Cultural relic digital resource sharing system and method based on cloud platform

Abstract

The invention relates to the technical field of data processing, in particular to a cultural relic digital resource sharing system and method based on a cloud platform. Comprising the following steps: 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, distributing a unique identifier for each cultural relic, and forming index information according to the digital cultural relic data and the cultural relic metadata; and when the user sends an access request, carrying out security authentication on each user, and when the security authentication is passed and the user has the access authority, searching the digital cultural relic data meeting the condition according to the access request of the user in combination with the index information, and carrying out encryption processing to obtain the encrypted digital cultural relic data. The technical problems that processing of cultural relic data is not accurate enough, the retrieval speed is low and the data security is low in cultural relic digital resource sharing are solved.

Description

Technical Field

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

[0002] With the continuous progress of technology, traditional cultural relic protection methods are facing increasing challenges. As an important witness of human civilization, the protection of cultural relics not only concerns the inheritance of cultural heritage but also directly affects historical research and social education. However, traditional cultural relic protection methods rely on physical protection means such as temperature and humidity control, fire and theft prevention, and physical sealing. Although these methods can temporarily and effectively prevent cultural relics from being damaged, they cannot completely avoid the threats brought by environmental changes, the passage of time, or human factors. In addition, these traditional cultural relic protection methods also have many limitations in the dissemination and display of cultural relics. Especially under the constraints of single display forms and the inability to cross time and space, it is difficult for the public to comprehensively understand the historical and artistic values of cultural relics.

[0003] With the rise of digital technology, digital protection of cultural relics has become a new solution. Through modern information acquisition, storage, management, and display technologies, digital cultural relics can, to a certain extent, overcome the limitations of traditional cultural relic protection means, maximize the retention of the historical information and artistic value of cultural relics, and avoid damage to cultural relics caused by environmental factors. At the same time, digital cultural relics also provide a wider range of channels for the dissemination of cultural relics. Especially with the support of the Internet, users around the world can view and learn at any time.

[0004] However, the existing cultural relic digital resource sharing methods have the following technical problems: inaccurate processing of cultural relic data, slow retrieval speed, and low data security in the sharing of cultural relic digital resources. Summary of the Invention

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

[0006] A cultural relic digital resource sharing system and method based on a cloud platform of the present invention specifically includes the following technical solutions: A cultural relic digital resource sharing method based on a cloud platform includes the following steps: S1. Collect and preprocess cultural relic data to obtain preprocessed cultural relic data; perform digital conversion processing on the preprocessed cultural relic data to obtain digital cultural relic data, store the digital cultural relic data and cultural relic metadata, assign a unique identifier to each cultural relic, and at the same time constitute index information according to the digital cultural relic data and cultural relic metadata; S2. After the storage procedure ends, when a user sends an access request, perform security authentication for each user. When the security authentication passes and the user has access rights, according to the user's access request, combined with the index information, use the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization to find the qualified digital cultural relic data, and encrypt the qualified digital cultural relic data to obtain the encrypted digital cultural relic data.

[0007] Preferably, the S1 specifically includes: Extract features from the digital cultural relic data and the cultural relic metadata generated during the collection and digital transformation of cultural relic data, extract features of different dimensions, and form the index information of the digital cultural relic data with the features of different dimensions, and establish an index based on the index information.

[0008] Preferably, the S2 specifically includes: When the user sends an access request, check whether there is a permission setting for the cultural relic data. When the cultural relic has restricted access, check whether the user role is allowed to access this cultural relic. When access is allowed, continue with the subsequent processing. When access is not allowed, return no permission to the user.

[0009] Preferably, the S2 specifically includes: During the implementation process of the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization, embed the multi-dimensional features of the cultural relics into a multi-dimensional vector space. Through the embedding process, represent the features of each cultural relic with high-dimensional feature vectors.

[0010] Preferably, the S2 specifically includes: During the implementation process of the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization, introduce an intelligent matching optimization model to perform similarity matching on the keywords in the access request, calculate the maximum matching degree score, and adjust the maximum matching degree score as the recursion depth increases.

