Oral talent score-based educational resource transaction execution method and system

Abstract

The invention provides an educational resource transaction execution method and system based on oral score, and the method comprises the steps: obtaining a transaction request initiated by a first transaction party, the transaction request comprising a buying request, a selling request and a transfer request; according to the transaction request, obtaining a first comprehensive score of the first transaction party in the talent dimension, and screening out a plurality of second transaction parties meeting a scoring condition, the scoring condition being that a difference value between the second comprehensive score of the second transaction party in the talent dimension and the first comprehensive score is kept in a preset range; according to the embodiment of the invention, the matching price between the first transaction party and each second transaction party is calculated based on the matching algorithm, and the fragmented educational resource transaction operation is executed under the condition that the transaction confirmation information between the first transaction party and any second transaction party is received, so that the matching degree between the resource and the level of the user can be improved, and the transaction success rate is improved.

Description

Technical Field

[0001] The present invention relates to the field of resource analysis and processing, and in particular to a method, system, device and medium for executing educational resource transactions based on eloquence scoring. Background Art

[0002] At present, when users need to purchase educational resources, they generally search for corresponding keywords on the system. The system selects resources that also contain the keywords in the resource name based on the keywords and pushes them to the users. Users can only judge whether the resources are in line with their level through the resource introduction. Correspondingly, the system also lacks corresponding level judgment, which makes the educational resources pushed by the system may not match the user's current ability level. There is even a large difference between the price of the resources selected by the system and the price expected by the user, resulting in low resource matching, which affects the efficiency of resource transactions. Summary of the invention

[0003] The embodiment of the present invention provides a method, system, device and medium for executing educational resource transactions based on eloquence scoring to solve the problems existing in related technologies. The technical solution is as follows:

[0004] In a first aspect, an embodiment of the present invention provides a method for executing educational resource transactions based on eloquence scoring, comprising:

[0005] Obtaining a transaction request initiated by the first transaction party, the transaction request including a buy request, a sell request, and a transfer request;

[0006] Obtaining a first comprehensive score of the first transaction party in the eloquence dimension according to the transaction request, and screening out a plurality of second transaction parties that meet a scoring condition, wherein the scoring condition is that the difference between the second comprehensive score of the second transaction party in the eloquence dimension and the first comprehensive score is within a preset range;

[0007] The matching price between the first transaction party and each second transaction party is calculated based on the matching algorithm, and the fragmented educational resource transaction operation is performed when the transaction confirmation information between the first transaction party and any second transaction party is received.

[0008] In one embodiment, the eloquence dimensions include fluency, pronunciation accuracy, speaking speed, vocabulary richness, sentence complexity, expressiveness, and content relevance.

[0009] In one embodiment, it further includes:

[0010] When the first transaction party issues a selling request, the educational resource content of the first transaction party is obtained, and a performance evaluation is performed on the educational resource content to obtain scoring information of the educational resource content in each eloquence dimension; the scoring information in each eloquence dimension is weighted averaged to obtain a first comprehensive score.

[0011] In one embodiment, it further includes:

[0012] When the first transaction party issues a selling request, an NFT is generated on the blockchain platform according to the smart contract, and the NFT is bound to the educational resource content, so that the NFT records the identity information of the first transaction party and the first comprehensive score of the educational resource content.

[0013] In one embodiment, it further includes:

[0014] According to preset rules, NFT is fragmented to obtain fragmented resource objects. Each fragmented resource object is labeled with its corresponding resource category and comprehensive score and stored in the blockchain. The fragmented resource object is the transaction object when performing fragmented educational resource transaction operations.

[0015] In one embodiment, the matching algorithm is:

[0016] The transaction price of the first transaction party is calculated according to the eloquence dimension weight of the first transaction party and the preliminary price proposed by the first transaction party; wherein the eloquence dimension weight of the first transaction party is calculated according to the first comprehensive score;

[0017] The transaction price of the second transaction party is calculated based on the eloquence dimension weight of the second transaction party and the preliminary price proposed by the second transaction party; wherein the eloquence dimension weight of the second transaction party is calculated based on the second comprehensive score;

[0018] The transaction price of the first transaction party and the transaction price of the second transaction party are averaged to obtain the matching price between the first transaction party and the second transaction party.

