CN117853289A - Learning resource data sharing method based on block chain - Google Patents
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Abstract
The invention discloses a learning resource data sharing method based on a block chain, which particularly relates to the technical field of block chains and comprises the following steps: step 1, establishing a learning resource data sharing platform; step 2, constructing a neural network model for screening learning resource data; step 3, screening the target learning resource data through the neural network model constructed in the step 2, and determining whether to uplink the target learning resource data to a learning resource data sharing platform according to a screening result; step 4, carrying out identity verification and authorization on the user in the learning resource data sharing platform; the invention ensures the safety, reliability and transparency of the learning resource data, realizes the resource sharing among users, maintains and adjusts the learning resource according to the feedback and evaluation of the users, ensures the timeliness and accuracy of the data, automatically screens and links the learning resource by using the neural network model, and improves the utilization rate of the resource and the operation efficiency of the platform.
Description
Technical Field
The invention relates to the technical field of blockchains, in particular to a learning resource data sharing method based on blockchains.
Background
Blockchains are terms in the field of information technology, essentially, are shared databases, and store data or information therein, and have the characteristics of 'non-counterfeitable', 'whole-course trace', 'traceable', 'open transparent', 'collective maintenance', and the like. Blockchains are essentially de-centralized databases, which are a series of data blocks that are generated in association using cryptographic methods to verify the validity of their information and to generate the next block. With the development of internet technology, learning resources are increasingly diversified in acquisition ways, and learners can acquire required learning resources through various ways. However, the existing learning resource data sharing method has some problems such as data security, privacy protection, difficult resource quality assurance and the like. In order to solve the problems, the invention provides a learning resource data sharing method based on a block chain.
Disclosure of Invention
In order to achieve the above purpose, the present invention provides the following technical solutions:
a learning resource data sharing method based on a block chain comprises the following steps:
step 1, establishing a learning resource data sharing platform;
step 2, constructing a neural network model for screening learning resource data;
step 3, screening the target learning resource data through the neural network model constructed in the step 2, and determining whether to uplink the target learning resource data to a learning resource data sharing platform according to a screening result;
step 4, carrying out identity verification and authorization on the user in the learning resource data sharing platform;
step 5, sharing and transmitting learning resource data for the authorized user;
step 6, collecting feedback and evaluation of the learning resource data by the user and maintaining and adjusting the learning resource data;
and 7, carrying out learning resource data sharing excitation on the user in the learning resource data sharing platform according to a preset excitation strategy.
In step 1, establishing a learning resource data sharing platform refers to: a learning resource data sharing platform based on a blockchain technology is built, and the platform comprises a plurality of nodes, wherein each node has a unique identity.
In a preferred embodiment, in step 2, constructing a neural network model for screening learning resource data refers to:
step one, preparing a data set containing different learning resources, wherein the data set is an MxN matrix, M represents columns, namely features, N represents rows, namely samples, and each sample is a feature vector of the learning resource;
training the data set in the first step, training a model by using the comparison loss, taking the feature vectors of any two learning resources as input data, and taking the similarity of the feature vectors of the two learning resources as output data;
and thirdly, evaluating the performance of the neural network model, stopping training when the accuracy reaches the expected value, and completing the construction of the neural network model for screening the learning resource data.
In a preferred embodiment, in step 3, the step of screening the target learning resource data by using the neural network model constructed in step 2, and determining whether to uplink the target learning resource data to the learning resource data sharing platform according to the screening result refers to:
screening target learning resource data through the neural network model constructed in the step 2, uploading screened target learning resource data to a learning resource data sharing platform, not uploading non-screened target learning resource data to the learning resource data sharing platform, then conducting encryption processing on the screened target learning resource data, generating a unique hash value for each screened target learning resource data, and uploading the encrypted learning resource data and the corresponding hash value to the learning resource data sharing platform.
In a preferred embodiment, in step 4, the authentication and authorization of the user in the learning resource data sharing platform means that:
when a user applies for acquiring learning resource data from the learning resource data sharing platform, identity verification is performed, identity information of the user is confirmed, a hash value of the learning resource data is provided, the user is authorized, and the authorized user can access and use the corresponding learning resource data.