[0011] Preferably, the S2 specifically includes: During the implementation process of the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization, calculate the final matching degree score based on the maximum matching degree score, and obtain the set of final matching degree scores between the access request and all cultural relics. Select the maximum matching degree score as the qualified cultural relic, so as to obtain the qualified digital cultural relic data.

[0012] Preferably, the S2 specifically includes: The digital cultural relics data that meet the conditions are encrypted using a multi - dimensional and multi - phase variable encryption algorithm. During the encryption process, a set of encryption keys and perturbation factors are initialized; the digital cultural relics data that meet the conditions are weighted by generating a key matrix, and the perturbation factors are introduced to perform preliminary perturbation on the digital cultural relics data that meet the conditions.

[0013] Preferably, S2 specifically includes: During the implementation of the multi - dimensional and multi - phase variable encryption algorithm, a non - linear perturbation function is introduced, combining exponential functions, logarithmic functions, and cosine functions to perform perturbation processing on the digital cultural relics data that meet the conditions, obtaining data after non - linear perturbation processing; a multi - dimensional space transformation operation is introduced, and the data after non - linear perturbation processing is subjected to space transformation through a rotation matrix and a scaling factor to obtain data after multi - dimensional space transformation.

[0014] Preferably, S2 specifically includes: During the implementation of the multi - dimensional and multi - phase variable encryption algorithm, a periodic perturbation mechanism is introduced, using sine waves and cosine waves to perform periodic perturbation on the data after multi - dimensional space transformation to obtain encrypted digital cultural relics data, thereby generating a decryption key, and returning the encrypted digital cultural relics data and the decryption key to the user.

[0015] A cultural relic digital resource sharing system based on a cloud platform includes the following parts: A cultural relic data digitization module, a data storage and management module, a permission management and user authentication module, a data sharing and access module, a data retrieval and query module, and a data encryption module; The cultural relic data digitization module collects cultural relic data, pre - processes the cultural relic data to obtain pre - processed cultural relic data; performs digital conversion processing on the pre - processed cultural relic data to obtain digital cultural relics data, and transmits the digital cultural relics data and the cultural relic metadata generated during the collection and conversion of cultural relic data to the data storage and management module; The data storage and management module stores the digital cultural relics data and its related cultural relic metadata in the distributed storage system of the cloud platform, assigns a unique identifier to each cultural relic, and at the same time forms index information based on the digital cultural relics data and the cultural relic metadata, and establishes an index based on the index information, and transmits the formed index information to the data sharing and access module and the data retrieval and query module; The permission management and user authentication module, after the storage process is completed, when a user sends an access request, performs security authentication on each user, and sends the access request that passes the security authentication to the data sharing and access module, and directly returns a refusal to access to the user for the request that does not pass the security authentication; 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 passes. Through this interface, users can access digital cultural relics data across platforms, and the corresponding digital cultural relics data will be returned according to the permissions during access. The standardized API interface is transmitted to the data retrieval and query module; The data retrieval and query module, when the access request security authentication passes, based on the standardized API interface provided by the data sharing and access module, according to the user's access request, combines the index information obtained from the data storage and management module, searches for digital cultural relics data that meets the conditions, and transmits the digital cultural relics data that meets the conditions to the data encryption module for encryption processing to obtain the encrypted digital cultural relics data, and returns the encrypted digital cultural relics data and the corresponding decryption key to the user; The data encryption module encrypts the digital cultural relics data that meets the conditions to obtain the encrypted digital cultural relics data, and at the same time generates the corresponding decryption key, and feeds back the encrypted digital cultural relics data and the corresponding decryption key to the data retrieval and query module.

[0016] The beneficial effects of the technical solution of the present invention are: 1. The digital cultural relics data that meets the conditions is searched through a cultural relics data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization. This algorithm maps the cultural relics features into a high-dimensional vector space, dynamically adjusts the importance of each cultural relics feature, and combines an intelligent matching optimization model to improve the efficiency and accuracy of cultural relics retrieval.