[0019] In one implementation, obtaining the educational resource content of the first transaction party includes:

[0020] Obtain resource data uploaded by the first transaction party, extract features of the resource data based on a convolutional neural network, and obtain resource features;

[0021] Classify resource features based on the MLP model to obtain the categories of resource data;

[0022] When the resource data conforms to the education category, the resource data is preprocessed to obtain the educational resource content.

[0023] In a second aspect, an embodiment of the present invention provides an educational resource transaction execution system based on eloquence scoring, which executes the educational resource transaction execution method based on eloquence scoring as described above.

[0024] In a third aspect, an embodiment of the present invention provides an electronic device, the device comprising: a memory and a processor. The memory and the processor communicate with each other through an internal connection path, the memory is used to store instructions, the processor is used to execute the instructions stored in the memory, and when the processor executes the instructions stored in the memory, the processor executes the method in any one of the above-mentioned embodiments.

[0025] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a computer, the method in any one of the above-mentioned embodiments is executed.

[0026] The advantages or beneficial effects of the above technical solution include at least:

[0027] The present invention uses the first comprehensive score of the first transaction party in the eloquence dimension as one of the conditions for resource transaction screening, and searches for the second transaction party that meets the scoring conditions from a large number of resources. At this time, the second comprehensive score of the first transaction party is at the same level as the first comprehensive score of the first transaction party. Based on this technology, the matching price between the first transaction party and the second transaction party is calculated based on the matching algorithm, and the educational resource transaction is carried out when both the first transaction party and the second transaction party agree to the transaction, which promotes the first transaction party and the second transaction party with similar scores to conduct transactions, improves the matching degree between the resources and the user's own level, and improves the transaction success rate.

[0028] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in the present invention and should not be regarded as limiting the scope of the present invention.

[0030] Figure 1 It is a flowchart of the educational resource transaction execution method of the present invention;

[0031] Figure 2 The structure block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0033] The embodiment of the present invention provides an educational resource transaction execution method based on eloquence scoring. In the transaction process, there are a first transaction party and a second transaction party. Assuming that the first transaction party is a buyer, the corresponding second transaction party is a seller; assuming that the first transaction party is a seller, the second transaction party is a buyer. The naming of the first transaction party and the second transaction party is only to distinguish the identities of different users.

[0034] like Figure 1 As shown, the educational resource transaction execution method includes the following steps:

[0035] Step S1: Obtain a transaction request initiated by the first transaction party, where the transaction request includes a buy request, a sell request, and a transfer request.

[0036] When receiving the transaction request from the first transaction party, a verification instruction is generated, and the identity and account balance of the first transaction party are verified according to the verification instruction to ensure the legitimacy and validity of the transaction request. Among them, the user identity can be verified through multi-factor identity authentication and smart contracts. The methods of multi-factor identity authentication and smart contract verification have been disclosed in the prior art and will not be described in detail here.

[0037] Step S2: Obtain a first comprehensive score of the first transaction party in the eloquence dimension according to the transaction request.

[0038] Assuming that the first transaction party issues a selling request, the first transaction party can upload resource data, which may be lecturer courses, speech videos, etc., which are the resources that the first transaction party wants to sell.

[0039] After acquiring the resource data, the resource data is analyzed and processed, that is, feature extraction is performed on the resource data based on the convolutional neural network to obtain resource features; and the resource features are classified based on the MLP model to obtain the category of the resource data; assuming that the category of the resource data conforms to the education category, the resource data can be traded as an education resource; assuming that the category of the resource data does not conform to the education category, the resource data cannot be traded as an education resource to ensure that the education resources must meet certain quality standards, including clear images and audio, high-quality video recording, and professional explanations and teaching content.

[0040] When the resource data meets the educational category, the resource data is preprocessed to obtain educational resource content. The preprocessing method includes confirming the copyright of the resource data, converting the file format of the resource data, etc. Among them, the first transaction party needs to sign a copyright authorization agreement when uploading the resource data to allow the content to be published and traded on the platform; and the file format conversion of the resource data is to unify the format of the resource data into a specified format, such as the video file format is MP4, the audio code is AAC, the video frame rate is 30fps, and the resolution is 1920x1080, to ensure the clarity of the educational resources.

[0041] After the platform obtains the educational resource content from the first transaction party, it scores the educational resource content to obtain the score of the educational resource content in each designated eloquence dimension, thereby understanding the level of the educational resource content.