In a preferred embodiment, in step 5, sharing and transmitting learning resource data to the authorized user means:
searching a hash value obtained by an authorized user during authorization, searching related data in a blockchain network according to the hash value, positioning learning resource data, and then storing the hash value obtained by the user during authorization and the positioned learning resource data in a blockchain, wherein when the two hash values are completely consistent, the positioned learning resource data is determined to be a sharing target and transmitted to the authorized user; when the two hash values are not completely consistent, the positioned learning resource data is not determined to be a sharing target, and no transmission operation is performed.
In a preferred embodiment, in step 6, collecting feedback and evaluation of learning resource data from the user and maintaining and adjusting the learning resource data means:
s1, collecting feedback of a user on learning resource data and maintaining the learning resource data according to the feedback:
s11, collecting comparison conditions of hash values obtained by a user during authorization and hash values stored in a block chain of positioned learning resource data, wherein when the two hash values are completely consistent, the collected feedback type is positive feedback; when the two hash values are not completely consistent, the collected feedback type is negative feedback;
s12, when the feedback type collected in S11 is positive feedback, maintenance of the learning resource data is not needed, and when the feedback type collected in S11 is negative feedback, maintenance of the learning resource data is needed;
s2, collecting the evaluation of the learning resource data by the user and adjusting the learning resource data according to the evaluation:
s21, counting the evaluation information of the learning resource data by a user in a preset time window, and summarizing the evaluation information to obtain an evaluation set;
s22, marking the evaluation item in the evaluation set as XMi, and obtaining the evaluation value pj=by a formula，/>The evaluation influence factors corresponding to preset evaluation items XMi are obtained, and Q is the total parameters and the number of users to be evaluated;
s23, setting the initial set effective time of the learning resource data as T1, comparing the evaluation value PJ with a preset evaluation threshold, and if the evaluation value PJ is larger than or equal to the preset evaluation threshold, adjusting the effective time of the learning resource data to be A times of the last effective time; if the evaluation value PJ is smaller than the preset evaluation threshold value for comparison, a correction prompt is sent to the user uploading the learning resource data, correction is completed within the effective time of the learning resource, otherwise, downlink processing is performed until the correction is completed, and uplink is performed again.
The invention has the technical effects and advantages that:
the invention ensures the safety, reliability and transparency of the learning resource data through the blockchain technology, adopts encryption processing and hash value verification, protects the privacy and copyright of the learning resource data, realizes the resource sharing among users, reduces the operation cost and risk of a centralized platform, maintains and adjusts the learning resource according to the feedback and evaluation of the users, ensures the timeliness and accuracy of the data, provides a decentralised learning resource data sharing mode, and promotes the wide spread and academic communication of the learning resource.
The invention utilizes the neural network model to automatically screen and uplink learning resources, improves the utilization rate of the resources and the operation efficiency of the platform, can effectively extract different characteristics of the two resources, then compares the two resources, finally realizes the aim of avoiding repeated uploading of the same resources, carries out learning resource data sharing excitation on users in the learning resource data sharing platform according to the preset excitation strategy, is beneficial to exciting the platform users to create more popular learning resources, and realizes a benign learning mechanism of sharing the learning resources.
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For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
fig. 1 is a schematic diagram of a blockchain-based learning resource data sharing method in the present invention.
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. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The following examples are obtained with reference to fig. 1:
example 1
A learning resource data sharing method based on a block chain comprises the following steps:
step 1, establishing a learning resource data sharing platform; a learning resource data sharing platform based on a blockchain technology is built, and the platform comprises a plurality of nodes, wherein each node has a unique identity.
Step 2, constructing a neural network model for screening learning resource data;
step 3, screening the target learning resource data through the neural network model constructed in the step 2, and determining whether to uplink the target learning resource data to a learning resource data sharing platform according to a screening result; the method comprises the steps of carrying out encryption processing on screened learning resource data, uploading the encrypted data to a blockchain network, wherein each learning resource data corresponds to a unique hash value for searching and verifying in the blockchain network, the hash value is a character string with a fixed length and is usually composed of numbers and letters, the hash value can be obtained by calculating the encrypted data through a hash algorithm such as SHA-256, the hash value has uniqueness and irreversibility, even if the encrypted data slightly changes, the hash value also changes obviously, and the blockchain is a decentralised distributed account book technology, so that the safety, reliability and non-tamper-ability of the data can be ensured; the workload evidence refers to: under the PoW consensus mechanism, the node participating in the creation of the new block needs to solve a complex mathematical problem to find a hash value meeting a specific condition, and the node solving the problem obtains a certain amount of rewards, so that the creation of the new block is ensured to consume a great amount of computing power, and the safety and reliability of the blockchain are ensured.
