CN114708127A - Student point system comprehensive assessment method and system - Google Patents

Student point system comprehensive assessment method and system Download PDF

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CN114708127A
CN114708127A CN202210397279.6A CN202210397279A CN114708127A CN 114708127 A CN114708127 A CN 114708127A CN 202210397279 A CN202210397279 A CN 202210397279A CN 114708127 A CN114708127 A CN 114708127A
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comprehensive assessment
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陈海龙
张俊婕
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Guangdong Yawai Technology Co ltd
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Abstract

The application provides a student point system comprehensive assessment method and system, which comprises the following steps: s1, carrying out digital processing on the paper documents in batches; s2, optimizing the digital processing result; s3, identifying the digital processing result to obtain name, school number, class, school information and score or grade information; s4, converting the identified result into an integral, accumulating and archiving the integral, and synchronizing the integral to the user side of the student or the parent; and S5, the user side can exchange different gifts by using the points. This application adopts artificial intelligence processing mode to carry out automatic identification to paper document and handles, has avoided the complexity of artifical input, has avoided the possibility of type mistake moreover, and this application can be accumulated the score that student's score corresponds constantly, can exchange the gift after reaching corresponding grade to this arouses student's interest in learning.

Description

Student point system comprehensive assessment method and system
Technical Field
The invention relates to the technical field of intelligent education, in particular to a student point system comprehensive assessment method and system.
Background
At present, domestic education subjects are various, evaluation dimensionality of students is increasingly diversified, and different subjects have different assessment modes. For example, some subjects adopt a scoring system, namely directly score on a test paper; some subjects adopt a grade evaluation mode: for example: only A, B, C, D notations are given on the test paper. Since the examination subjects of the students in the contemporary world are many, for example, many subjects such as language, data, foreign language, biology, chemistry, geography, history, politics, etc., and the frequency of examination or evaluation is high, such as: unit evaluation, weekly evaluation, monthly evaluation, and the like. As for students, frequent evaluation or examination makes the students very tired and hardly feel a little fun; as for teachers, if the teachers want to know the condition of each subject of a student in an all-round and multi-dimensional manner, the paper test papers to be evaluated at each time need to be frequently registered and counted, so that the method is very complicated, is easy to make mistakes, and enables the teachers to be tired.
Disclosure of Invention
The student point system comprehensive assessment method and system can achieve recognition of scores in an artificial intelligence mode, greatly reduce burden on a teacher side, can conduct point conversion and accumulation on multiple assessment results of students, can exchange gifts of corresponding grades after corresponding grades are achieved, arouse learning and assessment interests of the students, and are beneficial to arousing learning power of the students.
The invention provides a student point system comprehensive assessment method, which comprises the following steps:
s1, carrying out digital processing on the paper documents in batches;
s2, optimizing the digital processing result;
s3, identifying the digital processing result to obtain name, school number, class, school information and score or grade information;
s4, converting the score or grade information result into integral, accumulating and archiving, and synchronizing to the user end of the student or the parent;
and S5, the user side can exchange different gifts by using the points.
Optionally, the S1 further includes: and scanning the paper documents in batch, wherein the digital processing result is a digital image.
Optionally, the S2 further includes: tilt correction and enhancement and/or illumination compensation processes are performed.
Optionally, the S3 further includes: the identification is realized by adopting template matching or a deep neural network.
Optionally, the S4 includes: and when the identification result is a grade, correspondingly converting the grade into an integral.
Correspondingly, the application provides a student point system comprehensive assessment system, which comprises the following characteristics:
the digitization module is used for carrying out digitization processing on paper documents in batches;
the optimization processing module is used for optimizing the digital processing result;
the identification module is used for identifying the digital processing result to obtain name, school number, class, school information and score or grade information;
the conversion module is used for converting the score or grade information result into an integral, accumulating and archiving the integral and synchronizing the integral to a user side of a student or a parent;
and the exchange module is used for exchanging different gifts by the user side through the points.
Optionally, the digitizing module further comprises: and scanning the paper documents in batch, wherein the digital processing result is a digital image.
