A digital cultural relic resource sharing system and method based on cloud platform

Abstract

The present invention relates to the field of data processing technology, and in particular to a cloud platform-based digital cultural relic resource sharing system and method. The system comprises: collecting and preprocessing cultural relic data to obtain preprocessed cultural relic data; performing digital conversion processing on the preprocessed cultural relic data to obtain digital cultural relic data; storing the digital cultural relic data and cultural relic metadata, assigning a unique identifier to each cultural relic, and forming index information based on the digital cultural relic data and cultural relic metadata; performing security authentication on each user when a user sends an access request; and when the security authentication passes and the user has access rights, searching for qualified digital cultural relic data based on the user's access request and the index information, performing encryption processing, and obtaining encrypted digital cultural relic data. The system solves the technical problems of inaccurate processing of cultural relic data, slow retrieval speed, and low data security in the digital cultural relic resource sharing.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a cloud platform-based cultural relics digital resource sharing system and method. Background Art

[0002] With the continuous advancement of science and technology, traditional methods of cultural relic protection are facing increasing challenges. As important witnesses of human civilization, the protection of cultural relics is not only related to the inheritance of cultural heritage, but also directly affects historical research and social education. However, traditional cultural relic protection methods rely on physical protection measures, such as temperature and humidity control, fire prevention and theft prevention, and physical sealing. Although these methods can effectively prevent cultural relics from being damaged temporarily, they cannot completely avoid the threats posed by environmental changes, the passage of time, or human factors. In addition, these traditional cultural relic protection methods also have many limitations for the dissemination and display of cultural relics. In particular, due to the single display form and the inability to transcend time and space, it is difficult for the public to fully understand the historical and artistic value of cultural relics.

[0003] With the rise of digital technology, digital preservation of cultural relics has become an emerging solution. Through modern information collection, storage, management, and display technologies, digitized cultural relics can, to a certain extent, overcome the limitations of traditional cultural relic preservation methods, maximizing the preservation of their historical information and artistic value while preventing damage caused by environmental factors. Furthermore, digitized cultural relics provide a wider channel for dissemination, especially with the support of the internet, allowing users around the world to view and learn from them anytime, anywhere.

[0004] However, existing methods for sharing digital cultural relics resources have the following technical problems: the processing of cultural relics data in digital cultural relics resource sharing is not accurate enough, the retrieval speed is slow, and the data security is low. Summary of the Invention

[0005] The present invention provides a cloud platform-based cultural relic digital resource sharing system and method to solve the technical problems of inaccurate processing of cultural relic data, slow retrieval speed and low data security in cultural relic digital resource sharing.

[0006] The present invention provides a cloud-based cultural relic digital resource sharing system and method, which specifically includes the following technical solutions:

[0007] A method for sharing digital cultural relics resources based on a cloud platform, comprising the following steps:

[0008] S1. Collect and preprocess cultural relic data to obtain preprocessed cultural relic data; perform digital conversion on the preprocessed cultural relic data to obtain digital cultural relic data, store the digital cultural relic data and cultural relic metadata, and assign a unique identifier to each cultural relic, while constructing index information based on the digital cultural relic data and cultural relic metadata;

[0009] S2. After the storage process is completed, when the user sends an access request, security authentication is performed on each user. When the security authentication is passed and the user has access rights, according to the user's access request and combined with the index information, the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization is used to find the digital cultural relic data that meets the conditions, and the digital cultural relic data that meets the conditions is encrypted to obtain the encrypted digital cultural relic data.

[0010] Preferably, the S1 specifically includes:

[0011] Feature extraction is performed on the digitized cultural relic data and the cultural relic metadata generated when collecting and digitally converting the cultural relic data, and features of different dimensions are extracted. The features of different dimensions are then combined to form index information of the digitized cultural relic data, and an index is established based on the index information.

[0012] Preferably, the S2 specifically includes:

[0013] When the user sends an access request, check whether there is a permission setting for the cultural relic data. If the cultural relic is set to restrict access, check whether the user role allows access to this cultural relic. If access is allowed, continue with subsequent processing. If access is not allowed, return no permission to the user.

[0014] Preferably, the S2 specifically includes:

[0015] In the process of implementing the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization, the multi-dimensional features of the cultural relics are embedded into the multi-dimensional vector space. Through the embedding process, the characteristics of each cultural relic are represented by a high-dimensional feature vector.

