CN110737498B - Big data and artificial intelligence online examination method and system based on virtual container graphical interface - Google Patents
Big data and artificial intelligence online examination method and system based on virtual container graphical interface Download PDFInfo
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Abstract
A big data and artificial intelligence online examination method and system based on a virtual container graphical interface belongs to the technical field of virtualization; large-data and artificial intelligent multi-person simultaneous examination cannot be completed on one server; the method comprises the steps of setting virtual servers in a server, namely a mirror image server and a container server; setting a mirror image server, a container server and a Web server; the teacher end stores a new examination experiment mirror image on the basis mirror image, the student end generates a relatively independent student container according to the examination mirror image, a graphical interface is rendered to the student end in real time by socket communication, all operations of the student experiment are completed in real time, the contents in the student container are automatically stored, students submit experiment reports and the student container to examine and approve the teacher, the teacher gives corresponding scores, and the students log in and check the scores; the method can realize that the examination of multiple persons can be completed on one server; the system can reduce the cost of big data and artificial intelligence related examinations.
Description
Technical Field
The invention belongs to the technical field of virtualization, and particularly relates to a big data and artificial intelligence online examination method and system based on a virtual container graphical interface.
Background
With the development of technologies such as big data and artificial intelligence, more and more colleges and universities begin to build major data and artificial intelligence related specialties. Because a large amount of computer-related knowledge is involved, the mode of online examination can better meet the investigation of colleges and universities on big data and artificial intelligence related technologies of students.
Most of the existing online examination methods can only meet the investigation of theoretical contents and cannot complete large-data and artificial intelligent online practical examination, because the investigation of the related contents of the large data requires massive data resources and higher server hardware cost. The examination of the knowledge of human intelligence requires more expensive GPU resources. And a large-data and artificial intelligent multi-person simultaneous examination cannot be completed on one server.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a big data and artificial intelligence online examination method and a system based on a virtual container graphical interface, wherein the method effectively reduces the hardware cost of the server aiming at big data and artificial intelligence related examinations by constructing a virtual container server and a mirror image server on one server; on the basis of combining the method, the system can reduce the cost of big data and artificial intelligence related examinations, can support a graphical interface operating system, support cluster examination experiment operation and ensure the reasonability of the invention.
The technical scheme of the invention is as follows:
technical scheme one
A big data and artificial intelligence online examination method based on a virtual container graphical interface comprises the following steps:
step a, setting a virtual server in a server, namely a mirror image server and a container server; setting a mirror image server for storing mirror images, wherein the mirror image server is provided with an operating system mirror image of an image interface, is a system basic mirror image and is used as a basic template generated by a container; the mirror image supports two modes, one mode is a pseudo distribution mode, and a single mirror image supports generation of a master node and a plurality of slave node containers; the other is a cluster distribution mode mirror image, and the mirror image is a main node mirror image and a slave node mirror image;
step b, setting a container server for storing, generating and closing the container; selecting and configuring a single container server or a container server cluster according to the number of students; if the artificial intelligence examination is carried out, installing a GPU display card;
step c, setting a Web server for storing and running a Web program, wherein the Web program comprises the steps of creating a teacher question bank, generating an examination, displaying an experimental examination environment of students and operating big data of the students in the environment;
d, the container server cluster performs polling according to the configured residual condition of the container server source to ensure that a new container generates a server which is most distributed to the current resources;
step e, the teacher end calls a mirror image server, and stores a new examination experiment mirror image on the basis of the basic mirror image, wherein the examination experiment mirror image is used for making an experiment subject, making an experiment environment of the experiment subject, making an experiment subject library, manually selecting the subject to generate an experiment examination, selecting the subject by a template to generate the experiment examination and a test paper list to be examined;
step f, isolating resources including a CPU (central processing unit), a memory and a hard disk of a Docker container row server generated in a container server when a student terminal takes an examination, namely, a student terminal generates a relatively independent student container according to an examination mirror image, wherein the student container resource is relatively independent from student container resources generated by other student terminals in terms of occupying service resources, and the resources include the memory, the CPU and a storage space;
and step g, after the student containers are generated, a graphical interface is rendered to a student end in real time through socket communication, all operations of the student experiment are completed in real time, the contents in the student containers are automatically stored, students submit experiment reports and the student containers to a teacher for approval, the teacher selects whether the student containers are destroyed or not after the approval is finished, corresponding scores are given according to the experiment reports, and the student login account can check the current and previous examination scores, the submitting conditions of the experiment reports and the reading and approving conditions of the teacher on the experiment questions.
