WO2020177531A1 - 题目辅助方法及*** - Google Patents
题目辅助方法及*** Download PDFInfo
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- WO2020177531A1 WO2020177531A1 PCT/CN2020/075826 CN2020075826W WO2020177531A1 WO 2020177531 A1 WO2020177531 A1 WO 2020177531A1 CN 2020075826 W CN2020075826 W CN 2020075826W WO 2020177531 A1 WO2020177531 A1 WO 2020177531A1
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B19/00—Teaching not covered by other main groups of this subclass
- G09B19/02—Counting; Calculating
- G09B19/025—Counting; Calculating with electrically operated apparatus or devices
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/26—Techniques for post-processing, e.g. correcting the recognition result
- G06V30/262—Techniques for post-processing, e.g. correcting the recognition result using context analysis, e.g. lexical, syntactic or semantic context
- G06V30/274—Syntactic or semantic context, e.g. balancing
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/041—Abduction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
Definitions
- the present disclosure relates to the field of artificial intelligence technology, and in particular to a topic assistance method and system.
- An object of the present disclosure is to provide a method and system for assisting a topic.
- a method for assisting a topic including: acquiring an image including at least a first topic presented on a first surface through an image acquisition device; through a first computing device and a pre-trained first neural network The model, based on the image, identifies the first area where the first topic in the image is located; through a second computing device and a pre-trained second neural network model, based on the first area, identifies all areas The characters in the first area to obtain the first question; using a third computing device and a pre-trained third neural network model to determine the type of the first question based on the first question; if the The type of the first question is a calculation question, then: the first answer to the calculation question and the step-by-step problem-solving process are respectively generated by the fourth and fifth calculation devices; An answer and a step-by-step problem-solving process.
- a topic assistance system including: one or more neural network models pre-trained; one or more electronic devices with image acquisition functions and display functions, configured to acquire at least An image of the first topic presented on the first surface; and one or more computing devices configured to: based on the neural network model and the image, identify the first topic in the image where the first topic is located A region; based on the neural network model and the first region, identify the characters in the first region, thereby obtaining the first question; based on the neural network model and the first question, determine where State the type of the first question; if the type of the first question is a calculation question, a first answer to the calculation question and a step-by-step problem-solving process are generated, wherein the one or more electronic devices are also configured To display the title, the first answer, and the step-by-step problem solving process of the calculation problem.
- a topic assistance system including: one or more processors; and one or more memories, the one or more memories are configured to store a series of computer-executable instructions And computer-accessible data associated with the series of computer-executable instructions, wherein, when the series of computer-executable instructions are executed by the one or more processors, the one or more Each processor performs the method described above.
- a non-transitory computer-readable storage medium characterized in that a series of computer-executable instructions are stored on the non-transitory computer-readable storage medium, when the series of When the computer-executable instructions are executed by one or more computing devices, the one or more computing devices perform the methods described above.
- FIGS. 1A and 1B are schematic diagrams schematically showing a display screen of a display device on which a title assistance method according to an embodiment of the present disclosure is based.
- Fig. 2 is a flowchart schematically showing at least a part of a topic assisting method according to an embodiment of the present disclosure.
- Fig. 3 is a flowchart schematically showing at least a part of a topic assisting method according to an embodiment of the present disclosure.
- Fig. 4 is a structural diagram schematically showing at least a part of a question assistance system according to an embodiment of the present disclosure.
- Fig. 5 is a structural diagram schematically showing at least a part of a question assistance system according to an embodiment of the present disclosure.
- the present disclosure provides a topic assistance method, which can be used for teaching and learning, for example.
- the user can use the first electronic device with the image acquisition function to take photos or videos of the subject that needs assistance to obtain the image of the subject, and then can use the second electronic device with the display function (the first and second electronic devices can be The same device can also be a different device) to display the question (the question in the form of recognized characters or the image of the question can be displayed), the answer to the question, and the process of solving the question.
- the problem-solving process of the problem is a step-by-step problem-solving process, as shown in FIG. 1A, the user can easily understand the problem-solving method through the step-by-step problem-solving process.
- the problem-solving process of the problem is a graphical problem-solving process. As shown in FIG. 1B, the user can understand the problem-solving method from another perspective through the graphical problem-solving process.
- the method of the present disclosure can assist a single topic. In some embodiments, the method of the present disclosure can assist multiple questions in the entire test paper.
- Step S11 Obtain an image including at least the first topic presented on the first surface through the image acquisition device in the first electronic device.
- Images can include any form of visual presentation, such as photos or videos.
- the image acquisition device may include a camera, an imaging module, an image processing module, etc., and may also include a communication module for receiving or downloading images.
- image acquisition by the image acquisition device may include taking photos or videos, receiving or downloading photos or videos, and so on.
- the first surface may include paper (such as test papers, books or brochures, etc.), whiteboards, chalk boards, display screens (such as TV screens, computer screens, tablet screens, or learning machine screens, etc.), or various other surfaces.
- Step S12 Using the first computing device and the pre-trained first neural network model, based on the image, identify the first area where the first topic in the image is located.
- the input of the first neural network model is the image including the first topic, and the output is the first region where the first topic in the image is located.
- the first neural network model can use a large number of training samples, according to the above-mentioned input and output, and pre-trained by any known method. For example, it can be obtained through the following process of training: establishing a training set of image samples, in which each image sample includes at least one topic. Annotate each image sample to mark the location of at least one topic in each image sample; and train the first neural network through the image sample training set after annotation processing to obtain the first neural network Network model.
- the first neural network can be any known neural network, such as a deep residual network, a recurrent neural network, and so on.
- Training the first neural network may also include: testing the output accuracy of the trained first neural network based on the image sample test set; if the output accuracy is less than a predetermined first threshold, increasing the image sample training set The number of image samples, each image sample of the added image samples is subjected to the above-mentioned labeling processing; and the first neural network is retrained through the image sample training set after the number of image samples has been increased. Then the output accuracy of the retrained first neural network is tested again based on the image sample test set until the output accuracy of the first neural network meets the requirements, that is, not less than the predetermined first threshold. In this way, the trained first neural network whose output accuracy meets the requirements can be used as the pre-trained first neural network model in step S12.