[0016] Preferably, the S2 specifically includes:

[0017] In the implementation process of the cultural relics data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization, an intelligent matching optimization model is introduced to perform similarity matching on keywords in access requests, calculate the maximum matching score, and adjust the maximum matching score as the recursive depth increases.

[0018] Preferably, the S2 specifically includes:

[0019] In the process of implementing the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization, the final matching score is calculated based on the maximum matching score, and the final matching score set between the access request and all cultural relics is obtained. The cultural relic with the largest matching score is selected as the qualified cultural relic, thereby obtaining the qualified digital cultural relic data.

[0020] Preferably, the S2 specifically includes:

[0021] The qualified digital cultural relic data are encrypted using a multi-dimensional and multi-phase quantitative encryption algorithm. During the encryption process, a set of encryption keys and perturbation factors are initialized. The qualified digital cultural relic data are weighted by generating a key matrix, and the perturbation factor is introduced to perform preliminary perturbation on the qualified digital cultural relic data.

[0022] Preferably, the S2 specifically includes:

[0023] In the implementation process of the multi-dimensional and multi-phase quantitative encryption algorithm, a nonlinear perturbation function is introduced, and the exponential function, logarithmic function and cosine function are combined to perform perturbation processing on the digitized cultural relics data that meet the conditions, and obtain the data after nonlinear perturbation processing; a multi-dimensional space transformation operation is introduced, and the data after nonlinear perturbation processing is spatially transformed through the rotation matrix and scaling factor to obtain the data after multi-dimensional space transformation.

[0024] Preferably, the S2 specifically includes:

[0025] In the implementation process of the multi-dimensional and multi-phase quantitative encryption algorithm, a periodic perturbation mechanism is introduced, and sine waves and cosine waves are used to periodically perturb the data after multi-dimensional space transformation to obtain encrypted digitized cultural relic data, thereby generating a decryption key, and the encrypted digitized cultural relic data and decryption key are returned to the user.

[0026] A digital cultural relic resource sharing system based on a cloud platform, including the following parts:

[0027] Cultural relics data digitization module, data storage and management module, authority management and user authentication module, data sharing and access module, data retrieval and query module, data encryption module;

[0028] The cultural relic data digitization module collects cultural relic data, pre-processes the cultural relic data, and obtains pre-processed cultural relic data; performs digital transformation processing on the pre-processed cultural relic data to obtain digitized cultural relic data, and transmits the digitized cultural relic data and the cultural relic metadata generated when collecting and transforming the cultural relic data to the data storage and management module;

[0029] The data storage and management module stores the digitized cultural relic data and its related cultural relic metadata in the distributed storage system of the cloud platform, assigns a unique identifier to each cultural relic, constructs index information based on the digitized cultural relic data and cultural relic metadata, and creates an index based on the index information, and transmits the constructed index information to the data sharing and access module and the data retrieval and query module;

[0030] The permission management and user authentication module performs security authentication on each user when the user sends an access request after the stored procedure ends, and sends the access request that passes the security authentication to the data sharing and access module. Requests that do not pass the security authentication are directly returned to the user as access denial.

[0031] The data sharing and access module provides a standardized API interface after the access request security authentication of the permission management module and the user authentication module is passed. Through this interface, users can access digital cultural relic data across platforms. When accessing, the corresponding digital cultural relic data will be returned according to the permission, and the standardized API interface will be transmitted to the data retrieval and query module.

[0032] The data retrieval and query module, when the access request security authentication is passed, searches for qualified digitized cultural relic data based on the standardized API interface provided by the data sharing and access module and the index information obtained from the data storage and management module according to the user's access request, transmits the qualified digitized cultural relic data to the data encryption module for encryption processing, obtains the encrypted digitized cultural relic data, and returns the encrypted digitized cultural relic data and the corresponding decryption key to the user;

[0033] The data encryption module encrypts the digital cultural relic data that meets the conditions to obtain the encrypted digital cultural relic data, generates the corresponding decryption key, and feeds the encrypted digital cultural relic data and the corresponding decryption key back to the data retrieval and query module.

