A teaching material generation method, device, electronic equipment and program product

Abstract

The embodiment of the present application provides a teaching material generation method, device, electronic device and program product, which relates to the field of computer teaching technology; wherein, a teaching material generation method, the method comprising: obtaining target keywords for generating teaching materials; wherein, the target keywords include knowledge point keywords, regional keywords and audience keywords; the regional keywords are used to characterize the region to which the audience belongs or the region related to the knowledge point represented by the knowledge point keyword, and the audience keyword is used to characterize the age stage of the audience; based on the target keywords, each candidate scene object is determined; the determined candidate scene objects are displayed; in response to a selection operation for the displayed candidate scene object, teaching materials related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword are generated. It can be seen that this solution can enhance the fun of teaching materials.

Description

Technical Field

[0001] The present application relates to the field of computer teaching technology, and in particular to a teaching material generation method, device, electronic equipment and program product. Background Art

[0002] Knowledge points are the theoretical summaries of experience formed by humans through practical activities and thinking activities. Scholars extract core knowledge points and, through specialized study of these knowledge points, can apply these knowledge points more efficiently in life and work.

[0003] In actual teaching activities, lecturers can prepare lessons in advance to prepare teaching materials in advance; however, existing teaching materials are generally rigid and lack interest. It may be difficult for the audience to be interested in the knowledge points taught by the lecturers, which may result in the audience being unable to quickly understand the knowledge points taught by the lecturers.

[0004] At present, there is an urgent need for a teaching material generation method to enhance the interest of teaching materials. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a teaching material generation method, device, electronic device and program product to enhance the interest of teaching materials. The specific technical solution is as follows:

[0006] In a first aspect, an embodiment of the present application provides a method for generating teaching materials, which is applied to an electronic device, and the method includes:

[0007] Obtain target keywords for generating teaching materials; wherein the target keywords include knowledge point keywords, region keywords and audience keywords; the region keywords are used to represent the region to which the audience belongs or the region related to the knowledge point represented by the knowledge point keywords, and the audience keywords are used to represent the age stage of the audience;

[0008] Based on the target keyword, determine each candidate scene object; wherein the candidate scene object is: a scene object in the predetermined scene object of the region represented by the region keyword, which can be recognized by the age group represented by the audience keyword and is related to the knowledge point represented by the knowledge point keyword; the predetermined scene object is a scene object with regional characteristics;

[0009] Displaying the determined candidate scene objects;

[0010] In response to a selection operation on the displayed candidate scene object, a teaching material related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword is generated.

[0011] In a second aspect, an embodiment of the present application provides a teaching material generating device, which is applied to an electronic device, and the device includes:

[0012] An acquisition module, used to acquire target keywords for generating teaching materials; wherein the target keywords include knowledge point keywords, region keywords and audience keywords; the region keywords are used to represent the region to which the audience belongs or the region related to the knowledge point represented by the knowledge point keywords, and the audience keywords are used to represent the age stage of the audience;

[0013] The first determination module is used to determine each candidate scene object based on the target keyword; wherein the candidate scene object is: a scene object in the predetermined scene object of the region represented by the region keyword, which can be recognized by the age group represented by the audience keyword and is related to the knowledge point represented by the knowledge point keyword; the predetermined scene object is a scene object with regional characteristics;

[0014] A display module, used for displaying the determined candidate scene objects;

[0015] A generation module is used to generate, in response to a selection operation on the displayed candidate scene object, teaching materials related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword.

[0016] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0017] Memory, used to store computer programs;

[0018] The processor is used to implement the above-mentioned teaching material generation method when executing the program stored in the memory.

[0019] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program, which implements the above-mentioned teaching material generation method when executed by a processor.

[0020] Beneficial effects of the embodiments of the present application:

[0021] The teaching material generation method provided by the embodiment of the present application can obtain the target keywords for generating teaching materials, wherein the target keywords include knowledge point keywords, location keywords and audience keywords, and based on the target keywords, the candidate scene objects can be determined; subsequently, the teaching materials can be generated in response to the selection operation for the displayed candidate scene objects. It can be seen that the teaching materials generated by the embodiment of the present application not only include the knowledge points represented by the knowledge point keywords, but also are related to the target scene objects indicated by the selection operation, and the target scene objects indicated by the selection operation can be considered as the scene objects in the predetermined scene objects represented by the region keywords selected by the user, the scene objects that can be recognized by the age stage represented by the audience keywords and are related to the knowledge points represented by the knowledge point keywords, so the generated teaching materials can be combined with the audience's cognitive level and the regional environment in which they live; then, through the teaching materials, the fun of the teaching materials can be improved, thereby improving the audience's interest in the knowledge points taught by the lecturers. And, because the audience is interested in the knowledge points taught by the lecturers, the audience can also better understand the knowledge points taught by the lecturers, thereby improving the teaching quality and teaching effect.

[0022] Of course, implementing any product or method of the present application does not necessarily require achieving all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other embodiments can also be obtained based on these drawings.

[0024] Figure 1 A flow chart of a method for generating teaching materials provided in an embodiment of the present application;

[0025] Figure 2 A schematic diagram of the effect of a display interface provided in an embodiment of the present application;

[0026] Figure 3 A flowchart of another teaching material generation method provided in an embodiment of the present application;

[0027] Figure 4 A schematic diagram of the effect of a teaching plan template provided in an embodiment of the present application;

[0028] Figure 5 A schematic diagram of the effect of a target topic provided in an embodiment of the present application;

[0029] Figure 6A schematic diagram of the structure of a teaching material generating device provided in an embodiment of the present application;

[0030] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field based on the present application belong to the scope of protection of the present application.

[0032] First, in order to better understand this solution, the following is a brief introduction to the relevant technologies:

[0033] In actual teaching activities, lecturers can prepare lessons in advance to prepare teaching materials in advance, but the existing teaching materials all directly and rigidly introduce knowledge points, and do not combine the region or other aspects to introduce the knowledge points, which will make it difficult for the audience to be interested in the knowledge points taught by the lecturers; and due to the lack of teaching experience of the lecturers, the audience may not be able to efficiently understand the knowledge points taught by the lecturers. At the same time, different audiences have different cognitive levels and different abilities to absorb knowledge points, which will also affect the actual teaching effect.

[0034] In order to solve the above problems, the embodiments of the present application provide a teaching material generation method, device, electronic device and program product to enhance the interest of the teaching materials.

[0035] The following first introduces the teaching material generation method provided in the embodiment of the present application.

[0036] Among them, the teaching material generation method provided in the embodiment of the present application can be applied to electronic devices, and the electronic devices can specifically be terminal devices such as personal computers (PCs) and mobile devices. Of course, the electronic devices can also be servers, and the embodiment of the present application does not make specific limitations on this.

[0037] Specifically, the execution subject of the teaching material generation method may be a teaching material generation device. Exemplarily, when the teaching material generation method is applied to a terminal device, the teaching material generation device may be a client running in the terminal device and used for teaching material generation. Exemplarily, when the teaching material generation method is applied to a server, the teaching material generation device may be a computer program running in the server, and the computer program may be used for teaching material generation.

[0038] In addition, the specific form of the teaching material generating device is not limited to the form described above, but can also be a plug-in form, or a web form, which is not specifically limited in the embodiment of the present application. For example, the instructor logs in to the web end through an account, thereby generating teaching materials on the web end.

[0039] Furthermore, the application scenario in the embodiments of the present application can be any teaching and lesson preparation scenario, and is not limited to a campus scenario, but can also be other scenarios, such as certain training scenarios, which are not specifically limited in the embodiments of the present application; and the location of use can also be anywhere in the world, which is not specifically limited in the present application.

[0040] In addition, in order to better understand the present solution, before introducing the teaching material production method, the knowledge point keywords, regional keywords and audience keywords in the embodiment of the present application are exemplarily introduced and explained:

[0041] The knowledge point keywords may be keywords of relevant knowledge points of different disciplines in different regions and countries around the world. Any knowledge point keyword may represent a knowledge point under a discipline; wherein the different disciplines may be exemplarily divided into the following categories:

[0042] Language and Literature: includes reading, writing, grammar and literature learning in both native and foreign languages ​​(such as English, French, German, etc.).

[0043] Mathematics: basic arithmetic (addition, subtraction, multiplication and division), geometry, statistics and some basic algebra.

[0044] Science: The basics of natural sciences, including biology, physics, chemistry, etc., usually based on experiments and observations.

[0045] Social sciences: history, geography, sociology, etc.

[0046] Art and Music: painting, handicrafts, music appreciation, playing musical instruments, etc., to cultivate creativity and artistic sensibility.

[0047] Physical Education: includes various sports and games that promote physical development and health.

[0048] Moral and Civic Education: Teaching basic social values ​​and ethics, encouraging good social behavior and civic responsibility.

[0049] Information Technology: Basic computer skills and Internet safety.

