Learning incentive method and device, electronic equipment, storage medium and product

Abstract

The invention provides a learning incentive method and device, electronic equipment, a storage medium and a product, and the method comprises the steps: obtaining to-be-corrected data of a user; acquiring bright spot information which is extracted based on the to-be-corrected data and is used for representing the learning performance of the user; and generating information to be fed back according to the bright spot information, and controlling printing equipment to output the information to be fed back so as to provide forward excitation for a user. According to the technical scheme, the man-machine interaction experience between the user and the printing equipment can be improved, the technical equipment can better meet the emotional requirement of an education scene, and the learning internal driving force and initiative of students can be stimulated.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent education technology, and more specifically, to a learning motivation method, device, electronic device, storage medium, and product. Background Technology

[0002] With the gradual development of technology, smart technology has gradually entered people's lives, and people are increasingly fond of various smart devices, such as smart printing devices. It is now very common to use printing devices at home to print assignments and then submit them for review via scanner.

[0003] However, existing printing devices are designed with limited functionality, and their interaction modes have long remained at a simple level of receiving and responding to instructions. When students complete the printing process, the sounds emitted by the printer—starting up, the mechanical noises during printing, and the sound of paper ejection—create a negative conditioned reflex in students' minds after being repeatedly associated with the act of "doing homework." This causes the printing device to gradually evolve into a symbolic representation of "homework tasks" in the home learning environment. Its startup sounds and operating status can easily trigger negative emotions in students, such as stress, anxiety, and resistance, thus adversely affecting their learning motivation.

[0004] Currently, most technological improvements to printing equipment on the market focus on increasing printing speed, improving print quality, reducing energy consumption, or achieving wireless connectivity and other physical performance aspects. Printing equipment has been regarded as a purely functional tool, and its design philosophy ignores the emotional factors in human-computer interaction and the psychological feedback loop of user experience. This makes it impossible for printing equipment to provide differentiated and positive interactive experiences based on the user's identity and scenario needs. Summary of the Invention

[0005] This disclosure provides at least one learning incentive method, apparatus, electronic device, storage medium, and product.

[0006] In a first aspect, embodiments of this disclosure provide a learning incentive method, the method comprising: Obtain the user's data pending approval; Obtain the highlights information that characterize the user's learning performance based on the data to be corrected; Based on the highlighted information, feedback information is generated, and the printing device is controlled to output the feedback information to provide positive incentives to the user.

[0007] In one optional implementation, the extracted highlight information characterizing the user's learning performance based on the data to be corrected includes: Obtain the grading result of the data to be graded; wherein, the grading result includes at least the graded score; and, The data to be corrected is analyzed for highlights to obtain target identification results; wherein, the target identification results are used to indicate at least one of the following information: neatness of handwriting, innovativeness of problem-solving, and progress.

[0008] In one optional implementation, the highlight feature analysis of the data to be corrected includes: Highlight feature analysis is performed on the data to be corrected based on its multimodal data; wherein, the multimodal data includes image data and text data; The image data and text data of the data to be corrected are analyzed for highlights respectively to obtain the target recognition result determined based on the highlights feature analysis results.

[0009] In one optional implementation, the feedback information includes voice information; the step of generating the feedback information based on the highlight information and controlling the printing device to output the feedback information to provide positive encouragement to the user includes: Based on the graded scores, determine the attribute information of the generated voice information; Control the printing device to play the voice information.

[0010] In one optional implementation, the attribute information of the voice information includes information content; determining the attribute information of the generated voice information based on the grading score includes: When the graded score is greater than or equal to a preset score, the information content of the generated voice information is determined to include the graded score and / or the target recognition result; When the graded score is less than the preset score, it is determined that the information content of the generated voice information includes the target recognition result.

[0011] In one optional implementation, the feedback information includes an electronic certificate; the step of generating feedback information based on the highlight information and controlling the printing device to output the feedback information to provide positive incentives to the user includes: When the graded score is greater than or equal to a preset score, the electronic certificate is generated; Send a print job for the electronic certificate to the printing device so that the printing device prints the electronic certificate to obtain a physical certificate.

[0012] In one optional implementation, generating the electronic certificate includes: In a pre-set certificate template library, determine a certificate template that matches the highlight information and / or the data to be corrected; or, Retrieve the certificate template created by the user in advance using the template editor; or, The award certificate template that matches the highlighted information and / or the data to be corrected is retrieved using an artificial intelligence model; The electronic certificate is generated based on the certificate template.

[0013] In one optional implementation, the feedback information includes integral data; the step of generating feedback information based on the highlight information and controlling the printing device to output the feedback information to provide positive incentives to the user includes: Based on the highlighted information, indicators for judging the cumulative score are determined; wherein, the indicators include at least one of the following: the graded score, the excellence level of the target recognition result, the complexity of the data to be graded, and the number of times the user's graded score has exceeded a preset score consecutively. The final score is determined according to the score allocation rules and the aforementioned indicators; The user's points data is updated according to the final points value, and the printing device is controlled to display the points data.

[0014] In one optional implementation, before controlling the printing device to output the feedback information to provide positive incentive to the user, the following steps are included: The system sends a prompt message to the user requesting feedback on the pending information via a printing device. In response to the user's confirmation of the feedback information, the printing device is controlled to output the feedback information to the user.

[0015] In one optional implementation, the control printing device outputs the feedback information to provide positive incentives to the user, including: When the graded score is greater than or equal to a preset score, the printing device is controlled to immediately or within a preset time to output feedback information, which includes voice information and physical certificate. When the graded score is less than the preset score, the printing device is controlled to output feedback information immediately or within a preset time. The feedback information includes voice information. The information content of the voice information includes the graded score and / or the target recognition result.

[0016] Secondly, embodiments of this disclosure provide a learning incentive device, the device comprising: The acquisition unit is used to acquire the user's data to be modified. The data analysis unit is used to acquire, based on the data to be corrected, and extract highlight information to characterize the user's learning performance; The reward generation unit is used to generate feedback information based on the highlight information and control the printing device to output the feedback information to provide positive incentives to the user.

[0017] Thirdly, embodiments of this disclosure also provide an electronic device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps of the first aspect above, or any possible implementation of the first aspect, are performed.

[0018] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the first aspect or any possible implementation of the first aspect.

[0019] Fifthly, embodiments of this disclosure also provide a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the first aspect described above, or any possible implementation of the first aspect.

[0020] This disclosure provides a learning incentive method, apparatus, electronic device, storage medium, and product. In the embodiments of this application, firstly, user data to be corrected is acquired; then, highlight information representing the user's learning performance is extracted based on the data to be corrected; finally, feedback information is generated based on the highlight information, and a printing device is controlled to output the feedback information to provide positive incentives to the user.

[0021] In the above embodiments, highlight information representing user learning performance is extracted from the data to be graded, and positive feedback information is generated, transforming the traditional printing device into a physical carrier representing recognition and reward. This technical solution endows the printing device with the ability to "discover highlights and offer praise," upgrading it from a simple output terminal into a learning partner with preliminary cognitive and motivational capabilities. This transformation from tool to partner significantly improves the human-computer interaction experience, making the technology more aligned with the emotional needs of educational scenarios. When students receive and read this encouraging feedback information, their psychological feeling shifts from being forced to complete a task to receiving recognition of achievement, thus breaking the negative conditioned reflex triggered by the sound of the printing device and greatly stimulating students' intrinsic motivation and initiative in learning.