[0034] The beneficial effects of the technical solution of the present invention are:

[0035] 1. Search for qualified digitized cultural relic data through a cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization. The algorithm maps cultural relic features into a high-dimensional vector space and dynamically adjusts the importance of each cultural relic feature. Combined with an intelligent matching optimization model, it improves the efficiency and accuracy of cultural relic retrieval.

[0036] 2. Use a multi-dimensional and multi-phase quantitative encryption algorithm to encrypt the qualified digital cultural relic data. First, the qualified digital cultural relic data is represented as a data vector. By randomly generating a key matrix and a perturbation factor, the qualified digital cultural relic data is preliminarily perturbed to enhance the complexity of the encryption. By introducing a nonlinear perturbation function, the qualified digital cultural relic data is further perturbed to make the encryption result more complex. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a structural diagram of a cloud platform-based cultural relics digital resource sharing system according to the present invention;

[0038] Figure 2 This is a flow chart of a cloud platform-based cultural relics digital resource sharing method described in the present invention. DETAILED DESCRIPTION

[0039] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0040] 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 invention belongs.

[0041] The following describes in detail a specific solution of a cloud platform-based cultural relics digital resource sharing system and method provided by the present invention with reference to the accompanying drawings.

[0042] Refer to the attached Figure 1 , which shows a structure diagram of a cultural relic digital resource sharing system based on a cloud platform provided by one embodiment of the present invention. The system includes the following parts:

[0043] Cultural relics data digitization module, data storage and management module, authority management and user authentication module, data sharing and access module, data retrieval and query module, data encryption module;

[0044] The cultural relic data digitization module collects cultural relic data using data acquisition equipment such as 3D scanners and photographic equipment. The cultural relic data includes image data, audio data, video data, 3D scanning data, and document description data. The cultural relic data is pre-processed to obtain pre-processed cultural relic data. The pre-processed cultural relic data is digitally converted to obtain digitized cultural relic data. The digitized cultural relic data and the cultural relic metadata generated during the collection and conversion of the cultural relic data are transmitted to the data storage and management module. The cultural relic metadata includes the name, description, historical information, artistic style, age, region, etc. of the cultural relic.

[0045] The data storage and management module stores the digitized cultural relic data and its related cultural relic metadata in the distributed storage system of the cloud platform, assigns a unique identifier to each cultural relic, constructs index information based on the digitized cultural relic data and cultural relic metadata, and creates an index based on the index information. The constructed index information is then transmitted to the data sharing and access module and the data retrieval and query module to ensure subsequent search and access;

[0046] The permission management and user authentication module performs security authentication on each user when the user sends an access request after the stored procedure ends, and sends the access request that passes the security authentication to the data sharing and access module. Requests that do not pass the security authentication are directly returned to the user as access denial.

[0047] The data sharing and access module provides a standardized API interface after the access request security authentication of the permission management module and the user authentication module is passed. Through this interface, users can access digital cultural relic data across platforms. When accessing, the corresponding digital cultural relic data will be returned according to the permission, and the standardized API interface will be transmitted to the data retrieval and query module.

[0048] The data retrieval and query module, when the access request security authentication is passed, searches for qualified digitized cultural relic data based on the standardized API interface provided by the data sharing and access module and the index information obtained from the data storage and management module according to the user's access request, transmits the qualified digitized cultural relic data to the data encryption module for encryption processing, obtains the encrypted digitized cultural relic data, and returns the encrypted digitized cultural relic data and the corresponding decryption key to the user, ensuring efficient and accurate query and realizing resource sharing;

[0049] The data encryption module encrypts the digitized cultural relic data that meets the conditions to obtain the encrypted digitized cultural relic data, generates the corresponding decryption key, and feeds the encrypted digitized cultural relic data and the corresponding decryption key back to the data retrieval and query module;

[0050] Refer to the attached Figure 2, which shows a flow chart of a method for sharing digital cultural relics resources based on a cloud platform according to an embodiment of the present invention. The method includes the following steps:

[0051] S1. Collect and preprocess cultural relic data to obtain preprocessed cultural relic data; perform digital conversion on the preprocessed cultural relic data to obtain digital cultural relic data, store the digital cultural relic data and cultural relic metadata, and assign a unique identifier to each cultural relic, while constructing index information based on the digital cultural relic data and cultural relic metadata;

