Knowledge point review method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a knowledge point review method and device, electronic equipment and a storage medium. The method comprises the steps that knowledge point information corresponding to multiple first knowledge points is acquired; according to the last review time point and the review interval corresponding to each first knowledge point, determining a memory retention value corresponding to each first knowledge point; determining a review value corresponding to each first knowledge point according to the last review duration corresponding to each first knowledge point; determining a review recommendation score corresponding to each first knowledge point according to the memory retention value and the review value corresponding to each first knowledge point; and sorting the plurality of first knowledge points according to the review recommendation scores corresponding to the first knowledge points to obtain a knowledge point recommendation sequence. By implementing the embodiment, the learning efficiency of the target user can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a knowledge point review method, device, electronic device and storage medium. Background Art

[0002] At present, users have fragmented learning knowledge. Due to the lack of timely and scientific review and consolidation, they are prone to completely forgetting the content they have scanned. Every time they completely forget it, they need to relearn it, spending a lot of time learning the same knowledge point multiple times, which reduces the user's learning efficiency. Summary of the invention

[0003] The embodiments of the present application disclose a knowledge point review method, device, electronic device and storage medium, which can improve the learning efficiency of users.

[0004] The present application embodiment discloses a knowledge point review method, the method comprising:

[0005] Acquire knowledge point information corresponding to the plurality of first knowledge points respectively, wherein the knowledge point information includes the last review time point, the review interval and the last review duration;

[0006] Determining a memory retention value corresponding to each of the first knowledge points according to the last review time point and the review interval corresponding to each of the first knowledge points;

[0007] Determine the review value corresponding to each of the first knowledge points according to the last review duration corresponding to each of the first knowledge points;

[0008] Determine a review recommendation score corresponding to each of the first knowledge points according to the memory retention value and review value corresponding to each of the first knowledge points;

[0009] The plurality of first knowledge points are sorted according to the review recommendation scores corresponding to the respective first knowledge points to obtain a knowledge point recommendation sequence.

[0010] In one embodiment, determining the memory retention value corresponding to each of the first knowledge points according to the last review time point and the review interval corresponding to each of the first knowledge points includes:

[0011] Determine the time interval according to the current time point and the last review time point corresponding to the target first knowledge point, the target first knowledge point being any of the first knowledge points;

[0012] The memory retention value corresponding to the target first knowledge point is determined according to the time interval and the review interval corresponding to the target first knowledge point.

[0013] In one embodiment, determining the review value corresponding to each of the first knowledge points according to the last review duration corresponding to each of the first knowledge points includes:

[0014] Obtaining an average review duration corresponding to a knowledge category to which a target first knowledge point belongs, wherein the target first knowledge point is any of the first knowledge points;

[0015] The duration ratio between the last review duration corresponding to the target first knowledge point and the average review duration is calculated, and the review value corresponding to the target first knowledge point is determined according to the duration ratio.

[0016] In one embodiment, the knowledge point information further includes a collection status value;

[0017] The determining of the review recommendation score corresponding to each of the first knowledge points according to the memory retention value and the review value corresponding to each of the first knowledge points includes:

[0018] According to the memory retention value, the review value and the collection status value corresponding to each of the first knowledge points, the review recommendation score corresponding to each of the first knowledge points is determined.

[0019] In one embodiment, determining the review recommendation score corresponding to each of the first knowledge points according to the memory retention value, the review value and the collection status value corresponding to each of the first knowledge points includes:

[0020] The memory retention value, review value and collection status value corresponding to each of the first knowledge points are weightedly calculated to obtain a review recommendation score corresponding to each of the first knowledge points.

[0021] In one embodiment, the step of sorting the plurality of first knowledge points according to the review recommendation scores corresponding to the respective first knowledge points to obtain a knowledge point recommendation sequence includes:

[0022] The multiple first knowledge points are sorted in descending order of review recommendation scores, and the first N first knowledge points after sorting are selected to generate a knowledge point recommendation sequence.

[0023] In one embodiment, the method further comprises:

[0024] When it is detected that a review of the second knowledge point is completed, the latest review data corresponding to the second knowledge point is obtained; the latest review data includes a review score corresponding to the completion of the review of the second knowledge point;

[0025] updating the difficulty coefficient of the second knowledge point according to the review score and the corresponding memory retention value corresponding to the second knowledge point; the difficulty coefficient is used to represent the difficulty of the target user to remember the second knowledge point;

[0026] According to the updated difficulty coefficient, the review interval corresponding to the second knowledge point is updated.

[0027] In one embodiment, updating the difficulty coefficient of the second knowledge point according to the review score and the memory retention value corresponding to the second knowledge point includes:

[0028] Determining a target adjustment amount of a difficulty coefficient of the second knowledge point according to a review score and a corresponding memory retention value corresponding to the second knowledge point;

[0029] Based on the target adjustment amount, the difficulty coefficient of the second knowledge point is updated.

[0030] In one embodiment, determining the target adjustment amount of the difficulty coefficient of the second knowledge point according to the review score and the corresponding memory retention value corresponding to the second knowledge point includes:

[0031] If the review score corresponding to the second knowledge point is greater than a preset score threshold, determining the initial adjustment amount of the difficulty coefficient of the second knowledge point as a first adjustment amount;

[0032] If the review score corresponding to the second knowledge point is not greater than the preset score threshold, determining the initial adjustment amount of the difficulty coefficient of the second knowledge point as the second adjustment amount;

[0033] Subtract the memory retention value corresponding to the second knowledge point from the initial adjustment amount to obtain a target adjustment amount of the difficulty coefficient of the second knowledge point;

[0034] The updating of the difficulty coefficient of the second knowledge point based on the target adjustment amount includes:

[0035] The target adjustment amount is subtracted from the difficulty coefficient of the second knowledge point to obtain an updated difficulty coefficient.

[0036] In one embodiment, updating the review interval corresponding to the second knowledge point according to the updated difficulty coefficient includes:

[0037] According to the updated difficulty coefficient and the preset first adjustment coefficient, the review interval corresponding to the second knowledge point is adjusted to obtain a first review interval;

[0038] According to a preset second adjustment coefficient, the review interval corresponding to the second knowledge point is adjusted to obtain a second review interval;

[0039] According to the first review interval and the second review interval, the review interval corresponding to the second knowledge point is updated.

[0040] In one embodiment, the latest review data further includes a review time point, and the review time point is a time point when a review of the second knowledge point is detected to be completed;

[0041] After updating the review interval corresponding to the second knowledge point according to the updated difficulty coefficient, the method further includes:

[0042] According to the updated review interval and the review time point, the predicted review time point corresponding to the second knowledge point is determined, and the predicted review time point is the predicted time point when the second knowledge point needs to be reviewed next time.

[0043] In one embodiment, before acquiring the knowledge point information respectively corresponding to the plurality of first knowledge points, the method further includes:

[0044] Obtaining predicted review time points corresponding to a plurality of knowledge points included in the knowledge point record table; the predicted review time point is the predicted time point at which the next review is required;

[0045] According to the current time point and the predicted review time points corresponding to the respective knowledge points, a plurality of first knowledge points that need to be reviewed at the current time point are determined from the plurality of knowledge points.