- one or more image samples in the image sample training set can be placed in the image sample test set, or one or more image samples in the image sample test set can be placed in the image sample training set. .
- Step S13 Through the second computing device and the pre-trained second neural network model, the characters in the first area are recognized based on the first area, so as to obtain the first question.
- the input of the second neural network model is the first region in the image where the first topic is located (for example, the first region cut out from the complete image), and the output is the characters in the first region.
- the characters referred to in this article include text (including text, graphic text, letters, numbers, symbols, etc.) and pictures.
- the second neural network model can use a large number of training samples, according to the above-mentioned input and output, and pre-trained by any known method. For example, it can be obtained through the following process of training: establishing an image sample training set, where each image sample is an image of a region, and each region includes a topic. Annotation processing is performed on each image sample to annotate the characters in the region in each image sample; and the second neural network is trained through the image sample training set after the annotation processing to obtain the second neural network model.
- the second neural network can be any known neural network.
- training the second neural network can also include using the test set to verify the output accuracy of the model. If the accuracy does not meet the requirements, the sample set can be increased. Quantity and retrain.
- Step S14 Judge the type of the first question based on the first question through the third computing device and the third neural network model trained in advance.
- the types of questions can include calculation questions, applied questions, fill-in-the-blank questions, multiple-choice questions, operation questions, etc.
- the input of the third neural network model is the first question, and the output is the type of the first question.
- the third neural network model can be obtained by pre-training the third neural network by any known method using a large number of training samples according to the above-mentioned input and output.
- the third neural network can be any known neural network, such as a deep convolutional neural network.
- step S151 is: generating the first answer to the calculation problem and the step-by-step problem-solving process by the fourth and fifth computing devices, respectively.
- the first answer is a reference answer given by the application of the method of the present invention and used for calculation question assistance, and the fourth calculation device for generating the first answer may be any known calculation engine.
- the step-by-step problem-solving process of generating a calculation problem by the fifth calculation device includes: according to the form characteristics of the calculation problem (such as the number of unknowns, powers, positions, and calculation symbols), from a preset rule library Obtain the corresponding rules; and generate a step-by-step problem-solving process of calculation problems according to the corresponding rules.
- the following is a specific example to illustrate.
- the formal feature of the problem is determined to be a linear equation with a denominator.
- the obtained rule may be, for example, five steps including removing the denominator, removing the parentheses, shifting terms, merging similar terms, and converting the coefficient to 1. Then according to the rules including these five steps, the following step-by-step problem-solving process can be generated:
- the step of removing the denominator is usually to multiply both sides of the equation by the least common multiple of the two denominators (for example, in the above example, the denominators of 3 and 5 The least common multiple is 15).
- the step of removing the denominator can include two sub-steps: first eliminate the fraction in the denominator (for example, you can use the numerator and denominator to multiply the reciprocal of the denominator), and then multiply both sides of the equation by The least common multiple of the two denominators.
- equation For example: eliminate the fraction in the denominator, that is, the numerator and denominator on the left side of the equation are multiplied by the reciprocal 5 of the denominator on the left side of the equation, and the numerator and denominator on the right side of the equation are multiplied by the reciprocal 4/3 of the denominator on the right side of the equation to change the equation for: Then multiply both sides of the equation by the least common multiple of 3 of the two denominators, and the equation becomes: 15x 4(x+1). In this way, the result of the step of removing the denominator in the step-by-step problem-solving process of the above example is obtained.
- Step S152 is: displaying the title of the calculation problem and/or the recognized first area through the display device in the second electronic device, and displaying the first answer and the step-by-step problem-solving process.
- the first and second electronic devices may be the same device or different devices.
- the image capturing device and the display device can be located in the same electronic device or in different electronic devices.
- screen 100 of the display screen of the display device, refer to FIG. 1A.
- the screen 100 includes a title 106, a problem 101 of the calculation problem recognized by the second computing device and the second neural network model, an image area 107 where the problem of the calculation problem recognized by the first computing device and the first neural network model is located, The answer 102 of the calculation question generated by the fourth calculation device, and the step-by-step problem solving process 108, 109 (including 109-1, 109-2) generated by the fifth calculation device.
- the question 101 of the calculation question and its image area 107 are both displayed on the screen 100, those skilled in the art should understand that only one of the question 101 of the calculation question and its image area 107 needs to be displayed. That is, it is not even necessary to display any of the question 101 of the calculation question and the image area 107 thereof.
- the step-by-step problem solving process of the calculation problem is displayed when the first trigger is triggered.
- the first answer ie, the reference answer
- the user can first think about the steps of solving the problem, and then trigger when the user needs to view the steps of solving the problem (for example, by operating the second electronic device)
- the specific operation device in the display device, or a specific area in the display screen of the display device, etc. The display device displays these step-by-step problem solving processes.
- the method of the present invention can only display the calculation question 101 and the first answer 102 by default; when the area where the calculation question 101 is located on the display screen 100 of the display device, the image area 107 is located, and the calculation question is The area where the answer 102 is located, the blank area 103, and/or other designated areas (for example, the area where the partial title 105 is located, the area where the title 106 is located) is the first operation specified by the user (for example, tapping, two consecutive times) Only when tapping, long-pressing, deep-pressing, swiping, etc.), the step-by-step problem solving process 108, 109 is displayed.
- the indication of other designated areas in the drawings of the present application is only schematic, and other designated areas may obviously include other areas not shown in the drawings.
- name 108, the process 109-1, and the result 109-2 may not all be displayed, as long as one of them is displayed, or any two of them are displayed. can.
- the screen 100 may display the name 108 and result 109-2 of the operation corresponding to each step by default, as an aid to the user's question.
- the user wants to learn more about the operation, such as how to get the result 109-2, he can operate (for example, tap) a designated area (for example, the area where the special mark 104 is located) to trigger the display of the operation 109 -1.
