CN116192829A - WEB-based remote multi-mode experiment teaching method, device, equipment and medium - Google Patents

WEB-based remote multi-mode experiment teaching method, device, equipment and medium Download PDF

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CN116192829A
CN116192829A CN202310488730.XA CN202310488730A CN116192829A CN 116192829 A CN116192829 A CN 116192829A CN 202310488730 A CN202310488730 A CN 202310488730A CN 116192829 A CN116192829 A CN 116192829A
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习勇
陈翔
丹梅
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Dayao Information Technology Hunan Co ltd
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Abstract

The application relates to a WEB-based remote multi-mode experiment teaching method, a device, equipment and a medium, wherein the method comprises the steps of logging in an experiment cloud teaching server through a WEB browser, loading a target experiment task into a demonstration classroom mode, uploading experiment operation data of the target experiment task to the experiment cloud teaching server through a data compression algorithm, and downloading experiment instruction data of the target experiment task; after all operations of the target experiment task are completed, an experiment operation completion instruction is sent to the experiment cloud teaching server, and experiment result data generated after the experiment cloud teaching server performs distributed processing on experiment output data corresponding to each experiment operation data by adopting a distributed computing algorithm is received; and receiving experimental teaching recommended data generated after the experimental cloud teaching server adopts the deep neural network to conduct learning behavior analysis on each experimental operation data. The experimental simulation degree is high, and the interaction mode supports personalized recommendation.

Description

WEB-based remote multi-mode experiment teaching method, device, equipment and medium
Technical Field
The invention belongs to the technical field of teaching data transmission, and relates to a method, a device, equipment and a medium for teaching based on WEB remote multimode experiments.
Background
Along with the development of social demands and teaching technologies, experimental teaching plays a vital role in modern education, and experimental teaching methods based on WEB remote are receiving more and more attention and application due to the advantages of convenience, rapidness, cost saving and the like. The experimental teaching method based on WEB remote refers to a teaching mode that students participate in experimental teaching activities at different places by means of an online platform, a virtual laboratory and the like by utilizing the Internet and a remote technology. The comparison to traditional experimental teaching generally requires that a learner go to a laboratory physically to perform the experiment, but there are some limitations in this model, such as the limitation of laboratory equipment, geographical location, and time of the learner. The experimental teaching method based on the WEB remote can overcome the limitations, so that a learner can participate in the experimental teaching at any place and time, and the flexibility and convenience of the experimental teaching are improved.
Therefore, the experimental teaching method based on the WEB remote has been widely applied in many fields, such as physical, chemical, biological, mechanical and the like fields. In this way, a learner can perform experimental operations by using the virtual laboratory, thereby improving the efficiency of experimental teaching and the learning effect of the learner. However, the existing experimental teaching method based on WEB remote still has the problems of low experimental simulation degree and limited interaction mode, and has great influence on the quality and effect of experimental teaching, thereby severely restricting the development of experimental teaching.
Disclosure of Invention
Aiming at the problems existing in the traditional method, the invention provides a multi-mode experiment teaching method based on WEB remote, a multi-mode experiment teaching device based on WEB remote, a computer device and a computer readable storage medium, which can improve the experiment simulation degree and expand the interaction mode.
In order to achieve the above object, the embodiment of the present invention adopts the following technical scheme:
in one aspect, a WEB-based remote multi-mode experimental teaching method is provided, which includes the steps of:
logging in an experimental cloud teaching server through a WEB browser and loading a target experimental task into a demonstration class mode; the target experiment task is created and shared to the experiment cloud teaching server by a teacher user in a development mode;
in a demonstration classroom mode, uploading experimental operation data of a target experimental task to an experimental cloud teaching server by adopting a data compression algorithm and downloading experimental indication data of the target experimental task; the experiment indication data is used for indicating the next operation requirement of the target experiment task;
after all operations of the target experiment task are completed, an experiment operation completion instruction is sent to the experiment cloud teaching server, and experiment result data returned by the experiment cloud teaching server are received; the experimental result data are generated after the experimental cloud teaching server performs distributed processing on experimental output data corresponding to each experimental operation data by adopting a distributed computing algorithm;
Receiving experimental teaching recommended data returned by the experimental cloud teaching server; the experimental teaching recommended data are experimental interaction optimization data generated after the experimental cloud teaching server adopts the deep neural network to analyze learning behaviors of all experimental operation data.
In one embodiment, in a demonstration classroom mode, the step of uploading experimental operation data of a target experimental task to an experimental cloud teaching server and downloading experimental instruction data of the target experimental task by adopting a data compression algorithm includes:
in a demonstration classroom mode, the experimental operation data is compressed by using a lossy compression algorithm and then uploaded to an experimental cloud teaching server; the compressed experimental operation data are used for indicating the experimental cloud teaching server to obtain a current operation instruction after decompression and executing corresponding experimental operation;
after receiving and decompressing the experimental indication data, displaying the next operation requirement of the target experimental task in an operation interface in a demonstration classroom mode; the experimental indication data are data issued after the experimental cloud teaching server completes the current corresponding experimental operation and automatically generates and compresses the data by using a lossless compression algorithm according to the next operation requirement of the target experimental task.
In one embodiment, the lossless compression algorithm comprises LZ77 coding, LZW coding, or Huffman coding.
In one embodiment, the process of performing distributed processing on the experimental output data corresponding to each experimental operation data by the experimental cloud teaching server through a distributed computing algorithm includes:
after decomposing a target experiment task into a plurality of subtasks, the experiment cloud teaching server adopts different computing nodes to respectively process experiment output data of each subtask; each subtask is mutually independent and is processed in parallel by adopting a neural network;
and the experimental cloud teaching server combines the result data generated after processing the calculation nodes according to the disassembly sequence of the target experimental task to obtain experimental result data.
In one embodiment, after the process of receiving the experimental result data returned by the experimental cloud teaching server, the method further includes:
and carrying out accuracy check and reliability verification on the experimental result data according to the operation sequence of the target experimental task to obtain an accuracy label and a reliability label of the experimental result data.