[0050] It should be emphasized that the above is only an exemplary introduction, and the subjects involved in the specific knowledge point keywords may also include other classifications, and the embodiments of the present application do not make specific limitations on this.

[0051] Regional keywords are used to represent the region to which the audience belongs or the region related to the knowledge point represented by the knowledge point keyword; among them, different regions can have their own regional characteristics, among which the regional characteristics can be exemplarily divided into the following three categories, namely, regional culture, natural ecology and economy.

[0052] The first category is regional culture, which can be exemplified as follows:

[0053] Festivals: important local festivals, celebrations and customs, such as the Spring Festival, Mid-Autumn Festival, local festivals, etc. For example, region A has the custom of dragon and lion dances, region B has the custom of boat tours of ancient towns, and region C has the custom of Water Splashing Festival;

[0054] Painting-related art forms, such as court painting in region A and woodcut painting in region B;

[0055] Music, dance, drama and other art forms, such as shadow play in region A, flower drum in region B, and Naxi ancient music in region C;

[0056] Handicrafts, such as sculpture, textiles, ceramics…;

[0057] Architectural culture, for example: earthen buildings in region A, courtyard houses and royal buildings in region B, Hui-style architecture in region C, and Longmen Grottoes in region D;

[0058] Food culture, for example: barbecue in region A, spicy hot pot in region B, and casserole porridge in region C;

[0059] Clothing culture, for example: robes in region A;

[0060] Local social etiquette, traditional rituals and manners, for example: in the settlement of region A, when guests visit, the host will present Hada and offer kumis to express welcome; in region B, people will invite friends to drink tea together;

[0061] Local popular folk tales and stories, for example: the legend of White Snake in Region 1, the legend of Butterfly Spring in Region 2;

[0062] Among them, the second category is natural ecology, and examples thereof may include:

[0063] Topography and landforms, for example: plateau landforms in region 1, mountain city landforms in region 2 (cities built on mountains), basins in region 3;

[0064] Natural resources, such as water resources (rivers, lakes, etc.), mineral resources (gold, silver, copper, iron, oil, coal, natural gas, etc.);

[0065] Ecosystems, such as forests, grasslands, wetlands, plants, animals, microorganisms, etc.;

[0066] Among them, the third category is economic, and examples thereof may include:

[0067] Special agriculture, for example: flower market in area 1, cotton market in area 2, hairy crabs in area 3, terraced fields in area 4;

[0068] Specialized manufacturing / mining industries, for example, steel production in region 1, shoe production in region 2, gold and copper mining in region 3;

[0069] Special tourism, for example: mountain tourist attractions in region 1, lake tourist attractions in region 2;

[0070] Specialty agricultural products, for example: apples and cherries from region 1, Shaoxing wine from region 2, and ginseng from region 3.

[0071] It should be emphasized that the above is only an exemplary introduction, and specific regional keywords may also include other categories, which are not specifically limited in the embodiments of the present application.

[0072] Audience keywords represent age stages. Each age stage has objects that can be recognized. The following is an exemplary introduction to age stages and objects that can be recognized:

[0073] Preschool stage (3-6 years old);

[0074] Basic sensory objects: shape (round, square), color (red, blue), etc.;

[0075] Simple daily necessities: toys (such as building blocks, stuffed toys), daily necessities (cups, plates), etc.;

[0076] Common foods: apples, bananas, biscuits, etc.

[0077] Basic means of transportation: cars, trains, bicycles, etc.;

[0078] Toy characters: doctor toys, kitchen toys, etc.;

[0079] Lower grades of primary school (7-9 years old);

[0080] Basic Subject Objects: Geometric shapes: cube, cuboid, cylinder, etc.;

[0081] Scientific objects: magnets, water, basic parts of plants (roots, stems, leaves), etc.;

[0082] Daily necessities: household items, such as telephones, televisions, refrigerators, etc.;

[0083] School supplies: pencils, school bags, calculators, etc.;

[0084] Basic economic items: banknotes, coins, etc.;

[0085] Cultural items, traditional clothing, etc.;

[0086] Upper primary school (10-12 years old);

[0087] Scientific and technological objects: complex scientific instruments such as microscopes, telescopes, etc.;

[0088] Electronic devices: such as tablets, smartphones, etc.

[0089] Natural weather phenomena: thunderstorms, storms, rainbows, etc.;

[0090] Biological categories: insects, fish, birds, etc.;

[0091] Social and cultural heritage: historical relics, etc.;

[0092] Artworks: sculptures, paintings, etc.;

[0093] Social facilities: hospitals, libraries, museums, etc.;

[0094] Junior high school stage (13-15 years old);

[0095] Complex scientific objects: chemical substances; physical experiment equipment (circuit boards, mechanical models), etc.

[0096] Technical and engineering objects: mechanical devices such as engines, robots, etc.;

[0097] Information technology: network equipment, programming tools, etc.;

[0098] Social economic objects: market goods; global goods (international brands, multinational corporate products), etc.

[0099] High school stage (16-18 years old);

[0100] Professional scientific objects: advanced experimental equipment such as nuclear magnetic resonance equipment, mass spectrometer, etc.;

[0101] Research tools: laboratory analysis equipment, data analysis software, etc.

[0102] Complex machinery: airplanes, architectural models, etc.

[0103] High-tech products: virtual reality equipment, advanced computer hardware, etc.;

[0104] Social and cultural impacts: global issues (climate change, energy crisis), regional culture, etc.;

[0105] Social trends: artificial intelligence, global economic development, etc.

[0106] It should be emphasized that the audience keyword can also represent the audience's grade, for example: first grade, second grade, etc. The above is only an exemplary introduction, and the embodiments of the present application do not make specific limitations on this.

[0107] In addition, the "user" in the present application may be a lecturer or not, and the audience may be a person being taught or a person viewing teaching materials, and the embodiments of the present application do not make specific limitations on this.

[0108] Among them, a teaching material generation method is applied to an electronic device, and the method comprises:

[0109] Obtain target keywords for generating teaching materials; wherein the target keywords include knowledge point keywords, region keywords and audience keywords; the region keywords are used to represent the region to which the audience belongs or the region related to the knowledge point represented by the knowledge point keywords, and the audience keywords are used to represent the age stage of the audience;

[0110] Based on the target keyword, determine each candidate scene object; wherein the candidate scene object is: a scene object in the predetermined scene object of the region represented by the region keyword, which can be recognized by the age group represented by the audience keyword and is related to the knowledge point represented by the knowledge point keyword; the predetermined scene object is a scene object with regional characteristics;

[0111] Displaying the determined candidate scene objects;

[0112] In response to a selection operation on the displayed candidate scene object, a teaching material related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword is generated.

[0113] It can be seen that the teaching materials generated by the embodiment of the present application not only include the knowledge points represented by the knowledge point keywords, but also are related to the target scene objects indicated by the selection operation, and the target scene objects indicated by the selection operation can be considered as the predetermined scene objects in the region represented by the region keywords selected by the user, the scene objects that can be recognized by the age stage represented by the audience keywords and are related to the knowledge points represented by the knowledge point keywords, so the generated teaching materials can be combined with the audience's cognitive level and the regional environment in which they live; then, through the teaching materials, the fun of the teaching materials can be improved, thereby increasing the audience's interest in the knowledge points taught by the instructor. In addition, because the audience is interested in the knowledge points taught by the instructor, the audience can also better understand the knowledge points taught by the instructor, thereby improving the teaching quality and teaching effect.

[0114] A method for generating teaching materials provided in an embodiment of the present application is described below in conjunction with the accompanying drawings.

[0115] like Figure 1 As shown, a teaching material generation method provided in an embodiment of the present application is applied to an electronic device, and the method includes:

[0116] S101, obtaining target keywords for generating teaching materials;

[0117] The target keywords include knowledge point keywords, region keywords and audience keywords; the region keywords are used to represent the region to which the audience belongs or the region related to the knowledge point represented by the knowledge point keywords, and the audience keywords are used to represent the age stage of the audience;

[0118] Among them, the electronic device can be configured with a preview unit, such as: a display screen, and the preview unit is used to preview a display interface for generating teaching materials. Keywords used to generate teaching materials can be displayed on the display interface. The embodiment of the present application does not make specific limitations on this.

[0119] It can be understood that the target keywords can be keywords used to generate teaching materials obtained in response to selection instructions issued by the user through the display interface; and the target keywords can include three types of keywords, namely, knowledge point keywords, regional keywords and audience keywords, among which the regional keywords can be targeted at the region to which the audience belongs, or can be the region related to the knowledge point represented by the knowledge point keyword; illustratively, the audience is people in region A, then the regional keyword can be about region A; the knowledge point is about the knowledge related to the basin, then the region related to the knowledge point is region B, and the regional keyword is also region B.

