CN115908063A

CN115908063A - Training task generation method and device, electronic equipment and storage medium

Info

Publication number: CN115908063A
Application number: CN202110884527.5A
Authority: CN
Inventors: 崔颖
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2021-08-03
Filing date: 2021-08-03
Publication date: 2023-04-04

Abstract

The application relates to the technical field of computers, in particular to a training task generation method and device, electronic equipment and a storage medium, which are used for improving the training efficiency of a user. The method comprises the following steps: obtaining corresponding training data according to the training rule associated with the target object; based on training data, constructing an initial training node path, wherein each training node represents one key knowledge information in the training data, and the initial training node path represents an initial use sequence of each training node; screening and path recombination are carried out on each training node contained in the initial training node path based on historical training data associated with the target object, and a corresponding target training node path is obtained; and generating a training task for the target object based on the target training node path. The training tasks for the target object are generated based on the target training path, so that the personalized training tasks can be generated for different users, and the training efficiency of the users is effectively improved.

Description

Training task generation method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a training task generation method and apparatus, an electronic device, and a storage medium.

Background

With the rapid development of artificial intelligence, some training applications, training terminals, learning applications with training functions and the like are carried forward, an intelligent training mode is brought to trainers, and the trainers can train and practice anytime and anywhere.

Taking training exercises as example of dictation exercises, when a user needs to perform the dictation exercises, the user only needs to open the dictation terminal and select dictation contents, and the dictation terminal automatically reads the dictation contents, so that the user can complete the dictation exercises. However, when the user listens to the writing practice, the user listens to the writing practice according to the unified standard, the listening and writing contents are determined in advance, and the subject adaptability is poor. When the user carries out the exercise of independently dictating, the user is difficult to select the dictation content that is fit for its self, is unfavorable for promoting user's training efficiency. Therefore, how to generate personalized training tasks for different users is urgent to solve the problem of improving the training efficiency of the users.

Disclosure of Invention

The embodiment of the application provides a training task generation method and device, electronic equipment and a storage medium, which are used for improving the training efficiency of a user.

The training task generation method provided by the embodiment of the application comprises the following steps:

obtaining corresponding training data according to the training rule associated with the target object;

constructing an initial training node path based on the training data, wherein each training node represents one key knowledge information in the training data, and the initial training node path represents the initial use sequence of each training node;

screening and path recombination are carried out on each training node contained in the initial training node path based on historical training data associated with the target object, and a corresponding target training node path is obtained;

generating a training task for the target object based on the target training node path.

The training task generating device provided by the embodiment of the application comprises:

the data acquisition unit is used for acquiring corresponding training data according to the training rule associated with the target object;

the initial construction unit is used for constructing an initial training node path based on the training data, wherein each training node represents key knowledge information in the training data, and the initial training node path represents the initial use sequence of each training node;

the target construction unit is used for screening and path recombination of all training nodes contained in the initial training node path based on historical training data associated with the target object to obtain a corresponding target training node path;

and the task generating unit is used for generating a training task aiming at the target object based on the target training node path.

Optionally, the task generating unit is specifically configured to:

determining a media type corresponding to the training task according to a training style corresponding to the target object; and

acquiring a first auxiliary key information base associated with the current environmental factors;

and generating the training tasks of the media types according to the target training node path and the first auxiliary key information base.

Optionally, the apparatus further comprises:

the display unit is used for generating a training activity sequence and a training navigation aiming at the target object based on the target training node path and displaying the training activity sequence and the training navigation in a task interface; wherein,

the training activity sequence is used for representing the arrangement sequence of training items recommended according to the training style of the target object, the training navigation comprises at least one of global navigation and local navigation recommended according to the training style of the target object, the global navigation comprises a complete training system presented through a knowledge tree structure and the current training state of the target object, and the local navigation comprises the associated knowledge of the current training nodes presented through a knowledge concept graph structure.

Optionally, the apparatus further comprises:

the task updating unit is used for acquiring a second auxiliary key information base associated with the changed training state if the training state of the target object is detected to be changed;

updating target training nodes in the target training node path and a target using sequence among the target training nodes according to the second auxiliary key information base;

and regenerating the training task aiming at the target object based on the updated target training node path.

An electronic device provided by an embodiment of the present application includes a processor and a memory, where the memory stores program code, and when the program code is executed by the processor, the processor is caused to execute any one of the steps of the training task generating method.

Embodiments of the present application provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from the computer-readable storage medium by a processor of the computer device, and the computer instructions are executed by the processor to cause the computer device to perform the steps of any one of the training task generating methods described above.

An embodiment of the present application provides a computer-readable storage medium, which includes program code for causing an electronic device to perform any one of the steps of the training task generating method described above when the program product is run on the electronic device.

The beneficial effect of this application is as follows:

the embodiment of the application provides a training task generation method and device, electronic equipment and a storage medium. According to the method and the device, a general initial training path is generated based on training data, and then the initial training node path is adjusted by combining historical training data of the target object, so that a target training path for the target object is obtained, and a training task generated based on the target training path is also for the target object, is suitable for the target object and has high adaptability.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

FIG. 1A is a schematic diagram of a dictation method in the related art;

FIG. 1B is a schematic diagram of another dictation method in the related art;

FIG. 2 is an alternative diagram of an application scenario in an embodiment of the present application;

FIG. 3 is a schematic flowchart of a training task generation method according to an embodiment of the present application;

FIG. 4 is a schematic diagram of an initial training node path in an embodiment of the present application;

FIG. 5 is a schematic diagram of a target training node path in an embodiment of the present application;

fig. 6 is a schematic flowchart of a method for generating a target training node path according to an embodiment of the present application;

fig. 7 is a schematic flowchart of a dictation task generation method in an embodiment of the present application;

FIG. 8 is a diagram illustrating a generic model of an adaptive learning system according to an embodiment of the present application;

FIG. 9 is a reference model of an adaptive learning system in an embodiment of the present application;

FIG. 10 is a schematic illustration of a first sequence of learning activities in an embodiment of the present application;

FIG. 11 is a diagram illustrating a second sequence of learning activities in an embodiment of the present application;

FIG. 12 is a diagram illustrating global navigation and local navigation in an embodiment of the present application;

fig. 13 is a schematic flowchart of a dictation task generation method in an embodiment of the present application;

FIG. 14 is a schematic diagram of a training task generating device according to an embodiment of the present application;

fig. 15 is a schematic diagram of a hardware component of an electronic device to which the embodiments of the present application are applied;

fig. 16 is a schematic diagram of a hardware component structure of another electronic device to which the embodiment of the present application is applied.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the technical solutions of the present application. All other embodiments obtained by a person skilled in the art without any inventive step based on the embodiments described in the present application are within the scope of the protection of the present application.

Some concepts related to the embodiments of the present application are described below.