[0046] In one embodiment, the method is applied to an electronic device, the electronic device includes a scanning device, and before obtaining the review time points respectively corresponding to the plurality of first knowledge points, the method further includes:

[0047] Acquire scanned content of the scanning device, and extract multiple knowledge points from the scanned content;

[0048] Determine the knowledge category corresponding to each of the knowledge points;

[0049] According to the scanning information corresponding to each of the knowledge points and the corresponding knowledge category, a knowledge point record table is constructed, wherein the knowledge point record table includes the knowledge point identifier, knowledge category, last review time point, review interval, last review duration, difficulty coefficient, collection status value, and predicted review time point corresponding to each of the knowledge points;

[0050] The obtaining of knowledge point information respectively corresponding to the plurality of first knowledge points includes:

[0051] From the knowledge point record table, knowledge point information corresponding to the plurality of first knowledge points is obtained.

[0052] The present application embodiment discloses a knowledge point review device, the device comprising:

[0053] An information acquisition module, used to acquire knowledge point information corresponding to the plurality of first knowledge points, wherein the knowledge point information includes the last review time point, the review interval, and the last review duration;

[0054] A memory determination module, used to determine a memory retention value corresponding to each of the first knowledge points according to the last review time point and the review interval corresponding to each of the first knowledge points;

[0055] A value determination module, used to determine the review value corresponding to each of the first knowledge points according to the last review duration corresponding to each of the first knowledge points;

[0056] A score determination module, used to determine a review recommendation score corresponding to each of the first knowledge points according to the memory retention value and review value corresponding to each of the first knowledge points;

[0057] The sequence determination module is used to sort the multiple first knowledge points according to the review recommendation scores corresponding to the respective first knowledge points to obtain a knowledge point recommendation sequence.

[0058] The present application discloses an electronic device, including:

[0059] A memory storing executable program code;

[0060] a processor coupled to the memory;

[0061] The processor calls the executable program code stored in the memory to execute the method described in any one of the above embodiments.

[0062] An embodiment of the present application discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor executes the method described in any one of the above embodiments.

[0063] Through the knowledge point review method, device, electronic device and storage medium disclosed in the embodiments of the present application, knowledge point information corresponding to multiple first knowledge points can be obtained, and the knowledge point information can include the last review time point, review interval, and last review duration. The electronic device can determine the memory retention value corresponding to each first knowledge point based on the last review time point and review interval corresponding to each first knowledge point, and then determine the review value corresponding to each first knowledge point based on the last review duration corresponding to each first knowledge point, so that the review recommendation score corresponding to each first knowledge point can be determined based on the memory retention value and review value corresponding to each first knowledge point, and the electronic device then sorts the multiple first knowledge points according to the review recommendation score corresponding to each first knowledge point to obtain a knowledge point recommendation sequence.

[0064] In an embodiment of the present application, the electronic device can determine the memory retention value and the corresponding review value corresponding to each first knowledge point based on the knowledge point information corresponding to each of the multiple first knowledge points, so as to determine the review recommendation score corresponding to each first knowledge point. The review recommendation score comprehensively considers the target user's memory retention of the first knowledge point and the value of the target user's review of the first knowledge point. By sorting the knowledge point recommendation sequence according to the review recommendation score, the electronic device can provide targeted learning suggestions to the target user, that is, it can provide the target user with personalized review strategies to help the target user review and master the knowledge points more efficiently, thereby improving the target user's learning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0066] Figure 1-A It is a schematic diagram of an application scenario of a knowledge point review method disclosed in an embodiment of the present application;

[0067] Figure 1-B It is a schematic diagram of a knowledge point record table disclosed in an embodiment of the present application;

[0068] Figure 2-A It is a flowchart of a knowledge point review method disclosed in an embodiment of the present application;

[0069] Figure 2-B It is a schematic diagram of a forgetting curve disclosed in an embodiment of the present application;

[0070] Figure 3is an interactive schematic diagram of multiple modules included in an electronic device disclosed in an embodiment of the present application;

[0071] Figure 4 It is a flowchart of a knowledge point review method disclosed in an embodiment of the present application;

[0072] Figure 5 It is a flowchart diagram of a method for updating data after completing a review of a second knowledge point disclosed in an embodiment of the present application;

[0073] Figure 6 It is a modular schematic diagram of a knowledge point review device disclosed in an embodiment of the present application;

[0074] Figure 7 It is a structural block diagram of an electronic device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0076] It should be noted that the terms "including" and "having" in the embodiments of the present application and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0077] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first knowledge point may be referred to as a second knowledge point, and similarly, a second knowledge point may be referred to as a first knowledge point.

[0078] The first knowledge point and the second knowledge point are both knowledge points, but they are not the same knowledge point.

[0079] The embodiments of the present application disclose a knowledge point review method, device, electronic device and storage medium, which can improve the learning efficiency of target users.

[0080] The following is a detailed description with reference to the accompanying drawings.

[0081] like Figure 1-AAs shown, Figure 1-A : is a schematic diagram of an application scenario of a knowledge point review method disclosed in an embodiment of the present application, the application scenario may include an electronic device 110, the electronic device 110 may include but is not limited to an electronic pen, a mobile phone, a tablet computer, a wearable device, a laptop computer, a PC (Personal Computer), etc., the electronic device 110 may obtain knowledge points, such as obtaining knowledge points from a book, the electronic device 110 may include a knowledge point record table, wherein the knowledge point record table may include multiple knowledge points, the knowledge category to which the knowledge point belongs may include but is not limited to Chinese characters, Chinese pinyin, Chinese ancient poems, English words, English grammar, mathematics chapter knowledge points, etc., there is no limitation to this, for example, the knowledge point may be a Chinese character word "smile", and the knowledge point may also be an English word "abandon".

[0082] Among them, the knowledge point record table may also include the knowledge point identification, knowledge category, last review time point, review interval, last review duration, difficulty coefficient, collection status value, and predicted review time point corresponding to each knowledge point. The knowledge category may refer to the category or field to which the knowledge point belongs; the knowledge point identification may refer to the identifier or id (Identity Document) of the knowledge point, and the knowledge point identification is used to distinguish and identify different knowledge points in the same knowledge category; the last review time point may refer to the time when the target user last reviewed the corresponding knowledge point; the review interval refers to the interval between the last review time point and the next review time point predicted by the electronic device; the difficulty coefficient may be used to characterize the difficulty of the target user to remember the corresponding knowledge point; the collection status value may be used to characterize whether the target user has collected the corresponding knowledge point, such as a collection status value of "1" may characterize that the target user has collected the corresponding knowledge point, and a collection status value of "0" may characterize that the target user has not collected the corresponding knowledge point; the predicted review time point may characterize the time point when the electronic device predicts that the corresponding knowledge point needs to be reviewed next time, and more examples may be as follows: Figure 1-B As shown, Figure 1-B It is a schematic diagram of a knowledge point record table disclosed in an embodiment of the present application.