[0022] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0024] Figure 1 A flowchart of a learning incentive method provided by an embodiment of this disclosure is shown; Figure 2 A schematic diagram of a learning incentive system provided by an embodiment of this disclosure is shown; Figure 3 A flowchart illustrating a method for identifying highlight information in a learning incentive method provided by an embodiment of this disclosure is shown. Figure 4 A flowchart illustrating the method for generating speech information in a learning incentive method provided in this embodiment of the present disclosure is shown. Figure 5 A schematic diagram of a learning incentive device provided in an embodiment of this disclosure is shown; Figure 6 A schematic diagram of an electronic device provided in an embodiment of this disclosure is shown. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0026] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0027] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0028] With the gradual development of technology, smart technology has gradually entered people's lives, and people are increasingly fond of various smart devices, such as smart printing devices. It is now very common to use printing devices at home to print assignments and then submit them for review via scanner.

[0029] However, existing printing devices are designed with limited functionality, and their interaction modes have long remained at a simple level of receiving and responding to instructions. When students complete the printing process, the sounds emitted by the printer—starting up, the mechanical noises during printing, and the sound of paper ejection—create a negative conditioned reflex in students' minds after being repeatedly associated with the act of "doing homework." This causes the printing device to gradually evolve into a symbolic representation of "homework tasks" in the home learning environment. Its startup sounds and operating status can easily trigger negative emotions in students, such as stress, anxiety, and resistance, thus adversely affecting their learning motivation.

[0030] Currently, most technological improvements to printing equipment on the market focus on increasing printing speed, improving print quality, reducing energy consumption, or achieving wireless connectivity and other physical performance aspects. Printing equipment has been regarded as a purely functional tool, and its design philosophy ignores the emotional factors in human-computer interaction and the psychological feedback loop of user experience. This makes it impossible for printing equipment to provide differentiated and positive interactive experiences based on the user's identity and scenario needs.

[0031] Based on the above research, this disclosure provides a learning incentive method, apparatus, electronic device, storage medium, and product. In the embodiments of this application, firstly, the user's data to be corrected is acquired; then, highlight information representing the user's learning performance is extracted based on the data to be corrected; finally, feedback information is generated based on the highlight information, and the printing device is controlled to output the feedback information to provide positive incentives to the user.

[0032] In the above embodiments, highlight information representing user learning performance is extracted from the data to be graded, and positive feedback information is generated, transforming the traditional printing device into a physical carrier representing recognition and reward. This technical solution endows the printing device with the ability to "discover highlights and offer praise," upgrading it from a simple output terminal into a learning partner with preliminary cognitive and motivational capabilities. This transformation from tool to partner significantly improves the human-computer interaction experience, making the technology more aligned with the emotional needs of educational scenarios. When students receive and read this encouraging feedback information, their psychological feeling shifts from being forced to complete a task to receiving recognition of achievement, thus breaking the negative conditioned reflex triggered by the sound of the printing device and greatly stimulating students' intrinsic motivation and initiative in learning.

[0033] To facilitate understanding of this embodiment, a learning incentive method disclosed in this disclosure will first be described in detail. The execution subject of the learning incentive method provided in this disclosure is generally an electronic device with a certain computing power. In some possible implementations, this learning incentive method can be implemented by a processor calling computer-readable instructions stored in memory.

[0034] See Figure 1 The diagram shows a flowchart of a learning incentive method provided in an embodiment of this disclosure. The method includes steps S101 to S103, wherein: S101: Obtain the user's data to be modified.

[0035] The data to be graded includes, but is not limited to, electronic assignment documents submitted by users, such as scanned files of answers to paper assignments, or images of answers to paper assignments.

[0036] S102: Obtain key information based on the data to be graded, and extract it to characterize the user's learning performance.

[0037] Highlight information can be a single piece of information or a combination of multiple pieces of information. Highlight information refers to the excellent features that deserve recognition, praise, and reinforcement, which are extracted from the user's learning performance.

[0038] Here, the data to be modified can be analyzed from different dimensions, and the user's key information can be determined based on the analysis results of each dimension.

[0039] S103: Generate feedback information based on highlight information and control the printing device to output the feedback information to provide positive incentives to the user.

[0040] The feedback information refers to information highlighting key points for users, providing positive incentives. This feedback information can include one or more of the following: voice messages, highlight tags, electronic badges, electronic certificates, points data, and video information.

[0041] Here, the printing device can be a device with audio playback, video playback, printing, scanning, and points display functions. For example, if the printing device has a speaker, it can be controlled to output voice information through the speaker to provide positive incentives to the user; if the printing device also has a display screen, it can be controlled to display all accumulated points data and information such as virtual or physical gifts that can be redeemed with those points; for example, the printing device can display highlight labels or electronic badges to the user through the display screen; or it can be controlled to display an electronic certificate to be printed to the user through the display screen and print the electronic certificate to obtain a physical certificate; if the printing device also has a video player, it can be controlled to play video information through the video player.

[0042] In the embodiments of this application, the processes described in S101 to S103 above can be executed by any one of the following devices: terminal device, server, and printing device.

[0043] In one possible approach, the terminal device acquires data to be graded captured by the user through a camera, or acquires the content of an image pre-selected by the user from pre-taken pictures. The terminal device then sends this data to a server, allowing the server to extract key information representing the user's learning performance. Next, the server generates feedback information based on this key information. Finally, the server sends this feedback information back to the terminal device, enabling the terminal device to control a printing device to output the feedback information to provide positive reinforcement to the user; alternatively, the server directly controls the printing device to output the feedback information to provide positive reinforcement to the user.

[0044] In another possible approach, the terminal device acquires data to be graded captured by the user through a camera, or acquires the content of an image pre-selected by the user from pre-taken pictures as the data to be graded. Then, the terminal device extracts highlight information to characterize the user's learning performance based on the data to be graded; next, the terminal device generates feedback information based on the highlight information; finally, the terminal device controls a printing device to output the feedback information to provide positive reinforcement to the user.

[0045] In another possible approach, the printing device scans the answers to the paper-based assignment to obtain data to be graded. Then, the printing device sends this data to a server, allowing the server to extract key information representing the user's learning performance. Next, the server generates feedback information based on this key information. Afterward, the server sends this feedback information to either the terminal device or the printing device. Upon receiving the feedback information, the terminal device controls the printing device to output it to provide positive reinforcement to the user; the printing device, upon receiving the feedback information, either outputs it directly or according to the server's instructions.

[0046] The above content will be introduced below in the context of specific home learning scenarios.

[0047] In this scenario, the data to be graded is student A's homework, for example, data containing answers to a first-grade math unit test. Based on this data, key information representing student A's learning performance is extracted, and feedback information is generated from this information. This feedback information is then printed at student A's home, providing positive reinforcement to student A.

[0048] The feedback information can include voice messages, highlight labels, electronic badges, electronic certificates, points data, and video information. This tangible form of praise allows students to view, display, and even keep it repeatedly, thus continuously reinforcing their pride and confidence, and having a profound and lasting impact on cultivating a long-term, positive learning attitude.

[0049] In this embodiment of the application, step S102 above, based on the data to be graded, extracts highlight information to characterize the user's learning performance, specifically including the following steps: S11: Obtain the grading results of the data to be graded; the grading results shall include at least the graded score; S12: Perform highlight feature analysis on the data to be graded to obtain target identification results; wherein, the target identification results are used to indicate at least one of the following information: neatness of handwriting, innovativeness of problem-solving, and extent of progress.

[0050] Grading scores can be represented numerically, for example, as scores between 0 and 100; or they can be represented alphabetically, for example, as A+, A, B+, B, etc. Here, the grading score includes the total score of the data to be graded, or it can include sub-scores for the same type of questions within the data. For example, the data to be graded might be a user's answers to a first-grade math unit test, which includes calculation problems, fill-in-the-blank questions, true / false questions, multiple-choice questions, and word problems. In this case, the grading score can include the total score of the math unit test, for example, the user's total score for this math unit test is 96 points. Alternatively, the grading score can be sub-scores for calculation problems, fill-in-the-blank questions, true / false questions, multiple-choice questions, or word problems within the math unit test. For example, suppose the total score of the math unit test is 100 points, the total score for calculation problems is 20 points, the total score for fill-in-the-blank questions is 20 points, and so on. Suppose a user scored 20 points on calculation questions, 5 points on fill-in-the-blank questions, 15 points on true / false questions, 20 points on multiple-choice questions, and 4 points on word problems in this math unit test. In this case, it can be determined that the user performed better on calculation, true / false, and multiple-choice questions. Highlights can then be identified based on the total score of the data to be graded and / or the sub-scores of similar question types within the data.