[0017] 2. The multi-dimensional multi-phase variable encryption algorithm is used to encrypt the digital cultural relics data that meets the conditions. First, the digital cultural relics data that meets the conditions is represented in the form of a data vector. By randomly generating a key matrix and a perturbation factor, the digital cultural relics data that meets the conditions is initially perturbed to enhance the complexity of encryption. By introducing a non-linear perturbation function, the digital cultural relics data that meets the conditions is further perturbed to make the encryption result more complex. Description of the Drawings

[0018] Figure 1 It is a structure diagram of a cultural relic digital resource sharing system based on a cloud platform according to the present invention; Figure 2 It is a flowchart of a cultural relic digital resource sharing method based on a cloud platform according to the present invention. Detailed Embodiments

[0019] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0021] The following specifically describes the specific solutions of a cloud platform-based cultural relic digital resource sharing system and method provided by the present invention in conjunction with the accompanying drawings.

[0022] Refer to the attached Figure 1 , which shows the structural diagram of a cloud platform-based cultural relic digital resource sharing system provided by an embodiment of the present invention. The system includes the following parts: Cultural relic data digitization module, data storage and management module, permission 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 through data acquisition devices such as 3D scanners and photographic devices. The cultural relic data includes: image data, audio data, video data, 3D scan data, and literature description data. The cultural relic data is preprocessed to obtain preprocessed cultural relic data; the preprocessed cultural relic data is digitally transformed to obtain digital cultural relic data, and the digital cultural relic data and the cultural relic metadata generated during the acquisition and transformation of the cultural relic data are transmitted to the data storage and management module. The cultural relic metadata includes: cultural relic name, description, historical information, artistic style, age, region, etc.; The data storage and management module stores the digital 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, and at the same time constitutes index information based on the digital cultural relic data and the cultural relic metadata, and establishes an index based on the index information, and transmits the constituted index information to the data sharing and access module and the data retrieval and query module to ensure subsequent searching and access; The permission management and user authentication module, after the storage process is completed, when a user sends an access request, performs security authentication on each user, and sends the access request that passes the security authentication to the data sharing and access module. The request that does not pass the security authentication directly returns a denied access to the user; The data sharing and access module provides a standardized API interface after the security authentication of the access request from the permission management module and the user authentication module is passed. Through this interface, users can access digital cultural relics data across platforms, and the corresponding digital cultural relics data will be returned according to the permissions during access. The standardized API interface is transmitted to the data retrieval and query module; The data retrieval and query module, when the security authentication of the access request is passed, based on the standardized API interface provided by the data sharing and access module, according to the user's access request, combines the index information obtained from the data storage and management module, searches for the digital cultural relics data that meets the conditions, and transmits the digital cultural relics data that meets the conditions to the data encryption module for encryption processing to obtain the encrypted digital cultural relics data, and returns the encrypted digital cultural relics data and the corresponding decryption key to the user to ensure efficient and accurate query and realize resource sharing; The data encryption module encrypts the digital cultural relics data that meets the conditions to obtain the encrypted digital cultural relics data, generates the corresponding decryption key at the same time, and feeds back the encrypted digital cultural relics data and the corresponding decryption key to the data retrieval and query module; Refer to the appendix Figure 2 , which shows a flowchart of a method for sharing digital cultural relics resources based on a cloud platform provided by an embodiment of the present invention. The method includes the following steps: S1. Collect and preprocess the cultural relics data to obtain the preprocessed cultural relics data; perform digital conversion processing on the preprocessed cultural relics data to obtain digital cultural relics data, store the digital cultural relics data and the cultural relics metadata, assign a unique identifier to each cultural relic, and at the same time constitute index information according to the digital cultural relics data and the cultural relics metadata; Collect cultural relic data through data collection devices such as 3D scanners, photographic equipment, and recording equipment, including image data, audio data, video data, 3D scan data, and literature description data; preprocess the collected cultural relic data, and the preprocessing includes: using preprocessing methods such as denoising filtering, sharpening, and contrast adjustment for image data for denoising, optimization, format conversion, etc.; preprocessing methods for video data such as video editing and cropping, format conversion and compression; preprocessing methods for 3D scan data such as point cloud cleaning, meshing and refinement, optimization and simplification; preprocessing methods for literature description data such as text cleaning and standardization, to obtain preprocessed cultural relic data. The above preprocessing processes all adopt existing technical means and will not be elaborated here. After the cultural relic data is preprocessed, perform digital conversion processing on the preprocessed cultural relic data, that is, convert it into a structured digital format to obtain digital cultural relic data, including: converting 3D scan data into standard 3D file formats such as.obj,.ply,.stl, etc.; converting image data into common Web standard formats such as.jpg,.png, etc.; converting audio and video data into formats that support streaming transmission such as.mp3,.mp4, etc.; converting literature description data into structured metadata formats such as.xml,.json, etc.; at the same time, obtain the cultural relic metadata generated when collecting and converting cultural relic data, and the cultural relic metadata includes: cultural relic name, description, historical information, artistic style, age, region, etc.