Further, the specific method for setting the image server includes installing a Linux system in the image server, selecting a CentOS or Ubuntu system as an operating system, installing a Docker environment in the operating system, and presetting an operating system image with a graphical interface by using a Docker image warehouse.
Further, a specific method for setting the container server includes installing a Linux system in the container server, selecting a CentOS or Ubuntu system as an operating system, and installing a Docker environment in the operating system.
Further, the specific method for the container server cluster to poll according to the remaining condition of each server resource includes:
defining a server array sequence A (n) according to the number of servers, wherein n is the number of the servers, when a student requests the servers, polling the server array A (n), recording a current weight value as C, an initial value as max (S), and the max (S) is the maximum value of the weights in all the servers, finding the first server with the weight value larger than C, processing the student request, creating a container, reducing the weight value of the server, processing all the student requests, if the weight value reaches the tail of the array sequence A (n), enabling C- = Gcd (S) and the greatest common divisor of all the server weights of the Gcd (S), re-polling the servers, processing all the student requests until C is 0, and resetting C to be max (S).
Furthermore, the test question making comprises editable test examination questions, the test questions support online editing and making, and test question questions and reference answers are recorded online; the manufactured test question can be edited, modified, previewed and deleted; experimental topics support differentiation by knowledge points or chapters.
Furthermore, the experimental report supports similarity check of document contents and automatic check of experimental codes; the reports of two students are automatically decomposed into phrases, a single phrase of the student A and all phrases of the student B are subjected to polling check, and the cosine value of the included angle of the two vectors in the vector space is used as the measure of the difference between the two phrases; the similarity calculation formula is as follows:
cos (θ) is a cosine value, the closer the cosine value is to 1, the closer the student A's document is to the student B's document. Cut student A's document to x i Wherein x is the word frequency quantity of each phrase of student A, and the document of student B is y i Where y is the word frequency number of each phrase of student B,
for polynomial summation, n is the total number of phrases of the two documents. i is the sum starting from 1.
The similarity of the document content and the codes can be set by the teacher, and the names and the similarity ratios of the students can be automatically prompted when the similarity of the documents or the codes submitted by the students is higher than the value.
Technical scheme two
A system for realizing a big data and artificial intelligence online examination method based on a virtual container graphical interface in the technical scheme comprises a Web server side, a mirror image server side and a container server cluster side; the output end of the Web server end is communicated with the mirror image server end, the output end of the mirror image server end is communicated with the container server cluster end, and the container server cluster end is communicated with the Web server end in real time through a socket; the Web server side includes teacher's end and a plurality of student's end, the mirror image server side includes basic mirror image end and a plurality of examination mirror image end, container server cluster end includes a plurality of container server, teacher's end is through basic mirror image end and a plurality of examination mirror image end communication, a plurality of student's end and mirror image server end communication, examination mirror image end passes through container server cluster end and a plurality of student's container end communication.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a big data and artificial intelligence online examination method and a system based on a virtual container graphical interface, wherein the method effectively reduces the hardware cost of a server aiming at big data and artificial intelligence related examinations by constructing a virtual container server and a mirror image server on the same server; the method does not need to use a complex code writing tool for the investigation of big data and artificial intelligence technology and supports the operating system of a graphical interface. The method integrates the online editing, investigation, evaluation and examination of questions, and solves the problem that the existing online examination cannot be performed with manual intelligent experiment practical operation examination with graphic interface big data due to huge hardware cost.
On the basis of combining the method, the system can reduce the cost of big data and artificial intelligence related examinations, can support a graphical interface operating system, supports cluster examination experiment operation and ensures the reasonability of the invention.