- a graphical problem solving process of the calculation problem can be generated by the sixth calculation device, and when the second is triggered, the display device Display the title of the calculation problem and/or the first area recognized, and display the first answer and the stepwise and/or graphical problem solving process of the calculation problem.
- screen 200 For an illustrative example (screen 200) of the display screen of the display device, refer to FIG. 1B. Since the graphical problem-solving process 204 is more intuitive and easier to understand, displaying the graphical problem-solving process is more helpful to the effect of problem assistance.
- the graphical problem-solving process can be displayed only when the second trigger, for example, in the area where the problem 201 of the calculation problem is located on the display screen 200 of the display device,
- the area where the first answer 202 of the calculation question is located, the specific operation area (such as the area where the area title 205 is located, the area where the title 206 is located, etc.), and/or the blank area 203, etc., are specified by the user for the second operation (such as light Touch, double touch, long press, deep press, swipe, etc.).
- the method of the present invention may only display the title of the calculation question and the first answer by default, display the step-by-step problem-solving process at the first trigger, and display the graphical problem-solving process at the second trigger. In some embodiments, the method of the present invention may by default only display the calculation question, the first answer and the step-by-step problem-solving process, and display the graphical problem-solving process at the second trigger. In some embodiments, the method of the present invention may by default only display the question of the calculation problem, the first answer, and the graphical problem-solving process, and display the step-by-step problem-solving process at the first trigger.
- the graphical problem-solving process of generating the calculation problem by the sixth computing device may include: converting the calculation problem into a function graph based on the plotly library or the pm algorithm model; and generating the graphical problem-solving process of the calculation problem according to the function graph.
- converting the calculation problem into a function graph based on the plotly library or the pm algorithm model may include: converting the calculation problem into a function graph based on the plotly library or the pm algorithm model; and generating the graphical problem-solving process of the calculation problem according to the function graph.
- the question assistance method can also correct the second answer associated with the first question (for example, the user's answer to the first question) presented on the first surface. .
- the first question in the image is identified. Area and the second area where the second answer is located. Identify the characters in the first area through the second computing device and the pre-trained second neural network model, thereby obtaining the first question; and through the seventh computing device and the pre-trained fourth neural network model, identify the characters in the second area To get the second answer.
- the eighth calculating device compares the first and second answers to obtain the same or different results.
- the display device displays the title, the first answer, the second answer, the same or different results of the first and second answers, and the step-by-step problem solving process.
- the same or different results of the first and second answers can be displayed by a specific symbol (such as " ⁇ " or " ⁇ "), or a specific mark can be used to mark the second answer that is different from the first answer (reference answer)
- the answer (answer answer) is displayed.
- the training method of the fourth neural network model may be similar to the training method of the second neural network model.
- the second answer is used to identify the characters in the first area.
- the neural network model and the fourth neural network model for recognizing characters in the second region may be different models trained separately. It should be understood that the second neural network model and the fourth neural network model may also be the same model.
- Step S161 is: performing feature extraction on the word problem through the ninth computing device and the pre-trained fifth neural network model to generate a two-dimensional feature vector.
- the two-dimensional feature vector can be a feature map, which can be generated by any method known in the art, for example, a deep convolutional neural network can be used to process and extract the image area where the word problem is located. Among them, a first two-dimensional feature vector is generated for the text in the word problem, and a second two-dimensional feature vector is generated for the picture in the word problem; and the first and second two-dimensional feature vectors are spliced to obtain a two-dimensional feature vector.
- the input of the fifth neural network model is the first topic (including text and pictures), and the output is the two-dimensional feature vector corresponding to the first topic (composed of the first and second two-dimensional feature vectors).
- the fifth neural network model can be obtained by pre-training the fifth neural network by any known method using a large number of training samples according to the above-mentioned input and output.
- the fifth neural network can be any known neural network, such as a deep convolutional neural network.
- Step S162 is: searching for a topic vector matching the two-dimensional feature vector (for example, the vector of the topic closest to the first topic) from the preset vector index library through the tenth computing device.
- the vector index library includes multiple groups, and each group includes one or more vectors. These vectors are all feature extractions of known word problems (for example, questions in the test question library of pre-collected word problems) to generate two-dimensional feature vectors. Any two vectors from the same group have the same length, and any two vectors from different groups have different lengths.
- Searching for the topic vector from the vector index library can include: first find a group that matches the length of the two-dimensional feature vector in the vector index library according to the length of the two-dimensional feature vector; then search in the group that matches the length to find Topic vector. In this way, the topic vector that matches the two-dimensional feature vector can be searched more quickly.
- each group has its own index that matches (e.g., equal) the length of each vector in the group.
- Finding a group that matches the length of the two-dimensional feature vector in the vector index library includes: Index to the matched group according to the length of the two-dimensional feature vector.
- Step S163 is: through the eleventh computing device, generate the fourth answer (ie, the reference answer) of the applied problem based on the preset third answer associated with the problem vector; and step S164 is: display the applied problem through the display device
- the fourth answer may also come from a pre-collected test question bank of application questions.
- the test question bank includes questions and reference answers corresponding to the questions.
- the answer associated with the question is extracted from the question database, which is the third answer.
- the third answer is used as the mother board, and the third answer is deformed according to the difference between the first question and the closest question to obtain the fourth answer.
- Each of the above-mentioned pre-trained first to fifth neural network models can be stored as a whole on one or more storage media in any of the following items, or the first part can be stored in any of the following items One or more storage media in one item, and the second part is stored on one or more storage media in any of the following items: the first and/or second electronic device, one or more One or more of the remote server and the first to eleventh computing devices.
- any two of the first to eleventh computing devices that perform the processing of the above steps may be the same computing device, or may be different computing devices.
- Each of the first to eleventh computing devices may include one or more processors, and one or more processors belonging to one computing device may: all be located in the physical housing of the first and/or second electronic device, All are located in the physical housing of one or more remote servers, or the first part is located in the physical housing of the first and/or second electronic device and the second part is located in the physical housing of one or more remote servers.