[0052] Cultural relic data, including image data, audio data, video data, 3D scanning data and document description data, are collected through data acquisition equipment such as 3D scanners, photographic equipment, and recording equipment; the collected cultural relic data are preprocessed, and the preprocessing includes: denoising and optimization, format conversion, etc. of image data using preprocessing methods such as denoising filtering, sharpening, and contrast adjustment; video editing and cropping, format conversion and compression are performed on video data; point cloud cleaning, gridding and refinement, optimization and simplification are performed on 3D scanning data; and text cleaning and standardization are performed on document description data to obtain preprocessed cultural relic data. The above preprocessing processes all use existing technical means and will not be described in detail here. After the cultural relic data has been preprocessed, the preprocessed cultural relic data is digitally converted, that is, converted into a structured digital format to obtain digital cultural relic data, including: conversion into 3D scanning data using standard 3D file formats, such as .obj, .ply, .stl, etc.; conversion into common Web standard formats, such as .jpg, .png, etc. image data; conversion into formats that support streaming, such as .mp3, .mp4, etc. audio and video data; conversion into structured metadata formats, such as .xml, .json, etc. document description data; and at the same time, obtaining cultural relic metadata generated during the collection and conversion of cultural relic data, which includes: cultural relic name, description, historical information, artistic style, age, region, etc.

[0053] Furthermore, the digital cultural relic data and cultural relic metadata are stored in an existing distributed storage system (such as Hadoop HDFS). Specifically, the digital data of cultural relics (such as image data, video data, 3D scanning data, etc.) are stored as object data, and the data storage unit of each cultural relic can be an independent file; the metadata of cultural relics (such as cultural relic name, description, historical information, artistic style, age, region, etc.) are stored as structured data. JSON, XML, RDF and other formats can be used to ensure the readability and structuring of cultural relics metadata; further use existing strategies such as UUID (universally unique identifier), self-incrementing ID and prefix combination, and combined ID based on cultural relics features to assign a unique identifier to each cultural relic to identify its uniqueness in the distributed storage system; at the same time, use existing feature engineering technology to extract features of different dimensions in digitized cultural relic data and cultural relic metadata to form index information of digitized cultural relic data, and establish indexes based on the index information, including: image data index, video data index, 3D scanning data index, text data index, text metadata index, and structured metadata index; the establishment of the above index information adopts existing technical means such as content-based image retrieval, indexing based on model geometric features (such as grids, feature vectors of point clouds), text search engines, field-based indexing, etc., which will not be elaborated here.

[0054] S2. After the storage process is completed, when the user sends an access request, security authentication is performed on each user. When the security authentication is passed and the user has access rights, according to the user's access request and combined with the index information, the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization is used to find the digital cultural relic data that meets the conditions, and the digital cultural relic data that meets the conditions is encrypted to obtain the encrypted digital cultural relic data.

[0055] After the stored procedure ends, when the user sends an access request, user identity verification is performed on each user using existing authentication methods such as username / password verification, OAuth authentication, and API Token authentication. If the user authentication passes, the access request continues to be processed. If the user authentication fails, access denial is returned to the user.

[0056] When a user sends an access request, it is checked whether there is a special permission setting for the cultural relic data. If the cultural relic data is set to restrict access, it is further checked whether the user role is allowed to access this cultural relic. If access is allowed, subsequent processing continues. If access is not allowed, it returns no permission to the user.

[0057] When a user passes security authentication and has access rights, the user's access request is combined with index information to search for qualified digitized cultural relic data using a cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization. This algorithm maps cultural relic features serving as index information to a high-dimensional space, dynamically adjusts the importance of different cultural relic features, and improves the efficiency and accuracy of cultural relic data retrieval through intelligent matching optimization. The specific implementation process is as follows:

[0058] First, the multidimensional features of the cultural relics are embedded into the multidimensional vector space. The specific formula is as follows:

[0059] ,

[0060] in, It is The embedded high-dimensional feature vector of each cultural relic contains various information about the cultural relic, such as age, region, artistic style, etc., and can more effectively capture the similarities between cultural relics in high-dimensional space; It is The characteristic vector of the artifact, that is, Index information of cultural relics; It is a cultural relic feature The weight is used to adjust the importance of the feature in the embedded high-dimensional feature vector. It is determined according to expert experience and the reference value range is ; It is The first Features It is a cultural relic feature The optimization parameter for exponential transformation is used to control the influence of the feature in the embedded high-dimensional feature vector. It is obtained through experimental fitting and the reference value range is ; is the triangular transformation parameter used to control the characteristics of the artifacts The periodicity after transformation is determined according to specific needs. For example, if it is calculated based on the characteristic change period (such as the periodicity of dynasty change), the reference value range is ; Is the power index, which is used to control the scaling parameters of the entire embedded high-dimensional feature vector. It is determined according to the specific scenario and the reference value is , such as 1 or 2;

[0061] Through the above embedding process, the characteristics of each cultural relic will be represented by a high-dimensional feature vector, which captures the intrinsic properties of the cultural relic more finely.

