CN116681556A - Intelligent remote teaching cloud platform system and method based on education big data - Google Patents
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Abstract
The invention discloses an intelligent remote teaching cloud platform system and method based on education big data, which belong to the technical field of intelligent education and comprise a user terminal, a service cloud platform, a resource processing module, a lifting analysis module, a parameter adjustment module, a learning recording module, a period statistics module and a block storage module; the user terminal is used for inputting identity information by a user to log in the service cloud platform; the service cloud platform is used for verifying user identity information and improving online education resources for users; the invention can realize intelligent analysis of the learning condition of the user, intuitively feed back the learning condition of the related subjects to the user, realize accurate learning planning through education big data, improve the learning efficiency of the user, realize self-help parameter searching of the neural model, ensure the accuracy of parameters, improve the accuracy of the model on the analysis of the learning data of the user, reduce the operation difficulty, eliminate the need of manually adjusting the parameters and improve the use experience of the user.
Description
Technical Field
The invention relates to the technical field of intelligent education, in particular to an intelligent remote teaching cloud platform system and method based on education big data.
Background
Since the rapid development of information technology, it has continuously affected aspects of social life. In recent years, when information technology collides with education, glaring brilliance is generated. The application of the information technology in education and teaching promotes the change of the teaching process to pay more attention to experts, scholars, managers and first-line teachers, and the research of the application of the information technology in teaching is increased from the national level, the industry level or the first-line education and teaching management department; the information technology is applied to the education field, and the most prominent aspect is the supporting auxiliary effect of teaching resources on daily teaching, so that the construction of the teaching resources and the planning of a teaching resource service platform become an important component in the education informatization work.
The existing intelligent remote teaching cloud platform system and method cannot intelligently analyze the learning condition of a user, cannot realize accurate learning planning, and reduces the learning efficiency of the user; in addition, the existing intelligent remote teaching cloud platform system and method are low in model parameter accuracy, poor in analysis accuracy of learning data of users and high in operation difficulty, and therefore the intelligent remote teaching cloud platform system and method based on education big data are provided.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides an intelligent remote teaching cloud platform system and method based on education big data.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an intelligent remote teaching cloud platform system based on education big data comprises a user terminal, a service cloud platform, a resource processing module, a lifting analysis module, a parameter adjustment module, a learning record module, a period statistics module and a block storage module;
the user terminal is used for inputting identity information by a user to log in the service cloud platform;
the service cloud platform is used for verifying user identity information and improving online education resources for users;
the resource processing module is used for processing and mining education big data collected by the service cloud platform;
the lifting analysis module is used for carrying out deep analysis according to the learning condition uploaded by the user so as to formulate a related learning scheme;
the parameter adjustment module is used for updating and optimizing parameters of the lifting analysis module;
the learning record module is used for recording the learning condition of the user and the examination score of the learning course;
the period statistics module is used for generating a learning chart corresponding to the user according to the learning data recorded in the learning recording module and the period of the year or month;
and the block storage module is used for carrying out uplink storage on the learning information of each group of users.
As a further scheme of the invention, the user terminal specifically comprises a smart phone, a tablet personal computer, a notebook computer and a desktop computer.
As a further scheme of the invention, the resource processing module processes and mines the concrete steps as follows:
step one: the resource processing module is used for combing different interfaces, acquiring reasons for different received data of each group, acquiring and judging data source information of each group of data according to combing results, and classifying the acquired data of each group according to different data sources;
step two: counting the classified data, digging frequent item sets in the classified data, finding out the association and dissimilarity between different data tables based on the frequent item sets, combining the same data, digging subsequent frequent item sets, repeating the steps until the digging is completed to an empty set;
step three: and carrying out statistical analysis on the data in the table of the unified interface of each data source, dividing each group of data into strong available data, weak available data and invalid data according to analysis results, screening out the invalid data, and carrying out data restoration on the weak available data.
