A homework data collection method and system applied to an education platform

Abstract

The present invention discloses a method and system for collecting homework data applied to an education platform, and the method includes: setting a trigger condition for collecting electronic homework data on the education platform, determining data collection parameters according to the trigger condition; determining the standard format of electronic homework upload data based on the data collection parameters, and constructing a homework qualification judgment mechanism based on the standard format; collecting students' electronic homework online through the education platform and using the homework qualification judgment mechanism to make a qualified judgment on the electronic homework, and obtaining the judgment result; screening out unqualified homework according to the judgment result and marking the unqualified reason to generate a re-upload instruction to the terminal where the unqualified electronic homework data is located for re-uploading. Using the judgment mechanism to make a qualified judgment on the electronic homework uploaded by the student can intelligently review the uploaded homework without manual review, thereby improving work efficiency and practicality and reducing the cost of manpower.

Description

Technical Field

[0001] The present invention relates to the field of data collection technology, and in particular to a homework data collection method and system applied to an education platform. Background Art

[0002] At present, with the development of the times and the advancement of science and technology, the traditional teaching method has greatly reduced its appeal to the young people of the new era. Therefore, a new teaching method is needed to attract students' interest in learning and help them improve themselves better. Video teaching can visualize the knowledge points in writing, turning the invisible into tangible, which is more conducive to students' acceptance and understanding. At the same time, micro-classes can provide students with an environment for independent learning, better meet students' personalized learning of knowledge points in different subjects, and are an important supplement and expansion of traditional classroom learning. The content can be permanently preserved for easy reference and correction. Micro-classes are implemented through online education platforms. In the process of teaching, there will still be a function for teachers to assign homework and students to reply and upload online. The existing online collection method of homework data is usually through teachers to create a parent group, and the parents of students upload images to the group for teachers to review. It will not only cause homework confusion, but also fail to systematically make intelligent qualified judgments on the uploaded homework. Teachers need to check them one by one, which reduces practicality and work efficiency as well as the experience of teachers. Summary of the invention

[0003] In response to the above-mentioned problems, the present invention provides a homework data collection method and system for an education platform to solve the problem mentioned in the background technology that parents upload their homework to the group by uploading images for teachers to review, which not only causes homework confusion but also fails to systematically make intelligent qualification judgments on the uploaded homework, requiring teachers to manually check each homework one by one, reducing practicality, work efficiency and teacher experience.

[0004] A method for collecting homework data applied to an education platform comprises the following steps:

[0005] Set trigger conditions for collecting electronic homework data on the education platform, and determine data collection parameters based on the trigger conditions;

[0006] Determine the standard format of electronic homework upload data based on data collection parameters, and build a job qualification judgment mechanism based on the standard format;

[0007] Collect students' electronic homework online through the education platform and use the homework qualification judgment mechanism to judge the electronic homework and obtain the judgment result;

[0008] According to the judgment results, unqualified operations are screened out and the reasons for failure are marked to generate re-upload instructions to the terminal where the unqualified electronic operation data is located for re-upload.

[0009] Preferably, the setting of trigger conditions for collecting electronic homework data on the education platform and determining data collection parameters according to the trigger conditions include:

[0010] Identify multiple homework types on the education platform, obtain the average difficulty of each homework type, and set the submission period trigger conditions for each homework type based on the average difficulty;

[0011] Determine the answering method for each type of homework, and obtain the answer type for each type of homework according to the answering method;

[0012] Set the submission trigger conditions for each assignment type based on the answer type of each assignment type;

[0013] The data collection parameters are determined according to the submission period trigger conditions and submission method trigger conditions of each job type.

[0014] Preferably, the method of determining the standard format of the electronic homework upload data based on the data collection parameters and constructing a job qualification determination mechanism based on the standard format includes:

[0015] Determine the format of the collected data object according to the data collection parameters, and determine the standard format of the electronic homework upload data according to the collected object data format;

[0016] Determine the basic elements of the electronic homework upload data according to the standard format of the electronic homework upload data;

[0017] Determine the key indicators for judging the quality of electronic homework upload data based on the basic elements, and determine the scoring criteria for each key indicator;

[0018] A job qualification determination mechanism for electronic homework upload data is established based on the scoring criteria for each key indicator.

[0019] Preferably, collecting students' electronic homework online through the education platform and using the homework qualification judgment mechanism to judge the electronic homework to obtain the judgment result includes:

[0020] Determine the electronic homework upload method based on the answer sheet format of the homework type, and set the homework collection method of the education platform based on the electronic homework upload method;

[0021] Based on the homework collection method, the education platform collects students' electronic homework online, performs handwriting or drawing detection on the electronic homework, and obtains the detection results;

[0022] Determine whether the electronic homework is a blank homework according to the detection result, and if so, issue a homework submission error reminder;

[0023] If not, the job qualification judgment mechanism is used to determine the current scores of the key indicators of the electronic job, and whether the electronic job is qualified is determined based on the current scores of the key indicators.