In one embodiment, the process of experimental interaction optimization data generated after the experimental cloud teaching server performs learning behavior analysis on each experimental operation data by adopting a deep neural network includes:
The experiment cloud teaching server learns and analyzes all experiment operation data of the current terminal student by utilizing a pre-trained recurrent neural network model, and outputs experiment interaction optimization data of the current terminal student; the experimental interaction optimization data are used for displaying experimental steps and operation mode optimization suggestions for current terminal students, and experimental result interpretation and promotion suggestions.
In one embodiment, a process of sending an experiment operation completion instruction to an experiment cloud teaching server and receiving experiment result data returned by the experiment cloud teaching server includes:
when an experiment operation completion instruction is sent to an experiment cloud teaching server, a data key is sent to the experiment cloud teaching server; the data key is used for indicating the experimental cloud teaching server to encrypt experimental result data by utilizing the data key before issuing the experimental result data;
receiving encrypted experimental result data returned by the experimental cloud teaching server; the encrypted experimental result data carries an authority control field corresponding to the current terminal student;
and decrypting the encrypted experimental result data and obtaining the authority ID corresponding to the current terminal student to obtain the experimental result data available to the current terminal student.
On the other hand, still provide a remote multimode experimental teaching device based on WEB, include:
the experiment loading module is used for logging in the experiment cloud teaching server through the WEB browser and loading the target experiment task into a demonstration classroom mode; the target experiment task is created and shared to the experiment cloud teaching server by a teacher user in a development mode;
the data transmission module is used for uploading experimental operation data of the target experimental task to the experimental cloud teaching server by adopting a data compression algorithm in a demonstration classroom mode and downloading experimental indication data of the target experimental task; the experiment indication data is used for indicating the next operation requirement of the target experiment task;
the result recovery module is used for sending an experiment operation completion instruction to the experiment cloud teaching server after all the operations of the target experiment task are completed, and receiving experiment result data returned by the experiment cloud teaching server; the experimental result data are generated after the experimental cloud teaching server performs distributed processing on experimental output data corresponding to each experimental operation data by adopting a distributed computing algorithm;
the optimization receiving module is used for receiving the experiment teaching recommended data returned by the experiment cloud teaching server; the experimental teaching recommended data are experimental interaction optimization data generated after the experimental cloud teaching server adopts the deep neural network to analyze learning behaviors of all experimental operation data.
In still another aspect, a computer device is provided, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the WEB-based remote multi-mode experimental teaching method when executing the computer program.
In still another aspect, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor, implements the steps of the WEB-based remote multi-mode experimental teaching method described above.
One of the above technical solutions has the following advantages and beneficial effects:
according to the WEB-based remote multi-mode experiment teaching method, device, equipment and medium, after a current terminal student logs in an experiment cloud teaching server through a WEB browser, a target experiment task is loaded to a demonstration classroom mode to normally develop remote experiment operation, and data such as experiment operation data, experiment instruction data and the like are processed and transmitted by adopting a data compression algorithm, so that the experiment operation/instruction data are compressed, the size of data transmission is reduced, the transmission speed of an experiment interaction process is improved, the network bandwidth is saved, and the real-time performance of experiment interaction is improved to avoid distortion. After all operations of the target experiment task are completed, the current terminal learner can send an experiment operation completion instruction to the experiment cloud teaching server so as to acquire experiment result data returned by the experiment cloud teaching server. And finally, the current terminal student also receives experimental teaching recommended data given by the cloud teaching server aiming at the experimental teaching by adopting the deep neural network so as to analyze and optimize the experimental operation of the current terminal student and immediately give personalized suggestions for improving the experimental score and skill of the current terminal student. Therefore, the effects that the experimental simulation degree is high and the interaction mode supports personalized recommendation are effectively achieved, quality improvement is brought to the quality and effect of experimental teaching, and the development of modern remote experimental teaching is effectively accelerated.
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In order to more clearly illustrate the technical solutions of embodiments or conventional techniques of the present application, the drawings required for the descriptions of the embodiments or conventional techniques will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic flow chart of a multi-mode experimental teaching method based on WEB remote in one embodiment;
FIG. 2 is a schematic diagram of a process flow of uploading and downloading interactive data in one embodiment;
FIG. 3 is a flow diagram of an interactive data security process in one embodiment;
fig. 4 is a schematic block diagram of a remote multi-mode experimental teaching device based on WEB in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
It is noted that reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Those skilled in the art will appreciate that the embodiments described herein may be combined with other embodiments. The term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
The experimental teaching based on WEB remote refers to a teaching mode that students participate in experimental teaching activities at different places by utilizing the Internet and WEB remote technology and through modes of an online platform, a virtual laboratory and the like. Conventional experimental teaching typically requires a learner to physically go to a laboratory to perform an experiment, but there are significant limitations in this model, such as limited laboratory equipment, geographical location limitations, and learner time limitations. And the experimental teaching based on WEB remote is hopeful to overcome the limitations, so that a learner participates in the experimental teaching at any place and time, and the flexibility and convenience of the experimental teaching are improved.
At present, an experimental teaching method based on WEB remote has been applied to fields such as physics, chemistry, biology, machinery and other disciplines, and by the experimental teaching method, a learner can perform experimental operation by using a virtual laboratory, so that the efficiency of experimental teaching and the learning effect of the learner are improved. The existing experimental teaching method based on WEB remote has the advantages from the beginning, but has more defects due to objective restriction of the development stage, such as but not limited to network instability, low experimental simulation degree, limited experimental equipment model, limited interaction model, high professional knowledge support requirement and the like, which cannot be well solved for a long time. Aiming at the long-standing part of remote experiment teaching problems in the current practice, a novel WEB-based remote multimode experiment teaching technology is developed to improve the experiment simulation degree and expand the interaction mode, so that a learner can obtain more real experiment experience in a virtual experiment environment, the quality and effect of the WEB-based remote experiment teaching can be effectively improved, and the WEB-based remote experiment teaching technology has wide application prospect and market value.
Embodiments of the present invention will be described in detail below with reference to the attached drawings in the drawings of the embodiments of the present invention.
Referring to fig. 1, in one embodiment, the embodiment of the present application provides a WEB-based remote multi-mode experimental teaching method, which includes the following processing steps S12 to S18:
s12, logging in an experimental cloud teaching server through a WEB browser and loading a target experimental task into a demonstration classroom mode; the target experiment task is created and shared to the experiment cloud teaching server by a teacher user in a development mode.