[0051] Neatness of handwriting is used to characterize the regularity of a user's handwriting and / or the neatness of the layout of the data to be corrected. Neatness of handwriting can be expressed in the following levels: very neat, fairly neat; or, neatness of handwriting can also be expressed as a percentage value, such as 90%, where the larger the percentage value, the higher the neatness of handwriting.

[0052] Problem-solving innovation is used to characterize the degree to which unique ideas, novel methods, or creative expressions that go beyond the standard answer exist in the data to be graded. Problem-solving innovation can be expressed in the following levels: very innovative, relatively innovative; or, problem-solving innovation can also be expressed as a percentage value, such as 90%, where the higher the percentage value, the higher the degree of innovation.

[0053] Progress rate is used to characterize the difference between the current value and the historical value of at least one key indicator. It allows calculation of a user's growth and improvement in each key indicator. The at least one key indicator may include: graded score, neatness of handwriting, problem-solving creativity, and response time. Progress rate can be expressed in levels such as "very large" and "relatively large"; alternatively, it can be expressed as a percentage, for example, a 90% progress rate. This progress rate can be understood as the difference between the current value and the historical value of the key indicator, with a higher percentage indicating a greater progress rate.

[0054] After obtaining the data to be graded, an artificial intelligence (AI) model can be used to process the data and obtain a graded score. Furthermore, the AI ​​model can also be used to analyze the key features of the data to obtain target recognition results. Here, the AI ​​model that outputs the graded score and the target recognition result can be the same AI model or different AI models. For example, the AI ​​model could be a large language model, which can both grade the data to obtain a graded score and perform key feature analysis to obtain target recognition results.

[0055] After obtaining the graded scores and target identification results, highlight information is determined based on these results. Specifically, the total score in the graded score can be compared with a preset score. If the total score is greater than or equal to the preset score, then the total score and target identification result are identified as highlight information. For example, highlight information could be: a total score of 96 points, handwriting neatness as "very neat," problem-solving innovation as "very innovative," and improvement as "significant." If the total score is less than the preset score, then the highest-scoring sub-score is determined from among multiple sub-scores, and highlight information is determined based on the highest-scoring sub-score and the target identification result. For example, highlight information could be: a low total score of 80 points, but excellent performance on calculation problems (with a sub-score of 20 points), neat handwriting as "relatively neat," problem-solving innovation as "relatively innovative," and improvement as "significant." If the total score is less than the preset score, and there are no outstanding scores among the multiple sub-scores (i.e., no perfect score among the multiple sub-scores, or no score close to perfect score among the multiple sub-scores), then highlight information is determined solely based on the target recognition results. For example, highlight information could be: neatness of handwriting as "very neat", innovativeness of problem-solving as "highly innovative", or improvement as "significant improvement".

[0056] The above approach abandons the "one-size-fits-all" praise method and innovatively adjusts the generation strategy of highlight information dynamically according to the score level. For high-scoring students (e.g., 98 points), affirmation is given based on target identification results such as the creativity of problem-solving and the neatness of handwriting, which effectively avoids the apathy of high-scoring students. For average-scoring students (e.g., 80 points), their overall gap is intelligently ignored, and instead, the focus is on highlight information such as the question types they scored highest on, building a solid psychological advantage for them. This effectively prevents them from developing a comprehensive self-doubt due to unsatisfactory overall scores, thereby accurately rebuilding their learning confidence and igniting their enthusiasm for learning.

[0057] In this embodiment of the application, step S12, which involves analyzing the highlights of the data to be modified, specifically includes the following steps: Step S121: Perform highlight feature analysis on the multimodal data of the data to be corrected; whereby the multimodal data includes image data and text data.

[0058] After obtaining the data to be corrected, it can be converted from single-modal data to multimodal data. For example, if the data is image data, OCR technology can be used to recognize the text within the image data, thus obtaining the text data. Then, highlight feature analysis is performed on the multimodal data to obtain the target recognition result. Specifically, highlight feature analysis is performed on the multimodal data using an artificial intelligence model to obtain the target recognition result. This can be done on both image data and text data using an artificial intelligence model to obtain the target recognition result.

[0059] Specifically, after performing highlight feature analysis on image and text data using an artificial intelligence model, the following target recognition results can be obtained: "Handwriting neatness" is "very neat", and "Problem-solving innovation" is "highly innovative".

[0060] In this embodiment of the application, step S12, which involves analyzing the highlights of the data to be modified, further includes the following steps: Step S122: Perform highlight feature analysis on the image data and text data of the data to be corrected.

[0061] After obtaining the data to be graded, it can be converted from single-modal data to multi-modal data. For example, if the data is an image, OCR technology can be used to recognize the text within it. Then, an artificial intelligence model can be used to analyze the highlights of each modality. Specifically, analyzing the highlights of the image data using an AI model yields results on neat handwriting; similarly, analyzing the highlights of the text data using an AI model yields results on innovative problem-solving.

[0062] After obtaining the feature recognition results for each modality of data, the target recognition result is determined based on these feature recognition results. Specifically, after analyzing the highlight features of image data using an artificial intelligence model to obtain the feature recognition result for neatness of handwriting, if the "neatness of handwriting" is determined to be "very neat" based on this feature recognition result, then "neatness of handwriting is very neat" is taken as a target recognition result. After analyzing the highlight features of text data using an artificial intelligence model to obtain the feature recognition result for problem-solving innovation, if the "problem-solving innovation" is determined to be "highly innovative" based on this feature recognition result, then "problem-solving innovation is highly innovative" is taken as another target recognition result.

[0063] After performing highlight feature analysis on image and text data to obtain feature recognition results, the current feature recognition result can be compared with the feature recognition results obtained at previous times to obtain the improvement margin for that feature recognition result. If the improvement margin meets a preset margin threshold, then that improvement margin is determined as another target recognition result. Specifically, after performing highlight feature analysis on image data using an artificial intelligence model to obtain the feature recognition result for neatness of handwriting, the current feature recognition result for neatness of handwriting can be compared with the feature recognition results for neatness of handwriting obtained at previous times to obtain the improvement margin for "neatness of handwriting". If the improvement margin is greater than a preset margin threshold, then that improvement margin is determined as another target recognition result. Similarly, after performing highlight feature analysis on image data using an artificial intelligence model to obtain the feature recognition result for problem-solving innovation, the current feature recognition result for problem-solving innovation can be compared with the feature recognition results for problem-solving innovation obtained at previous times to obtain the improvement margin for "problem-solving innovation". If the improvement margin is greater than a preset margin threshold, then that improvement margin is determined as another target recognition result.

[0064] In this embodiment of the application, after obtaining the graded score, the graded score can be compared with the score obtained for the user in the previous grading. If it is determined from the comparison result that the user's graded score has improved compared with the previous score, and the improvement exceeds a preset threshold, then the improvement of the graded score is also regarded as highlight information.

[0065] In the above implementation, the data to be graded is deconstructed into multimodal data such as image data and text data, and the highlights and features of the image data and text data are analyzed from different analytical perspectives. This not only reveals the students' intrinsic thinking quality (such as the problem-solving innovation reflected in the text data), but also captures their extrinsic learning habits and attitudes (such as the neatness of handwriting reflected in the image data), thereby realizing a comprehensive and three-dimensional evaluation of students' learning performance, greatly improving the breadth and depth of highlight discovery, and making incentive feedback more comprehensive and accurate.