As a further scheme of the invention, the depth analysis of the lifting analysis module comprises the following specific steps:
step I: the lifting analysis module acquires learning data uploaded by a user or past learning data recorded by a platform, then takes the learning data or the past learning data recorded by the platform as a sample data set, calculates standard deviation of the sample data set at the same time, eliminates abnormal data in the sample data set according to the calculated standard deviation, and performs standardization and normalization processing on the residual data;
step II: dividing the data in the processed sample data set into a training set and a testing set, inputting the training set into a neural network with the set completed, acquiring a relevant linear combination through a neuron excitation function, calculating an energy function of the neural network, and ending the training process and outputting a depth analysis model when the energy function is smaller than a target error;
step III: the testing set is led into a depth analysis model for testing, the loss value of the depth analysis model is calculated, and if the loss value does not reach the expected value, the parameter updating is carried out on the depth analysis model;
step IV: the deep analysis model receives the user learning data, extracts the learning data of the corresponding subjects according to the user selection condition, screens out the learning data with poor characterization capability, simulates the user learning condition, outputs the learning prediction curve of each subject, formulates the learning plan of each subject for the user according to the prediction curve, and simultaneously extracts and feeds back the corresponding remote education resources to the corresponding user account.
As a further scheme of the invention, the standard deviation in the step I is specifically calculated as follows:
wherein v is n For the data deviation of the sample dataset, s is the standard deviation, if any data x i Deviation of (2)v n Satisfy |v n Judging the data as abnormal data and eliminating the abnormal data when the I is more than 3 sigma
The specific calculation formula of the standardized treatment in the step I is as follows:
wherein y represents the proposed characteristic parameter; mean (y) represents the average processing of the extracted feature parameters; std (y) represents standard deviation of the feature parameters.
As a further scheme of the invention, the block memory module uplink memory comprises the following specific steps:
step (1): the block storage module preprocesses the user learning data into a block meeting the condition, when the block is accessed to the network, each node in the block chain network generates a local public and private key pair as an identification code in the network, and when one node waits for the local role to become a candidate node, the leader application is broadcasted to other nodes in the network and sent;
step (2): when the candidate node becomes a leading node, the other nodes become following nodes, the leading node broadcasts the block record information, the following nodes broadcast the received information to the other following nodes after receiving the information, record the repetition times, and generate a block head by using the information with the largest repetition times;
step (3): and after the verification is passed, the leader node sends an addition command and enters a sleep stage, and after the follow-up node receives the confirmation information, each newly generated block group is added to the block chain and returns to the candidate identity.
An intelligent remote teaching method based on education big data comprises the following steps:
(1) Processing education big data in the service cloud platform;
(2) The user logs in the service cloud platform and uploads personal learning information;
(3) Constructing a depth analysis model and adjusting parameters of the depth analysis model;
(4) A learning scheme is formulated for the user according to the learning information of the user;
(5) Recording user learning information and counting according to a certain period;
(6) And uploading the user learning data to the blockchain for storage.
As a further aspect of the present invention, the parameter adjustment in step (3) specifically includes the following steps:
the first step: the parameter adjustment module initializes the network connection weight in a specified interval of the depth analysis model constructed by the lifting analysis module, submits training samples from a set of input and output pairs during training, calculates the output of the depth analysis model, compares expected network output with actual network output, and calculates local errors of all neurons;
and a second step of: training and updating the weight of the depth analysis model according to a learning rule equation after the local error exceeds a preset threshold value of a worker, and listing all possible data results according to a preset learning rate and step length;
and a third step of: for each group of data, selecting any subset as a test set, selecting the rest subsets as training sets, detecting the test set after training a test model, and counting root mean square errors of detection results;
fourth step: and replacing the test set with another subset, taking the rest subset as a training set, counting root mean square errors again until all data are predicted once, and selecting the corresponding combined parameter with the minimum root mean square error as the optimal parameter in the data interval and replacing the original parameter of the depth analysis model.
Compared with the prior art, the invention has the beneficial effects that:
1. the system carries out carding classification on various groups of education big data collected by a service cloud platform through a resource processing module, then finds out the association and difference among different data tables, merges the same data, carries out data cleaning and data restoration, then promotes an analysis module to obtain learning data uploaded by a user or past learning data recorded by the platform, screens abnormal data in the learning data, divides the rest data into a training set and a testing set after preprocessing, trains a set neural network through the training set, outputs a deep analysis model when an energy function value of the neural network meets a target value, tests the deep analysis model through the testing set, receives user learning data after the deep analysis model is tested to be qualified, extracts corresponding subject learning data according to user selection conditions, screens out learning data with poor characterization capability, simulates user learning conditions, outputs learning prediction curves of various subjects, and simultaneously extracts and feeds back corresponding remote education resources to corresponding users according to a learning plan of each subject of the user according to the prediction curves, can realize intelligent analysis of user learning conditions, intuitively and simultaneously carries out image feedback on the relevant subject learning data through the large-scale learning data, and realizes accurate user account number learning planning.