Drawings
FIG. 1 is a diagram of the structure of the present invention;
FIG. 2 is a second diagram of the inventive structure;
FIG. 3 is a schematic view of a student generated container summary interface.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
Detailed description of the invention
A big data and artificial intelligence online examination method based on a virtual container graphical interface comprises the following steps:
step a, setting a mirror image server for storing a mirror image, wherein the mirror image server is provided with an operating system mirror image of an image interface, is a system basic mirror image and is used as a basic template generated by a container; the mirror image supports two modes, one mode is a pseudo distribution mode, and a single mirror image supports generation of a master node and a plurality of slave node containers; the other is a cluster distribution mode mirror image, and the mirror images are a master node mirror image and a slave node mirror image;
step b, setting a container server for storing, generating and closing the container; selecting and configuring a single-machine container server or a container server cluster according to the number of students, wherein the large-data related examinations with more students and higher requirements on a server CPU, an internal memory and the like should configure the cluster; if the artificial intelligence examination is carried out, installing a GPU display card;
step c, setting a Web server for storing and operating a Web program, wherein the Web program comprises the steps of establishing a teacher question bank, generating an examination, displaying an experiment and examination environment of students and operating big data of the students in the environment;
d, the container server cluster performs polling according to the remaining condition of the container server source, if 3 container servers are configured, the container server cluster performs polling on the three servers, and a new container is ensured to generate a server which is most distributed to the current resources;
step e, the teacher end calls a mirror image server, and stores a new examination experiment mirror image on the basis of the basic mirror image, wherein the examination experiment mirror image is used for making an experiment subject, making an experiment environment of the experiment subject, making an experiment subject library, manually selecting the subject to generate an experiment examination, selecting the subject by a template to generate the experiment examination and a test paper list to be examined;
step f, using a Docker container generated in a container server to isolate resources of a CPU (central processing unit), a memory and a hard disk of the server when a student terminal takes an examination, namely, a student terminal generates a relatively independent student container according to an examination mirror image, as shown in figure 3, the resources of the student container are relatively independent from the resources of the student containers generated by other student terminals on the aspect of occupying service resources, the resources comprise the memory, the CPU and a storage space, the container supports the generation of a container cluster according to the cluster mirror image, and students can complete a cluster-related requirement experiment according to the requirement of an experimental subject;
step g, after the student container is generated, a graphical interface is rendered to a student end in real time through socket communication, all student experiment operations are completed in real time, a student exits from the virtual container, the content in the student container is automatically stored, and at the moment, the container releases occupied resources of a server CPU, a memory and a GPU and only occupies a small amount of physical disk space; students submit experiment reports and student containers to teachers for examination and approval, after the examination and approval, the teachers select whether the student containers are destroyed or not, corresponding scores are given according to the experiment reports, and the student login account numbers can check the current and past examination scores, the submitting conditions of the experiment reports and the reading and approving conditions of the teachers for experiment questions.
Specifically, the specific method for setting the mirror image server comprises the steps of installing a Linux system in the mirror image server, selecting a CentOS or Ubuntu system as an operating system, installing a Docker environment in the operating system, and presetting an operating system mirror image with a graphical interface by using a Docker mirror warehouse.
Specifically, the specific method for setting the container server includes installing a Linux system in the container server, selecting a CentOS or Ubuntu system as an operating system, and installing a Docker environment in the operating system.
Specifically, the mirror server and the container server are both virtual servers arranged in the same server;
specifically, the specific method for the container server cluster to poll according to the remaining condition of each server resource includes:
defining a server array sequence A (n) according to the number of servers, wherein n is the number of servers, when a student requests a server, polling the server array A (n), recording the current weight value as C, the initial value as max (S), and the max (S) is the maximum value of the weights in all servers, finding the first server with the weight value larger than C, processing a student request, creating a container, reducing the weight value of the server, processing all student requests, if the weight value reaches the tail of the array sequence A (n), enabling C- = Gcd (S), and the greatest common divisor of all server weights of the Gcd (S), re-polling the servers, processing all student requests until C is 0, and resetting C as max (S).