[0066] In this embodiment of the application, when the feedback information includes voice information, step S103 generates the feedback information based on the highlight information and controls the printing device to output the feedback information to provide positive encouragement to the user. Specifically, it includes the following steps: Step S21: Determine the attribute information of the generated voice information based on the graded score; Step S22: Control the printing device to play voice information.

[0067] Here, the graded score (i.e., the total score described above) can be compared with a preset score to determine the attribute information of the speech information. A corresponding strategy is set for each preset score, which determines the attribute information of the speech information to be generated. For example, for any preset score, if the graded score meets the preset score, one strategy is used to determine the attribute information of the speech information to be generated; if the graded score does not meet the preset score, another strategy is used to determine the attribute information of the speech information to be generated.

[0068] The number of preset scores can be multiple, such as 95, 90, 85, etc. The number and specific values ​​of the preset scores can be set according to actual needs; no specific limitations are imposed here. Then, the maximum preset score that the graded score must satisfy can be determined from among the multiple preset scores. For example, if the graded score is 94, it can be determined that the graded score satisfies preset scores 90 and 85, but not the preset score 95. Therefore, the maximum preset score is 90. In this case, the attribute information of the generated voice information can be determined according to the strategy of satisfying 90 but not 95.

[0069] For example, the attribute information of the voice information includes at least one of the following: the information content of the voice information, the sound effect information of the voice information (e.g., excited tone, appreciative tone, encouraging tone), the background music of the voice information, etc.

[0070] At this point, the strategy for a grade of 95 is: Message 1, sound effects indicating excitement, and background music that expresses celebration or excitement; the strategy for a grade of 90 but less than 95 is: Message 2, sound effects indicating appreciation, and background music that expresses appreciation, praise, or affirmation; the strategy for a grade of 85 but less than 90 is: Message 3, sound effects indicating encouragement, and background music that expresses encouragement or motivation; the strategy for a grade of less than 85 is: Message 4, sound effects indicating empathy and comfort, and background music that expresses warm companionship.

[0071] If student A's score for the data to be graded is 92, then the attribute information of the voice message can be determined as follows: message content 2, sound effect information indicating appreciation, and background music that can express appreciation, praise, or affirmation. At this time, the printing device can be controlled to play the voice message with the above attribute information.

[0072] In one alternative approach, the server determines the attribute information of the generated voice message based on whether the graded score meets a preset value, and then controls the printing device to play the voice message. For example, the server directly controls the printing device to play the voice message; or, the server controls the printing device to play the voice message through a terminal device.

[0073] In another alternative approach, the terminal device determines the attribute information of the generated voice information based on whether the graded score meets the preset score, and then controls the printing device to play the voice information. For example, the terminal device directly controls the printing device to play the voice information.

[0074] In another alternative approach, the printing device determines the attribute information of the generated voice message based on whether the graded score meets the preset score, and then plays the voice message directly; or, after receiving a playback confirmation message sent by the user through the terminal device, it plays the voice message.

[0075] By employing the above processing method, the score range of the user's feedback can be determined, allowing for more appropriate audio feedback. For example, for excellent performance that meets the preset score, an enthusiastic, high-pitched audio message with celebratory background music is generated and played; for performance that does not meet the preset score, a gentle, encouraging, and affirmative audio message is generated. This layered auditory feedback, precisely matched to achievement levels, allows students to directly experience the positive correlation between effort and reward, thus greatly enhancing their intrinsic motivation.

[0076] In this embodiment of the application, when the attribute information of the voice information includes the information content, the above steps determine the attribute information of the generated voice information based on whether the grading score meets the preset score, specifically including the following steps: When the graded score is greater than or equal to the preset score, the generated voice information is determined to include the graded score and / or the target recognition result. When the graded score is less than the preset score, the generated voice information is determined to include the target recognition result.

[0077] This involves comparing the graded score with a preset score. When the graded score is greater than or equal to the preset score, the information content of the speech information is determined based on the graded score and / or the target recognition result. For example, when the graded score is greater than or equal to the preset score of 95, the information content of the speech information is determined based on the graded score of 95 and / or the target recognition result. The information content of the speech information can be determined in the following ways: Method 1: For each graded score and target recognition result, corresponding text information is pre-set. Then, a database query can be used to find text information that matches the graded score and target recognition result, and this matching text information is identified as the content of the speech information. The found text information associated with the graded score and / or the text information associated with the target recognition result can then be converted into audio to obtain the speech information. For example, a database query reveals that the text information corresponding to a graded score of 95 is "Your score is almost perfect, you are really great, I'm proud of you"; and a database query reveals that the text information corresponding to the target recognition result is "Your handwriting is very neat, you're practically a human printer," and "You've made great progress this time, your handwriting is getting neater and neater, congratulations on upgrading from an ordinary human printer to a gold medal human printer."

[0078] Method 2: You can obtain text templates associated with the graded score and / or text templates associated with the target recognition result. Then, add the graded score and / or the target recognition result to the corresponding text templates to obtain the corresponding text information. For example, the text template associated with a graded score of 95 is "Your score is ( ), which is close to full marks. You did a great job." Adding the graded score to this text template yields the following text information: Your score is 95, which is close to full marks. You did a great job. For example, the text template associated with the target recognition result "neatness of handwriting" could be "The neatness of handwriting this time is ( ), we hope you can continue to maintain it." Adding the target recognition result to this text template yields the following text information: The neatness of handwriting this time is very neat. We hope you can continue to maintain it.

[0079] Method 3: Personalized praise messages can also be generated using AI models, linked to the graded score and / or the target recognition result. For example, if the graded score is 95, the AI ​​model will generate corresponding personalized praise messages based on the target recognition result, such as: "Congratulations on getting 95 points! Let's look at this 'high-scoring answer sheet': zero mistakes on the basic questions, over 90% accuracy on the difficult questions, and the ability to flexibly apply learned knowledge points during the problem-solving process. This 'steady + flexible' performance is truly at the level of a top student! Keep up this focus and dedication, and you'll definitely get a perfect score next time!" At this point, the content of the voice message can be determined based on the personalized praise message generated by the AI ​​model.

[0080] After obtaining the corresponding text information using methods one, two, or three, the content of the speech information can be determined based on the text information. For example, when there is only one piece of text information, it can be directly identified as the content of the speech information. When there are multiple pieces of text information, they can be concatenated to obtain a concatenated text, which can then be used as the content. The content can then be converted into audio to obtain the speech information. When concatenating multiple pieces of text information, corresponding connecting words can be identified, and the multiple pieces of text information can be concatenated according to the connecting words to obtain the concatenated text. For example, you can combine the text messages "Your score is almost perfect, you're amazing, I'm so proud of you" and "Your handwriting is so neat, you're practically a human printer" and "You've made great progress this time, your handwriting is getting neater and neater, congratulations on upgrading from an ordinary human printer to a gold medal human printer" to get the following combined text: Your score is almost perfect, you're amazing, I'm so proud of you; I also noticed that your handwriting is very neat, you're practically a human printer; You've made great progress this time, your handwriting is getting neater and neater, congratulations on upgrading from an ordinary human printer to a gold medal human printer.

[0081] When the graded score is found to be less than a preset score, the information content of the speech information can be determined based on the target recognition result. Here, the text information corresponding to the target recognition result can be determined using the methods described in Method 1, Method 2, or Method 3 above, and the information content of the speech information can be determined based on the text information. Then, the information content is converted into audio to obtain the speech information. Here, the methods for determining text information using Method 1, Method 2, and Method 3 will not be elaborated further.

