A file management system and method based on blockchain technology

Abstract

The present invention provides an archive management system and method based on blockchain technology, which relates to the technical field of data storage management. The archive management system based on blockchain technology includes: an archive data collection unit, an edge importance analysis unit, an archive data encryption unit and an archive data storage unit, and the archive data collection unit, the edge importance analysis unit, the archive data encryption unit and the archive data storage unit are connected in sequence. The present invention performs importance analysis on archive digitized data through the mean shift method, which helps to prioritize and store more important data, improves the efficiency of data management, and makes the system have broad application prospects and important practical application value in the field of archive management.

Description

Technical Field

[0001] The present invention relates to the technical field of data storage management, and in particular to an archive management system and method based on blockchain technology. Background Art

[0002] Blockchain technology integrates a variety of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism and encryption algorithm to form an innovative application technology platform. This technology realizes the decentralization and trustlessness of data storage and management by building a multi-node collaborative end-to-end network. In this structure, each participating node holds a complete copy of the data, which enhances the transparency and security of the data, and also greatly reduces the risk of data tampering. Especially in the field of medical research, the accuracy, traceability and confidentiality of data are particularly critical. Using blockchain technology for archival management of medical research data can not only ensure the security of data during collection, storage and transmission, but also automate the data processing process through smart contracts, thereby improving efficiency and reducing the possibility of human error. Every change or access to data leaves an unalterable record on the blockchain, so that the source and modification history of the data can be tracked and verified, which is crucial for scientific research integrity and compliance.

[0003] Currently, in traditional data storage systems, data is usually stored in a single server or data center. This centralized storage model is vulnerable to physical or network attacks, resulting in a higher risk of data leakage, tampering or loss. In addition, data is not encrypted during transmission or encryption measures are insufficient, resulting in insufficient protection of sensitive information.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of this, in view of the problems in the related art, the present invention provides a file management system and method based on blockchain technology to solve the above-mentioned problems.

[0006] In order to solve the above problems, the specific technical solutions adopted by the present invention are as follows:

[0007] According to one aspect of the present invention, there is provided an archive management system based on blockchain technology. The archive management system based on blockchain technology comprises: an archive data collection unit, an edge importance analysis unit, an archive data encryption unit and an archive data storage unit, and the archive data collection unit, the edge importance analysis unit, the archive data encryption unit and the archive data storage unit are connected in sequence;

[0008] The archive data collection unit is used to collect archive data and digitize the archive data to obtain archive digitized data;

[0009] The edge importance analysis unit is used to perform importance analysis on the digital archive data based on the mean shift method to obtain the importance analysis result;

[0010] The archive data encryption unit is used to encrypt the archive digital data with importance by using the lattice additive homomorphic encryption algorithm according to the importance analysis result to obtain encrypted data;

[0011] An archive data storage unit, used for storing encrypted data and unencrypted archive digitized data based on blockchain technology;

[0012] Preferably, the edge importance analysis unit comprises: a data preprocessing module, a cluster analysis module and an importance evaluation module, and the data preprocessing module, the cluster analysis module and the importance evaluation module are connected in sequence;

[0013] The data preprocessing module is used to preprocess the digital data of the archives to obtain standardized data. The preprocessing includes data cleaning and data standardization.

[0014] A cluster analysis module is used to perform cluster analysis on standardized data using the mean shift method to obtain cluster analysis results;

[0015] The importance assessment module is used to perform importance analysis on each cluster analysis result, identify importance factors and evaluate the importance level of the archive digitized data based on the importance factor identification results.

[0016] Preferably, performing cluster analysis on the standardized data using the mean shift method to obtain the cluster analysis results comprises the following steps:

[0017] Step 1: Use multivariate normal distribution as kernel function and set bandwidth parameters;

[0018] Step 2: randomly select an archive data point in the standardized data as the initial center point, and calculate the average displacement of all data points within a preset radius of the initial center point;

[0019] Step 3: update the position of the center point according to the obtained average displacement, and repeat steps 2 to 3 until a preset number of iterations is reached to obtain the cluster center;

[0020] Step 4: According to the obtained cluster center, each archive data point is assigned to the nearest cluster center to obtain the cluster analysis result.