- each of the first to eleventh computing devices may also include one or more memories to store instructions that can be executed by the one or more processors and data required to execute the instructions, such as one or more At least part of a plurality of neural network models.
- the process of processing a single item (a calculation problem or an application problem) is described.
- the question assisting method of the present invention can also process multiple questions in the whole test paper together. It should be understood that the process of processing a single topic in the above embodiment is also applicable to the process of processing multiple topics together. For the sake of brevity, when describing the following embodiments, the method for applying the above process will not be repeated.
- the image of basically the entire test paper is acquired through the image acquisition device in the first electronic device.
- the entire test paper includes multiple questions, and the types of multiple questions may be the same or different.
- the types of questions can include calculation questions, applied questions, fill-in-the-blank questions, multiple-choice questions, operation questions, etc.
- the first computing device and the first neural network model Through the first computing device and the first neural network model, multiple respective regions where multiple topics in the image are located are identified.
- the second computing device and the second neural network model the characters in the above-mentioned multiple regions are respectively recognized, thereby obtaining multiple questions included in the image of the entire test paper.
- the third calculation device and the third neural network model the type of each of the multiple questions is judged.
- test paper also includes the answer
- this method can also identify the area where the answer for each question is located when identifying the area where each question is located. Then, the corresponding model is used to identify the characters in the area where each answer is located, so as to correct the answer in the whole test paper by comparing the answer with the reference answer.
- judging the type of each question in the multiple questions is based on each question (for example, the text and pictures included in the question, etc.) and the position of each question in the entire test paper (for example, where each question is The position of the area in the image of the whole test paper).
- each question for example, the text and pictures included in the question, etc.
- the position of each question in the entire test paper for example, where each question is The position of the area in the image of the whole test paper.
- the distribution of question types is relatively fixed. For example, calculation questions are distributed at the beginning of the test paper, followed by multiple-choice or fill-in-the-blank questions, and finally applied questions and operation questions. Therefore, when identifying the question type, consider the position of the question in the entire test paper, which is conducive to the accuracy of recognition.
- the location can be a detailed location, such as coordinates; it can also be a rough location, such as which part of the test paper is distributed (such as the upper left part, the middle right part, etc.); it can also be the order of the questions, such as the part of the first big question Wait.
- the input of the third neural network model is each item and the corresponding position of each item in the whole test paper, and the output is the type of each item.
- each question in the sample and the location of the area where the answer is located and the question type are marked.
- using the first neural network model to identify multiple areas in the image where multiple questions are located includes the following process: using a deep convolutional neural network to extract the two-dimensional feature vector of the entire test paper picture.
- An anchor point (anchor, also called an anchor box) of a different shape is generated for each grid of the two-dimensional feature vector.
- Each anchor point includes the center coordinates of the label box and the length and height of the label box. Because the text lines in the test paper are mostly long bars, you can define multiple anchor points in advance, including rectangular boxes with an aspect ratio of 2:1, 3:1, 4:1, and other ratios.
- the area of each identified question is marked with a rectangular frame of appropriate shape.
- the image samples used include ground truth boxes that mark each question in the sample and the real area where the answer is located. For example, it can be passed Manually labeled). Among them, the pictures and texts in the title are marked with real frames. In the training process, the generated anchor points are regressed with the real frame, so that the labeling frame is closer to the real position of the topic, and the first neural network model can better identify the area where each topic is located.
- Questions are usually printed in font, and answers are usually handwritten; and especially for application questions, the character set contained in the question and the character set contained in the answer are often different, and the character set contained in the answer is usually smaller than the question.
- the included character set for example, the characters in the answer are usually Chinese characters plus numbers, letters and symbols.
- different models can be used to recognize characters in the question and the answer, and the two models can be trained using different training image sample sets. Nevertheless, the method of model recognition can use hole convolution to extract features of characters (including text and pictures), so that the extracted features have a larger receptive field.
- hollow convolution can be recognized according to the context of handwritten text; it can also be recognized at intervals, without recognizing text by text, which is convenient for machine parallel processing. Then the feature is decoded through the attention model, and finally the text with variable length is output.
- the method of the present invention further includes the process shown in FIG. 3.
- Step S21 Through the ninth computing device and the fifth neural network model, feature extraction is performed on the image of the problem areas of multiple word problems ⁇ T1, T2,..., Tn ⁇ to generate multiple two-dimensional feature vectors ⁇ a1, a2, ...,An ⁇ .
- Step S22 Search a plurality of nearest vectors ⁇ b1, b2,..., bn ⁇ that are respectively the closest to a plurality of two-dimensional feature vectors from a preset vector index library through the tenth computing device.
- Step S23 According to the pre-set mark of each vector in the vector index library (the mark of each vector is the identification ID of the test paper from which the vector comes), obtain multiple test papers corresponding to the multiple nearest vectors ⁇ P1, P2, ..., Pn ⁇ .
- Step S24 Determine the test paper with the most occurrences among the multiple test papers as the matching test paper P.
- Step S25 For each of the multiple questions, it is determined that the test paper corresponding to the closest vector of the two-dimensional feature vector of each question is a matching test paper. Taking item T1 as an example, it is determined that the test paper P1 corresponding to the closest vector b1 of the two-dimensional feature vector a1 of T1 is the matching test paper P.
- step S261 determine the closest vector b1 closest to the two-dimensional feature vector a1 of the question T1 as the question vector t of the first question; if not, proceed to step S262: change the two-dimensional feature vector of the question T1 a1. Perform the shortest edit distance matching among multiple vectors with the identification marks of the matching test paper P, find the vector s with the smallest edit distance to the two-dimensional feature vector a1 of question T1, and set the vector with the smallest edit distance s is determined as the topic vector t of the first topic.
- Step S27 Using the eleventh computing device, generate the fourth answer (ie, the reference answer) of the question T1 according to the preset third answer (for example, the mother board answer) associated with the question vector t of the question T1.
- Step S28 Display the fourth answers to these application questions on the display device.
- FIG. 4 is a structural diagram schematically showing at least a part of a question assistance system 400 according to an embodiment of the present disclosure.