[0024] Preferably, the method of screening out unqualified operations according to the determination results and marking the reasons for the unqualified operations to generate a re-upload instruction to the terminal where the unqualified electronic operation data is located for re-uploading includes:

[0025] Determine the qualification level of each electronic assignment according to the determination result, wherein the qualification level includes: qualified, slightly unqualified, unqualified and seriously unqualified;

[0026] Screen out unqualified assignments according to the degree of eligibility of each electronic assignment, and obtain the non-compliance index of each unqualified assignment;

[0027] Determine the reasons for failure of each unqualified operation based on the unqualified indicators of the operation and mark them;

[0028] The identity information of the uploading student is determined based on each unqualified assignment, and a re-upload instruction is generated according to the identity information and sent to the student's terminal.

[0029] Preferably, the performing handwriting or drawing detection on the electronic homework and obtaining the detection result includes:

[0030] Scan the electronic assignments, and determine the system printed handwriting / drawings and non-system printed handwriting / drawings in the electronic assignments according to the scanning results;

[0031] Obtaining a preset handwriting job and a drawing job, and extracting handwriting features and drawing features from the preset handwriting job and the drawing job;

[0032] Based on the handwriting features and drawing features, matching is performed in non-system printed handwriting / drawings through a preset matching algorithm to obtain a matching result;

[0033] Determine the detection handwriting and the detection drawing in the non-system printed handwriting / drawing according to the matching result;

[0034] Among them, the preset matching algorithms include: brute force matching, nearest neighbor matching and FLANN matching.

[0035] Preferably, matching is performed in non-system printed handwriting / drawings by using a preset matching algorithm based on handwriting features and drawing features to obtain matching results, including:

[0036] Obtaining recognition parameters of handwriting features and drawing features, comparing the recognition parameters with standard recognition parameters, and obtaining the comparison difference;

[0037] When the comparison difference is higher than a preset difference threshold, a second matching algorithm having an algorithm recognition rate higher than a preset algorithm recognition rate threshold is selected from the plurality of first matching algorithms;

[0038] Determine the degree of differentiation between handwritten and drawn features and system printed handwriting features;

[0039] Determine the identification matching factor hyperparameter quantification requirement according to the discrimination, and select a third matching algorithm that meets the requirement from the second matching algorithm as the preset matching algorithm according to the identification matching factor hyperparameter quantification requirement;

[0040] And through the preset matching algorithm, matching is performed in non-system printed handwriting / drawings to obtain matching results.

[0041] Preferably, the setting of the submission mode triggering condition of each homework type based on the answer type of each homework type includes:

[0042] Determine the student's operation goal and operation flow parameters for each type of homework according to the answer type of the homework type;

[0043] Determine the submission object of each operation type according to the operation target and operation flow parameters, and determine the corresponding bearing mode of the submission object of each operation type;

[0044] Determine the trigger condition requirement parameters for the submission mode of each job type according to the bearer mode corresponding to the submission object of each job type;

[0045] Set the submission trigger conditions for each job type based on the required parameters.

[0046] Preferably, the basic elements contained in the electronic homework upload data are determined according to the standard format of the electronic homework upload data, including:

[0047] Determine the standard form of answers based on the standard format of electronic homework upload data, and determine the distribution of answer information based on the standard form of answers;

[0048] Based on the morphological algorithm, the boundary range of each type of information in the electronic homework upload data is determined according to the distribution of answer information;

[0049] Determine the distribution area of ​​each type of information according to the boundary range of each type of information in the electronic homework upload data, and extract information features from the distribution area of ​​each type of information in the electronic homework upload data;

[0050] Analyze the information characteristics to determine the system elements in each distribution area, perform mapping evolution processing on the system elements, and obtain processing results;

[0051] Determine the evolutionary characteristics in each distribution area according to the processing results, and obtain the explanatory variables corresponding to the evolutionary characteristics;

[0052] Determine the representation information in each distribution area based on the explanatory variables and determine the description method of the representation information;

[0053] Determine the semantic components in each distribution area according to the description method of the representation information, and determine the aggregation condition elements of the semantic components;

[0054] Determine the structural features of the answers within each distribution area based on the aggregation conditional elements of the semantic components;

[0055] Construct the digital coding genealogy of the operating elements through the relationship between the preset structural features and element functions;

[0056] Using the digital coding spectrum of the task elements, the corresponding vector element set in each distribution area is determined based on the structural characteristics of the answer;

[0057] The basic elements contained in the electronic job upload data are determined based on the corresponding vector element sets in each distribution area.

