A customized data loading and presentation method and system

Abstract

The present invention relates to a customized data loading and presentation method, platform and system, the method comprising: obtaining a set of training segment data packets for training topics: loading and presenting the training segments in the training segment data packets to users in order: detecting in real time whether the customization conditions are met during the presentation process: selecting the customized training segments to be replaced from the training segment data packet set based on the interest feature set: pairing the training segments in the current customized training segment package, and replacing the paired training segments corresponding to the customized training segments to be replaced with the customized training segments to be replaced. The present invention ensures the long-term accuracy of customization by combining global and local optimization for continuous correction, and can continuously perform adaptive customization adjustments according to the static characteristics and dynamic mastery of the user.

Description

[Technical field]

[0001] The present invention belongs to the technical field of online training, and in particular, relates to a customized data loading and presentation method and system. [Background technology]

[0002] At the same time, with the rapid development of smart phones, cloud computing and big data technology, the Internet has become an important tool for people to work, shop, manage finances, make friends and entertain. With the continuous development of Internet technology, more and more people use online training to learn relevant courses. For example, primary and secondary school students can learn English, Chinese, mathematics and other courses online, factory employees can learn the operation of new production lines online, and company employees can receive technical training online, etc., which enables people to learn relevant knowledge without leaving home, bringing a lot of convenience. In the big data application system, many industries currently obtain different data from multiple data sources, and the data types include structured data, semi-structured data, and unstructured data; the data content format is disorganized, and the information is a combination of virtual and real. The information types are diverse, and a large amount of information is presented at the same time, such as: content, audio, text information, video, pictures, etc., and it also includes the application of virtual reality technology and augmented reality technology in the field of training, which has become a normal teaching form. Virtual reality technology training makes up for the shortcomings of traditional training models and brings more customized options. In other words, with the development of big data technology, training materials for different topics are becoming more and more abundant, and training types are becoming more and more diverse.

[0003] There are two problems in the existing technology: First, with the acceleration of the pace of life, people's demand for education and training has shown a new trend of becoming more flexible and diverse. The traditional fixed-point, long-term, high-cost, and low-efficiency online training methods based on static customization are far from meeting people's needs. Second: The existing way of providing content to users is often based on static feature matching: With the complexity of training materials, training plans can be carried out for a long time, and in the long-term training process, the different needs of different users will be very different: For example: the same fourth-grade students will be provided with the same training content in the static customization process, but in fact, they must have different strengths and weaknesses in the long-term training process. Therefore, in order for them to achieve the same training goals, the required training details are different. In short: the existing technology only relies on how to dynamically customize the training content according to the characteristics of different users, which is a technical problem to be solved. Therefore, in order to better exert the training effect, the training content should highlight the long-term adaptability and pertinence. In view of the above problems, the present invention is based on a combination of static and dynamic customization, and continuously corrects through a combination of global and local optimization, thereby ensuring the long-term accuracy of customization, and can continuously perform adaptive customization adjustments based on the user's static characteristics and dynamic grasp of the situation. [Summary of the invention]

[0004] In order to solve the above problems in the prior art, the present invention proposes a customized data loading and presentation method and system, the method comprising:

[0005] Step S1: obtaining a set of training segment data packets for a training topic; the set of training segment data packets includes one or more training segment data packets, each independent training segment data packet includes training content developed around the training topic, and the training for the training topic can be considered completed after the presentation of the independent training segment data packet is completed; the training segment data packet includes one or more training segments arranged in sequence: wherein: the attributes of the training segment include a topic rating attribute and a semantic attribute;

[0006] Step S2: Obtain the current customized training segment data package for the training topic; load and present the training segments in the training segment data package to the user in order: during the presentation process, detect in real time whether the customization conditions are met, and if they are met, proceed to the next step:

[0007] Step S3: obtaining the user's interest features for each completed training segment in the current customized training segment data package, and the interest features of all completed training segments constitute an interest feature set; selecting a customized training segment to be replaced from the training segment data package set based on the interest feature set; wherein: the interest feature is a dynamic semantic attribute generated during the actual presentation of each training segment, and the elements in the interest feature and the semantic attribute are corresponding;

[0008] Step S4: for each customized training segment to be replaced, pairing training segments in the current customized training segment package in turn, and replacing the paired training segment corresponding to the customized training segment to be replaced with the customized training segment to be replaced;