[0021] Preferably, randomly selecting an archive data point in the standardized data as the initial center point, and calculating the average displacement of all data points within a preset radius of the initial center point includes:

[0022] Based on the selected initial center point, the search area is determined by a pre-set radius;

[0023] The kernel density estimation method is used to calculate the density estimate of each archival data point in the normalized data within the search area;

[0024] Based on the density estimate of all data points in the search area, the average displacement of the initial center point in all data points in the search area is calculated.

[0025] Preferably, the density estimation formula for each archival data point in the standardized data within the search area is calculated using the kernel density estimation method:

[0026] ;

[0027] In the formula, f ( d ) indicates an archive data point d The density estimate of ;

[0028] m Represents the total number of data points;

[0029] p n k Indicates n The kernel radius of the data points;

[0030] k Represents the data space dimension;

[0031] represents the kernel function;

[0032] Represents archive data points d With archive data points d n The normalized distance between .

[0033] Preferably, according to the density estimation value of all data points in the search area, the calculation formula for calculating the average displacement of the initial center point in all data points in the search area is:

[0034] ;

[0035] In the formula, M ( c ) represents the initial center point c The average displacement of all data points in the search area;

[0036] f ( d ) indicates an archive data point d The density estimate of ;

[0037] S Indicates the initial center point c At the preset radius R The set of all data points in the search area.

[0038] Preferably, performing importance analysis on each cluster analysis result, identifying importance factors and evaluating the importance level of the archive digitized data according to the importance factor identification results include:

[0039] Conduct attribute analysis on the archival digitized data in each cluster analysis result;

[0040] Using the FAST method, characteristic frequencies are assigned to the attributes of the archive digitized data in each cluster and a conversion function is constructed;

[0041] The constructed conversion function is used to convert the digital data attributes of archives into one-dimensional variables, and the first-order sensitivity index of each digital data attribute of archives is calculated by Fourier coefficients;

[0042] The archival digitized data in each cluster were rated in importance based on the first-order sensitivity index.

[0043] Preferably, the archive data encryption unit comprises: an encrypted data selection module, a key generation module, and a data encryption module, and the encrypted data selection module, the key generation module, and the data encryption module are connected in sequence;

[0044] The encrypted data selection module is used to select the digitalized archive data that meets the rating threshold through a preset rating threshold;

[0045] A key generation module, used to generate a key based on a trapdoor generation algorithm, the key including a public key and a private key;

[0046] The data encryption module is used to encrypt the digitalized data of the archives that meet the rating threshold through the public key in the key to obtain encrypted data.

[0047] Preferably, generating a key matrix based on a trapdoor generation algorithm includes:

[0048] A safety factor is set based on the storage requirements of digital archive data, and odd and prime numbers are determined based on the safety factor;

[0049] Determine the encryption parameters of the trapdoor generation algorithm based on the security factor, odd prime numbers, and prime numbers;

[0050] A trapdoor generation algorithm is used to generate a random matrix and a trapdoor matrix, and the random matrix and the trapdoor matrix are used as a public key and a private key, respectively.

[0051] Preferably, the digitalized data of the archives that meet the rating threshold is encrypted by the public key in the key, and the encrypted data obtained includes:

[0052] Perform data encoding processing on the digitalized archive data that meets the rating threshold to obtain a set of encoding vectors;

[0053] A uniformly distributed vector is randomly selected from the encoding vector set, and the randomly selected vector is encrypted based on the public key to obtain encrypted data.

[0054] According to another aspect of the present invention, a file management method based on blockchain technology is provided, and the file management method based on blockchain technology comprises the following steps:

[0055] S1. Collect archival data and digitize the archival data to obtain digital archival data;

[0056] S2. Conduct importance analysis on digital archive data based on the mean shift method to obtain importance analysis results;

[0057] S3. Based on the importance analysis results, the lattice additive homomorphic encryption algorithm is used to encrypt the digital data of the important archives to obtain encrypted data;

[0058] S4. Based on blockchain technology, encrypted data and unencrypted archival digital data are stored.

[0059] Compared with the prior art, the present invention provides an archive management system and method based on blockchain technology, which has the following beneficial effects:

[0060] (1) The present invention uses a lattice additive homomorphic encryption algorithm to encrypt important digital archival data, thereby ensuring the confidentiality of the data and preventing unauthorized access and leakage. The distributed storage and non-tamperability characteristics of blockchain technology are used to further enhance the security of the data, making the archival data more secure and reliable during storage and transmission. The importance of digital archival data is analyzed by the mean shift method, which helps to prioritize the processing and storage of more important data, thereby improving the efficiency of data management. This system has broad application prospects and important practical application value in the field of archival management.