Step 4, carrying out identity verification and authorization on the user in the learning resource data sharing platform; the user in the learning resource data sharing platform refers to that after the learner passes identity verification, the user can apply for obtaining the learning resource data from the platform, and the platform authorizes the learner according to the identity information of the learner and the hash value of the learning resource data.
Step 5, sharing and transmitting learning resource data for the authorized user; after authorization, the learner can acquire learning resource data from the platform. When the resource sharing is carried out among learners, the data transmission can be carried out in a point-to-point mode, so that the safety and privacy of the data are ensured.
Step 6, collecting feedback and evaluation of the learning resource data by the user and maintaining and adjusting the learning resource data; the platform periodically checks the learning resource data, and can optimize and adjust the learning resource data according to feedback and evaluation of learners, so that timeliness and accuracy of the data are ensured.
And 7, carrying out learning resource data sharing excitation on the user in the learning resource data sharing platform according to a preset excitation strategy.
The invention adopts the blockchain technology, ensures the safety, reliability and transparency of the learning resource data, ensures the privacy and copyright protection of the learning resource data through encryption processing and hash value verification, realizes the resource sharing among learners, improves the utilization efficiency of the learning resource, provides a decentralizing learning resource data sharing mode, and reduces the operation cost and risk of a centralizing platform.
In step 1, establishing a learning resource data sharing platform refers to: a learning resource data sharing platform based on a blockchain technology is built, and the platform comprises a plurality of nodes, wherein each node has a unique identity.
In step 2, constructing a neural network model for screening learning resource data refers to:
step one, preparing a data set containing different learning resources, wherein the data set is an MxN matrix, M represents columns, namely features, N represents rows, namely samples, and each sample is a feature vector of the learning resource;
training the data set in the first step, wherein preprocessing is required to be performed on the data before training a neural network model, the preprocessing comprises the steps of data cleaning, standardization, coding and the like so as to ensure the quality and consistency of the data, preprocessing the data belongs to the prior art, excessive redundancy is not performed here, then the model is trained by using contrast loss, the feature vectors of any two learning resources are used as input data, and the similarity of the feature vectors of the two learning resources is used as output data; contrast loss refers to: by minimizing the distance between the positive samples and the negative samples, in the training process, the distance between the positive samples of the same resource which the model learns should be as small as possible, and the distance between the negative samples of different resources should be as large as possible, so that the model can better distinguish the same and different resources, and the similarity of the feature vectors of two learning resources is taken as output data, the higher the similarity is, namely the smaller the distance between the positive samples of the same resource is, which indicates that the probability of the same learning resource between the two samples is greater;
thirdly, evaluating the performance of the neural network model, stopping training when the accuracy reaches the expected value, and completing the construction of the neural network model for screening the learning resource data; the trained neural network model can be applied to an actual learning resource screening task to avoid repeated uploading of learning resources, improve the effective utilization rate of a blockchain, take the similarity of feature vectors of two learning resources as output data and compare the similarity with a preset threshold value, when the output data is greater than or equal to the preset threshold value, the learning resources are not screened and marked as a result 1, when the output data is smaller than the preset threshold value, the learning resources are screened and marked as a result 0, the final screening result is 0 or 1, and whether the learning resources pass the screening can be directly judged.
In step 3, screening the target learning resource data through the neural network model constructed in step 2, and determining whether to uplink the target learning resource data to the learning resource data sharing platform according to the screening result refers to:
screening target learning resource data through the neural network model constructed in the step 2, inputting two learning resources in the data by the neural network model, wherein one learning resource is a learning resource which is already uplink and kept in an effective state, the other learning resource is a new learning resource which is to be uplink, the screened target learning resource data is uplink to a learning resource data sharing platform, the non-screened target learning resource data is not uplink to the learning resource data sharing platform, then the screened target learning resource data is subjected to encryption processing, a unique hash value is generated for each screened target learning resource data, and then the encrypted learning resource data and the corresponding hash value are uploaded to the learning resource data sharing platform together.
In step 4, performing authentication and authorization on the user in the learning resource data sharing platform means that:
when a user applies for acquiring learning resource data from the learning resource data sharing platform, identity verification is performed, identity information of the user is confirmed, a hash value of the learning resource data is provided, the user is authorized, and the authorized user can access and use the corresponding learning resource data.