[0083] In one embodiment, the electronic device 110 can detect a review recommendation request, or the electronic device 110 can perform the steps of the knowledge point review method when the current time point is in a preset daily review time period. Optionally, the electronic device 110 can obtain the knowledge point information corresponding to the multiple first knowledge points, and determine the memory retention value corresponding to each first knowledge point according to the last review time point and review interval corresponding to each first knowledge point, and can determine the review value corresponding to each first knowledge point according to the last review duration corresponding to each first knowledge point. The electronic device 110 can also determine the review recommendation score corresponding to each first knowledge point according to the memory retention value and review value corresponding to each first knowledge point, and then sort the multiple first knowledge points according to the review recommendation score corresponding to each first knowledge point to obtain a knowledge point recommendation sequence.

[0084] In one embodiment, the electronic device 110 may also include a scanning device, and the target user may scan through the scanning device. The electronic device 110 may obtain the scanned content of the scanning device, thereby extracting multiple knowledge points from the scanned content, and determining the knowledge category corresponding to each knowledge point. The electronic device 110 constructs a knowledge point record table according to the scan information corresponding to each knowledge point and the corresponding knowledge category. The knowledge point record table includes the knowledge point identification, knowledge category, last review time point, review interval, last review duration, difficulty coefficient, collection status value, and predicted review time point corresponding to each knowledge point. Therefore, the electronic device 110 can obtain the knowledge point information corresponding to multiple first knowledge points from the knowledge point record table.

[0085] Among them, the last review time point can be the scanning time point corresponding to the scanned content, the initial review interval can be a first preset value, such as one day, the initial last review duration can be the average review duration corresponding to the knowledge category to which the knowledge point belongs, the initial difficulty coefficient can be a second preset value, and the predicted review time point can be determined based on the initial review time interval and the last review time point. Optionally, the electronic device 110 can also distinguish between the extracted multiple knowledge points, determine whether each knowledge point matches the target user, and if not, determine it as an invalid knowledge point and do not save it in the knowledge point record table. Specifically, the electronic device 110 can obtain the grade information corresponding to the target user, and determine whether each knowledge point is in the learning content of the grade information corresponding to the target user. If so, the knowledge point matches the target user, and if not, the knowledge point does not match the target user.

[0086] By implementing this embodiment, timely and scientific review can be provided for the knowledge points scanned by the scanning device, the knowledge points can be consolidated, a closed loop of the learning path of checking, learning and remembering can be formed, and the learning efficiency of the user can be improved.

[0087] like Figure 2-AAs shown, Figure 2-A : is a flow chart of a knowledge point review method disclosed in an embodiment of the present application. The knowledge point review method can be applied to the electronic device in the above embodiment. The knowledge point review method may include the following steps:

[0088] Step 210, obtaining knowledge point information corresponding to the plurality of first knowledge points respectively.

[0089] The electronic device can obtain the knowledge point information corresponding to the multiple first knowledge points from the knowledge point record table. Optionally, the knowledge point record table can be stored locally in the electronic device or in the cloud, and there is no limitation on this. The electronic device can select multiple knowledge points in the knowledge point record table to determine multiple first knowledge points.

[0090] In one embodiment, the electronic device can obtain the predicted review time points corresponding to the multiple knowledge points included in the knowledge point record table, and determine the multiple first knowledge points that need to be reviewed at the current time point from the multiple knowledge points according to the current time point and the predicted review time points corresponding to each knowledge point. Among them, the electronic device can compare the predicted review time points corresponding to the current time point and each knowledge point, and determine the knowledge points whose corresponding predicted review time points are not later than the current time point from the multiple knowledge points as the multiple first knowledge points that need to be reviewed at the current time point. Among them, the current time point can be the current date or the current moment, and the embodiment of the present application does not limit the accuracy of the current time point. Optionally, the electronic device can also calculate the time difference between the predicted review time point corresponding to each knowledge point and the current time point, and when the time difference is less than the preset time difference threshold, the corresponding knowledge point can be used as the first knowledge point. Implementing this embodiment, the electronic device can accurately determine whether each knowledge point needs to be reviewed at the current time point in terms of time according to the predicted review time, thereby improving the learning efficiency of the user.

[0091] Optionally, the electronic device may determine multiple first knowledge points based on the current time point. Optionally, the electronic device may also determine multiple first knowledge points based on the user itinerary of the target user.

[0092] As an optional implementation, the electronic device can determine the type of common knowledge points corresponding to the current time point according to the current time point, and determine multiple first knowledge points from multiple knowledge points according to the type of common knowledge points. Among them, the electronic device can use a fixed time period as a review cycle, such as one week as a cycle, one month as a cycle, and the electronic device can determine the time sequence number of the current time point in the current review cycle, and the time sequence number is used to characterize the number of days of the current time point in the current review cycle. For example, if one week is a cycle and the current time point is Wednesday, the time sequence number is 3, because Wednesday is the third day of the week. The electronic device can determine the historical review time points corresponding to the current time point in multiple historical review cycles according to the time sequence number, and the electronic device obtains the knowledge points reviewed by the target user at multiple historical review time points, and determines the knowledge point type with the highest review frequency according to the reviewed knowledge points as the common knowledge point type corresponding to the current time point. By implementing this embodiment, the first knowledge point can be determined according to the user's review habits, thereby improving the user's review interest and the user's review efficiency.

[0093] As an optional implementation, the electronic device can also analyze the types of important knowledge points that need to be reviewed based on the user itinerary of the target user, and determine multiple first knowledge points from multiple knowledge points based on the types of important knowledge points. Among them, the electronic device can obtain the user itinerary of the target user in the future time period, for example, the user itinerary in the next 10 days, and analyze the target itinerary of the target user's exam itinerary, interview itinerary, work itinerary, etc. that require knowledge points from the user itinerary, and the electronic device then determines the type of knowledge point corresponding to the target itinerary as the important knowledge point type. For example, the electronic device can determine from the user itinerary that the target user has a math test in a week, and the electronic device can determine that the type of important knowledge point is math chapter knowledge. Implementing this implementation can improve the intelligence of the electronic device and accurately push the knowledge points needed by the target user.

[0094] Step 220, determining the memory retention value corresponding to each first knowledge point according to the last review time point and the review interval corresponding to each first knowledge point.

[0095] The electronic device can use specific algorithms and models to calculate the memory retention value corresponding to each first knowledge point according to the last review time point and review interval corresponding to each first knowledge point. Among them, the specific algorithms and models may include a time memory algorithm and a forgetting curve model. The time memory algorithm refers to a machine learning algorithm for processing sequence data. The time memory algorithm can be used for time series prediction. The memory algorithm may include but is not limited to recurrent neural networks, long short-term memory networks, and transformer models. The forgetting curve model can be used to forget old information when processing new time series data, thereby simulating the process of user memory forgetting, and can also further accurately simulate the process of user memory forgetting according to the rule of first fast and then slow.

[0096] The memory retention value is used to characterize the target user's memory retention of the first knowledge point, wherein the higher the memory retention value, the stronger the target user's memory of the first knowledge point, and the lower the probability of the target user forgetting the first knowledge point. It should be understood that there is a corresponding relationship between the memory retention value and time. The longer the current time point is from the last review time point, the lower the memory retention value, which is consistent with the characteristic that the target user will gradually forget the knowledge point if he does not review it for a long time. Optionally, the electronic device can determine the memory retention value corresponding to each first knowledge point based on the current time point, the last review time point corresponding to each first knowledge point, and the review interval.