[0061] (2) The present invention cleans and standardizes the digitalized archival data, thereby improving the quality of the data. The mean shift method is used to perform cluster analysis on the standardized data, which can cluster similar archival data together, helping to discover patterns and regularities in the data. Importance analysis is performed on each cluster, importance factors are identified, and the importance level of the digitalized archival data is evaluated based on these factors, thereby achieving refined classification and management of the data. The multivariate normal distribution is used as the kernel function and the bandwidth parameter is set, so that the mean shift method is more adapted to the characteristics of the archival data and the accuracy of cluster analysis is improved. The first-order sensitivity index of each attribute of the digitalized archival data is calculated using the FAST method and Fourier coefficients, which can accurately evaluate the importance of each attribute and provide a scientific basis for importance rating.

[0062] (3) The present invention selects the digitalized archival data that needs to be encrypted through a pre-set rating threshold, ensuring that only data with an importance level reaching a certain level will be encrypted, thereby improving the security of the data. The public key and private key are generated by a trapdoor generation algorithm, and the selected data is encrypted with the public key, ensuring the confidentiality of the data and preventing unauthorized access and leakage. This improves the security, flexibility, efficiency of key management and data integrity of the archival data, and provides a strong data security guarantee for the archive management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0064] Figure 1 This is a principle block diagram of an archive management system based on blockchain technology according to an embodiment of the present invention;

[0065] Figure 2 is a flowchart of a file management method based on blockchain technology according to an embodiment of the present invention.

[0066] In the figure:

[0067] 1. Archive data collection unit; 2. Edge importance analysis unit; 3. Archive data encryption unit; 4. Archive data storage unit. DETAILED DESCRIPTION

[0068] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0069] According to an embodiment of the present invention, a file management system and method based on blockchain technology are provided.

[0070] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, there is provided an archive management system and method based on blockchain technology, the archive management system based on blockchain technology comprises: an archive data collection unit 1, an edge importance analysis unit 2, an archive data encryption unit 3 and an archive data storage unit 4, and the archive data collection unit 1, the edge importance analysis unit 2, the archive data encryption unit 3 and the archive data storage unit 4 are connected in sequence;

[0071] The archive data collection unit 1 is used to collect archive data and digitize the archive data to obtain archive digitized data;

[0072] It should be noted that archival data is data related to medical research. The collection and digitization of archival data related to medical research is a key process, including the following steps:

[0073] Collect raw data from medical and scientific research institutions such as hospitals and laboratories. These data may include medical records, experimental results, clinical trial data, etc.

[0074] Converting paper records to digital format involves scanning the documents and using optical character recognition technology to convert them into an editable and searchable format.

[0075] The edge importance analysis unit 2 is used to perform importance analysis on the archive digitized data based on the mean shift method to obtain the importance analysis result;

[0076] As a preferred embodiment, the edge importance analysis unit 2 includes: a data preprocessing module, a cluster analysis module and an importance evaluation module, and the data preprocessing module, the cluster analysis module and the importance evaluation module are connected in sequence;

[0077] The data preprocessing module is used to preprocess the digital data of the archives to obtain standardized data. The preprocessing includes data cleaning and data standardization.

[0078] It should be noted that preprocessing the digital data of archives to obtain standardized data will help improve data quality and lay the foundation for subsequent data analysis and utilization. The preprocessing process mainly includes two aspects: data cleaning and data standardization. Data cleaning is the primary task of preprocessing, which aims to remove noise, errors and inconsistencies in the data to make the data more accurate, complete and reliable. Data standardization is the process of converting cleaned data into a standard format or standard range for unified analysis and utilization. In the field of medical research, data standardization includes:

[0079] Coding standardization: Unify the coding of medical terms, disease names, drug names, etc. to facilitate data transmission and sharing between different systems. This helps reduce misunderstandings and errors caused by inconsistent coding.

[0080] Numerical standardization: For continuous numerical data (such as blood pressure, blood sugar levels, etc.), standardization processing (such as Z-score standardization, Min-Max standardization, etc.) may be required to eliminate dimensional differences and scale effects and make different variables comparable.

[0081] Classification standardization: For categorical variables (such as gender, blood type, etc.), classification standards and coding rules need to be clarified to ensure the accuracy and consistency of the data.