[0023] Furthermore, store the digital cultural relic data and cultural relic metadata in an existing distributed storage system (such as Hadoop HDFS). Specifically, store the digital data of cultural relics (such as image data, video data, 3D scan data, etc.) as object data, and the data storage unit of each cultural relic can be an independent file; store the metadata of cultural relics (such as cultural relic name, description, historical information, artistic style, age, region, etc.) as structured data. Formats such as JSON, XML, and RDF can be used to ensure the readability and structuring of cultural relic metadata; further use existing strategies such as UUID (Universally Unique Identifier), combination of auto-increment ID and prefix, and combination ID based on cultural relic characteristics 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 techniques in the digital cultural relic data and cultural relic metadata to extract features in different dimensions, form the index information of the digital cultural relic data, and establish an index based on the index information, including: image data index, video data index, 3D scan data index, text data index, text metadata index, structured metadata index; the establishment of the above index information all adopts existing technical means such as content-based image retrieval, index establishment based on model-based geometric features (such as feature vectors of meshes and point clouds), text search engines, and field-based indexes, and will not be elaborated here.

[0024] S2. After the storage process ends, when a user sends an access request, perform security authentication for each user. When the security authentication passes and the user has access rights, according to the user's access request, combined with the index information, use the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization to find the qualified digital cultural relic data, and encrypt the qualified digital cultural relic data to obtain the encrypted digital cultural relic data.

[0025] After the storage process ends, when a user sends an access request, use existing authentication methods such as username / password verification, OAuth authentication, and API Token authentication to authenticate the user's identity. If the user identity authentication passes, continue to process the access request. If the user identity authentication fails, return a denied access to the user.

[0026] When a user sends an access request, check whether there are special permission settings for the cultural relic data. If the cultural relic data has restricted access settings, further check whether the user role is allowed to access this cultural relic. If access is allowed, continue with subsequent processing. If access is not allowed, return no permission to the user.

[0027] When the user's security authentication passes and the user has access rights, according to the user's access request, combined with the index information, use the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization to find the qualified digital cultural relic data. The cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization maps the cultural relic features used as index information to a high-dimensional space, dynamically adjusts the importance of different cultural relic features, and improves the retrieval efficiency and accuracy of cultural relic data through intelligent matching optimization. The specific implementation process is as follows: First, embed the multi-dimensional features of the cultural relics into a multi-dimensional vector space. The specific formula is as follows: , where, is the embedded high-dimensional feature vector of the th cultural relic, which contains various information of the cultural relic, such as age, region, artistic style, etc., and can more effectively capture the similarity between cultural relics in the high-dimensional space; is the feature vector of the th cultural relic, that is, the index information of the th cultural relic; is the weight of the cultural relic feature , which is used to adjust the importance of this feature in the embedded high-dimensional feature vector. It is determined according to the expert experience method, and the reference value range is ; is the the th feature of the cultural relic; is the optimization parameter when performing exponential transformation on the cultural relic feature, used to control the influence of this feature in the embedded high-dimensional feature vector, obtained through experimental fitting, and the reference value range is ; ; is the triangular transformation parameter, used to control the periodicity of the cultural relic feature after transformation, determined according to specific requirements, such as calculating based on the feature change period (such as the periodicity of dynasty change), and the reference value range is ; is the power exponent, a parameter used to control the scaling of the entire embedded high-dimensional feature vector, determined according to specific scenarios, and the reference value is , such as 1 or 2; Through the above embedding process, the features of each cultural relic will be represented by a high-dimensional feature vector, and this representation can more precisely capture the intrinsic attributes of the cultural relic.