[0120] For a better understanding, the display interface described above will be described below in conjunction with the accompanying drawings. Figure 2 As shown:

[0121] There are multiple interactive option areas on the display interface, namely "Please select the knowledge field", "Select the age group you are targeting", "Optional: expected scene objects, region, specific knowledge points" and "Generate"; among them, under "Please select the knowledge field", there are multiple options "First grade of primary school, second grade of primary school, third grade of primary school...", and "Mathematics upper, Mathematics lower, Chinese upper, Chinese lower". By selecting the above options, specific knowledge points will be displayed later, so that you can select knowledge point keywords; among them, under "Select the age group you are targeting", there are also multiple options "Kindergarten small and medium classes, kindergarten large classes, first and second grades of primary school, third and fourth grades of primary school, fifth and sixth grades of primary school...", by selecting the above options, you can select audience keywords; among them, in "Optional: expected scene objects, region, specific knowledge points", you can select the expected scene objects, of course, you can also not select them.

[0122] In addition, this display interface is a display interface that does not require the selection of regional keywords. After selecting the above keywords, you can click "Generate" to generate teaching materials.

[0123] Of course, the above display interface is only for exemplary purposes and does not specifically limit the embodiments of the present application.

[0124] Exemplarily, in one implementation, the step of obtaining target keywords for generating teaching materials may include step A1 or A2:

[0125] Step A1, obtaining knowledge point keywords and audience keywords specified by the user, and obtaining location keywords based on positioning information obtained by the positioning device;

[0126] or,

[0127] Step A2, obtaining the knowledge point keywords, audience keywords and region keywords specified by the user.

[0128] It is understandable that the user can indicate the knowledge point keywords and the audience keywords based on the selection instruction, and can also indicate the knowledge point keywords, the audience keywords and the regional keywords. Whether to select the regional keywords can be pre-configured in the electronic device; then, without indicating the regional keywords, the regional keywords can be obtained based on the positioning information obtained by the positioning device; wherein the positioning device can be a device in the electronic device, and the positioning device can locate the electronic device, for example: GPS (Global Positioning System, Global Positioning System) device, and the embodiments of the present application do not make specific limitations on this. Exemplarily, the electronic device can obtain the knowledge point keywords, audience keywords and regional keywords specified by the user: basic digital addition and subtraction, 7-9 years old, region 1; or, the electronic device can obtain the knowledge point keywords and audience keywords specified by the user: basic digital addition and subtraction and 7-9 years old, and the positioning information obtained based on the GPS device, and obtain the regional keyword region 1. The following is an exemplary introduction to the situation where users select regional keywords based on a campus scenario: lecturers can log in to the teaching material generation client through a student account, and then use the teaching material client to generate teaching materials. Campus leaders can manage student accounts to decide whether lecturers select regional keywords. If lecturers do not select regional keywords, the default regional keyword is the campus address. The embodiments of the present application do not make specific limitations on this.

[0129] Among them, the granularity of the regional keywords (that is, the degree of refinement of the region represented by the regional keywords) can be set according to the actual situation. For example, the regional granularity that can be represented by the regional keywords can be provinces / municipalities, or the regional granularity that can be represented by the regional keywords can also be more subdivided areas under provinces / municipalities. These are all reasonable.

[0130] S102, determining each candidate scene object based on the target keyword;

[0131] The candidate scene objects are: the scene objects in the predetermined scene objects of the region represented by the region keyword, the scene objects that can be recognized by the age group represented by the audience keyword and are related to the knowledge point represented by the knowledge point keyword; the predetermined scene objects are scene objects with regional characteristics;

[0132] It is understandable that the candidate scene objects can also be considered as scene objects that simultaneously meet the scope of the regional keywords, audience keywords and knowledge point keywords, that is, the scene objects with regional characteristics in the region represented by the regional keywords, the scene objects that can be recognized by the age stage represented by the audience keywords and the knowledge points represented by the knowledge point keywords. Exemplarily, the regional keyword is region A, the audience keyword is 7-9 years old, and the knowledge point keyword is a cuboid. Then, the determined candidate scene objects are: the predetermined scene objects under the regional scope of region A, and the objects related to the cuboid in the cognitive objects of 7-9 year old children. And, the representation form of the candidate scene objects can be image data, text data, or audio data, etc., which is not specifically limited in the embodiments of the present application. In addition, the determined candidate scene objects can be the candidate scene objects found in the database, or the candidate scene objects found on the Internet. The specific description will be made in the subsequent embodiments, and no further elaboration will be given here.

[0133] Exemplarily, in one implementation, the electronic device can directly search for each candidate scene object from a preset database based on the target keyword to obtain the scene objects that are related to the knowledge point represented by the knowledge point keyword and that can be recognized by the age group represented by the audience keyword in the predetermined scene objects of the region represented by the region keyword; wherein the preset database can record the mapping relationship between the predetermined scene objects for different knowledge points that can be recognized by each age group in each region, so that the candidate scene objects corresponding to the target keyword can be directly found from the target database. For example: the preset database records the predetermined scene objects a, b and c related to arithmetic knowledge points that can be recognized by age group 1 in region A, then, if the target keyword includes region A, age group 1 and arithmetic knowledge points, then at this time, the predetermined scene objects a, b and c can be found from the preset database to obtain each candidate scene object.

[0134] In another implementation, each candidate scene object can also be determined based on a specified large language model. For example, a query statement is issued to the specified large language model so that the specified large language model can respond to the query statement and feedback each candidate scene object, wherein the semantics of the query statement is used to indicate the scene objects in the predetermined scene objects under the region represented by the query region keyword, which are recognizable by the age group represented by the audience keyword and are related to the knowledge point represented by the knowledge point keyword. The specified large language model can be a natural speech processing tool belonging to artificial intelligence with functions such as question answering that exists in the related technology. For the sake of clear layout, the specific content of each candidate scene object is determined by calling the large language model, which will be introduced in subsequent embodiments and will not be elaborated here.

[0135] The above-mentioned implementation method of determining each candidate scene object based on the target keyword is only an example and does not constitute a limitation to the present application.

[0136] S103, displaying the determined candidate scene objects;

[0137] It is understandable that the determined candidate scene objects can be displayed in the display interface described above, or in other interfaces, and the present application embodiment does not specifically limit this. For example, all objects related to the rectangular block in the predetermined scene objects within the area range of area A and the cognitive objects of children aged 7-9 can be displayed in the display interface.

[0138] S104, in response to a selection operation on the displayed candidate scene object, generating teaching materials for the knowledge point represented by the knowledge point keyword and related to the target scene object indicated by the selection operation.

[0139] It is understandable that, in response to the selection operation for the displayed candidate scene objects, the target scene object can be determined from the candidate scene objects, and teaching materials related to the target scene object indicated by the selection operation for the knowledge point represented by the knowledge point keyword are generated. In order to better understand the process of determining the target scene object, an exemplary introduction is given below: in response to the user performing a selection operation for the displayed candidate scene object, the target scene object is determined to be an insect breeding box, an exhibition cabinet of the museum in Region A, etc. In addition, the generated teaching materials are materials for the knowledge point represented by the knowledge point keyword, and the related objects involved in the teaching materials for introducing or describing the knowledge point are the target scene objects, that is, the target scene objects are integrated into the description or introduction of the knowledge point.

[0140] It should be emphasized that in response to the selection operation on the displayed candidate scene object, the teaching materials related to the target scene object indicated by the selection operation for the knowledge point represented by the knowledge point keyword are not generated immediately. There may be a certain delay after responding to the selection operation on the displayed candidate scene object, and the embodiments of the present application do not make specific limitations on this.

[0141] Furthermore, after the teaching materials are generated, an option for seeking user feedback can be displayed on the display interface, and the user can provide feedback on the generated teaching materials. The electronic device can then use quantitative or qualitative evaluation to evaluate the user's opinions, which is not specifically limited in the embodiments of the present application. In addition, during the process of generating teaching materials, the user can modify any keyword at any time, which does not result in the termination of the generation of teaching materials, and the electronic device can regenerate teaching materials for the modified keywords.

[0142] For the sake of clarity of layout, the specific content of the teaching materials generated for the knowledge points represented by the knowledge point keywords and related to the target scene objects indicated by the selection operation will be introduced in subsequent embodiments and will not be elaborated on again.

[0143] It can be seen that the teaching materials generated by the embodiment of the present application not only include the knowledge points represented by the knowledge point keywords, but also are related to the target scene objects indicated by the selection operation, and the target scene objects indicated by the selection operation can be considered as the predetermined scene objects in the region represented by the region keywords selected by the user, the scene objects that can be recognized by the age stage represented by the audience keywords and are related to the knowledge points represented by the knowledge point keywords, so the generated teaching materials can be combined with the audience's cognitive level and the regional environment in which they live; then, through the teaching materials, the fun of the teaching materials can be improved, thereby increasing the audience's interest in the knowledge points taught by the instructor. In addition, because the audience is interested in the knowledge points taught by the instructor, the audience can also better understand the knowledge points taught by the instructor, thereby improving the teaching quality and teaching effect.