[0062] Furthermore, similarity matching will be performed on the keywords in the access request to select the cultural relic that is closest to the high-dimensional feature vector of the cultural relic. The intelligent matching optimization model is introduced to achieve intelligent matching by calculating the maximum matching score. The intelligent matching optimization model calculates the maximum matching score function Measure the similarity between the keywords in the access request and the characteristics of the cultural relics, and adjust the maximum matching score as the recursion depth increases to ensure that each level of optimization can more accurately optimize the maximum matching score. The specific form is:

[0063] ,

[0064] in, It is The first level artifacts and The maximum matching score between access requests. The larger the value, the more the cultural relic matches the query condition. is the weight coefficient, which is used to adjust the relative influence of the calculation results of the previous level. It can be determined by the existing heuristic rules. The purpose is to balance the influence of the recursive level. The reference value is ; It is The first level artifacts and The maximum matching score between access requests; It is access request feature vectors; is the weight coefficient, which is used to adjust the relative influence of the calculation results of the current level. It can be determined by the existing heuristic rules. The purpose is to balance the influence of the recursive level. The reference value is ; is the transpose; It is the inner product of the two eigenvectors, indicating the similarity between the artifact and the query. The larger the inner product, the more relevant the artifact is to the query. It is a modulo operation; It is the The embedded high-dimensional feature vector of each cultural relic is modulo; It is the The access request feature vector is modulo; Is the attenuation factor, which is used to control the rate at which the recursive depth affects the maximum matching score. The reference value is ; is the recursive level, indicating the recursive level in the current query process; is an exponential function used to control the recursive level Impact on the maximum matching score; It is a logarithmic function used to balance the impact of the increase in recursion depth on the final matching result.

[0065] Furthermore, based on the existing recursive optimization algorithm, the final matching score is calculated using the following formula:

[0066] ,

[0067] in, It is artifacts and The final matching score between access requests; is the maximum recursion depth level, which is determined according to the specific scenario; Is a weighting factor that decreases as the recursive level increases, ensuring that higher recursive levels have less impact on the final matching score. The value range is .

[0068] Further obtain the final matching score set between the access request and all artifacts , select the one with the largest matching score as the qualified cultural relic, and extract the digitized cultural relic data corresponding to the qualified cultural relic through the existing standardized API interface to obtain the qualified digitized cultural relic data; and encrypt the qualified digitized cultural relic data using a multi-dimensional and multi-phase quantitative encryption algorithm to obtain the encrypted digitized cultural relic data. The specific implementation process is as follows:

[0069] First, the digital cultural relics data that meet the conditions are represented as a data vector , the data vector contains multiple digital cultural relics data items. Assume that this data vector contains digital cultural relic data items, namely , the dimension is , where each A digital cultural relic data item corresponding to the cultural relic, such as age, species, description, etc.

[0070] Furthermore, in the encryption process, a set of encryption keys and perturbation factors are first initialized. In order to enhance the security of the multi-dimensional multi-phase quantitative encryption algorithm, a random key matrix is ​​generated. To achieve the weighting of the digital cultural relics data that meet the conditions, the dimension of this key matrix is , the values ​​in the key matrix are randomly generated and will change with each step in the encryption process. In addition, a perturbation factor is introduced , is a The vector is used to perform preliminary perturbations on the digitized cultural relic data that meets the conditions, thereby increasing the randomness and complexity of the encryption. The purpose of these initial settings is to provide a randomized baseline for subsequent steps, making the encryption results more difficult to predict.