[0009] The step S4 specifically includes the following steps:

[0010] Step S41: The part starting from the next training segment of the completed training segment in the current customized training segment package is used as a replaceable part:

[0011] Step S42: obtaining an unprocessed customized training segment to be replaced sgu;

[0012] Step S43: Calculate the attribute distance DAtt between the unprocessed customized training segment to be replaced sgu and any training segment sgv in the replaceable part sgu，sgv ; Select the training segment sgv corresponding to the smallest attribute distance as the to-be-determined paired training segment of the unprocessed custom segment to be replaced sgu; if the training segment sgv corresponding to the smallest attribute distance is used as the to-be-determined paired training segment for the first time, set the pairing threshold value to the DAtt sgu，sgv value, and use the to-be-determined paired segment as the paired training segment of the unprocessed replacement customized training segment sgu; otherwise, the DAtt obtained this time sgu，sgv The value is compared with the pairing threshold value, only DAtt sgu，sgv When the value is less than the pairing threshold, the pairing threshold is updated to be equal to the DAtt obtained this time. sgu，sgv value, and use the to-be-determined paired segment as the paired training segment of the unprocessed replacement customized training segment sgu; then, set the unprocessed replacement customized training segment sgu as processed;

[0013] The attribute distance DAtt is calculated using the following formula (2): sgu，sgv ; Among them: SgCh sgu，ky is the kyth element in the semantic attributes of the unprocessed customized training segment to be replaced sgu: SgCh sgv，ky is the kyth element in the semantic attributes of any training segment sgv in the replaceable part:

[0014]

[0015] Step S44: If there is still unprocessed customized training segment sgu to be replaced and the replacement of the replaceable part does not meet the replacement condition, then return to step S42, otherwise, proceed to the next step:

[0016] Step S45: For all the customized training segments to be replaced that have paired training segments, the paired training segments corresponding to the customized training segments to be replaced are replaced with the customized training segments to be replaced. After the replacement is completed, an updated customized training segment package is formed:

[0017] Step S5: Calculate whether the topic score of the updated customized training segment data package is greater than the current customized training segment package before the update; if so, use the updated customized training segment package as the current customized training segment package, and load and present the training segments based on the current customized training segment package; otherwise, cancel the replacement operation without updating the current customized training segment package.

[0018] Furthermore, the order is a logical order, a semantic order, a preset order and / or an order of corresponding training topics.

[0019] Furthermore, there is no sequential relationship between two training segments in the training segment data packet. In this case, the two training segments can be arranged in any manner.

[0020] Furthermore, the customized condition is when a preset number of training segments are presented, an independent training segment is about to be presented, and / or the training time length reaches a preset time length.

[0021] Furthermore, a pairing threshold is set for any training segment sgv in each replaceable part.

[0022] Furthermore, the replacement condition is that all training segments in the replaceable part or training segments higher than a preset ratio have become paired training segments.

[0023] A customized data loading and presentation system is provided, wherein the customized data loading and presentation system is used to implement the customized data loading and presentation method.

[0024] A customized data loading and presenting platform comprises a processor, the processor is coupled to a memory, the memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, the customized data loading and presenting method is implemented.

[0025] A computer-readable storage medium includes a program, which, when executed on a computer, enables the computer to execute the customized data loading and presentation method.

[0026] An online education server is configured to execute the customized data loading and presentation method.

[0027] The beneficial effects of the present invention include:

[0028] (1) With the customized training clip package as the center, a binary classification based on the user's real-time interests is performed for each training clip. In this process, a minimum disordered semantic distance target optimization is formulated for each training clip, and a global optimization target or constraint condition based on the topic score is formulated for the customized training clip package. The global optimal customization method is found through local optimization through replacement and cancellation. Through continuous correction, the long-term accuracy of customization is guaranteed, and adaptive customization adjustments can be made continuously according to the user's static characteristics and dynamic mastery:

[0029] (2) During the training process, based on the static optimal customized training package, a dynamic interest feature set that changes over time is collected, and dynamic optimization is performed based on the interest feature set. According to the current training progress and user interests of the current user, the training segments are dynamically replaced, ultimately improving the training efficiency;

[0030] Furthermore, by comprehensively utilizing the local disorder and long-term effectiveness of the training segments in the training process, on the basis of feature distance, the minimum disordered semantic distance is used to quantitatively and efficiently perform dynamic optimization, and in the dynamic replacement process, the pairing threshold is coordinated to select the best training segment pairing combination to support dynamic customization.