[0144] Exemplarily, in one implementation, the step of determining each candidate scene object based on the target keyword includes steps B1-B2:

[0145] Step B1, using at least the region represented by the region keyword and the age stage represented by the audience keyword as conditional contents, generating a combined query statement for searching for scene objects related to the knowledge point represented by the knowledge point keyword; wherein, in the combined query statement, the query priority for the region represented by the region keyword is higher than the query priority for the age stage represented by the audience keyword;

[0146] It can be understood that the combined query statement can be considered as a statement that uses the region represented by the region keyword and the age stage represented by the audience keyword as conditional content to query scene objects related to the knowledge point represented by the knowledge point keyword. Then, through the combined query statement, the query requests for the region keyword, the knowledge point keyword and the audience keyword can be combined; it should be emphasized that in this implementation, in the combined query statement, the query priority for the region represented by the region keyword is higher than the query priority for the age stage represented by the audience keyword. Exemplarily, with region A and 7-9 years old as conditional content, a combined query statement for querying scene objects related to a cuboid is generated.

[0147] It should be emphasized that there can be multiple conditional contents in the combined query statement introduced above, that is, there can be multiple regional keywords and audience keywords, and the knowledge point keyword is the core of the combined query statement. When generating a combined query statement, usually only one can be entered to achieve accurate input. The combined query statement generated in this way is generated with the knowledge point represented by the knowledge point keyword as the core. Subsequently, based on the combined query statement, more accurate candidate scene objects can be queried.

[0148] Exemplarily, in one implementation, the region represented by the regional keyword and the knowledge point represented by the knowledge point keyword can be used as conditional content to generate a combined query statement for querying scene objects related to the age stage represented by the audience keyword. In this implementation, the age stage represented by the audience keyword is used as the core to query candidate scene objects that are more closely combined with the audience keyword. Of course, this implementation is only introduced as an example and does not specifically limit the embodiments of the present application.

[0149] In addition, in another implementation method, the knowledge points represented by the knowledge point keywords and the age stages represented by the audience keywords can be used as conditional content to generate a combined query statement for querying scene objects related to the region represented by the regional keywords. In this implementation method, the region represented by the regional keywords is used as the core, and candidate scene objects that are more closely combined with the regional keywords can be queried. Of course, this implementation method is only an exemplary introduction and does not specifically limit the embodiments of the present application.

[0150] Step B2: input the combined query statement into the large language model to search for each candidate scene object from a preset target database, wherein the target database contains the correspondence between each region and the corresponding predetermined scene object, the correspondence between each knowledge point and the corresponding knowledge point information, and the correspondence between audiences of different age groups and the scene objects that can be recognized.

[0151] Among them, the large language model is a model pre-trained based on the keywords in the training data and the target database. The large language model is used to find the candidate scene objects that meet the combined query statement from the target database based on any combined query statement. The target keyword is input into the large language model, and the large language model can output each candidate scene object. Any neural network model that can perform data screening can be used as the large language model in this application. Exemplarily, when training the large language model, the sample data can be each sample combined query statement, and the true value can be a predetermined scene object that matches each sample combined query statement; the embodiment of the present application does not make specific limitations on this.

[0152] It is understandable that the target database can be a pre-established database, which can include the correspondence between each region and the corresponding scene objects with regional characteristics, for example: the correspondence between region A-high mountains, yaks, and barley, and can include the correspondence between knowledge points and corresponding knowledge point information, for example: the correspondence between mathematics-basic arithmetic, geometry, and statistics, and can also include the correspondence between audiences of various ages and recognizable scene objects, for example: the correspondence between 3-6 years old-graphics and colors; of course, the target database also includes various scene objects, and the embodiments of the present application do not specifically limit this; then, the candidate scene object can be a scene object in the preset target database that has a corresponding relationship with the target keyword. In addition, there can be multiple target databases, and a single target database may not be able to store all scene objects. The specifics will be introduced in the subsequent embodiments, and no further details will be given here.

[0153] It can be understood that after the combined query statement is input into the large language model, the large language model will first query the target database for the predetermined scene objects of the region represented by the regional keywords, and then query the scene objects that can be recognized by the age group represented by the audience keywords from the predetermined scene objects, and finally query the scene objects related to the knowledge points represented by the knowledge point keywords from the scene objects that can be recognized by the age group represented by the audience keywords, as each candidate scene object. Exemplarily, based on the regional keyword of region A, the audience keyword of 7-9 years old, and the knowledge point keyword of cuboid, a combined query statement is formed and input into the large language model. The large language model can preferentially search for the predetermined scene objects of region A from the target database, and then query the scene objects that can be recognized by the age group of 7-9 years old from the predetermined scene objects, and then query the scene objects related to the cuboid from the scene objects that can be recognized by the age group of 7-9 years old to obtain various candidate scene objects; or, the large language model can preferentially query the scene objects related to the cuboid from the target database, and then search for the predetermined scene objects belonging to region A from the searched scene objects, and then search for the scene objects that can be recognized by the age group of 7-9 years old from the searched predetermined scene objects to obtain various candidate scene objects.

[0154] In addition, in the subsequent steps, the target scene object determined for the selection operation of the displayed candidate scene objects can be used as the training truth value of the large language model, so that the candidate scene objects output by the large language model are more inclined to the user's preferences, which can improve the user experience.

[0155] It can be seen that the embodiment of the present application generates a combined query statement based on the target keyword, and inputs the combined query statement into the large language model to search for each candidate scene object from the target database; each candidate scene object can be found more simply and quickly through the large language model, so as to facilitate the subsequent display of each candidate scene object, generate teaching materials more quickly, and the generated teaching materials can be combined with the audience's cognitive level and the regional environment in which they live, which also improves the interest of the teaching materials. In addition, the region represented by the regional keyword and the age stage represented by the audience keyword are used as conditional content to generate a combined query statement for querying scene objects related to the knowledge point represented by the knowledge point keyword. The knowledge point represented by the knowledge point keyword can be used as the core, and the generated combined query statement is also more in line with the knowledge point keyword.

[0156] Exemplarily, in one implementation, the target keyword further includes: a custom keyword;

[0157] After obtaining the target keywords for generating teaching materials, the method further includes:

[0158] Based on the semantics of the custom keyword, determining a keyword among the knowledge point keyword, the region keyword, and the audience keyword that is relevant to the content represented by the custom keyword as the keyword to be modified;

[0159] Based on the semantics represented by the custom keyword, modify the content represented by the keyword to be modified;

[0160] The step of modifying the content represented by the keyword to be modified based on the semantics represented by the custom keyword includes:

[0161] Replacing the content represented by the keyword to be modified with the semantic content represented by the custom keyword;

[0162] or,

[0163] The content represented by the keyword to be modified and the content with semantics represented by the custom keyword are subjected to intersection processing on the content range to obtain a processing result, and the content represented by the keyword to be modified is replaced with the content represented by the processing result.

[0164] It is understandable that in this implementation, the target keywords also include custom keywords, which can be set by the user. Unlike regional keywords and audience keywords, custom keywords are not necessary keywords. Custom keywords can be keywords that are relevant to regional keywords, audience keywords, and knowledge point keywords. Keywords that are relevant to the content represented by custom keywords can be used as keywords to be revised. Then, regional keywords, audience keywords, and knowledge point keywords may all be used as keywords to be revised; for example: when the custom keyword is multiplication, at this time, the custom keyword is relevant to the knowledge point keyword, and at this time, the knowledge point keyword can be used as a keyword to be revised.

[0165] And, after determining the keywords to be revised, the keywords to be revised can be adjusted according to the semantics represented by the custom keywords, and the content range of the content represented by the adjusted keywords to be revised is consistent with the semantics represented by the custom keywords; exemplarily, the custom keywords are multiplication, the keywords to be revised are knowledge point keywords, and the content represented by the knowledge point keywords is basic arithmetic, then the content range of the content represented by the knowledge point keywords can be revised, and the content range of the content represented by the knowledge point keywords can be revised to multiplication; the custom keywords are snow mountains, the keywords to be revised are regional keywords, and the content represented by the regional keywords is region 1, then the content range of the content represented by the regional keywords can be revised according to the semantics represented by the custom, and the content range of the content represented by the regional point keywords can be revised to snow mountains; the custom keywords are 5 years old, the keywords to be revised are audience keywords, and the content represented by the audience keywords is preschool, then the content range of the content represented by the audience keywords can be revised, and the content range of the content represented by the audience keywords can be revised to 5 years old.

[0166] Then, custom keywords can also be considered as keywords used to narrow the scope of regional keywords, audience keywords and knowledge point keywords, that is, custom keywords further narrow the content scope of the content represented by the knowledge point keywords.

[0167] In addition, after adjusting the content range of the content represented by the keyword to be modified according to the semantics represented by the custom keyword, the step of determining each candidate scene object based on the target keyword can be continued; since the content range of the content represented by the keyword to be modified has been modified, the various candidate scene objects finally obtained are more in line with the user's expectations.