[0082] Time standardization: For data involving time (such as consultation time, treatment time, etc.), it is necessary to unify the time format and time zone settings to facilitate time series analysis or comparison across time points.

[0083] A cluster analysis module is used to perform cluster analysis on standardized data using the mean shift method to obtain cluster analysis results;

[0084] As a preferred implementation, cluster analysis is performed on the standardized data using the mean shift method to obtain cluster analysis results, including the following steps:

[0085] Step 1: Use multivariate normal distribution as kernel function and set bandwidth parameters;

[0086] It should be noted that the kernel function is used to estimate the density of sample points around a given point. Multivariate normal distribution (also known as Gaussian distribution) as a kernel function can well describe the distribution of data points in multidimensional space. The bandwidth parameter is set to control the scope of the kernel function, thereby affecting the results of clustering.

[0087] When setting the bandwidth parameter, you need to consider the density and distribution range of the data. If the bandwidth parameter is set too large, the kernel function will have a wide range of action, which may cause different clusters to be merged together; if the bandwidth parameter is set too small, the kernel function will have a narrow range of action, which may cause the same cluster to be split into multiple small clusters. Specifically, the bandwidth parameter can be selected through methods such as cross-validation to select the optimal bandwidth parameter.

[0088] Step 2: randomly select an archive data point in the standardized data as the initial center point, and calculate the average displacement of all data points within a preset radius of the initial center point;

[0089] As a preferred implementation, randomly selecting an archive data point in the standardized data as the initial center point, and calculating the average displacement of all data points within a preset radius of the initial center point includes:

[0090] Based on the selected initial center point, the search area is determined by a pre-set radius;

[0091] It should be noted that the initial center point is used as the starting point of the mean shift algorithm, and the radius defines the size of the search area centered on the initial center point. The radius should be set based on the characteristics and distribution of the data to ensure that enough data points are covered, but not too wide to include too many irrelevant points (specifically, the radius can be set as a fixed proportion of the standard deviation of all data points, such as selecting 20% ​​to 30% of the average standard deviation as the radius). Based on the selected initial center point and the set radius, a circular (or hypersphere in high-dimensional data) search area is created. All data points within this area will be taken into account for calculating the displacement in the next step.

[0092] The kernel density estimation method is used to calculate the density estimate of each archival data point in the normalized data within the search area;

[0093] Specifically, the density estimation formula for each archival data point in the standardized data within the search area is calculated using the kernel density estimation method:

[0094] ;

[0095] In the formula, f ( d ) indicates an archive data point d The density estimate of ;

[0096] m Represents the total number of data points;

[0097] p n k Indicates nThe kernel radius of the data points;

[0098] k Represents the data space dimension;

[0099] represents the kernel function;

[0100] Represents archive data points d With archive data points d n The normalized distance between .

[0101] Based on the density estimate of all data points in the search area, the average displacement of the initial center point in all data points in the search area is calculated.

[0102] Specifically, according to the density estimation value of all data points in the search area, the calculation formula for calculating the average displacement of the initial center point in all data points in the search area is:

[0103] ;

[0104] In the formula, M ( c ) represents the initial center point c The average displacement of all data points in the search area;

[0105] f ( d ) indicates an archive data point d The density estimate of ;

[0106] S Indicates the initial center point c At the preset radius R The set of all data points in the search area.

[0107] Step 3: update the position of the center point according to the obtained average displacement, and repeat steps 2 to 3 until a preset number of iterations is reached to obtain the cluster center;

[0108] Specifically, the average displacement is used to update the current center point position. The new center point position is the original center point plus the average displacement. When all center points no longer move significantly, the cluster center is considered to be determined. These center points represent high-density areas in the data, and the data points around these center points can be considered as a cluster.

[0109] Step 4: According to the obtained cluster center, each archive data point is assigned to the nearest cluster center to obtain the cluster analysis result.

[0110] It should be noted that, according to the obtained cluster center, each archive data point is assigned to the nearest cluster center, and the cluster analysis result is obtained, including the steps of:

[0111] For each archive data point, calculate its distance to each cluster center. The distance is usually calculated using the Euclidean distance.

[0112] Assign each data point to the cluster center closest to it, indicating that the data point belongs to the cluster represented by the cluster center;

[0113] In this way, all data points are assigned to a cluster to form the final clustering result.

