CN113722506A - Intelligent knowledge point identification method, system, intelligent equipment and storage medium - Google Patents
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Abstract
The invention discloses a method, a system, intelligent equipment and a storage medium for intelligently identifying knowledge points, wherein the method comprises the following steps: collecting working data and weak knowledge point sets of each local end, wherein the working data comprises question making records; respectively acquiring a knowledge point set corresponding to each piece of working data, wherein the knowledge point set consists of a plurality of knowledge points; comparing each knowledge point set with the rest knowledge point sets to obtain a plurality of similar knowledge point sets corresponding to each knowledge point set respectively; obtaining a plurality of similar local terminals corresponding to each local terminal according to a plurality of similar knowledge point sets corresponding to each knowledge point set; and identifying the weak knowledge point set corresponding to each local end as the weak knowledge point sets of a plurality of similar local ends corresponding to the local end. The method and the device can identify weak knowledge points of each user, and improve the learning efficiency of the user.
Description
Technical Field
The invention relates to the technical field of intelligent equipment, in particular to a method and a system for identifying points, intelligent equipment and a storage medium.
Background
With the increase of the per-capita knowledge level of the society, most parents tend to pay more and more attention to the education problems of student users at present, and the intelligent learning machine is used as an important hardware facility in the education industry, can be used for learning daily courses and providing exercises for the users, can also provide exercise correcting and explaining functions for the users at present, and greatly improves the learning efficiency of the users.
When the improvement of user's education level and the deepening of the teaching degree of difficulty, the intelligent learning machine carries out the exercise planning exercise according to daily course, can lead to user's academic burden aggravation because every knowledge point exercise quantity is too big to can waste most of time managers on the exercise to the knowledge point of having become familiar with and mastering, and can have to neglect to the knowledge point of weakness. Although the user spends a lot of time in learning and practicing, since weak knowledge points of each user cannot be identified, learning efficiency of the user is greatly reduced, and user experience is poor.
In order to solve the problem that most of time and energy of users are wasted in practice aiming at familiar knowledge points, an intelligent knowledge point identification method is needed at present, weak knowledge points which may exist in each user can be identified, and the learning efficiency of the users is improved.
Disclosure of Invention
In order to solve the technical problem that most of time managers are wasted on practice aiming at familiar and mastered knowledge points, the invention provides a knowledge point intelligent identification method, a knowledge point intelligent identification system, intelligent equipment and a storage medium, wherein the specific technical scheme is as follows:
the invention provides an intelligent knowledge point identification method, which comprises the following steps:
collecting working data and weak knowledge point sets of each local end, wherein the working data comprises question making records;
respectively acquiring a knowledge point set corresponding to each piece of working data, wherein the knowledge point set consists of a plurality of knowledge points;
comparing each knowledge point set with the rest knowledge point sets to obtain a plurality of similar knowledge point sets corresponding to each knowledge point set respectively;
obtaining a plurality of similar local terminals corresponding to each local terminal according to a plurality of similar knowledge point sets corresponding to each knowledge point set;
and identifying the weak knowledge point set corresponding to each local end as the weak knowledge point sets of a plurality of similar local ends corresponding to the local end.
The intelligent knowledge point identification method provided by the invention identifies a plurality of local ends with similar working data by comparing the working data of each local end, shares weak knowledge points among the similar local ends, avoids the problem that most time managers are wasted on the practice of the familiar knowledge points when a user uses the local ends for learning, and the weak knowledge points are possibly overlooked, can identify the possible weak knowledge points of each user, and improves the learning efficiency of the user.
Further, the present invention also provides an intelligent knowledge point identification method, wherein the comparing of each knowledge point set with the rest knowledge point sets to obtain a plurality of similar knowledge point sets corresponding to each knowledge point set specifically includes:
respectively calculating the similarity between each knowledge point set and the rest knowledge point sets;
and judging a plurality of knowledge point sets with the similarity larger than a preset threshold in the rest knowledge point sets as the similar knowledge point sets.
According to the intelligent knowledge point identification method, the similarity between the knowledge point sets is calculated, the two knowledge point sets with the similarity larger than the preset threshold are judged to be similar knowledge point sets, and the corresponding similar local ends are determined by judging the similarity of the knowledge point sets.
