A method for processing mathematical morphological signals, a computer device, and a storage medium

Abstract

The present invention provides a method for processing mathematical morphological signals, a computer device, and a storage medium. The processing method includes: obtaining a preset image, and using a test paper recognition model based on mathematical morphology to recognize the test paper image in the preset image; processing the test paper image to obtain the test questions in the test paper image, and storing the test questions in a preset exercise question bank; when a local user terminal accesses a local area network, sharing the preset exercise question bank with foreign user terminals within the local area network. Due to the technical solution of the present invention, the user terminal can update the exercise questions in the preset exercise question bank in a state where it cannot be connected to the network. Therefore, the problem in the prior art that the user terminal cannot be connected to the network in time and affects the user's learning efficiency can be solved, thereby achieving the purpose of improving the user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing based on mathematical models, and particularly to a method for processing mathematical morphological signals, a computer device, and a storage medium. Background Art

[0002] An online teaching system is an educational platform that utilizes the Internet for teaching. In this teaching system, there are usually a teaching server and user terminals. A teaching database for storing teaching resources such as exercises and courseware is set up on the teaching server. The user terminals can be connected to the teaching server through the network and access the teaching server through an application program to obtain teaching resources from the teaching database for users to learn.

[0003] For areas with low network coverage, such as remote mountainous areas, the user terminals cannot consistently maintain a communication connection with the teaching server. In such cases, the user terminals need to download teaching resources from the teaching database when communicating with the teaching server so that users can learn when the user terminals are in an offline state. The user terminals usually use terminal devices such as mobile phones and tablet computers. Since the data storage capacity of these terminal devices is limited, the teaching resources that can be downloaded each time are also limited. If users cannot connect to the teaching server in time to update the teaching resources after using the downloaded teaching resources, it will cause the problem that users cannot continue to learn on the user terminals, thus affecting the learning efficiency of the students. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a method for processing mathematical morphological signals, a computer device, and a storage medium that overcome the above problems or at least partially solve the above problems, and can solve the problem in the prior art that the learning efficiency of users is affected due to the inability of user terminals to connect to the network in time.

[0005] To at least solve the above technical problems, the present invention provides the following technical solutions:

[0006] A method for processing mathematical morphological signals, which is used for the user terminal of a teaching system; the processing method includes:

[0007] Obtain a preset image, and use a test paper recognition model based on mathematical morphology to recognize the test paper image in the preset image;

[0008] Process the test paper image to obtain the test questions in the test paper image, and store the test questions in a preset exercise bank;

[0009] When the local user terminal accesses the local area network, share the preset exercise bank with the remote user terminals within the local area network.

[0010] According to an embodiment of the present invention, when processing the test paper image to obtain the questions in the test paper image, it further includes: obtaining the test paper type of the test paper in the test paper image;

[0011] Storing the questions in the preset exercise question bank includes:

[0012] Judging whether the test paper type matches the terminal type of the local user terminal;

[0013] If so, store the questions in the preset exercise question bank.

[0014] According to an embodiment of the present invention, storing the questions in the preset exercise question bank includes:

[0015] Obtain the storage capacity of the preset exercise question bank according to the networking frequency of the local user terminal;

[0016] Screen the questions in the test paper image according to the storage capacity, and store the selected exercises in the preset exercise question bank.

[0017] According to an embodiment of the present invention, sharing the preset exercise question bank with the foreign user terminals within the local area network includes:

[0018] Obtain the terminal types of the foreign user terminals within the local area network, and use the foreign user terminals whose terminal types match the test paper type as the target foreign user terminals;

[0019] Share the preset exercise question bank with the target foreign user terminals.

[0020] According to an embodiment of the present invention, when the local user terminal accesses the local area network, it further includes:

[0021] When the local user terminal receives the preset exercise question bank shared by the foreign user terminal, judge whether the local user terminal meets the preset update conditions;

[0022] If so, control the local user terminal to download the exercises in the preset exercise question bank.

[0023] According to an embodiment of the present invention, the preset update conditions include:

[0024] The learning progress of the local user terminal reaches the preset progress and / or receives the preset update instruction.