[0071] ,

[0072] In the above process, the disturbance factor The introduction of greatly enhances the initial perturbation effect of qualified digital cultural relics data, each element of which The unpredictability of the encryption process is ensured by random generation, such as uniform distribution and Gaussian distribution. In the subsequent encryption process, the disturbance factor The interaction with eligible digitized cultural relics data will have a key impact on the subsequent encryption effect.

[0073] In order to make the encryption process more nonlinear and to form a highly complex encryption result, a nonlinear perturbation function is introduced based on the composite mapping mechanism of multiple nonlinear functions and the random perturbation superposition model. The design of this function combines the exponential function, logarithmic function and cosine function. Through the combination of these functions, the digital cultural relics data that meet the conditions are disturbed multiple times to obtain the data after nonlinear perturbation processing. Nonlinear perturbation function The output is the data after nonlinear perturbation processing, and it is represented as a new data vector , which has the following form:

[0074] ,

[0075] in, It performs exponential calculations on each qualified digital cultural relic data to achieve the effect of data compression; Each digitized cultural relic data that meets the conditions is smoothed to avoid extreme values ​​in the data; It is the introduction of periodic disturbance to each qualified digital cultural relic data, which is used to increase the complex periodic changes of the data, making the disturbance degree of each data random and enhancing the irreversibility of encryption.

[0076] Furthermore, multi-dimensional space transformation operations are introduced, through the rotation matrix and scaling factor Perform spatial transformation on the data after nonlinear perturbation. Rotation matrix By introducing an orthogonal matrix to transform the spatial distribution of data, ,in is the identity matrix; and the scaling factor The weighting is then performed by changing the amplitude of the data after nonlinear perturbation processing. Through multidimensional space transformation operations, each qualified digital cultural relic data will be mapped from the original space to a new space, thereby effectively increasing the complexity of the data and the difficulty of encryption.

[0077] The formula for multidimensional space transformation is:

[0078] ,

[0079] in, It is the data after multi-dimensional space transformation.

[0080] Furthermore, in order to increase the complexity of the encryption process, a periodic perturbation mechanism is introduced. The periodic perturbation mechanism uses sine and cosine waves to periodically perturb the data after multi-dimensional space transformation, thereby forming a more complex encryption feature and obtaining the encrypted digital cultural relic data:

[0081] ,

[0082] in, It is the encrypted digitized cultural relic data; It is a periodic disturbance factor, which is used to control the frequency of disturbance. It is determined according to specific needs. The reference value is .

[0083] Furthermore, a decryption key is generated by performing an inverse operation based on the multi-dimensional and multi-phase quantitative encryption algorithm, and the encrypted digitized cultural relic data and the decryption key are returned to the user, thereby realizing the sharing of digital cultural relic resources.

[0084] In summary, a cloud platform-based cultural relics digital resource sharing system and method have been completed.

[0085] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0086] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0087] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for sharing digital cultural relics resources based on a cloud platform, characterized in that: The following steps are involved: S1. Collect and preprocess cultural relic data to obtain preprocessed cultural relic data; Performing digital transformation on the pre-processed cultural relic data to obtain digital cultural relic data, storing the digital cultural relic data and cultural relic metadata, assigning a unique identifier to each cultural relic, and constructing index information based on the digital cultural relic data and cultural relic metadata; S2. After the storage process is complete, when a user sends an access request, security authentication is performed on each user. If the security authentication passes and the user has access rights, the user's access request is combined with index information to search for qualified digitized cultural relic data using a cultural relic data retrieval algorithm based on multidimensional dynamic embedded feature learning and intelligent matching optimization. The qualified digitized cultural relic data is then encrypted using a multidimensional multi-phase quantitative variation encryption algorithm to obtain the encrypted digitized cultural relic data. In the implementation process of the multi-dimensional and multi-phase quantitative encryption algorithm, a nonlinear perturbation function is introduced, and the exponential function, logarithmic function and cosine function are combined to perform perturbation processing on the digitized cultural relic data that meets the conditions, and obtain the data after nonlinear perturbation processing; a multi-dimensional space transformation operation is introduced, and the data after nonlinear perturbation processing is spatially transformed through the rotation matrix and scaling factor to obtain the data after multi-dimensional space transformation; a periodic perturbation mechanism is introduced, and the data after multi-dimensional space transformation is periodically perturbed using sine waves and cosine waves to obtain the encrypted digitized cultural relic data, thereby generating a decryption key, and the encrypted digitized cultural relic data and the decryption key are returned to the user.