Specifically, the mirror image production comprises that a basic mirror image can be uploaded on a mirror image server, an experiment mirror image is produced, the mirror image server can be called through a web project, and the experiment mirror image is stored as a new examination experiment mirror image on the basis of the basic mirror image. For the examination related to the big data, a technical environment related to the big data, such as a Hadoop environment, needs to be installed in the basic mirror image. The related art of artificial intelligence requires installation of related environments and tools of artificial intelligence, such as Python environment, jupyter, etc. And after the mirror image is stored, the name, the icon and the description of the mirror image are required to be filled in for selective use when the experimental environment of the experimental subject is manufactured. The mirror image, the mirror image description and the mirror image icon support modification, and the whole mirror image supports deletion. The mirror image is made in support of a pseudo-distribution mode and a cluster mode.
Specifically, the test question making comprises editable test examination questions, the test questions support online editing and making, and test question questions and reference answers are recorded online; the manufactured test question can be edited, modified, previewed and deleted; experimental topics support differentiation by knowledge points or chapters.
Specifically, the experimental subject experimental environment manufacturing comprises test subject self-manufacturing supporting configuration and examination editing experimental environment editing, and a mirror image of an examination container can be generated when a student examines. The selection of the mirror image supports the viewing of the name, description and icon of the mirror image, and if the mirror image cannot meet the requirements which the teacher wants to meet, the teacher can edit the mirror image, upload data and tools related to the big data experimental exam and then store the data and tools as a new mirror image. The configuration of the test experiment may select a pseudo distribution pattern and a cluster distribution pattern. The pseudo distribution mode is a single mirror image, and the cluster distribution mode is a cluster mode with multiple master nodes and multiple slave nodes. The configuration of the examination experiment may select the size of the resources allocated for the container when it is generated. The CPU supports 1 core, 2 cores and 4 cores. And the space system of the hard disk is dynamically distributed according to the occupation condition of the student on the disk. The resource system of the GPU is dynamically allocated according to the usage of the GPU by students.
Specifically, the experimental question bank comprises a plurality of experimental questions which are made to generate an experimental question bank, and the question bank supports batch import of table texts. The question bank supports retrieval by chapter or knowledge point.
In particular, manually selecting questions to generate an experimental exam includes generating an exam with manually selecting questions, which may define an exam name and duration. Test subjects for which tests are desired can be selected, and scores for the subjects can be defined.
Specifically, the template topic selection for generating the experimental examination comprises the step of generating the examination by using a test question template, wherein the template can be configured according to chapters, scores and the number of questions, the template is a constraint, the experimental questions can be manually selected under the constraint of the template, the questions can be randomly extracted from a question bank according to the template to generate a test paper, and the name and the duration of the examination can be defined by the examination paper.
Specifically, the list of examination papers to be examined includes the teacher creating an examination, which is entered into the list of examination papers to be examined. When the examination is needed, the teacher can start the examination, and the students receive the examination in real time and complete the examination within the specified time. The test papers in the list to be examined support the preview. The previewed test paper questions are supported to be downloaded as documents.
Specifically, when the teacher approves the experimental examination report, the teacher can check the name of the test paper, the examination duration, the examination ending time, the examination state, preview the test paper questions, and check the experimental examination reports and containers submitted by all students. The teacher may give a score according to the student's submission.
Specifically, the experimental report supports similarity verification of document contents and automatic check of experimental codes; the reports of two students are automatically decomposed into phrases, a single phrase of the student A and all phrases of the student B are subjected to polling check, and the cosine value of the included angle of the two vectors in the vector space is used as the measure of the difference between the two phrases; the similarity calculation formula is as follows:
cos (θ) is a cosine value, the closer the cosine value is to 1, the closer the student A's document is to the student B's document. Cut student A's document to x i Wherein x is the word frequency quantity of each phrase of student A, and the document of student B is y i Where y is the word frequency number of each phrase of student B,
for polynomial summation, n is the total number of phrases for the two documents. i is the sum starting from 1.