[0168] Specifically, based on the semantics represented by the custom keyword, the content represented by the keyword to be modified is modified in two ways:

[0169] It can be understood that the first method is to directly replace the content represented by the keyword to be revised with the semantic content represented by the custom keyword. For example, the custom keyword is multiplication, and the keyword to be revised is the knowledge point keyword, specifically basic arithmetic. Then, the knowledge point keyword can be directly replaced from basic arithmetic to multiplication; the custom keyword is snow mountain, and the keyword to be revised is a regional keyword, specifically region 1. Then, the regional keyword can be directly replaced from region 1 to snow mountain.

[0170] It can be understood that the second method is to perform an intersection processing on the content represented by the keyword to be corrected and the content of the semantics represented by the custom keyword with respect to the content range to obtain a processing result; illustratively, in one implementation method, if the content of the semantics represented by the custom keyword belongs to a subset of the content represented by the keyword to be corrected, the intersection of the two can be the content of the semantics represented by the custom keyword, and the semantics represented by the custom keyword can be used as the processing result; and, subsequently, the content represented by the keyword to be corrected can be replaced with the content represented by the processing result, that is, replaced with the content of the semantics represented by the custom keyword; for example, the custom keyword is multiplication, and the keyword to be corrected is a knowledge point keyword, specifically basic arithmetic, and the two are subjected to an intersection processing, and the obtained processing result is multiplication, then, the content represented by the knowledge point keyword can be directly replaced with multiplication.

[0171] Furthermore, when the semantic content represented by the custom keyword does not belong to the subset of the content represented by the keyword to be corrected, the intersection of the two can also be used as the processing result, and the content represented by the keyword to be corrected can be replaced with the content represented by the processing result. For example, the custom keyword is plain, the keyword to be corrected is a regional keyword, and the content represented by the regional keyword is grassland. Then, the intersection processing on the content range can be performed to obtain the processing result. The processing result represents region 1. Region 1 belongs to both plains and grasslands. Then, the content represented by the regional keyword can be replaced with region 1.

[0172] Of course, the above two methods can be applied to custom keywords, and the first method can be used for regional keywords, and the second method can be used for knowledge point keywords and audience keywords. The embodiments of the present application do not make specific limitations on this.

[0173] It can be seen that the target keywords can also include custom keywords. Based on the semantics of the custom keywords, the keywords among the knowledge point keywords, regional keywords and audience keywords that are relevant to the content represented by the custom keywords can be determined as keywords to be revised, and based on the semantics represented by the custom keywords, the content represented by the keywords to be revised can be revised; then, the content scope of the content represented by the keywords to be revised can be revised, so that the various candidate scene objects subsequently obtained are more in line with the user's expectations, the flexibility of generating teaching materials is improved, and the user experience is improved.

[0174] Alternatively, in another embodiment, Figure 3 As shown, the present application also provides another teaching material generation method, which may include:

[0175] S301, obtaining target keywords for generating teaching materials;

[0176] S302, determining each candidate scene object based on the target keyword;

[0177] S303, displaying the determined candidate scene objects;

[0178] It is understandable that steps S301-S303 are the same as the above steps S101-S103, so they will not be described in detail here.

[0179] S304, determining the demand type of the teaching materials to be generated as the target type; wherein the demand type includes a teaching plan demand type or a topic demand type;

[0180] It can be understood that the demand type can be determined by the user. When the teaching material generated by the user's demand is a lesson plan, the demand type can be a lesson plan demand type. When the teaching material generated by the user's demand is a question, the demand type can also be a question demand type. The demand type of the teaching material to be generated can be determined as the target type.

[0181] Of course, the demand types for teaching materials are not limited to the above two types. There are also test paper demand types / outline demand types, which are not specifically limited in the embodiments of the present application.

[0182] S305: Generate teaching materials related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword according to the target type.

[0183] It can be understood that when the target type is a teaching plan requirement type, a teaching plan related to the target scene object indicated by the selection operation can be generated for the knowledge point represented by the knowledge point keyword; when the target type is a question requirement type, a question related to the target scene object indicated by the selection operation can be generated for the knowledge point represented by the knowledge point keyword. And, for the two target types, the specific process of generating teaching materials will be introduced in subsequent embodiments. Exemplarily, in one implementation method, generating teaching materials related to the target scene object indicated by the selection operation for the knowledge point represented by the knowledge point keyword according to the target type includes steps C1-C2:

[0184] Step C1, if the target type is a teaching plan requirement type, generating a teaching plan template for combining scene objects related to the knowledge point represented by the knowledge point keyword, wherein the teaching plan template is provided with a field filling position for a name field of the scene object to be combined and / or an image filling area for the scene object to be combined;

[0185] It can be understood that steps C1 and C2 are both for the case where the target type is a lesson plan requirement type, and a lesson plan template related to the knowledge points represented by the knowledge point keywords and used to combine scene objects can be generated; and the lesson plan template can be provided with a field filling position for the name field of the scene object to be combined, and / or, the lesson plan template can be provided with an image filling area for the scene object to be combined, which can be filled with scene objects of different forms later.

[0186] Step C2, using the target scene object indicated by the selection operation as the scene object to be combined, filling the teaching plan template with content to obtain teaching materials.

[0187] It is understandable that the target scene object can be used as the scene object to be combined and directly filled into the lesson plan model to obtain teaching materials; then, when the target scene object is text data, the target scene object in the form of text data can be filled into the field filling position of the name field of the target scene object, and when the target scene object is image data, the target scene object in the form of image data can be filled into the image filling area of ​​the target scene object to obtain teaching materials of the type required by the lesson plan. Exemplarily, the knowledge point keyword is the preliminary understanding of angles, the audience keyword is the kindergarten class, and the target scene object is the picture data showing kindergarten tables and chairs, scissors, and building blocks. Then, the picture data showing kindergarten tables and chairs, scissors, and building blocks can be used as the scene object to be combined, and the content of the lesson plan template is filled. The obtained teaching materials contain scene objects such as kindergarten tables and chairs, scissors, and building blocks, which can enable children in the kindergarten class to better understand the knowledge taught.

[0188] In order to better understand the lesson plan template, the following is an introduction with the attached pictures. Figure 4 As shown:

[0189] The lesson plan template may include a scene text + three image data; wherein the scene text may represent: XXXX (scene object) may be regarded as XXXX (knowledge point), and its XXX (features of the scene object) conforms to XXX (core features of the knowledge point); and the image data may be image data in the target scene object.

[0190] Of course, the above teaching plan template is only used as an exemplary introduction and does not constitute a specific limitation on the embodiments of the present application.

[0191] It can be seen that if the target type is a lesson plan requirement type, a lesson plan template related to the knowledge point represented by the knowledge point keyword and used to combine the scene object can be generated. Subsequently, the target scene object is filled into the lesson plan template to obtain a lesson plan requirement type lesson plan template. The lesson plan template can be adjusted according to user needs, thereby improving the user experience; and the generated teaching materials can be combined with the audience's cognitive level and the geographical environment in which they live, thereby improving the interest of the teaching materials.

[0192] In another implementation, generating, according to the target type, teaching materials for the knowledge point represented by the knowledge point keyword and related to the target scene object indicated by the selection operation includes steps D1-D2:

[0193] Step D1, if the target type is a question requirement type, determine a question template matching the knowledge point represented by the knowledge point keyword from a preset question bank;

[0194] Step D2, based on the target scene object indicated by the selection operation, replace the field content belonging to the object field in the question template to obtain teaching materials.

[0195] It can be understood that step D1 is for the case where the target type is a question requirement type, and a question template that matches the knowledge point represented by the knowledge point keyword can be determined from a preset question bank, wherein the question bank can be pre-set or a question bank on the Internet, and the embodiment of the present application does not make specific limitations on this; then, a question template that matches the knowledge point represented by the knowledge point keyword can be found from the question bank, and the question template can be a blank template or a question, and the embodiment of the present application does not make specific limitations on this.

[0196] It is understandable that, for step D2, the field content belonging to the object field in the title template can be replaced based on the target scene object indicated by the selection operation, so that teaching materials of the type required by the title can be obtained; by replacing the target scene object indicated by the selection operation with the field content belonging to the object field in the title template, and the target scene object is an object common to the audience, the audience can be more familiar with the object in the title template and easier to understand. Exemplarily, the instructors in area 1 can make more use of images or models of landmarks in area 1 to generate target topics, and the target topic is "There are 10 seats in the theater, 6 people have sat down, how many seats are left now?"; and the instructors in area 2 can make more use of the natural environment to generate target topics, and the target topic is "There are 7 yaks on the grassland, and 2 of them have been transferred to another pasture. How many are left now? There are 7 plateau plants on the grassland, and the herders have picked 3 of them. How many are left now? There are 5 barley plants planted in the field, and 3 more were planted. How many are there in total now?"