[0114] The importance assessment module is used to perform importance analysis on each cluster analysis result, identify importance factors and evaluate the importance level of the archive digitized data based on the importance factor identification results.

[0115] As a preferred implementation, performing importance analysis on each cluster analysis result, identifying importance factors and evaluating the importance level of the archive digitized data according to the importance factor identification results include:

[0116] Conduct attribute analysis on the archival digitized data in each cluster analysis result;

[0117] It should be noted that the attribute analysis of the archival digitized data in each cluster analysis result includes identifying key attributes. By reviewing the archival digitized data in each cluster, key attributes that can describe and distinguish these data are identified. These attributes include but are not limited to: data type (such as text, image, audio, etc.), data source (such as department, project, etc.), time of data creation or collection, size or format of data, etc.

[0118] Using the FAST (Fourier Amplitude Sensitivity Testing, a framework for applying systems thinking) method, characteristic frequencies are assigned to the attributes of the digitalized archival data in each cluster and a conversion function is constructed;

[0119] It should be noted that for each attribute, its possible value range or domain is predefined. For example, for the "data type" attribute, the domain may include "text", "image", "audio", etc.

[0120] In each cluster, calculate the frequency of each attribute value. This is done by simply counting the number of times each attribute value appears in the cluster and then dividing by the total number of data points in the cluster. For example, if there are 150 data points with a "data type" of "text" in a cluster and the total number of data points is 300, then the frequency of the "text" type is 150 / 300 = 0.5.

[0121] The calculated frequency is used as the characteristic frequency of the attribute value in the cluster, which reflects the representativeness and importance of the attribute value in the cluster.

[0122] Construct a conversion function to map the attribute value to a continuous range of values ​​for Fourier transform. The conversion function can be linear or nonlinear, depending on the characteristics of the attribute and the analysis requirements. For example, you can use the frequency of occurrence of the attribute value as input and output a frequency-adjusted sine wave variable X=sin(2πfx), where f is the characteristic frequency and x is the original attribute value.

[0123] The constructed conversion function is used to convert the digital data attributes of archives into one-dimensional variables, and the first-order sensitivity index of each digital data attribute of archives is calculated by Fourier coefficients;

[0124] It should be noted that the following steps are involved in converting the digital data attributes of archives into one-dimensional variables using the constructed conversion function and calculating the first-order sensitivity index of each digital data attribute of archives using Fourier coefficients:

[0125] Convert each attribute value into a one-dimensional variable using a previously constructed transformation function (e.g., a frequency-based sine wave function). This transformation maps multidimensional, categorical, or continuous attribute values ​​into a numerical range suitable for Fourier analysis based on the frequency of occurrence of the attribute.

[0126] Applying the Fourier transform to the transformed one-dimensional variable can be done with a computer program such as the NumPy library in Python; the Fourier transform converts a time or space series into the frequency domain, revealing the magnitudes of different frequency components;

[0127] The fundamental frequency coefficients of each attribute are extracted from the Fourier transform results. These coefficients represent the dominant influence of the frequency component on the change of the data set. The first-order sensitivity index calculation is based on the amplitude of the Fourier coefficient, which quantifies the average impact on the output results when a single attribute changes. This can usually be achieved by analyzing the amplitude of the main frequency in the Fourier transform results.

[0128] The archival digitized data in each cluster were rated in importance based on the first-order sensitivity index.

[0129] It should be noted that, according to the first-order sensitivity index, the importance rating of the digitalized archival data in each cluster includes the following steps:

[0130] The first-order sensitivity index for each attribute was extracted from the previous Fourier analysis;

[0131] According to the numerical range of the first-order sensitivity index, the standard of importance level is set. For example, the index can be divided into three levels: high, medium, and low. Then, according to the first-order sensitivity index, each attribute is classified into the corresponding importance level. The data points corresponding to the attributes with higher sensitivity index are considered more important in the cluster.

[0132] The archive data encryption unit 3 is used to encrypt the archive digital data with importance by using the lattice additive homomorphic encryption algorithm according to the importance analysis result to obtain encrypted data;

[0133] As a preferred embodiment, the archive data encryption unit 3 includes: an encrypted data selection module, a key generation module, and a data encryption module, and the encrypted data selection module, the key generation module, and the data encryption module are connected in sequence;

[0134] The encrypted data selection module is used to select the digitalized archive data that meets the rating threshold through a preset rating threshold;

[0135] It should be noted that the threshold represents the minimum acceptance standard for data. For example, the threshold can be set as a specific percentile of the first-order sensitivity index, such as above the median or the highest 25%. Using the set rating threshold, the digitalized archive data whose first-order sensitivity index reaches or exceeds the threshold can be screened out.