Optionally, the optimization processing module further includes: tilt correction and enhancement and/or illumination compensation processes are performed.
Optionally, the identification module further includes: the identification is realized by adopting template matching or a deep neural network.
Optionally, the conversion module further comprises: and when the identification result is a grade, correspondingly converting the grade into an integral.
The technical effects of this application lie in:
1. the intelligent automatic identification processing of the paper test paper is realized, the score registration efficiency is improved, the error rate of manual input is reduced, and the time and energy cost of manual input are reduced.
2. The study interest of students is stimulated, and the study efficiency and the study effect are improved in order to exchange favorite gifts for more diligent study.
3. The intelligent algorithm provided by the application improves the intelligent recognition efficiency, is suitable for recognition and input of paper test paper, belongs to the original invention, and can be popularized to various schools.
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FIG. 1 is a principal logic sequence diagram of the present invention.
Detailed Description
As shown in fig. 1, in order to solve the above problems, the present application provides a student point system comprehensive assessment method and system, which can realize score identification in an artificial intelligence manner, thereby greatly reducing the burden on the teacher side, and can convert and accumulate points of a plurality of assessment results of students, and after reaching a corresponding grade, gifts of the corresponding grade can be exchanged, so as to stimulate the learning and assessment interests of the students, thereby being beneficial to stimulating the learning power of the students.
The invention provides a student point system comprehensive assessment method, which comprises the following steps:
s1, carrying out digital processing on the paper documents in batches;
s2, optimizing the digital processing result;
s3, identifying the digital processing result to obtain name, school number, class, school information and score or grade information;
s4, converting the score or grade information result into integral, accumulating and archiving, and synchronizing to the user end of the student or the parent;
and S5, the user side can exchange different gifts by using the points.
Optionally, the S1 further includes: and scanning the paper documents in batch, wherein the digital processing result is a digital image.
Optionally, the S2 further includes: tilt correction and enhancement and/or illumination compensation processes are performed.
Optionally, the S3 further includes: the identification is realized by adopting template matching or a deep neural network.
The template matching method specifically comprises the following steps:
positioning a sealed area of the test paper, detecting at least one of a school, a class, a name and a school number, preferably detecting the name and a score or grade area, and identifying the content of the area;
during identification, firstly extracting a feature vector of an identification region, optionally, the identification region may be a sealed region, and then calculating similarity S between the feature vector to be identified and each preset template vector, wherein the similarity S is expressed as:
Figure BDA0003599521860000031
wherein S represents a similarity value, j is more than or equal to 1 and less than or equal to N, and N represents the total number of the feature points; alpha, beta>0.1 and α + β ═ 1, PiFor the ith feature point to be recognized, QjRepresenting the feature point in the jth template corresponding to the ith position.
And if the S full is equal to or greater than a preset similarity threshold, the recognition is considered to be successful, and the object corresponding to the template is considered as a recognition result, wherein the object can be a single character or a character string.
Optionally, the deep neural network includes an input layer, one or more hidden layers, and an output layer;
the input layer is used for receiving the optimized picture, and the picture only reserves a sealing area, wherein the sealing area comprises: at least one of school, class, name, school number, score, or rating, preferably, includes name and score or rating information.
Optionally, the hidden layer comprises one or more convolutional layers, one or more pooling layers; the loss function adopted by the deep neural network is a log-likelihood loss function.
Optionally, the pooling method is as follows:
xe＝f(weφ(ue))
ue＝(1-we)φ(xe-1)；
wherein x iseRepresents the output of the current layer, ueFor representing the input, w, of a function phieRepresents the weight of the current layer, phi represents the log-likelihood loss function, xe-1Representing the output of the previous layer.
Optionally, the
Figure BDA0003599521860000041
N represents the size of the sample data set, i takes values of 1-N, and yi represents a label corresponding to the sample xi; qyiRepresents the weight of the sample xi at its label yi, MyiDenotes the deviation of the sample xi at its label yi, MjRepresents the deviation at output node j; thetaj,iIs the weighted angle between the sample xi and its corresponding label yi.