In step 5, sharing and transmitting learning resource data for the authorized user means:
searching a hash value obtained by an authorized user during authorization, searching related data in a blockchain network according to the hash value, positioning learning resource data, and then storing the hash value obtained by the user during authorization and the positioned learning resource data in a blockchain, wherein when the two hash values are completely consistent, the positioned learning resource data is determined to be a sharing target and transmitted to the authorized user; when the two hash values are not completely consistent, the positioned learning resource data is not determined to be a sharing target, and no transmission operation is performed.
In step 6, collecting feedback and evaluation of learning resource data from a user and maintaining and adjusting the learning resource data means that:
s1, collecting feedback of a user on learning resource data and maintaining the learning resource data according to the feedback:
s11, collecting comparison conditions of hash values obtained by a user during authorization and hash values stored in a block chain of positioned learning resource data, wherein when the two hash values are completely consistent, the collected feedback type is positive feedback; when the two hash values are not completely consistent, the collected feedback type is negative feedback;
s12, when the feedback type collected in S11 is positive feedback, maintenance of the learning resource data is not needed, and when the feedback type collected in S11 is negative feedback, maintenance of the learning resource data is needed; the maintenance of the learning resource data means that when the two hash values are not completely consistent, the positioned learning resource data is not determined to be a sharing target, and no transmission operation is performed, which means that the learning resource data at the moment may be tampered or damaged, and maintenance is required to ensure the integrity and the correctness of the learning resource data;
s2, collecting the evaluation of the learning resource data by the user and adjusting the learning resource data according to the evaluation:
s21, counting the evaluation information of the learning resource data by a user in a preset time window, and summarizing the evaluation information to obtain an evaluation set;
s22, marking the evaluation item in the evaluation set as XMi, and obtaining the evaluation value pj=by a formula，/>The evaluation influence factors corresponding to preset evaluation items XMi are obtained, and Q is the total parameters and the number of users to be evaluated;
wherein, the information in the evaluation set can be obtained by the following ways:
1. the platform can be provided with a scoring system to score the learning resources by the learner, and the scoring can be based on various aspects of the learning resources, such as content quality, usability, practicability, and the like. By collecting scores of learners, the platform can know the overall performance of learning resources, optimize and adjust the overall performance according to the overall performance, and the content quality, usability, practicability and the like can be used as evaluation items;
2. the platform can provide a comment function, so that a learner can give his own opinion and advice to the learning resources, and the learner can give improved opinions to the learning resources in comments or share his own experience and mind in practical application. The comments can help other learners to know the advantages and the disadvantages of learning resources, simultaneously provide the direction of optimizing the resources for the platform, search the preset words with different tendencies in the comments, and take the occurrence frequency of the words with the tendencies of exaggeration, depreciation and neutrality as an evaluation item;
3. the platform can regularly or irregularly develop questionnaires, collect the demands and satisfaction of learners on learning resources, the questionnaires can comprise questions about learning resource content, form, difficulty and the like, and the learners evaluate the overall experience of the platform, and the platform can better know the demands of the learners by analyzing the questionnaire results, so that the learning resources are optimized and adjusted, and the evaluation results of the questions about the learning resource content, form, difficulty and the like can be used as evaluation items;
4. the platform can know the using habit and preference of the learner by analyzing the behavior data of the learner in the process of using the learning resources, for example, the platform can track the browsing, downloading, collecting and other operations of the learner on the platform, the stay time, the repeated watching times and the like of the learner on the learning resources, the platform can find hot spots and cold spots of the learning resources through the data, so that the learning resources can be optimized and adjusted in a targeted manner, and the stay time, the repeated watching times and the like on the learning resources can be used as evaluation items;
the determination of the evaluation items can be set according to the actual situation, andan evaluation influence factor corresponding to a preset evaluation item XMi is not 0 and is +.>The size of the item reflects the evaluation importance of the evaluation item to the whole;
s23, setting the initial set effective time of the learning resource data as T1, comparing the evaluation value PJ with a preset evaluation threshold, and if the evaluation value PJ is larger than or equal to the preset evaluation threshold, adjusting the effective time of the learning resource data to be A times of the last effective time; if the initial set effective time of a learning resource is set to be T1, the evaluation value PJ is greater than or equal to a preset evaluation threshold, the next effective time is set to be a×t1, if the next evaluation value PJ is still greater than or equal to the preset evaluation threshold, the set effective time is set to be a×aχt1, if the evaluation value PJ is smaller than the preset evaluation threshold for comparison, a correction prompt is sent to a user who uploads the learning resource data, the correction is completed within the effective time of the learning resource, otherwise, downlink processing or deletion processing is performed until the correction is completed, and uplink is performed again, when the user who uploads the learning resource data sends the correction prompt, the learning quality of the learning resource data is at a lower level, including but not limited to factors such as too large learning difficulty, too large content deletion degree, and low practicality.