[0097] like Figure 2-B As shown, Figure 2-BIt is a schematic diagram of a forgetting curve disclosed in an embodiment of the present application, wherein point A can represent the target user's first learning of a knowledge point, and the memory retention value corresponding to point A is the highest value. The AB curve can represent the process in which the memory retention value begins to decrease after the target user learns the knowledge point for the first time. There is a sudden change in the memory retention value from point B to point C, which can represent the target user's first review of the knowledge point, so the memory retention value returns to the highest value, that is, the memory retention value corresponding to point C is equal to the memory retention value corresponding to point A. If the target user does not review the knowledge point for the first time at point B, the memory retention value will continue to decrease according to the dotted line corresponding to the AB curve, and it can be seen from the AB curve and the dotted line that the speed of decrease of the memory retention value is first fast and then slow. Next, the CD curve can represent the process in which the memory retention value begins to decrease after the target user reviews the knowledge point for the first time. It should be noted that as the number of reviews increases, the speed at which the target user forgets the knowledge point will slow down, that is, the speed of decrease of the memory retention value corresponding to the CD curve is less than the speed of decrease of the memory retention value corresponding to the AB curve. Therefore, as the number of reviews increases, the review interval can also be increased while ensuring that the memory retention value is greater than the preset threshold. There is a sudden change in the memory retention value from point D to point E, which can represent the target user's second review of the knowledge point. The EF curve can represent the process in which the memory retention value begins to decrease after the target user reviews the knowledge point for the second time. There is a sudden change in the memory retention value from point F to point G, which can represent the target user's third review of the knowledge point. The subsequent process will not be elaborated on.

[0098] Step 230, determining the review value corresponding to each first knowledge point according to the last review duration corresponding to each first knowledge point.

[0099] The electronic device can also determine the review value of each knowledge point based on the last review time corresponding to each first knowledge point using a preset calculation method, wherein the review value can be proportional to the last review time, that is, a longer last review time can correspond to a higher review value, because the target user may need or be willing to spend more time to review and master the corresponding first knowledge point.

[0100] Step 240 , determining a review recommendation score corresponding to each first knowledge point according to the memory retention value and review value corresponding to each first knowledge point.

[0101] In order to determine a first knowledge point that is more worthy of review among multiple first knowledge points, optionally, the electronic device can perform weighted calculation on the memory retention value and review value corresponding to the target first knowledge point to determine a review recommendation score corresponding to the target first knowledge point.

[0102] Step 250 , sorting the plurality of first knowledge points according to the review recommendation scores corresponding to the respective first knowledge points to obtain a knowledge point recommendation sequence.

[0103] The electronic device can sort multiple first knowledge points according to the review recommendation scores corresponding to each first knowledge point to obtain a knowledge point recommendation sequence. It can be understood that since the target user has limited review time every day and the number of knowledge points that the target user reviews each time cannot be too many, the knowledge point recommendation sequence may include some or all of the multiple first knowledge points, thereby providing the first knowledge points with higher review recommendation scores to the target user for review.

[0104] Optionally, after obtaining the knowledge point recommendation sequence, the electronic device can output the knowledge point content and knowledge point title corresponding to each first knowledge point in the knowledge point recommendation sequence in sequence according to the knowledge point recommendation sequence. Among them, for any first knowledge point in the knowledge point recommendation sequence, the electronic device can obtain the knowledge point identifier corresponding to the first knowledge point, and obtain the knowledge point content corresponding to the first knowledge point from the first database according to the knowledge point identifier corresponding to the first knowledge point, and obtain the knowledge point title corresponding to the first knowledge point from the second database, the first database including knowledge point content corresponding to multiple knowledge points, and the second database including knowledge point titles corresponding to multiple knowledge points. When it is detected that the target user has completed the knowledge point title corresponding to the first knowledge point, the electronic device can score the completed knowledge point title to obtain the review score corresponding to the first knowledge point.

[0105] In order to more clearly describe the process of storing, recommending and acquiring knowledge points by an electronic device, as an example, the electronic device may include a data processing module, a data storage module and a review recommendation module, so as to implement the method of the above embodiment. Figure 3 As shown, Figure 3 It is an interactive schematic diagram of multiple modules included in an electronic device disclosed in an embodiment of the present application. The data processing module can obtain the scanned content of the scanning device of the electronic device, and extract multiple knowledge points from the scanned content, and then save the multiple knowledge points to the knowledge point information library included in the data storage module. The review recommendation module can execute steps 210 to 250 in the above embodiment. In step 210, the review recommendation module can obtain knowledge point information from the knowledge point information library included in the data storage module. After step 250, when the electronic device outputs knowledge point content and knowledge point titles, it can obtain knowledge point content from the first database included in the data storage module, and obtain knowledge point titles from the second database included in the data storage module. It should be understood that Figure 3 This is only an example and does not mean that the electronic device must include these modules.

[0106] In an embodiment of the present application, the electronic device can determine the memory retention value and the corresponding review value corresponding to each first knowledge point based on the knowledge point information corresponding to each of the multiple first knowledge points, so as to determine the review recommendation score corresponding to each first knowledge point. The review recommendation score comprehensively considers the target user's memory retention of the first knowledge point and the value of the target user's review of the first knowledge point. By sorting the knowledge point recommendation sequence according to the review recommendation score, the electronic device can provide targeted learning suggestions to the target user, that is, it can provide the target user with personalized review strategies to help the target user review and master the knowledge points more efficiently, thereby improving the target user's learning efficiency.

[0107] like Figure 4 As shown, Figure 4 : is a flow chart of a knowledge point review method disclosed in an embodiment of the present application. The knowledge point review method can be applied to the electronic device in the above embodiment. The knowledge point review method may include the following steps:

[0108] Step 410, obtaining knowledge point information corresponding to the plurality of first knowledge points respectively.

[0109] Optionally, the knowledge point information may also include a collection status value. When the electronic device detects a collection operation on the first knowledge point, the collection status value corresponding to the first knowledge point may be determined as a collected value, such as "1". If the electronic device does not detect a collection operation on the first knowledge point, the electronic device may set the collection status value corresponding to the first knowledge point to a non-collected value, such as "0" by default.

[0110] Step 420, determining the time interval according to the current time point and the last review time point corresponding to the target first knowledge point, where the target first knowledge point is any first knowledge point.

[0111] Step 430, determining a memory retention value corresponding to the target first knowledge point according to the time interval and the review interval corresponding to the target first knowledge point.

[0112] The electronic device can calculate the time interval between the current time point and the last review time point corresponding to the target first knowledge point, and calculate the interval ratio between the time interval and the review interval corresponding to the target first knowledge point, and the electronic device can determine the memory retention value corresponding to the target first knowledge point based on the interval ratio. It can be understood that since the current time point and the last review time point are both time points at which the target user actually reviews, the time interval is obtained by the current time point and the last review time point corresponding to the target first knowledge point, that is, the time interval can be considered to be a real interval between two reviews, and the review interval is the interval between the last review time point and the next review time point predicted by the electronic device, that is, the review interval can be considered to be a predicted interval between two reviews, and the two are different.