Dictation: it is a way to strengthen memory when learning human language. Dictation, also known as merry writing, is a teaching way in which a person (usually a teacher or a parent) reads words or sentences and a student (or a child) writes corresponding words. Generally, the method is mainly embodied in learning Chinese and English. For example, when learning Chinese, students can listen to the words of teachers or students and spell the words on books with pens while pronouncing the words aside; when learning English, dictation mainly refers to that a teacher plays the contents to be dictated by dictation or multimedia, and students write the contents while listening.

An adaptive learning system: the system can provide an individualized learning service for learners, and the realization system adopts corresponding teaching strategies to recommend individualized learning paths and learning resources according to various characteristics and behavior trends of learners, such as learning targets, preferences, cognitive levels and the like.

Training: training and training are used to enable trainees to master a certain skill. Specifically, the method comprises the following steps: in order to achieve unified scientific and technical specifications and standardized operation, trainees can achieve the expected level and improve the target through a certain education training technical means by the modern informatization processes of target planning setting, knowledge and information transmission, skill proficient drilling, operation achievement evaluation, result exchange bulletin and the like, and the training of fighting capacity, personal ability and working capacity is improved. In the embodiment of the present application, word dictation training is mainly used as an example for illustration, and the training enables a user to master related words.

Training a node path: for characterizing an order of use between training nodes, wherein each training node characterizes a key knowledge information in the training data. In the application, the training node paths are divided into two types: an initial training node path and a target training node path. The initial training node paths are constructed based on training data associated with training rules, and are the same for different users, so that the initial using sequence of each training node is represented; the target training node paths are obtained by screening and path recombination of training nodes in the initial training node paths, and the target training node paths may be different for different users.

Key knowledge information: the training data is classified and obtained, and can be a character, a word, or a radical, pinyin, stroke method (such as left-right structure, upper-lower structure, upper-middle-lower structure) and the like.

Artificial Intelligence (AI): the method is a theory, method, technology and application system for simulating, extending and expanding human intelligence by using a digital computer or a machine controlled by the digital computer, sensing the environment, acquiring knowledge and obtaining the best result by using the knowledge. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the implementation method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making. The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence base technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like. In the embodiment of the application, the terminal device or the server can implement adaptive recommendation and the like of dictation content based on the AI technology.

The following briefly introduces the design concept of the embodiments of the present application:

taking a training task as an example of dictation, dictation is an important way for detecting learning results in the learning process of students, and with the development of science and technology, students usually choose to use electronic equipment (such as a family education machine) for dictation exercise. The conventional electronic device adds the dictation content by people and then reads and writes according to the added dictation content.

Under the intelligent dictation mode, a dictating person can practice dictation anytime and anywhere. However, the related technical solutions are divided into two types, and both of them need to depend on teachers, parents or students themselves, are not intelligent, and have low effects. The two technical schemes are introduced below respectively:

the first scheme comprises the following steps: the user actively selects, and the specific implementation flow is shown in fig. 1A:

the first step is as follows: electronizing and storing the dictation content of the textbook in a datamation way, and labeling label information; the second step is that: after the data is processed, page presentation is carried out according to the book directory structure, and clicking and checking by a user are supported; the third step: after seeing the visual directory, the user manually selects the words to be dictated; the fourth step: to listen and write.

However, in this way, the user needs to select which dictation contents, how to dictation, how many dictations today, whether to be dictated in tomorrow, and whether to be dictated in tomorrow do not have a scientific guidance, and only learns to listen to and write one lesson according to the progress of teacher teaching, and from head to tail, the teacher can only adjust the scheme and arrange the homework according to the overall situation of this class, and there is no realization of refined teaching guidance and help for each student, and a scientific and reasonable teaching scheme suitable for the individual intelligence level, learning ability, learning score, mastery degree, and acceptance degree cannot be customized for each student, so this way is low in efficiency, poor in effect, and unable to locate the weak points of students, perform targeted dictation training, and review and consolidate.

Fang Anshi two: the job distribution, the specific implementation flow is shown in fig. 1B:

the first step is as follows: arranging electronic dictation operation or dictation requirements according to the teaching experience of a teacher, and dictating new words in 1-2 courses in the current day or dictating all the new words in 1-4 units on a 3-grade book today; the second step is that: the students listen and write according to the requirements of teachers.

However, this method is a homework arrangement for all students of a class, because students with different learning degrees, different learning abilities and proficiency need to complete the same task, and for students with good learning performance, it takes a certain time to complete such homework, and the meaning is not great, because these words he has already mastered; for students with poor learning performance, the scheme takes a lot of time, because for the students with poor learning performance, the proficiency and mastery degree of words are low, long time is needed for completing the scheme, and the effect is not good, so that teachers can not arrange jobs according to the abilities of the students in a layered mode, and different solutions and different learning plans can be provided for different students.

In conclusion, the two schemes are very dependent on experienced teaching teachers, guidance is conducted according to the teaching experience of the teachers, and on one hand, high-quality human input is needed; on the other hand, when the teacher does not know the conditions of the students and updates the conditions in time completely in the process of collecting the actual data, the provided experience is unreasonable, and the final teaching effect is damaged.

In view of this, embodiments of the present application provide a training task generation method and apparatus, an electronic device, and a storage medium. According to the method and the device, a general initial training path is generated based on training data, and then the initial training node path is adjusted by combining historical training data of the target object, so that a target training path for the target object is obtained, and a training task generated based on the target training path is also for the target object, is suitable for the target object and has high adaptability.

The preferred embodiments of the present application will be described below with reference to the accompanying drawings of the specification, it should be understood that the preferred embodiments described herein are merely for illustrating and explaining the present application, and are not intended to limit the present application, and that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.

Fig. 2 is a schematic view of an application scenario according to an embodiment of the present application. The application scenario diagram includes two terminal devices 210 and a server 220. The terminal device 210 in the embodiment of the present application may be installed with a training client, and the training client is used for training a trainer at any time and any place.

Specifically, the training in the embodiment of the present application may refer to chinese word dictation training, english word dictation training, mathematical formula training, and the like, and is mainly exemplified herein by the chinese word dictation training. For example, the terminal device 210 may be installed with a dictation client, and the dictation client is used for a dictating person to perform dictation exercises anytime and anywhere.

Each terminal device may have a client installed therein, which may be used for training, for example, an Application (APP) for chinese dictation, an APP for english dictation, and other learning APPs including a dictation function. The client related to the embodiment of the present application may be a pre-installed client (e.g., software), a client embedded in a certain application (e.g., an applet), or a web page version client, and the specific type of the client is not limited. The server is a server corresponding to software, a web page, an applet, or the like.

In a dictation scenario, a dictation job is typically performed periodically, when a new class is completed, during weekend review, during unit review, and before a mid-term end exam, and is therefore a common, high-frequency form of dictation. The initiator of the common dictation operation comprises a teacher, a parent or a student, wherein the teacher arranges the dictation as the operation for the student, the parent requires to learn more after class, and the child wants to prepare for review before an examination.