The similarity of the document content and the codes can be set by the teacher, and the names and the similarity ratios of the students can be automatically prompted when the similarity of the documents or the codes submitted by the students is higher than the value.
Detailed description of the invention
A system for implementing a virtual container graphical interface-based big data and artificial intelligence online examination method according to an embodiment of the present invention is shown in fig. 1-2, and includes a Web server, a mirror image server, and a container server cluster; the output end of the Web server end is communicated with a mirror image server end, the output end of the mirror image server end is communicated with a container server cluster end, and the container server cluster end is communicated with the Web server end in real time through a socket; the Web server side includes teacher's end and a plurality of student's end, the mirror image server side includes basic mirror image end and a plurality of examination mirror image end, container server cluster end includes a plurality of container server, teacher's end is through basic mirror image end and a plurality of examination mirror image end communication, a plurality of student's end and mirror image server end communication, examination mirror image end passes through container server cluster end and a plurality of student's container end communication.
Detailed description of the invention
The method is built in a big data examination system of colleges and universities based on the first specific implementation mode and the second specific implementation mode; the role of the system is divided into teachers and students. The teacher creates a class, and imports students in the class. The teacher creates an question bank to input big data and artificial intelligence related test questions. And creating a mirror image of the test questions based on the basic mirror image, storing the mirror image in the system, and generating the test mirror image. And the teacher uploads big data experimental examination data and big data related tools in the mirror image. And after selecting the mirror image, selecting the CPU and the memory configuration of the container generated according to the mirror image.
The teacher generates an examination paper according to a template in the system, wherein the template is preset in advance. The template is N experimental questions, and each question is divided into M points. The experimental exam took S minutes. When in examination, the teacher selects a class to take an examination, and the students enter the system according to the time appointed by the teacher.
And during examination, the students enter the system and then generate containers according to the mirror images made by the teachers. The containers for each student are relatively independent. The students apply big data tools to order to respectively operate and complete the relevant requirements of the big data examination. And compiling an experimental examination report and completing the examination.
The teacher approves the report submitted by the student and enters a container submitted by the student. The completion of the student's experimental exam is checked. And checks the repetition degree of the student codes and reports using the code and document duplication checking function. The students in question are recorded.
The teacher gives examination scores of all students in the whole class according to the answering conditions of the students. The teacher chooses to delete the containers submitted by the students after giving the score.
The students log in the own account numbers to check the scores obtained by the students in the score inquiry column.
Claims (7)
1. A big data and artificial intelligence online examination method based on a virtual container graphical interface is characterized by comprising the following steps:
step a, setting a virtual server in a server, namely a mirror image server and a container server; setting a mirror image server for storing mirror images, wherein the mirror image server is provided with an operating system mirror image of an image interface, is a system basic mirror image and is used as a basic template generated by a container; the mirror image supports two modes, one mode is a pseudo distribution mode, and a single mirror image supports generation of a master node and a plurality of slave node containers; the other is a cluster distribution mode mirror image, and the mirror image is a main node mirror image and a slave node mirror image;
step b, setting a container server for storing, generating and closing the container; selecting and configuring a single container server or a container server cluster according to the number of students; if the artificial intelligence examination is carried out, installing a GPU display card;
step c, setting a Web server for storing and running a Web program, wherein the Web program comprises the steps of creating a teacher question bank, generating an examination, displaying an experimental examination environment of students and operating big data of the students in the environment;
d, the container server cluster performs polling according to the residual condition of the container server source to ensure that a new container generates a server which is most distributed to the current resources;
step e, the teacher end calls a mirror image server, and stores a new examination experiment mirror image on the basis of the basic mirror image, wherein the examination experiment mirror image is used for making an experiment subject, making an experiment environment of the experiment subject, making an experiment subject library, manually selecting the subject to generate an experiment examination, selecting the subject by a template to generate the experiment examination and a test paper list to be examined;
step f, using a Docker container generated in a container server to isolate resources of a CPU (central processing unit), a memory and a hard disk of the server when a student terminal takes an examination, namely, a student terminal generates a relatively independent student container according to an examination mirror image, the student container resources are relatively independent from the student container resources generated by other student terminals on the aspect of occupying service resources, and the resources comprise the memory, the CPU and a storage space;
step g, after the student containers are generated, a graphical interface is rendered to a student end in real time through socket communication, all operations of a student experiment are completed in real time, the contents in the student containers are automatically stored, students submit experiment reports and the student containers to teachers for approval, after approval is finished, the teachers select whether the student containers are destroyed or not, corresponding scores are given according to the experiment reports, and the student login account number can check the scores of the current and past examinations, the submitting conditions of the experiment reports and the reading conditions of the teachers on experiment questions.