[0113] Optionally, the electronic device may multiply the interval ratio by a preset coefficient to obtain a product, and then use the product as an exponent with the natural constant e as the base to calculate the memory retention value corresponding to the target first knowledge point. Specifically, the calculation formula of the memory retention value MR is shown in formula (1):

[0114]

[0115] Among them, MR is the memory retention value corresponding to the first target knowledge point, T Diff is the time interval between the current time point and the last review time point corresponding to the first target knowledge point, RI is the review interval corresponding to the first target knowledge point, and exp{·} is an exponential function with the natural constant e as the base. The time interval T Diff The formula can be shown as formula (2),

[0116] T Diff =now date -last_review data Formula (2);

[0117] Among them, now data is the current time point, last_review date The time point of the last review.

[0118] Step 440, obtaining the average review duration corresponding to the knowledge category to which the target first knowledge point belongs.

[0119] The electronic device can obtain the knowledge category to which the target first knowledge point belongs from the knowledge point record table, and determine the average review time corresponding to the knowledge category to which the target first knowledge point belongs, and the average review time is used to characterize the average time consumed in reviewing the knowledge points included in the knowledge category. The average review time can be determined based on multiple historical review times corresponding to the knowledge points included in the knowledge category, or it can be determined based on network big data, and there is no limitation on this.

[0120] Step 450, calculate the duration ratio between the last review duration corresponding to the target first knowledge point and the average review duration, and determine the review value corresponding to the target first knowledge point according to the duration ratio.

[0121] Optionally, the electronic device can use the duration ratio as the review value corresponding to the target first knowledge point. Optionally, the electronic device can also multiply the duration ratio by a preset value coefficient to obtain the review value corresponding to the target first knowledge point. The embodiment of the present application does not limit the method for determining the review value.

[0122] Step 460 , determining the review recommendation score corresponding to each first knowledge point according to the memory retention value, the review value and the collection status value corresponding to each first knowledge point.

[0123] In one embodiment, the electronic device may perform weighted calculation on the memory retention value, review value, and collection status value corresponding to each first knowledge point to obtain a review recommendation score corresponding to each first knowledge point. The memory retention value may correspond to a first weight, the review value may correspond to a second weight, and the collection status value may correspond to a third weight. The first weight, the second weight, and the third weight may all be preset weights. Specifically, the formula for calculating the review recommendation score by the electronic device is shown in formula (3):

[0124] rcy_score=w r *(MR)+w dr *(duration radio )+w c *(is_collect) formula (3);

[0126] Among them, rcy_score is the review recommendation score, w r is the first weight corresponding to the memory retention value, w dr is the second weight corresponding to the review value, w c is the third weight corresponding to the collection status value, duration radio is the review value, is_collect is the collection status value.

[0127] Step 470 , sorting the multiple first knowledge points in descending order of review recommendation scores, and selecting the first N first knowledge points after sorting to generate a knowledge point recommendation sequence.

[0128] Among them, compared with other first knowledge points, the first N first knowledge points are N first knowledge points with higher review recommendation scores, and the electronic device generates a knowledge point recommendation sequence based on the N first knowledge points, and the knowledge point recommendation sequence can also sort the N first knowledge points in descending order of review recommendation scores. Among them, N is an integer not less than 1, and N can be pre-set by the target user or determined by the electronic device according to the review history of the target user, and there is no limitation on this.

[0129] In an embodiment of the present application, the electronic device can also determine the time interval based on the current time point and the last review time point corresponding to the target first knowledge point, and determine the memory retention value corresponding to the target first knowledge point based on the time interval and the review interval corresponding to the target first knowledge point. The electronic device can also obtain the average review time corresponding to the knowledge category to which the target first knowledge point belongs, calculate the time ratio between the last review time corresponding to the target first knowledge point and the average review time, and determine the review value corresponding to the target first knowledge point based on the time ratio. Through scientific calculation methods, the accuracy of memory retention value and review value can be improved, and the electronic device then obtains each knowledge point. The collection status value corresponding to a knowledge point determines the review recommendation score corresponding to each first knowledge point according to the memory retention value, the corresponding review value and the corresponding collection status value corresponding to each first knowledge point. This not only improves the accuracy of the review recommendation score, but also takes into account the user's wishes, thereby recommending the knowledge points needed by the user and improving the user's learning efficiency. The electronic device also sorts multiple first knowledge points in order from high to low according to the review recommendation score, and selects the top N first knowledge points after sorting to generate a knowledge point recommendation sequence, which fully controls the quantity and quality of the knowledge points that the user needs to review, and further improves the user's learning efficiency.

[0130] like Figure 5 As shown, Figure 5 : is a flowchart of a method for updating data after completing a review of a second knowledge point disclosed in an embodiment of the present application. The method for updating data can be applied to the electronic device in the above embodiment. The method for updating data can include the following steps:

[0131] Step 510, when it is detected that a review of the second knowledge point is completed, the latest review data corresponding to the second knowledge point is obtained.

[0132] Among them, the latest review data includes a review score corresponding to the completed review of the second knowledge point, and the review score is the score corresponding to the knowledge point title corresponding to the second knowledge point completed by the target user. Among them, the electronic device can determine that a review of the second knowledge point has been completed when it detects that the knowledge point title corresponding to the second knowledge point has been completed. Optionally, the latest review data corresponding to the second knowledge point may also include a review time point, and the review time point may be the time point when the electronic device detects that a review of the second knowledge point has been completed. It should be understood that the current time point in the above content refers to the time point when the electronic device determines the knowledge point recommendation sequence, and the review time point refers to the time point when the electronic device determines that the knowledge point review is completed. The two may be the same or different, and the embodiments of the present application do not limit this.

[0133] Step 520, updating the difficulty coefficient of the second knowledge point according to the review score and the corresponding memory retention value corresponding to the second knowledge point.

[0134] Among them, since there is a relationship between the memory retention value and time, when the review time point and the current time point are not the same time point, the memory retention value corresponding to the second knowledge point may refer to the memory retention value corresponding to the second knowledge point at the review time point, and the memory retention value corresponding to the first knowledge point in the above step 210 may refer to the memory retention value corresponding to the first knowledge point at the current time point.

[0135] The review score corresponding to the second knowledge point can represent the target user's mastery of the second knowledge point, and the memory retention value corresponding to the second knowledge point can represent the target user's memory retention of the second knowledge point at the review time point. Generally speaking, the higher the memory retention value, the higher the review score. Optionally, the electronic device can determine whether the review score corresponding to the second knowledge point meets the score requirement. If it does, the difficulty coefficient of the second knowledge point can be reduced. The memory retention amount can be used to determine the degree of reduction in the difficulty coefficient. The memory retention amount is inversely proportional to the degree of reduction. The higher the memory retention amount, the lower the degree of reduction. If it does not meet the requirement, the difficulty coefficient of the second knowledge point can be increased. The memory retention amount can be used to determine the degree of improvement in the difficulty coefficient. The memory retention amount is proportional to the degree of improvement. The higher the memory retention amount, the higher the degree of improvement.

