Digital cultural heritage display platform and method based on multi-chain fusion
Patent Information
- Application Number
- CN202610975250.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-02
- Publication Date
- 2026-09-15
Smart Images
Figure CN122760239A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing technology, and in particular to a digital display platform and method for cultural heritage based on multi-chain integration. Background Technology
[0002] The field of data processing technology involves the use of computers and their algorithms to acquire, store, analyze, manage, and display information, including technologies such as data acquisition, data storage, data transmission, data analysis and processing, and data visualization.
[0003] Among them, the cultural heritage digital display platform refers to a platform that uses financial technology to digitally display and manage cultural heritage.
[0004] These platforms combine technologies such as blockchain, smart contracts, and digital asset management to digitally preserve cultural heritage and build a tradable and interactive cultural asset display system on this basis.
[0005] In practice, existing digital cultural heritage display platforms rely primarily on traditional centralized database records and manual review for the valuation and ownership management of cultural heritage after its digital preservation. This lack of in-depth quantitative analysis of the physical and textural characteristics of the assets results in a lack of objective mathematical basis for the value anchoring of digital assets, making them susceptible to subjective factors. In cross-platform transactions, the uniqueness verification process for assets is complex and inefficient. Relying on simple hash verification cannot effectively distinguish between pirated or duplicated assets with highly similar content. This presents significant limitations in risk control and compliance assurance for financial assets, resulting in technical deficiencies in ensuring the secure trading and financial reliability of digital cultural and creative assets. Summary of the Invention
[0006] To address the technical problems existing in the prior art, embodiments of the present invention provide a digital display platform and method for cultural heritage based on multi-chain fusion.
[0007] On the one hand, it provides a digital display platform for cultural heritage based on multi-chain integration, which includes: The feature sequence generation module obtains the three-dimensional scan point cloud coordinate set and multi-angle texture mapping map of cultural relics, extracts the features of the two and performs weighted fusion to construct the feature sequence of digital cultural and creative assets. The index building module obtains the preset standard financial asset value benchmark centroid data, performs feature space projection and dimension alignment based on financial valuation attributes on the feature sequence of the digital cultural and creative assets, and divides the asset value assessment cluster index. The hash code generation module performs binary quantization encoding on the digital cultural and creative asset feature sequence and asset value assessment clustering index to generate a cross-chain asset uniqueness retrieval hash code; The risk control verification module uses the unique retrieval hash code of the cross-chain asset as the retrieval anchor point, performs full network broadcasting and location extraction in the preset distributed state database, and generates multi-chain asset ownership confirmation risk control verification results. The collection trading and display module extracts the corresponding digital cultural and creative asset characteristics from the financial digital asset entries marked as having unique ownership in the multi-chain asset ownership confirmation and risk control verification results, and generates a financial digital collection trading and display list.
[0008] As a further aspect of the present invention, the digital cultural and creative asset feature sequence includes geometric curvature feature data, pixel grayscale difference data, and spatial coordinate mapping fusion attributes; the asset value assessment clustering index includes asset risk assessment category labels, standard financial asset value benchmark reference coordinates, and Euclidean distance measurement values; the cross-chain asset uniqueness retrieval hash code includes binary quantitative signature encoding, multi-dimensional hyperplane projection feature bits, and distributed ledger block addressing identifiers; the multi-chain asset ownership confirmation risk control verification results include asset ownership exclusivity determination markers, cosine similarity comparison values, and asset evidence association status codes; and the financial digital collectibles transaction display list includes compliant financial digital asset entries, initial transaction pricing assignment data, and financial ownership binding information.
[0009] As a further aspect of the present invention, the feature sequence generation module includes: The spatial discrete point calculation submodule collects the coordinate set of the three-dimensional scanned point cloud of cultural relics, traverses the coordinate position of each spatial discrete point in the set, calculates the normal vector of the tangent plane where the discrete point is located, extracts geometric curvature features based on the degree of directional deflection of the normal vectors of adjacent discrete points, and generates geometric curvature feature extraction data. The pixel grayscale difference statistics submodule obtains the multi-angle texture mapping map of the cultural relic surface, performs grayscale conversion on the map pixel array, extracts adjacent pixels in the horizontal and vertical directions of the map, calculates the difference in brightness intensity between adjacent pixels, and obtains pixel grayscale difference calculation data. The feature weighted fusion submodule calls the pixel grayscale difference calculation data and the geometric curvature feature extraction data, matches the spatial position of the two-dimensional texture features and the three-dimensional geometric features according to the spatial coordinate mapping alignment relationship, sets fusion weight coefficients for the matched geometric curvature features and pixel grayscale difference calculation data respectively, performs linear weighted superposition operation of feature values, and generates a digital cultural and creative asset feature sequence.
[0010] As a further aspect of the present invention, the index construction module includes: The value benchmark acquisition submodule acquires preset standard financial asset value benchmark centroid data and calculates the financial valuation correlation value between features and financial valuation attributes in the feature sequence of the digital cultural and creative assets. The feature space projection submodule performs feature space projection and dimension alignment based on financial valuation attributes on the feature sequence of the digital cultural and creative assets, maps the projected feature sequence to the vector space corresponding to the centroid data of the standard financial asset value benchmark, and calculates the Euclidean distance between the mapped feature sequence and the centroid data of the standard financial asset value benchmark. The evaluation clustering submodule performs clustering classification judgment on the feature sequence points in the feature sequence of digital cultural and creative assets, classifies the feature sequence points with the smallest Euclidean distance value into the corresponding asset risk assessment category, marks all feature sequence points in the same category, and generates an asset value assessment clustering index.