Further, the present invention also provides an intelligent knowledge point identification method, where the calculating of the similarity between each knowledge point set and each of the other knowledge point sets includes:
and respectively calculating the proportion of the category quantity of the same knowledge points in each knowledge point set and the other knowledge point sets in the same knowledge point set to the total category quantity of the knowledge points as the similarity.
The intelligent knowledge point identification method provided by the invention calculates the similarity of the two knowledge point sets by calculating the proportion of the same knowledge points in the two knowledge point sets to the total knowledge points, can accurately identify the similar knowledge point sets, matches with the similar local end and improves the accuracy of identifying the weak knowledge points of each user.
Further, the present invention also provides an intelligent knowledge point identification method, where the method separately calculates similarity between each knowledge point set and each of the other knowledge point sets, and specifically includes:
acquiring preset relation weight among the knowledge points, wherein the relation weight is used for representing the similarity degree among the knowledge points;
and respectively calculating the similarity between each knowledge point set and the rest knowledge point sets according to the relation weight among the knowledge points.
The intelligent knowledge point identification method provided by the invention introduces the relation weight between the knowledge points, calculates the similarity of the two knowledge point sets, avoids the error of similarity identification caused by the membership of the knowledge points in the knowledge point sets, can accurately match with similar local ends, and improves the accuracy of identifying the weak knowledge points of each user.
Further, the present invention also provides an intelligent knowledge point identification method, where after the knowledge point sets corresponding to the respective working data are respectively obtained, before the comparison between each knowledge point set and the rest knowledge point sets, the method further includes:
and eliminating the knowledge point sets of which the number of the knowledge points is less than the preset number.
According to the intelligent knowledge point identification method provided by the invention, the knowledge point sets are screened according to the number of the knowledge points in the knowledge point sets for different knowledge point sets, and the knowledge point sets with the number of the knowledge points larger than the preset number are compared, so that the similarity between the different knowledge point sets has a reference value, and the accuracy of identifying the weak knowledge points of each user is improved.
Further, the present invention also provides an intelligent knowledge point identification method, where after identifying that the weak knowledge point set corresponding to each local end is the weak knowledge point sets of a plurality of similar local ends corresponding to the local end, the method further includes:
and pushing information to the local end according to the weak knowledge point set of each local end.
According to the intelligent knowledge point identification method provided by the invention, after the weak knowledge points of the local end are intelligently identified, corresponding information is pushed to the local end according to the weak knowledge points, and a customized learning plan and learning content are generated according to each local end, so that the learning efficiency of a user can be improved, and the experience of the user is enhanced.
Additionally, the present invention provides a knowledge point intelligent recognition system, comprising:
the collecting module is used for collecting working data and weak knowledge point sets of each local end, wherein the working data comprises question making records;
the first acquisition module is connected with the collection module and is used for respectively acquiring a knowledge point set corresponding to each piece of working data, and the knowledge point set consists of a plurality of knowledge points;
the comparison module is connected with the first acquisition module and used for comparing each knowledge point set with the rest knowledge point sets to obtain a plurality of similar knowledge point sets corresponding to the knowledge point sets respectively;
the second acquisition module is connected with the comparison module and used for acquiring a plurality of similar local terminals corresponding to each local terminal according to a plurality of similar knowledge point sets corresponding to each knowledge point set;
and the identification module is connected with the second acquisition module and the collection module and is used for identifying the weak knowledge point set corresponding to each local end as a plurality of similar weak knowledge point sets of the local end corresponding to the local end.
Further, the invention also provides an intelligent knowledge point identification system, wherein the first acquisition module comprises:
the calculating unit is used for calculating the similarity between each knowledge point set and the rest knowledge point sets respectively;
and the judging unit is connected with the calculating unit and is used for judging that the plurality of the knowledge point sets with the similarity greater than a preset threshold in the rest knowledge point sets are the similar knowledge point sets.
Additionally, the present invention provides an intelligent device, which includes a processor, a storage and a computer program stored in the storage and executable on the processor, wherein the processor is configured to execute the computer program stored in the storage, and implement the operations performed by the above-mentioned intelligent knowledge point identification method.
Additionally, the present invention provides a storage medium having at least one instruction stored therein, which is loaded and executed by a processor to implement the operations performed by the intelligent knowledge point identification method as described above.