[0028] 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. An intelligent matching optimization model is introduced, aiming to achieve intelligent matching by calculating the maximum matching degree score. The intelligent matching optimization model measures the similarity between the keywords in the access request and the cultural relic features through the maximum matching degree score calculation function and adjusts the maximum matching degree score as the recursion depth increases to ensure that each level of optimization can more precisely optimize the maximum matching degree score. The specific form is: , where is the maximum matching degree score between the th cultural relic and the th access request in the th level. The larger this value is, the more closely the cultural relic matches the query condition; is the weight coefficient, used to adjust the relative influence of the calculation result of the previous level, which can be determined by existing heuristic rules, aiming to balance the influence of the recursion level, and the reference value is ; is the maximum matching degree score between the th cultural relic and the th access request in the th level; is the th access request feature vector; is the weight coefficient, used to adjust the relative influence of the calculation result of the current level, which can be determined by existing heuristic rules, aiming to balance the influence of the recursion level, and the reference value is ; is the transpose; is the inner product of two eigenvectors, representing the similarity between the cultural relic and the query condition. The larger the inner product, the more relevant the cultural relic is to the query condition; is the modulo operation; is for the th cultural relic to perform modulo operation on the embedded high-dimensional eigenvector; is for the th access request eigenvector to perform modulo operation; is the attenuation factor, used to control the influence rate of the recursion depth on the maximum matching degree score. The reference value is ; is the level of recursion, representing the level of recursion in the current query process; is the exponential function, used to control the influence of the recursion level on the maximum matching degree score; is the logarithmic function, used to balance the influence of the growth of the recursion depth on the final matching result.

[0029] Furthermore, based on the existing recursive optimization algorithm, calculate the final matching degree score. The calculation formula is as follows: , where, is the final matching degree score between the th cultural relic and the th access request; is the maximum recursion depth level, determined according to the specific scenario; is the weighting factor, which gradually decreases as the recursion level increases, ensuring that the higher recursion levels have less influence on the final matching degree score. The value range is .

[0030] Furthermore, obtain the set of the final matching degree scores between the access request and all cultural relics, select the largest matching degree score from it as the qualified cultural relic, and extract the digital cultural relic data corresponding to the qualified cultural relic through the existing standardized API interface to obtain the qualified digital cultural relic data; and encrypt the qualified digital cultural relic data using the multi-dimensional multi-phase variable encryption algorithm to obtain the encrypted digital cultural relic data. The specific implementation process is as follows: First, represent the qualified digital cultural relic data in the form of a data vector , and this data vector contains multiple digital cultural relic data items. Assume this data vector contains digital cultural relic data items, that is, , with the dimension of , where each A digital cultural relic data item corresponding to a cultural relic, such as age, species, description, etc.

[0031] Furthermore, in the encryption process, a group of encryption keys and perturbation factors are first initialized. To enhance the security of the multi-dimensional and multi-phase variable encryption algorithm, by generating a random key matrix to achieve the weighting of the eligible digital cultural relic data. The dimension of this key matrix is , the values in the key matrix are randomly generated and will change with the steps during the encryption process. In addition, a perturbation factor is introduced, which is a vector used to perform preliminary perturbation on the eligible digital cultural relic data, thereby increasing the randomness and complexity of encryption. The purpose of these initialization settings is to provide a randomized benchmark for subsequent steps, making the encryption result more difficult to predict.