[0011] As a further aspect of the present invention, the hash code generation module includes: The hyperplane projection calculation submodule constructs a multidimensional hyperplane projection space with multiple randomly distributed normal vectors. It maps the geometric curvature feature data and pixel grayscale feature data in the feature sequence of digital cultural and creative assets and the category information in the asset value assessment clustering index to the multidimensional hyperplane projection space, and calculates the vector dot product projection value of the feature data in each hyperplane direction. The binarization quantization encoding submodule performs binarization quantization encoding on the positive and negative polarities of the vector dot product projection values, maps positive values to binary state bits and negative values to their opposite binary state bits, extracts the quantized binary data bits in sequence and performs bit string concatenation processing to generate a discrete signature sequence. The cross-chain hash addressing submodule uses the discrete signature sequence as the addressing index basis to locate the corresponding block storage location in the distributed ledger storage network. Based on the address information of the block storage location and the encoded content of the discrete signature sequence, it performs hash operation and mapping processing to obtain the unique retrieval hash code of cross-chain assets.
[0012] As a further aspect of the present invention, the risk control verification module includes: The evidence storage location extraction submodule uses the unique retrieval hash code of the cross-chain asset as the retrieval anchor point, performs full network broadcast and location extraction in the preset distributed state database, points to the corresponding evidence storage block in the blockchain ledger according to the index of the hash code, extracts the encrypted record associated with the retrieval anchor point from the located block, and generates encrypted historical asset evidence storage feature data. The data decryption and alignment submodule restores the original plaintext data structure of the encrypted record, parses the feature vector dimension information in the plaintext data, performs vector dimension alignment processing on the restored encrypted historical asset evidence feature data, and generates restored historical asset evidence feature data. The exclusive ownership determination submodule maps the restored historical asset evidence feature data and the digital cultural and creative asset feature sequence to the same vector space, calculates the cosine value of the angle between the two, compares it with the preset asset repetition circuit breaker threshold, determines the financial digital asset entries with unique ownership or conflicting ownership, and generates multi-chain asset ownership confirmation risk control verification results.
[0013] As a further aspect of the present invention, the collection trading display module includes: The unique asset extraction submodule extracts financial digital asset entries marked as having unique ownership from the multi-chain asset ownership confirmation risk control verification results, removes financial digital asset entries marked as having conflicting ownership, and generates a unique asset feature data set. The ownership binding pricing submodule calls the asset valuation clustering index to perform financial ownership binding on the unique asset feature data set, assigns the asset entries to the corresponding financial asset categories and performs initial transaction pricing assignment, and generates initial transaction pricing assignment results; The transaction list generation submodule sorts and arranges the contents of the transaction display list based on the initial transaction pricing assignment result, and generates a financial digital collectible transaction display list.
[0014] On the other hand, a multi-chain fusion-based digital display method for cultural heritage, which is executed based on the aforementioned multi-chain fusion-based digital display platform for cultural heritage, includes the following steps: S1: Obtain the set of three-dimensional scanned point cloud coordinates and multi-angle texture mapping map of cultural relics, extract the features of the two and perform weighted fusion to construct a feature sequence of digital cultural and creative assets; S2: Obtain the preset standard financial asset value benchmark centroid data, perform feature space projection and dimension alignment based on financial valuation attributes on the digital cultural and creative asset feature sequence, and divide the asset value assessment cluster index. S3: Perform binary quantization encoding on the digital cultural and creative asset feature sequence and asset value assessment clustering index to generate a cross-chain asset uniqueness retrieval hash code; S4: Using the unique retrieval hash code of the cross-chain asset as the retrieval anchor point, perform full network broadcasting and location extraction in the preset distributed state database to generate multi-chain asset ownership confirmation risk control verification results; S5: Extract the corresponding digital cultural and creative asset features from the financial digital asset entries marked as having unique ownership in the multi-chain asset ownership confirmation and risk control verification results, and generate a financial digital collectible transaction display list.
[0015] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By weighted fusion of the three-dimensional geometric curvature features and two-dimensional pixel grayscale difference features of cultural relics, a digital cultural and creative asset feature sequence that combines physical attributes and appearance details is constructed, solving the problem of insufficient objectivity in value assessment. The feature sequence is used to perform projection and clustering of financial valuation attributes, anchoring the value of digital assets to the standard financial asset value benchmark, forming an asset value assessment clustering index based on market risk, improving the rationality of initial transaction pricing. Locality-sensitive hashing technology is used to binarize and encode the feature sequence and clustering information, generating a cross-chain asset uniqueness retrieval hash code with feature fingerprint and block addressing capabilities, realizing rapid location and decentralized verification of asset ownership in a multi-chain environment. By comparing cosine similarity to restore historical evidence feature data and current asset feature sequence, the exclusivity of ownership is strictly determined, effectively avoiding the risks of duplicate issuance and piracy, and ensuring the compliance and transaction security of the financial digital collectibles transaction list. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a schematic diagram of a digital display platform for cultural heritage based on multi-chain fusion provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the platform framework of the present invention; Figure 3 This is a flowchart of the feature sequence generation module in this invention; Figure 4 This is a flowchart of the index construction module in this invention; Figure 5 This is a flowchart of the hash code generation module in this invention; Figure 6 This is a flowchart of the risk control verification module in this invention; Figure 7 This is a flowchart of the collection trading display module in this invention; Figure 8 This is a flowchart of a method for digitally displaying cultural heritage based on multi-chain fusion, provided in an embodiment of the present invention. Detailed Implementation
[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.
[0019] In embodiments of the present invention, words such as "example" and "for example" are used to indicate that something is used as an example, illustration, or explanation.
[0020] Any embodiment or design described as "example" in this invention should not be construed as being more preferred or advantageous than other embodiments or designs.
[0021] To be precise, the use of the word "example" is intended to present the concept in a concrete way.