The invention provides a method, a system, intelligent equipment and a storage medium for intelligently identifying knowledge points, which at least comprise the following technical effects:
(1) the local ends with similar working data are identified by comparing the working data of the local ends, weak knowledge points are shared among the similar local ends, the problem that most of time managers are wasted on the practice of knowledge points which are familiar to master when a user uses the local ends for learning, and the weak knowledge points are possibly overlooked when the user uses the local ends for learning is solved, the weak knowledge points which are possibly existing in each user can be identified, and the learning efficiency of the user is improved;
(2) the similarity between the knowledge point sets is calculated, and the two knowledge point sets with the similarity larger than the preset threshold are judged to be similar knowledge point sets, so that similar local ends are determined;
(3) the similarity of the two knowledge point sets is calculated by calculating the proportion of the same knowledge points in the two knowledge point sets to the total amount of the knowledge points, so that the similar knowledge point sets can be accurately identified and matched with similar local ends, and the accuracy of identifying the weak knowledge points of each user is improved;
(4) relationship weight between the knowledge points is introduced, similarity of the two knowledge point sets is calculated, errors in similarity identification caused by membership of the knowledge points in the knowledge point sets are avoided, similar local ends can be accurately matched, and accuracy of identifying weak knowledge points of each user is improved;
(5) screening the knowledge point sets according to the number of knowledge points in the knowledge point sets for different knowledge point sets, so that the similarity between the different point sets has a reference value, and the accuracy of identifying the weak knowledge points of each user is improved;
(6) after the weak knowledge points of the local end are intelligently identified, corresponding information is pushed to the local end according to the weak knowledge points, and customized learning plans and learning contents are generated according to each local end, so that the learning efficiency of a user can be improved, and the experience of the user is enhanced.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flow chart of a method for intelligent knowledge point identification according to the present invention;
FIG. 2 is a flow chart of determining similarity between knowledge point sets in an intelligent knowledge point identification method of the present invention;
FIG. 3 is a flow chart of calculating the similarity between different knowledge point sets in the intelligent knowledge point identification method of the present invention;
FIG. 4 is another flowchart of calculating the similarity between different knowledge point sets in the intelligent knowledge point identification method of the present invention;
FIG. 5 is another block diagram of a method for intelligent knowledge point identification according to the present invention;
FIG. 6 is an exemplary diagram of a knowledge point intelligent recognition system of the present invention;
FIG. 7 is a diagram of an exemplary comparison module in the intelligent knowledge point identification system of the present invention;
fig. 8 is an exemplary diagram of a smart device of the present invention.
Reference numbers in the figures: a collection module-10, a first acquisition module-20, a calculation unit-21, a judgment unit-22, a comparison module-30, a second acquisition module-40, an identification module-50, a smart device-100, a processor-110, a memory-120 and a computer program 121.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
For the sake of simplicity, the drawings only schematically show the parts relevant to the present invention, and they do not represent the actual structure as a product. In addition, in order to make the drawings concise and understandable, components having the same structure or function in some of the drawings are only schematically depicted, or only one of them is labeled. In this document, "one" means not only "only one" but also a case of "more than one".
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
In addition, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not intended to indicate or imply relative importance.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
Example 1
One embodiment of the present invention, as shown in fig. 1, provides a knowledge point intelligent identification method, including the steps of:
s100, working data and weak knowledge point sets of each local end are collected.
Specifically, the local end comprises intelligent learning machines, learning computers, learning watches, learning point-to-read machines and other intelligent equipment which help users to perform academic tutoring. After receiving the operation of the user and the input data, the local end periodically stores the operation of the user and the input data as working data and uploads the working data to the cloud end for corresponding processing.
Optionally, after receiving the operation of the user and the input data, the local end stores the operation of the user and the input data as working data, stores the working data in the local end, and performs corresponding processing on the working data through a processor mounted on the local end.
Specifically, the weak knowledge point set comprises a plurality of weak knowledge points, and the weak knowledge points are identified by executing identification operation of the weak knowledge points through a processor or a cloud deployed at the local end, so that a plurality of weak knowledge points corresponding to each local end are identified.
S200, acquiring knowledge point sets corresponding to the working data respectively.