It can be understood that the WEB browser is a client used by a current terminal student when participating in a remote experiment teaching task, and is carried on a computer terminal or various mobile terminals used by the current terminal student for contact interaction with the experiment cloud teaching server. The experimental cloud teaching server can at least support a development mode and a demonstration classroom mode, a learner or a teacher can use different modes according to different identity authorities, a new experimental task can be created and shared in the development mode, and the experimental task can be browsed, entered and analyzed in the demonstration classroom mode. For each current terminal student who needs to participate in experimental teaching at present, the current terminal student can remotely log in the same experimental cloud teaching server through a WEB browser so as to search an experimental task (namely a target experimental task) appointed by a teacher in the current experimental teaching course, load the experimental task to the local and display the experimental task in a demonstration classroom mode, and operate and use the current terminal student.
S14, in a demonstration classroom mode, uploading experimental operation data of a target experimental task to an experimental cloud teaching server by adopting a data compression algorithm and downloading experimental indication data of the target experimental task; the experimental indicating data is used to indicate the next operational requirement of the target experimental task.
It will be appreciated that, since the amount of simulation data generated during the remote experimental teaching process is generally large, the conventional remote teaching method is limited by network infrastructure and the current state of the art, and is generally transmitted by adopting a data direct transmission manner. However, with the complexity and diversification of experimental contents, it is found that the conventional data transmission mode leads to network congestion and slower transmission speed, so that the real-time performance of experimental interaction is greatly reduced. Therefore, data compression algorithms are adopted in the long-line and downlink processes of experimental (operation, indication and the like) data respectively, so that experimental simulation data are reliably compressed and then transmitted, the time and network bandwidth required by transmission are reduced, the transmission efficiency is improved, and the real-time performance of experimental interaction is improved in the data transmission layer.
S16, after all operations of the target experiment task are completed, sending an experiment operation completion instruction to the experiment cloud teaching server, and receiving experiment result data returned by the experiment cloud teaching server; the experimental result data are generated after the experimental cloud teaching server performs distributed processing on experimental output data corresponding to each experimental operation data by adopting a distributed computing algorithm.
It can be understood that after the current terminal student completes all experimental operations on the target experimental task, the experimental cloud teaching server can obtain the complete experimental result data of the current terminal student corresponding to the target experimental task almost synchronously by means of efficient data processing of the distributed computing algorithm. In the traditional experimental teaching method, the system basically responds and accumulates the middle experimental data corresponding to each operation execution of the current terminal student, and the system has low efficiency, large time delay and easy error. In the embodiment, a distributed computing algorithm is introduced in the experimental result data processing process of the experimental cloud teaching server for the first time to execute task processing, and the computing efficiency and the computing speed can be greatly improved by distributing the simulated subtask data in the experimental process to a plurality of computing nodes for parallel processing, so that the real-time performance and the accuracy of the data are greatly improved in the experimental response level.
S18, receiving experiment teaching recommended data returned by the experiment cloud teaching server; the experimental teaching recommended data are experimental interaction optimization data generated after the experimental cloud teaching server adopts the deep neural network to analyze learning behaviors of all experimental operation data.
It can be understood that after the current terminal student completes the current target experiment task and obtains the corresponding experiment result data, the experiment cloud teaching server also returns the experiment teaching recommended data for the current terminal student and the current remote experiment teaching to the current terminal student, for example, in the Web remote-based experiment teaching, the experiment cloud teaching server automatically learns the learning interaction characteristics and preferences of the current terminal student by utilizing the experiment interaction data of the current terminal student through a deep neural network, and gives personalized experiment interaction optimization suggestions for the current terminal student, thereby providing more personalized experiment interaction services for the current terminal student. In addition, the deep neural network can also be applied to the scene of experimental data analysis, for example, the deep neural network is utilized to classify and predict experimental result data of the current terminal student so as to establish an experimental teaching dynamic file of the current terminal student and guide the current terminal student to adopt more optimized learning behaviors to improve experimental operation skills and learning results. Therefore, the diversity, interactivity and customization of remote experiment teaching can be effectively improved by providing the personalized feedback interaction mode, so that the learning requirements of students can be better met, and the interests of experimental exploration are stimulated.
According to the WEB-based remote multi-mode experiment teaching method, after a current terminal student logs in an experiment cloud teaching server through a WEB browser, a target experiment task is loaded to a demonstration classroom mode to normally develop remote experiment operation, and the experimental operation data, the experimental instruction data and other incoming and outgoing data are processed and transmitted by adopting a data compression algorithm, so that the experimental operation/instruction data are compressed, the size of data transmission is reduced, the transmission speed of an experiment interaction process is improved, the network bandwidth is saved, and the real-time performance of the experiment interaction is improved to avoid distortion. After all operations of the target experiment task are completed, the current terminal learner can send an experiment operation completion instruction to the experiment cloud teaching server so as to acquire experiment result data returned by the experiment cloud teaching server. And finally, the current terminal student also receives experimental teaching recommended data given by the cloud teaching server aiming at the experimental teaching by adopting the deep neural network so as to analyze and optimize the experimental operation of the current terminal student and immediately give personalized suggestions for improving the experimental score and skill of the current terminal student. Therefore, the effects that the experimental simulation degree is high and the interaction mode supports personalized recommendation are effectively achieved, quality improvement is brought to the quality and effect of experimental teaching, and the development of modern remote experimental teaching is effectively accelerated.
In one embodiment, as shown in fig. 2, regarding the above step S14, the following processing steps may be specifically included:
s142, in a demonstration classroom mode, the experimental operation data is compressed by using a lossy compression algorithm and then uploaded to an experimental cloud teaching server; the compressed experimental operation data are used for indicating the experimental cloud teaching server to obtain a current operation instruction after decompression and executing corresponding experimental operation;
s144, after receiving and decompressing the experiment instruction data, displaying the next operation requirement of the target experiment task in an operation interface in a demonstration classroom mode; the experimental indication data are data issued after the experimental cloud teaching server completes the current corresponding experimental operation and automatically generates and compresses the data by using a lossless compression algorithm according to the next operation requirement of the target experimental task.