[0082] It should be noted that the grading score described in the above embodiments refers to the total score described above. That is, when the total score of the data to be graded is less than the preset score, the information content of the voice information can be determined based on the target recognition result; when the total score of the data to be graded is greater than or equal to the preset score, the information content of the voice information is determined by the grading score and / or the target recognition result.

[0083] In addition, when the total score of the data to be corrected is less than the preset score, the highest sub-score can be determined from the multiple sub-scores of the data to be corrected. If the highest sub-score meets the preset score requirement, the information content of the speech information is determined based on the highest sub-score and / or the target recognition result.

[0084] By using the above processing methods, the incentive strategies can be refined and optimized, the relevance of the feedback information can be improved, and more targeted incentive strategies can be developed for students, avoiding the uniformity of incentive strategies.

[0085] In this embodiment of the application, when the feedback information includes an electronic certificate, the above steps generate the feedback information based on the highlight information and control the printing device to output the feedback information to provide positive incentives to the user. Specifically, the steps include the following: Step S31: When the graded score is greater than or equal to the preset score, generate an electronic certificate; Step S32: Send a print job for the electronic certificate to the printing device so that the printing device can print the electronic certificate and obtain the physical certificate.

[0086] The system can compare the graded score with a preset score. For example, when the graded score is greater than or equal to the preset score of 95, an electronic certificate can be generated for the user. Then, the printing device can be controlled to print the electronic certificate to obtain a physical certificate.

[0087] Here, there can be one or more preset scores. When there is only one preset score, if the graded score is greater than or equal to the preset score, an electronic certificate matching the preset score will be generated and printed to obtain a physical certificate.

[0088] When there are multiple preset scores, the maximum preset score that the corrected score can satisfy can be determined from these multiple preset scores. Then, an electronic certificate matching the maximum preset score is generated and printed to obtain a physical certificate. For example, the preset scores include 85, 90, and 95. Suppose the user's data to be corrected has a score of 94. In this case, the maximum preset score that the corrected score can satisfy can be determined to be 90. An electronic certificate matching the maximum preset score of 90 can be determined, and the printing device can be controlled to print the electronic certificate to obtain a physical certificate.

[0089] It should be noted that the content of the electronic certificate matched with each preset score is different. For example, the electronic certificate matched with a preset score of 85 points can read, "In this assessment, you made rapid progress and performed excellently, and have been awarded the title of 'Star of Progress'". The electronic certificate matched with a preset score of 90 points can read, "In this assessment, you achieved excellent results and have been awarded the title of 'Model Student'". The electronic certificate matched with a preset score of 95 points can read, "In this assessment, you achieved top results and have been awarded the title of 'Elite Student'".

[0090] In this embodiment, when there is one preset score, if the graded score is less than that preset score; or when there are multiple preset scores, if the graded score is less than the minimum preset score, then an encouraging voice message can be generated based on the graded score. For example, if a student's graded score is 70, an encouraging voice message can be generated. The encouraging content of this voice message could be: "In this learning process, you demonstrated admirable perseverance and willpower. Although the questions were quite challenging, you carefully wrote out the solution process for each question. This attitude of facing difficulties head-on is a more valuable asset than a score. Every effort you make is accumulating strength for future breakthroughs! Therefore, you are rated as a potential star."

[0091] It should be noted that if the feedback information includes both voice information and electronic certificate, then when the graded score is less than the preset score, voice information can be generated in the manner described in the above implementation method. This voice information can be used to encourage students.

[0092] In this embodiment of the application, after identifying highlights in the target correction data and obtaining the target identification result, corresponding highlight labels and / or electronic badges can be generated based on the target identification result. The specific implementation method is as follows: Based on the result level corresponding to the target recognition result, a corresponding highlight label and / or electronic badge will be determined. The highlight labels and / or electronic badges corresponding to different result levels of target recognition results will differ, for example, in content and / or display effects.

[0093] For example, if the target recognition result is "the neatness of the handwriting reaches the 'very neat' standard", then a "Handwriting Star" highlight label will be added to the corresponding position of the correction result, or a red "Neat" electronic badge will be generated.

[0094] If the target recognition result is "the innovativeness of the problem-solving reaches a 'very high' standard", then the "Innovation Master" highlight label will be annotated in the corresponding position of the grading result, or a gold "Innovation" electronic badge will be generated.

[0095] If the target recognition result is "the progress has reached the 'significant' standard", then a "Progress Pioneer" highlight label will be added to the corresponding position of the correction result, or a blue "Progress" electronic badge will be generated.

[0096] In the above embodiments, by generating highlight labels, electronic badges, and electronic certificates, and then printing the electronic certificates to obtain physical certificates, virtual incentives can be elevated to physical incentives, thereby enhancing the user's sense of accomplishment and ritual. This processing method not only allows users to grade data but also provides positive incentives based on grading scores, increasing user stickiness to the printing device and deepening the interactive experience between the user and the printing device.

[0097] In this embodiment of the application, the electronic certificate can be generated in the following ways, specifically including: Method 1: In the pre-set certificate template library, select a certificate template that matches the highlight information and / or the data to be corrected.

[0098] Here, a certificate template library is pre-set, which contains multiple electronic certificates. These electronic certificates can be templates pre-created by the system or templates created by users through their terminal devices.

[0099] Each electronic certificate in the certificate template library was categorized according to preset category labels, where the preset category labels are associated with the data type of highlight information and / or data to be corrected.

[0100] For example, if the data to be graded is of subject information such as mathematics, Chinese, English, or drawing, then the electronic certificates can be categorized according to this data type in the certificate template. For instance, the data type "mathematics" corresponds to one or more electronic certificates, the data type "Chinese" corresponds to one or more electronic certificates, and so on. These will not be listed one by one here.

[0101] For each type of electronic certificate, further classification can be made based on key information. For example, key information may include graded score, neatness of handwriting, innovativeness in problem-solving, and rate of improvement. In this case, corresponding electronic certificates can be set for different grades, different levels of neatness of handwriting, different levels of innovativeness in problem-solving, and different rates of improvement.

[0102] In practice, an electronic certificate matching the highlight information can be found in the certificate template library. For example, if the highlight information is a graded score of 95, then an electronic certificate matching 95 can be found in the certificate template library. Alternatively, an electronic certificate matching the data to be graded can be found in the certificate template library. For example, if the data type to be graded is "mathematics," then an electronic certificate matching "mathematics" can be found in the certificate template library. Then, among the electronic certificates matching "mathematics," the electronic certificate matching the highlight information (e.g., a graded score of 95) can be identified.

[0103] If there are multiple types of highlight information, such as grading scores and neatness of handwriting, one option is to merge the content of the electronic certificate corresponding to each type of highlight information and use the merged electronic certificate as the certificate template that matches the highlight information and / or the data to be graded. Another option is to use the electronic certificate corresponding to the highest priority highlight information as the certificate template that matches the highlight information and / or the data to be graded. A corresponding priority is pre-set for each type of highlight information; for example, the priority decreases sequentially for grading scores, neatness of handwriting, innovative problem-solving, and the extent of improvement.

[0104] Method 2: Obtain the certificate template created by the user in advance through the template editor; then, generate an electronic certificate based on the certificate template.

[0105] Here, users can pre-install a client on their terminal device, which provides the ability to edit certificate templates. Responding to the user's template editing command, the client enters the template editor's editing page. This page contains at least one template element, and users can select and / or modify multiple template elements to complete the creation of the certificate template. After creation, the client uploads the certificate template to the server and stores it.

[0106] Users can add template tags to the created award certificate templates. These template tags can determine the data type of the data to be reviewed that the award certificate template can be applied to, and / or determine the highlight information that the award certificate template can be applied to.

[0107] In practice, after identifying the key information, a certificate template matching the data to be corrected and / or the key information can be selected from the user's pre-created certificate templates, and an electronic certificate can be generated based on the matching template. For example, if the matching certificate template does not contain the information to be filled, the matching certificate template is directly used as the electronic certificate. If the matching certificate template contains the information to be filled, the information is filled in, and the filled certificate template is then designated as the electronic certificate. The information to be filled in can be the user's name, grade, class, etc.