The excitation function R is:
Figure BDA0003599521860000042
n represents the size of a sample data set; yi denotes the sample feature vector xiA corresponding tag value; wyiRepresenting a sample feature vector xiWeight at its label yi, θyiDenoted as sample xiThe vector angle with its corresponding label yi.
And continuously training the deep neural network until a preset condition is met, and obtaining a trained deep neural network model.
The output layer is used for outputting classification results such as names, scores/grades and the like, and optionally, identification results such as classes, schools and the like can also be output.
Optionally, the S4 includes: when the identification result is grade, correspondingly converting the grade into integral; alternatively, A may be converted to 100 points, B may be converted to 80 points, C may be converted to 60 points, and D may be converted to 40 points. The above conversion rules are only one specific exemplary embodiment and can be set by those skilled in the art.
For a specific score, the score may be directly used as an integral, or may be multiplied by a certain coefficient to perform score-to-integral conversion, which is not specifically limited herein.
The gifts in the points store can be provided with specific demands by students, funds can be funded by school exits or family members, and the points are associated according to the prices of commodities, wherein the higher the price is, the more points are needed.
Correspondingly, the application provides a student integral comprehensive assessment system, which comprises the following characteristics:
the digitization module is used for carrying out digitization processing on paper documents in batches;
the optimization processing module is used for optimizing the digital processing result;
the identification module is used for identifying the digital processing result to obtain name, school number, class, school information and score or grade information;
the conversion module is used for converting the score or grade information result into an integral, accumulating and archiving the integral and synchronizing the integral to a user side of a student or a parent;
and the exchange module is used for exchanging different gifts by the user side through the points.
Optionally, the digitizing module further comprises: and scanning the paper documents in batch, wherein the digital processing result is a digital image.
Optionally, the optimization processing module further includes: tilt correction and enhancement and/or illumination compensation processes are performed.
Optionally, the identification module further includes: the identification is realized by adopting template matching or a deep neural network.
The template matching method specifically comprises the following steps:
positioning a sealed area of the test paper, detecting at least one of a school, a class, a name and a school number, preferably detecting the name and a score or grade area, and identifying the content of the area;
during identification, firstly extracting a feature vector of an identification area, and then calculating the similarity S between the feature vector to be identified and each preset template vector, wherein the similarity S is expressed as:
Figure BDA0003599521860000051
wherein S represents a similarity value, j is more than or equal to 1 and less than or equal to N, and N represents the total number of the feature points; alpha, beta>0.1 and α + β ═ 1, PiFor the ith feature point to be recognized, QjRepresenting the feature point in the jth template corresponding to the ith position.
And if the S is equal to or greater than a preset similarity threshold, the recognition is considered to be successful, the object corresponding to the template is considered as a recognition result, and the object can be a single character or a character string.
Optionally, the deep neural network includes an input layer, one or more hidden layers, and an output layer;
the input layer is used for receiving the optimized picture, and the picture only reserves a sealing area, wherein the sealing area comprises: at least one of school, class, name, school number, score, or rating, preferably, includes name and score or rating information.
Optionally, the hidden layer comprises one or more convolutional layers, one or more pooling layers; the loss function adopted by the deep neural network is a log-likelihood loss function.
Optionally, the pooling method is as follows:
xe＝f(weφ(ue))
ue＝(1-we)φ(xe-1)；
wherein x iseRepresents the output of the current layer, ueFor expressing the function phiInput, weRepresents the weight of the current layer, phi represents the log-likelihood loss function, xe-1Representing the output of the previous layer.
Optionally, the
Figure BDA0003599521860000061
N represents the size of the sample data set, i takes values from 1 to N, and yi represents a label corresponding to a sample xi; qyiRepresents the weight of the sample xi at its label yi, MyiDenotes the deviation of the sample xi at its label yi, MjRepresents the deviation at output node j; thetaj,iIs the weighted angle between the sample xi and its corresponding label yi.