The training task generation method in the embodiment of the application can be executed by the server or the terminal device independently, or can be executed by the server and the terminal device together. For example, the server obtains corresponding training data according to training rules associated with the target object, and an initial training node path is constructed based on the training data; and then, the server screens and recombines paths of all training nodes contained in the initial training node path based on historical training data associated with the target object to obtain a corresponding target training node path, and issues the target training node path to the terminal equipment, and the terminal equipment generates and displays a training task for the target object based on the target training node path, such as a word dictation task.

In an alternative embodiment, the terminal device 210 and the server 220 may communicate with each other via a communication network.

In an alternative embodiment, the communication network is a wired network or a wireless network.

In this embodiment, the terminal device 210 is an electronic device used by a user, and the electronic device may be an electronic device having a certain computing capability and running instant messaging software and a website or social software and a website, such as a personal computer, a notebook, a learning mobile phone, a learning tablet, a mobile phone, a mobile tablet, a learning machine, a point-to-read machine, a family education machine, a television, an electronic book reader, a vehicle-mounted terminal, and a personal digital assistant, or may be an intelligent education hardware product having a training capability, such as an intelligent sound box, an intelligent learning platform, and an intelligent learning desk lamp. Each terminal device 210 is connected to a server 220 through a wireless network, and the server 220 is a server or a server cluster or a cloud computing center formed by a plurality of servers, or is a virtualization platform.

It should be noted that fig. 2 is only an example, and the number of the terminal devices and the servers is not limited in practice, and is not specifically limited in the embodiment of the present application.

The training task generating method provided by the exemplary embodiment of the present application is described below with reference to the accompanying drawings in conjunction with the application scenarios described above, it should be noted that the above application scenarios are only shown for the convenience of understanding the spirit and principles of the present application, and the embodiments of the present application are not limited in this respect.

Referring to fig. 3, an implementation flowchart of a training task generating method provided in the embodiment of the present application is illustrated by taking an execution subject as a terminal device as an example, and a specific implementation flow of the method is as follows:

s31: the terminal equipment obtains corresponding training data according to the training rule associated with the target object;

in a dictation scene, the training rules mainly refer to teaching requirements, teaching lines and the like related to Chinese word learning, and the training data refer to dictation related words specified by the teaching requirements, the teaching lines and the like. Firstly, the dictation content of a relevant textbook needs to be electronized, stored in a datamation mode and labeled with label information, such as ' primary school under the first unit of the first great tree on the first level of the first class in the teaching version ', the new words are divided into two different contents which can be written and recognized by me, from the teaching angle, the requirement of writing can be higher than that of recognizing by me, for example, ' i can write: day, earth, human … "; "I will recognize: you, I, he … "; the words "sky, white clouds …", and the like.

S32: the method comprises the steps that terminal equipment constructs an initial training node path based on training data, wherein each training node represents key knowledge information in the training data, and the initial training node path represents an initial using sequence of each training node;

wherein the initial training node path refers to a generic learning path for all subjects.

Optionally, when an initial training node path is constructed based on training data, since each training node in the path represents one piece of key knowledge information, each corresponding training node and an initial using sequence among the training nodes can be determined by classifying all pieces of key knowledge information in the training data; and further, constructing an initial training node path based on each training node and the initial using sequence among the training nodes.

The key knowledge information may be a word, or a radical, pinyin, stroke method (such as left-right structure, top-bottom structure, top-middle-bottom structure), etc., which is not limited herein.

Assuming that the training data are dictation words in a class book of a grade of primary school, and the training nodes represent a specific word, all the training data can be classified and divided to determine all the words to be mastered, one word corresponds to one training node, and further, an initial use sequence, namely a learning sequence, among all the training nodes is determined based on teaching requirements.

Fig. 4 is a schematic diagram of an initial training node path according to an embodiment of the present application. For example, firstly, the teaching data is divided according to textbook units to obtain a first unit, a second unit, a third unit, and a fourth unit …, and then training nodes in each unit are sorted based on teaching requirements, as shown in fig. 4, the sequence is: training node one, training node two, training node three, training node four, …, training node thirteen, ….

S33: the terminal equipment screens and recombines paths of all training nodes contained in the initial training node path based on historical training data associated with the target object to obtain a corresponding target training node path;

s34: and the terminal equipment generates a training task aiming at the target object based on the target training node path.

Fig. 5 is a schematic diagram of a target training node path according to an embodiment of the present application. Compared with the initial training node path shown in fig. 4, the training nodes and sequences that are not mastered by the screened target object temporarily have: the training node eight, the training node three, the training node four, the training node nine, the training node two, the training node twelve and the training node eleven, and obviously, for the target object, the training nodes in the target training node path are different from the training nodes shown in fig. 4 in number and sequence.

Specifically, the embodiment of the present application considers that the learning levels, learning abilities, and the like of different subjects are not necessarily the same, and therefore, after determining the initial training node path that is common to all subjects based on step S32, it is further necessary to generate a personalized target training node path for the target subject based on the historical training data associated with the target subject. And then generating a personalized training task for the target object on the basis of the training task. The method can provide scientific and accurate training scheme and path guidance for the user, so that the user can train at high quality, training efficiency is improved, and training effect is guaranteed.

The following mainly takes dictation as an example, and a method for generating a dictation task in the embodiment of the present application is described in detail.

An optional implementation manner is that S32, which is a flowchart of a method for generating a target training node path in the embodiment of the present application, may be implemented according to the flowchart shown in fig. 6, and includes the following steps:

s61: the terminal equipment determines target key knowledge information which accords with preset conditions in historical training data and training standards corresponding to the target key knowledge information;

the historical training data refers to data of learning software used by a user on electronic equipment such as a desk lamp, a tablet and a mobile phone, which is acquired under the condition of user authorization, for example, learning data generated by the user A in learning applications APP related to various languages, and after the data are acquired, data information related to words and preset conditions can be extracted to serve as target key knowledge information to construct a knowledge path graph for word learning, namely a target training node path.

Optionally, the target key knowledge information meeting the preset condition includes at least one of the following:

in the corresponding historical training process, the error rate reaches the key knowledge information of a first preset threshold;

adding the key knowledge information of the specified mark in the corresponding historical training process;

and in the corresponding historical training process, searching key knowledge information with the searching times reaching a second preset threshold value.

Based on the historical training data, the following data can be obtained within a period of time: a certain type of key knowledge information with the error rate higher than a first preset threshold value, such as words of a certain component; the user adds the words in the key knowledge information (such as the words added to the wrong textbook, the wrong textbook and the new textbook) of the specified marks, such as the wrong marks, the key marks and the like; in the process of learning based on a learning APP, the user searches for at least one of words and phrases whose number of times reaches a second preset threshold, which is not specifically limited herein. According to the user data, the weak area of the user can be positioned, and a learning scheme and content are provided for the user to match with the corresponding learning path.