[0058] A homework data collection system applied to an education platform, the system comprising:

[0059] A setting module, used to set trigger conditions for collecting electronic homework data on the education platform, and determine data collection parameters according to the trigger conditions;

[0060] A construction module is used to determine a standard format for uploading data of electronic jobs based on data collection parameters, and to construct a job qualification determination mechanism based on the standard format;

[0061] A determination module is used to collect students' electronic homework online through the education platform and use the homework qualification determination mechanism to determine the electronic homework and obtain the determination result;

[0062] The screening module is used to screen out unqualified operations according to the judgment results, mark the reasons for unqualified operations, and generate re-upload instructions to the terminal where the unqualified electronic operation data is located for re-upload.

[0063] Through the above technical solution, the present invention achieves the following technical effects:

[0064] 1. By intelligently setting the starting conditions for collecting electronic homework data, the collection timing of students' electronic homework and the specified collection content can be intelligently determined, and then the content and specific format of the collected homework can be determined, and then a qualified judgment mechanism for the homework can be generated. The judgment mechanism can be used to judge the eligibility of the electronic homework uploaded by the students, and the uploaded homework can be intelligently reviewed without manual review, which improves work efficiency and practicality and reduces manpower costs. At the same time, each student uploads electronic homework through a personal terminal and binds it to his or her own identity information, which can avoid confusion in the uploaded electronic homework and improve stability.

[0065] 2. Set the submission period trigger conditions for each type of homework according to the average difficulty of each homework. At the same time, set the submission method trigger conditions for each type of homework according to the answer method of each homework, and determine the data collection parameters. This can more accurately set the appropriate collection trigger conditions and data collection parameters for each type of homework according to the answer attributes, and can intelligently review multiple types of questions, thereby improving compatibility, adaptability and practicality.

[0066] 3. Determine the standard format of the electronic homework upload data based on the data collection parameters, determine the key indicators of the quality of the electronic homework upload data based on the standard format, and determine the scoring criteria for each indicator, and build a job qualification judgment mechanism for the electronic homework upload data. Automated evaluation can be performed based on the scoring criteria for each key indicator to improve the objectivity and fairness of the job evaluation.

[0067] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0068] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0070] Figure 1 A workflow diagram of a method for collecting homework data applied to an education platform provided by the present invention;

[0071] Figure 2 Another workflow diagram of a method for collecting homework data applied to an education platform provided by the present invention;

[0072] Figure 3 Another work flow chart of a method for collecting homework data applied to an education platform provided by the present invention;

[0073] Figure 4 This is a structural schematic diagram of a homework data collection system applied to an education platform provided by the present invention. DETAILED DESCRIPTION

[0074] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0075] At present, with the development of the times and the advancement of science and technology, the appeal of traditional teaching methods to young people in the new era has greatly decreased. Therefore, a new teaching method is needed to attract students' interest in learning and help them improve themselves better. Video teaching can visualize the knowledge points in writing, turn the invisible into tangible, and is more conducive to students' acceptance and understanding. At the same time, micro-classes can provide students with an environment for autonomous learning, better meet students' personalized learning of knowledge points in different subjects, and are an important supplement and expansion of traditional classroom learning. The content can be permanently preserved for easy reference and correction. Micro-classes are implemented through online education platforms. In the process of teaching, there will still be a function for teachers to assign homework and students to reply and upload online. The existing online collection method of homework data is usually to create a parent group through teachers, and the parents of students upload it to the group through image uploading for teachers to review. It will not only cause homework confusion, but also fail to systematically perform intelligent qualified judgment on the uploaded homework. Teachers need to check them one by one, which reduces practicality and work efficiency as well as the experience of teachers. In order to solve the above problems, this embodiment discloses a homework data collection method applied to an education platform.

[0076] A homework data collection method applied to an education platform, such as Figure 1 As shown, the following steps are included:

[0077] Step S101, setting a trigger condition for collecting electronic homework data on the education platform, and determining data collection parameters according to the trigger condition;

[0078] Step S102: determining a standard format for uploading electronic homework data based on data collection parameters, and building a job qualification determination mechanism based on the standard format;

[0079] Step S103: Collecting students' electronic homework online through the education platform and using the homework qualification judgment mechanism to judge the electronic homework, and obtaining the judgment result;

[0080] Step S104: Filter out unqualified operations according to the determination result, mark the reasons for the unqualified operations, and generate a re-upload instruction to the terminal where the unqualified electronic operation data is located for re-upload.