Specifically, the working data further comprises teaching video information and teaching text information, wherein each teaching video in the teaching video information corresponds to a plurality of knowledge points, each teaching text in the teaching text information corresponds to a plurality of knowledge points, and each question searching record in the question searching record corresponds to a plurality of knowledge points.
S300, comparing each knowledge point set with the rest knowledge point sets to obtain a plurality of similar knowledge point sets corresponding to each knowledge point set respectively.
Illustratively, the similarity degree between different point sets can be quantified to be the similarity degree, and a plurality of similar knowledge point sets respectively corresponding to each knowledge point set are obtained by comparing the similarity degrees between different knowledge point sets.
S400, according to the plurality of similar knowledge point sets corresponding to each knowledge point set, a plurality of similar local ends corresponding to each local end are obtained.
Specifically, each knowledge point set belongs to one local end, and the similarity relation between the local ends can be directly obtained by obtaining the similarity relation between the knowledge point sets.
S500 identifies the weak knowledge point set corresponding to each local peer as weak knowledge point sets of a plurality of similar local peers corresponding to the local peer.
Illustratively, the local peer B, the local peer C, and the local peer D are judged to be similar local peers to the local peer a, and when the weak knowledge point set a1 of the local peer a is recognized, the judgment a1 is also judged to be weak knowledge points of the local peer B, the local peer C, and the local peer D.
The intelligent knowledge point identification method provided by the embodiment identifies a plurality of local ends with similar working data by comparing the working data of each local end, shares weak knowledge points among the similar local ends, avoids the problem that most time managers are wasted on the practice of the familiar knowledge points when a user uses the local ends for learning, and the weak knowledge points are possibly overlooked, can identify the possible weak knowledge points of each user, and improves the learning efficiency of the user.
Example 2
Based on embodiment 1, as shown in fig. 2 to 4, in the intelligent knowledge point identification method provided by the present invention, step S300 compares each knowledge point set with the other knowledge point sets to obtain a plurality of similar knowledge point sets corresponding to each knowledge point set, and specifically includes:
s310, calculating the similarity between each knowledge point set and the rest knowledge point sets respectively.
Illustratively, if the four local terminals respectively correspond to A, B, C, D four knowledge point sets, the similarity between the knowledge point set a and the knowledge point set B, the similarity between the knowledge point set a and the knowledge point set C, and the similarity between the knowledge point set a and the knowledge point set D are respectively calculated. And then respectively calculating the similarity between the knowledge point B set and the rest knowledge point sets A, C, D, and sequentially calculating the similarity between each knowledge point set and the rest knowledge point sets according to the method.
Optionally, as shown in fig. 3, step S310 calculates the similarity between each knowledge point set and the other knowledge point sets, specifically including:
s311 respectively calculating the similarity of each knowledge point set and the rest knowledge point sets, wherein the proportion of the category number of the same knowledge points to the total category number of the knowledge points is used as the similarity.
Illustratively, the knowledge point set A comprises a knowledge point a, a knowledge point B and a knowledge point c, the knowledge point set B comprises a knowledge point B, a knowledge point d and a knowledge point f, and the similarity between the knowledge point set A and the knowledge point set B is calculated by the following formula:
W(AB)＝|A∩B|÷|A∪B|＝1÷5＝0.2。
optionally, as shown in fig. 4, step S310 calculates the similarity between each knowledge point set and the other knowledge point sets, and specifically includes:
s312 obtains preset relationship weights between the knowledge points.
Specifically, the relationship weight is used to represent the degree of similarity between the respective knowledge points.
Illustratively, if two knowledge points are the same, the relationship weight is 1, if one knowledge point is in an affiliation with another knowledge point, the weight is defined according to the relevance, the primary relationship weight is 0.8, the secondary relationship weight is 0.6, and the tertiary relationship weight is 0.4.
For example, if a first knowledge point "recognizes four directions of east, south, west, and north" is defined as a first-order knowledge point, a second knowledge point "recognizes other three directions according to a certain direction" is defined as a second-order knowledge point, and a third knowledge point "recognizes three directions of east, south, west, and north" is defined as a third-order knowledge point, and the three knowledge points have membership relationships, the relationship weight between the first knowledge point and the second knowledge point is 0.8 as the first-order relationship weight, and the relationship weight between the first knowledge point and the third knowledge point is 0.6 as the second-order relationship weight.