S62: and the terminal equipment screens and recombines paths of all training nodes contained in the initial training node path based on the target key knowledge information and the corresponding training standard to generate a target training node path.

The training standard mainly refers to a learning requirement and word attributes for words, such as: i will read and i will write. According to the training standard and the target key knowledge information capable of reflecting the learning level of the target object, each training node contained in the initial training node path can be further screened, the training nodes mastered by the target object are screened out, a plurality of training nodes not mastered by the target object are selected, and then the use sequence of the selected training nodes is rearranged, namely path recombination is carried out, and the target training node path is generated.

Optionally, step S62 may be further divided into the following steps:

s621: the terminal equipment screens out all target training nodes related to target key knowledge information in all training nodes;

for example, the target key knowledge information screened out based on the historical training data of the user a is words beside a single person, words beside a wood word, pinyin and sentences related to the words, and the like, and based on the target key knowledge information, related target training nodes can be screened out from the initial training node path, for example, 10 training nodes are screened out from 100 initial training nodes as target training nodes, and the numbers are 1 to 10 respectively. Or, 20 training nodes are screened out from 100 initial training nodes, and the nodes related to each other in the 20 training nodes are combined to finally generate 10 target training nodes, and the like. The merging of the training nodes may be performed based on the learning level, the learning style, and the like of the target object, and is not particularly limited herein.

S622: the terminal equipment determines difficulty labels of the associated target training nodes based on training standards corresponding to the target key knowledge information;

in addition to the above listed training criteria, there may be more complex criteria, such as i making words, i making sentences, etc. Based on the training criteria, difficulty labels of each target training node can be determined, for example, i will read as level 1, i will write as level 2, i will make words as level 3, i will make sentences as level 4, and so on.

S623: the terminal equipment conducts path recombination on each target training node based on the difficulty label of each target training node and the initial using sequence among all target training nodes in the general training path, and determines the target using sequence among all target training nodes;

s624: and the terminal equipment constructs a target training node path based on each target training node and the target using sequence among the target training nodes.

After the difficulty labels of the target training nodes are determined based on the process, the target training nodes can be rearranged based on the initial use sequence among the target training nodes and the previously determined difficulty labels, when the target training nodes are specifically rearranged, the sequence among the target training nodes follows the principle that the higher the difficulty labels of the target training nodes of the same type are, the later the target training nodes of different types are determined according to the initial use sequence, and the like.

For example, one target training node has 10 target training nodes, which can be classified according to radicals, structures, and the like, wherein 5 target training nodes belong to the character side and can be classified into a class of target training nodes (class a for short, and numbers are 1 to 5 respectively), and the other 5 target training nodes belong to the character side and can be classified into a class of target training nodes (class B for short, and numbers are 1 to 5 respectively). When 5 class-A target training nodes are ranked, the higher the difficulty label is, the later the difficulty label is, for example, the order is: the target training node 1- > target training node 3- > target training node 2- > target training node 4- > target training node 5; when 5B-class target training nodes are ranked, the higher the difficulty label is, the later the difficulty label is, for example, the order is: the target training node 10- > the target training node 8- > the target training node 7- > the target training node 9- > the target training node 6; for class A and class B, wherein the majority of the initial use order of class A is in front of the majority of the initial use order of class B, and the majority of the initial use order of class B is behind the majority of the initial use order of class B, therefore, the final target use order is determined based on the above, and the target training node path is generated as follows: target training node 1- > target training node 3- > target training node 2- > target training node 4- > target training node 5- > target training node 10- > target training node 8- > target training node 7- > target training node 9- > target training node 6.

Of course, an average value of the initial use sequence of each target training node in the class of target training nodes may be obtained, and inter-class ranking may be performed based on the average value. It should be noted that the above-mentioned manner for determining the usage order of the targets is only an example, and practically any manner for determining the usage order of the targets based on the difficulty labels of the target training nodes and the initial usage order among the target training nodes in the universal training path is applicable in the present application, and is not limited in particular.

Based on the above embodiment, the order of textbook arrangement can be broken, and students do not need to listen and write strictly according to the requirements of teachers, parents and the like, and do not need to listen and write in the order of book catalogues, such as listening and writing in the first course first, or listening and writing in the first unit 1-5 courses first. In this way, all words which should be mastered by students in the learning section can be scattered according to the personal learning ability of the students, and the word sequence is recombined according to the learning condition of the students, so as to achieve the purpose of better teaching.

In an optional embodiment, when generating the training task for the target object based on the target training node path, the specific process is as follows:

determining a media type corresponding to a training task according to a training style corresponding to a target object; acquiring a first auxiliary key information base associated with the current environmental factors; and generating the training task of the media type according to the target training node path and the first auxiliary key information base.

The training style mainly refers to a learning style and can be generally divided into: information processing (lively, meditation), perception (comprehension, intuition), information input (vision, speech), content understanding (sequence, synthesis), and the like. The media type is used for describing the tendency of a user to select a resource media format and is divided into: pictures, video, text, etc. For example, when the training style is an active style, the media type corresponding to the active style is more suitable for the video type; when the training style is the meditation style, the media type corresponding to the style is more suitable for the picture and text type; when the training style is a visual style, the media type corresponding to the style is more suitable for video and picture types; when the training style is a speech style, the media type corresponding to the style is more suitable for text types and the like.

In the embodiment of the present application, the current environmental factors include social hotspot factors, weather factors, and the like. For example, in the current rainy weather, some information bases related to the rainy weather can be acquired as the first auxiliary key information base. When a training task of a certain media type is generated based on the first auxiliary key information base and the target training node path acquired before,

the media type may be at least one of a video, a picture, and a text, for example, the media type may be in a video form, or may be in a picture + text form, etc., and the media type is determined with reference to an actual situation, and is not specifically limited herein.

In the above embodiment, different target training node paths can be provided according to factors such as periods and learning environments, and in the whole learning cycle of a user, special periods exist, such as review and pre-study in summer and chills; in the period, there will be new class study, zhou Xiaojie, month review, examination in the middle period, examination at the end of the period; in the whole learning period, special periods such as an upgradable examination exist, and based on the embodiment, different training schemes can be provided for students according to different occasions.

Optionally, considering that the learning score of the user is dynamic change, when the word browsing or dictation detection state changes, the user needs to adjust and update in time, dynamically updates the position in the word learning knowledge path, regenerates a proper learning position and task, fully achieves the supplement of school education, solves the problem that the teacher cannot achieve accurate teaching for a single student, thereby reducing the burden of the teacher, parents and students, and ensuring the final teaching effect and efficiency.