[0025] According to an embodiment of the present invention, when the local terminal accesses the local area network, it further includes:

[0026] Obtain the learning progress of the foreign user terminal;

[0027] Using a first preset mathematical model, obtain a progress threshold according to the number of the remote user terminals and the learning progress of the local user terminal;

[0028] Determine whether the learning progress of each remote user terminal is greater than the progress threshold;

[0029] If not, correct the common exercise question bank of the remote user terminal according to the answering records of the local user terminal, so as to reduce the difficulty of the common exercise question bank on the remote user terminal.

[0030] According to an embodiment of the present invention, the correcting the common exercise question bank of the remote user terminal according to the answering records of the local user terminal includes:

[0031] Using a second preset mathematical model, obtain a corresponding correction coefficient according to the learning progress and answering records of the remote user terminal;

[0032] Correct the common exercise question bank of the remote user terminal according to the correction coefficient and the answering records of the local user terminal.

[0033] On the other hand, the present invention also provides a machine-readable storage medium, on which a machine-executable program is stored. When the machine-executable program is executed by a processor, the method for processing a mathematical morphology signal according to any one of the above embodiments is implemented.

[0034] On another aspect, the present invention also provides a computer device, including a memory, a processor, and a machine-executable program stored on the memory and running on the processor. When the processor executes the machine-executable program, the method for processing a mathematical morphology signal according to any one of the embodiments is implemented.

[0035] In the technical solution provided by the present invention, the user terminal of the teaching system can adopt a test paper recognition model based on mathematical morphology to obtain a test paper image from a preset image; then recognize the test questions in the test paper image and store them in a preset exercise question bank, so that when the user terminal is in an off-grid state, the preset exercise question bank can be updated. And when the local user terminal accesses the local area network, the local user terminal also shares the preset exercise question bank on the local user terminal with remote user terminals in the local area network, so that all user terminals in the local area network can obtain the test questions on the test paper. Due to the technical solution of the present invention, the user terminal can update the exercises in the preset exercise question bank when it is unable to connect to the network, so the problem in the prior art that the user learning efficiency is affected due to the user terminal being unable to connect to the network in time can be solved, thereby achieving the purpose of improving the user experience.

[0036] Those skilled in the art will better understand the above and other objects, advantages and features of the present invention from the following detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings. Description of the Drawings

[0037] Some specific embodiments of the present invention will be described in detail hereinafter with reference to the accompanying drawings in an illustrative rather than restrictive manner. The same reference numerals in the drawings denote the same or similar components or parts. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0038] Figure 1 is a schematic flowchart of a method for processing a mathematical morphological signal according to an embodiment of the present invention;

[0039] Figure 2 is a schematic flowchart of storing test questions in a preset practice question bank on a local user terminal from a test paper image according to an embodiment of the present invention;

[0040] Figure 3 is a schematic flowchart of storing test questions in a preset practice question bank on a local user terminal from a test paper image according to another embodiment of the present invention;

[0041] Figure 4 is a schematic flowchart of sharing a preset practice question bank with foreign user terminals within a local area network according to an embodiment of the present invention;

[0042] Figure 5 is a schematic flowchart of a method for correcting a common practice question bank of a foreign user terminal according to an embodiment of the present invention;

[0043] Figure 6 is a schematic flowchart of a method for correcting a common practice question bank of a foreign user terminal according to the answer records of a local user terminal according to an embodiment of the present invention;

[0044] Figure 7 is a schematic diagram of a machine-readable storage medium according to an embodiment of the present invention;

[0045] Figure 8 is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Embodiments

[0046] The following refers to Figures 1 to 8To describe a method for processing a mathematical morphological signal, a computer device, and a storage medium according to an embodiment of the present invention. In the description of this embodiment, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one of such features, that is, include one or more of such features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined. When a certain feature "includes or contains" a certain or certain features it covers, unless otherwise specifically described, this indicates that other features are not excluded and other features may be further included.

[0047] Unless otherwise clearly specified and limited, the terms "set", "installed", "connected", "coupled", "fixed", etc. should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal connection of two components or the interaction relationship between two components, unless otherwise clearly limited. Those of ordinary skill in the art should be able to understand the specific meanings of the above terms in the present invention according to specific circumstances.