S313, according to the relation weight among the knowledge points, the similarity between each knowledge point set and the rest knowledge point sets is calculated respectively.
Illustratively, the knowledge point set a includes knowledge points a, B and c, and the knowledge point set B includes knowledge points a 'and c', where the knowledge points a 'and a are subordinate, the relationship weight of the knowledge points a' and a is a primary relationship weight of 0.8, the knowledge points c 'and a are subordinate, and the relationship weight of the knowledge points c' and c is a primary relationship weight of 0.8.
Calculating the similarity between the knowledge point set A and the knowledge point set B by the following formula:
s320, judging the knowledge point sets with the similarity larger than the preset threshold in the rest knowledge point sets to be similar knowledge point sets.
Illustratively, the similarity W of the knowledge point set A and the knowledge point set B is respectively calculated1Similarity W between knowledge point set A and knowledge point set C2Similarity W between knowledge point set A and knowledge point set D3Then, if W1And W2And if the value is smaller than the preset threshold value, judging that the knowledge point set B and the knowledge point set C are both similar knowledge point sets to the knowledge point set A.
The preset threshold may be set to 0.4, 0.6, or 0.8, etc.
The intelligent knowledge point identification method provided by the embodiment calculates the similarity between the knowledge point sets, judges that the two knowledge point sets with the similarity larger than the preset threshold are similar knowledge point sets, and determines the corresponding similar local end by judging the similarity of the knowledge point sets, and calculates the similarity between the knowledge point sets by calculating the proportion of the same knowledge points in the two knowledge point sets to the total knowledge point amount and calculating the similarity of the two knowledge point sets in two different methods, so that the problem that the similarity identification has errors due to the membership of the knowledge points in the knowledge point sets can be avoided, the similar local end can be accurately matched, and the accuracy of identifying the weak knowledge points of each user can be improved. The method for judging the similar local ends in the specific learning scene introduced by the embodiment is convenient for sharing weak knowledge points among the similar local ends.
Example 3
Based on any one of embodiments 1 to 2, as shown in fig. 5, the method for intelligently identifying knowledge points according to the present invention, after the step S200 of respectively obtaining knowledge point sets corresponding to each piece of working data, the step S300 of comparing each knowledge point set with the rest knowledge point sets, before obtaining a plurality of similar knowledge point sets corresponding to each knowledge point set, further includes:
s210, eliminating the knowledge point sets with the number of knowledge points smaller than the preset number.
Illustratively, the preset number may be set to 4, 6, 8, 10, etc. When the preset number is set to be 8, the knowledge point sets with the knowledge point number smaller than 8 do not participate in the judgment and comparison process of the similar knowledge point sets.
Optionally, after the step S500 identifies the weak knowledge point set corresponding to each local peer as weak knowledge point sets of a plurality of similar local peers corresponding to the local peer, the method further includes:
s600, information is pushed to the local end according to the weak knowledge points of the local ends.
Specifically, after the weak knowledge point set of the local end is identified, learning contents such as learning plan, course arrangement, exercise type and the like are generated according to the weak knowledge point set and are sent to the corresponding local end.
Optionally, a detection question is generated according to the identified weak knowledge point set and sent to the local end for the user of the local end to practice, and whether the identified weak knowledge point set is accurate or not is verified according to the detection question.
The intelligent knowledge point identification method provided by the embodiment screens different knowledge point sets according to the number of knowledge points in the knowledge point sets, compares the knowledge point sets with the number of knowledge points larger than the preset number, so that the similarity among the different knowledge point sets has a higher reference value, pushes corresponding information to a local end according to weak knowledge points after intelligently identifying the weak knowledge points of the local end, and generates customized learning plans and learning contents according to each local end, thereby improving the learning efficiency of users and enhancing the experience of users.
Example 4
One embodiment of the present invention, as shown in fig. 6, provides an intelligent knowledge point identification system, which includes a collection module 10, a first acquisition module 20, a comparison module 30, a second acquisition module 40, and an identification module 50.
Wherein the collecting module 10 is used for collecting the working data and weak knowledge point sets of each local end.