[0108] Method 3: An AI model is used to retrieve a certificate template that matches the highlights and / or the data to be corrected; then, an electronic certificate is generated based on the certificate template.

[0109] Here, an AI model can be used to obtain a certificate template associated with the graded score and / or the target recognition result, and then an electronic certificate can be generated based on this template. For example, when the graded score is 95 and the AI ​​identifies the target recognition result as "Excellent in problem-solving innovation and 96% in neatness," the AI ​​model will retrieve the "Top Student Gold Award" certificate template. This certificate template has a unique gold border, dual honor labels of "Star of Problem Solving Innovation" and "Exemplary Handwriting," and also includes a module displaying the 95-point score. Then, the AI ​​model fills in personalized information such as the user's name, graded score, the identified target recognition result, and the date of award, automatically generating an electronic certificate with a unique number. If the graded score is 87 and the target recognition result is "Improvement of 15 points," the AI ​​model will retrieve the "Star of Progress" certificate template and fill in highlight information such as the improvement score, graded score, and target recognition result, thereby generating the corresponding electronic certificate.

[0110] The above implementation introduces a diversified electronic template generation mechanism, which can provide users with a more comprehensive way to generate electronic certificates and realize personalized customization of electronic certificates. This makes the certificates no longer uniform, but a visual interpretation of the student's unique strengths, so that the student feels deeply understood and recognized, greatly enhancing the accuracy and emotional impact of the incentive, and fully affirming the child's efforts and talents.

[0111] In this embodiment of the application, when the feedback information includes points data, the above steps generate feedback information based on the highlight information and control the printing device to output the feedback information to provide positive incentives to the user, specifically including the following steps: Step S41: Based on the highlight information, determine the indicators used for score accumulation judgment; wherein, the indicator values ​​include at least one of the following: the graded score, the excellent level of the target recognition result, the complexity of the data to be graded, and the number of times the user's graded score has exceeded the preset score consecutively. Step S42: Determine the final score value according to the score allocation rules and indicators; Step S43: Update the user's points data according to the final points value, and control the printing device to display the points data.

[0112] The "excellent" rating for target recognition results indicates the degree of excellence in the recognition outcome. For example, the "excellent" rating for neat handwriting can be: very neat, fairly neat, or reasonably neat; the "excellent" rating for problem-solving innovation can be: very innovative, fairly innovative, or reasonably innovative; the "excellent" rating for improvement can be: very significant improvement, relatively significant improvement, or reasonably significant improvement; and the "excellent" rating for graded scores can be divided using multiple preset score values, namely 95, 90, and 85. A graded score greater than 95 is considered very excellent, a graded score less than 95 but greater than 90 is considered fairly excellent, and a graded score less than 90 but greater than 85 is considered reasonably excellent.

[0113] The complexity of the data to be graded can be determined by the complexity of the questions contained within it. If the data contains complex labels, the complexity can be determined by these labels; if the data does not contain complex labels, additional questions can be identified, and the complexity can be determined based on the number and / or difficulty of the identified additional questions. A higher number of additional questions and greater difficulty in the additional questions indicate higher complexity, and vice versa.

[0114] The number of times a user's graded score exceeds a preset score can be understood as the number of times a user's graded data exceeds 95 points consecutively, for example, 3 times, 5 times, etc.

[0115] For each indicator, corresponding point allocation rules can be pre-set. For example, for graded scores, the following point allocation rules can be determined: A score higher than 95 will earn 50 points. A score higher than 90 but lower than 95 will earn 30 points. A score higher than 85 but lower than 90 will earn 15 points. No points will be awarded for scores higher than 80 but lower than 85. If the graded score is below 80 (excluding 80), 10 points will be deducted.

[0116] Based on the neatness of handwriting, the following point allocation rules can be determined: The neatness of the handwriting is described as "very neat," earning 50 points. The neatness of the handwriting is rated as "relatively neat," earning 30 points. The neatness of the handwriting is "generally neat," earning 15 points. The neatness of the handwriting is considered sloppy, resulting in a score of -5 points.

[0117] Regarding the innovativeness of problem-solving, the following integral allocation rules can be determined: The problem-solving demonstrates a high degree of innovation, earning 50 points. The problem-solving demonstrates a high level of innovation, earning 30 points. The innovativeness of the solution is rated as average, and 15 points will be awarded.

[0118] Based on the magnitude of progress, the following rules for allocating points can be determined: The improvement is significant, earning 50 points. The improvement is considered significant, and 30 points are awarded. The improvement rate is average, and 15 points are awarded.

[0119] At this point, the points value corresponding to each indicator can be determined according to the above-mentioned point allocation rules. The points values ​​corresponding to multiple indicators are then added together to obtain the final point value. Afterward, the user's point data can be updated according to the final point value, and the printing device can be controlled to display this point data, or the point data can be displayed through a terminal device. Here, users can use their point data to redeem desired rewards from their parents (such as game time or small gifts), or, under parental supervision, redeem various gifts in the client's online "points mall," transforming learning achievements into tangible fun and motivation.

[0120] In this embodiment of the application, step S103, which controls the printing device to output the feedback information, specifically includes the following steps: When the graded score is greater than or equal to the preset score, the printing device is controlled to immediately output voice information including the graded score and / or the target recognition result, as well as an electronic certificate; If the graded score is less than the preset score, the printer will immediately output voice information containing the graded score and / or the target recognition result.

[0121] or: When the graded score is greater than or equal to the preset score, the printing device is controlled to output voice information including the graded score and / or the target recognition result and an electronic certificate at a preset time. When the graded score is less than the preset score, the printing device is controlled to output voice information containing the graded score and / or the target recognition result at a preset time.

[0122] Specifically, when the graded score is greater than or equal to a preset score, controlling the printing device to immediately execute the feedback information means that the printing device automatically executes the feedback information output operation when a specific trigger condition is met: When the score of the data to be graded is greater than or equal to 95, the feedback information includes voice information and electronic certificate. The printing device will immediately play the voice information and simultaneously print the electronic certificate to generate a physical certificate. In this scenario, "the graded score is greater than or equal to 95" is the core condition for triggering voice playback and certificate printing. In other words, "the graded score is greater than or equal to 95" is the specific triggering condition that needs to be met to play the voice information and print the electronic certificate.

[0123] When the score of the data to be corrected is less than 95, the feedback information is voice information containing the score (or / and the target recognition result), and the printing device will play the voice information immediately. In this scenario, "the score of correction is less than 95" is the core condition for triggering voice playback. In other words, "the score of correction is greater than or equal to 95" is the specific trigger condition that needs to be met to play the voice information.

[0124] Additionally, when the graded score is greater than or equal to a preset score, the printing device is controlled to execute the feedback information at a preset time. This means that the printing device executes the feedback information output operation at a time node set by the user. At this time, both the "preset score condition" and the "preset time condition" must be met simultaneously. When the score of the data to be graded is greater than or equal to 95, the feedback information includes voice information and an electronic certificate. At this time, the printing device will play the voice information and print the electronic certificate to generate a physical certificate within the preset time. The preset time is a specific execution time set by the user in advance. The preset time set for the voice information and the electronic certificate can be the same or different.

[0125] When the score of the data to be graded is less than 95, the feedback information will be voice information containing the grading score (or / and the target recognition result), and the printing device will play the voice information at the preset time. The preset time is a specific execution time set by the user.

[0126] The preset time can be set to 19:00-20:00 daily, suitable for parents' after-get off work companionship, allowing parents and children to witness the feedback results together, thus enhancing the incentive effect. Users can set the preset time according to their actual needs; there are no specific limitations here, as long as it is feasible.