[0022] Furthermore, in embodiments of the present invention, the meaning of "and / or" can be both, or either one of them.
[0023] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that when the distinction is not emphasized, their intended meanings are consistent.
[0024] The words “of,” “corresponding,” and “corresponding” can sometimes be used interchangeably. It should be noted that when their distinction is not emphasized, they convey the same meaning.
[0025] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.
[0026] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.
[0027] This invention provides a digital display platform for cultural heritage based on multi-chain fusion, such as... Figure 1 The diagram shown illustrates a digital display platform for cultural heritage based on multi-chain integration. This platform includes: The feature sequence generation module obtains the three-dimensional scan point cloud coordinate set and multi-angle texture mapping map of cultural relics, extracts the features of the two and performs weighted fusion to construct the feature sequence of digital cultural and creative assets. The index building module obtains the preset standard financial asset value benchmark centroid data, performs feature space projection and dimension alignment based on financial valuation attributes on the feature sequence of digital cultural and creative assets, and divides the asset value assessment cluster index. The hash code generation module performs binary quantization encoding on the feature sequence of digital cultural and creative assets and the clustering index of asset value assessment to generate a unique hash code for cross-chain asset retrieval. The risk control verification module uses the unique retrieval hash code of cross-chain assets as the retrieval anchor point, performs full network broadcast and location extraction in the preset distributed state database, and generates multi-chain asset ownership confirmation risk control verification results. The collection trading and display module extracts the corresponding digital cultural and creative asset characteristics from the financial digital asset entries marked as having unique ownership in the multi-chain asset ownership confirmation and risk control verification results, and generates a list of financial digital collection transactions for display.
[0028] The feature sequence of digital cultural and creative assets includes geometric curvature feature data, pixel grayscale difference data, and spatial coordinate mapping fusion attributes. The asset value assessment clustering index includes asset risk assessment category labels, standard financial asset value benchmark reference coordinates, and Euclidean distance measurement values. The cross-chain asset uniqueness retrieval hash code includes binary quantitative signature encoding, multi-dimensional hyperplane projection feature bits, and distributed ledger block addressing identifiers. The multi-chain asset ownership confirmation risk control verification results include asset ownership exclusivity determination markers, cosine similarity comparison values, and asset evidence association status codes. The financial digital collectibles transaction display list includes compliant financial digital asset entries, initial transaction pricing assignment data, and financial ownership binding information.
[0029] Please see Figure 2 and Figure 3 The feature sequence generation module includes: The spatial discrete point calculation submodule collects the coordinate set of the three-dimensional scanned point cloud of cultural relics, traverses the coordinate position of each spatial discrete point in the set, calculates the normal vector of the tangent plane where the discrete point is located, extracts geometric curvature features based on the degree of directional deflection of the normal vectors of adjacent discrete points, and generates geometric curvature feature extraction data. First, the original point cloud data stream of the artifact surface is received by laser scanning or structured light projection equipment. The header information in the data stream is analyzed to determine the origin and scale factor of the point cloud coordinate system. All point cloud data are then mapped to a unified, standardized three-dimensional Cartesian coordinate system.
[0030] Subsequently, a traversal operation is performed on the coordinate set. For each target discrete point in the set, K-nearest neighbor search is used to find the nearest neighbor point within a preset search radius to construct a local neighborhood point set.
[0031] The principal component analysis logic is invoked to construct the covariance matrix of the local neighborhood point set. The covariance matrix is then decomposed into eigenvalues to extract the eigenvector corresponding to the smallest eigenvalue. This eigenvector is then established as the normal vector of the tangent plane containing the target discrete point.
[0032] After obtaining the normal vectors of all discrete points, the system further traverses adjacent discrete point pairs, extracts the normal vector coordinates of the current point and the adjacent points respectively, performs vector dot product operation, and calculates the angle between the two normal vectors by combining the inverse cosine function.
[0033] At this point, based on the geometric curvature determination logic, it is necessary to set a quantitative standard for curvature characteristics, namely the critical threshold between planes and curved surfaces.
[0034] The threshold is set based on the arithmetic mean of the angle between the normal vectors of the point clouds of all flat regions in the statistical standard geometry sample library, and the standard deviation is calculated. The result of adding three times the standard deviation to the arithmetic mean is established as the critical threshold between the plane and the curved surface.
[0035] For example, if the mean angle of the flat area obtained by statistics is 0.05 radians and the standard deviation is 0.01 radians, then the critical threshold is set to 0.05 plus 3 multiplied by 0.01, which is 0.08 radians.
[0036] If the calculated angle between the normal vectors is greater than the critical threshold, it is determined that there is a sudden change in geometric curvature at that position, and the angle value is recorded as a geometric curvature feature value; if it is less than the critical threshold, the feature value is set to zero.
[0037] Finally, all the calculated non-zero geometric curvature feature values are arranged in spatial coordinate order to generate geometric curvature feature extraction data containing spatial location information and curvature intensity information.
[0038] The pixel grayscale difference statistics submodule obtains the multi-angle texture mapping map of the cultural relic surface, performs grayscale conversion on the map pixel array, extracts adjacent pixels in the horizontal and vertical directions of the map, calculates the difference in brightness intensity between adjacent pixels, and obtains pixel grayscale difference calculation data. Read the multi-angle texture mapping map of the cultural relic surface, identify the bitmap format of the map file, and separate the color components of the red, green, and blue channels.
[0039] Based on the differences in human eye sensitivity to different colors of light, a weighted average method is used to process the red, green, and blue components into grayscale. The specific weighting is set according to the International Commission on Illumination (ICI) standard. The red channel value is multiplied by 0.299, the green channel value by 0.587, and the blue channel value by 0.114. The three products are then added together to obtain the single-channel grayscale value of the pixel, thereby converting the color spectrum into a grayscale pixel array.