Example 2
In step 7 of example 1, it is proposed that: according to a preset incentive strategy, carrying out study resource data sharing incentive on users in a study resource data sharing platform, wherein the method comprises the following steps:
excitation strategy one, integral excitation regime: the platform can set up a point system, the learner and the teacher can obtain points in the interaction actions such as sharing, commenting, quoting and the like, the points can be used for exchanging services or rewards provided by the platform, such as course offers, physical rewards and the like, and in the mode, the learner and the teacher can also obtain actual returns while actively participating in the platform activities.
Excitation strategy two, ranking list excitation system: the platform can set up various ranking charts, such as a popular resource chart, a high-quality author chart and the like, so as to show the performances of learning resources and authors, and the learning resources and authors with the top ranking can obtain a certain exposure degree and honor, so that other learners and teachers are stimulated to improve the contribution quality of the learners and the teachers.
Incentive strategy three, bonus incentive system: the platform can set up a bonus system, and for widely cited and praised learning resources, authors can obtain a certain cash prize or other forms of economic compensation, which will help to motivate the enthusiasm of learners and teachers, causing them to pay more attention to the quality and practicability of learning resources.
Excitation policy four, authentication and honor excitation system: the platform can provide certification and honor marks for excellent learning resources and authors, such as gold medal lecturer, high-quality resources and the like, and the certification and honor marks can improve the authority and credibility of the learning resources and authors, thereby attracting more learners to pay attention to and use.
Excitation strategy five, community activity excitation system: the platform can regularly hold various online and offline activities such as lectures, seminars, competitions and the like, invite excellent learning resources and authors to participate, and through the activities, the learners and teachers can mutually communicate, learn and cooperate, so that the quality and influence of the learning resources are improved together.
Incentive strategies six, personalized recommended incentive systems: the platform can recommend high-quality learning resources for learners according to interests and demands of the learners, and can recommend the learning resources which are approved by a large number of learners preferentially, so that the exposure and influence of the learning resources are improved, and meanwhile, the platform is helpful for stimulating the learners and teachers to create more popular learning resources.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (7)
1. The learning resource data sharing method based on the block chain is characterized by comprising the following steps of:
step 1, establishing a learning resource data sharing platform;
step 2, constructing a neural network model for screening learning resource data;
step 3, screening the target learning resource data through the neural network model constructed in the step 2, and determining whether to uplink the target learning resource data to a learning resource data sharing platform according to a screening result;
step 4, carrying out identity verification and authorization on the user in the learning resource data sharing platform;
step 5, sharing and transmitting learning resource data for the authorized user;
step 6, collecting feedback and evaluation of the learning resource data by the user and maintaining and adjusting the learning resource data;
and 7, carrying out learning resource data sharing excitation on the user in the learning resource data sharing platform according to a preset excitation strategy.
2. The method for sharing learning resource data based on blockchain as in claim 1, wherein in step 1, establishing a learning resource data sharing platform means: a learning resource data sharing platform based on a blockchain technology is built, and the platform comprises a plurality of nodes, wherein each node has a unique identity.
3. The blockchain-based learning resource data sharing method of claim 2, wherein in step 2, constructing a neural network model for screening learning resource data refers to:
step one, preparing a data set containing different learning resources, wherein the data set is an MxN matrix, M represents columns, namely features, N represents rows, namely samples, and each sample is a feature vector of the learning resource;
training the data set in the first step, training a model by using the comparison loss, taking the feature vectors of any two learning resources as input data, and taking the similarity of the feature vectors of the two learning resources as output data;
and thirdly, evaluating the performance of the neural network model, stopping training when the accuracy reaches the expected value, and completing the construction of the neural network model for screening the learning resource data.