[0042] The content of educational resources can be evaluated on the eloquence dimensions through AI technology. The eloquence dimensions include fluency, pronunciation accuracy, speaking speed, vocabulary richness, sentence complexity, expressiveness and content relevance. These evaluation indicators ensure the professionalism and effectiveness of the educational content.

[0043] Among them, AI technology can use appropriate AI models, such as deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN) or long short-term memory network (LSTM), etc., use pre-collected training set data to train the specified AI model, and learn the relationship between MFCC features and eloquence dimension scores. Use the trained AI model to analyze the MFCC features of educational resource content and predict the score information corresponding to each specified eloquence dimension.

[0044] Specifically, the fluency score can be obtained by analyzing the short-term energy and zero-crossing rate in the MFCC sequence and using the change of the short-term energy to detect pauses and non-fluency.

[0045] The pronunciation accuracy score can be calculated by comparing the MFCC features with a known accurate pronunciation template and calculating the distance between the MFCC features and the template. The smaller the distance, the higher the pronunciation accuracy score.

[0046] The speaking rate score can evaluate the speaking rate by measuring the number of phonemes or words per unit time, and use MFCC features to assist in the detection of phoneme boundaries.

[0047] Expressiveness can analyze the dynamic range, pitch changes, and rhythm changes in MFCC features, and use changes in pitch, volume, and duration to evaluate the expressiveness of speech and obtain an expressiveness score.

[0048] In addition, the vocabulary richness score can be based on the results of text analysis after speech-to-text conversion. NLP technology can be used to evaluate the diversity and complexity of vocabulary and obtain the corresponding vocabulary richness score.

[0049] The sentence complexity score can be based on text analysis to evaluate the complexity of sentence structure. It can analyze grammatical features such as sentence length, clause usage, passive voice, etc. to obtain a sentence complexity score.

[0050] The content relevance score can also be combined with NLP technology to perform semantic analysis on the voice content and evaluate the degree of match between the voice content and a given topic or question, thereby obtaining a content relevance score.

[0051] After obtaining the scoring information of the educational resource content in each eloquence dimension, the scoring information in each eloquence dimension may be weighted averaged to obtain a comprehensive score of the educational resource content corresponding to the first transaction party.

[0052] In some embodiments, the scoring information corresponding to each designated eloquence dimension can also be analyzed based on a multiple regression model to obtain a comprehensive score of the educational resource content corresponding to the first transaction party in the eloquence dimension. That is, the scores of the educational resource content corresponding to the first transaction party in each eloquence dimension are used as independent variables, and the comprehensive score is used as the dependent variable. A multiple regression model is established to determine the influence weight (regression coefficient) of each variable on the comprehensive score, and the regression coefficient obtained by the multiple regression model analysis is combined with the scoring information to calculate the comprehensive score.

[0053] Among them, the comprehensive score is the result of a linear regression model, which is used to comprehensively evaluate the eloquence performance of educational resource content. The specific formula is as follows:

[0054] S composite =β1F fluency +β2A accuracy +β3R rate +β4V vocabulary +β5C omplexity +β6E expressiveness +β7R relevance +∈

[0055] Among them, βi is the regression coefficient corresponding to each eloquence dimension; ∈ is the error term;

[0056] -F fluency is the fluency score;

[0057] -A accuracy is the pronunciation accuracy score;

[0058] -R rate It is the speech rate score;

[0059] -V vocabularyis the vocabulary richness score;

[0060] -C com p lexity is the sentence complexity score;

[0061] -E expressiveness is the performance score;

[0062] -R relevance is the content relevance score.

[0063] In a multiple regression model, evaluating the combined effect of a variable (such as pronunciation accuracy) on other dimensions (such as fluency, speaking rate, vocabulary richness, etc.) can be achieved by calculating the partial derivative of the variable. Higher-order partial derivatives can be used to measure interactions and nonlinear relationships between variables.

[0064] Specifically, calculate the first-order partial derivative of the dependent variable Y with respect to each independent variable Xi to obtain the marginal effect of each variable: This indicates the effect of the independent variable Xi on Y while keeping other variables constant.

[0065] Compute higher-order partial derivatives of the dependent variable Y with respect to some combinations of the independent variables to assess interactions between the variables:

[0066]

[0067] If this value is not zero, it indicates that there is an interaction between Xi and Xj.