An optional implementation manner is that the target training node path is updated based on the following process:

if the training state of the target object is detected to change, a second auxiliary key information base associated with the changed training state is obtained; updating target training nodes in a target training node path and a target using sequence among the target training nodes according to the second auxiliary key information base; and regenerating the training task aiming at the target object based on the updated target training node path.

The training state mainly refers to the learning state of the user, and can be determined based on the learning receptivity, the learning level and the like of the user, and the training state of a general user can change along with the increase of knowledge points learned by the user. The second auxiliary key information base is the information base related to the changed training state. For example, the user sets a target for acquiring a related prize, that is, the information base related to the prize can be used as the second auxiliary key information base; or, the user obtains a word-related award, that is, a new word stock that is more complex than the award can be used as a second auxiliary key information base, and the like, which is determined with specific reference to the actual situation and is not specifically limited herein.

When the target training node path is updated based on the second auxiliary key information base and the training state of the target object, the target using sequence of the target training nodes is updated, the target training nodes are further updated, namely the target training nodes mastered by the user are deleted, and then some new target training nodes are reselected.

Based on the embodiment, words required to be mastered by students can be scattered and recombined according to different students, different periods and different mastering degrees; providing a customized dictation plan for each student; providing different contents to each student; the dictation is more intelligent, efficient and effective, and the optimal plan of the optimal scheme is used for guiding the user to perform dictation so as to ensure the final learning effect.

The following mainly takes dictation task generation as an example, and details the method in the embodiment of the present application are described:

because different dictation capabilities of different students are not considered in related technologies, the situation that the difficulty of dictation contents is not matched with the dictation capabilities of the students possibly occurs in the dictation process, and the dictation task generation method provided by the application. Fig. 7 is a schematic flow chart of a dictation task generation method according to the present application. The method specifically comprises the following steps:

1. and data processing means electronically processing the words which should be mastered by the student.

The specific implementation mode can be as follows: photographing a paper book surface in a photographing area of a photographing module by using the photographing module of the electronic equipment to obtain a book image; the book content is obtained by analyzing the book image, and words contained in the book content are recorded as dictation words.

For example, according to the requirements of "course standards", student books, teaching lines, and examinees, the words to be mastered by the section (primary school, junior middle school, and senior high school) are extracted, stored in a data manner, and stored with detailed word label attributes, such as publishing company, grade, upper and lower books, unit, text title, i's meeting, i's writing, and the like.

2. And constructing a general knowledge path for word learning, namely an initial training node path in the text.

Specifically, a word learning general knowledge path, i.e., a standardized path, can be constructed according to the grade, the order of book entry and text, the difficulty, the word source relationship, the calligraphic relationship, and the like, and the path is a content which is presented according to the grade characteristics of the students and is suitable for the students to grasp all the contents.

The word source relationship refers to the relationship between the word and the evolution process of the word, and the stroke relationship refers to the relationship between the writing strokes of the word, and the common strokes include a left-right structure, an upper-lower structure, an upper-middle-lower structure and the like.

3. And data acquisition, namely acquiring user data generated in the learning APP under the condition of user authorization, and meanwhile, collecting each error word and performing attribute marking. If the user inquires the new words, the user adds the words entering the new word book, the user listens to the written words, and the user takes pictures of the corrected words for subsequent positioning use. Wherein, the property is a word property, such as writing, recognizing, backing, etc.

4. Generating a customized knowledge path with the user, i.e. a target training node path

Specifically, firstly, a knowledge path diagram suitable for word learning of the user is generated, and the knowledge path diagram comprises a learning sequence and difficulty labels of knowledge; and then, positioning the weakest word learning area of the user by the collected Chinese character information and combining the current state of the user, and matching the corresponding learning path.

5. And generating a dictation task.

Specifically, dictation content of the current area is given preferentially, and according to a subsequent learning and dictation plan of path planning, daily tasks mastered by the dictation content and words are generated; when the word browsing or dictation detection state changes, the position in the word learning knowledge path is dynamically updated, and a proper learning position and task are regenerated.

In the embodiment of the application, a self-adaptive learning system can be constructed to plan words and learn knowledge paths for users.

Fig. 8 is a schematic diagram of a general model of an adaptive learning system according to an embodiment of the present application. The model can specifically comprise the following parts:

1. a Domain Model (Domain Model) describing Domain knowledge structures, including concepts and connections between concepts; 2. a Student Model (UM) which is also called a User Model and represents Student characteristics and describes information such as knowledge, tendency, hobbies and interests of each User; 3. an education Model (Pedagogogical Model) which defines rules for accessing each part of the field Model according to the information in the student Model; 4. an Adaptive Engine (Adaptive Engine) is a whole software environment for creating and updating the field concepts and links, and uses information in other models to perform personalized selection, annotation and presentation of learning contents for learners; 5. an Interface Module (Interface Module) represents and defines the interaction between the user and the adaptive learning system.

In the embodiment of the application, the system can carry out semantic description on the learning resources, carry out semantic diagnosis on the learning style and the cognitive level, dynamically present the learning resources and the learning path according to the learner model, and realize resource sharing, reusing and personalized recommendation. Realizing the learner adaptation system and the bidirectional adaptation of the system to the learner.

In the following, an adaptive learning system reference model applied to the dictation task generation method in the embodiment of the present application is proposed in combination with the adaptive system general learning model shown in fig. 8.

Fig. 9 is a diagram illustrating a reference model of an adaptive learning system according to an embodiment of the present application. The model specifically comprises the following parts:

1. user Model (i.e., student Model) and Learning behavior (Learning belloviors):

the user model describes individual characteristics of the user, such as learner (student) basic information description (name, gender, birth date, telephone, e-mail, education level, occupation, etc.), learning style, cognitive level, interest preference, etc.

The learning behavior records the learning history process of the learner (such as the media type, learning time, access times and the like of the learner accessing the learning resources), and the system can continuously update the user model according to the learning history record of the user.

2. Domain Model (Domain Model):

structures describing domain knowledge include concepts and associations between concepts, each concept may have different attributes, and concepts having the same attribute may be different data types. An inter-concept connection is an object that connects two or more concepts, with a unique identification value and attributes.

3. Adaptive Model (i.e. educational Model):

the model defines how to access various parts of the domain model based on information in the user model, to generate adaptive actions, and to modify a set of rules of the user model that embody the idea of teaching design of the course.

4. Adaptive Engine (Adaptive Engine):

the part executes adaptive rules corresponding to the implementation of the system, selects, assembles and presents pages according to the user model, and implements the modification and maintenance of the user model according to the user learning behavior history record, and the like.

5. Presentation Model (Presentation Model):

the system realizes adaptive display of at least one of Content (Content), navigation (presentation), sequence (Sequence) and the like through an adaptive engine according to a user model, a domain model and an adaptive model.