[0197] In order to better understand the teaching materials of the question requirements, the following is an introduction with the accompanying drawings, such as Figure 5 As shown:

[0198] The generated teaching materials of the question requirement type may include questions, options (A, B, C, D) and correct answers, and may also include image data, wherein the image data form may be a data form of a scene object.

[0199] It can be seen that if the target type is a question requirement type, a question template that matches the knowledge point represented by the knowledge point keyword can be determined from the preset question bank, and based on the target scene object indicated by the selection operation, the field content belonging to the object field in the question template is replaced to obtain teaching materials. The target question can be adjusted according to the user's needs, thereby improving the user experience; and the generated teaching materials can be combined with the audience's cognitive level and the geographical environment in which they live, thereby improving the interest of the teaching materials.

[0200] It can be seen that the embodiment of the present application can determine the demand type of the teaching material to be generated as the target type, and generate teaching materials for the knowledge points represented by the knowledge point keywords and related to the target scene object indicated by the selection operation according to the target type; then, the teaching materials can be generated according to the user's demand type, thereby improving the user experience; and the generated teaching materials can be combined with the audience's cognitive level and the geographical environment in which they live; then, through the teaching materials, the fun of the teaching materials can be improved, thereby increasing the audience's interest in the knowledge points taught by the lecturers. In addition, since the audience is interested in the knowledge points taught by the lecturers, the audience can also better understand the knowledge points taught by the lecturers, thereby improving the teaching quality and teaching effect.

[0201] Exemplarily, in one implementation, the data form of the determined candidate scene object is an image data form;

[0202] The step of generating, in response to a selection operation on the displayed candidate scene object, teaching materials related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword, comprises steps E1-E2:

[0203] Step E1, in response to a selection operation on a displayed candidate scene object, performing image optimization processing on a target scene object indicated by the selection operation to obtain the target scene object after image optimization processing.

[0204] In one implementation, the image optimization process may include highlighting the content of the semantics represented by the knowledge point keyword;

[0205] Step E2, generating teaching materials related to the target scene object after the image optimization processing for the knowledge point represented by the knowledge point keyword; wherein the target scene object after the image optimization processing is displayed in the teaching materials.

[0206] It is understandable that, for step E1, the target scene object indicated by the selection operation can be optimized for the selection operation of the displayed candidate scene object, wherein the image optimization process can include highlighting the content of the semantics represented by the knowledge point keyword, specifically, the image optimization process can include retaining only the image data of the target scene object, removing the background image data, highlighting the content of the semantics represented by the knowledge point keyword, thereby obtaining the target scene object after image optimization process, and the image optimization process can also be the addition of stroke processing to the content of the semantics represented by the knowledge point keyword in the target scene object, so as to highlight the content of the semantics represented by the knowledge point keyword, thereby obtaining the target scene object after image optimization process, and the present application embodiment does not specifically limit the image optimization process; and, the display effect of the content of the semantics represented by the knowledge point keyword of the target scene object after image optimization process is better, so that the audience can better understand the knowledge point keyword. Of course, in order to ensure the display effect of the target scene object in the teaching material, a transparent background can also be added to the target scene object during the image optimization process, and the present application embodiment does not specifically limit the process of image optimization process. For example, there is an image data depicting sheep on a grassland, and the target scene object is the sheep. Then, the grassland background image data can be eliminated to obtain image data about the sheep as the target scene object after image optimization processing.

[0207] It can be understood that, for step E2, teaching materials related to the target scene objects after image optimization processing can be generated for the knowledge points represented by the knowledge point keywords, and the target scene objects after image optimization processing can be displayed in the generated teaching materials; and, step E2 is similar to step S104, so the embodiments of the present application will not be described in detail here.

[0208] It can be seen that the embodiments of the present application can perform image optimization processing on the target scene object indicated by the selection operation to obtain the target scene object after image optimization processing, and can generate teaching materials related to the target scene object after image optimization processing for the knowledge points represented by the knowledge point keywords, so that the target scene object can be highlighted in the teaching materials to enhance the display effect of the target scene object.

[0209] Exemplarily, in one implementation, the step of determining each candidate scene object based on the target keyword may include step F1:

[0210] Step F1, based on the target keyword, performing scene object search processing on each target database to obtain each candidate scene object;

[0211] Each target database contains the correspondence between each region and the corresponding predetermined scene object, the correspondence between each knowledge point and the corresponding knowledge point information, and the correspondence between the audience of each age stage and the scene object that can be recognized; different target databases correspond to different regional characteristic types, and the predetermined scene objects in each target database are objects under the regional characteristic type corresponding to the target database, and different target databases correspond to their own weights;

[0212] Accordingly, the scene object search process for each target database includes:

[0213] The scene object search process for each target database includes:

[0214] For each target database, based on the target keyword, searching for candidate scene objects from the target database to obtain a search result corresponding to each target database;

[0215] According to the weight corresponding to each target data, the search results corresponding to each target database are screened again;

[0216] The re-screening process includes: a first processing method or a second processing method, wherein the first processing method is used to make, for each target database, the ratio of the number of objects corresponding to the target database to the number of each candidate scene object equal to the corresponding weight of the target database, and the number of objects corresponding to the target database is the number of candidate scene objects among the each candidate scene object that belong to the target database;

[0217] The second processing method is used to select, for each target database, candidate scene objects of a target ratio from the search results corresponding to the target database, and the value of the target ratio is the value of the weight corresponding to the target database.

[0218] It can be understood that in the process of performing scene object search processing on each target database based on the target keyword, the electronic device can perform scene object search processing on each target database to obtain each candidate scene object, and the electronic device can also call the large language model to enable the large language model to perform scene object search processing on each target database to obtain each candidate scene object. The embodiments of the present application do not make specific limitations on this. Furthermore, different target databases may correspond to different types of regional characteristics, for example, target database A corresponds to three types of regional characteristics: handicrafts, architectural culture and food culture, target database B corresponds to two types of regional characteristics: clothing culture and social etiquette, and target database C corresponds to two types of regional characteristics: local popular folk legends and stories and festivals; then, the predetermined scene objects in each target database are also different, and the predetermined scene objects in each target database are objects under the regional characteristic type corresponding to the target database; and each target database contains the correspondence between each region and the corresponding predetermined scene object (using this correspondence, the predetermined scene object corresponding to the regional keyword can be found in the target database), the correspondence between each knowledge point and the corresponding knowledge point information (using this correspondence, the knowledge point information corresponding to the knowledge point represented by the knowledge point keyword can be found in the target database), and the correspondence between audiences of different age groups and recognizable scene objects (using this correspondence, the scene objects that the audience can recognize represented by the audience keyword can be found in the target database). In addition, each target database has its own weight. Specifically, the weight can be determined based on the access popularity of the predetermined scene objects existing in each target database. Then, the higher the access popularity of the predetermined scene objects in any target database, the higher the weight set for the target database. Alternatively, the weight corresponding to each target database can also be obtained by configuring through a user configuration interface. The user can directly configure the configuration interface according to his or her own needs. The embodiment of the present application does not make specific limitations on this. Among them, the access popularity can be the network access popularity, which is not specifically limited in the embodiment of the present application. It should be emphasized that the sum of the weights corresponding to each target database should be 1. The weight can be a decimal or a percentage, and the embodiment of the present application does not make specific limitations on this; for example: there are three target databases, among which the corresponding weight of target database 1 is 40%, and it can be considered that the objects of the regional characteristics type in target database 1 have a network access popularity of 40%, the corresponding weight of target database 2 is 30%, and it can be considered that the objects of the regional characteristics type in target database 2 have a network access popularity of 30%, and the corresponding weight of target database 3 is 30%, and it can be considered that the objects of the regional characteristics type in target database 3 have a network access popularity of 30%.

[0219] Exemplarily, in one implementation, the method of calculating the weight corresponding to the target database may include: for each target database, calculating the average of the heat of all predetermined scene objects / part of the predetermined scene objects in the target database as the heat value of the target database, and calculating the ratio of the heat value of the target database to the sum of the heat values ​​of each target database, so as to obtain the weight corresponding to the target database. The method of calculating the weight is similar to normalization. Of course, the method of calculating the weight corresponding to the target database introduced above is only an exemplary introduction, and the embodiments of the present application do not make specific limitations on this.

[0220] Specifically, the process of searching for scene objects in each target database is as follows: for each target database, based on the target keyword, search for candidate scene objects from the target database to obtain the search results corresponding to each target database; since the corresponding relationship introduced above exists in each target database, the candidate scene objects existing in the target database can be directly found based on the target keyword to obtain the search results corresponding to the target database; and, the search results corresponding to each target database can be screened again according to the weight corresponding to each target database, so as to obtain the final required candidate scene objects, that is, the candidate scene objects that need to be displayed to the user. Among them, the re-screening process can include: the first processing method or the second processing method.