[0048] In addition, in the description of this embodiment, the first feature being "above" or "below" the second feature may include the first and second features being in direct contact, or may include the first and second features not being in direct contact but in contact through other features therebetween. That is, in the description of this embodiment, the first feature being "above", "over", and "on" the second feature includes the first feature being directly above and obliquely above the second feature, or merely indicating that the first feature is at a higher horizontal height than the second feature. The first feature being "under", "beneath", or "below" the second feature may be the first feature being directly below or obliquely below the second feature, or merely indicating that the first feature is at a lower horizontal height than the second feature.

[0049] In the description of this embodiment, the description with reference to terms such as "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples.

[0050] Please refer to Figure 1 , Figure 1Shown is a schematic flowchart of a method for processing a mathematical morphological signal in an embodiment of the present invention. This processing method is set on the user terminal of the teaching system to update the exercises in the preset exercise bank on the user terminal when the user terminal cannot connect to the Internet, so as to improve the user experience.

[0051] As Figure 1 shown, the method for processing the mathematical morphological signal of the present invention includes the following steps:

[0052] Step S1: Obtain a preset image, and use a test paper recognition model based on mathematical morphology to recognize the test paper image in the preset image;

[0053] Step S2: Process the above test paper image to obtain the questions in the test paper image, and store the questions in the preset exercise bank on the local user terminal;

[0054] Step S3: When the local user terminal accesses the local area network, share the preset exercise bank on the local user terminal with foreign user terminals within the local area network.

[0055] In the above step S1, the preset image can be obtained by collecting images of the preset area, or the preset image can be received from other terminal devices such as mobile phones. Since there may be multiple test papers or the background of the test paper in the obtained preset image. For example, when the test paper is placed on the desktop, in addition to the test paper image, there is also a desktop image in the preset image. Therefore, in this embodiment, after the preset image is obtained, the preset image is input into the test paper recognition model based on mathematical morphology to recognize the test paper image in the preset image through the test paper recognition model.

[0056] In this embodiment, the method for recognizing the preset image by using the test paper recognition model includes:

[0057] First, preprocess the preset image. The preprocessing includes gray-scale transformation processing and median filtering processing. Among them, the gray-scale transformation processing includes enhancing the gray-scale of the preset image to enhance the contrast of the preset image; the median filtering processing is to obtain the neighborhood corresponding to each pixel point in the preset image, and use the average value of the pixel values of the pixel points in each neighborhood as the pixel value of the corresponding pixel point. The median filtering processing can overcome the problem of image detail blurring caused by linear filtering processing to effectively filter out the noise in the preset image.

[0058] Then, obtain the binarization threshold, and then use the binarization threshold to perform binarization processing on the preprocessed preset image. Set the pixel points with gray-scale values greater than the binarization threshold in the preprocessed preset image as white pixel points, and set the pixel points with gray-scale values not greater than the binarization threshold as black pixel points.

[0059] In this embodiment, the method for obtaining the binarization threshold includes:

[0060] Step S101: Use the average value of the maximum gray value and the minimum gray value of the preprocessed preset image as the initial value of the binarization threshold;

[0061] Step S102: Segment the preprocessed preset image into two groups of images according to the binarization threshold, where the gray values of the first group of images are less than the binarization threshold, and the gray values of the second group of images are greater than the binarization threshold;

[0062] Step S103: Calculate the average gray value of the first group of images and the average gray value of the second group of images;

[0063] Step S104: Use the average value of the average gray value of the first group of images and the average gray value of the second group of images as the updated binarization threshold;

[0064] Step S105: Determine whether the updated binarization threshold is equal to the binarization threshold before update;

[0065] If so, stop updating the binarization threshold and use the updated binarization threshold as the final binarization threshold;

[0066] If not, return to execute Step S102.

[0067] Next, detect the edge of the test paper image in the preset image according to mathematical morphology. In this embodiment, when detecting the edge of the test paper image in the preset image according to mathematical morphology, first perform erosion processing on the preprocessed preset image. For example, a black dot block with a size of 3×3 can be used to perform erosion processing on the preprocessed preset image to obtain a preset image after removing the object boundary; then subtract the preprocessed preset image from the preset image after erosion processing to obtain the edge of the test paper image in the preset image.