[0136] In one embodiment, the electronic device can determine the target adjustment amount of the difficulty coefficient of the second knowledge point according to the review score and the corresponding memory retention value corresponding to the second knowledge point, and update the difficulty coefficient of the second knowledge point based on the target adjustment amount. Optionally, the electronic device can subtract the memory retention value corresponding to the second knowledge point from the initial adjustment amount corresponding to the review score to obtain the target adjustment amount. The target adjustment amount can be a positive value or a negative value. If the target adjustment amount is a positive value, the difficulty coefficient of the second knowledge point can be reduced. If the target adjustment amount is a negative value, the difficulty coefficient of the second knowledge point can be increased. Implementation of this embodiment improves the accuracy of updating the difficulty coefficient of the second knowledge point.

[0137] As an optional implementation, if the review score corresponding to the second knowledge point is greater than a preset score threshold, the electronic device can determine that the review score corresponding to the second knowledge point meets the score requirement, and determine the initial adjustment amount of the difficulty coefficient of the second knowledge point to be the first adjustment amount, which may be a preset adjustment amount. If the review score corresponding to the second knowledge point is not greater than the preset score threshold, the electronic device can determine that the review score corresponding to the second knowledge point does not meet the score requirement, and determine the initial adjustment amount of the difficulty coefficient of the second knowledge point to be the second adjustment amount, which may also be a preset adjustment amount. The electronic device may subtract the memory retention value corresponding to the second knowledge point from the initial adjustment amount to obtain the target adjustment amount of the difficulty coefficient of the second knowledge point, and subtract the target adjustment amount from the difficulty coefficient of the second knowledge point to obtain an updated difficulty coefficient.

[0138] Optionally, after obtaining the updated difficulty coefficient, the electronic device can determine whether the updated difficulty coefficient is within a preset difficulty coefficient range, and the difficulty coefficient range refers to the range between the minimum difficulty coefficient and the maximum difficulty coefficient. If the updated difficulty coefficient is within the preset difficulty coefficient range, it can be determined that the updated difficulty coefficient is normal. If the updated difficulty coefficient is not within the preset difficulty coefficient range, it can be determined that the updated difficulty coefficient is less than the minimum difficulty coefficient, or the updated difficulty coefficient is greater than the maximum difficulty coefficient. If the updated difficulty coefficient is less than the minimum difficulty coefficient, the minimum difficulty coefficient is used as the updated difficulty coefficient. If the updated difficulty coefficient is greater than the maximum difficulty coefficient, the maximum difficulty coefficient is used as the updated difficulty coefficient. Specifically, the updating method of the difficulty coefficient is as shown in formula (4),

[0139] D next =min{D max ,max{D min ,D last -score end +MR}} Formula (4);

[0141] Among them, Dnext is the updated difficulty coefficient, D max is the maximum difficulty coefficient, D min is the minimum value of the difficulty coefficient, D last is the current difficulty coefficient of the second knowledge point, score end can be the initial adjustment amount corresponding to the review score, where score end The determination method of can be shown as formula (5),

[0142]

[0143] Among them, score is the last review score, and the full score is 100.

[0144] Step 530: Update the review interval corresponding to the second knowledge point according to the updated difficulty coefficient.

[0145] Generally speaking, the difficulty coefficient after updating is proportional to the review interval after updating. The more the review interval corresponding to the second knowledge point increases, the larger the review interval after updating is.

[0146] In one embodiment, the electronic device may adjust the review interval corresponding to the second knowledge point according to the updated difficulty coefficient and the preset first adjustment coefficient to obtain the first review interval, adjust the review interval corresponding to the second knowledge point according to the preset second adjustment coefficient to obtain the second review interval, and update the review interval corresponding to the second knowledge point according to the first review interval and the second review interval. The electronic device may compare the first review interval with the second review interval, and if the first review interval is greater than the second review interval, use the first review interval as the updated review interval corresponding to the second knowledge point, and if the first review interval is not greater than the second review interval, use the second review interval as the updated review interval corresponding to the second knowledge point.

[0147] Among them, the first adjustment coefficient may include an interval growth coefficient and a difficulty attenuation coefficient. The interval growth coefficient is used to increase the review interval, and the difficulty attenuation coefficient is used to reduce the updated difficulty coefficient. The updated difficulty coefficient is proportional to the updated review interval, that is, the difficulty attenuation coefficient can be used to reduce the review interval. The interval growth coefficient and the difficulty attenuation coefficient respectively control the growth and attenuation of the review interval, thereby improving the accuracy of the review interval update.

[0148] Optionally, after updating the review interval corresponding to the second knowledge point, the electronic device may also compare the updated review interval with a preset minimum review interval value. If the updated review interval is greater than the preset minimum review interval value, it is determined that the updated review interval is normal. If the updated review interval is not greater than the preset minimum review interval value, the preset minimum review interval value is used as the updated review interval, thereby avoiding the review interval being too small, causing the user to frequently review the same knowledge point, thereby improving the user's learning efficiency.

[0149] Specifically, the update method of the review interval can be shown as formula (6):

[0150] RI next =max{1,max{0.5*RI last ,a*D - b*RI las}} Formula (6);

[0151] Among them, RI next is the updated review interval, a is the interval growth coefficient in the first adjustment coefficient, b is the difficulty attenuation coefficient in the first adjustment coefficient, 0.5 is the second adjustment coefficient, RI last is the current review interval, and 1 is the minimum review interval.

[0152] Step 540, determining the predicted review time point corresponding to the second knowledge point according to the updated review interval and review time point.

[0153] The electronic device may add the updated review interval to the review time point to obtain a predicted review time point corresponding to the second knowledge point.

[0154] In an embodiment of the present application, the electronic device can obtain the latest review data corresponding to the second knowledge point when detecting that a review of the second knowledge point has been completed, and update the difficulty coefficient of the second knowledge point according to the review score corresponding to the second knowledge point and the corresponding memory retention value, and then update the review interval corresponding to the second knowledge point according to the updated difficulty coefficient, and finally determine the predicted review time point corresponding to the second knowledge point according to the updated review interval and review time point. The electronic device updates the review interval based on the data of this review, thereby determining the time point of the user's next review, which can improve the accuracy of the review interval and the predicted review time point, so that the user can review the corresponding knowledge point at the appropriate time point, thereby improving the user's learning efficiency.

[0155] like Figure 6 As shown, Figure 66 is a modular schematic diagram of a knowledge point review device disclosed in an embodiment of the present application. The knowledge point review device 600 may include an information acquisition module 610, a memory determination module 620, a value determination module 630, a score determination module 640, and a sequence determination module 650, wherein:

[0156] The information acquisition module 610 is used to acquire knowledge point information corresponding to the plurality of first knowledge points, wherein the knowledge point information includes the last review time point, the review interval, and the last review duration;

[0157] A memory determination module 620, for determining a memory retention value corresponding to each first knowledge point according to the last review time point and the review interval corresponding to each first knowledge point;

[0158] A value determination module 630, for determining the review value corresponding to each first knowledge point according to the last review duration corresponding to each first knowledge point;

[0159] A score determination module 640, for determining a review recommendation score corresponding to each first knowledge point according to the memory retention value and review value corresponding to each first knowledge point;

[0160] The sequence determination module 650 is used to sort the multiple first knowledge points according to the review recommendation scores corresponding to the respective first knowledge points to obtain a knowledge point recommendation sequence.