[0040] Next, a sliding window is constructed that slides row by row and column by column along the horizontal and vertical axes of the grayscale pixel array.
[0041] In the horizontal direction, extract the grayscale value of the current pixel and the adjacent pixel to its right, perform a subtraction operation and take the absolute value; in the vertical direction, extract the grayscale value of the current pixel and the adjacent pixel below it, and similarly perform a subtraction operation and take the absolute value.
[0042] To quantify the significance of these differential data, a criterion for determining effective texture features is established.
[0043] The benchmark is set based on the following: select a set of pixels in the background smooth area of the map, calculate the root mean square value of the gray-level difference of the pixels in the set, and set twice the root mean square value as the criterion for determining effective texture features.
[0044] For example, if the root mean square value of the gray-level difference in the smooth region is 5, then the criterion is set to 2 times 5, that is, 10.
[0045] Any difference value calculated that exceeds the criterion is considered a valid texture feature point. These difference values and their row and column indices in the pixel array are recorded and finally compiled to form pixel grayscale difference calculation data.
[0046] The feature weighted fusion submodule calls pixel grayscale difference calculation data and geometric curvature feature extraction data, matches the spatial position of two-dimensional texture features and three-dimensional geometric features according to the spatial coordinate mapping alignment relationship, sets fusion weight coefficients for the matched geometric curvature features and pixel grayscale difference calculation data respectively, performs linear weighted superposition operation of feature values, and generates digital cultural and creative asset feature sequence. Read the pixel grayscale difference calculation data and the geometric curvature feature extraction data. Since the two belong to the two-dimensional pixel coordinate system and the three-dimensional spatial coordinate system respectively, a coordinate mapping and alignment operation needs to be performed.
[0047] This operation uses texture mapping parameters to project each spatial coordinate point in the 3D point cloud onto the coordinate plane of the 2D texture map, thereby establishing a one-to-one correspondence index relationship between 3D geometric features and 2D texture features.
[0048] After completing the position matching, the feature fusion stage begins, where it is necessary to determine the fusion weight coefficients of the geometric curvature features and the pixel grayscale difference data.
[0049] The weights are set dynamically based on the principle of information entropy: the information entropy values of the geometric curvature feature set and the pixel gray-level difference set are calculated separately, the sum of the two information entropy values is calculated, and the information entropy of the geometric curvature feature is divided by the sum to obtain the fusion weight coefficient of the geometric feature. Similarly, the fusion weight coefficient of the pixel feature is calculated.
[0050] For example, if the information entropy of the geometric curvature feature set is 4.0 and the information entropy of the pixel gray-level difference set is 6.0, then the sum is 10.0; at this time, the geometric feature weight coefficient is 4.0 divided by 10.0 equals 0.4, and the pixel feature weight coefficient is 6.0 divided by 10.0 equals 0.6.
[0051] Subsequently, for each aligned feature point, the geometric curvature feature value is multiplied by the geometric feature weight coefficient, the pixel grayscale difference value is multiplied by the pixel feature weight coefficient, and the two product results are linearly weighted and superimposed.
[0052] For example, if the geometric curvature feature value of a certain point is 0.8 and the normalized pixel grayscale difference value is 0.5, then the fused feature value is 0.8 multiplied by 0.4 plus 0.5 multiplied by 0.6, which is 0.62.
[0053] This operation is performed by traversing all feature points, and the resulting numerical sequence is the feature sequence of digital cultural and creative assets.
[0054] Please see Figure 2 and Figure 4 The index building module includes: The value benchmark acquisition submodule acquires the preset standard financial asset value benchmark centroid data and calculates the financial valuation correlation between features and financial valuation attributes in the feature sequence of digital cultural and creative assets. Access the financial market data analysis interface and retrieve the preset standard financial asset value database.
[0055] The database covers different categories of historical trading data and volatility indicators.
[0056] Clustering algorithms are used to process this financial asset data, grouping asset data of the same category together and calculating the geometric center, or centroid vector, of each cluster.
[0057] These centroid vectors constitute the centroid data for the benchmark value of standard financial assets.
[0058] Next, the characteristic sequence of digital cultural and creative assets is read. In order to measure the correlation between the sequence and financial attributes, the Pearson correlation coefficient is used as the calculation logic.
[0059] The characteristic sequence of digital cultural and creative assets is regarded as the first variable, and a certain financial value benchmark centroid data is regarded as the second variable. The covariance of the first variable and the second variable and the product of the standard deviations of the first variable and the second variable are calculated respectively. The correlation coefficient is obtained by dividing the covariance by the product of the standard deviations.
[0060] This value ranges from -1 to +1.
[0061] For example, if the calculated covariance is 0.85 and the product of the standard deviations of the two variables is 1.0, then the correlation coefficient is 0.85, indicating that there is a strong positive correlation between the two.
[0062] Traverse all financial value benchmark centroids, calculate the correlation values between the characteristic sequences of digital cultural and creative assets and them one by one, and record these values as financial valuation correlation values.
[0063] The feature space projection submodule performs feature space projection and dimension alignment based on financial valuation attributes on the feature sequence of digital cultural and creative assets. It maps the projected feature sequence to the vector space corresponding to the centroid data of the standard financial asset value benchmark and calculates the Euclidean distance between the mapped feature sequence and the centroid data of the standard financial asset value benchmark. The dimensional transformation of the feature sequence of digital cultural and creative assets is performed to map it from the original physical feature space to the financial valuation feature space.
[0064] First, analyze the vector dimension of the standard financial asset value benchmark centroid data and construct a projection matrix.