[0068] Finally, locate the test paper image in the preset image. In this embodiment, locating the test paper image in the preset image means determining the position of the test paper image in the preset image, and there are various methods for determination, such as the horizontal projection method, that is, projecting the test paper image in the preset image onto a two-dimensional coordinate system to obtain the coordinates of the preset test paper image in the two-dimensional coordinate system.

[0069] In the above step S2, optical character recognition technology can be used to recognize the text in the test paper image to obtain the test questions in the test paper image, and store the test questions in a preset exercise question bank on the local user terminal. In this embodiment, the preset exercise question bank is an exercise question bank set on the user terminal for storing test paper questions. Since the method of recognizing text in images has been widely used in the prior art, for example, using OCR (Optical Character Recognition) technology to recognize text in images, it will not be elaborated in this embodiment.

[0070] In the above step S3, when the local user terminal accesses the local area network, it can interact with other user terminals in the local area network, that is, foreign user terminals. At this time, the exercise questions in the preset exercise question bank on the local user terminal are sent to the foreign user terminals in the local area network, so that the foreign user terminals receive the exercise questions in the preset exercise question bank on the local user terminal.

[0071] In summary, in this embodiment, the user terminal of the teaching system can use a test paper recognition model based on mathematical morphology to obtain a test paper image from a preset image; then recognize the test questions in the test paper image and store them in a preset exercise question bank, so that when the user terminal is in an off-line state, the preset exercise question bank can be updated. And when the local user terminal accesses the local area network, the local user terminal also shares the preset exercise question bank on the local user terminal with foreign user terminals in the local area network, so that all user terminals in the local area network can obtain the test questions on the test paper. Due to the setting method of this embodiment, the user terminal can update the exercise questions in the preset exercise question bank in a state where it cannot be connected to the network, so the problem in the prior art that the user learning efficiency is affected due to the user terminal not being able to be connected to the network in time can be solved, thus achieving the purpose of improving the user experience.

[0072] In some embodiments of the present invention, when processing the test paper image to obtain the test questions in the test paper image in the above step S2, it further includes: obtaining the test paper type of the test paper in the test paper image.

[0073] In this embodiment, the test paper type of the test paper can be determined according to the knowledge level corresponding to the test paper. For example, the test papers can be classified according to the grades applicable to the test papers, and the test papers applicable to the same grade are regarded as test papers of the same test paper type.

[0074] The method of storing the test questions in the test paper image in the preset exercise question bank on the local user terminal in the above step S2 is as Figure 2 shown and includes the following steps:

[0075] Step S201: Determine whether the test paper type in the test paper image matches the terminal type of the local user terminal; if so, execute step S202:

[0076] Step S202: Store the questions in the test paper image in a preset exercise question bank on the local user terminal.

[0077] In the above step S201, the terminal type of the user terminal is determined according to the type of the user corresponding to the user terminal. For example, it can be classified according to the grade of the user, and the users in the same grade are regarded as users of the same type, and an identifier corresponding to the corresponding user type is set on the user terminal. In this embodiment, the type of the local user terminal can be obtained through the identifier of the local user terminal.

[0078] In the above step S202, store the questions in the test paper image in a preset exercise question bank on the local user terminal, and at the same time store the test paper type in the test paper image in the preset exercise question bank.

[0079] Through the setting method of this embodiment, it can be ensured that the exercise questions in the preset exercise question bank on the local user terminal correspond to the user's knowledge level, so as to achieve the purpose of improving the user experience.

[0080] In some embodiments of the present invention, the method of storing the questions in the test paper image in a preset exercise question bank in the above step S2 is as Figure 3 shown, including the following steps:

[0081] Step S211: Obtain the storage capacity of the preset exercise question bank on the local user terminal according to the network connection frequency of the local user terminal;

[0082] Step S212: Screen the questions in the test paper image according to the storage capacity of the preset exercise question bank on the local user terminal, and store the screened exercise questions in the preset exercise question bank on the local user terminal.