[0068] If there is a nonlinear relationship or interaction between variables, an interaction term can be added to the multiple regression model. In this case, the multiple regression model expression is:

[0069] Y=β0+β1X1+β2X2+…+βijXiXj+∈

[0070] Among them, β0 is the intercept, β1, β2,…,βn are regression coefficients, ∈ is the error term, and βij is the coefficient of the interaction term. There is an interaction between Xi and Xj.

[0071] The dependent variable in this embodiment is the comprehensive score S composite , the independent variable combination is pronunciation accuracy score A accuracv and vocabulary richness score V vocabularv , then the interaction between variables is:

[0072]

[0073] Among them, γ is the influence coefficient.

[0074] During the model training process, the regression coefficients βi can be obtained through techniques such as the least squares method. These coefficients are the values ​​of partial derivatives, which reflect the contribution of each eloquence dimension to the comprehensive score.

[0075] Analyze the educational resource content uploaded by the first transaction party, and the comprehensive score obtained by the analysis is called the first comprehensive score. At this time, the educational resource content can be traded on the platform; in order to ensure the security of the transaction, the educational resource content can be uploaded to the blockchain, that is, NFT is generated on the blockchain platform according to the smart contract, and the NFT is bound to the educational resource content, so that the NFT records the identity information of the first transaction party and the first comprehensive score of the educational resource content.

[0076] In some embodiments, NFTs can also be fragmented according to preset rules to obtain fragmented resource objects, and each fragmented resource object is marked with its corresponding resource category and comprehensive score and stored in the blockchain. The fragmented resource object is the transaction object when performing the fragmented educational resource transaction operation. The preset rule can be to fragment the NFT according to the percentage or fixed share rule while ensuring the integrity of the resource content. The fragmentation rule can be adjusted according to factors such as the value of the content and user needs. For example, the first trading party wants to sell a 100-minute educational resource video, which contains three themes of content explanation. At this time, the theme content contained in the educational resource video can be identified according to AI technology, and the educational resource video can be fragmented into three segments. Each segment tells the content of a theme. At the same time, it is necessary to ensure that the theme content told by each segment is complete. At this time, the three fragmented segments are stored as transaction objects on the blockchain, and the eloquence performance analysis is performed on the video content of the three segments, and the comprehensive scores corresponding to the three video contents are obtained respectively, and each comprehensive score is marked on its corresponding transaction object to facilitate subsequent transaction matching.

[0077] In the case where the first transaction party is the buyer, the self-assessment answers uploaded by the first transaction party can be obtained, wherein the self-assessment answers are answers made according to preset self-assessment questions, which include questions corresponding to multiple specified eloquence dimensions. Multiple specified eloquence dimensions can be pre-set according to actual needs, and the specified eloquence dimensions include fluency, pronunciation accuracy, speaking speed, vocabulary richness, sentence complexity, expressiveness, and content relevance, etc. Set the corresponding self-assessment questions for each specified eloquence dimension, and display the self-assessment questions on the interactive page for the first transaction party to answer.

[0078] Each preset question can be pre-configured with its corresponding preset weight. The score corresponding to each question is obtained based on the self-assessment answer made by the first transaction party. The comprehensive score of the first transaction party in the eloquence dimension is calculated based on the score of each question and the corresponding weight of each question. The comprehensive score reflects the level of eloquence of the first transaction party.

[0079] For example, during the registration process on the interactive page, the first trading party answered a self-assessment question about pronunciation accuracy, such as "How accurate do you think your pronunciation is during a speech? (1-5 points)", and the user selected 4 points. Assuming that the weight of this question is 0.3, this answer will be multiplied by the weight when calculating the comprehensive score, and the score for this question will be 1.2. After the questions corresponding to each eloquence dimension are answered, the scores of all questions are accumulated, and the average score is calculated to form the comprehensive score of the first trading party.

[0080] In addition, when the first transaction party is the buyer, the first transaction party may also upload its own speech video, and use AI technology to score the speech video according to the indicators of each eloquence dimension. The scores corresponding to each eloquence dimension indicator may be weighted averaged or analyzed based on a multivariate regression model to obtain a comprehensive score of the first transaction party, which may also reflect the eloquence level of the first transaction party.

[0081] When the first transaction party initiates a transaction request, it will search the blockchain for a suitable second transaction party and suitable educational resource content based on the transaction request. If so, step S3 will be executed; if there is no suitable second transaction party, the transaction request, the educational resource content to be sold and other data can be stored in the blockchain, and the resources will not be removed from the blockchain until the transaction is successful.