It will be appreciated that the data compression algorithms employed may include lossless compression algorithms and lossy compression algorithms, and may be selected or used in combination based on the upload and download transmission processing characteristics of the experimental (operational and indicative) data. In the experimental operation process, the experimental data are compressed by using a data compression algorithm and then uploaded to an experimental cloud teaching server, and the experimental cloud teaching server correspondingly decompresses the uploaded experimental data, so that the experimental cloud teaching server can be normally used. Because the uplink experimental operation data only needs to know the operation instruction of the current terminal learner, and does not need to judge which step of which experimental subtask the operation instruction aims at (the information is locked when the experimental cloud teaching server issues the operation instruction), the uplink experimental operation data can be processed and transmitted by adopting a lossy compression algorithm, a certain data precision is allowed to be sacrificed to realize a higher compression ratio, so that the storage space and the transmission bandwidth of the uplink data are effectively reduced, and meanwhile, the problem of operation response errors is avoided.
For the downlink experiment indication data, the experiment cloud teaching server needs to accurately specify which step of which experiment subtask is currently required to perform corresponding experiment operation to the current terminal trainee, so that the lossless compression algorithm can be adopted to process and transmit the downlink experiment indication data, the storage space and the transmission bandwidth of the data are reduced as much as possible under the condition of not losing the data precision, the compression and the accurate transmission of the uplink and downlink processes of the data are realized, the real-time performance of experiment interaction is practically improved on the data transmission layer, and the effective improvement of the experiment simulation degree is ensured.
In one embodiment, the lossless compression algorithm comprises LZ77 coding, LZW coding, or Huffman coding. It can be appreciated that the lossy compression algorithm may employ an existing gzip format compression algorithm or bzip2 format compression algorithm according to the type of experimental operation data, such as but not limited to text data, picture data or audio data, and they achieve the purpose of high compression ratio by employing the rules of repeated characters, words and the like in the text data and employing techniques such as dictionary coding or huffman coding; or the existing JPEG format compression algorithm or H.264 format compression algorithm can be adopted to process the picture data so as to achieve the purpose of high compression ratio; or the existing AAC format compression algorithm or WMA format compression algorithm can be adopted, and based on the characteristics of the human auditory system, some signals insensitive to human ear hearing are removed, so that the aim of high compression ratio is fulfilled. Specific compression and decompression implementation manners of applying the above various lossy compression algorithms can be understood by referring to the corresponding processing flow of the existing compression algorithm, and will not be described in detail in this specification.
In this embodiment, the compression and transmission of the downlink data may be specifically realized by using these algorithms such as LZ77 coding, LZW coding or Huffman coding, and the specific compression and decompression implementation manner of applying the above various lossless compression algorithms may be understood by referring to the processing flow of the corresponding existing compression algorithm in the same way, which is not described in detail in this specification.
Specifically, on the experimental cloud teaching server, the experimental indication data is compressed through a lossless compression algorithm (such as LZ77, LZW or Huffman coding, etc.), so that the data size is reduced, and the transmission data size is reduced. And then transmitting the compressed experimental indication data to the current terminal student end through a network. During transmission, techniques such as protocol compression (e.g., HTTP compression) may be employed to further reduce the amount of data transmitted. The compressed experimental indication data can be restored to original experimental indication data by decompression operation at the current terminal student end, so that the decompressed experimental indication data can be subjected to corresponding data processing, such as data conversion and visualization, and the accurate and efficient receiving and displaying use of downlink data are realized.
And in the data uplink stage, the current terminal student end performs lossy compression on experimental operation data input by the current terminal student end, so that the data quantity transmitted back to the experimental cloud teaching server from the current terminal student end is reduced. And then transmitting the compressed experimental operation data back to the experimental cloud teaching server through a network. In the transmission process, the transmission data volume can be further reduced by adopting the technologies such as protocol compression and the like. On the experimental cloud teaching server, decompression operation is carried out on the transmitted experimental operation data, so that the transmitted experimental operation data can be restored into original experimental operation data for generating corresponding experimental responses (for forming corresponding experimental result data parts), and accurate and efficient transmission and use of uplink data are realized.
By the steps, the compression and transmission of experimental data can be realized on the premise of guaranteeing the integrity and the precision of the data, the transmission data quantity is reduced, and the transmission efficiency is improved.
In one embodiment, regarding the above step S16, the process of performing, by the experiment cloud teaching server, the distributed processing on the experiment output data corresponding to each experiment operation data by using the distributed computing algorithm may specifically include the following processes:
after decomposing a target experiment task into a plurality of subtasks, the experiment cloud teaching server adopts different computing nodes to respectively process experiment output data of each subtask; each subtask is mutually independent and is processed in parallel by adopting a neural network;
and the experimental cloud teaching server combines the result data generated after processing the calculation nodes according to the disassembly sequence of the target experimental task to obtain experimental result data.
It can be understood that the experimental cloud teaching server is used as a core system of the whole cloud teaching platform, and a large number of computing nodes can be configured for respectively processing different tasks. In this embodiment, for a target experiment task, the experiment cloud teaching server decomposes the target experiment task into a plurality of subtasks, and distributes each subtask to different computing nodes for processing, and simultaneously ensures that each subtask can be processed in parallel independently of each other. And calling the existing algorithm of the system suitable for the corresponding subtask on each computing node to process and calculate experimental data, and generating result data of the computing node.
And merging the result data of each calculation node obtained in a distributed and real-time manner by the experiment cloud teaching server according to the disassembly sequence of the target experiment task, and continuously executing the distributed processing and merging process along with the promotion of the target experiment task until all operations of the target experiment task are completed. Finally, the experimental cloud teaching server can directly obtain complete experimental result data aiming at the target experimental task so as to return the experimental result data to the current terminal student by adopting a data compression algorithm (such as lossless compression), and in addition, the experimental cloud teaching server can habitually backup and submit and store the experimental result data in the background so as to complete data archiving and result submission to teacher users. The design and application of the above-mentioned distributed computation can be specifically implemented by using an existing distributed computation model such as an existing MapReduce model or Spark model, for example, a distributed computation algorithm such as MapReduce:
MapReduce is a distributed computing model that mainly contains two phases: map phase and Reduce phase. In this model, tasks are divided into many small sub-tasks that are executed in parallel on distributed computing nodes.