Specifically, the local end comprises intelligent learning machines, learning computers, learning watches, learning point-to-read machines and other intelligent equipment which help users to perform academic tutoring. After receiving the operation of the user and the input data, the local end periodically stores the operation of the user and the input data as working data and uploads the working data to the cloud end for corresponding processing.
Optionally, the collection module 10 stores the operation of the user and the input data as working data after receiving the operation of the user and the input data, stores the working data in the local side, and performs corresponding processing on the working data through a processor mounted on the local side.
Specifically, the weak knowledge point set comprises a plurality of weak knowledge points, and the weak knowledge points are identified by executing identification operation of the weak knowledge points through a processor or a cloud deployed at the local end, so that a plurality of weak knowledge points corresponding to each local end are identified.
The first obtaining module 20 is connected to the collecting module 10, and is configured to obtain the knowledge point sets corresponding to the respective working data.
Specifically, the working data further comprises teaching video information and teaching text information, wherein each teaching video in the teaching video information corresponds to a plurality of knowledge points, each teaching text in the teaching text information corresponds to a plurality of knowledge points, and each question searching record in the question searching record corresponds to a plurality of knowledge points.
The comparison module 30 is connected to the first obtaining module 20, and is configured to compare each knowledge point set with the remaining knowledge point sets to obtain a plurality of similar knowledge point sets corresponding to each knowledge point set.
For example, the comparison module 30 may quantify the similarity between different sets of points into similarity, and obtain a plurality of similar sets of knowledge points corresponding to each set of knowledge points by comparing the similarities between different sets of knowledge points.
The second obtaining module 40 is connected to the comparing module 30, and is configured to obtain a plurality of similar local ends corresponding to each local end according to the plurality of similar knowledge point sets corresponding to each knowledge point set.
Specifically, each knowledge point set belongs to a local end, and the second obtaining module 40 can directly obtain the similarity relationship between the local ends by obtaining the similarity relationship between the knowledge point sets.
And the identifying module 50 is connected to the second obtaining module 40 and the collecting module 10, and is configured to identify the weak knowledge point set corresponding to each local peer as a plurality of similar weak knowledge point sets of the local peers corresponding to the local peer.
Illustratively, the local peer B, the local peer C, and the local peer D are judged to be similar local peers to the local peer a, and when the weak knowledge point set a1 of the local peer a is recognized, the judgment a1 is also judged to be weak knowledge points of the local peer B, the local peer C, and the local peer D.
The intelligent knowledge point identification system provided by the embodiment identifies a plurality of local ends with similar working data by comparing the working data of the local ends, shares weak knowledge points among the similar local ends, avoids the problem that most time managers are wasted on the practice of knowledge points which are familiar and mastered when a user uses the local ends for learning, and possibly neglects the weak knowledge points, can identify the weak knowledge points which possibly exist in each user, and improves the learning efficiency of the user.
Example 5
Based on embodiment 4, as shown in fig. 7, in the intelligent knowledge point identification system provided by the present invention, the first obtaining module 20 includes a calculating unit 21 and a judging unit 22.
Wherein the calculating unit 21 is configured to calculate the similarity between each knowledge point set and each of the other knowledge point sets.
Illustratively, if the four local terminals respectively correspond to A, B, C, D four knowledge point sets, the similarity between the knowledge point set a and the knowledge point set B, the similarity between the knowledge point set a and the knowledge point set C, and the similarity between the knowledge point set a and the knowledge point set D are respectively calculated. And then respectively calculating the similarity between the knowledge point B set and the rest knowledge point sets A, C, D, and sequentially calculating the similarity between each knowledge point set and the rest knowledge point sets according to the method.
The judging unit 22 is connected to the calculating unit 21, and is configured to judge, as similar knowledge point sets, some knowledge point sets in which the similarity among the remaining knowledge point sets is greater than a preset threshold.
Illustratively, after calculating the similarity W1 between the knowledge point set a and the knowledge point set B, the similarity W2 between the knowledge point set a and the knowledge point set C, and the similarity W3 between the knowledge point set a and the knowledge point set D, respectively, if W1 and W2 are less than a preset threshold, it is determined that the knowledge point set B and the knowledge point set C are both similar to the knowledge point set a.