[0083] In the above step S211, the storage capacity corresponding to multiple frequency intervals can be set first. After obtaining the network connection frequency of the local user terminal, according to the storage capacity corresponding to the frequency interval where the network connection frequency is located, this storage capacity is the storage capacity of the preset exercise question bank on the local user terminal; then obtain the update coefficient α of the local user terminal, and the value of the update coefficient α is between 0 and 1; finally, multiply the storage capacity of the preset exercise question bank of the local user terminal by the update coefficient to obtain the preset exercise question update amount of the local user terminal.

[0084] In the above step S212, questions with a quantity not greater than the storage capacity of the preset exercise question bank can be screened out from the questions in the test paper image, and the screening can be random selection or, according to the set screening rules, questions with a set serial number in the test paper image.

[0085] Through the setting method of this embodiment, the storage capacity of the preset exercise question bank on the local user terminal can be made to correspond to the networking frequency, so the reliability of updating the preset exercise question bank of the local user terminal can be improved.

[0086] In some embodiments of the present invention, the method for sharing the preset exercise question bank with the foreign user terminals in the local area network in the above step S3 is as Figure 4 shown and includes the following steps:

[0087] Step S301: Obtain the terminal types of the foreign user terminals in the local area network;

[0088] Step S302: Use the foreign user terminals whose terminal types correspond to the test paper types in the test paper images as the target foreign user terminals;

[0089] Step S303: Share the preset exercise question bank on the local user terminal with the target foreign user terminals.

[0090] In the above step S301, after the local user terminal accesses the local area network, obtain the terminal types of the corresponding foreign user terminals according to the identifiers of the foreign user terminals in the local area network.

[0091] In the above step S302, the terminal types of the foreign user terminals in the local area network can be compared one by one to determine whether the terminal types of the foreign user terminals correspond to the test paper types in the test paper images, so as to obtain the target foreign user terminals from the foreign user terminals.

[0092] In the above step S303, the addresses of the target foreign user terminals can be obtained first, and then the exercises in the preset exercise question bank can be sent to the corresponding target foreign user terminals according to the addresses.

[0093] Through the setting method of this embodiment, the preset exercise question bank on the local user terminal can be shared only with the foreign user terminals whose terminal types correspond to the test paper types in the test paper images, so that each user terminal can only obtain the preset exercise question bank corresponding to the corresponding terminal type, improving the reliability of the user terminal.

[0094] In some embodiments of the present invention, when the local user terminal accesses the local area network, the processing method of the mathematical morphology signal in this embodiment further includes:

[0095] When the local user terminal receives the preset exercise question bank shared by the foreign user terminal, determine whether the local user terminal meets the preset update conditions;

[0096] If so, control the local user terminal to download the exercises in the preset exercise question bank shared by the foreign user terminal.

[0097] With the setting method of this embodiment, when the local user terminal accesses the local area network, only when the local user terminal meets the preset update conditions, will it be controlled to download the exercises in the preset exercise question bank shared by the remote user terminal. Compared with the embodiment without setting the preset update conditions, it can improve the controllability of the update of the preset exercise question bank of the local user terminal, prevent the preset exercise question bank of the local user terminal from being updated frequently, and improve the reliability of the local user terminal.

[0098] In some embodiments of the present invention, when determining whether the local user terminal meets the preset update conditions, the preset update conditions include: the learning progress of the local user terminal reaches the preset progress, and / or the local user terminal receives a preset update instruction.

[0099] In this embodiment, the method for calculating the learning progress of the local user terminal includes: first, obtaining the number N of exercise types in the commonly used exercise question bank on the local user terminal, and the number N' of exercise types learned by the user of the local user terminal, and then the learning progress T of the local user terminal can be calculated as T = N ′ / N.

[0100] In this embodiment, the commonly used exercise question bank on the local user terminal is the exercise question bank used to store the exercises downloaded from the teaching database on the local user terminal, and the commonly used exercise question bank is independent of the preset exercise question bank.

[0101] With the setting method of this embodiment, when the local user terminal accesses the local area network, when the learning progress of the local user terminal reaches the preset progress, and / or when the local user terminal receives a preset update instruction input by the user, the exercises in the preset exercise question bank shared by the remote user terminal can be downloaded to update the preset exercise question bank of the local user terminal. Since the learning progress of the local user terminal can reflect the learning efficiency of the user and the preset update instruction can be issued by the user, the reliability and controllability of the update of the preset exercise question bank on the local user terminal can be improved through the setting method of this embodiment.