[0065] The projection matrix is constructed based on the eigenvector space after dimensionality reduction by principal component analysis, ensuring that the dimensions after projection are consistent with the dimensions of the financial benchmark data.
[0066] The feature sequence of digital cultural and creative assets is represented as a row vector, and multiplied by the projection matrix to obtain the projected feature sequence.
[0067] Subsequently, the Euclidean distance between the mapped feature sequence and the centroid data of the benchmark value of each standard financial asset is calculated.
[0068] The calculation process is as follows: For each dimension component in the feature sequence vector, subtract the corresponding dimension component of the reference centroid vector, and square the difference; add up the squared differences of all dimensions, and finally take the square root of the sum. The result is the Euclidean distance value.
[0069] For example, if the coordinates of the feature sequence in the two-dimensional simplified space are (2, 2) and the coordinates of a certain reference centroid are (5, 6), then the difference in the first dimension is 3, the difference in the second dimension is 4, the sum of squares is 9 plus 16 equals 25, and the square root gives the Euclidean distance as 5.
[0070] The distance between the feature sequence and each reference centroid data needs to be calculated to form a set of distance values.
[0071] The evaluation clustering submodule performs clustering classification judgment on the feature sequence points in the feature sequence of digital cultural and creative assets, assigns the feature sequence points with the smallest Euclidean distance to the corresponding asset risk assessment category, marks all feature sequence points in the same category, and generates an asset value assessment clustering index. Receive the set of Euclidean distance values and execute the minimum distance determination logic.
[0072] Iterate through the set, compare the values of all distances, and identify the minimum value.
[0073] The category to which the minimum value corresponds to the benchmark centroid of the standard financial asset (such as high volatility and high return or stable value preservation) is determined as the category to which the digital cultural and creative asset characteristic sequence belongs.
[0074] To build the index, an index library with a hash table structure is built in memory, using the asset risk assessment category as the key and the list of asset identifiers belonging to that category as the value.
[0075] Add the asset identifier corresponding to the currently processed feature sequence to the list of the corresponding category, and mark and store the coordinate information of the feature sequence point in multi-dimensional space.
[0076] This process enables the discretization of continuous feature data into category information with financial attribute labels.
[0077] The generated asset valuation cluster index not only includes category affiliation but also records the specific distance deviation between the asset and its affiliation centroid. This deviation value can be used as a correction factor for subsequent refined pricing.
[0078] Ultimately, all processed asset feature sequence points were organized in an orderly manner under various risk assessment categories, forming a structured asset valuation cluster index library.
[0079] Please see Figure 2 and Figure 5 The hash code generation module includes: The hyperplane projection calculation submodule constructs a multidimensional hyperplane projection space with multiple randomly distributed normal vectors. It maps the geometric curvature feature data and pixel grayscale feature data in the feature sequence of digital cultural and creative assets and the category information in the asset value assessment clustering index to the multidimensional hyperplane projection space, and calculates the vector dot product projection value of the feature data in each hyperplane direction. The process of constructing a multidimensional hyperplane projection space with multiple randomly distributed normal vectors is as follows: Set the seed parameter for random number generation, and call the standard normal distribution function to generate a set of random numbers with a mean of zero and a variance of one; Based on the sum of the dimensions of the feature sequence of digital cultural and creative assets and the preset hash encoding bits, the random numerical set is constructed into a random projection matrix, and each column vector in the random projection matrix is established as a randomly distributed normal vector in the multidimensional hyperplane projection space.
[0080] Initialize a pseudo-random number generator, setting a fixed integer as the seed parameter to ensure that the generated random sequence is reproducible.
[0081] Call the standard normal distribution function to generate a series of floating-point random values with a mean of 0 and a variance of 1.
[0082] Based on the total number of dimensions of the feature sequence of digital cultural and creative assets and the preset number of bits of hash encoding, a corresponding number of values are extracted from the random numerical set to construct a random projection matrix.
[0083] Each column vector of this matrix represents a randomly generated hyperplane normal vector in multidimensional space.
[0084] Subsequently, the feature sequence of digital cultural and creative assets and the category codes in the asset value assessment clustering index are read and concatenated into a comprehensive feature vector.
[0085] Perform matrix multiplication on the composite eigenvector and the random projection matrix: that is, project the eigenvector onto the direction of each column normal vector of the matrix and calculate the vector dot product.
[0086] The calculation process is as follows: multiply each component of the eigenvector by the corresponding component of the normal vector, and sum the product of all components to obtain the projection value of the eigenvector on the corresponding hyperplane direction.
[0087] For example, if the eigenvector is [1, 2] and the normal vector is [0.5, 0.5], then the dot product projection value is 1 multiplied by 0.5 plus 2 multiplied by 0.5, which equals 1.5.
[0088] The final result will be multiple scalar projection values, which together form a projection result vector.
[0089] The binarization quantization encoding submodule performs binarization quantization encoding on the positive and negative polarities of the vector dot product projection values, maps positive values to binary state bits and negative values to their opposite binary state bits, extracts the quantized binary data bits in sequence and performs bit string concatenation processing to generate a discrete signature sequence. It receives a vector containing multiple projected values and performs sign function operations on each of these values one by one.
[0090] The specific quantization logic is as follows: if a certain projected value is greater than or equal to 0, the quantization result at that position is determined to be a binary status bit "1"; if the projected value is less than 0, it is determined to be a binary status bit "0".
[0091] Based on the order of the projected values in the vector, the corresponding binary status bits are extracted sequentially.
[0092] For example, if the projected numerical sequence is [0.5, -1.2, 3.0, -0.1], then the corresponding binary status bits are 1, 0, 1, and 0, respectively.
[0093] Then, these extracted binary bits are concatenated in order from the most significant bit to the least significant bit to form a binary string.
[0094] This binary string is the discrete signature sequence.