[0081] In this embodiment, the trigger conditions are the timing trigger conditions and content trigger conditions for collecting electronic homework data, for example: setting that students must upload electronic homework before 10:30 p.m., and at the same time, the content of the homework must contain handwriting;

[0082] In this embodiment, the data collection parameters are represented as data collection items for electronic homework uploaded by students, such as documents, images, etc.;

[0083] In this embodiment, the homework qualification determination mechanism is used to determine whether the student submits a blank homework or a completed homework.

[0084] The working principle of the above technical solution is: set the trigger conditions for collecting electronic homework data on the education platform, and determine the data collection parameters according to the trigger conditions; determine the standard format of the electronic homework upload data based on the data collection parameters, and build a homework qualification judgment mechanism based on the standard format; collect students' electronic homework online through the education platform and use the homework qualification judgment mechanism to judge the electronic homework and obtain the judgment results; filter out unqualified homework according to the judgment results and mark the reasons for unqualified homework to generate a re-upload instruction to the terminal where the unqualified electronic homework data is located for re-upload.

[0085] The beneficial effects of the above technical solution are: by intelligently setting the starting conditions for collecting electronic homework data, the collection timing of students' electronic homework and the specified collection content can be intelligently determined, and then the content and specific format of the collected homework can be determined, and then a qualified judgment mechanism for the homework can be generated. The judgment mechanism can be used to judge the qualification of the electronic homework uploaded by the students, and the uploaded homework can be intelligently reviewed without manual review, which improves work efficiency and practicality and reduces manpower costs. At the same time, each student uploads electronic homework through a personal terminal and binds it to his or her own identity information, which can avoid confusion in the uploaded electronic homework and improve stability. It solves the problem mentioned in the prior art that uploading images to the group for teacher review will not only cause homework confusion, but also fail to systematically perform intelligent qualification judgment on the uploaded homework, and requires teachers to manually check them one by one, which reduces practicality, work efficiency and teacher experience.

[0086] In one embodiment, Figure 2As shown, the triggering conditions for collecting electronic homework data on the education platform are set, and data collection parameters are determined according to the triggering conditions, including:

[0087] Step S201: determine multiple homework types on the education platform, obtain the average difficulty of each homework type, and set a submission period trigger condition for each homework type according to the average difficulty;

[0088] Step S202, determining the answering method for each type of homework, and obtaining the answer type for each type of homework according to the answering method;

[0089] Step S203, setting a submission mode trigger condition for each assignment type based on the answer type of each assignment type;

[0090] Step S204: determine data collection parameters according to the submission period triggering condition and the submission mode triggering condition of each job type.

[0091] In this embodiment, the homework type may be painting homework, Chinese homework, math homework, physics homework, etc.;

[0092] In this embodiment, the answering method is represented by the method for answering each type of assignment, for example: by drawing, by selecting options, by subjective description and writing, etc.

[0093] The beneficial effects of the above technical solution are: setting the submission period trigger conditions for each type of homework according to the average difficulty of each homework, and at the same time, setting the submission method trigger conditions for each type of homework according to the answering method of each homework, and determining the data collection parameters. It can more accurately set the adaptive collection trigger conditions and data collection parameters for each type of homework according to the answering attributes, and can intelligently review multiple types of questions, thereby improving compatibility, adaptability and practicality.

[0094] In one embodiment, Figure 3 As shown, the standard format of the electronic homework upload data is determined based on the data collection parameters, and the job qualification determination mechanism is constructed based on the standard format, including:

[0095] Step S301, determining the format of the collected data object according to the data collection parameters, and determining the standard format of the electronic homework upload data according to the collected object data format;

[0096] Step S302, determining basic elements included in the electronic homework upload data according to the standard format of the electronic homework upload data;

[0097] Step S303: determining key indicators for judging the quality of the uploaded electronic homework data based on the basic elements, and determining the scoring criteria for each key indicator;

[0098] Step S304: construct a job qualification determination mechanism for the electronic homework uploaded data based on the scoring criteria for each key indicator.

[0099] In this embodiment, the basic elements include: handwriting elements, image elements, name elements, etc.;

[0100] In this embodiment, the key indicators include: length, font neatness, answer composition, etc.;

[0101] The beneficial effects of the above technical solution are: determining the standard format of electronic homework upload data according to data collection parameters, determining the key indicators of the quality of electronic homework upload data according to the standard format, and determining the scoring criteria for each indicator, building a job qualification judgment mechanism for electronic homework upload data, and performing automated evaluation based on the scoring criteria for each key indicator, thereby improving the objectivity and fairness of job evaluation.