2. The method for sharing digital cultural relics resources based on a cloud platform according to claim 1, characterized in that: Said S1 specifically includes: Feature extraction is performed on the digitized cultural relic data and the cultural relic metadata generated when collecting and digitally converting the cultural relic data, and features of different dimensions are extracted. The features of different dimensions are then combined to form index information of the digitized cultural relic data, and an index is established based on the index information.

3. The method for sharing digital cultural relics resources based on a cloud platform according to claim 1, characterized in that: Said S2 specifically includes: When the user sends an access request, check whether there is a permission setting for the cultural relic data. If the cultural relic is set to restrict access, check whether the user role allows access to this cultural relic. If access is allowed, continue with subsequent processing. If access is not allowed, return no permission to the user.

4. The method for sharing digital cultural relics resources based on a cloud platform according to claim 1, characterized in that: Said S2 specifically includes: In the process of implementing the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization, the multi-dimensional features of the cultural relics are embedded into the multi-dimensional vector space. Through the embedding process, the characteristics of each cultural relic are represented by a high-dimensional feature vector.

5. The method for sharing digital cultural relics resources based on a cloud platform according to claim 1, characterized in that: Said S2 specifically includes: In the implementation process of the cultural relics data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization, an intelligent matching optimization model is introduced to perform similarity matching on keywords in access requests, calculate the maximum matching score, and adjust the maximum matching score as the recursive depth increases.

6. The method for sharing digital cultural relics resources based on a cloud platform according to claim 5, characterized in that: Said S2 specifically includes: In the process of implementing the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization, the final matching score is calculated based on the maximum matching score, and the final matching score set between the access request and all cultural relics is obtained. The cultural relic with the largest matching score is selected as the qualified cultural relic, thereby obtaining the qualified digital cultural relic data.

7. The method for sharing digital cultural relics resources based on a cloud platform according to claim 6, characterized in that: Said S2 specifically includes: During the encryption process, a set of encryption keys and perturbation factors are initialized; the qualified digitized cultural relic data are weighted by generating a key matrix, and the perturbation factor is introduced to perform preliminary perturbation on the qualified digitized cultural relic data.

8. A cloud platform-based cultural relic digital resource sharing system, applied to the cloud platform-based cultural relic digital resource sharing method according to claim 1, characterized in that: Includes the following sections: Cultural relics data digitization module, data storage and management module, authority management and user authentication module, data sharing and access module, data retrieval and query module, data encryption module; The cultural relic data digitization module collects cultural relic data, pre-processes the cultural relic data, and obtains pre-processed cultural relic data; Performing digital transformation on the pre-processed cultural relic data to obtain digital cultural relic data, and transmitting the digital cultural relic data and cultural relic metadata generated when collecting and transforming the cultural relic data to the data storage and management module; The data storage and management module stores the digitized cultural relic data and its related cultural relic metadata in the distributed storage system of the cloud platform, assigns a unique identifier to each cultural relic, constructs index information based on the digitized cultural relic data and cultural relic metadata, and creates an index based on the index information, and transmits the constructed index information to the data sharing and access module and the data retrieval and query module; The permission management and user authentication module performs security authentication on each user when the user sends an access request after the stored procedure ends, and sends the access request that passes the security authentication to the data sharing and access module. Requests that do not pass the security authentication are directly returned to the user as access denial. The data sharing and access module provides a standardized API interface after the access request security authentication of the permission management module and the user authentication module is passed. Through this interface, users can access digital cultural relic data across platforms. When accessing, the corresponding digital cultural relic data will be returned according to the permission, and the standardized API interface will be transmitted to the data retrieval and query module. The data retrieval and query module, when the access request security authentication is passed, searches for qualified digitized cultural relic data based on the standardized API interface provided by the data sharing and access module and the index information obtained from the data storage and management module according to the user's access request, transmits the qualified digitized cultural relic data to the data encryption module for encryption processing, obtains the encrypted digitized cultural relic data, and returns the encrypted digitized cultural relic data and the corresponding decryption key to the user; The data encryption module encrypts the digital cultural relic data that meets the conditions to obtain the encrypted digital cultural relic data, generates the corresponding decryption key, and feeds the encrypted digital cultural relic data and the corresponding decryption key back to the data retrieval and query module.

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