering the data obtained from the information interaction on the target component.

[0095] In this embodiment, the target animation effect is applied to the target file to obtain the target component. Then, rendered data obtained from the information interaction is displayed on the target component. Specifically, the state management mode module interacts with the Arduino to receive data sent from the Arduino's process module; the browser renders the data to obtain the rendered data; the rendered data is displayed on the target component; when a user clicks a target button in the browser, the current data information is sent to the Arduino's process module to control the components in the Arduino. Specifically, the processing module receives data from the hardware and renders it in the browser; when the user operates in the browser, for example, pressing a button, data is sent to the processing module to control the Arduino components.

[0096] Understandably, this solution incorporates the machine learning platform TensorFlow.js to automatically classify and analyze Arduino component images. Based on the classification results, it automatically matches a pre-built component library and generates corresponding SVG components. This effectively solves the problem of automatically generating corresponding SVG component files from Arduino component images when it's currently impossible to control the Arduino via a browser. This allows for more accurate simulation of on-site hardware components and animations, enabling a highly immersive interaction with the Arduino from the browser.

[0097] For details regarding step S31, please refer to the corresponding content disclosed in the foregoing embodiments, which will not be repeated here.

[0098] As can be seen, this application embodiment receives images uploaded by a machine learning platform and the corresponding analysis results; determines whether a matching component exists in the accessory component library based on the analysis results; if no matching component exists, obtains the corresponding target setting item based on the analysis results and a preset setting item matching instruction; matches the corresponding animation effect from a preset animation script library based on the analysis results to obtain the target animation effect; inputs the target setting item into a preset file conversion plugin to obtain the target plugin; uses the target plugin to convert the image to obtain the target file; applies the target animation effect to the target file to obtain the target component; and displays the rendered data after rendering the data obtained from information interaction on the target component, thereby improving the sense of interaction and user experience, while also improving development efficiency.

[0099] See Figure 5 As shown in the illustration, this application also discloses an information interaction device applied to a front end, comprising:

[0100] The receiving module 11 is used to receive images uploaded by the machine learning platform and the corresponding analysis results of the images;

[0101] The component judgment module 12 is used to determine whether there is a matching component in the accessory component library based on the analysis results;

[0102] The component generation module 13 is used to generate a target component based on the analysis results and the image by using a preset component generation method if no matching component exists, so as to display the rendered data after rendering the data obtained from the information interaction on the target component.

[0103] As can be seen, this application includes: receiving images uploaded by a machine learning platform and the corresponding analysis results; determining whether a matching component exists in the accessory component library based on the analysis results; if no matching component exists, generating a target component using a preset component generation method, based on the analysis results and the image, so as to display rendered data obtained from information interaction on the target component. Thus, this application determines whether a matching component exists based on the received analysis results from the machine learning platform; if not, it creates a corresponding component. That is, it generates an accurate target component based on the analysis results of real hardware component images, and uses this component to display the rendered data obtained from interaction, improving interactivity and user experience while increasing development efficiency.

[0104] In some specific embodiments, the receiving module 11 specifically includes:

[0105] The image sample dataset acquisition unit is used by the machine learning platform to acquire a preset image sample dataset and load the software development kit of the machine learning platform.

[0106] The dataset information acquisition unit is used to create a first learning channel through a preset channel establishment code, so as to use the first learning channel to analyze the image sample dataset and obtain dataset information.

[0107] The model creation unit is used to create a target model based on the dataset information and preset model code.

[0108] A model storage unit is used to store the target model on the machine learning platform;

[0109] Image receiving unit, used by the machine learning platform to receive images uploaded by users;

[0110] The image information acquisition unit is used to create a second learning channel through the preset channel establishment code, so as to analyze the image using the second learning channel and obtain the current image information;

[0111] An analysis unit is used to read the target model and use the target model to analyze and judge the current image information to obtain analysis results;

[0112] A sending unit is used to send the analysis results and the image from the machine learning platform to the front end;

[0113] The receiving unit is used to receive images uploaded by the machine learning platform and the corresponding analysis results of the images.

[0114] In some specific embodiments, the component determination module 12 specifically includes:

[0115] The component judgment unit is used to determine whether a matching component exists in the accessory component library based on the analysis results;

[0116] The component acquisition unit is used to directly acquire the corresponding target component and receive or send data through the state management mode module if a matching component exists.

[0117] In some specific embodiments, the component generation module 13 specifically includes:

[0118] The target setting item acquisition unit is used to acquire the corresponding target setting item based on the analysis result and the preset setting item matching instruction if no matching component exists.

[0119] An animation effects matching unit is used to match corresponding animation effects from a preset animation script library based on the analysis results to obtain the target animation effects;

[0120] The target plugin acquisition unit is used to input the target settings into a preset file conversion plugin to obtain the target plugin;

[0121] The target file generation unit is used to transform the image using the target plugin to obtain a target file;

[0122] A target component generation unit is used to apply the target animation effect to the target file to obtain the target component;

[0123] An interaction unit is used to interact with the Arduino through the state management mode module to receive the data sent from the process module of the Arduino;

[0124] A data rendering unit is used to perform rendering operations on the data through a browser to obtain the rendered data;

[0125] The data display unit is used to display the rendered data on the target component;

[0126] The component control unit is used to send current data information to the process module of the Arduino when it detects that the user clicks the target button on the browser, so as to control the components in the Arduino;

[0127] The component connection unit is used to connect all the target components through the state management mode module, so as to transmit data between different target components through the state management mode module.