[0161] In one embodiment, the memory determination module 620 is also used to determine the time interval based on the current time point and the last review time point corresponding to the target first knowledge point, where the target first knowledge point is any first knowledge point; and determine the memory retention value corresponding to the target first knowledge point based on the time interval and the review interval corresponding to the target first knowledge point.

[0162] In one embodiment, the value determination module 630 is also used to obtain the average review time corresponding to the knowledge category to which the target first knowledge point belongs, where the target first knowledge point is any first knowledge point; calculate the time ratio between the last review time corresponding to the target first knowledge point and the average review time, and determine the review value corresponding to the target first knowledge point based on the time ratio.

[0163] In one embodiment, the knowledge point information also includes a collection status value, and the score determination module 640 is further used to determine the review recommendation score corresponding to each first knowledge point based on the memory retention value, the corresponding review value and the corresponding collection status value corresponding to each first knowledge point.

[0164] In one embodiment, the score determination module 640 is further used to perform weighted calculation on the memory retention value, review value and collection status value corresponding to each first knowledge point to obtain a review recommendation score corresponding to each first knowledge point.

[0165] In one embodiment, the sequence determination module 650 is used to sort the multiple first knowledge points in descending order of review recommendation scores, and select the first N first knowledge points after sorting to generate a knowledge point recommendation sequence.

[0166] In one embodiment, the knowledge point review device 600 also includes a data update module, which is used to obtain the latest review data corresponding to the second knowledge point when it is detected that a review of the second knowledge point has been completed; the latest review data includes a review score corresponding to the completed review of the second knowledge point; the difficulty coefficient of the second knowledge point is updated according to the review score corresponding to the second knowledge point and the corresponding memory retention value; the difficulty coefficient is used to characterize the difficulty of the target user to remember the second knowledge point; according to the updated difficulty coefficient, the review interval corresponding to the second knowledge point is updated.

[0167] In one embodiment, the data update module is also used to determine the target adjustment amount of the difficulty coefficient of the second knowledge point according to the review score and the corresponding memory retention value corresponding to the second knowledge point; and update the difficulty coefficient of the second knowledge point based on the target adjustment amount.

[0168] In one embodiment, the data update module is also used to determine the initial adjustment amount of the difficulty coefficient of the second knowledge point as the first adjustment amount if the review score corresponding to the second knowledge point is greater than a preset score threshold; if the review score corresponding to the second knowledge point is not greater than the preset score threshold, determine the initial adjustment amount of the difficulty coefficient of the second knowledge point as the second adjustment amount; subtract the memory retention value corresponding to the second knowledge point from the initial adjustment amount to obtain the target adjustment amount of the difficulty coefficient of the second knowledge point; subtract the target adjustment amount from the difficulty coefficient of the second knowledge point to obtain the updated difficulty coefficient.

[0169] In one embodiment, the data update module is also used to adjust the review interval corresponding to the second knowledge point according to the updated difficulty coefficient and the preset first adjustment coefficient to obtain the first review interval; adjust the review interval corresponding to the second knowledge point according to the preset second adjustment coefficient to obtain the second review interval; and update the review interval corresponding to the second knowledge point according to the first review interval and the second review interval.

[0170] In one embodiment, the latest review data also includes a review time point, which is the time point when a review of the second knowledge point is detected to be completed; the data update module is also used to determine the predicted review time point corresponding to the second knowledge point based on the updated review interval and review time point, and the predicted review time point is the predicted time point when the second knowledge point needs to be reviewed next time.

[0171] In one embodiment, the information acquisition module 610 is also used to obtain the predicted review time points corresponding to the multiple knowledge points included in the knowledge point record table; the predicted review time point is the predicted time point when review is required next time; based on the current time point and the predicted review time point corresponding to each knowledge point, multiple first knowledge points that need to be reviewed at the current time point are determined among the multiple knowledge points.

[0172] In one embodiment, a knowledge point review device 600 is applied to an electronic device, which includes a scanning device. The knowledge point review device 600 also includes a knowledge scanning module, which is used to obtain the scanning content of the scanning device and extract multiple knowledge points from the scanning content; determine the knowledge category corresponding to each knowledge point; construct a knowledge point record table based on the scanning information corresponding to each knowledge point and the corresponding knowledge category, and the knowledge point record table includes the knowledge point identification, knowledge category, last review time point, review interval, last review duration, difficulty coefficient, collection status value, and predicted review time point corresponding to each knowledge point; the information acquisition module 610 is also used to obtain knowledge point information corresponding to multiple first knowledge points from the knowledge point record table.

[0173] In an embodiment of the present application, the electronic device can determine the memory retention value and the corresponding review value corresponding to each first knowledge point based on the knowledge point information corresponding to each of the multiple first knowledge points, so as to determine the review recommendation score corresponding to each first knowledge point. The review recommendation score comprehensively considers the target user's memory retention of the first knowledge point and the value of the target user's review of the first knowledge point. By sorting the knowledge point recommendation sequence according to the review recommendation score, the electronic device can provide targeted learning suggestions to the target user, that is, it can provide the target user with personalized review strategies to help the target user review and master the knowledge points more efficiently, thereby improving the target user's learning efficiency.

[0174] like Figure 7 As shown, in one embodiment, an electronic device is provided, which may include:

[0175] A memory 710 storing executable program codes;

[0176] a processor 720 coupled to the memory 710;

[0177] The processor 720 calls the executable program code stored in the memory 710 to implement the knowledge point review method provided in the above embodiments.

[0178] The memory 710 may include a random access memory (RAM) or a read-only memory (ROM). The memory 710 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 710 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area may also store data created by the electronic device during use, etc.

[0179] The processor 720 may include one or more processing cores. The processor 720 uses various interfaces and lines to connect various parts of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 710, and calling data stored in the memory 710. Optionally, the processor 720 can be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 720 can integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 720, but may be implemented separately through a communication chip.

[0180] It is understandable that the electronic device may include more or fewer structural elements than those in the above structural block diagram, for example, a power module, physical buttons, a WiFi (Wireless Fidelity) module, a speaker, a Bluetooth module, a sensor, etc., and no limitation is made here.

[0181] An embodiment of the present application discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the methods described in the above embodiments.

[0182] In addition, an embodiment of the present application further discloses a computer program product. When the computer program product is run on a computer, the computer can execute all or part of the steps in any one of the knowledge point review methods described in the above embodiments.

[0183] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0184] The above is a detailed introduction to a knowledge point review method, device, electronic device and storage medium disclosed in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A knowledge point review method, characterized in that: The method comprises: Acquire knowledge point information corresponding to the plurality of first knowledge points respectively, wherein the knowledge point information includes the last review time point, the review interval and the last review duration; Determine the memory retention value corresponding to each of the first knowledge points according to the last review time point and the review interval corresponding to each of the first knowledge points; Determine the review value corresponding to each of the first knowledge points according to the last review duration corresponding to each of the first knowledge points; Determine a review recommendation score corresponding to each of the first knowledge points according to the memory retention value and review value corresponding to each of the first knowledge points; The plurality of first knowledge points are sorted according to the review recommendation scores corresponding to the respective first knowledge points to obtain a knowledge point recommendation sequence.