For the generation of experimental result data, a MapReduce manner may be used. In particular, a large target experiment task may be divided into multiple small subtasks, each of which is assigned to a different computing node for processing. In the Map stage, each computing node processes the assigned subtasks and outputs the processing results as a series of key-value pairs (key-value pairs). In the Reduce stage, the key pairs are combined into a smaller result set and the final experimental result data is output.
Specifically, in an experimental teaching system based on WEB remote, an experimental task can be divided into a plurality of subtasks, each of which represents a specific experimental operation. In the Map stage, each computing node can process a small task in parallel and output the result as a series of key value pairs, wherein the key represents a specific experimental result, and the value represents data corresponding to the result. In the Reduce stage, the key pairs are combined into a smaller result set and the final experimental result data is output.
It should be noted that, when the MapReduce is used for generating the experimental result data, the Map function and the Reduce function need to be designed reasonably so as to ensure that the experimental result data can be generated efficiently and accurately. Meanwhile, the transmission and storage problems of data are also required to be considered so as to ensure the reliability and the safety of the data. For example, the Map function and the Reduce function are designed with the characteristics and requirements of a specific experimental task in mind. In general, the Map function is used to convert experimental operation data into the form of key-value pairs, and the Reduce function is used to aggregate, calculate and combine the key-value pairs output by the Map function to generate final experimental result data.
In order to ensure the efficiency and accuracy of the MapReduce algorithm, for example, the following method may be adopted:
reasonably dividing the data blocks: the original experimental operation data is divided into a plurality of data blocks, and the data blocks are distributed to different Map tasks in a cluster of distributed computing nodes for processing. Therefore, a plurality of computing nodes in the cluster can be fully utilized, and the parallelism degree and the processing speed of data processing are improved.
Selecting an appropriate computing node: in the Map function, a proper computing node can be selected for data processing according to the existing characteristics of experimental operation data and given task requirements. Therefore, the communication delay between the nodes and the extra burden brought by data transmission can be avoided, and the execution efficiency of the Map function is improved.
And reasonably setting a Reduce function: in the Reduce function, proper merging strategy and data structure may be adopted to merge the key-value pairs output by the Map function orderly and effectively to generate final experimental result data. Suitable merging algorithms and data structures, such as hash tables or binary trees, may be selected based on the nature and requirements of a particular experimental task.
Data backup and fault tolerance processing: in a distributed computing environment, problems such as node failure and data loss may occur due to factors such as network and computing nodes. In order to ensure the reliability and safety of the data, data backup and fault-tolerant processing strategies such as existing data redundancy backup, fault transfer and the like can be adopted. Thus, the durability and the reliability of the data can be ensured, and the influence on the generation and analysis of experimental results caused by data loss is avoided.
By the distributed computing processing, the efficiency and accuracy of data processing are improved.
In one embodiment, further, after the process of receiving the experimental result data returned by the experimental cloud teaching server in step S16, the method specifically may further include:
and carrying out accuracy check and reliability verification on the experimental result data according to the operation sequence of the target experimental task to obtain an accuracy label and a reliability label of the experimental result data.
Specifically, after receiving and decompressing the experimental result data returned by the experimental cloud teaching server, the current terminal student terminal also needs to perform accuracy verification and reliability verification on the experimental result data according to the operation sequence set by the target experimental task, for example, whether the logic sequence of the experimental result data accurately corresponds to the operation sequence set by the target experimental task or not is verified, so as to determine whether the situation that the logic sequence of the result data is wrong or not due to data transmission errors, compression or decompression errors or the like is caused; for example, whether the result data corresponding to each operation step in the experimental result data has obvious abnormality (such as numerical distortion, loss and the like) is detected. If the test result data is verified by the accuracy check sum and the reliability is not abnormal, the test result data is obtained, the terminal can set the accuracy label and the reliability label of the test result data as preset labels, for example, the accuracy label is set to represent a character string or a graphic identifier passing the verification, and the reliability label can also be set to represent a character string or a graphic identifier passing the verification. Otherwise, if the test result data is found to be abnormal through the verification of the accuracy check sum reliability, the test result data is judged to be failed, the terminal can set the accuracy label and the reliability label of the test result data as another preset label, for example, the accuracy label is set to be a character string or a graphic identifier representing that the verification fails, and the reliability label can be set to be a character string or a graphic identifier representing that the verification fails.
Through the processing, the accuracy and the reliability of experimental result data can be ensured more effectively.
In one embodiment, as shown in fig. 3, further, in the step S16, a process of sending an experiment operation completion instruction to the experiment cloud teaching server and receiving the experiment result data returned by the experiment cloud teaching server may specifically further include the following processing:
s162, when an experiment operation completion instruction is sent to the experiment cloud teaching server, a data key is sent to the experiment cloud teaching server; the data key is used for indicating the experimental cloud teaching server to encrypt experimental result data by utilizing the data key before issuing the experimental result data;
s164, receiving encrypted experimental result data returned by the experimental cloud teaching server; the encrypted experimental result data carries an authority control field corresponding to the current terminal student;
s166, decrypting the encrypted experimental result data and obtaining the authority ID corresponding to the current terminal student to obtain the experimental result data available to the current terminal student.
It can be appreciated that in the remote experiment teaching, due to the consideration of secret related experiments or anti-cheating among different students, the reliability and the safety of the downlink experiment result data need to be further ensured. The data key may be set by the current end student or may be given in advance by the teacher.
Specifically, after the experiment cloud teaching server receives the data key sent by the current terminal after the experiment operation is completed, the collected experiment result data is encrypted by using the data key, and a permission control field is added in front of or behind the encrypted experiment result data to ensure that only the current terminal student has permission to decrypt and use the experiment result data, and then the experiment cloud teaching server can normally perform data compression and return to the current terminal so as to ensure the safety of the data in the transmission and storage processes, remove the opportunity of intentionally or unintentionally using other experiment result data among different students, and prevent cheating. The authority control field may be set as a user ID number of the current terminal learner, or ID information such as an IP address or a mobile phone number, as long as the experimental result data can be uniquely identity-bound. After receiving and decompressing the returned experimental result data, the current terminal can decrypt by using the data key and input the corresponding authority ID, thereby obtaining complete and available experimental result data.