[0032] , In the above process, the introduction of the perturbation factor greatly enhances the preliminary perturbation effect of the eligible digital cultural relic data. Each of its elements is randomly generated, ensuring the unpredictability during the encryption process, such as randomly generated using uniform distribution or Gaussian distribution. In the subsequent encryption process, the interaction between the perturbation factor and the eligible digital cultural relic data will have a crucial impact on the subsequent encryption effect.

[0033] To make the encryption process more non-linear and able to form a high-complexity encryption result, further, based on the multiple non-linear function composite mapping mechanism and the random perturbation superposition model, a non-linear perturbation function is introduced. The design of this function combines exponential function, logarithmic function and cosine function, and through the combination of these functions, multiple perturbations are performed on the eligible digital cultural relic data to obtain the data after non-linear perturbation processing. The output of the non-linear perturbation function is the data after non-linear perturbation processing, and it is represented as a new data vector , and its form is as follows: , where, performs an exponential operation on each eligible digital cultural relic data, achieving the effect of data compression; then performs smoothing processing on each eligible digital cultural relic data to avoid extreme values of the data; It is the introduction of periodic perturbations to each qualified digital cultural relic data, which is used to increase the complex periodic changes of the data, making the perturbation degree of each data random and enhancing the irreversibility of encryption.

[0034] Furthermore, a multi-dimensional space transformation operation is introduced. Through the rotation matrix and the scaling factor perform space transformation on the data after non-linear perturbation processing. The rotation matrix transforms the spatial distribution of the data by introducing an orthogonal matrix, satisfying , where is the identity matrix; while the scaling factor weights by changing the amplitude of the data after non-linear perturbation processing. Through the multi-dimensional space transformation operation, each qualified digital cultural relic data will be mapped from the original space to a new space, thus effectively increasing the complexity of the data and the difficulty of encryption.

[0035] The formula for multi-dimensional space transformation is: , where is the data after multi-dimensional space transformation.

[0036] Furthermore, in order to improve the complexity of the encryption process, a periodic perturbation mechanism is introduced. The periodic perturbation mechanism uses sine waves and cosine waves to perform periodic perturbations on the data after multi-dimensional space transformation, thus forming more complex encryption characteristics to obtain the encrypted digital cultural relic data: , where is the encrypted digital cultural relic data; is the periodic perturbation factor, which is used to control the frequency of perturbation and is determined according to specific requirements. The reference value is .

[0037] Furthermore, the decryption key is generated through the reverse operation of the above multi-dimensional multi-phase variable encryption algorithm, and the encrypted digital cultural relic data and the decryption key are returned to the user to realize the sharing of digital cultural relic resources.

[0038] In summary, a system and method for sharing digital cultural relic resources based on a cloud platform are completed.

[0039] The sequence order of the invention embodiments is only for description and does not represent the advantages or disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0040] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0041] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for sharing digital cultural relic resources based on a cloud platform, characterized in that, It includes the following steps: S1. Collect and preprocess cultural relic data to obtain preprocessed cultural relic data; Perform digital conversion processing on the preprocessed cultural relic data to obtain digital cultural relic data, store the digital cultural relic data and cultural relic metadata, assign a unique identifier to each cultural relic, and at the same time constitute index information according to the digital cultural relic data and cultural relic metadata; S2. After the storage process ends, when a user sends an access request, perform security authentication on each user. When the security authentication is passed and the user has access rights, according to the user's access request, combined with the index information, use the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization to find the digital cultural relic data that meets the conditions, and encrypt the digital cultural relic data that meets the conditions to obtain the encrypted digital cultural relic data.

2. The method for sharing digital cultural relic resources based on a cloud platform according to claim 1, characterized in that The S1 specifically includes: Extract features from the digital cultural relic data and the cultural relic metadata generated during the collection and digital conversion of cultural relic data, extract features of different dimensions, and constitute the index information of the digital cultural relic data with the features of different dimensions, and establish an index based on the index information.