[0221] Exemplarily, in the first processing method, the weight corresponding to each target database is used to indicate: the proportion of objects belonging to the target database among the candidate scene objects finally obtained, that is, the proportion of objects related to the target database among the candidate scene objects that need to be displayed to the user. Exemplarily, the corresponding weight of target database 1 is 40%, indicating that among the candidate scene objects that need to be displayed to the user, the number of objects belonging to target database 1 accounts for 40%; the corresponding weight of target database 2 is 30%, indicating that among the candidate scene objects that need to be displayed to the user, the number of objects belonging to target database 2 accounts for 30%; the corresponding weight of target database 3 is 30%, indicating that among the candidate scene objects that need to be displayed to the user, the number of objects belonging to target database 3 accounts for 30%; on the premise that the total amount of each candidate scene object to be obtained has been determined in advance, the number of objects required for each target database in the obtained candidate scene objects can be determined according to the total amount and the corresponding weight of each target database. Assuming that 20 candidate scene objects are needed now, 8 candidate scene objects can be screened out from the search results of target database 1, 6 candidate scene objects can be screened out from the search results of target database 2, and 6 candidate scene objects can be screened out from the search results of target database 3. Of course, the total number of candidate scene objects required to be obtained may not be set. In this case, after determining the search results corresponding to each target database, the search results currently corresponding to each target database are screened again according to the weight corresponding to each target database, so that the number of objects in each target database in the final candidate scene objects obtained accounts for the weight corresponding to the target database. The embodiments of the present application do not make specific limitations on this.

[0222] And, in the second processing method, for each target database, the predetermined scene objects of the target ratio are screened from the search results corresponding to the target database, the value of the target ratio is the value of the weight corresponding to the target database, and the weight in the second processing method can be considered as the weight used for re-screening the objects initially selected from the target database; for example: the weights corresponding to target database 1, target database 2 and target database 3 are 40%, 30%, and 30% respectively. 10 objects are found in target database 1, and 4 candidate scene objects can be screened according to the target ratio of 40%; 20 objects are found in target database 2, and 6 candidate scene objects can be screened according to the target ratio of 30%; 10 objects are found in target database 3, and 3 candidate scene objects can be screened according to the target ratio of 30%; thus, 13 candidate scene objects can be finally obtained.

[0223] Of course, the screening process described above is only an exemplary introduction, and the embodiments of the present application do not make any specific limitations on this.

[0224] It can be seen that the embodiment of the present application can introduce the weight corresponding to the target database in the process of determining each candidate scene object, so that each determined candidate scene object can be a candidate scene object that better meets user needs, which can improve the user experience. In addition, subsequent users select the target scene object from each candidate scene object and generate teaching materials related to the target scene object, which can enhance the interest of the teaching materials and thus increase the audience's interest in the knowledge points taught by the instructor.

[0225] Exemplarily, in one implementation, after generating, in response to a selection operation on the displayed candidate scene object, teaching materials related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword, the method further includes step G1:

[0226] Step G1, based on the target scene object indicated by the selection operation, updating the weight of the target database containing the target scene object; wherein the updating process is used to increase the weight.

[0227] It is understandable that after the user sends a selection operation for each candidate scene object, thereby selecting the target scene object, the weight of the target database containing the target scene object can be updated based on the target scene object indicated by the selection operation. Since the target scene object is a scene object that better meets the user's needs, the weight of the target database containing the target scene object can be increased, so that each candidate scene object determined subsequently is more inclined to the user's needs, thereby better meeting the user's needs. It is understandable that after the weight of the target database containing the target scene object is increased, since the sum of the weights corresponding to each target database is 1, the weights corresponding to other target databases can be reduced so that the sum of the weights corresponding to each target database remains 1; when the weights corresponding to each other target database are reduced, the increase in the weight of the target database containing the target scene object can be used as the total reduction, and the total reduction is evenly distributed to each other target database to reduce the weights corresponding to each other target database.

[0228] It can be seen that based on the target scene object indicated by the selection operation, the weight of the target database containing the target scene object can be updated, so that the weight of the target database containing the target scene object is increased, which can improve the user experience; and, subsequent users select the target scene object from various candidate scene objects and generate teaching materials related to the target scene object, which can enhance the interest of the teaching materials, thereby increasing the audience's interest in the knowledge points taught by the instructor.

[0229] Based on the above method embodiment, Figure 6 As shown, a teaching material generating device is applied to an electronic device, and the device comprises:

[0230] The acquisition module 610 is used to acquire target keywords for generating teaching materials; wherein the target keywords include knowledge point keywords, region keywords and audience keywords; the region keywords are used to represent the region to which the audience belongs or the region related to the knowledge point represented by the knowledge point keywords, and the audience keywords are used to represent the age stage of the audience;

[0231] The first determination module 620 is used to determine each candidate scene object based on the target keyword; wherein the candidate scene object is: a scene object in the predetermined scene object of the region represented by the region keyword, which can be recognized by the age group represented by the audience keyword and is related to the knowledge point represented by the knowledge point keyword; the predetermined scene object is a scene object with regional characteristics;

[0232] A display module 630 is used to display the determined candidate scene objects;

[0233] The generation module 640 is used to generate, in response to a selection operation on the displayed candidate scene object, teaching materials related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword.

[0234] Optionally, the determining module is specifically configured to:

[0235] At least the region represented by the region keyword and the age stage represented by the audience keyword are used as conditional contents to generate a combined query statement for querying scene objects related to the knowledge point represented by the knowledge point keyword; wherein, in the combined query statement, the query priority for the region represented by the region keyword is higher than the query priority for the age stage represented by the audience keyword;

[0236] Inputting the combined query statement into a large language model to search for each candidate scene object from a preset target database;

[0237] The target database includes the correspondence between each region and the corresponding predetermined scene object, the correspondence between each knowledge point and the corresponding knowledge point information, and the correspondence between audiences of different age groups and the scene objects that can be recognized.

[0238] Optionally, the target keywords also include: custom keywords;

[0239] The device also includes:

[0240] A second determination module is used to determine, after obtaining the target keywords for generating teaching materials, keywords among the knowledge point keywords, regional keywords and audience keywords that are relevant to the content represented by the custom keywords based on the semantics of the custom keywords as keywords to be modified;

[0241] A correction module, used for correcting the content represented by the keyword to be corrected based on the semantics represented by the custom keyword;

[0242] Wherein, the correction module is specifically used for:

[0243] Replacing the content represented by the keyword to be modified with the semantic content represented by the custom keyword;

[0244] or,

[0245] The content represented by the keyword to be modified and the content with semantics represented by the custom keyword are subjected to intersection processing on the content range to obtain a processing result, and the content represented by the keyword to be modified is replaced with the content represented by the processing result.

[0246] Optionally, the device further comprises:

[0247] A third determination module is used to determine the demand type of the teaching material to be generated as the target type before generating the teaching material for the knowledge point represented by the knowledge point keyword and related to the target scene object indicated by the selection operation; wherein the demand type includes a teaching plan demand type or a question demand type;

[0248] The generating module comprises:

[0249] A generating submodule, for generating, according to the target type, teaching materials related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword;

[0250] Wherein, the generation submodule is specifically used for:

[0251] If the target type is a teaching plan requirement type, generating a teaching plan template for combining scene objects related to the knowledge point represented by the knowledge point keyword, wherein the teaching plan template is provided with a field filling position for a name field of the scene object to be combined and / or an image filling area for the scene object to be combined;

[0252] Using the target scene object indicated by the selection operation as the scene object to be combined, filling the teaching plan template with content to obtain teaching materials;

[0253] or,

[0254] If the target type is a question requirement type, determining a question template matching the knowledge point represented by the knowledge point keyword from a preset question bank;

[0255] Based on the target scene object indicated by the selection operation, the field content belonging to the object field in the question template is replaced to obtain teaching materials.

[0256] Optionally, the first determining module includes:

[0257] A search processing submodule is used to perform scene object search processing on each target database based on the target keyword to obtain each candidate scene object;

[0258] Each target database contains the correspondence between each region and the corresponding predetermined scene object, the correspondence between each knowledge point and the corresponding knowledge point information, and the correspondence between the audience of each age stage and the scene object that can be recognized; different target databases correspond to different regional characteristic types, and the predetermined scene objects in each target database are objects under the regional characteristic type corresponding to the target database, and different target databases correspond to their own weights;

[0259] The search processing submodule is specifically used for:

[0260] For each target database, based on the target keyword, searching for candidate scene objects from the target database to obtain a search result corresponding to each target database;

[0261] According to the weight corresponding to each target data, the search results corresponding to each target database are screened again;

[0262] The re-screening process includes: a first processing method or a second processing method, wherein the first processing method is used to make, for each target database, the ratio of the number of objects corresponding to the target database to the number of each candidate scene object equal to the corresponding weight of the target database, and the number of objects corresponding to the target database is the number of candidate scene objects among the each candidate scene object that belong to the target database;

[0263] The second processing method is used to select, for each target database, candidate scene objects of a target ratio from the search results corresponding to the target database, and the value of the target ratio is the value of the weight corresponding to the target database.