[0082] Step S3: Filter out multiple second transaction parties that meet the scoring conditions, where the scoring conditions are that the difference between the second comprehensive score of the second transaction party in the eloquence dimension and the first comprehensive score remains within a preset range; calculate the matching price between the first transaction party and each second transaction party based on the matching algorithm, and execute the fragmented educational resource transaction operation when the transaction confirmation information between the first transaction party and any second transaction party is received.

[0083] In the process of screening the second transaction party, the search platform is used to determine whether there is a second transaction party whose second comprehensive score is similar to the first comprehensive score of the first transaction party. If the difference between the two scores is within a preset range, the second transaction party that meets the score conditions can be screened out for subsequent matching algorithm calculation. The number of second transaction parties can be screened according to a preset number.

[0084] Assuming that the first transaction party is the buyer and the second transaction party is the seller, the second comprehensive score of the second transaction party can be scored according to the educational resource content uploaded by the second transaction party; assuming that the first transaction party is the seller and the second transaction party is the buyer, the second comprehensive score of the second transaction party can be scored and analyzed according to the self-assessment answers or speech videos uploaded by the second transaction party. The naming of the second comprehensive score is only used to distinguish it from the first comprehensive score. Its scoring method has been explained above and will not be repeated here.

[0085] Among them, the matching algorithm is as follows:

[0086] The transaction price of the first transaction party is calculated according to the eloquence dimension weight of the first transaction party and the preliminary price proposed by the first transaction party; wherein the eloquence dimension weight of the first transaction party is calculated according to the first comprehensive score;

[0087] The transaction price of the second transaction party is calculated based on the eloquence dimension weight of the second transaction party and the preliminary price proposed by the second transaction party; wherein the eloquence dimension weight of the second transaction party is calculated based on the second comprehensive score;

[0088] The transaction price of the first transaction party and the transaction price of the second transaction party are averaged to obtain the matching price between the first transaction party and the second transaction party.

[0089] The formula of the matching algorithm is as follows:

[0090]

[0091] Among them, Pm is the matching price, Pb is the preliminary price proposed by the first trading party, Ps is the preliminary price proposed by the second trading party, and Wb score is the eloquence dimension weight of the first trading party, Ws score It is the weight of the eloquence dimension of the second transaction party.

[0092] The matching price can be pushed to the first and second trading parties, so that the first and second trading parties can understand their price changes and the other party's information. After the first and second trading parties have initiated transaction confirmation information on the platform to agree to the transaction, the platform can execute the fragmented educational resource transaction operation, that is, after the transaction matching is completed, the system creates a smart contract to define the specific terms and conditions of the transaction, where the smart contract is deployed on the blockchain platform; during the transaction, the smart contract automatically executes the transaction according to predefined rules, including asset transfer and transaction settlement, and the transaction record is on the blockchain to ensure transparency and immutability, so that the first trading party can accurately purchase educational resource content that meets its own eloquence level.

[0093] An embodiment of the present invention further provides an educational resource transaction execution system based on eloquence scoring, which executes the educational resource transaction execution method based on eloquence scoring as described above.

[0094] The functions of each module in the system of the embodiment of the present invention can be found in the corresponding description of the above method, which will not be repeated here.

[0095] An embodiment of the present invention further provides an electronic device, Figure 2 FIG. 2 shows a structural block diagram of an electronic device according to an embodiment of the present invention. Figure 2 As shown, the electronic device includes: a memory 100 and a processor 200, wherein the memory 100 stores a computer program that can be run on the processor 200. When the processor 200 executes the computer program, the educational resource transaction execution method based on eloquence scoring in the above embodiment is implemented. The number of the memory 100 and the processor 200 can be one or more.

[0096] The electronic device also includes:

[0097] The communication interface 300 is used to communicate with external devices and perform data exchange transmission.

[0098] If the memory 100, the processor 200 and the communication interface 300 are implemented independently, the memory 100, the processor 200 and the communication interface 300 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0099] Optionally, in a specific implementation, if the memory 100, the processor 200 and the communication interface 300 are integrated on a chip, the memory 100, the processor 200 and the communication interface 300 can communicate with each other through an internal interface.