2. The virtual container graphical interface-based big data and artificial intelligence online examination method according to claim 1, wherein the specific method for setting the image server comprises installing a Linux system in the image server, selecting a CentOS or an Ubuntu system as the operating system, installing a Docker environment in the operating system, and using a Docker image warehouse to preset an operating system image with the graphical interface.
3. The virtual container graphical interface-based big data and artificial intelligence online examination method according to claim 1, wherein the specific method for setting the container server comprises installing a Linux system in the container server, selecting a CentOS or Ubuntu system as an operating system, and installing a Docker environment in the operating system.
4. The virtual container graphical interface-based big data and artificial intelligence online examination method according to claim 1, wherein the specific method for polling the container server cluster according to the remaining conditions of the server resources comprises the following steps:
defining a server array sequence A (n) according to the number of servers, wherein n is the number of servers, when a student requests a server, polling the server array A (n), recording the current weight value as C, the initial value as max (S), and the max (S) is the maximum value of the weights in all servers, finding the first server with the weight value larger than C, processing a student request, creating a container, reducing the weight value of the server, processing all student requests, if the weight value reaches the tail of the array sequence A (n), enabling C- = Gcd (S), and the greatest common divisor of all server weights of the Gcd (S), re-polling the servers, processing all student requests until C is 0, and resetting C as max (S).
5. The virtual container graphical interface-based big data and artificial intelligence online examination method according to claim 1, wherein the production of the test questions comprises editable test examination questions, the test questions support online editing and production, and the test question questions and reference answers are recorded online; the manufactured test question can be edited, modified, previewed and deleted; experimental topics support differentiation by knowledge points or chapters.
6. The virtual container graphical interface-based big data and artificial intelligence online examination method according to claim 1, wherein the experimental report supports similarity verification of document contents and automatic check of experimental codes; the reports of two students are automatically decomposed into phrases, a single phrase of the student A and all phrases of the student B are subjected to polling check, and the cosine value of the included angle of the two vectors in the vector space is used as the measure of the difference between the two phrases; the similarity calculation formula is as follows:
cos (θ) is a cosine value, the closer the cosine value is to 1, the closer the student A document is to the student B document; cut student A's document to x i Wherein x is the word frequency quantity of each phrase of student A, and the document of student B is y i Where y is the word frequency number of each phrase of student B,
is a polynomial summation, n is the total number of phrases of two documents; i is the sum starting from 1;
the similarity of the document content and the codes can be set by the teacher, and the name and the similarity ratio of the students can be automatically prompted when the similarity of the documents or the codes submitted by the students is higher than the value.
7. The system for realizing the big data and artificial intelligence online examination method based on the virtual container graphical interface is characterized by comprising a Web server side, a mirror image server side and a container server cluster side; the output end of the Web server end is communicated with the mirror image server end, the output end of the mirror image server end is communicated with the container server cluster end, and the container server cluster end is communicated with the Web server end in real time through a socket; the Web server side includes teacher's end and a plurality of student's end, the mirror image server side includes basic mirror image end and a plurality of examination mirror image end, container server cluster end includes a plurality of container server, teacher's end is through basic mirror image end and a plurality of examination mirror image end communication, a plurality of student's end and mirror image server end communication, examination mirror image end passes through container server cluster end and a plurality of student's container end communication.
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