Through the steps, the reliability and the safety of the downlink experimental result data can be ensured. In addition, experiment cloud teaching server can also regularly back up the experimental result data of student, prevents unexpected loss or the damage of data to the same field experiment of different students can keep apart the storage with the data of different students, in order to ensure that the data of different students can not mutual interference. For data access from the new authority ID, the experimental cloud teaching server can also conduct online security audit on data access and use of the experimental cloud teaching server, and whether unauthorized students or users outside the domain illegally access the data is determined according to the set authority of a teacher, so that the teacher can timely find and process the data security problem.
In one embodiment, regarding the step S18, the process of generating the experimental interaction optimization data after the experimental cloud teaching server performs the learning behavior analysis on each experimental operation data by using the deep neural network may specifically include the following processes:
the experiment cloud teaching server learns and analyzes all experiment operation data of the current terminal student by utilizing a pre-trained recurrent neural network model, and outputs experiment interaction optimization data of the current terminal student; the experimental interaction optimization data are used for displaying experimental steps and operation mode optimization suggestions for current terminal students, and experimental result interpretation and promotion suggestions.
It will be appreciated that the recurrent neural network (Recurrent Neural Network, RNN) is more suitable for modeling of sequence data, and is capable of capturing time correlation and long-term dependence, and therefore, in this embodiment, RNN is employed to enable learning and analysis of experimental operation data of the current end student. It should be noted that, for different types of experimental teaching, according to different experimental contents and different data forms, other deep neural network models may be alternatively adopted to realize learning and analyzing each experimental operation data of the current terminal learner, for example, but not limited to, convolutional neural network (Convolutional Neural Network, CNN), long Short-Term Memory (LSTM), self-encoder (AE), or generating countermeasure network (Generative Adversarial Network, GAN), and the application method thereof may be basically understood by referring to the flow method recommended by the deep neural network model itself.
The pre-trained recurrent neural network model may be obtained by: 1. and (3) data acquisition: after the student finishes the experimental operation, the experimental operation data of the student is collected, such as information including experimental steps, time, operation modes, experimental results and the like. 2. Data cleaning: and denoising, missing value filling, outlier processing and other operations are performed on the collected experimental operation data, so that the quality and the integrity of the data are ensured. 3. Feature extraction: for the cleaned experimental operation data, the data features related to the experimental operation and experimental results of the students are extracted, for example, the data features can be extracted by means of statistical analysis, data mining and the like by utilizing the feature engineering of the model. 4. Model training: according to the extracted data characteristics, a recurrent neural network model is constructed, and training and optimizing are carried out on the model by combining with the history experimental operation data of a learner until the set model precision index is reached, so that the trained recurrent neural network model is obtained.
Specifically, after the trained deep neural network model is utilized to analyze and output the experimental operation data of the current terminal student, personalized experimental interaction optimization data can be generated, so that the content such as the optimization experimental step, the optimization operation mode, the interpretation of experimental results and the improvement suggestion suitable for the current terminal student is recommended according to the experimental operation data and the historical experimental operation data of the current terminal student. And finally, the generated personalized experimental interaction optimization data are presented to the current terminal students, for example, the contents of suggestions can be displayed to the current terminal students in a graphical interface, a text prompt and other modes, and more customized and flexible experimental teaching interaction modes are realized, so that the improvement of the remote experimental teaching quality is promoted.
It should be understood that, although the steps in the flowcharts of fig. 1 to 3 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Furthermore, at least a portion of the steps of fig. 1-3 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or stages are performed necessarily occur sequentially, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Referring to fig. 4, in one embodiment, a WEB-based remote multi-mode experiment teaching apparatus 100 is provided, which includes an experiment loading module 11, a data transmission module 13, a result recycling module 15, and an optimization receiving module 17. The experiment loading module 11 is used for logging in the experiment cloud teaching server through a WEB browser and loading a target experiment task into a demonstration classroom mode; the target experiment task is created and shared to the experiment cloud teaching server by a teacher user in a development mode. The data transmission module 13 is used for uploading experimental operation data of a target experimental task to the experimental cloud teaching server and downloading experimental instruction data of the target experimental task by adopting a data compression algorithm in a demonstration classroom mode; the experimental indicating data is used to indicate the next operational requirement of the target experimental task. The result recovery module 15 is configured to send an experiment operation completion instruction to the experiment cloud teaching server after all operations of the target experiment task are completed, and receive experiment result data returned by the experiment cloud teaching server; the experimental result data are generated after the experimental cloud teaching server performs distributed processing on experimental output data corresponding to each experimental operation data by adopting a distributed computing algorithm. The optimization receiving module 17 is used for receiving the experiment teaching recommended data returned by the experiment cloud teaching server; the experimental teaching recommended data are experimental interaction optimization data generated after the experimental cloud teaching server adopts the deep neural network to analyze learning behaviors of all experimental operation data.
According to the remote multi-mode experimental teaching device 100 based on the WEB, after a current terminal student logs in an experimental cloud teaching server through the WEB browser, a target experimental task is loaded to a demonstration classroom mode to normally develop remote experimental operation, and the experimental operation data, the experimental indication data and other incoming and outgoing data are processed and transmitted by adopting a data compression algorithm, so that the experimental operation/indication data are compressed, the size of data transmission is reduced, the transmission speed of an experimental interaction process is improved, the network bandwidth is saved, and the real-time performance of experimental interaction is improved to avoid distortion. After all operations of the target experiment task are completed, the current terminal learner can send an experiment operation completion instruction to the experiment cloud teaching server so as to acquire experiment result data returned by the experiment cloud teaching server. And finally, the current terminal student also receives experimental teaching recommended data given by the cloud teaching server aiming at the experimental teaching by adopting the deep neural network so as to analyze and optimize the experimental operation of the current terminal student and immediately give personalized suggestions for improving the experimental score and skill of the current terminal student. Therefore, the effects that the experimental simulation degree is high and the interaction mode supports personalized recommendation are effectively achieved, quality improvement is brought to the quality and effect of experimental teaching, and the development of modern remote experimental teaching is effectively accelerated.