[0102] In some embodiments of the present invention, when the local user terminal accesses the local area network, the processing method of the mathematical morphology signal in this embodiment further includes a method for correcting the commonly used exercise question bank of the remote user terminal, and the method is as Figure 5 shown, including the following steps:

[0103] Step S311: Obtain the learning progress of the remote user terminal in the local area network;

[0104] Step S312: Adopt the first preset mathematical model to obtain the progress threshold according to the number of remote user terminals in the local area network and the learning progress of the local user terminal;

[0105] Step S313: Determine whether the learning progress of each external user terminal within the local area network is greater than the progress threshold;

[0106] If not, then execute Step S314;

[0107] Step S314: Modify the common practice question bank of the external user terminal according to the answering records of the local user terminal, so as to reduce the difficulty of the common practice question bank on the external user terminal.

[0108] In the above Step S311, the calculation method of the learning progress of the external user terminal within the local area network is the same as that of the learning progress of the local user terminal in the above text. Since this calculation method has been introduced in detail in the above text, in order to avoid repetition, it will not be elaborated in this embodiment.

[0109] In the above Step S312, the number of external user terminals within the local area network and the learning progress of the local user terminal can be input into the first preset mathematical model. The first preset mathematical model can calculate the corresponding progress threshold Q according to the number of external user terminals and the learning progress of the local user terminal.

[0110] In this embodiment, assuming that the number of external user terminals within the local area network is n, when calculating the corresponding progress threshold Q using the first preset mathematical model, the following calculation formula is used:

[0111] Q = T - de sn + g

[0112] Among them, s is the terminal quantity correction coefficient, d is the terminal matching coefficient, and g is the correction constant.

[0113] In the above Step S313, the learning progress of each external user terminal can be compared with the above progress threshold to determine whether the learning progress of each external user terminal is greater than the above progress threshold. If so, it is determined that the learning progress of each external user terminal is synchronized with the learning progress of the local user terminal, and at this time, there is no need to modify the common practice question bank of the external user terminal. If the learning progress of any external user terminal is not greater than the above progress threshold, it is determined that the learning progress of this external user terminal is not synchronized with the learning progress of the local user terminal, and it is necessary to modify the common practice question bank of this external user terminal.

[0114] In the above Step S314, the method of modifying the common practice question bank of the external user terminal according to the answering records of the local user terminal includes:

[0115] First, according to the answering records of the local user terminal, obtain the exercises with difficulty less than the corresponding preset difficulty among various exercises;

[0116] Then, send a question bank correction instruction to the remote user terminal to delete the exercises with a difficulty level greater than the corresponding preset difficulty level in the commonly used question bank on the remote user terminal, thereby achieving the correction of the commonly used question bank on the remote user terminal.

[0117] For example, there are a total of ten exercises of a certain type in the commonly used question bank of the local user terminal. Among the answering records of the local user terminal, six exercises of this type are answered correctly. Then, it is determined that the difficulty levels of these six exercises in this type of exercise are less than the corresponding preset difficulty levels, and the difficulty levels of the other four exercises are greater than the corresponding preset difficulty levels. When correcting the commonly used question bank of the remote user terminal, delete the four exercises with a difficulty level greater than the corresponding preset difficulty level in this type of exercise in the commonly used question bank on the remote user terminal, and only retain the four exercises with a difficulty level less than the corresponding preset difficulty level.

[0118] Through the setting method of this embodiment, when the local user terminal meets the preset update conditions, the learning progress of the remote user terminal can be obtained, and the first preset mathematical model can be used to obtain the progress threshold according to the number of remote user terminals within the local area network and the learning progress of the local user terminal; then, the commonly used question bank of the remote user terminal with a learning progress greater than the high progress threshold is corrected according to the answering records of the local user terminal. Since the difficulty level of the exercises in the commonly used question bank of the remote user terminal can be reduced after correcting the commonly used question bank of the remote user terminal, when the user of the remote user terminal uses this commonly used question bank for learning, the learning difficulty can be reduced to improve the learning speed of the user, so that the learning progress of the remote user terminal can quickly catch up with the learning progress of the local user terminal.