[0102] In one embodiment, the collecting of students' electronic homework online through the education platform and performing qualification determination on the electronic homework using the homework qualification determination mechanism to obtain the determination result includes:

[0103] Determine the electronic homework upload method based on the answer sheet format of the homework type, and set the homework collection method of the education platform based on the electronic homework upload method;

[0104] Based on the homework collection method, the education platform collects students' electronic homework online, performs handwriting or drawing detection on the electronic homework, and obtains the detection results;

[0105] Determine whether the electronic homework is a blank homework according to the detection result, and if so, issue a homework submission error reminder;

[0106] If not, the job qualification judgment mechanism is used to determine the current scores of the key indicators of the electronic job, and whether the electronic job is qualified is determined based on the current scores of the key indicators.

[0107] The beneficial effects of the above technical solution are: setting the homework collection method of the education platform according to the answer sheet format of the homework type, collecting students' electronic homework, performing handwriting or drawing detection on the electronic homework, and judging whether it is a submission error, which can improve the efficiency and quality of homework collection.

[0108] In one embodiment, the method of screening out unqualified operations according to the determination results, marking the reasons for the unqualified operations, and generating a re-upload instruction to the terminal where the unqualified electronic operation data is located for re-upload includes:

[0109] Determine the qualification level of each electronic assignment according to the determination result, wherein the qualification level includes: qualified, slightly unqualified, unqualified and seriously unqualified;

[0110] Screen out unqualified assignments according to the degree of eligibility of each electronic assignment, and obtain the non-compliance index of each unqualified assignment;

[0111] According to the unqualified indicators of each unqualified assignment, the reasons for the unqualified assignment are determined and marked; the assignment content is marked here, and the assignment is collected. The marking method is, for example, marking the unqualified area in yellow, etc. For example, if the length is too short and does not meet the standard, it can be marked in yellow;

[0112] The identity information of the uploading student is determined based on each unqualified assignment, and a re-upload instruction is generated according to the identity information and sent to the student's terminal.

[0113] The beneficial effects of the above technical solution are: determining the degree of qualification of each electronic homework according to the judgment result, screening out unqualified homework, obtaining the non-standard indicators of each unqualified homework, determining the reasons for failure and marking them, and generating re-upload instructions, which can effectively improve the pass rate of electronic homework and reduce the number of unqualified homework. At the same time, it is convenient for students to check unqualified homework in time.

[0114] In one embodiment, the step of performing handwriting or drawing detection on the electronic homework and obtaining the detection result includes:

[0115] Scan the electronic assignments, and determine the system printed handwriting / drawings and non-system printed handwriting / drawings in the electronic assignments according to the scanning results;

[0116] Obtaining a preset handwriting job and a drawing job, and extracting handwriting features and drawing features from the preset handwriting job and the drawing job;

[0117] Based on the handwriting features and drawing features, matching is performed in non-system printed handwriting / drawings through a preset matching algorithm to obtain a matching result;

[0118] Determine the detection handwriting and the detection drawing in the non-system printed handwriting / drawing according to the matching result;

[0119] Among them, the preset matching algorithms include: brute force matching, nearest neighbor matching and FLANN matching.

[0120] The beneficial effects of the above technical solution are: scanning the electronic homework, determining the printed handwriting / drawings in the electronic homework according to the scanning results, and matching the non-system printed handwriting / drawings according to the handwriting features and drawing features, obtaining the matching results, and determining the detection handwriting and detection drawing in the non-system printed handwriting / drawings, which can improve the recognition accuracy and reduce the false alarm rate. At the same time, it can serve as an important basis for evaluating students' academic performance, help teachers better understand students' learning situation and ability level, and provide students with targeted guidance.

[0121] In one embodiment, matching is performed in non-system printed handwriting / drawings through a preset matching algorithm based on handwriting features and drawing features to obtain matching results, including:

[0122] Obtaining recognition parameters of handwriting features and drawing features, comparing the recognition parameters with standard recognition parameters, and obtaining the comparison difference;

[0123] When the comparison difference is higher than a preset difference threshold, a second matching algorithm having an algorithm recognition rate higher than a preset algorithm recognition rate threshold is selected from the plurality of first matching algorithms;

[0124] Determine the degree of differentiation between handwritten and drawn features and system printed handwriting features;

[0125] Determine the identification matching factor hyperparameter quantification requirement according to the discrimination, and select a third matching algorithm that meets the requirement from the second matching algorithm as the preset matching algorithm according to the identification matching factor hyperparameter quantification requirement;

[0126] And through the preset matching algorithm, matching is performed in non-system printed handwriting / drawings to obtain matching results.

[0127] In the above technical solution, the recognition parameters refer to the features that can be recognized in the handwriting and drawing process, such as horizontal, vertical, left-falling, right-falling, and hooks in the handwriting process, or lines, or contours, colors, etc. in the drawing process. By comparing the recognition parameters with the standard recognition parameters, if the comparison difference is greater, an algorithm with a higher recognition rate is required, and a second matching algorithm that meets the requirements is screened out from multiple first matching algorithms according to the recognition rate.