In one embodiment, the data transmission module 13 may specifically include an uplink compression sub-module and a downlink decompression sub-module, where the uplink compression sub-module is configured to compress experimental operation data by using a lossy compression algorithm in a demonstration classroom mode and upload the compressed experimental operation data to an experimental cloud teaching server; the compressed experimental operation data are used for indicating the experimental cloud teaching server to obtain a current operation instruction after decompression and executing corresponding experimental operation. The downlink decompression sub-module is used for receiving and decompressing the experimental indication data and then displaying the next operation requirement of the target experimental task in an operation interface in a demonstration classroom mode; the experimental indication data are data issued after the experimental cloud teaching server completes the current corresponding experimental operation and automatically generates and compresses the data by using a lossless compression algorithm according to the next operation requirement of the target experimental task.
In one embodiment, the lossless compression algorithm comprises LZ77 coding, LZW coding, or Huffman coding.
In one embodiment, the process of performing distributed processing on the experimental output data corresponding to each experimental operation data by using the experimental cloud teaching server by adopting a distributed computing algorithm may include:
After decomposing a target experiment task into a plurality of subtasks, the experiment cloud teaching server adopts different computing nodes to respectively process experiment output data of each subtask; each subtask is mutually independent and is processed in parallel by adopting a neural network;
and the experimental cloud teaching server combines the result data generated after processing the calculation nodes according to the disassembly sequence of the target experimental task to obtain experimental result data.
In one embodiment, after receiving the experimental result data returned by the experimental cloud teaching server, the result recycling module 15 may be further configured to perform accuracy check and reliability verification on the experimental result data according to the operation sequence of the target experimental task, so as to obtain an accuracy tag and a reliability tag of the experimental result data.
In one embodiment, the process of generating experimental interaction optimization data after the experimental cloud teaching server performs learning behavior analysis on each experimental operation data by using the deep neural network may include:
the experiment cloud teaching server learns and analyzes all experiment operation data of the current terminal student by utilizing a pre-trained recurrent neural network model, and outputs experiment interaction optimization data of the current terminal student; the experimental interaction optimization data are used for displaying experimental steps and operation mode optimization suggestions for current terminal students, and experimental result interpretation and promotion suggestions.
In one embodiment, the result recycling module 15 may be further configured to send a data key to the experimental cloud teaching server when sending an experimental operation completion instruction to the experimental cloud teaching server; the data key is used for indicating the experimental cloud teaching server to encrypt experimental result data by utilizing the data key before issuing the experimental result data; receiving encrypted experimental result data returned by the experimental cloud teaching server; the encrypted experimental result data carries an authority control field corresponding to the current terminal student; and decrypting the encrypted experimental result data and obtaining the authority ID corresponding to the current terminal student to obtain the experimental result data available to the current terminal student.
For specific limitations of the WEB-based remote multimode experimental teaching device 100, reference may be made to the corresponding limitations of the WEB-based remote multimode experimental teaching method hereinabove, and thus, the description thereof will not be repeated. The above-mentioned various modules in the WEB-based remote multi-mode experiment teaching apparatus 100 may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a device with a data processing function, or may be stored in a memory of the device in software, so that the processor may call and execute operations corresponding to the above modules, where the device may be, but is not limited to, various data computing and processing devices existing in the art.
In one embodiment, there is also provided a computer device including a memory and a processor, the memory storing a computer program, the processor implementing the following processing steps when executing the computer program: logging in an experimental cloud teaching server through a WEB browser and loading a target experimental task into a demonstration class mode; the target experiment task is created and shared to the experiment cloud teaching server by a teacher user in a development mode; in a demonstration classroom mode, uploading experimental operation data of a target experimental task to an experimental cloud teaching server by adopting a data compression algorithm and downloading experimental indication data of the target experimental task; the experiment indication data is used for indicating the next operation requirement of the target experiment task; after all operations of the target experiment task are completed, an experiment operation completion instruction is sent to the experiment cloud teaching server, and experiment result data returned by the experiment cloud teaching server are received; the experimental result data are generated after the experimental cloud teaching server performs distributed processing on experimental output data corresponding to each experimental operation data by adopting a distributed computing algorithm; receiving experimental teaching recommended data returned by the experimental cloud teaching server; the experimental teaching recommended data are experimental interaction optimization data generated after the experimental cloud teaching server adopts the deep neural network to analyze learning behaviors of all experimental operation data.
It will be appreciated that the above-mentioned computer device may include other software and hardware components not listed in the specification besides the above-mentioned memory and processor, and may be specifically determined according to the model of the specific computer device in different application scenarios, and the detailed description will not be listed in any way.
In one embodiment, the processor may further implement the steps or sub-steps added in the embodiments of the above-described remote multi-mode experimental teaching method based on WEB.
In one embodiment, there is also provided a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the following processing steps: logging in an experimental cloud teaching server through a WEB browser and loading a target experimental task into a demonstration class mode; the target experiment task is created and shared to the experiment cloud teaching server by a teacher user in a development mode; in a demonstration classroom mode, uploading experimental operation data of a target experimental task to an experimental cloud teaching server by adopting a data compression algorithm and downloading experimental indication data of the target experimental task; the experiment indication data is used for indicating the next operation requirement of the target experiment task; after all operations of the target experiment task are completed, an experiment operation completion instruction is sent to the experiment cloud teaching server, and experiment result data returned by the experiment cloud teaching server are received; the experimental result data are generated after the experimental cloud teaching server performs distributed processing on experimental output data corresponding to each experimental operation data by adopting a distributed computing algorithm; receiving experimental teaching recommended data returned by the experimental cloud teaching server; the experimental teaching recommended data are experimental interaction optimization data generated after the experimental cloud teaching server adopts the deep neural network to analyze learning behaviors of all experimental operation data.
In one embodiment, the computer program when executed by the processor may further implement the steps or sub-steps added in the embodiments of the above-described WEB-based remote multi-mode experimental teaching method.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program, which may be stored on a non-transitory computer readable storage medium and which, when executed, may comprise the steps of the above-described embodiments of the methods. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus dynamic random access memory (Rambus DRAM, RDRAM for short), and interface dynamic random access memory (DRDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples represent only a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, and are intended to be within the scope of the present application. The scope of the patent is therefore intended to be covered by the appended claims.