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

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

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

6. The method for sharing digital cultural relic resources based on a cloud platform according to claim 5, characterized in that, The S2 specifically includes: In the implementation process of the cultural relic data retrieval algorithm based on multi-dimensional dynamic embedded feature learning and intelligent matching optimization, calculate the final matching degree score based on the maximum matching degree score, and obtain the final matching degree score set between the access request and all cultural relics, select the maximum matching degree score as the cultural relic that meets the conditions, so as to obtain the digital cultural relic data that meets the conditions.

7. A method for sharing digital cultural relic resources based on a cloud platform according to claim 6, characterized in that The S2 specifically includes: Use the multi-dimensional multi-phase variable encryption algorithm to encrypt the digital cultural relic data that meets the conditions. During the encryption process, initialize a set of encryption keys and perturbation factors; weight the digital cultural relic data that meets the conditions by generating a key matrix, introduce the perturbation factors, and perform preliminary perturbation on the digital cultural relic data that meets the conditions.

8. A method for sharing digital cultural relic resources based on a cloud platform according to claim 7, characterized in that The S2 specifically includes: In the implementation process of the multi-dimensional and multi-phase variable encryption algorithm, a non-linear perturbation function is introduced, which combines exponential functions, logarithmic functions, and cosine functions to perform perturbation processing on the digital cultural relics data that meets the conditions, obtaining the data after non-linear perturbation processing; a multi-dimensional space transformation operation is introduced, and the data after non-linear perturbation processing is subjected to space transformation through a rotation matrix and a scaling factor, obtaining the data after multi-dimensional space transformation.

9. A method for sharing digital cultural relic resources based on a cloud platform according to claim 8, characterized in that, The S2 specifically includes: In the implementation process of the multi-dimensional and multi-phase variable encryption algorithm, a periodic perturbation mechanism is introduced, using sine waves and cosine waves to perform periodic perturbation on the data after multi-dimensional space transformation, obtaining the encrypted digital cultural relics data, thereby generating a decryption key, and returning the encrypted digital cultural relics data and the decryption key to the user.

10. A cultural relic digital resource sharing system based on a cloud platform, which is applied to a cultural relic digital resource sharing method based on a cloud platform described in claim 1, and is characterized in that, It includes the following parts: Cultural relics data digitization module, data storage and management module, permission management and user authentication module, data sharing and access module, data retrieval and query module, data encryption module; The cultural relics data digitization module collects cultural relics data, preprocesses the cultural relics data, and obtains the preprocessed cultural relics data; Performs digital conversion processing on the preprocessed cultural relics data to obtain digital cultural relics data, and transmits the digital cultural relics data and the cultural relics metadata generated during the collection and conversion of cultural relics data to the data storage and management module; The data storage and management module stores the digital cultural relics data and its related cultural relics metadata in the distributed storage system of the cloud platform, assigns a unique identifier to each cultural relic, simultaneously constructs index information based on the digital cultural relics data and cultural relics metadata, and establishes 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, after the storage process ends, when a user sends an access request, performs security authentication on each user, and sends the access request that passes the security authentication to the data sharing and access module, and directly returns a denied access to the user for the request that fails the security authentication; The data sharing and access module, after the access request security authentication of the permission management module and the user authentication module passes, provides a standardized API interface. Through this interface, users can access digital cultural relics data across platforms, and the corresponding digital cultural relics data will be returned according to the permissions during access, and transmits the standardized API interface to the data retrieval and query module; The data retrieval and query module, when the access request security authentication passes, based on the standardized API interface provided by the data sharing and access module, according to the user's access request, combines the index information obtained from the data storage and management module, searches for the digital cultural relics data that meets the conditions, and transmits the digital cultural relics data that meets the conditions to the data encryption module for encryption processing, obtaining the encrypted digital cultural relics data, and returns the encrypted digital cultural relics data and the corresponding decryption key to the user; The data encryption module encrypts the eligible digital cultural relics data to obtain the encrypted digital cultural relics data, generates the corresponding decryption key at the same time, and feeds back the encrypted digital cultural relics data and the corresponding decryption key to the data retrieval and query module.

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