[0100] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which implements the method provided in the embodiment of the present invention when the program is executed by a processor.

[0101] An embodiment of the present invention further provides a chip, which includes a processor for calling and executing instructions stored in the memory from the memory, so that a communication device equipped with the chip executes the method provided by the embodiment of the present invention.

[0102] An embodiment of the present invention also provides a chip, including: an input interface, an output interface, a processor and a memory, wherein the input interface, the output interface, the processor and the memory are connected via an internal connection path, and the processor is used to execute the code in the memory. When the code is executed, the processor is used to execute the method provided by the embodiment of the invention.

[0103] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor supporting the advanced RISC machines (ARM) architecture.

[0104] Further, optionally, the above-mentioned memory may include a read-only memory and a random access memory, and may also include a non-volatile random access memory. The memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may include a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may include a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct rambus RAM (DR RAM).

[0105] 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 according to the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0106] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0107] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0108] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for executing educational resource transactions based on eloquence scoring, characterized in that: include: Acquire a transaction request initiated by the first transaction party, wherein the transaction request includes a buy request, a sell request, and a transfer request; Obtaining a first comprehensive score of the first transaction party in the eloquence dimension according to the transaction request, and screening out a plurality of second transaction parties that meet a scoring condition, wherein the scoring condition is that the difference between the second comprehensive score of the second transaction party in the eloquence dimension and the first comprehensive score is within a preset range; The matching price between the first transaction party and each of the second transaction parties is calculated based on the matching algorithm, and the fragmented educational resource transaction operation is performed when the transaction confirmation information between the first transaction party and any of the second transaction parties is received.

2. The educational resource transaction execution method based on eloquence scoring according to claim 1 is characterized in that: The dimensions of eloquence include fluency, pronunciation accuracy, speaking speed, vocabulary richness, sentence complexity, expressiveness, and content relevance.

3. The educational resource transaction execution method based on eloquence scoring according to claim 1 is characterized in that: Also includes: When the first transaction party issues the selling request, the educational resource content of the first transaction party is obtained, a performance evaluation is performed on the educational resource content, and scoring information of the educational resource content in each eloquence dimension is obtained; the scoring information in each eloquence dimension is weighted averaged to obtain the first comprehensive score.

4. The educational resource transaction execution method based on eloquence scoring according to claim 3 is characterized in that: Also includes: When the first transaction party issues the selling request, an NFT is generated on the blockchain platform according to the smart contract, and the NFT is bound to the educational resource content, so that the NFT records the identity information of the first transaction party and the first comprehensive score of the educational resource content.

5. The educational resource transaction execution method based on eloquence scoring according to claim 4 is characterized in that: Also includes: The NFT is fragmented according to preset rules to obtain fragmented resource objects. Each fragmented resource object is labeled with its corresponding resource category and comprehensive score and stored in the blockchain. The fragmented resource object is the transaction object when performing fragmented educational resource transaction operations.

6. The educational resource transaction execution method based on eloquence scoring according to claim 1 is characterized in that: The matching algorithm is: The transaction price of the first transaction party is calculated according to the eloquence dimension weight of the first transaction party and the preliminary price proposed by the first transaction party; wherein the eloquence dimension weight of the first transaction party is calculated according to the first comprehensive score; The transaction price of the second transaction party is calculated according to the eloquence dimension weight of the second transaction party and the preliminary price proposed by the second transaction party; wherein the eloquence dimension weight of the second transaction party is calculated according to the second comprehensive score; The transaction price of the first transaction party and the transaction price of the second transaction party are averaged to obtain a matching price between the first transaction party and the second transaction party.

7. The educational resource transaction execution method based on eloquence scoring according to claim 1 is characterized in that: The obtaining of the educational resource content of the first transaction party includes: Obtaining resource data uploaded by the first transaction party, and performing feature extraction on the resource data based on a convolutional neural network to obtain resource features; Classifying the resource features based on the MLP model to obtain the category of the resource data; In the case where the resource data conforms to the education category, the resource data is preprocessed to obtain the education resource content.

8. An educational resource transaction execution system based on eloquence scoring, characterized in that: Execute the educational resource transaction execution method based on eloquence scoring as described in any one of claims 1 to 7.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement the educational resource transaction execution method based on eloquence scoring as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the educational resource transaction execution method based on eloquence scoring as described in any one of claims 1 to 7 is implemented.

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