- the system 400 may include one or more neural network models 410, one or more electronic devices 420, one or more computing devices 430, one or more remote servers 440, and a network 450.
- one or more neural network models 410, one or more electronic devices 420, one or more computing devices 430, and one or more remote servers 440 may be connected to each other through a network 450.
- the network 450 may be any wired or wireless network, and may also include a cable.
- one or more neural network models 410 are independent of one or more electronic devices 420, one or more computing devices 430, one or more remote servers 440, and a separate network 450 in the system 400
- the box shows that it should be understood that one or more neural network models 410 may be actually stored on any one of the other entities 420, 430, 440, 450 included in the system 400.
- one or more computing devices may include server computing devices that operate as a load-balanced server farm.
- server computing devices that operate as a load-balanced server farm.
- various aspects of the subject matter described herein can be implemented by multiple computing devices communicating with each other, for example, through a network.
- Each of the one or more electronic devices 420, one or more computing devices 430, and one or more remote servers 440 may be located at different nodes of the network 450, and can directly or indirectly communicate with other nodes of the network 450 Communication.
- the system 500 may also include other devices not shown in FIG. 4, where each different device is located at a different node of the network 450.
- Various protocols and systems can be used to interconnect the network 450 and the components in the system described herein, so that the network 450 can be part of the Internet, the World Wide Web, a specific intranet, a wide area network, or a local area network.
- the network 450 may utilize standard communication protocols such as Ethernet, WiFi, and HTTP, protocols that are proprietary to one or more companies, and various combinations of the foregoing protocols. Although certain advantages are obtained when transferring or receiving information as described above, the subject matter described herein is not limited to any specific information transfer method.
- Each of one or more electronic devices 420, one or more computing devices 430, and one or more remote servers 440 may be configured to be similar to the system 500 shown in FIG. 5, that is, have one or more processors 510, one or more memories 520, and instructions and data.
- Each of the one or more electronic devices 420, the one or more computing devices 430, and the one or more remote servers 440 may be a personal computing device intended to be used by a user or a commercial computer device used by an enterprise, and has All components commonly used in combination with personal computing devices or commercial computer devices, such as a central processing unit (CPU), memory for storing data and instructions (for example, RAM and internal hard drives), such as displays (for example, monitors with screens, One or more I/O devices such as touch screens, projectors, televisions or other devices operable to display information), mice, keyboards, touch screens, microphones, speakers, and/or network interface devices.
- the one or more electronic devices 420 may also include one or more cameras for capturing still images or recording video streams, and all components for connecting these elements to each other.
- one or more electronic devices 420 may each include a full-sized personal computing device, they may optionally include a mobile computing device capable of wirelessly exchanging data with a server through a network such as the Internet.
- the one or more electronic devices 420 may be a mobile phone, or a device such as a PDA with wireless support, a tablet PC, or a netbook that can obtain information via the Internet.
- one or more electronic devices 420 may be a wearable computing system.
- FIG. 5 is a structural diagram schematically showing at least a part of a question assistance system 500 according to an embodiment of the present disclosure.
- the system 500 includes one or more processors 510, one or more memories 520, and other components (not shown) generally present in devices such as computers.
- Each of the one or more memories 520 can store content that can be accessed by one or more processors 510, including instructions 521 that can be executed by one or more processors 510, and can be executed by one or more processors 510. retrieve, manipulate, or store data522.
- the instruction 521 may be any instruction set to be directly executed by the one or more processors 510, such as machine code, or any instruction set to be executed indirectly, such as a script.
- the terms "instruction”, “application”, “process”, “step” and “program” in this article can be used interchangeably in this article.
- the instructions 521 may be stored in an object code format for direct processing by one or more processors 510, or stored in any other computer language, including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance.
- the instructions 521 may include instructions that cause, for example, one or more processors 510 to act as various neural networks herein. The rest of this article explains the functions, methods, and routines of instruction 521 in more detail.
- the one or more memories 520 may be any temporary or non-transitory computer-readable storage media capable of storing content accessible by the one or more processors 510, such as hard drives, memory cards, ROM, RAM, DVD, CD, USB memory, writable memory and read-only memory, etc.
- One or more of the one or more memories 520 may include a distributed storage system, where the instructions 521 and/or data 522 may be stored on multiple different storage devices physically located at the same or different geographic locations.
- One or more of the one or more memories 520 may be connected to the one or more first devices 510 via a network, and/or may be directly connected to or incorporated into any one of the one or more processors 510.
- One or more processors 510 may retrieve, store, or modify data 522 according to instructions 521.
- the data 522 stored in the one or more memories 520 may include various images to be recognized, various image sample sets, and parameters used for various neural networks as described above. Other data not associated with images or neural networks may also be stored in one or more memories 520.
- the data 522 may also be stored in computer registers (not shown), as a table or XML document with many different fields and records. Type database.
- the data 522 may be formatted in any computing device readable format, such as but not limited to binary values, ASCII, or Unicode.
- the data 522 may include any information sufficient to identify related information, such as numbers, descriptive text, proprietary codes, pointers, references to data stored in other storage such as other network locations, or used by functions to calculate related information. Data information.
- the one or more processors 510 may be any conventional processors, such as a central processing unit (CPU), a graphics processing unit (GPU), etc., which are commercially available. Alternatively, the one or more processors 510 may also be dedicated components, such as an application specific integrated circuit (ASIC) or other hardware-based processors. Although not required, one or more processors 510 may include dedicated hardware components to perform specific calculation processes faster or more efficiently, such as image processing on images.
- CPU central processing unit
- GPU graphics processing unit
- ASIC application specific integrated circuit
- processors 510 may include dedicated hardware components to perform specific calculation processes faster or more efficiently, such as image processing on images.
- processors 510 and one or more memories 520 are schematically shown in the same box in FIG. 5, the system 500 may actually include multiple processors that may exist in the same physical housing or different Multiple processors or memories in a physical housing.