[0128] Furthermore, embodiments of this application also provide an electronic device. Figure 6 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0129] Figure 6 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the information interaction method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0130] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0131] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0132] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including computer programs capable of performing the information interaction methods executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0133] Furthermore, this application also discloses a storage medium storing a computer program, which, when loaded and executed by a processor, implements the information interaction method steps disclosed in any of the foregoing embodiments.

[0134] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0135] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0136] The above provides a detailed description of the information interaction method, apparatus, device, and storage medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An information exchange method, characterized in that, Applied to front-end, including: Receive images uploaded by the machine learning platform and the corresponding analysis results; Based on the analysis results, it is determined whether there is a matching component in the parts and components library; wherein, if the analysis result is obtained for the first time, it is determined that there is no matching component in the parts and components library, and if the analysis result is not obtained for the first time, it is determined that there may be a matching component in the parts and components library. If no matching component is found, a target component is generated based on the analysis results and the image using a preset component generation method, so that the rendered data obtained from the information interaction is displayed on the target component. The step of generating the target component based on the analysis results and the image further includes: All the target components are connected through the state management mode module so that data can be transferred between different target components through the state management mode module; The step of generating a target component based on the analysis results and the image, so as to display rendered data after rendering operations on the data obtained from information interaction on the target component, includes: Based on the analysis results and the image, a target component is generated, and the component interacts with the Arduino through the state management mode module to receive the data sent from the Arduino's process module. The data is rendered using a browser to obtain the rendered data. The rendered data is displayed on the target component; When a user clicks a target button on the browser, the current data information is sent to the process module of the Arduino in order to control the components in the Arduino; If no matching component exists, a target component is generated based on the analysis results and the image using a preset component generation method, including: If no matching component is found, the corresponding target setting is obtained based on the analysis results and the preset setting matching instruction. Based on the analysis results, the corresponding animation effects are matched from the preset animation script library to obtain the target animation effects; Input the target settings into the preset file conversion plugin to obtain the target plugin; The image is transformed using the target plugin to obtain the target file; The target animation effect is applied to the target file to obtain the target component.

2. The information interaction method according to claim 1, characterized in that, Also includes: The machine learning platform acquires a preset image sample dataset and loads the machine learning platform's software development kit. A first learning channel is created by establishing code through a preset channel, so as to analyze the image sample dataset using the first learning channel and obtain dataset information; Create a target model based on the dataset information and the preset model code; The target model is stored in the machine learning platform.

3. The information interaction method according to claim 2, characterized in that, Also includes: The machine learning platform receives the images uploaded by the user; A second learning channel is created by establishing code through the preset channel, so that the image can be analyzed using the second learning channel to obtain the current image information; The target model is read, and the target model is used to analyze and judge the current image information to obtain the analysis results; The analysis results and the image are sent from the machine learning platform to the front end.

4. The information interaction method according to claim 1, characterized in that, After determining whether a matching component exists in the parts and components library based on the analysis results, the process further includes: If a matching component exists, the corresponding target component is directly obtained and data is received or sent through the state management mode module.

5. An information interaction device, characterized in that, Applied to front-end, including: The receiving module is used to receive images uploaded by the machine learning platform and the corresponding analysis results of the images; The component judgment module is used to determine whether there is a matching component in the parts component library based on the analysis results; wherein, if the analysis result is obtained for the first time, it is determined that there is no matching component in the parts component library, and if the analysis result is not obtained for the first time, it is determined that there may be a matching component in the parts component library. The component generation module is used to generate a target component based on the analysis results and the image by using a preset component generation method if no matching component exists, so as to display the rendered data after rendering the data obtained from the information interaction on the target component. The component generation module specifically includes: The target setting item acquisition unit is used to acquire the corresponding target setting item based on the analysis result and the preset setting item matching instruction if no matching component exists. An animation effects matching unit is used to match corresponding animation effects from a preset animation script library based on the analysis results to obtain the target animation effects; The target plugin acquisition unit is used to input the target settings into a preset file conversion plugin to obtain the target plugin; The target file generation unit is used to transform the image using the target plugin to obtain a target file; A target component generation unit is used to apply the target animation effect to the target file to obtain the target component; An interaction unit is used to interact with the Arduino through a state management mode module to receive the data sent from the process module of the Arduino; A data rendering unit is used to perform rendering operations on the data through a browser to obtain the rendered data; The data display unit is used to display the rendered data on the target component; The component control unit is used to send current data information to the process module of the Arduino when it detects that the user clicks the target button on the browser, so as to control the components in the Arduino; The component connection unit is used to connect all the target components through the state management mode module, so as to transmit data between different target components through the state management mode module.

6. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the information interaction method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, Used to store computer programs; wherein, when the computer programs are executed by a processor, they implement the information interaction method as described in any one of claims 1 to 4.

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