The preset threshold may be set to 0.4, 0.6, or 0.8, etc.
The intelligent knowledge point identification method provided by this embodiment calculates the similarity between the knowledge point sets, determines that two knowledge point sets with the similarity greater than a preset threshold are similar knowledge point sets, and determines corresponding similar local ends by determining the similarity of the knowledge point sets.
Example 6
One embodiment of the present invention, as shown in fig. 8, provides an intelligent device 100, which includes a processor 110, a memory 120, wherein the memory 120 is used for storing a computer program 121; the processor 110 is configured to execute the computer program 121 stored in the memory 120 to implement the intelligent knowledge point identification method in the corresponding method embodiment.
The smart device 100 may be a smart learning machine, a learning computer, a learning watch, a learning point-reading machine, or the like. The smart device 100 may include, but is not limited to, a processor 110, a memory 120. Those skilled in the art will appreciate that fig. 8 is merely an example of the smart device 100 and does not constitute a limitation of the smart device 100 and may include more or fewer components than illustrated, or some components in combination, or different components, such as: the smart device 100 may also include input/output interfaces, display smart devices, network access smart devices, communication buses, communication interfaces, and the like. A communication interface and a communication bus, and may further include an input/output interface, wherein the processor 110, the memory 120, the input/output interface and the communication interface complete communication with each other through the communication bus. The memory 120 stores a computer program 121, and the processor 110 is configured to execute the computer program 121 stored in the memory 120, so as to implement the intelligent knowledge point identification method in the corresponding method embodiment.
The Processor 110 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 120 may be an internal storage unit of the smart device 100, such as: hard disk or memory of the intelligent device. The memory may also be an external storage smart device to the smart device, such as: the intelligent device is provided with a plug-in hard disk, an intelligent memory Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like. Further, the memory 120 may also include both an internal storage unit of the smart device 100 and an external storage smart device. The memory 120 is used for storing the computer program 121 and other programs and data required by the smart device 100. The memory may also be used to temporarily store data that has been output or is to be output.
A communication bus is a circuit that connects the described elements and enables transmission between the elements. For example, the processor 110 receives commands from other elements through the communication bus, decrypts the received commands, and performs calculations or data processing according to the decrypted commands. The memory 120 may include program modules such as a kernel (kernel), middleware (middleware), an Application Programming Interface (API), and applications. The program modules may be comprised of software, firmware or hardware, or at least two of the same. The input/output interface forwards commands or data entered by a user via the input/output interface (e.g., sensor, keyboard, touch screen). The communication interface connects the smart device 100 with other network smart devices, user smart devices, networks. For example, the communication interface may be connected to the network by wired or wireless connection to connect to external other network smart devices or user smart devices. The wireless communication may include at least one of: wireless fidelity (WiFi), Bluetooth (BT), Near Field Communication (NFC), Global Positioning Satellite (GPS) and cellular communications, among others. The wired communication may include at least one of: universal Serial Bus (USB), high-definition multimedia interface (HDMI), asynchronous transfer standard interface (RS-232), and the like. The network may be a telecommunications network and a communications network. The communication network may be a computer network, the internet of things, a telephone network. The smart device 100 may be connected to a network via a communication interface, and protocols used by the smart device 100 to communicate with other network smart devices may be supported by at least one of an application, an Application Programming Interface (API), middleware, a kernel, and a communication interface.
Example 7
In an embodiment of the present invention, a storage medium stores at least one instruction, and the instruction is loaded and executed by a processor to implement the operations performed by the corresponding embodiments of the intelligent knowledge point identification method. For example, the storage medium may be a read-only memory (ROM), a Random Access Memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage smart device, and the like.