- one of the one or more memories 520 may be a hard disk drive or other storage medium located in a housing different from the housing of each of the one or more computing devices (not shown) described above. . Therefore, references to processors, computers, computing devices, or memories should be understood to include references to collections of processors, computers, computing devices, or memories that may or may not operate in parallel.
- references to “one embodiment” or “some embodiments” means that the feature, structure, or characteristic described in conjunction with the embodiment is included in at least one embodiment or at least some embodiments of the present disclosure. Therefore, the appearances of the phrases “in one embodiment” and “in some embodiments” in various places in this disclosure do not necessarily refer to the same or the same embodiments. In addition, in one or more embodiments, any suitable combination and/or sub-combination may be used to combine features, structures, or characteristics.
- the word "exemplary” means “serving as an example, instance, or illustration” and not as a “model” to be copied exactly. Any implementation described exemplarily herein is not necessarily interpreted as being preferred or advantageous over other implementations. Moreover, the present disclosure is not limited by any expressed or implied theory given in the above technical field, background art, summary of the invention, or specific embodiments.
- the word “substantially” means to include any minor changes caused by design or manufacturing defects, device or component tolerances, environmental influences, and/or other factors.
- the word “substantially” also allows the difference between the perfect or ideal situation caused by parasitic effects, noise, and other practical considerations that may be present in the actual implementation.
- connection means that one element/node/feature is electrically, mechanically, logically, or otherwise directly connected (or Direct communication).
- coupled means that one element/node/feature can be directly or indirectly connected to another element/node/feature mechanically, electrically, logically, or in other ways. Allow interaction, even if the two features may not be directly connected. In other words, “coupled” intends to include direct connection and indirect connection of elements or other features, including the connection of one or more intermediate elements.
- a component may be, but is not limited to, a process, an object, an executable state, an execution thread, and/or a program running on the processor.
- an application program running on a server and the server may be one component.
- One or more components may exist inside an executing process and/or thread, and a component may be located on one computer and/or distributed between two or more computers.
- embodiments of the present disclosure may also include the following examples:
- An auxiliary method for a topic including:
- the first answer to the calculation problem and the step-by-step problem-solving process are respectively generated by the fourth and fifth computing devices.
- the title of the calculation question and/or the first area is displayed by a display device, and the first answer and the step-by-step problem solving process are displayed.
- step-by-step question-solving process of generating the calculation question by the fifth calculation device comprises:
- the step-by-step problem solving process of the calculation problem is generated according to the corresponding rule.
- step-by-step problem-solving process includes one or more steps
- displaying the step-by-step problem-solving process on a display device includes: displaying all the steps in order. Describe the operation result corresponding to one or more steps.
- displaying the step-by-step question-solving process on a display device further comprises: matching the one or more steps in the screen of the display device The area associated with the corresponding result displays the operation name and/or process corresponding to the one or more steps.
- the first trigger comprises: the area where the question of the calculation question is located on the display screen of the display device, the first of the calculation question The area where the answer is located, the blank area, and/or the specified area are subjected to the specified first operation.
- the graphical problem solving process of the calculation problem is displayed by the display device.
- a graphical problem solving process of the calculation problem is generated according to the function graph.
- the second trigger comprises: the area where the question of the calculation question is located on the display screen of the display device, the first of the calculation question The area where the answer is located, the specific operation area, and/or the blank area are subjected to the designated second operation.
- the second answer and the result are also displayed by the display device.
- the fourth answer to the applied problem is displayed on the display device.
- the vector index library includes a plurality of groups, and each group includes one or more vectors, wherein any two vectors from the same group have the same Length, any two vectors from different groups have different lengths,
- searching for the topic vector from the vector index library includes:
- each group has its own index, the index matches the length of the vector in the group, and the index is found in the vector index library.
- the length matching group of the two-dimensional feature vector includes:
- the question assisting method according to claim 1 wherein the image comprises substantially the entire test paper where the first question presented on the first surface is located, wherein the first question is determined
- the type is also based on the position of the first area in the entire test paper.
- test paper further includes a plurality of second questions of practical type other than the first question, and the method further comprises:
- test paper corresponding to the nearest vector that is closest to the two-dimensional feature vector of the word problem is the matching test paper, then:
- test paper corresponding to the nearest vector that is closest to the two-dimensional feature vector of the word problem is not the matching test paper, then:
- the two-dimensional feature vector of the word problem is matched with the shortest edit distance among multiple vectors from the matching test paper, and the vector with the shortest edit distance to the two-dimensional feature vector of the word problem is found, and the The vector with the shortest edit distance is determined as the topic vector of the word problem;
- the fourth answer to the applied problem is displayed on the display device.
- a topic assistance system including:
- One or more pre-trained neural network models are One or more pre-trained neural network models
- One or more electronic devices with an image acquisition function and a display function, configured to acquire an image including at least the first question presented on the first surface;
- One or more computing devices configured to:
- the type of the first question is a calculation question, then generate the first answer to the calculation question and a step-by-step problem-solving process,
- the one or more electronic devices are also configured to display the title, the first answer, and the step-by-step problem solving process of the calculation problem.
- the one or more computing devices are further configured to: if the type of the first question is a calculation question, generate a graphical problem-solving process of the calculation question; and
- the one or more electronic devices are further configured to display the graphical problem solving process of the calculation problem.
- the one or more computing devices are further configured to: if the type of the first question is a practical question, then:
- the one or more electronic devices are also configured to display a fourth answer to the word problem.
- the question assistance system according to claim 18, wherein the image includes substantially the entire test paper where the first question presented on the first surface is located, wherein the first question is determined
- the type is also based on the position of the first area in the entire test paper.
- the one or more computing devices are also configured to:
- feature extraction is performed on the first topic and the multiple second topics respectively to generate multiple two-dimensional feature vectors
- test paper corresponding to the nearest vector that is closest to the two-dimensional feature vector of the word problem is the matching test paper, then:
- test paper corresponding to the nearest vector that is closest to the two-dimensional feature vector of the word problem is not the matching test paper, then:
- the two-dimensional feature vector of the word problem is matched with the shortest edit distance among multiple vectors from the matching test paper, and the vector with the shortest edit distance to the two-dimensional feature vector of the word problem is found, and the The vector with the shortest edit distance is determined as the topic vector of the word problem;
- the one or more electronic devices are further configured to display a fourth answer to the word problem.