[0119] In some embodiments of the present invention, the method for correcting the commonly used question bank of the remote user terminal according to the answering records of the local user terminal in the above step S314 is as Figure 6 shown, and includes the following steps:

[0120] Step S321: Use the second preset mathematical model to obtain the corresponding correction coefficient according to the learning progress and answering records of the remote user terminal;

[0121] Step S322: Correct the commonly used question bank of the remote user terminal according to the above correction coefficient and the answering records of the local user terminal.

[0122] In the above step S321, let the learning progress of one of the remote user terminals be L, and in the answering records of this remote user terminal, the number of exercise types completed is h, and the correct rate of the i-th exercise type is P i , then the correction coefficient k of this remote user terminal calculated by using the second preset mathematical model is:

[0123]

[0124] where β is the progress correction value, γ is the answer correction value, a is the progress coefficient, b is the answer coefficient, and c 1 and c 2 are the first correction constant and the second correction constant respectively.

[0125] In the above step S322, taking one of the exercise types as an example, assume that among the exercises of a certain exercise type in the answer record of the local user terminal, t exercises are answered correctly. Then, select ⌊kt⌋ exercises from these t exercises, generate a correction instruction based on these ⌊kt⌋ exercises, and send it to the corresponding remote user terminal.

[0126] After receiving the above correction instruction, the remote user terminal obtains multiple exercises corresponding to this exercise type from the corresponding common exercise library, and deletes the other exercises except these ⌊kt⌋ exercises from the multiple exercises, so that only these ⌊kt⌋ exercises are saved for this exercise type, thereby realizing the correction of this exercise type in the common exercise library on the remote user terminal.

[0127] Through the setting method of this embodiment, the corresponding correction coefficient can be obtained according to the learning progress and answer record of the remote user terminal, and the common exercise library of the remote user terminal can be corrected according to the correction coefficient of the remote user terminal and the answer record of the local user terminal. Since the learning progress and answer record of the remote user terminal can accurately reflect the learning ability of the corresponding user, correcting the common exercise library of the remote user terminal according to the correction coefficient of the remote user terminal and the answer record of the local user terminal can make the modified common exercise library more matched with the learning ability of the corresponding user, thereby improving the reliability of correcting the common exercise library on the remote user terminal.

[0128] This embodiment also provides a machine-readable storage medium and a computer device. Figure 7 is a schematic diagram of a machine-readable storage medium 830 according to an embodiment of the present invention; Figure 8 is a schematic diagram of a computer device 900 according to an embodiment of the present invention. The machine-readable storage medium 830 stores a machine-executable program 840 thereon. When the machine-executable program 840 is executed by a processor, it implements the data replication status test method of the disaster recovery database in any of the above embodiments.

[0129] The computer device 900 may include a memory 920, a processor 910, and a machine-executable program 840 stored on the memory 920 and running on the processor 910. When the processor 910 executes the machine-executable program 840, it implements the data replication status test method of the disaster recovery database in any of the above embodiments.

[0130] Note that the logic and / or steps represented in the flowchart or described otherwise herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any machine-readable storage medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices.

[0131] For the description of this embodiment, the machine-readable storage medium 830 can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the machine-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0132] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0133] The computer device 900 can be, for example, a server, a desktop computer, a laptop computer, a tablet computer, or a smartphone. In some examples, the computer device 900 can be a cloud computing node. The computer device 900 can be described in the general context of computer system-executable instructions, such as program modules, executed by a computer system. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc. that perform specific tasks or implement specific abstract data types. The computer device 900 can be implemented in a distributed cloud computing environment where tasks are executed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0134] The computer device 900 may include a processor 910 adapted to execute stored instructions and a memory 920 that provides temporary storage space for the operation of the instructions during operation. The processor 910 may be a single-core processor, a multi-core processor, a computing cluster, or any number of other configurations. The memory 920 may include random access memory (RAM), read-only memory, flash memory, or any other suitable storage system.