[0095] This sequence exhibits locality sensitivity, meaning that for assets with highly similar original features, the Hamming distance of the generated discrete signature sequences will also be very small.
[0096] This binarization process greatly compresses the data volume while preserving the quadrant distribution information of asset features in multidimensional space, providing a basic data structure for subsequent efficient retrieval.
[0097] The cross-chain hash addressing submodule uses the discrete signature sequence as the addressing index to locate the corresponding block storage location in the distributed ledger storage network. Based on the address information of the block storage location and the encoded content of the discrete signature sequence, it performs hash operations and mapping processing to obtain the unique retrieval hash code for cross-chain assets. The generated discrete signature sequence is used as the lookup key in the distributed hash table for routing lookup in the storage network nodes of the distributed ledger.
[0098] Calculate the XOR distance between the discrete signature sequence and the identifiers of each node in the network, locate the nearest storage node, and obtain the block storage location information recorded on that node.
[0099] To generate a unique cross-chain retrieval identifier, the obtained block storage location address string is concatenated with the binary string of the discrete signature sequence to form a new string to be processed.
[0100] Next, a secure hash algorithm is invoked to perform a digest operation on the concatenated string.
[0101] The operation process includes filling the message to a fixed length multiple, initializing the hash buffer value, executing multiple rounds of compression function processing, and finally outputting a fixed-length hexadecimal string.
[0102] This string is the unique retrieval hash code for cross-chain assets. It not only contains the asset's characteristic fingerprint information, but also binds its physical storage address in the blockchain network, ensuring uniqueness in a multi-chain environment.
[0103] Please see Figure 2 and Figure 6 The risk control verification module includes: The evidence storage location extraction submodule uses the unique retrieval hash code of cross-chain assets as the retrieval anchor point, performs full network broadcast and location extraction in the preset distributed state database, points to the corresponding evidence storage block in the location blockchain ledger according to the index of the hash code, extracts the encrypted record associated with the retrieval anchor point from the location block, and generates encrypted historical asset evidence storage feature data. It receives the uniqueness retrieval hash code of cross-chain assets as input, and uses it as the retrieval anchor to launch a network-wide broadcast query request to the preset distributed state database.
[0104] Database nodes use Bloom filters to quickly determine if the hash code exists in the local index. If it does, the corresponding index pointer is returned.
[0105] Based on the logical address pointed to by the pointer, the specific blockchain ledger node is located.
[0106] In the located ledger, based on the block height information contained in the hash code, the block header and Merkle tree are traversed to verify the validity of the transaction path, and finally the evidence block containing the record of the target asset is locked.
[0107] Extract the data payload associated with the retrieval anchor from the transaction data segment of the block.
[0108] Since on-chain data is usually protected by encryption, the extracted data is a binary stream in ciphertext form. This data is then encapsulated to generate encrypted historical asset evidence feature data, along with corresponding timestamps and signature metadata.
[0109] The data decryption and alignment submodule restores the original plaintext data structure of the encrypted record, parses the feature vector dimension information in the plaintext data, performs vector dimension alignment processing on the restored encrypted historical asset evidence feature data, and generates restored historical asset evidence feature data. The specific process of performing vector dimension alignment on the recovered encrypted historical asset evidence feature data is as follows: The total number of feature dimensions in the feature sequence of digital cultural and creative assets is analyzed and set as the standard alignment length. Traverse and reconstruct each feature vector in the historical asset evidence feature data, and detect the dimensional length of the feature vector; If the dimension length of the restored historical asset evidence feature data is less than the standard alignment length, zero values are padded at the end of the vector of the restored historical asset evidence feature data until the dimension length is consistent with the standard alignment length. The system invokes a pre-set private key or authorized decryption key to perform decryption operations on encrypted historical asset evidence data, restoring the ciphertext binary stream to the original plaintext data structure.
[0110] Parse the plaintext data, read the array of feature vectors recorded within it, and identify the number of dimensions of the historical feature vectors.
[0111] At the same time, read the total number of dimensions of the current digital cultural and creative asset feature sequence to be verified and set it as the standard alignment length.
[0112] Execution dimension detection logic: Iterate through each historical feature vector after decryption and restoration, and count the number of its elements.
[0113] If the dimension of a historical feature vector is found to be less than the standard alignment length, the padding mechanism is activated.
[0114] The padding process involves appending zeros to the end of the historical feature vectors until their total length equals the standard alignment length.
[0115] For example, if the standard alignment length is 128 dimensions, but the parsed historical vector is only 120 dimensions, then the positions from the 121st to the 128th dimension need to be filled with the value 0.
[0116] After the filling is completed, the historical asset evidence feature data is generated to ensure that it has dimensional consistency with the current asset feature sequence in vector operations.
[0117] The exclusive ownership determination submodule maps the restored historical asset evidence feature data and the digital cultural and creative asset feature sequence to the same vector space, calculates the cosine value of the angle between the two, compares it with the preset asset repetition circuit breaker threshold, determines the financial digital asset entries with unique ownership or conflicting ownership, and generates multi-chain asset ownership confirmation risk control verification results. The received dimension-aligned restored historical asset evidence feature data and current digital cultural and creative asset feature sequence.
[0118] The logic for calculating cosine similarity is as follows: First, calculate the dot product of the two vectors, which is to multiply the values of the corresponding dimensions of the two vectors and then sum them; then calculate the magnitude of each vector, which is the square root of the sum of the squares of each component; finally, divide the dot product result by the product of the two magnitudes to obtain the cosine value of the included angle.
[0119] The value ranges from -1 to +1.
[0120] To determine whether there is a conflict of ownership, a repeated asset circuit breaker threshold is introduced.