[0128] In the process of recognizing handwriting features and drawing features, it is necessary to use recognition matching factors for recognition. In the process of quantification, the recognition matching factors need to identify matching factor hyperparameters. Each recognition matching factor will correspond to an recognition matching factor hyperparameter. The quantification requirement refers to the quantity requirement for the recognition matching factor hyperparameters. It is sufficient to meet the quantity. By determining the quantification requirement, a third matching algorithm that further meets the requirement is screened out from a large number of second matching algorithms, and the screened out third matching algorithm is set as the preset matching algorithm.

[0129] The present invention can ensure the precision and accuracy of the matching results by selecting a matching algorithm as a preset matching algorithm based on multiple parameters from the recognition angle (such as recognition rate, etc.), while also ensuring the matching compatibility of handwriting features and drawing features, avoiding the occurrence of matching omissions, and improving matching efficiency and reliability.

[0130] In one embodiment, the setting of the submission mode triggering condition of each assignment type based on the answer type of each assignment type includes:

[0131] Determine the student's operation goal and operation flow parameters for each type of homework according to the answer type of the homework type;

[0132] Determine the submission object of each operation type according to the operation target and operation flow parameters, and determine the corresponding bearing mode of the submission object of each operation type;

[0133] Determine the trigger condition requirement parameters for the submission mode of each job type according to the bearer mode corresponding to the submission object of each job type;

[0134] Set the submission trigger conditions for each job type based on the required parameters.

[0135] The beneficial effects of the above technical solution are: determining the submission object of each type of assignment based on the answer type of each type of assignment, thereby determining the corresponding carrier mode of the submission object of each type of assignment, which can more effectively track and manage assignments and ensure that each assignment is properly processed, obtain the submission method trigger condition requirement parameters of each type of assignment, set the submission method trigger condition of each type of assignment, and better manage the assignment process.

[0136] In one embodiment, the basic elements contained in the electronic homework upload data are determined according to the standard format of the electronic homework upload data, including:

[0137] Determine the standard form of answers based on the standard format of electronic homework upload data, and determine the distribution of answer information based on the standard form of answers;

[0138] Based on the morphological algorithm, the boundary range of each type of information in the electronic homework upload data is determined according to the distribution of answer information;

[0139] Determine the distribution area of ​​each type of information according to the boundary range of each type of information in the electronic homework upload data, and extract information features from the distribution area of ​​each type of information in the electronic homework upload data;

[0140] Analyze the information characteristics to determine the system elements in each distribution area, perform mapping evolution processing on the system elements, and obtain processing results;

[0141] Determine the evolutionary characteristics in each distribution area according to the processing results, and obtain the explanatory variables corresponding to the evolutionary characteristics;

[0142] Determine the representation information in each distribution area based on the explanatory variables and determine the description method of the representation information;

[0143] Determine the semantic components in each distribution area according to the description method of the representation information, and determine the aggregation condition elements of the semantic components;

[0144] Determine the structural features of the answers within each distribution area based on the aggregation conditional elements of the semantic components;

[0145] Construct the digital coding genealogy of the operating elements through the relationship between the preset structural features and element functions;

[0146] Using the digital coding spectrum of the task elements, the corresponding vector element set in each distribution area is determined based on the structural characteristics of the answer;

[0147] The basic elements contained in the electronic job upload data are determined based on the corresponding vector element sets in each distribution area.

[0148] In this embodiment, the standard form of the answer is represented by the description form of the answer to the electronic homework uploaded data, for example: text form or picture form.

[0149] In this embodiment, the answer information distribution is represented by the information distribution of the student's answer, including: handwriting information distribution, identity information distribution, drawing information distribution, etc.;

[0150] In this embodiment, the information features are represented as answer information features within each distribution area;

[0151] In this embodiment, the system elements are represented as descriptive elements of the printed fonts within the job;

[0152] In this embodiment, the mapping evolution process is represented as the answer mapping evolution process for the job printing font;

[0153] The beneficial effects of the above technical solution are: by determining the information structure characteristics in each area of ​​the electronic assignment and then determining the basic elements, the most basic text form elements, image form elements, name elements, etc. contained in the assignment can be determined based on the standard answer of the electronic assignment, thereby maximizing the rationality, objectivity and accuracy of the quality inspection of the electronic assignment.