2. Compared with the prior teaching method, the method comprises initializing a network connection weight in a specified interval of a depth analysis model constructed by a lifting analysis module through a parameter adjustment module, calculating the output of the depth analysis model, comparing the expected network output with the actual network output, calculating the local errors of all neurons, training and updating the weight of the depth analysis model according to a learning rule equation after the local errors exceed a preset threshold value of a worker, listing all possible data results according to a preset learning rate and step length, selecting any subset as a test set for each group of data, taking the rest subset as a training set, detecting the test set after training the test model, counting the root mean square error of the detection result, replacing the test set with another subset, taking the rest subset as the training set, counting the root mean square error again until all data are predicted once, and realizing the self-walking parameter accuracy of the neural model, improving the learning difficulty of the model for a user, reducing the manual operation parameters, and improving the user experience parameters.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
FIG. 1 is a system block diagram of an intelligent remote teaching cloud platform system based on educational big data;
fig. 2 is a block flow diagram of an intelligent remote teaching method based on educational big data.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1, an intelligent remote teaching cloud platform system based on education big data includes a user terminal, a service cloud platform, a resource processing module, a lifting analysis module, a parameter adjustment module, a learning recording module, a period statistics module and a block storage module.
The user terminal is used for inputting identity information by a user to log in the service cloud platform.
It should be further noted that the user terminal specifically includes a smart phone, a tablet computer, a notebook computer, and a desktop computer.
The service cloud platform is used for verifying the identity information of the user and improving online education resources for the user; the resource processing module is used for processing and mining education big data collected by the service cloud platform.
Specifically, the resource processing module is used for carding different interfaces, acquiring reasons for different received data of each group, acquiring data source information for judging each group of data according to a carding result, classifying the acquired data of each group according to different data sources, counting the classified data, digging frequent item sets in the classified data, finding out association and difference among different data tables based on the frequent item sets, merging the same data, digging subsequent frequent item sets, repeating the steps until the data in the table of the unified interface of each data source is stopped after the data are mined to an empty set, carrying out statistical analysis on the data in the table of each data source, dividing each group of data into strong available data, weak available data and invalid data according to an analysis result, simultaneously screening out invalid data, and carrying out data restoration on the weak available data.
The lifting analysis module is used for carrying out deep analysis according to the learning condition uploaded by the user so as to formulate a related learning scheme; the parameter adjustment module is used for updating and optimizing the parameters of the lifting analysis module.
Specifically, the lifting analysis module acquires learning data uploaded by a user or past learning data recorded by a platform, then takes the learning data or the past learning data recorded by the platform as a sample data set, calculates standard deviation of the sample data set at the same time, eliminates abnormal data in the sample data set according to the calculated standard deviation, performs standardization and normalization processing on the residual data, divides the processed data in the sample data set into a training set and a test set, inputs the training set into a neural network with completed setting, acquires relevant linear combination through a neuron excitation function, calculates an energy function of the neural network, ends the training process when the energy function is smaller than a target error, outputs a deep analysis model, introduces a test set into the deep analysis model for testing, calculates a loss value of the deep analysis model, and performs parameter updating on the deep analysis model if the loss value does not reach a desired value, receives the user learning data, extracts learning data of corresponding subjects according to a user selection condition, eliminates learning data with poor characterization capability, simulates the user learning condition, outputs learning curves of the subjects, and then prepares corresponding learning prediction curves of the subjects according to the prediction curves of the subjects as user subjects, and simultaneously extracts corresponding learning source of the remote education account numbers.
In this embodiment, the specific calculation formula of the standard deviation is as follows:
wherein v is n For the data deviation of the sample dataset, s is the standard deviation, if any data x i Deviation v of (2) n Satisfy |v n Judging the data as abnormal data and eliminating the abnormal data when the I is more than 3 sigma
The specific calculation formula of the normalization process is as follows:
wherein y represents the proposed characteristic parameter; mean (y) represents the average processing of the extracted feature parameters; std (y) represents standard deviation of the feature parameters.