2. The method according to claim 1, characterized in that The determining the memory retention value corresponding to each of the first knowledge points according to the last review time point and the review interval corresponding to each of the first knowledge points includes: Determine the time interval according to the current time point and the last review time point corresponding to the target first knowledge point, the target first knowledge point being any of the first knowledge points; The memory retention value corresponding to the target first knowledge point is determined according to the time interval and the review interval corresponding to the target first knowledge point.

3. The method according to claim 1, characterized in that The determining the review value corresponding to each of the first knowledge points according to the last review duration corresponding to each of the first knowledge points includes: Obtaining an average review duration corresponding to a knowledge category to which a target first knowledge point belongs, wherein the target first knowledge point is any of the first knowledge points; The duration ratio between the last review duration corresponding to the target first knowledge point and the average review duration is calculated, and the review value corresponding to the target first knowledge point is determined according to the duration ratio.

4. The method according to any one of claims 1 to 3, characterized in that: The knowledge point information also includes a collection status value; and determining the review recommendation score corresponding to each of the first knowledge points according to the memory retention value and review value corresponding to each of the first knowledge points includes: According to the memory retention value, the review value and the collection status value corresponding to each of the first knowledge points, the review recommendation score corresponding to each of the first knowledge points is determined.

5. The method according to claim 4, characterized in that Determining the review recommendation score corresponding to each of the first knowledge points according to the memory retention value, the review value and the collection status value corresponding to each of the first knowledge points includes: The memory retention value, review value and collection status value corresponding to each of the first knowledge points are weightedly calculated to obtain a review recommendation score corresponding to each of the first knowledge points.

6. The method according to claim 1, characterized in that The step of sorting the plurality of first knowledge points according to the review recommendation scores corresponding to the respective first knowledge points to obtain a knowledge point recommendation sequence includes: The multiple first knowledge points are sorted in descending order of review recommendation scores, and the first N first knowledge points after sorting are selected to generate a knowledge point recommendation sequence.

7. The method according to claim 1, characterized in that The method further comprises: When it is detected that a review of the second knowledge point is completed, the latest review data corresponding to the second knowledge point is obtained; the latest review data includes a review score corresponding to the completion of the review of the second knowledge point; updating the difficulty coefficient of the second knowledge point according to the review score and the corresponding memory retention value corresponding to the second knowledge point; the difficulty coefficient is used to represent the difficulty of the target user to remember the second knowledge point; According to the updated difficulty coefficient, the review interval corresponding to the second knowledge point is updated.

8. The method according to claim 7, characterized in that The updating of the difficulty coefficient of the second knowledge point according to the review score and the memory retention value corresponding to the second knowledge point includes: Determining a target adjustment amount of a difficulty coefficient of the second knowledge point according to a review score and a corresponding memory retention value corresponding to the second knowledge point; Based on the target adjustment amount, the difficulty coefficient of the second knowledge point is updated.

9. The method according to claim 8, characterized in that The step of determining a target adjustment amount of the difficulty coefficient of the second knowledge point according to the review score and the memory retention value corresponding to the second knowledge point includes: If the review score corresponding to the second knowledge point is greater than a preset score threshold, determining the initial adjustment amount of the difficulty coefficient of the second knowledge point to be the first adjustment amount; If the review score corresponding to the second knowledge point is not greater than the preset score threshold, determining the initial adjustment amount of the difficulty coefficient of the second knowledge point as the second adjustment amount; Subtract the memory retention value corresponding to the second knowledge point from the initial adjustment amount to obtain a target adjustment amount of the difficulty coefficient of the second knowledge point; The updating of the difficulty coefficient of the second knowledge point based on the target adjustment amount includes: The target adjustment amount is subtracted from the difficulty coefficient of the second knowledge point to obtain an updated difficulty coefficient.

10. The method according to claim 7, characterized in that The updating of the review interval corresponding to the second knowledge point according to the updated difficulty coefficient includes: According to the updated difficulty coefficient and the preset first adjustment coefficient, the current review interval corresponding to the second knowledge point is adjusted to obtain a first review interval; According to a preset second adjustment coefficient, the review interval currently corresponding to the second knowledge point is adjusted to obtain a second review interval; According to the first review interval and the second review interval, the review interval corresponding to the second knowledge point is updated.

11. The method according to any one of claims 7 to 10, characterized in that: The latest review data also includes a review time point, and the review time point is a time point when a review of the second knowledge point is detected to be completed; After updating the review interval corresponding to the second knowledge point according to the updated difficulty coefficient, the method further includes: According to the updated review interval and the review time point, the predicted review time point corresponding to the second knowledge point is determined, and the predicted review time point is the predicted time point when the second knowledge point needs to be reviewed next time.

12. The method according to claim 1, characterized in that Before acquiring the knowledge point information respectively corresponding to the plurality of first knowledge points, the method further includes: Obtaining predicted review time points corresponding to a plurality of knowledge points included in the knowledge point record table; According to the current time point and the predicted review time points corresponding to the respective knowledge points, a plurality of first knowledge points that need to be reviewed at the current time point are determined from the plurality of knowledge points.

13. The method according to claim 1, characterized in that The method is applied to an electronic device, the electronic device comprising a scanning device, and before obtaining the review time points respectively corresponding to the plurality of first knowledge points, the method further comprises: Acquire scanned content of the scanning device, and extract multiple knowledge points from the scanned content; Determine the knowledge category corresponding to each of the knowledge points; According to the scanning information corresponding to each of the knowledge points and the corresponding knowledge category, a knowledge point record table is constructed, wherein the knowledge point record table includes one or more of the knowledge point identifier, knowledge category, last review time point, review interval, last review duration, difficulty coefficient, collection status value, and predicted review time point corresponding to each of the knowledge points; The obtaining of knowledge point information respectively corresponding to the plurality of first knowledge points includes: From the knowledge point record table, knowledge point information corresponding to the plurality of first knowledge points is obtained.

14. A knowledge point review device, characterized in that: The device comprises: An information acquisition module, used to acquire knowledge point information corresponding to the plurality of first knowledge points, wherein the knowledge point information includes the last review time point, the review interval, and the last review duration; A memory determination module, used to determine a memory retention value corresponding to each of the first knowledge points according to the last review time point and the review interval corresponding to each of the first knowledge points; A value determination module, used to determine the review value corresponding to each of the first knowledge points according to the last review duration corresponding to each of the first knowledge points; A score determination module, used to determine a review recommendation score corresponding to each of the first knowledge points according to the memory retention value and review value corresponding to each of the first knowledge points; The sequence determination module is used to sort the multiple first knowledge points according to the review recommendation scores corresponding to the respective first knowledge points to obtain a knowledge point recommendation sequence.

15. An electronic device, characterized in that: include: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the method according to any one of claims 1 to 13.

16. A computer storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 13.

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