Brief Description of the Drawings

[0031] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present application, but do not constitute an improper limitation of the present invention. In the drawings:

[0032] Figure 1 A schematic diagram of the customized data loading and presentation method provided by the present invention. [Specific implementation method]

[0033] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the illustrative embodiments and descriptions are only used to explain the present invention but are not intended to limit the present invention.

[0034] The present invention proposes a customized data loading and presentation method and system, as shown in the attached Figure 1 As shown, the method comprises the following steps:

[0035] Step S1: Obtain a set of training segment data packets for a training topic; the training segment data packet set includes one or more training segment data packets, each independent training segment data packet includes training content centered on the training topic, and the training for the training topic is considered completed after the presentation of the independent training segment data packets is completed: the training segment data packet includes one or more training segments arranged in sequence; wherein: the attributes of the training segment include a topic scoring attribute and a semantic attribute; the semantic attribute is used for local optimization, and the topic scoring attribute is used for global optimization guidance:

[0036] Preferably: the order is a logical order, a semantic order, a preset order, an order of the topics to which it is applied, etc.:

[0037] Preferably: there is no order relationship between two training segments in the training segment data packet. In this case, the two training segments can be arranged in any manner:

[0038] Preferably: the semantic attributes are semantic features such as keywords, keyword groups, key sentence frequencies, etc. involved in the training clips and related to the topic;

[0039] Preferably: the segmentation of the training segment is a relatively independent training content that conforms to the semantic logic of the training content;

[0040] Step S2: obtaining a current customized training segment data package for a training topic; presenting the training segments in the training segment data package to the user in order; detecting in real time during the presentation whether the customization conditions are met, and if they are met, proceeding to the next step: wherein: the current customized training segment data package is initialized to be set as a default training segment data package;

[0041] Preferably: the default training segment data package is selected according to the training needs of the current user; the default training segment data package is a subset of the training segment set;

[0042] Preferably: the customized condition is when a preset number of training segments are presented, an independent training segment is about to be presented, and / or the training time length reaches a preset time length;

[0043] Step S3: obtaining the user's interest features for each completed training segment in the current customized training segment data package; selecting the customized training segment to be replaced from the training segment data package set based on the interest features; wherein: the interest features are dynamic semantic attributes generated during the actual presentation of each training segment, that is, the keywords, keyword groups, and key sentence frequencies involved in the training segment content corresponding to the user's stay time during the presentation process; obviously, the interest features and semantic attributes are corresponding, and are both plural groups, corresponding to each keyword, keyword group and / or key sentence frequency and other semantic features; the interest features can be regarded as dynamic semantic attributes;

[0044] Specifically: Step S3 includes the following steps:

[0045] Step S31: Obtain the user's semantic attributes for each completed training segment; the semantic attributes of each completed training segment together constitute an interest feature set; the interest features included in the interest feature set are unordered:

[0046] Step S32: selecting an unprocessed training segment data packet br from the training segment data packet set;

[0047] Step S33: calculating the minimum unordered semantic distance between the unprocessed training segment data packet br and the interest feature set; the unordered semantic distance represents the semantic distance between two elements calculated in an unordered manner;

[0048] The step S33 specifically includes the following steps:

[0049] Step S331: From the interest feature set {(fr sgn，ky )} to obtain an unprocessed interest feature (fr sgn，ky ), and calculate the feature distance DCH between the unprocessed interest feature and each unprocessed training segment sg1 in the unprocessed training segment data packet br sg1，sgn Specifically: the characteristic distance DCH is calculated using the following formula (1): sg1，sgn ; Among them: fr sgn，ky is the kyth element in the sgnth interest feature in the interest feature set; SgCh sg1，ky is the kyth element in the semantic attribute of the unprocessed sg1th training segment in the one unprocessed training segment data packet;

[0050]