The learning record module is used for recording the learning condition of the user and the examination score of the learning course; the period statistics module is used for generating a learning chart corresponding to the user according to the learning data recorded in the learning recording module and the period of the year or month; the block storage module is used for carrying out uplink storage on each group of user learning information.
Specifically, the block storage module preprocesses user learning data into blocks meeting the conditions, when the blocks enter a network, each node in the block chain network generates a local public and private key pair as an identification code in the network, when one node waits for a local role to become a candidate node, a leader application is broadcasted and sent to other nodes in the network, when the candidate node becomes the leader node, the other nodes become following nodes, the leader node broadcasts block record information, the following nodes broadcast the received information to the other following nodes and record the repetition times after receiving the information, and an information generating block head with the maximum repetition times is used, a verification application is sent to the leader node, after verification is passed, the leader node sends an addition command and enters a sleep stage, and after the following nodes receive confirmation information, each newly generated block is added to the block chain and returns to the candidate identity.
Example 2
Referring to fig. 2, an intelligent remote teaching method based on education big data, the teaching method is specifically as follows:
and processing the education big data in the service cloud platform.
The user logs in the service cloud platform and uploads personal learning information.
And constructing a depth analysis model and carrying out parameter adjustment on the depth analysis model.
Specifically, the parameter adjustment module initializes the network connection weight in a specified interval of the depth analysis model constructed by the lifting analysis module, submits a training sample from a set of input and output pairs during training, calculates the output of the depth analysis model, compares the expected network output with the actual network output, calculates the local errors of all neurons, trains and updates the weight of the depth analysis model according to a learning rule equation after the local errors exceed a preset threshold value of a worker, lists all possible data results according to a preset learning rate and step length, selects any subset as a test set for each group of data, and after the rest subset is used as a training set, the test set is detected after the test model is trained, counts root mean square error of the detection result, replaces the test set with another subset, then takes the rest subset as the training set, counts root mean square error again until all data are predicted once, and selects a corresponding combination parameter with the minimum root mean square error as an optimal parameter in the data interval and replaces the original parameter of the depth analysis model.
And formulating a learning scheme for the user according to the user learning information.
And recording the learning information of the user and counting according to a certain period.
And uploading the user learning data to the blockchain for storage.
Claims (8)
1. The intelligent remote teaching cloud platform system based on the education big data is characterized by comprising a user terminal, a service cloud platform, a resource processing module, a lifting analysis module, a parameter adjustment module, a learning recording module, a period statistics module and a block storage module;
the user terminal is used for inputting identity information by a user to log in the service cloud platform;
the service cloud platform is used for verifying user identity information and improving online education resources for users;
the resource processing module is used for processing and mining education big data collected by the service cloud platform;
the lifting analysis module is used for carrying out deep analysis according to the learning condition uploaded by the user so as to formulate a related learning scheme;
the parameter adjustment module is used for updating and optimizing parameters of the lifting analysis module;
the learning record module is used for recording the learning condition of the user and the examination score of the learning course;
the period statistics module is used for generating a learning chart corresponding to the user according to the learning data recorded in the learning recording module and the period of the year or month;
and the block storage module is used for carrying out uplink storage on the learning information of each group of users.
2. The educational big data based intelligent remote teaching cloud platform system according to claim 1, wherein the user terminal specifically comprises a smart phone, a tablet computer, a notebook computer and a desktop computer.
3. The intelligent remote teaching cloud platform system based on education big data according to claim 2, wherein the specific steps of the resource processing module processing and mining are as follows:
step one: the resource processing module is used for combing different interfaces, acquiring reasons for different received data of each group, acquiring and judging data source information of each group of data according to combing results, and classifying the acquired data of each group according to different data sources;
step two: counting the classified data, digging frequent item sets in the classified data, finding out the association and dissimilarity between different data tables based on the frequent item sets, combining the same data, digging subsequent frequent item sets, repeating the steps until the digging is completed to an empty set;
step three: and carrying out statistical analysis on the data in the table of the unified interface of each data source, dividing each group of data into strong available data, weak available data and invalid data according to analysis results, screening out the invalid data, and carrying out data restoration on the weak available data.