[0121] The threshold is set based on the following: the cosine similarity between feature vectors of large-scale unrelated genuine assets in the statistical historical database is calculated, the 99th percentile of its distribution is calculated, and this percentile is set as the threshold.
[0122] For example, if statistical analysis shows that 99% of non-duplicate assets have a similarity of less than 0.85, then 0.85 will be set as the circuit breaker threshold.
[0123] If the calculated cosine of the included angle is greater than the circuit breaker threshold, for example, if the calculated result is 0.92, then the two are determined to be the same or highly similar assets, triggering an ownership conflict alarm and marking the financial digital asset entry as having an ownership conflict; conversely, if it is less than the threshold, then it is determined to be unique in ownership.
[0124] Finally, based on the judgment result of each item, a multi-chain asset ownership confirmation risk control verification result containing status markers is generated.
[0125] Please see Figure 2 and Figure 7 The collection trading and display module includes: The unique asset extraction submodule extracts financial digital asset entries marked as having unique ownership from the multi-chain asset ownership confirmation risk control verification results, removes financial digital asset entries marked as having conflicting ownership, and generates a unique asset feature data set. Iterate through all record entries in the multi-chain asset ownership confirmation risk control verification results.
[0126] For each record, examine its status flag field.
[0127] If the field indicates a conflict of rights or a suspected high risk, perform a removal operation to remove the entry from the current processing queue and archive it to the exception log database for subsequent manual review.
[0128] If the field shows that the ownership is unique, then the asset identifier and feature data corresponding to the entry are extracted and kept in the processing queue.
[0129] This process rigorously selects compliant assets that are eligible for listing and trading.
[0130] After a full traversal and filtering, all the retained entries are repackaged and constructed into a unique asset feature data set.
[0131] Each asset in this collection has undergone mathematical uniqueness verification, eliminating the risk of double-spending or piracy.
[0132] The ownership binding pricing submodule calls the asset valuation clustering index to perform financial ownership binding on the unique asset feature data set, assigns the asset entries to the corresponding financial asset categories and performs initial transaction pricing assignment, and generates the initial transaction pricing assignment result; Read each asset data item from the unique asset feature data set and call the previously generated asset valuation cluster index.
[0133] Based on the asset's characteristic sequence, find the asset risk assessment category to which it belongs in the index.
[0134] Once the category is determined, the corresponding benchmark market price range is queried.
[0135] The initial transaction pricing calculation process is as follows: obtain the benchmark average price of the category, and fine-tune it by combining the distance deviation value between the asset characteristic sequence and the category centroid.
[0136] The specific calculation logic is as follows: the initial price equals the benchmark average price multiplied by the correction factor, where the correction factor equals 1 minus the product of the distance deviation value and the adjustment coefficient.
[0137] The adjustment coefficient is set based on the rate of decay of the price of similar assets with the degree of deviation in the historical transactions of the statistical market.
[0138] For example, if the benchmark average price of a certain category is 1,000 yuan, the adjustment coefficient is set to 0.1; if the distance deviation of an asset from the centroid is 0.5, the correction factor is 1 minus 0.5 multiplied by 0.1, which is 0.95; finally, the initial price of the asset is 1,000 multiplied by 0.95, which equals 950 yuan.
[0139] Perform this pricing calculation on each asset to generate an initial transaction pricing assignment result containing specific monetary values.
[0140] The transaction list generation submodule sorts and arranges the contents of the transaction display list based on the initial transaction pricing assignment results, generating a transaction display list of financial digital collectibles. Receive the initial transaction pricing result, which contains a series of digital cultural and creative asset items that have been confirmed and priced.
[0141] These items are arranged according to preset sorting rules.
[0142] The sorting rules are based on the initial pricing amount from highest to lowest, or grouped and displayed according to the priority of the risk category to which the asset belongs.
[0143] Traverse the sorted data structure, extract key metadata such as asset thumbnail index, name, initial pricing, and risk category tags, and generate standardized data packages according to the requirements of the front-end display framework.
[0144] This data package is the display list of financial digital collectibles transactions. It is directly connected to the user's terminal display interface to ensure that the transaction list seen by the user is a collection of compliant digital assets that has undergone strict risk control screening, intelligent classification, and reasonable pricing.
[0145] Please see Figure 8 The method for digitally displaying cultural heritage based on multi-chain integration is executed on the aforementioned digital display platform for cultural heritage based on multi-chain integration, and includes the following steps: S1: Obtain the set of three-dimensional scanned point cloud coordinates and multi-angle texture mapping map of cultural relics, extract the features of the two and perform weighted fusion to construct a feature sequence of digital cultural and creative assets; S2: Obtain the preset standard financial asset value benchmark centroid data, perform feature space projection and dimension alignment based on financial valuation attributes on the feature sequence of digital cultural and creative assets, and divide the asset value assessment cluster index. S3: Perform binary quantization encoding on the feature sequence of digital cultural and creative assets and the clustering index of asset value assessment to generate a unique cross-chain asset retrieval hash code; S4: Using the unique retrieval hash code of cross-chain assets as the retrieval anchor point, perform full network broadcasting and location extraction in the preset distributed state database to generate multi-chain asset ownership confirmation risk control verification results; S5: Extract the corresponding digital cultural and creative asset characteristics from the financial digital asset entries marked as having unique ownership in the multi-chain asset ownership confirmation and risk control verification results, and generate a financial digital collectible transaction display list.
[0146] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.
[0147] Therefore, the scope of protection of this invention should be determined by the scope of the claims.