[0127] In this embodiment of the application, before controlling the printing device to output feedback information to provide positive incentives to the user, the method further includes the following steps: S51: Send a notification message to the user via the printing device indicating that feedback is pending. S52: In response to the user's confirmation operation for the feedback information, control the printing device to output the feedback information to the user.

[0128] Here, after extracting the highlight information to characterize the user's learning performance, feedback information can be generated based on this highlight information. Optionally, a prompt message for reviewing the feedback information can be sent to the user via the printing device or terminal device. For example, the prompt message can be played through the printer's speaker: "Incentive information has been generated; please review it promptly." Simultaneously, the prompt message and a confirmation button can be displayed on the printer's screen. If the printer detects the following voice message via its microphone: "Incentive information confirmed; please output," then the user's confirmation action regarding the feedback information has been detected; or, if the user triggers the confirmation button, then the user's confirmation action regarding the feedback information has been detected. In this case, the printer can be controlled to output the feedback information to the user.

[0129] In this embodiment, a prompt message "Incentive information has been generated, please review it promptly" and a confirmation button can also be displayed through a client or mini-program installed on the terminal device. If a user's confirmation operation displayed in the client or mini-program is detected, it is confirmed that the user has confirmed the feedback information to be provided. At this time, the printing device can be controlled to output the feedback information to the user.

[0130] Here, after extracting the highlight information to characterize the user's learning performance, feedback information can be generated based on this highlight information. Alternatively, the feedback information can be directly input into the printing device.

[0131] In the above implementation, by allowing the user to confirm the feedback information before outputting it, material waste caused by incorrect, inappropriate, or unsuitable information is effectively avoided, significantly improving resource utilization and operational economy. At the same time, it enhances the user's sense of participation, control, and overall service satisfaction during the interaction process.

[0132] like Figure 2 The diagram shown illustrates a learning incentive system applied to the learning incentive method described in the above embodiments. Figure 2As shown, this learning record system includes a printing device, a server, and terminal devices. The printing device and the server can communicate via HTTP (Hypertext Transfer Protocol) and MQTT (Message Queuing Telemetry Transport), while the server and terminal devices communicate via the internet. Here, the printing device can be a device with both printing and scanning capabilities, and the server includes: an AI analysis engine, a certificate generation system, a points management platform, and a parental control center.

[0133] In this embodiment, the server can send a homework printing task to the printing device. After the printing device outputs the homework result, the student can answer the homework result and scan the answer result using the scanning function of the printing device. Then, the scan result is uploaded to the server as data to be graded; or, the student can take a picture of the answer result using a terminal device and upload the picture as data to be graded to the server.

[0134] After receiving the data to be graded, the server uses an AI analysis engine to process the data and obtain key information representing the user's learning performance. This key information includes at least one of the following: grading score, neatness of handwriting, innovativeness in problem-solving, and rate of improvement. Simultaneously, the AI ​​analysis engine can generate voice feedback based on this key information.

[0135] After receiving the highlighted information, the certificate generation system can generate an electronic certificate when the graded score is greater than or equal to a preset value. It then sends a print job to the printing device to print the electronic certificate, resulting in a physical certificate. Simultaneously, the certificate generation system also provides certificate creation functionality. The system includes a certificate editor, which users can access via a client or app installed on their terminal device to create certificate templates.

[0136] After obtaining the highlights information, the points management platform can determine the indicators used for points accumulation based on the highlights information. The indicators include at least one of the following: the graded score, the excellence level of the target recognition result, the complexity of the data to be graded, and the number of times the user's graded score has exceeded the preset score. Then, according to the points allocation rules and indicators, the final points value is determined. The user's points data is updated according to the final points value, and the printing device is controlled to display the points data.

[0137] The parental control center on the server has the following functions: device management, rule configuration, content moderation, and content viewing.

[0138] The device management function manages the printing equipment. For example, users can manage the printing equipment by connecting to a terminal device. The rule configuration function configures the point allocation rules. For example, users can set point allocation rules for each indicator by connecting to a terminal device. The content review function allows users to review feedback information. For example, after identifying feedback information, a reminder message can be sent to the user to review the feedback information, and the printing device can be controlled to output the feedback information after the user approves the feedback information. The content viewing function allows users to view graded scores and target recognition results, etc.

[0139] like Figure 3 The diagram shown is a flowchart illustrating a method for identifying key information in a learning motivation approach. Figure 3 As shown, the method includes the following steps: S301: Obtain the user's data to be modified; S302: Convert the data to be corrected into multimodal data; multimodal data includes image data and text data; S303: Perform image analysis on the image data to obtain the feature recognition results of the neatness of handwriting; S304: Perform text analysis on the text data to obtain feature recognition results that demonstrate the innovativeness of the problem-solving approach; S305: Determine the target recognition result based on the feature recognition results of neat handwriting and innovative problem-solving.

[0140] For example, if the handwriting neatness is determined to be "very neat" based on the feature recognition result, then "handwriting neatness is very neat" is taken as a target recognition result; if the problem-solving innovation is determined to be "highly innovative" based on the feature recognition result, then "highly innovative" is taken as another target recognition result.

[0141] After performing highlight feature analysis on each modality of data and obtaining feature recognition results, the feature recognition results obtained at the current time can be compared with the feature recognition results obtained at historical time to obtain the improvement range for the feature recognition result. If the improvement range meets the preset range threshold, the improvement range is determined as another target recognition result.

[0142] like Figure 4 The diagram shown is a flowchart illustrating a method for generating speech information in a learning incentive approach. Figure 4 As shown, the method includes the following steps: S401: Get the graded score of the data to be graded; S402: Determine the score interval corresponding to the graded score from multiple preset score intervals; wherein, the multiple preset score intervals are determined by multiple preset score values; S403: Determine the tone information based on the score range corresponding to the graded score; for example, if the score range is greater than or equal to 95 points, the tone information is "excited tone"; if the score range is [94, 90], the tone information is "appreciative tone"; if the score range is [85-89] points, the tone information is "encouraging tone"; if the score range is less than 84 points, the tone is "empathetic and comforting tone".

[0143] S404: Determine the information content of the voice message to be generated based on the graded scores; S405: Generate speech information based on information content and tone information.

[0144] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0145] Based on the same inventive concept, this disclosure also provides a learning incentive device corresponding to the learning incentive method. Since the principle of the device in this disclosure for solving the problem is similar to the learning incentive method described above in this disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0146] Reference Figure 5 The diagram shown is a schematic representation of a learning incentive device provided in an embodiment of this disclosure. The device includes: an acquisition unit 51, a data analysis unit 52, and a reward generation unit 53; wherein, Acquisition unit 51 is used to acquire the user's data to be modified; The data analysis unit 52 is used to obtain, based on the data to be corrected, highlight information that characterizes the user's learning performance; The reward generation unit 53 is used to generate feedback information based on the highlight information and control the printing device to output the feedback information to provide positive incentives to the user.

[0147] In one possible implementation, the data analysis unit 52 is further configured to: Obtain the grading result of the data to be graded; wherein, the grading result includes at least the graded score; and, The data to be corrected is analyzed for highlights and features to obtain target identification results; wherein, the target identification results are used to indicate at least one of the following information: neatness of handwriting, innovativeness of problem-solving, and progress.

[0148] In one possible implementation, the data analysis unit 52 is further configured to: perform highlight feature analysis on the data to be corrected based on the multimodal data of the data to be corrected; wherein the multimodal data includes image data and text data; the data analysis unit 52 is further configured to: perform highlight feature analysis on the image data and text data of the data to be corrected respectively.

[0149] In one possible implementation, when the feedback information includes voice information, the reward generation unit 53 is further configured to: The attribute information of the generated voice information is determined based on whether the graded score meets the preset score. Control the printing device to play the voice information.