They may be implemented in program code that is executable by a computing device such that it is executed by the computing device, or separately, or as individual integrated circuit modules, or as a plurality or steps of individual integrated circuit modules. Thus, the present invention is not limited to any specific combination of hardware and software.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or recited in detail in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed intelligent knowledge point identification method, system, intelligent device and storage medium may be implemented in other ways. For example, the above-described embodiments of a knowledge point intelligent identification method, system, intelligent device and storage medium are merely illustrative, and for example, the division of the modules or units is only a logical division, and other divisions may be realized in practice, for example, a plurality of units or modules may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the communication links shown or discussed may be through interfaces, devices or units, or integrated circuits, and may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
It should be noted that the above-mentioned embodiments are only preferred embodiments of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. An intelligent knowledge point identification method is characterized by comprising the following steps:
collecting working data and weak knowledge point sets of each local end, wherein the working data comprises question making records;
respectively acquiring a knowledge point set corresponding to each piece of working data, wherein the knowledge point set consists of a plurality of knowledge points;
comparing each knowledge point set with the rest knowledge point sets to obtain a plurality of similar knowledge point sets corresponding to each knowledge point set respectively;
obtaining a plurality of similar local terminals corresponding to each local terminal according to a plurality of similar knowledge point sets corresponding to each knowledge point set;
and identifying the weak knowledge point set corresponding to each local end as the weak knowledge point sets of a plurality of similar local ends corresponding to the local end.
2. The method according to claim 1, wherein the comparing of each knowledge point set with the rest knowledge point sets to obtain a plurality of similar knowledge point sets corresponding to each knowledge point set specifically comprises:
respectively calculating the similarity between each knowledge point set and the rest knowledge point sets;
and judging the knowledge point sets with the similarity larger than a preset threshold in the rest knowledge point sets to be similar knowledge point sets.
3. The intelligent knowledge point identification method according to claim 2, wherein the calculating the similarity between each knowledge point set and each of the other knowledge point sets respectively comprises:
and respectively calculating the proportion of the category quantity of the same knowledge points in each knowledge point set and the other knowledge point sets in the same knowledge point set to the total category quantity of the knowledge points as the similarity.
4. The intelligent knowledge point identification method according to claim 2, wherein the calculating of the similarity between each knowledge point set and each of the other knowledge point sets respectively further comprises:
acquiring preset relation weights among the knowledge points, wherein the relation weights are used for representing the similarity degree among the knowledge points;
and respectively calculating the similarity between each knowledge point set and the rest knowledge point sets according to the relation weight among the knowledge points.
5. The intelligent knowledge point identification method according to claim 1, wherein after the obtaining of the knowledge point sets corresponding to the respective working data, and before the comparing of each knowledge point set with the rest of the knowledge point sets, further comprises:
and eliminating the knowledge point sets of which the number of the knowledge points is less than the preset number.
6. The method according to claim 1, wherein after identifying the weak knowledge point set corresponding to each local peer as the weak knowledge point sets of a plurality of similar local peers corresponding to the local peer, the method further comprises:
and pushing information to the local end according to the weak knowledge point set of each local end.
7. An intelligent knowledge point identification system, comprising:
the collecting module is used for collecting working data and weak knowledge point sets of each local end, wherein the working data comprises question making records;
the first acquisition module is connected with the collection module and is used for respectively acquiring a knowledge point set corresponding to each piece of working data, and the knowledge point set consists of a plurality of knowledge points;
the comparison module is connected with the first acquisition module and used for comparing each knowledge point set with the rest knowledge point sets to obtain a plurality of similar knowledge point sets corresponding to the knowledge point sets respectively;
the second acquisition module is connected with the comparison module and used for acquiring a plurality of similar local terminals corresponding to each local terminal according to a plurality of similar knowledge point sets corresponding to each knowledge point set;
and the identification module is connected with the second acquisition module and the collection module and is used for identifying the weak knowledge point set corresponding to each local end as a plurality of similar weak knowledge point sets of the local end corresponding to the local end.
8. The intelligent knowledge point recognition system of claim 7, wherein the first acquisition module comprises:
the calculating unit is used for calculating the similarity between each knowledge point set and the rest knowledge point sets respectively;
and the judging unit is connected with the calculating unit and is used for judging that the plurality of the knowledge point sets with the similarity greater than a preset threshold in the rest knowledge point sets are the similar knowledge point sets.
9. An intelligent device, comprising a processor, a storage and a computer program stored in the storage and operable on the processor, wherein the processor is configured to execute the computer program stored in the storage, and implement the operations performed by the intelligent knowledge point identification method according to any one of claims 1 to 6.
10. A storage medium, wherein at least one instruction is stored in the storage medium, and the instruction is loaded and executed by a processor to implement the operation performed by the intelligent knowledge point identification method according to any one of claims 1 to 6.
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