The excitation function R is:
Figure BDA0003599521860000062
n represents the size of the sample data set; yi denotes the sample feature vector xiA corresponding tag value; wyiRepresenting a sample feature vector xiWeight at its label yi, θyiDenoted as sample xiThe vector angle with its corresponding label yi.
And continuously training the deep neural network until a preset condition is met, and obtaining a trained deep neural network model.
The output layer is used for outputting classification results such as names, scores/grades and the like, and can also output identification results such as classes, schools and the like.
Optionally, the conversion module further comprises: and when the identification result is a grade, correspondingly converting the grade into an integral.
Optionally, when the identification result is a grade, correspondingly converting the grade into an integral; alternatively, a may be converted for 100 minutes, B may be converted for 80 minutes, C may be converted for 60 minutes, and D may be converted for 40 minutes. The above conversion rules are only one specific exemplary embodiment and can be set by those skilled in the art.
For a specific score, the score may be directly used as an integral, or may be multiplied by a certain coefficient to perform score-to-integral conversion, which is not specifically limited herein.
The gifts in the points store can be provided with specific demands by students, funds can be funded by school exits or family members, and the points are associated according to the prices of commodities, wherein the higher the price is, the more points are needed.
It should be noted that the above embodiments and further limitations, which can be combined and used without conflict, constitute the practical disclosure of the present invention, are limited by space and are not listed, but all combinations fall within the scope of protection of the present application.
It will be understood by those skilled in the art that all or part of the steps of the above methods may be implemented by instructing the relevant hardware through a program, and the program may be stored in a computer readable storage medium, such as a read-only memory, a magnetic or optical disk, and the like. Alternatively, all or part of the steps of the above embodiments may be implemented using one or more integrated circuits. Accordingly, each module/unit in the above embodiments may be implemented in the form of hardware, and may also be implemented in the form of a software functional module. The present invention is not limited to any specific form of combination of hardware and software.
The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it should be understood that various changes and modifications can be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. A student point system comprehensive assessment method comprises the following steps:
s1, carrying out digital processing on the paper documents in batches;
s2, optimizing the digital processing result;
s3, identifying the digital processing result to obtain name, school number, class, school information and score or grade information;
s4, converting the score or grade information result into integral, accumulating and archiving, and synchronizing to the user end of the student or the parent;
and S5, the user side can exchange different gifts by using the points.
2. The student point system comprehensive assessment method according to claim 1, wherein the S1 further comprises: and scanning the paper documents in batch, wherein the digital processing result is a digital image.
3. The student integral comprehensive assessment method according to claim 1, wherein the S2 further comprises: tilt correction and enhancement and/or illumination compensation processes are performed.
4. The student point system comprehensive assessment method according to claim 1, wherein the S3 further comprises: the identification is realized by adopting template matching or a deep neural network.
5. The student point system comprehensive assessment method according to claim 1, wherein the S4 comprises: and when the identification result is a grade, correspondingly converting the grade into an integral.
6. A student integral comprehensive assessment system comprises the following characteristics:
the digitization module is used for carrying out digitization processing on paper documents in batches;
the optimization processing module is used for optimizing the digital processing result;
the identification module is used for identifying the digital processing result to obtain name, school number, class, school information and score or grade information;
the conversion module is used for converting the score or grade information result into an integral, accumulating and archiving the integral and synchronizing the integral to a user side of a student or a parent;
and the exchange module is used for exchanging different gifts by the user side through the points.
7. The student point system comprehensive assessment system according to claim 6, the digitizing module further comprising: and scanning the paper documents in batch, wherein the digital processing result is a digital image.
8. The student point system comprehensive assessment system according to claim 6, the optimization processing module further comprising: tilt correction and enhancement and/or illumination compensation processes are performed.
9. The student point system comprehensive assessment system according to claim 6, the identification module further comprising: the identification is realized by adopting template matching or a deep neural network.
10. The student point system comprehensive assessment system according to claim 6, the transformation module further comprising: and when the identification result is a grade, correspondingly converting the grade into an integral.
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