When the content is displayed, the system displays different media types (such as videos, pictures, texts and the like) according to the learning style of the user; the system can also present learning content with different factual or abstract characteristics according to the learning style of the user.

When the navigation display is performed, the system can be divided into global navigation and local navigation according to the learning style and the cognitive level, and the detailed description is provided below.

The cognitive level estimation mainly estimates the mastering condition of a student on a certain knowledge point through the exercise test records of the student, and then the system recommends knowledge resources of corresponding levels according to different cognitive levels of the user, so that a more personalized learning process and a more personalized learning target are formulated.

When the learning activity sequence is displayed, the system can adapt to the recommended learning sequence according to the learning style of the learner, specifically, in order to achieve the learning goal, the system can present a set of feasible learning scheme according to the individual difference of the user, and the learning scheme integrates the learning goal, the training task, the operation steps, the interaction form, the evaluation mechanism and the like, which will be described in detail in the following.

Based on the self-adaptive learning system, the method for providing word learning path planning and content planning for the user by means of the self-adaptive learning system is provided, so that the problems that when the user listens and writes words, the process only needs to be in place from the beginning, the time is consumed in the process, and the effect is poor are solved, and different scientific and effective methods and personalized teaching guidance solutions can be provided for students with different learning degrees and different periods. In addition, the method solves the problems that a teacher in a school can only provide universal education and cannot provide a personalized and targeted learning scheme, and students can only listen and write words with lower proficiency and difficulty and can master insecure words.

In an alternative embodiment, the task interface may be presented based on:

and generating a training activity sequence and a training navigation aiming at the target object based on the target training node path, and displaying the training activity sequence and the training navigation in a task interface.

The training activity sequence is used for representing the arrangement sequence of training items recommended according to the training style of the target object, and is also called a learning activity sequence, and the system can adapt and recommend the learning sequence according to the learning style of the learner.

Fig. 10 is a schematic diagram of a first learning activity sequence according to an embodiment of the present application. Wherein, the left side is the learning activity sequence corresponding to the active user, and the right side is the learning activity sequence corresponding to the meditation user. For an active learner, the system then recommends a sequence of learning activities that is: outline → resource → summary → practice → forum → example → test; for a meditation learner, the system recommends a sequence of learning activities that is: outline → resource → summary → example → forum → practice → test.

Fig. 11 is a schematic diagram of a second learning activity sequence according to the embodiment of the present application. Similar to fig. 10, the left side in fig. 11 is a learning activity sequence corresponding to a speech-type user, which is the same as the left side sequence in fig. 10, and the right side is a learning activity sequence corresponding to a visual-type user, which is the same as the left side sequence in fig. 10. Fig. 11 further illustrates different media types for users of different learning styles. Wherein, for the language type user, the learning resources can be displayed through the text; for visual users, the learning resources may be presented by at least one of video and pictures.

In addition to those enumerated in fig. 10 and 11, the system may also recommend a sequence of learning activities for an active learner: participation in discussion (optional) → reading learning materials (recommended) → case study (recommended) → doing practice (optional) → completing testing (optional); for a meditation type learner, the system may also recommend that the sequence of learning activities be: reading learning materials (optional) → case study (optional) → participation in discussion (recommendation) → doing exercise (optional) → completing testing (optional).

It should be noted that the above listed learning activity sequences are only examples, and may be specifically determined according to practical situations, and are not specifically limited herein.

The training navigation in the embodiment of the present application is described in detail below.

Wherein the training navigation includes at least one of global navigation and local navigation recommended according to a training style of the target object.

Fig. 12 is a schematic diagram of global navigation and local navigation in the embodiment of the present application. The knowledge tree shown on the left in fig. 12, i.e. the global navigation, may comprise a complete training architecture presented by a (domain) knowledge tree structure. As shown in fig. 12, which includes a plurality of nodes, the present illustration only shows the sixth node (training node), and other nodes that are not currently displayed can be viewed by sliding up and down. In addition, a node may also include multiple child nodes, for example, child nodes including word creation, sentence creation, and the like, or child nodes obtained by further dividing the node according to strokes, word source relationships, and the like, which is not specifically limited herein.

In addition, the current training state of the target object can be displayed through the tree structure, for example, the learning state mark such as different colors and different patterns is adopted to display the current knowledge mastering state of the learner, which is not limited specifically herein.

The knowledge point structure, i.e., local navigation, shown on the right side of fig. 12 includes associated knowledge of the current training node (i.e., xx under the fourth child node) presented by the knowledge concept graph structure, and specifically, the knowledge concept graph provided for the learner can clearly show related knowledge, previous knowledge, next knowledge, and the like of the current knowledge point. Among them, the related knowledge is, for example: pinyin, word composition, sentence making, etc.; antecedent knowledge such as: knowledge that needs review; the latter knowledge is for example: requiring pre-learned knowledge.

In the above embodiment, when a student wants to complete a dictation task, the student sees a word learning map customized for himself, and a clear learning path and a current progress are marked, so as to provide a detailed plan, such as which words are dictated and what dictation frequency is; once the student uses the device, the learning achievement and the progress are changed and updated, a path is planned, and the task is dynamically adjusted to be the most suitable according to the learning condition and the mastery degree of the student.

In conclusion, the method and the system provide targeted and scientific dictation plans, schemes and contents for the user by means of AI self-adaptive capacity, thereby achieving the effects of greatly improving training efficiency and effect and avoiding the problem sea tactics; the real dictation level of the student can be reflected, the student can be helped to clearly know whether the dictation ability of the student is improved, so that the student can make great progress in the dictation training and the learning effect is improved.

Fig. 13 is a schematic flow chart of another dictation task generation method in the embodiment of the present application. The specific implementation flow of the method is as follows:

step S1301: the terminal equipment obtains corresponding words according to the course dictation rule associated with the student A;

step S1302: the terminal equipment constructs an initial dictation node path for word learning based on the obtained words;

step S1303: the method comprises the steps that terminal equipment collects user data of a student A generated in each learning APP under the authorization of the student A;

step S1304: the terminal equipment determines target key knowledge information which meets preset conditions and dictation standards corresponding to the target key knowledge information based on the collected user data;

step S1305: the terminal equipment screens out all target dictation nodes which are associated with the target key knowledge information from all dictation nodes;

step 1306: the terminal equipment determines a difficulty label of the associated target dictation node based on the dictation standard corresponding to the target key knowledge information;

step S1307: the terminal equipment conducts path recombination on each target dictation node based on the difficulty label of each target dictation node and the initial use sequence among each target dictation node in the universal dictation path, and determines the target use sequence among each target dictation node;

step S1308: the terminal equipment constructs a target dictation node path based on each target dictation node and a target use sequence among the target dictation nodes;

step S1309: the terminal equipment determines a media type corresponding to the dictation task according to the dictation style corresponding to the student A;

step 1310: the terminal equipment generates a dictation task of the media type according to the target dictation node path;

step S1311: if the terminal equipment detects that the dictation state of the student A changes, acquiring an auxiliary key information base associated with the changed dictation state;

step S1312: the terminal equipment updates target dictation nodes in a target dictation node path and a target use sequence among the target dictation nodes according to the auxiliary key information base;

step S1313: and the terminal equipment regenerates the dictation task for the student A based on the updated target dictation node path.