[0051] Preferably, the step of taking an unprocessed training segment sg1 from the unprocessed training segment data packet br is as follows: taking an unprocessed training segment sg1 from a front part of the unprocessed training segment data packet br; the length of the front part is greater than or equal to the number of elements in the interest feature set, and less than or equal to the redundancy multiplied by the number of elements in the interest feature set; the unprocessed training segment data packet includes a front part located at the front and a rear part located after the front part:

[0052] Preferably, the redundancy is a preset value, for example, 20%. Of course, the redundancy can be set to be related to the size of a specific set of interest features:

[0053] Step S332: For each value of sg1, select DCH sg1，sgn MIN(DCH sg1，sgn ), and set the interest feature corresponding to the smallest one as processed, and the corresponding unprocessed training segment sg1 as processed:

[0054] Step S333: Update the minimum unordered semantic distance DuQ corresponding to the unprocessed training segment data packet br br =DuQ br +MIN(DCH sg1，sgn ); where: set the minimum unordered semantic distance DuQ br The initial value of is 0;

[0055] Step S334: determine whether all interest features in the interest feature set have been processed, if yes, then end, otherwise return to step S331;

[0056] Step S34: determining whether all training segment data packets in the training segment data packet set have been processed, if so, proceeding to the next step, otherwise, returning to step S32;

[0057] Step S35: For each training segment data packet br, select the unordered semantic distance DuQ from the training segment data packet set br The smallest one is used as the target training segment data packet: that is, the matching between the training segment data packet and the interest feature set does not distinguish the order of the interest features; this greatly increases the possibility of successful matching and increases the redundancy of content provision;

[0058] Alternative: Select the unordered semantic distance DuQ from the training segment data set br The minimum NM training segment data packets are used as the target training segment data packets; where NM is a quantity value, for example, 3 to 5:

[0059] Step S36: The first part of the target training segment data packet that does not have the minimum feature distance MIN (DCH sg1，sgn ) other training segments other than the corresponding training segment sg1 as the customized training segments to be replaced;

[0060] Alternatively: the step S35 is specifically as follows: determining the hotspot area of ​​the target training segment data packet, and taking the one with the smallest non-feature distance MIN(DCH sg1，sgn ) other training segments other than the corresponding training segment sg1 as the customized training segments to be replaced: when determining the hotspot area, the target training segment data packet with the smallest non-characteristic distance MIN(DCH sg1，sgn ) corresponds to the area covered by the training segment sg1 in the target training segment data packet (that is, the area with the smallest distance to the feature MIN(DCH sg1，sgn ) in the training segment sg1 corresponding to the training segment, the training segment before the front training segment and the training segment after the last training segment are not in the hot spot area to be determined) as the hot spot area to be determined: when the size of the hot spot area to be determined is greater than or equal to the redundancy multiplied by the size of the feature set of interest, the hot spot area to be determined is taken as the hot spot area; otherwise, the hot spot area to be determined is extended backward (the direction of progress of the training progress in the target training segment data packet) to the redundancy multiplied by the size of the feature set of interest, and the area obtained after the extension is taken as the hot spot area; the one with the smallest distance MIN(DCH in the hot spot area) not being the feature sg1，sgn ) other training segments other than the corresponding training segment sg1 are used as the customized training segments to be replaced:

[0061] Step S4: for each customized training segment to be replaced, pair the training segments in the current customized training segment package in turn, and replace the paired training segment corresponding to the customized training segment to be replaced with the customized training segment to be replaced:

[0062] The step S4 specifically includes the following steps:

[0063] Step S41: The part starting from the next training segment of the completed training segment in the current customized training segment package is used as a replaceable part:

[0064] Step S42: obtaining an unprocessed customized training segment to be replaced sgu;

[0065] Step S43: Calculate the attribute distance DAtt between the unprocessed customized training segment to be replaced sgu and any training segment sgv in the replaceable part sgu，sgv; Select the training segment sgv corresponding to the smallest attribute distance as the to-be-determined paired training segment of the unprocessed custom segment to be replaced sgu; if the training segment sgv corresponding to the smallest attribute distance is used as the to-be-determined paired training segment for the first time, set the pairing threshold value to the DAtt sgu，sgv value, and use the to-be-determined paired segment as the paired training segment of the unprocessed replacement customized training segment sgu; otherwise, the DAtt obtained this time sgu，sgv The value is compared with the pairing threshold value, only DAtt sgu，sgv When the value is less than the pairing threshold, the pairing threshold is updated to be equal to the DAtt obtained this time. sgu，sgv value, and use the to-be-determined paired segment as the paired training segment of the unprocessed replacement customized training segment sgu; then, set the unprocessed replacement customized training segment sgu as processed:

[0066] Preferably: the attribute distance DAtt is calculated using the following formula (2): sgu，sgv :Of which: SgCh sgu，ky is the kyth element in the semantic attributes of the unprocessed customized training segment to be replaced sgu: SgCh sgv，ky is the kyth element in the semantic attributes of any training segment sgv in the replaceable part:

[0067]

[0068] Preferably: In this process, if a to-be-replaced customized training segment becomes a paired training segment of another replacement customized training segment because its paired training segment (because the DAtt of the other replacement customized training segment sgu，sgv value is less than the matching threshold value), the customized training segment to be replaced is reset to unprocessed; of course, this step is optional, because there is a large similarity between the multiple replacement customized segments that enter the agreed threshold, so the possibility of repeatedly entering other thresholds is low:

[0069] Preferably: a pairing threshold value is set for any training segment sgv in each replaceable part, and its initial value is set to a larger value;

[0070] Step S44: if there is still unprocessed customized training segment sgu to be replaced and the replacement of the replaceable part does not meet the replacement condition, then return to step S42, otherwise, proceed to the next step;

[0071] Preferably: the replacement condition is that all training segments in the replaceable part or training segments with a ratio higher than a preset ratio have become paired training segments:

[0072] Step S45: For all the customized training segments to be replaced that have paired training segments, the paired training segments corresponding to the customized training segments to be replaced are replaced with the customized training segments to be replaced. After the replacement is completed, an updated customized training segment package is formed:

[0073] Step S5: Calculate whether the topic score of the updated customized training segment data package is greater than the current customized training segment package before the update; if so, use the updated customized training segment package as the current customized training segment package, and load and present the training segments based on the current customized training segment package; otherwise, cancel the replacement operation without updating the current customized training segment package:

[0074] Preferably: pre-set the topic score of each training segment, and determine the topic score of the corresponding customized training segment package by weighted summation:

[0075] Alternative: Split the topic into multiple subtopics, and set a subtopic score for each subtopic for the training segment, and determine the subtopic score of the corresponding customized training segment package by weighted summation: If each subtopic score of the updated customized training segment data package is greater than the customized training segment package before the update, then it is considered that the topic score of the updated customized training segment data package is greater than the customized training segment package before the update:

[0076] Preferably: before presenting the training segments based on the current customized training segment package, the order of existence of the training segments in the customized training segment package is sorted based on the sequential logic between the training segments:

[0077] Preferably: pre-build the logical order of all training segments and form a logical dependency graph, and sort out the existence order of the total training segments in the customized training segment based on the logical dependency graph:

[0078] Based on the same inventive concept, the present invention also provides a customized data loading and presentation system, the system comprising: a customized server and a presentation terminal: the customized server is used to implement the above-mentioned customized data loading and presentation method: the presentation terminal is used to present the training segment based on the customized training segment package:

[0079] Preferably: the customized server is one or more:

[0080] Preferably: the presentation terminal is a mobile phone, a notebook, a projector, etc.

[0081] A computer program (also referred to as a program, software, software application, script, or code) can be written in any form of a programming language, including an assembled or interpreted language, a declarative or procedural language, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple collaborative files (e.g., files storing one or more modules, subroutines, or code portions). A computer program can be deployed to execute on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