[0135] The processor 910 may be connected through a system interconnect (such as PCI, PCI-Express, etc.) to an I / O interface (input / output interface) adapted to connect the computer device 900 to one or more I / O devices (input / output devices). The I / O devices may include, for example, a keyboard and a pointing device, where the pointing device may include a touchpad or a touch screen, etc. The I / O devices may be built-in components of the computer device 900 or may be devices externally connected to the computing device.

[0136] The processor 910 may also be linked through a system interconnect to a display interface adapted to connect the computer device 900 to a display device. The display device may include a display screen as a built-in component of the computer device 900. The display device may also include a computer monitor, a television, a projector, etc. externally connected to the computer device 900. In addition, a network interface controller (NIC) may be adapted to connect the computer device 900 to a network through a system interconnect. In some embodiments, the NIC may use any suitable interface or protocol (such as Internet Small Computer System Interface, etc.) to transmit data. The network may be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, etc. Remote devices may be connected to the computing device through the network.

[0137] At this point, those skilled in the art should recognize that although multiple exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications that conform to the principles of the present invention can still be directly determined or derived from the content disclosed in the present invention without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention should be understood and determined to cover all these other variations or modifications.

Claims

1. A method for processing mathematical morphological signals, which is used for the user terminal of a teaching system; Characterized in that, The processing method includes: Obtain a preset image, and use a test paper recognition model based on mathematical morphology to recognize the test paper image in the preset image; Process the test paper image to obtain the questions in the test paper image, and store the questions in a preset exercise question bank on the local user terminal, including: Obtain the storage capacity of the preset exercise question bank on the local user terminal according to the networking frequency of the local user terminal; Screen the questions in the test paper image according to the storage capacity, and store the selected exercises in the preset exercise question bank on the local user terminal; When the local user terminal accesses the local area network, share the preset exercise question bank on the local user terminal with the foreign user terminals in the local area network, and When the local user terminal receives the preset exercise question bank shared by the foreign user terminal, judge whether the local user terminal meets the preset update condition; If so, control the local user terminal to download the exercises in the preset exercise question bank shared by the foreign user terminal.

2. The method for processing mathematical morphological signals according to claim 1, Characterized in that, When processing the test paper image to obtain the questions in the test paper image, it further includes: obtaining the test paper type of the test paper in the test paper image; The storing the questions in the preset exercise question bank includes: Judge whether the test paper type matches the terminal type of the local user terminal; If so, store the questions in the preset exercise question bank.

3. The method for processing mathematical morphological signals according to claim 2, Characterized in that, The sharing the preset exercise question bank with the foreign user terminals in the local area network includes: Obtain the terminal types of the foreign user terminals in the local area network, and use the foreign user terminals whose terminal types match the test paper type as the target foreign user terminals; Share the preset exercise question bank with the target foreign user terminals.

4. The method for processing mathematical morphological signals according to claim 1, Characterized in that, The preset update condition includes: The learning progress of the local user terminal reaches the preset progress and / or receives a preset update instruction.

5. The method for processing mathematical morphological signals according to claim 1, Characterized in that, When the local user terminal accesses the local area network, it further includes: Obtain the learning progress of the foreign user terminal; Adopt a first preset mathematical model to obtain a progress threshold according to the number of foreign user terminals and the learning progress of the local user terminal; Judge whether the learning progress of each foreign user terminal is greater than the progress threshold; If not, correct the common exercise question bank of the foreign user terminal according to the answering record of the local user terminal to reduce the difficulty of the common exercise question bank on the foreign user terminal.

6. The method for processing mathematical morphological signals according to claim 5, Characterized in that, The correcting the common exercise question bank of the foreign user terminal according to the answering record of the local user terminal includes: Using a second preset mathematical model, a corresponding correction coefficient is obtained according to the learning progress and answering records of the remote user terminal; According to the correction coefficient and the answering records of the local user terminal, the common exercise question bank of the remote user terminal is corrected.

7. A machine-readable storage medium, on which a machine-executable program is stored. When the machine-executable program is executed by a processor, the method for processing a mathematical morphology signal according to any one of claims 1 to 6 is implemented.

8. A computer device, including a memory, a processor, and a machine-executable program stored on the memory and running on the processor. When the processor executes the machine-executable program, the method for processing a mathematical morphology signal according to any one of claims 1 to 6 is implemented.

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