[0154] In one embodiment, this embodiment also discloses a homework data collection system applied to an education platform, such as Figure 4 As shown, the system includes:

[0155] A setting module 401 is used to set a trigger condition for collecting electronic homework data on the education platform and determine data collection parameters according to the trigger condition;

[0156] A construction module 402 is used to determine a standard format for uploading electronic homework data based on data collection parameters, and to construct a job qualification determination mechanism based on the standard format;

[0157] The determination module 403 is used to collect students' electronic homework online through the education platform and use the homework qualification determination mechanism to determine the electronic homework and obtain the determination result;

[0158] The screening module 404 is used to screen out unqualified operations according to the determination results, mark the reasons for the unqualified operations, and generate a re-upload instruction to the terminal where the unqualified electronic operation data is located for re-upload.

[0159] The working principle and beneficial effects of the above technical solution have been described in the method claims and will not be repeated here.

[0160] Those skilled in the art should understand that the first and second in the present invention merely refer to different application stages.

[0161] Other embodiments of the present disclosure will be readily apparent to those skilled in the art after considering the specification and practicing the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0162] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A homework data collection method applied to an education platform, characterized in that: The following steps are involved: S101: setting a trigger condition for collecting electronic homework data on the education platform, and determining data collection parameters according to the trigger condition; S102: Determine a standard format for uploading electronic homework data based on data collection parameters, and build a job qualification determination mechanism based on the standard format; S103: Collecting students' electronic homework online through the education platform and using the homework qualification judgment mechanism to judge the electronic homework, and obtaining the judgment result; S104: Filter out unqualified operations according to the determination results, mark the reasons for unqualified operations, generate a re-upload instruction to the terminal where the unqualified electronic operation data is located for re-upload; Step S102 includes: Determine the electronic homework upload method according to the answer sheet format of the homework type, set the homework collection method of the education platform according to the electronic homework upload method, and based on the homework collection method, the education platform collects students' electronic homework online at the same time; Scan the electronic assignments, and determine the system printed handwriting / drawings and non-system printed handwriting / drawings in the electronic assignments according to the scanning results; Obtaining a preset handwriting job and a drawing job, and extracting handwriting features and drawing features from the preset handwriting job and the drawing job; Obtaining recognition parameters of handwriting features and drawing features, comparing the recognition parameters with standard recognition parameters, and obtaining the comparison difference; When the comparison difference is higher than a preset difference threshold, a second matching algorithm having an algorithm recognition rate higher than a preset algorithm recognition rate threshold is selected from the plurality of first matching algorithms; Determine the degree of differentiation between handwritten and drawn features and system printed handwriting features; Determine the identification matching factor hyperparameter quantification requirement according to the discrimination, and select a third matching algorithm that meets the requirement from the second matching algorithm as the preset matching algorithm according to the identification matching factor hyperparameter quantification requirement; and matching is performed in the non-system printed handwriting / drawings through a preset matching algorithm to obtain a matching result, and the detection handwriting and the detection drawing in the non-system printed handwriting / drawings are determined according to the matching result. Perform handwriting or drawing detection on the electronic homework, obtain the detection result, and determine whether the electronic homework is a blank homework according to the detection result. If so, issue a homework submission error reminder; If not, the job qualification judgment mechanism is used to determine the current scores of the key indicators of the electronic job, and whether the electronic job is qualified is determined based on the current scores of the key indicators.

2. The homework data collection method applied to the education platform according to claim 1 is characterized in that: The triggering conditions for collecting electronic homework data on the education platform are set, and data collection parameters are determined according to the triggering conditions, including: Identify multiple homework types on the education platform, obtain the average difficulty of each homework type, and set the submission period trigger conditions for each homework type based on the average difficulty; Determine the answering method for each type of homework, and obtain the answer type for each type of homework according to the answering method; Set the submission trigger conditions for each assignment type based on the answer type of each assignment type; The data collection parameters are determined according to the submission period trigger conditions and submission method trigger conditions of each job type.

3. The homework data collection method applied to the education platform according to claim 1 is characterized in that: The method of determining the standard format of the electronic homework upload data based on the data collection parameters and constructing a job qualification determination mechanism based on the standard format includes: Determine the format of the collected data object according to the data collection parameters, and determine the standard format of the electronic homework upload data according to the collected object data format; Determine the basic elements of the electronic homework upload data according to the standard format of the electronic homework upload data; Determine the key indicators for judging the quality of electronic homework upload data based on the basic elements, and determine the scoring criteria for each key indicator; A job qualification determination mechanism for electronic homework upload data is established based on the scoring criteria for each key indicator.