4. The intelligent remote teaching cloud platform system based on education big data according to claim 3, wherein the elevation analysis module depth analysis comprises the following specific steps:
step I: the lifting analysis module acquires learning data uploaded by a user or past learning data recorded by a platform, then takes the learning data or the past learning data recorded by the platform as a sample data set, calculates standard deviation of the sample data set at the same time, eliminates abnormal data in the sample data set according to the calculated standard deviation, and performs standardization and normalization processing on the residual data;
step II: dividing the data in the processed sample data set into a training set and a testing set, inputting the training set into a neural network with the set completed, acquiring a relevant linear combination through a neuron excitation function, calculating an energy function of the neural network, and ending the training process and outputting a depth analysis model when the energy function is smaller than a target error;
step III: the testing set is led into a depth analysis model for testing, the loss value of the depth analysis model is calculated, and if the loss value does not reach the expected value, the parameter updating is carried out on the depth analysis model;
step IV: the deep analysis model receives the user learning data, extracts the learning data of the corresponding subjects according to the user selection condition, screens out the learning data with poor characterization capability, simulates the user learning condition, outputs the learning prediction curve of each subject, formulates the learning plan of each subject for the user according to the prediction curve, and simultaneously extracts and feeds back the corresponding remote education resources to the corresponding user account.
5. The intelligent remote teaching cloud platform system based on education big data according to claim 4, wherein the standard deviation concrete calculation formula in the step I is as follows:
wherein v is n For the data deviation of the sample dataset, s is the standard deviation, if any data x i Deviation v of (2) n Satisfy |v n Judging the data as abnormal data and eliminating the abnormal data when the I is more than 3 sigma
The specific calculation formula of the standardized treatment in the step I is as follows:
wherein y represents the proposed characteristic parameter; mean (y) represents the average processing of the extracted feature parameters; std (y) represents standard deviation of the feature parameters.
6. The intelligent remote teaching cloud platform system based on education big data according to claim 1, wherein the block storage module uplink storage comprises the following specific steps:
step (1): the block storage module preprocesses the user learning data into a block meeting the condition, when the block is accessed to the network, each node in the block chain network generates a local public and private key pair as an identification code in the network, and when one node waits for the local role to become a candidate node, the leader application is broadcasted to other nodes in the network and sent;
step (2): when the candidate node becomes a leading node, the other nodes become following nodes, the leading node broadcasts the block record information, the following nodes broadcast the received information to the other following nodes after receiving the information, record the repetition times, and generate a block head by using the information with the largest repetition times;
step (3): and after the verification is passed, the leader node sends an addition command and enters a sleep stage, and after the follow-up node receives the confirmation information, each newly generated block group is added to the block chain and returns to the candidate identity.
7. An intelligent remote teaching method based on education big data is characterized in that the teaching method specifically comprises the following steps:
(1) Processing education big data in the service cloud platform;
(2) The user logs in the service cloud platform and uploads personal learning information;
(3) Constructing a depth analysis model and adjusting parameters of the depth analysis model;
(4) A learning scheme is formulated for the user according to the learning information of the user;
(5) Recording user learning information and counting according to a certain period;
(6) And uploading the user learning data to the blockchain for storage.
8. The intelligent remote teaching method based on educational big data according to claim 7, wherein the parameter adjustment in step (3) specifically comprises the following steps:
the first step: the parameter adjustment module initializes the network connection weight in a specified interval of the depth analysis model constructed by the lifting analysis module, submits training samples from a set of input and output pairs during training, calculates the output of the depth analysis model, compares expected network output with actual network output, and calculates local errors of all neurons;
and a second step of: training and updating the weight of the depth analysis model according to a learning rule equation after the local error exceeds a preset threshold value of a worker, and listing all possible data results according to a preset learning rate and step length;
and a third step of: for each group of data, selecting any subset as a test set, selecting the rest subsets as training sets, detecting the test set after training a test model, and counting root mean square errors of detection results;
fourth step: and replacing the test set with another subset, taking the rest subset as a training set, counting root mean square errors again until all data are predicted once, and selecting the corresponding combined parameter with the minimum root mean square error as the optimal parameter in the data interval and replacing the original parameter of the depth analysis model.
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| CN117668497B (en) * | 2024-01-31 | 2024-05-07 | 山西卓昇环保科技有限公司 | Carbon emission analysis method and system based on deep learning under environment protection |
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