Based on the embodiment, when the student finishes the dictation task, the student can see the word learning map customized for himself, and the word learning map is marked with a clear learning path and a current progress, so that a detailed plan is provided, such as which words are dictated and what dictation frequency is; once the student uses the device, the learning achievement and the progress are changed and updated, the path is planned again, and the task is dynamically adjusted to be the most suitable according to the learning condition and the mastery degree of the student.

Based on the same inventive concept, the embodiment of the application also provides a training task generating device. As shown in fig. 14, which is a schematic structural diagram of the training task generating device 1400, the training task generating device may include:

a data obtaining unit 1401, configured to obtain corresponding training data according to a training rule associated with a target object;

an initial construction unit 1402, configured to construct an initial training node path based on the training data, where each training node represents one piece of key knowledge information in the training data, and the initial training node path represents an initial usage order of each training node;

a target construction unit 1403, configured to perform screening and path recombination on each training node included in the initial training node path based on historical training data associated with the target object, and obtain a corresponding target training node path;

a task generating unit 1404 configured to generate a training task for the target object based on the target training node path.

Optionally, the initial building unit 1402 is specifically configured to:

determining corresponding training nodes and an initial using sequence among the training nodes by classifying all key knowledge information in the training data;

and constructing an initial training node path based on each training node and the initial using sequence among the training nodes.

Optionally, target building unit 1403 is specifically configured to:

determining target key knowledge information which accords with preset conditions in historical training data and training standards corresponding to the target key knowledge information;

and screening and path recombination are carried out on each training node contained in the initial training node path based on the target key knowledge information and the corresponding training standard, and a target training node path is generated.

Optionally, target building unit 1403 is specifically configured to:

screening out all target training nodes associated with target key knowledge information from all training nodes;

determining difficulty labels of the associated target training nodes based on training standards corresponding to the target key knowledge information;

based on the difficulty labels of all target training nodes and the initial use sequence among all target training nodes in the general training path, path recombination is carried out on all target training nodes, and the target use sequence among all target training nodes is determined;

and constructing a target training node path based on each target training node and the target use sequence among the target training nodes.

Optionally, the task generating unit 1404 is specifically configured to:

determining a media type corresponding to a training task according to a training style corresponding to a target object; and

and generating a training task of a media type according to the target training node path and the first auxiliary key information base.

Optionally, the apparatus further comprises:

the display unit 1405 is used for generating a training activity sequence and a training navigation aiming at a target object based on the target training node path and displaying the training activity sequence and the training navigation in a task interface; wherein,

the training activity sequence is used for representing the arrangement sequence of training items recommended according to the training style of the target object, the training navigation comprises at least one of global navigation and local navigation recommended according to the training style of the target object, the global navigation comprises a complete training system presented through a knowledge tree structure and the current training state of the target object, and the local navigation comprises associated knowledge of the current training nodes presented through a knowledge concept graph structure.

Optionally, the apparatus further comprises:

the task updating unit 1406 is configured to, if it is detected that the training state of the target object changes, obtain a second auxiliary key information base associated with the changed training state;

updating target training nodes in a target training node path and a target using sequence among the target training nodes according to the second auxiliary key information base;

and regenerating the training task for the target object based on the updated target training node path.

For convenience of description, the above parts are separately described as modules (or units) according to functional division. Of course, the functionality of the various modules (or units) may be implemented in the same one or more pieces of software or hardware when implementing the present application.

Having described the training task generating method and apparatus according to an exemplary embodiment of the present application, an electronic device for training task generation according to another exemplary embodiment of the present application is described next.

As will be appreciated by one skilled in the art, aspects of the present application may be embodied as a system, method or program product. Accordingly, various aspects of the present application may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

The electronic equipment is based on the same inventive concept as the method embodiment. In one embodiment, the electronic device may be a server, such as server 220 shown in FIG. 2. In this embodiment, the electronic device may be configured as shown in fig. 15, and may include a memory 1501, a communication module 1503, and one or more processors 1502.

A memory 1501 for storing computer programs executed by the processor 1502. The memory 1501 may mainly include a program storage area and a data storage area, where the program storage area may store an operating system, programs needed for running an instant messaging function, and the like; the storage data area can store various instant messaging information, operation instruction sets and the like.

The memory 1501 may be a volatile memory (volatile memory), such as a random-access memory (RAM); the memory 1501 may also be a non-volatile memory (non-volatile memory), such as a read-only memory (rom), a flash memory (flash memory), a hard disk (HDD) or a solid-state drive (SSD); or memory 1501 is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory 1501 may be a combination of the above memories.

The processor 1502 may include one or more Central Processing Units (CPUs), or be a digital processing unit, etc. A processor 1502 for implementing the above-described training task generation method when calling a computer program stored in the memory 1501.

The communication module 1503 is used for communicating with terminal devices and other servers.

The embodiment of the present application does not limit the specific connection medium among the memory 1501, the communication module 1503 and the processor 1502. In fig. 15, the memory 1501 and the processor 1502 are connected by a bus 1504, the bus 1504 is depicted by a thick line in fig. 15, and the connection manner between other components is merely illustrative and not limited. The bus 1504 may be divided into an address bus, a data bus, a control bus, and the like. For ease of description, only one thick line is depicted in fig. 15, but not only one bus or one type of bus.

The memory 1501 stores a computer storage medium having computer-executable instructions stored therein, which are used to implement the training task generating method according to the embodiments of the present application. The processor 1502 is configured to perform the training task generating method described above and shown in FIG. 3.

In another embodiment, the electronic device may also be other electronic devices, such as the terminal device 210 shown in fig. 2. In this embodiment, the structure of the electronic device may be as shown in fig. 16, including: a communications component 1610, a memory 1620, a display unit 1630, a camera 1640, a sensor 1650, audio circuitry 1660, a bluetooth module 1670, a processor 1680, and the like.

The communication component 1610 is configured to communicate with a server. In some embodiments, a Wireless Fidelity (WiFi) module may be included, the WiFi module being a short-range Wireless transmission technology, through which the electronic device may help the user to transmit and receive information.

Memory 1620 may be used to store software programs and data. The processor 1680 executes various functions of the terminal device 210 and data processing by executing software programs or data stored in the memory 1620. The memory 1620 may comprise high speed random access memory and may also comprise non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. The memory 1620 stores an operating system that enables the terminal device 210 to operate. The memory 1620 may store an operating system and various application programs, and may further store codes for executing the training task generating method according to the embodiment of the present application.