[0136] A key generation module, used to generate a key based on a trapdoor generation algorithm, the key including a public key and a private key;

[0137] As a preferred implementation, generating a key matrix based on a trapdoor generation algorithm includes:

[0138] A safety factor is set based on the storage requirements of digital archive data, and odd and prime numbers are determined based on the safety factor;

[0139] It should be noted that when setting the security factor of archival digital data, it is necessary to comprehensively consider the sensitivity and importance of the data. Methods such as risk matrix can be used to quantify the sensitivity and importance of the data, and comprehensively determine the safety factor based on the possibility and impact of security threats.

[0140] In addition, the odd prime number q and prime number p are determined according to the safety factor r. The odd prime number q is defined as q=poly(r), that is, q is a polynomial function of r, and the prime number p should satisfy , and p <q<p 3 , Represents a constant used to multiply the cube of r when calculating the lower bound of the prime number p, and poly(r) represents a polynomial function about r.

[0141] Determine the encryption parameters of the trapdoor generation algorithm based on the security factor, odd prime numbers, and prime numbers;

[0142] It should be noted that the encryption parameters of the trapdoor generation algorithm are the settings of the Gaussian noise distribution parameters. Gaussian noise is used to increase the difficulty of the computational problem and make it more resistant to analytical attacks. The introduction of noise can interfere with the statistical analysis of encrypted data and increase the difficulty of obtaining sensitive information. Among them, the Gaussian parameter noise distribution is , m is an integer, and m=O(rlogq); the parameter is β, and β=poly(r), O represents a mathematical symbol used to describe the upper bound of a function as the input value grows.

[0143] A trapdoor generation algorithm is used to generate a random matrix and a trapdoor matrix, and the random matrix and the trapdoor matrix are used as a public key and a private key, respectively.

[0144] It should be noted that, by establishing an integer set consisting of 0 to q-1 according to an odd prime number q;

[0145] With r and m as the number of rows and columns of the matrix respectively, randomly select elements from the integer set to generate a random matrix, and with m as the number of rows and columns of the matrix, randomly select elements from the integer set to generate a trapdoor matrix.

[0146] The data encryption module is used to encrypt the digitalized data of the archives that meet the rating threshold through the public key in the key to obtain encrypted data.

[0147] As a preferred implementation, the digitalized archive data that meets the rating threshold is encrypted by the public key in the key, and the encrypted data obtained includes:

[0148] Perform data encoding processing on the digitalized archive data that meets the rating threshold to obtain a set of encoding vectors;

[0149] It should be noted that the selected data is encoded and converted into a series of vectors. This step usually involves formatting the data into a form that can be processed by the encryption algorithm, such as converting text, images or other data types into numerical vectors and ensuring that each vector is evenly distributed, which helps to improve the randomness and security of the encryption process.

[0150] A uniformly distributed vector is randomly selected from the encoding vector set, and the randomly selected vector is encrypted based on the public key to obtain encrypted data.

[0151] It should be noted that vectors are randomly selected from the set of coded vectors. The selection process ensures that the selection of vectors is uniform and random to avoid any possible prediction or pattern analysis. These randomly selected vectors are then encrypted using a public key. The public key comes from the previously generated random matrix and is combined with Gaussian noise and other algorithm parameters for secure encryption. The encrypted set of vectors forms an encrypted data packet. This data contains an encrypted form of the original information, and only individuals with the corresponding private key (trapdoor matrix) can decrypt and access the original content.

[0152] The archive data storage unit 4 is used to store encrypted data and unencrypted archive digitized data based on blockchain technology;

[0153] It should be noted that using blockchain technology to store encrypted data and unencrypted digital archive data is an innovative solution that can provide additional security and traceability. The immutability and distributed nature of blockchain makes it an ideal choice for protecting sensitive data. Storing encrypted data and unencrypted digital archive data includes the following steps:

[0154] Record encrypted and unencrypted data as transactions on the blockchain. Each data item can be an independent transaction or part of a series of transactions. Use smart contracts to automate the process of data storage. Smart contracts can define data storage, access rights, and other rules.