Claims (10)

1. A multi-mode experiment teaching method based on WEB remote is characterized by comprising the following steps:
logging in an experimental cloud teaching server through a WEB browser and loading a target experimental task into a demonstration class mode; the target experiment task is created and shared to the experiment cloud teaching server by a teacher user in a development mode;
in the demonstration classroom mode, uploading experimental operation data of the target experimental task to the experimental cloud teaching server by adopting a data compression algorithm and downloading experimental indication data of the target experimental task; the experiment indication data is used for indicating the next operation requirement of the target experiment task;
After all the operations of the target experiment task are completed, sending an experiment operation completion instruction to the experiment cloud teaching server, and receiving experiment result data returned by the experiment cloud teaching server; the experimental result data are generated after the experimental cloud teaching server performs distributed processing on experimental output data corresponding to each experimental operation data by adopting a distributed computing algorithm;
receiving experimental teaching recommended data returned by the experimental cloud teaching server; the experimental teaching recommended data are experimental interaction optimization data generated after the experimental cloud teaching server adopts a deep neural network to conduct learning behavior analysis on each experimental operation data.
2. The WEB-based remote multi-mode experimental teaching method according to claim 1, wherein the step of uploading experimental operation data of the target experimental task to the experimental cloud teaching server and downloading experimental indication data of the target experimental task using a data compression algorithm in the demonstration classroom mode comprises the steps of:
in the demonstration classroom mode, the experimental operation data is compressed by using a lossy compression algorithm and then uploaded to the experimental cloud teaching server; the compressed experimental operation data are used for indicating the experimental cloud teaching server to obtain a current operation instruction after decompression and executing corresponding experimental operation;
After receiving and decompressing the experimental instruction data, displaying the next operation requirement of the target experimental task in an operation interface in the demonstration classroom mode; and the experiment indication data are data which are automatically generated according to the next operation requirement of the target experiment task after the experiment cloud teaching server finishes the current corresponding experiment operation and are issued after compression processing by using a lossless compression algorithm.
3. The WEB-based remote multimode experimental teaching method according to claim 2, wherein the lossless compression algorithm comprises LZ77 coding, LZW coding or Huffman coding.
4. The WEB-based remote multimode experimental teaching method according to claim 1, wherein the experimental cloud teaching server performs a distributed processing process on experimental output data corresponding to each experimental operation data by using a distributed computing algorithm, and the process comprises the following steps:
after decomposing the target experiment task into a plurality of subtasks, the experiment cloud teaching server adopts different computing nodes to respectively process experiment output data of each subtask; each subtask is mutually independent and is processed in parallel by adopting a neural network;
And the experimental cloud teaching server combines the result data generated after processing the calculation nodes according to the disassembly sequence of the target experimental task to obtain the experimental result data.
5. The WEB-based remote multimode experimental teaching method according to claim 4, further comprising, after the process of receiving the experimental result data returned by the experimental cloud teaching server:
and performing accuracy check and reliability verification on the experimental result data according to the operation sequence of the target experimental task to obtain an accuracy label and a reliability label of the experimental result data.
6. The WEB-based remote multimode experimental teaching method according to claim 1, wherein the experimental cloud teaching server adopts a deep neural network to perform a process of learning behavior analysis on each experimental operation data and generating experimental interaction optimization data, and the process comprises the following steps:
the experimental cloud teaching server learns and analyzes each experimental operation data of the current terminal student by utilizing a pre-trained recurrent neural network model, and outputs the experimental interaction optimization data of the current terminal student; the experimental interaction optimization data are used for displaying experimental steps and operation mode optimization suggestions for current terminal students, and experimental result interpretation and promotion suggestions.
7. The WEB-based remote multimode experimental teaching method according to claim 6, wherein the process of sending an experimental operation completion instruction to the experimental cloud teaching server and receiving experimental result data returned by the experimental cloud teaching server comprises the steps of:
when an experiment operation completion instruction is sent to the experiment cloud teaching server, a data key is sent to the experiment cloud teaching server; the data key is used for indicating the experiment cloud teaching server to encrypt the experiment result data by utilizing the data key before issuing the experiment result data;
receiving encrypted experimental result data returned by the experimental cloud teaching server; the encrypted experimental result data carries an authority control field corresponding to the current terminal student;
and decrypting the encrypted experimental result data and obtaining the authority ID corresponding to the current terminal student to obtain the experimental result data available to the current terminal student.
8. Remote multi-mode experiment teaching device based on WEB, which is characterized by comprising:
the experiment loading module is used for logging in the experiment cloud teaching server through the WEB browser and loading the target experiment task into a demonstration classroom mode; the target experiment task is created and shared to the experiment cloud teaching server by a teacher user in a development mode;
The data transmission module is used for uploading experimental operation data of the target experimental task to the experimental cloud teaching server by adopting a data compression algorithm under the demonstration classroom mode and downloading experimental indication data of the target experimental task; the experiment indication data is used for indicating the next operation requirement of the target experiment task;
the result recovery module is used for sending an experiment operation completion instruction to the experiment cloud teaching server after all the operations of the target experiment task are completed, and receiving experiment result data returned by the experiment cloud teaching server; the experimental result data are generated after the experimental cloud teaching server performs distributed processing on experimental output data corresponding to each experimental operation data by adopting a distributed computing algorithm;
the optimization receiving module is used for receiving the experiment teaching recommended data returned by the experiment cloud teaching server; the experimental teaching recommended data are experimental interaction optimization data generated after the experimental cloud teaching server adopts a deep neural network to conduct learning behavior analysis on each experimental operation data.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the WEB-based remote multi-mode experimental teaching method according to any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the WEB-based remote multimodal experimental teaching method according to any of claims 1 to 7.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116931888A (en) * 2023-09-14 2023-10-24 大尧信息科技(湖南)有限公司 Teaching experiment construction method, system, equipment and medium based on software definition
CN116931888B (en) * 2023-09-14 2023-12-01 大尧信息科技(湖南)有限公司 Teaching experiment construction method, system, equipment and medium based on software definition

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