4. The homework data collection method applied to an education platform according to claim 1, characterized in that: The method of screening out unqualified operations according to the determination results and marking the reasons for the unqualified operations to generate a re-upload instruction to the terminal where the unqualified electronic operation data is located for re-uploading includes: Determine the qualification level of each electronic assignment according to the determination result, wherein the qualification level includes: qualified, slightly unqualified, unqualified and seriously unqualified; Screen out unqualified assignments according to the degree of eligibility of each electronic assignment, and obtain the non-compliance index of each unqualified assignment; Determine the reasons for failure of each unqualified operation based on the unqualified indicators of the operation and mark them; The identity information of the uploading student is determined based on each unqualified assignment, and a re-upload instruction is generated according to the identity information and sent to the student's terminal.

5. The homework data collection method applied to the education platform according to claim 2 is characterized in that: The triggering condition for setting the submission mode of each homework type based on the answer type of each homework type includes: Determine the student's operation goal and operation flow parameters for each type of homework according to the answer type of the homework type; Determine the submission object of each operation type according to the operation target and operation flow parameters, and determine the corresponding bearing mode of the submission object of each operation type; Determine the trigger condition requirement parameters for the submission mode of each job type according to the bearer mode corresponding to the submission object of each job type; Set the submission trigger conditions for each job type based on the required parameters.

6. The homework data collection method applied to an education platform according to claim 3, characterized in that: The basic elements of the electronic homework upload data shall be determined according to the standard format of the electronic homework upload data, including: Determine the standard form of answers based on the standard format of electronic homework upload data, and determine the distribution of answer information based on the standard form of answers; Based on the morphological algorithm, the boundary range of each type of information in the electronic homework upload data is determined according to the distribution of answer information; Determine the distribution area of ​​each type of information according to the boundary range of each type of information in the electronic homework upload data, and extract information features from the distribution area of ​​each type of information in the electronic homework upload data; Analyze the information characteristics to determine the system elements in each distribution area, perform mapping evolution processing on the system elements, and obtain processing results; Determine the evolutionary characteristics in each distribution area according to the processing results, and obtain the explanatory variables corresponding to the evolutionary characteristics; Determine the representation information in each distribution area based on the explanatory variables and determine the description method of the representation information; Determine the semantic components in each distribution area according to the description method of the representation information, and determine the aggregation condition elements of the semantic components; Determine the structural features of the answers within each distribution area based on the aggregation conditional elements of the semantic components; Construct the digital coding genealogy of the operating elements through the relationship between the preset structural features and element functions; Using the digital coding spectrum of the task elements, the corresponding vector element set in each distribution area is determined based on the structural characteristics of the answer; The basic elements contained in the electronic job upload data are determined based on the corresponding vector element sets in each distribution area.

7. A homework data collection system applied to an education platform, characterized in that: The system includes: A setting module, used to set trigger conditions for collecting electronic homework data on the education platform, and determine data collection parameters according to the trigger conditions; A construction module is used to determine a standard format for uploading data of electronic jobs based on data collection parameters, and to construct a job qualification determination mechanism based on the standard format; A determination module is used to collect students' electronic homework online through the education platform and use the homework qualification determination mechanism to determine the electronic homework and obtain the determination result; A screening module is used to screen out unqualified operations according to the determination results, mark the reasons for unqualified operations, and generate a re-upload instruction to the terminal where the unqualified electronic operation data is located for re-upload; The determination module specifically includes: determining the electronic homework upload method according to the answer sheet form of the homework type, setting the homework collection method of the education platform according to the electronic homework upload method, and collecting the electronic homework of the students online on the education platform based on the homework collection method; Scan the electronic assignments, and determine the system printed handwriting / drawings and non-system printed handwriting / drawings in the electronic assignments according to the scanning results; Obtaining a preset handwriting job and a drawing job, and extracting handwriting features and drawing features from the preset handwriting job and the drawing job; Obtaining recognition parameters of handwriting features and drawing features, comparing the recognition parameters with standard recognition parameters, and obtaining the comparison difference; When the comparison difference is higher than a preset difference threshold, a second matching algorithm having an algorithm recognition rate higher than a preset algorithm recognition rate threshold is selected from the plurality of first matching algorithms; Determine the degree of differentiation between handwritten and drawn features and system printed handwriting features; Determine the identification matching factor hyperparameter quantification requirement according to the discrimination, and select a third matching algorithm that meets the requirement from the second matching algorithm as the preset matching algorithm according to the identification matching factor hyperparameter quantification requirement; and matching is performed in the non-system printed handwriting / drawings through a preset matching algorithm to obtain a matching result, and the detection handwriting and the detection drawing in the non-system printed handwriting / drawings are determined according to the matching result. Perform handwriting or drawing detection on the electronic homework, obtain the detection result, and determine whether the electronic homework is a blank homework according to the detection result. If so, issue a homework submission error reminder; If not, the job qualification judgment mechanism is used to determine the current scores of the key indicators of the electronic job, and whether the electronic job is qualified is determined based on the current scores of the key indicators.

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