[0155] Set up access control and role-based access control (RBAC) policies to ensure that only authorized users can access specific data. Set stricter access conditions for sensitive data (encrypted data);

[0156] Leverage the immutability of blockchain to record every attempt to access and modify data, create a verifiable, unchangeable log for each data transaction, configure and implement automated monitoring and alarm systems to detect and respond to abnormal access or transaction behaviors, and ensure data security and compliance.

[0157] like Figure 2 According to another embodiment of the present invention, a file management method based on blockchain technology is provided, and the file management method based on blockchain technology includes the following steps:

[0158] S1. Collect archival data and digitize the archival data to obtain digital archival data;

[0159] S2. Conduct importance analysis on digital archive data based on the mean shift method to obtain importance analysis results;

[0160] S3. Based on the importance analysis results, the lattice additive homomorphic encryption algorithm is used to encrypt the digital data of the important archives to obtain encrypted data;

[0161] S4. Based on blockchain technology, encrypted data and unencrypted archival digital data are stored.

[0162] In summary, with the help of the above-mentioned technical scheme of the present invention, the present invention ensures the confidentiality of the data and prevents unauthorized access and leakage by encrypting the important digital archival data using the lattice additive homomorphic encryption algorithm, and further enhances the security of the data by using the distributed storage and non-tamperable characteristics of blockchain technology, making the archival data more secure and reliable during storage and transmission. The importance analysis of the digital archival data is performed through the mean shift method, which helps to prioritize the processing and storage of more important data, improves the efficiency of data management, and makes the system have broad application prospects and important practical application value in the field of archival management. The present invention cleans and standardizes the archive digitized data, improves the quality of the data, uses the mean shift method to perform cluster analysis on the standardized data, can cluster similar archive data together, helps to discover patterns and rules in the data, performs importance analysis on each cluster, identifies importance factors, and evaluates the importance level of the archive digitized data according to these factors, realizes the refined classification and management of the data, uses the multivariate normal distribution as the kernel function and sets the bandwidth parameter, so that the mean shift method is more adapted to the characteristics of the archive data, improves the accuracy of cluster analysis, uses the FAST method and Fourier coefficients to calculate the first-order sensitivity index of each archive digitized data attribute, can accurately evaluate the importance of each attribute, and provide a scientific basis for importance rating. The present invention selects the archive digitized data to be encrypted through a pre-set rating threshold, ensures that only data with an importance level reaching a certain level will be encrypted, thereby improving the security of the data, generates a public key and a private key using a trapdoor generation algorithm, and encrypts the selected data through the public key, ensures the confidentiality of the data, prevents unauthorized access and leakage, improves the security and flexibility of the archive data, the efficiency of key management and the integrity of the data, and provides a strong data security guarantee for the archive management system.

[0163] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0164] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A file management system based on blockchain technology, characterized in that: The archive management system based on blockchain technology includes: an archive data collection unit, an edge importance analysis unit, an archive data encryption unit and an archive data storage unit, and the archive data collection unit, the edge importance analysis unit, the archive data encryption unit and the archive data storage unit are connected in sequence; The archive data collection unit is used to collect archive data and digitize the archive data to obtain archive digitized data; The edge importance analysis unit is used to perform importance analysis on the archive digitized data based on the mean shift method to obtain importance analysis results; The archive data encryption unit is used to encrypt the archive digitized data with importance using a lattice additive homomorphic encryption algorithm according to the importance analysis result to obtain encrypted data; The archive data storage unit is used to store encrypted data and unencrypted archive digitized data based on blockchain technology; The edge importance analysis unit includes: a data preprocessing module, a cluster analysis module and an importance evaluation module, and the data preprocessing module, the cluster analysis module and the importance evaluation module are connected in sequence; The data preprocessing module is used to preprocess the digital archive data to obtain standardized data, and the preprocessing includes data cleaning and data standardization processing; The cluster analysis module is used to perform cluster analysis on the standardized data using the mean shift method to obtain cluster analysis results; The importance evaluation module is used to perform importance analysis on each cluster analysis result, identify importance factors and evaluate the importance level of the archive digitized data according to the importance factor identification results; The method of performing cluster analysis on the standardized data by using the mean shift method to obtain the cluster analysis result comprises the following steps: Step 1: Use multivariate normal distribution as kernel function and set bandwidth parameters; Step 2: randomly select an archive data point in the standardized data as the initial center point, and calculate the average displacement of all data points within a preset radius of the initial center point; Step 3: update the position of the center point according to the obtained average displacement, and repeat steps 2 to 3 until a preset number of iterations is reached to obtain the cluster center; Step 4: According to the obtained cluster center, each archive data point is assigned to the nearest cluster center to obtain the cluster analysis result; The aforementioned steps of performing importance analysis on each cluster analysis result, identifying importance factors, and evaluating the importance level of the archive digitized data based on the importance factor identification results include: Conduct attribute analysis on the archival digitized data in each cluster analysis result; Using the FAST method, characteristic frequencies are assigned to the attributes of the archive digitized data in each cluster and a conversion function is constructed; The constructed conversion function is used to convert the digital data attributes of archives into one-dimensional variables, and the first-order sensitivity index of each digital data attribute of archives is calculated by Fourier coefficients; The archival digitized data in each cluster were rated in importance based on the first-order sensitivity index.