The display unit 1630 may also be used to display a Graphical User Interface (GUI) of information input by or provided to the user and various menus of the terminal apparatus 210. Specifically, the display unit 1630 may include a display screen 1632 provided on the front surface of the terminal device 210. The display 1632 may be configured in the form of a liquid crystal display, a light emitting diode, or the like. The display unit 1630 may be used to display a task interface and the like in the embodiment of the present application.

The display unit 1630 may also be used to receive input numeric or character information, generate signal input related to user settings and function control of the terminal device 210, and specifically, the display unit 1630 may include a touch screen 1631 disposed on the front side of the terminal device 210, and may collect touch operations by the user thereon or nearby, such as clicking a button, dragging a scroll box, and the like.

The touch screen 1631 may cover the display screen 1632, or the touch screen 1631 and the display screen 1632 may be integrated to implement the input and output functions of the terminal device 210, and after integration, the touch screen may be referred to as a touch display screen for short. The display unit 1630 can display the application programs and the corresponding operation steps.

The camera 1640 may be used to capture still images, and the user may post comments on images captured by the camera 1640 through the application. The number of the cameras 1640 may be one or plural. The object generates an optical image through the lens and projects the optical image to the photosensitive element. The photosensitive element may be a Charge Coupled Device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The light sensing elements convert the optical signals to electrical signals that are then passed to processor 1680 for conversion to digital image signals.

The terminal device may also include at least one sensor 1650, such as an acceleration sensor 1651, a distance sensor 1652, a fingerprint sensor 1653, a temperature sensor 1654. The terminal device may also be configured with other sensors such as a gyroscope, barometer, hygrometer, thermometer, infrared sensor, light sensor, motion sensor, and the like.

The audio circuitry 1660, speaker 1661, microphone 1662 may provide an audio interface between the user and the terminal device 210. The audio circuit 1660 may convert the received audio data into an electrical signal, transmit the electrical signal to the speaker 1661, and convert the electrical signal into an acoustic signal by the speaker 1661 for output. The terminal device 210 may also be provided with a volume button for adjusting the volume of the sound signal. On the other hand, the microphone 1662 converts collected sound signals into electrical signals, which are received by the audio circuit 1660 and converted into audio data, which are output to the communication component 1610 for transmission to, for example, another terminal device 210, or to the memory 1620 for further processing.

The bluetooth module 1670 is used for information interaction with other bluetooth devices having a bluetooth module through a bluetooth protocol. For example, the terminal device may establish a bluetooth connection with a wearable electronic device (e.g., a smart watch) that is also equipped with a bluetooth module via the bluetooth module 1670, thereby performing data interaction.

The processor 1680 is a control center of the terminal device, connects various parts of the entire terminal device using various interfaces and lines, and performs various functions of the terminal device and processes data by running or executing software programs stored in the memory 1620 and calling data stored in the memory 1620. In some embodiments, processor 1680 may include one or more processing units; the processor 1680 may also integrate an application processor, which primarily handles operating systems, user interfaces, application programs, etc., and a baseband processor, which primarily handles wireless communications. It is to be appreciated that the aforementioned baseband processor may not be integrated into processor 1680. The processor 1680 may run an operating system, an application program, a user interface display, a touch response, and the training task generation method according to the embodiments of the present disclosure. Additionally, processor 1680 is coupled with display unit 1630.

In some possible embodiments, the aspects of the training task generating method provided by the present application may also be implemented in the form of a program product including program code for causing an electronic device to perform the steps of the training task generating method according to various exemplary embodiments of the present application described above in this specification when the program product is run on the electronic device, for example, the electronic device may perform the steps as shown in fig. 3.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The program product of embodiments of the present application may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a computing device. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with a command execution system, apparatus, or device.

A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with a command execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user computing device, partly on the user equipment, as a stand-alone software package, partly on the user computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functions of two or more units described above may be embodied in one unit, according to embodiments of the application. Conversely, the features and functions of one unit described above may be further divided into embodiments by a plurality of units.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims

1. A training task generating method, the method comprising:

constructing an initial training node path based on the training data, wherein each training node represents one piece of key knowledge information in the training data, and the initial training node path represents the initial using sequence of each training node;

2. The method of claim 1, wherein constructing an initial training node path based on the training data comprises:

constructing the initial training node path based on the training nodes and the initial using sequence among the training nodes.

3. The method of claim 1, wherein the filtering and path reorganizing each training node included in the initial training node path based on historical training data associated with the target object to obtain a corresponding target training node path comprises:

determining target key knowledge information which accords with preset conditions in the historical training data and training standards corresponding to the target key knowledge information;

and screening and path recombination are carried out on each training node contained in the initial training node path based on the target key knowledge information and the corresponding training standard, so as to generate the target training node path.

4. The method as claimed in claim 3, wherein the target key knowledge information meeting the preset condition comprises at least one of:

adding appointed marked key knowledge information in a corresponding historical training process;

5. The method of claim 3, wherein the obtaining the target training node path by filtering and path recombination of each training node included in the initial training node path based on the target key knowledge information and corresponding training criteria comprises:

screening out all target training nodes associated with the target key knowledge information from all the training nodes;

based on the difficulty labels of the target training nodes and the initial using sequence among the target training nodes in the general training path, path recombination is carried out on the target training nodes, and the target using sequence among the target training nodes is determined;

and constructing the target training node path based on the target training nodes and the target use sequence among the target training nodes.

6. The method of claim 1, wherein generating a training task for the target object based on the target training node path comprises:

7. The method of claim 1, wherein the method further comprises:

generating a training activity sequence and a training navigation aiming at the target object based on the target training node path, and displaying the training activity sequence and the training navigation in a task interface; wherein,

8. The method of any one of claims 1 to 7, further comprising:

if the training state of the target object is detected to be changed, a second auxiliary key information base associated with the changed training state is obtained;

9. A training task generating apparatus, comprising:

the initial construction unit is used for constructing an initial training node path based on the training data, wherein each training node represents one key knowledge information in the training data, and the initial training node path represents the initial use sequence of each training node;

10. The apparatus of claim 9, wherein the initial building unit is specifically configured to:

constructing the initial training node path based on the training nodes and the initial use sequence among the training nodes.

11. The apparatus of claim 9, wherein the object construction unit is specifically configured to:

12. The apparatus of claim 11, wherein the target key knowledge information meeting the preset condition comprises at least one of:

13. The apparatus of claim 11, wherein the object construction unit is specifically configured to:

14. An electronic device, comprising a processor and a memory, wherein the memory stores program code which, when executed by the processor, causes the processor to perform the steps of the method of any of claims 1 to 8.

15. A computer-readable storage medium, characterized in that it comprises program code for causing an electronic device to perform the steps of the method of any one of claims 1 to 8, when said storage medium is run on said electronic device.