[0264] Optionally, the device further comprises:

[0265] An update processing module is used to, in response to a selection operation on a displayed candidate scene object, generate teaching materials for a knowledge point represented by a knowledge point keyword and related to a target scene object indicated by the selection operation, and then, based on the target scene object indicated by the selection operation, update the weight of a target database containing the target scene object; wherein the update processing is used to increase the weight.

[0266] Optionally, the data form of the determined candidate scene object is an image data form;

[0267] The generating module is specifically used for:

[0268] In response to a selection operation on the displayed candidate scene object, an image optimization process is performed on the target scene object indicated by the selection operation to obtain the target scene object after the image optimization process; wherein the image optimization process includes highlighting the content of the semantics represented by the knowledge point keyword;

[0269] Generate teaching materials related to the target scene object after the image optimization processing for the knowledge point represented by the knowledge point keyword; wherein the target scene object after the image optimization processing is displayed in the teaching materials.

[0270] In the technical solution of this application, the operations involved in obtaining, storing, using, processing, transmitting, providing and disclosing user personal information are all carried out with the user's authorization.

[0271] The present application also provides an electronic device, such as Figure 7 As shown, including:

[0272] Memory 701, used for storing computer programs;

[0273] The processor 702 is used to implement a method for generating teaching materials when executing the program stored in the memory 701.

[0274] Furthermore, the electronic device may further include a communication bus and / or a communication interface, and the processor 702, the communication interface, and the memory 701 communicate with each other via the communication bus.

[0275] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0276] The communication interface is used for communication between the above electronic device and other devices.

[0277] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0278] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0279] In another embodiment provided in the present application, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, any of the above-mentioned teaching material generation methods is implemented.

[0280] In another embodiment provided by the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute any teaching material generating method in the above embodiments.

[0281] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a solid-state hard disk (SSD), etc.

[0282] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0283] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0284] The above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application are included in the protection scope of the present application.

Claims

1. A method for generating teaching materials, characterized in that: Applied to electronic equipment, the method comprises: Obtaining target keywords for generating teaching materials; wherein the target keywords include knowledge point keywords, region keywords and audience keywords; the region keywords are used to characterize the region to which the audience belongs, and the audience keywords are used to characterize the age stage of the audience; the region keywords are set based on a user's selection instruction, or determined based on a positioning device of the electronic device; Based on the target keyword, each candidate scene object is determined; wherein the candidate scene object is: a scene object in the predetermined scene object of the region represented by the region keyword, a scene object that can be recognized by the age group represented by the audience keyword and is related to the knowledge point represented by the knowledge point keyword; the predetermined scene object is a scene object with regional characteristics; and each candidate scene object is determined from a preset target database; the target database contains a correspondence between each region and a corresponding predetermined scene object, a correspondence between each knowledge point and corresponding knowledge point information, and a correspondence between audiences of each age group and recognizable scene objects; Displaying the determined candidate scene objects; In response to a selection operation on the displayed candidate scene object, a teaching material related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword is generated.

2. The method according to claim 1, characterized in that The determining each candidate scene object based on the target keyword includes: At least the region represented by the region keyword and the age stage represented by the audience keyword are used as conditional contents to generate a combined query statement for querying scene objects related to the knowledge point represented by the knowledge point keyword; wherein, in the combined query statement, the query priority for the region represented by the region keyword is higher than the query priority for the age stage represented by the audience keyword; The combined query statement is input into a large language model to search for each candidate scene object from a preset target database.

3. The method according to claim 1, characterized in that The target keywords also include: custom keywords; After obtaining the target keywords for generating teaching materials, the method further includes: Based on the semantics of the custom keyword, determining a keyword among the knowledge point keyword, the region keyword, and the audience keyword that is relevant to the content represented by the custom keyword as the keyword to be modified; Based on the semantics represented by the custom keyword, modify the content represented by the keyword to be modified; The step of modifying the content represented by the keyword to be modified based on the semantics represented by the custom keyword includes: Replacing the content represented by the keyword to be modified with the semantic content represented by the custom keyword; or, The content represented by the keyword to be modified and the content with semantics represented by the custom keyword are subjected to intersection processing on the content range to obtain a processing result, and the content represented by the keyword to be modified is replaced with the content represented by the processing result.

4. The method according to any one of claims 1 to 3, characterized in that: Before generating teaching materials related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword, the method further includes: Determine the demand type of the teaching materials to be generated as the target type; wherein the demand type includes a teaching plan demand type or a topic demand type; The generating of teaching materials related to the target scene object indicated by the selection operation and targeting the knowledge point represented by the knowledge point keyword comprises: Generating, according to the target type, teaching materials for the knowledge point represented by the knowledge point keyword and related to the target scene object indicated by the selection operation; The step of generating teaching materials related to the target scene object indicated by the selection operation and targeting the knowledge point represented by the knowledge point keyword according to the target type includes: If the target type is a teaching plan requirement type, generating a teaching plan template for combining scene objects related to the knowledge point represented by the knowledge point keyword, wherein the teaching plan template is provided with a field filling position for a name field of the scene object to be combined and / or an image filling area for the scene object to be combined; Using the target scene object indicated by the selection operation as the scene object to be combined, filling the teaching plan template with content to obtain teaching materials; or, If the target type is a question requirement type, determining a question template matching the knowledge point represented by the knowledge point keyword from a preset question bank; Based on the target scene object indicated by the selection operation, the field content belonging to the object field in the question template is replaced to obtain teaching materials.

5. The method according to claim 1, characterized in that The number of the target databases is multiple; and the determining of each candidate scene object based on the target keyword includes: Based on the target keyword, performing scene object search processing on each target database to obtain each candidate scene object; Different target databases correspond to different regional characteristic types. The predetermined scene objects in each target database are objects under the regional characteristic type corresponding to the target database, and different target databases correspond to their own weights. The scene object search process for each target database includes: For each target database, based on the target keyword, searching for candidate scene objects from the target database to obtain a search result corresponding to each target database; According to the weight corresponding to each target data, the search results corresponding to each target database are screened again; The re-screening process includes: a first processing method or a second processing method, wherein the first processing method is used to make, for each target database, the ratio of the number of objects corresponding to the target database to the number of each candidate scene object equal to the corresponding weight of the target database, and the number of objects corresponding to the target database is the number of candidate scene objects among the each candidate scene object that belong to the target database; The second processing method is used to select, for each target database, candidate scene objects of a target ratio from the search results corresponding to the target database, and the value of the target ratio is the value of the weight corresponding to the target database.

6. The method according to claim 5, characterized in that After generating, in response to the selection operation on the displayed candidate scene object, teaching materials related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword, the method further includes: Based on the target scene object indicated by the selection operation, the weight of the target database containing the target scene object is updated; wherein the update process is used to increase the weight.

7. The method according to claim 1, characterized in that The data form of the determined candidate scene object is an image data form; The step of generating, in response to a selection operation on the displayed candidate scene object, teaching materials related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword, comprises: In response to a selection operation on the displayed candidate scene object, an image optimization process is performed on the target scene object indicated by the selection operation to obtain the target scene object after the image optimization process; wherein the image optimization process includes highlighting the content of the semantics represented by the knowledge point keyword; Generate teaching materials related to the target scene object after the image optimization processing for the knowledge point represented by the knowledge point keyword; wherein the target scene object after the image optimization processing is displayed in the teaching materials.

8. A teaching material generating device, characterized in that: Applied to electronic equipment, the device comprises: an acquisition module, used to acquire target keywords for generating teaching materials; wherein the target keywords include knowledge point keywords, region keywords and audience keywords; the region keywords are used to characterize the region to which the audience belongs, and the audience keywords are used to characterize the age stage of the audience; the region keywords are set based on the user's selection instruction, or determined based on the positioning device of the electronic device; The first determination module is used to determine each candidate scene object based on the target keyword; wherein the candidate scene object is: a scene object in the predetermined scene object of the region represented by the region keyword, a scene object that can be recognized by the age stage represented by the audience keyword and is related to the knowledge point represented by the knowledge point keyword; the predetermined scene object is a scene object with regional characteristics; and each candidate scene object is determined from a preset target database; the target database contains a correspondence between each region and the corresponding predetermined scene object, a correspondence between each knowledge point and the corresponding knowledge point information, and a correspondence between the audience of each age stage and the scene object that can be recognized; A display module, used for displaying the determined candidate scene objects; A generation module is used to generate, in response to a selection operation on the displayed candidate scene object, teaching materials related to the target scene object indicated by the selection operation and for the knowledge point represented by the knowledge point keyword.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, for implementing any of the methods described in claims 1-7 when executing a program stored in a memory.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.

Images