[0150] In one possible implementation, when the attribute information of the voice information includes information content, the reward generation unit 53 is further configured to: When the graded score is greater than or equal to the preset score, it is determined that the information content of the generated voice information includes the graded score and / or the target recognition result; When the graded score is less than the preset score, it is determined that the information content of the generated voice information includes the target recognition result.

[0151] In one possible implementation, when the feedback information includes an electronic certificate, the reward generation unit 53 is further configured to: When the graded score is greater than or equal to a preset score, the electronic certificate is generated; Send a print job for the electronic certificate to the printing device so that the printing device prints the electronic certificate to obtain a physical certificate.

[0152] In one possible implementation, the reward generation unit 53 is further configured to: In a pre-set certificate template library, determine a certificate template that matches the highlight information and / or the data to be corrected; or Retrieve the certificate template created by the user in advance using the template editor; or The award certificate template is retrieved using an artificial intelligence model to match the highlights and / or the data to be corrected. The electronic certificate is generated based on the certificate template.

[0153] In one possible implementation, when the feedback information includes points data, the reward generation unit 53 is further configured to: Based on the highlighted information, indicators for judging the cumulative score are determined; wherein, the indicators include at least one of the following: the graded score, the excellence level of the target recognition result, the complexity of the data to be graded, and the number of times the user's graded score has exceeded a preset score consecutively. The final score is determined according to the score allocation rules and the aforementioned indicators; The user's points data is updated according to the final points value, and the printing device is controlled to display the points data.

[0154] In one possible implementation, the device is also used for: Before controlling the printing device to output the feedback information to provide positive incentives to the user, a prompt message for reviewing the feedback information is sent to the user through the printing device; In response to the user's confirmation of the feedback information, the printing device is controlled to output the feedback information to the user.

[0155] In one possible implementation, the reward generation unit 53 is further configured to: When the graded score is greater than or equal to a preset score, the printing device is controlled to immediately output voice information and a physical certificate including the graded score and / or the target recognition result; When the graded score is less than the preset score, the printing device is controlled to immediately output voice information containing the graded score and / or the target recognition result.

[0156] In one possible implementation, the reward generation unit 53 is further configured to: When the graded score is greater than or equal to a preset score, the printing device is controlled to output voice information and physical certificate including the graded score and / or the target recognition result at a preset time. When the graded score is less than the preset score, the printing device is controlled to output voice information containing the graded score and / or the target recognition result at a preset time.

[0157] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0158] Corresponding to Figure 1 In addition to the learning incentive methods mentioned above, this disclosure also provides an electronic device 600, such as... Figure 6 The diagram shown is a structural schematic of an electronic device 600 provided in an embodiment of this disclosure, including: The system includes a processor 61, a memory 62, and a bus 63. The memory 62 stores execution instructions and includes main memory 621 and external memory 622. The main memory 621, also called internal memory, temporarily stores the computational data in the processor 61, as well as data exchanged with external memory such as a hard disk. The processor 61 exchanges data with the external memory 622 through the main memory 621. When the electronic device 600 is running, the processor 61 communicates with the memory 62 through the bus 63, causing the processor 61 to execute the following instructions: Obtain the user's data pending approval; Based on the data to be corrected, extract the highlights that characterize the user's learning performance; Based on the highlighted information, feedback information is generated, and the printing device is controlled to output the feedback information to provide positive incentives to the user.

[0159] This disclosure also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the learning incentive method described in the above-described method embodiments. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0160] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the learning incentive method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0161] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0162] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0163] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0164] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0165] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0166] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A learning motivation method, characterized in that, The method includes: Obtain the user's data pending approval; Obtain the highlights information that characterize the user's learning performance based on the data to be corrected; Based on the highlighted information, feedback information is generated, and the printing device is controlled to output the feedback information to provide positive incentives to the user.

2. The method according to claim 1, characterized in that, The step of obtaining the highlight information used to characterize the user's learning performance based on the data to be corrected includes: Obtain the grading result of the data to be graded; wherein, the grading result includes at least the graded score; and, The data to be corrected is analyzed for highlights to obtain target identification results; wherein, the target identification results are used to indicate at least one of the following information: neatness of handwriting, innovativeness of problem-solving, and progress.

3. The method according to claim 2, characterized in that, The analysis of highlight features in the data to be corrected, to obtain target identification results, includes: Highlight feature analysis is performed on the data to be corrected based on its multimodal data; wherein, the multimodal data of the data to be corrected includes image data and text data; The image data and text data of the data to be corrected are analyzed for highlights respectively to obtain the target recognition result determined based on the highlights feature analysis results.

4. The method according to claim 2, characterized in that, The feedback information includes voice information; the step of generating feedback information based on the highlight information and controlling the printing device to output the feedback information to provide positive encouragement to the user includes: Based on the graded scores, determine the attribute information of the generated voice information; Control the printing device to play the voice information.

5. The method according to claim 4, characterized in that, The attribute information of the voice information includes the information content; determining the attribute information of the generated voice information based on the graded score includes: When the graded score is greater than or equal to a preset score, the information content of the generated voice information is determined to include the graded score and / or the target recognition result; When the graded score is less than the preset score, it is determined that the information content of the generated voice information includes the target recognition result.

6. The method according to claim 2, characterized in that, The feedback information includes an electronic certificate; the process of generating feedback information based on the highlight information and controlling the printing device to output the feedback information to provide positive incentives to the user includes: When the graded score is greater than or equal to a preset score, the electronic certificate is generated; Send a print job for the electronic certificate to the printing device so that the printing device prints the electronic certificate to obtain a physical certificate.

7. The method according to claim 6, characterized in that, The generation of the electronic certificate includes: In a pre-set certificate template library, determine a certificate template that matches the highlight information and / or the data to be corrected; or, Retrieve the certificate template created by the user in advance using the template editor; or, The award certificate template that matches the highlighted information and / or the data to be corrected is retrieved using an artificial intelligence model; The electronic certificate is generated based on the certificate template.

8. The method according to claim 2, characterized in that, The feedback information includes points data; the step of generating feedback information based on the highlight information and controlling the printing device to output the feedback information to provide positive incentives to the user includes: Based on the highlighted information, indicators for judging the cumulative score are determined; wherein, the indicators include at least one of the following: the graded score, the excellence level of the target recognition result, the complexity of the data to be graded, and the number of times the user's graded score has exceeded a preset score consecutively. The final score is determined according to the score allocation rules and the aforementioned indicators; The user's points data is updated according to the final points value, and the printing device is controlled to display the points data.

9. The method according to claim 1, characterized in that, Before controlling the printing device to output the feedback information to provide positive incentives to the user, the process includes: The system sends a prompt message to the user requesting feedback on the pending information via a printing device. In response to the user's confirmation of the feedback information, the printing device is controlled to output the feedback information to the user.

10. The method according to claim 2, characterized in that, The control printing device outputs the feedback information to provide positive incentives to the user, including: When the graded score is greater than or equal to a preset score, the printing device is controlled to immediately or within a preset time to output feedback information, which includes voice information and physical certificate. When the graded score is less than the preset score, the printing device is controlled to output feedback information immediately or within a preset time. The feedback information includes voice information. The information content of the voice information includes the graded score and / or the target recognition result.

11. A learning incentive device, characterized in that, The device includes: The acquisition unit is used to acquire the user's data to be modified. The data analysis unit is used to obtain, based on the data to be corrected, highlight information that characterizes the user's learning performance. The reward generation unit is used to generate feedback information based on the highlight information and control the printing device to output the feedback information to provide positive incentives to the user.

12. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and the machine-readable instructions, when executed by the processor, perform the steps of the learning stimulation method as described in any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the learning incentive method as described in any one of claims 1 to 10.

14. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the learning incentive method according to any one of claims 1 to 10.

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