- the item assistance system further comprises one or more remote servers, and one or more of the one or more neural network models are stored in the One or more storage media in one or more remote servers.
- the question assistance system according to claim 18, wherein the question assistance system further comprises one or more remote servers, and one or more of the one or more computing devices are located in the one or more Multiple remote servers in the physical enclosure.
- a topic assistance system including:
- One or more processors are One or more processors.
- One or more memories configured to store a series of computer-executable instructions and computer-accessible data associated with the series of computer-executable instructions
- a non-transitory computer-readable storage medium wherein a series of computer-executable instructions are stored on the non-transitory computer-readable storage medium.
- the one or more computing devices are caused to perform the method according to any one of claims 1-17.
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Abstract
Description
Claims (20)
- 一种题目辅助方法,包括:通过影像获取装置获取至少包括呈现在第一表面的第一题目的影像;通过第一计算装置和预先训练的第一神经网络模型,基于所述影像,识别出所述影像中的所述第一题目所在的第一区域;通过第二计算装置和预先训练的第二神经网络模型,基于所述第一区域,识别出所述第一区域中的字符,从而得到所述第一题目;通过第三计算装置和预先训练的第三神经网络模型,基于所述第一题目,判断所述第一题目的类型;若所述第一题目的类型为计算题,则:通过第四计算装置和第五计算装置分别生成所述计算题的第一答案和步骤化的解题过程;以及通过显示装置显示所述计算题的题目和/或所述第一区域,并且显示所述第一答案以及所述步骤化的解题过程。
- 根据权利要求1所述的题目辅助方法,其特征在于,通过所述第五计算装置生成所述计算题的步骤化的解题过程包括:根据所述计算题的题目的形式特征,从预先设置的规则库中获取对应的规则;以及根据所述对应的规则生成所述计算题的步骤化的解题过程;其中,所述步骤化的解题过程包括一个或多个步骤,通过显示装置显示所述步骤化的解题过程包括:按顺序显示所述一个或多个步骤所对应的操作结果。
- 根据权利要求2所述的题目辅助方法,其特征在于,通过显示装置显示所述步骤化的解题过程还包括:在所述显示装置的画面中的与所述一个或多个步骤所对应的结果相关联的区域,显示所述一个或多个步骤所对 应的操作名称和/或过程。
- 根据权利要求1所述的题目辅助方法,其特征在于,所述计算题的步骤化的解题过程在第一触发时才被显示,所述第一触发包括:所述显示装置的显示画面中的所述计算题的题目所在的区域、所述计算题的第一答案所在的区域、空白区域和/或指定的区域被进行指定的第一操作。
- 根据权利要求1所述的题目辅助方法,其特征在于,若所述第一题目的类型为计算题,则所述方法还包括:通过第六计算装置生成所述计算题的图形化的解题过程;以及在第二触发时,通过所述显示装置显示所述计算题的图形化的解题过程,所述第二触发包括:所述显示装置的显示画面中的所述计算题的题目所在的区域、所述计算题的第一答案所在的区域、特定的操作区域和/或空白区域被进行指定的第二操作。
- 根据权利要求5所述的题目辅助方法,其特征在于,通过所述第六计算装置生成所述计算题的图形化的解题过程包括:基于plotly库或pm算法模型将所述计算题转换为函数图;以及根据所述函数图生成所述计算题的图形化的解题过程。
- 根据权利要求1所述的题目辅助方法,其特征在于,所述影像还包括呈现在所述第一表面的与所述第一题目相关联的第二答案,所述方法还包括:通过所述第一计算装置和所述第一神经网络模型,基于所述影像,还识别出所述影像中的所述第二答案所在的第二区域;通过第七计算装置和预先训练的第四神经网络模型,识别出所述第二区域中的字符,从而得到所述第二答案;若所述第一题目的类型为计算题,则:通过第八计算装置比较所述第一答案和第二答案,以得到相同或不同的结果;以及通过所述显示装置还显示所述第二答案以及所述结果。
- 根据权利要求1所述的题目辅助方法,其特征在于,还包括:若所述第一题目的类型为应用题,则:通过第九计算装置和预先训练的第五神经网络模型,对所述应用题进行特征提取以生成二维特征向量;通过第十计算装置,从预先设置的向量索引库中搜索与所述二维特征向量相匹配的题目向量;通过第十一计算装置,根据预先设置的与所述题目向量相关联的第三答案,生成所述应用题的第四答案;以及通过显示装置显示所述应用题的第四答案。
- 根据权利要求8所述的题目辅助方法,其特征在于,对所述应用题进行特征提取以生成二维特征向量包括:对所述应用题中的文字生成第一二维特征向量,并对所述应用题中的图片生成第二二维特征向量;以及拼接所述第一和第二二维特征向量以得到所述二维特征向量。
- 根据权利要求8所述的题目辅助方法,其特征在于,所述向量索引库包括多个组,每个组包括一个或多个向量,其中,来自同一组的任意两个向量具有相同的长度,来自不同组的任意两个向量具有不同的长度,其中,从所述向量索引库中搜索所述题目向量包括:根据所述二维特征向量的长度,在所述向量索引库中找到与所述二维特征向量的长度匹配的组;在所述组中进行搜索,以找到所述题目向量。