4. The blockchain-based learning resource data sharing method of claim 3, wherein in step 3, the target learning resource data is screened by the neural network model constructed in step 2, and determining whether to uplink the target learning resource data to the learning resource data sharing platform according to the screening result refers to:
screening target learning resource data through the neural network model constructed in the step 2, uploading screened target learning resource data to a learning resource data sharing platform, not uploading non-screened target learning resource data to the learning resource data sharing platform, then conducting encryption processing on the screened target learning resource data, generating a unique hash value for each screened target learning resource data, and uploading the encrypted learning resource data and the corresponding hash value to the learning resource data sharing platform.
5. The blockchain-based learning resource data sharing method of claim 4, wherein in step 4, performing identity verification and authorization on the user in the learning resource data sharing platform means:
when a user applies for acquiring learning resource data from the learning resource data sharing platform, identity verification is performed, identity information of the user is confirmed, a hash value of the learning resource data is provided, the user is authorized, and the authorized user can access and use the corresponding learning resource data.
6. The method for sharing learning resource data based on blockchain as in claim 5, wherein in step 5, sharing and transmitting learning resource data to the authorized user means:
searching a hash value obtained by an authorized user during authorization, searching related data in a blockchain network according to the hash value, positioning learning resource data, and then storing the hash value obtained by the user during authorization and the positioned learning resource data in a blockchain, wherein when the two hash values are completely consistent, the positioned learning resource data is determined to be a sharing target and transmitted to the authorized user; when the two hash values are not completely consistent, the positioned learning resource data is not determined to be a sharing target, and no transmission operation is performed.
7. The blockchain-based learning resource data sharing method of claim 6, wherein in step 6, collecting feedback and evaluation of learning resource data from a user and maintaining and adjusting the learning resource data means:
s1, collecting feedback of a user on learning resource data and maintaining the learning resource data according to the feedback:
s11, collecting comparison conditions of hash values obtained by a user during authorization and hash values stored in a block chain of positioned learning resource data, wherein when the two hash values are completely consistent, the collected feedback type is positive feedback; when the two hash values are not completely consistent, the collected feedback type is negative feedback;
s12, when the feedback type collected in S11 is positive feedback, maintenance of the learning resource data is not needed, and when the feedback type collected in S11 is negative feedback, maintenance of the learning resource data is needed;
s2, collecting the evaluation of the learning resource data by the user and adjusting the learning resource data according to the evaluation:
s21, counting the evaluation information of the learning resource data by a user in a preset time window, and summarizing the evaluation information to obtain an evaluation set;
s22, marking the evaluation item in the evaluation set as XMi, and obtaining the evaluation value pj=by a formula，/>The evaluation influence factors corresponding to preset evaluation items XMi are obtained, and Q is the total parameters and the number of users to be evaluated;
s23, setting the initial set effective time of the learning resource data as T1, comparing the evaluation value PJ with a preset evaluation threshold, and if the evaluation value PJ is larger than or equal to the preset evaluation threshold, adjusting the effective time of the learning resource data to be A times of the last effective time; if the evaluation value PJ is smaller than the preset evaluation threshold value for comparison, a correction prompt is sent to the user uploading the learning resource data, correction is completed within the effective time of the learning resource, otherwise, downlink processing is performed until the correction is completed, and uplink is performed again.
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| CN112348717A (en) * | 2020-11-25 | 2021-02-09 | 山东师范大学 | Knowledge uplink and acquisition method and system based on block chain |
| CN113297625A (en) * | 2021-07-23 | 2021-08-24 | 北京笔新互联网科技有限公司 | Data sharing system and method based on block chain and electronic equipment |
| CN116108505A (en) * | 2023-02-14 | 2023-05-12 | 成都康赛信息技术有限公司 | Educational resource sharing safety house method based on alliance chain |
| CN117114484A (en) * | 2023-08-29 | 2023-11-24 | 联通(海南)产业互联网有限公司 | Construction method of resource quality index system of learning resource platform |
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| CN113297625A (en) * | 2021-07-23 | 2021-08-24 | 北京笔新互联网科技有限公司 | Data sharing system and method based on block chain and electronic equipment |
| CN116108505A (en) * | 2023-02-14 | 2023-05-12 | 成都康赛信息技术有限公司 | Educational resource sharing safety house method based on alliance chain |
| CN117114484A (en) * | 2023-08-29 | 2023-11-24 | 联通(海南)产业互联网有限公司 | Construction method of resource quality index system of learning resource platform |
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