[0082] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0084] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0085] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A customized data loading and presentation method, characterized in that: The method comprises: Step S1: obtaining a set of training segment data packets for a training topic; the set of training segment data packets includes one or more training segment data packets, each independent training segment data packet includes training content developed around the training topic, and the training for the training topic can be considered completed after the presentation of the independent training segment data packet is completed; the training segment data packet includes one or more training segments arranged in sequence; wherein: the attributes of the training segment include a topic rating attribute and a semantic attribute; Step S2: obtaining a current customized training segment data package for the training topic; loading and presenting the training segments in the training segment data package to the user in order; during the presentation process, detecting in real time whether the customization conditions are met, and if so, proceeding to the next step; Step S3: obtaining the user's interest features for each completed training segment in the current customized training segment data package, and the interest features of all completed training segments constitute an interest feature set; selecting a customized training segment to be replaced from the training segment data package set based on the interest feature set; wherein: the interest feature is a dynamic semantic attribute generated during the actual presentation of each training segment, and the elements in the interest feature and the semantic attribute are corresponding, which are the frequencies of keywords, keyword phrases and / or key sentences involved in the training segment content corresponding to the user's stay time during the presentation process; Step S4: for each customized training segment to be replaced, pairing training segments in the current customized training segment package in turn, and replacing the paired training segment corresponding to the customized training segment to be replaced with the customized training segment to be replaced; The step S4 specifically includes the following steps: Step S41: taking the part starting from the next training segment of the completed training segment in the current customized training segment package as a replaceable part; Step S42: obtaining an unprocessed customized training segment to be replaced sgu; Step S43: Calculate the attribute distance DAtt between the unprocessed customized training segment to be replaced sgu and any training segment sgv in the replaceable part sgu,sgv ; Select the training segment sgv corresponding to the smallest attribute distance as the to-be-determined paired training segment of the unprocessed custom segment to be replaced sgu; if the training segment sgv corresponding to the smallest attribute distance is used as the to-be-determined paired training segment for the first time, set the pairing threshold value to the DAtt sgu，sgv value, and use the to-be-determined paired segment as the paired training segment of the unprocessed replacement customized training segment sgu; otherwise, the DAtt obtained this time sgu,sgv The value is compared with the pairing threshold value, only DAtt sgu,sgv When the value is less than the pairing threshold, the pairing threshold is updated to be equal to the DAtt obtained this time. sgu，sgv , value, and use the to-be-determined paired segment as the paired training segment of the unprocessed replacement customized training segment sgu; then, set the unprocessed replacement customized training segment sgu as processed; The attribute distance DAtt is calculated using the following formula (2): sgu,sgv ; Among them: sgCh sgu，ky is the kyth element in the semantic attribute of the unprocessed customized training segment to be replaced sgu; sgCh sgv，ky is the kyth element in the semantic attributes of any training segment sgv in the replaceable part; Step S44: if there is still unprocessed customized training segment sgu to be replaced and the replacement of the replaceable part does not meet the replacement condition, then return to step S42, otherwise, proceed to the next step; Step S45: for all the to-be-replaced customized training segments that have paired training segments, the paired training segments corresponding to the to-be-replaced customized training segments are replaced with the to-be-replaced customized training segments; after the replacement is completed, an updated customized training segment package is formed; Step S5: Calculate whether the topic score of the updated customized training segment data package is greater than the current customized training segment package before the update; if so, use the updated customized training segment package as the current customized training segment package, and load and present the training segments based on the current customized training segment package; otherwise, cancel the replacement operation without updating the current customized training segment package.

2. The customized data loading and presentation method according to claim 1, characterized in that: The sequence is a logical sequence, a semantic sequence, a preset sequence and / or an sequence that corresponds to the training topic.

3. The customized data loading and presentation method according to claim 2, characterized in that: There is no sequential relationship between two training segments in the training segment data packet. In this case, the two training segments can be arranged in any manner.

4. The customized data loading and presentation method according to claim 3, characterized in that: The customized condition is when a preset number of training segments are presented, an independent training segment is about to be presented, and / or the training time length reaches a preset time length.

5. The customized data loading and presentation method according to claim 4, characterized in that: A pairing threshold is set for any training segment sgv in each replaceable part.

6. The customized data loading and presentation method according to claim 5, characterized in that: The replacement condition is that all training segments in the replaceable part or training segments with a proportion higher than a preset proportion have become paired training segments.

7. A customized data loading and presentation system, characterized in that: The customized data loading and presentation system is used to implement the customized data loading and presentation method described in any one of claims 1 to 6.

8. A customized data loading and presentation platform, characterized in that: The method comprises a processor, wherein the processor is coupled to a memory, wherein the memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, the customized data loading and presenting method according to any one of claims 1 to 5 is implemented.

9. A computer-readable storage medium, characterized in that: The invention comprises a program which, when being executed on a computer, enables the computer to execute the customized data loading and presenting method according to any one of claims 1 to 5.

10. An online education server, characterized in that: The online education server is configured to execute the customized data loading and presenting method according to any one of claims 1-5.

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