- 根据权利要求1所述的题目辅助方法,其特征在于,所述影像包括呈现在所述第一表面的所述第一题目所在的基本上整张试卷,其中,判断所述第一题目的类型还基于所述第一区域在所述整张试卷中的位置。
- 根据权利要求11所述的题目辅助方法,其特征在于,所述整张试卷还包括除所述第一题目之外的多个类型为应用题的第二题目,所述方法还包括:通过所述第一计算装置和所述第一神经网络模型,基于所述影像,识别出所述影像中的所述多个第二题目所在的多个第三区域;通过所述第二计算装置和所述第二神经网络模型,基于所述多个第三区域,分别识别所述多个第三区域中的字符,从而得到所述多个第二题目;若所述第一题目的类型为应用题,则:通过第九计算装置和预先训练的第五神经网络模型,分别对所述第一题目和所述多个第二题目进行特征提取以生成多个二维特征向量;通过第十计算装置:从预先设置的向量索引库中搜索分别与所述多个二维特征向量距离最近的多个最近向量;根据所述向量索引库中各个向量被预先设置的标记,得到所述多个最近向量所分别对应的多个试卷,所述标记为所述向量所来自的试卷的识别;将所述多个试卷中出现次数最多的试卷确定为匹配试卷;若与所述应用题的二维特征向量距离最近的所述最近向量所对应的试卷是所述匹配试卷,则:将与所述应用题的二维特征向量距离最近的所述最近向量确定为所述应用题的题目向量;若与所述应用题的二维特征向量距离最近的所述最近向量所对应的试卷不是所述匹配试卷,则:将所述应用题的二维特征向量,在来自所述匹配试卷的多个向量中进行最短编辑距离匹配,找到与所述应用题的二维特征向量的最短编辑距离最小的向量,将所述最短编辑距离最小的向量确定为所述应用题的题目向量;通过第十一计算装置,根据预先设置的与所述题目向量相关联的第三答案,生成所述应用题的第四答案;以及通过显示装置显示所述应用题的第四答案。
- 根据权利要求12所述的题目辅助方法,其特征在于,所述第一至第五以及第九至第十一计算装置中的任意两者为相同或不同的计算装置。
- 一种题目辅助***,包括:预先训练的一个或多个神经网络模型;具有影像获取功能和显示功能的一个或多个电子设备,被配置为获取至少包括呈现在第一表面的第一题目的影像;以及一个或多个计算装置,被配置为:基于所述神经网络模型和所述影像,识别出所述影像中的所述第一题目所在的第一区域;基于所述神经网络模型和所述第一区域,识别出所述第一区域中的字符,从而得到所述第一题目;基于所述神经网络模型和所述第一题目,判断所述第一题目的类型;若所述第一题目的类型为计算题,则生成所述计算题的第一答案和步骤化的解题过程,其中,所述一个或多个电子设备还被配置为显示所述计算题的题目、第一答案以及步骤化的解题过程。
- 根据权利要求14所述的题目辅助***,其特征在于,所述一个或多个计算装置还被配置为:若所述第一题目的类型为计算题,则生成所述计算题的图形化的解题过程;以及所述一个或多个电子设备还被配置为:显示所述计算题的图形化的解题过程。
- 根据权利要求14所述的题目辅助***,其特征在于,所述一个或多个计算装置还被配置为:若所述第一题目的类型为应用题,则:基于所述神经网络模型,对所述应用题进行特征提取以生成二维特征向量;从预先设置的向量索引库中搜索与所述二维特征向量相匹配的题目向量;根据预先设置的与所述题目向量相关联的第三答案,生成所述应用题的第四答案;以及所述一个或多个电子设备还被配置为:显示所述应用题的第四答案。
- 根据权利要求14所述的题目辅助***,其特征在于,所述影像包括呈现在所述第一表面的所述第一题目所在的基本上整张试卷,其中,判断所述第一题目的类型还基于所述第一区域在所述整张试卷中的位置。
- 根据权利要求17所述的题目辅助***,其特征在于,所述整张试卷还包括除所述第一题目之外的多个类型为应用题的第二题目,所述一个或多个计算装置还被配置为:基于所述神经网络模型和所述影像,识别出所述影像中的所述多个第二题目所在的多个第三区域;基于所述神经网络模型和所述多个第三区域,分别识别所述多个第三区域中的字符,从而得到所述多个第二题目;若所述第一题目的类型为应用题,则:基于神经网络模型,分别对所述第一题目和所述多个第二题目进行特征提取以生成多个二维特征向量;从预先设置的向量索引库中搜索分别与所述多个二维特征向量距离最近的多个最近向量;根据所述向量索引库中各个向量被预先设置的标记,得到所述多个最近向量所分别对应的多个试卷,所述标记为所述向量所来自的试卷的识别;将所述多个试卷中出现次数最多的试卷确定为匹配试卷;若与所述应用题的二维特征向量距离最近的所述最近向量所对应的试卷是所述匹配试卷,则:将所述应用题的二维特征向量距离最近的所述最近向量确定为所述应用题的题目向量;若与所述应用题的二维特征向量距离最近的所述最近向量所对应的试卷不是所述匹配试卷,则:将所述应用题的二维特征向量,在来自所述匹配试卷的多个向量中进行最短编辑距离匹配,找到与所述应用题的二维特征向量的最短编辑距离最小的向量,将所述最短编辑距离最小的向量确定为所述应用题的题目向量;根据预先设置的与所述题目向量相关联的第三答案,生成所述应用题的第四答案;以及所述一个或多个电子设备还被配置为:显示所述应用题的第四答案。
- 一种题目辅助***,包括:一个或多个处理器;以及一个或多个存储器,所述一个或多个存储器被配置为存储一系列计算机可执行的指令以及与所述一系列计算机可执行的指令相关联的计算机可访问的数据,其中,当所述一系列计算机可执行的指令被所述一个或多个处理器执 行时,使得所述一个或多个处理器进行如权利要求1-13中任一项所述的方法。
- 一种非临时性计算机可读存储介质,其特征在于,所述非临时性计算机可读存储介质上存储有一系列计算机可执行的指令,当所述一系列计算机可执行的指令被一个或多个计算装置执行时,使得所述一个或多个计算装置进行如权利要求1-13中任一项所述的方法。
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CN110598642A (zh) * | 2019-09-16 | 2019-12-20 | 杭州大拿科技股份有限公司 | 一种计算题在线练习方法、装置、设备和存储介质 |
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CN112183253A (zh) * | 2020-09-15 | 2021-01-05 | 北京大米科技有限公司 | 数据处理方法、装置、电子设备及计算机可读存储介质 |
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