2. According to claim 1, a file management system based on blockchain technology is characterized in that: The randomly selecting an archive data point in the standardized data as the initial center point and calculating the average displacement of all data points within a preset radius of the initial center point includes: Based on the selected initial center point, the search area is determined by a pre-set radius; The kernel density estimation method is used to calculate the density estimate of each archival data point in the normalized data within the search area; Based on the density estimate of all data points in the search area, the average displacement of the initial center point in all data points in the search area is calculated.

3. According to claim 2, a file management system based on blockchain technology is characterized in that: The formula for calculating the density estimation value of each archival data point in the standardized data within the search area using the kernel density estimation method is: ； In the formula, f ( d ) indicates an archive data point d The density estimate of ; m Represents the total number of data points; p n k Indicates n The kernel radius of the data points; k Represents the data space dimension; represents the kernel function; Represents archive data points d With archive data points d n The normalized distance between .

4. According to claim 3, a file management system based on blockchain technology is characterized in that: The calculation formula for calculating the average displacement of all data points of the initial center point in the search area according to the density estimation value of all data points in the search area is: ； In the formula, M ( c ) represents the initial center point c The average displacement of all data points in the search area; f ( d ) indicates an archive data point d The density estimate of ; S Indicates the initial center point c At the preset radius R The set of all data points in the search area.

5. According to claim 1, a file management system based on blockchain technology is characterized in that: The archive data encryption unit includes: an encrypted data selection module, a key generation module, and a data encryption module, and the encrypted data selection module, the key generation module, and the data encryption module are connected in sequence; The encrypted data selection module is used to select the digitalized archive data that meets the rating threshold through a preset rating threshold; The key generation module is used to generate a key based on a trapdoor generation algorithm, wherein the key includes a public key and a private key; The data encryption module is used to encrypt the digitalized data of the archives that meet the rating threshold using the public key in the key to obtain encrypted data.

6. According to claim 5, a file management system based on blockchain technology is characterized in that: The generating of the key matrix based on the trapdoor generation algorithm comprises: A safety factor is set based on the storage requirements of digital archive data, and odd and prime numbers are determined based on the safety factor; Determine the encryption parameters of the trapdoor generation algorithm based on the security factor, odd prime numbers, and prime numbers; A trapdoor generation algorithm is used to generate a random matrix and a trapdoor matrix, and the random matrix and the trapdoor matrix are used as a public key and a private key, respectively.

7. According to claim 6, a file management system based on blockchain technology is characterized in that: The digitized archive data that meets the rating threshold is encrypted by the public key in the key to obtain the encrypted data including: Perform data encoding processing on the digitalized archive data that meets the rating threshold to obtain a set of encoding vectors; A uniformly distributed vector is randomly selected from the encoding vector set, and the randomly selected vector is encrypted based on the public key to obtain encrypted data.

8. A file management method based on blockchain technology, used to implement the execution of the file management system based on blockchain technology as described in any one of claims 1 to 7, characterized in that: The archive management method based on blockchain technology includes the following steps: S1. Collect archival data and digitize the archival data to obtain digital archival data; S2. Conduct importance analysis on digital archive data based on the mean shift method to obtain importance analysis results; S3. Based on the importance analysis results, the lattice additive homomorphic encryption algorithm is used to encrypt the digital data of the important archives to obtain encrypted data; S4. Based on blockchain technology, encrypted data and unencrypted archival digital data are stored.

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