Claims
1. A multi-chain fusion-based digital cultural heritage exhibition platform, characterized in that, The platform includes: The feature sequence generation module obtains the three-dimensional scan point cloud coordinate set and multi-angle texture mapping map of cultural relics, extracts the features of the two and performs weighted fusion to construct the feature sequence of digital cultural and creative assets. The index building module obtains the preset standard financial asset value benchmark centroid data, performs feature space projection and dimension alignment based on financial valuation attributes on the feature sequence of the digital cultural and creative assets, and divides the asset value assessment cluster index. The hash code generation module performs binary quantization encoding on the digital cultural and creative asset feature sequence and asset value assessment clustering index to generate a cross-chain asset uniqueness retrieval hash code; The risk control verification module uses the unique retrieval hash code of the cross-chain asset as the retrieval anchor point, performs full network broadcasting and location extraction in the preset distributed state database, and generates multi-chain asset ownership confirmation risk control verification results. The collection trading and display module extracts the corresponding digital cultural and creative asset characteristics from the financial digital asset entries marked as having unique ownership in the multi-chain asset ownership confirmation and risk control verification results, and generates a financial digital collection trading and display list.
2. The multi-chain fusion-based cultural heritage digitization exhibition platform according to claim 1, characterized in that: The digital cultural and creative asset feature sequence includes geometric curvature feature data, pixel grayscale difference data, and spatial coordinate mapping fusion attributes. The asset value assessment clustering index includes asset risk assessment category labels, standard financial asset value benchmark reference coordinates, and Euclidean distance measurement values. The cross-chain asset uniqueness retrieval hash code includes binary quantitative signature encoding, multi-dimensional hyperplane projection feature bits, and distributed ledger block addressing identifiers. The multi-chain asset ownership confirmation risk control verification results include asset ownership exclusivity determination markers, cosine similarity comparison values, and asset evidence association status codes. The financial digital collectible transaction display list includes compliant financial digital asset entries, initial transaction pricing assignment data, and financial ownership binding information.
3. The multi-chain fusion-based cultural heritage digitization exhibition platform according to claim 1, characterized in that: The feature sequence generation module includes: The spatial discrete point calculation submodule collects the coordinate set of the three-dimensional scanned point cloud of cultural relics, traverses the coordinate position of each spatial discrete point in the set, calculates the normal vector of the tangent plane where the discrete point is located, extracts geometric curvature features based on the degree of directional deflection of the normal vectors of adjacent discrete points, and generates geometric curvature feature extraction data. The pixel grayscale difference statistics submodule obtains the multi-angle texture mapping map of the cultural relic surface, performs grayscale conversion on the map pixel array, extracts adjacent pixels in the horizontal and vertical directions of the map, calculates the difference in brightness intensity between adjacent pixels, and obtains pixel grayscale difference calculation data. The feature weighted fusion submodule calls the pixel grayscale difference calculation data and the geometric curvature feature extraction data, matches the spatial position of the two-dimensional texture features and the three-dimensional geometric features according to the spatial coordinate mapping alignment relationship, sets fusion weight coefficients for the matched geometric curvature features and pixel grayscale difference calculation data respectively, performs linear weighted superposition operation of feature values, and generates a digital cultural and creative asset feature sequence.
4. The multi-chain fusion-based cultural heritage digitization exhibition platform according to claim 1, characterized in that: The index building module includes: The value benchmark acquisition submodule acquires preset standard financial asset value benchmark centroid data and calculates the financial valuation correlation value between features and financial valuation attributes in the feature sequence of the digital cultural and creative assets. The feature space projection submodule performs feature space projection and dimension alignment based on financial valuation attributes on the feature sequence of the digital cultural and creative assets, maps the projected feature sequence to the vector space corresponding to the centroid data of the standard financial asset value benchmark, and calculates the Euclidean distance between the mapped feature sequence and the centroid data of the standard financial asset value benchmark. The evaluation clustering submodule performs clustering classification judgment on the feature sequence points in the feature sequence of digital cultural and creative assets, classifies the feature sequence points with the smallest Euclidean distance value into the corresponding asset risk assessment category, marks all feature sequence points in the same category, and generates an asset value assessment clustering index.
5. The cultural heritage digital display platform based on multi-chain fusion according to claim 1, characterized in that: The hash code generation module includes: The hyperplane projection calculation submodule constructs a multidimensional hyperplane projection space with multiple randomly distributed normal vectors. It maps the geometric curvature feature data and pixel grayscale feature data in the feature sequence of digital cultural and creative assets and the category information in the asset value assessment clustering index to the multidimensional hyperplane projection space, and calculates the vector dot product projection value of the feature data in each hyperplane direction. The binarization quantization encoding submodule performs binarization quantization encoding on the positive and negative polarities of the vector dot product projection values, maps positive values to binary state bits and negative values to their opposite binary state bits, extracts the quantized binary data bits in sequence and performs bit string concatenation processing to generate a discrete signature sequence. The cross-chain hash addressing submodule uses the discrete signature sequence as the addressing index basis to locate the corresponding block storage location in the distributed ledger storage network. Based on the address information of the block storage location and the encoded content of the discrete signature sequence, it performs hash operation and mapping processing to obtain the unique retrieval hash code of cross-chain assets.
6. The cultural heritage digital display platform based on multi-chain fusion according to claim 5, characterized in that: The process of constructing a multidimensional hyperplane projection space with multiple randomly distributed normal vectors is as follows: Set the seed parameter for random number generation, and call the standard normal distribution function to generate a set of random numbers with a mean of zero and a variance of one; Based on the sum of the dimensions of the feature sequence of digital cultural and creative assets and the preset hash encoding bits, the random numerical set is constructed into a random projection matrix, and each column vector in the random projection matrix is established as a randomly distributed normal vector in the multidimensional hyperplane projection space.
7. The digital display platform for cultural heritage based on multi-chain fusion as described in claim 1, characterized in that: The risk control verification module includes: The evidence storage location extraction submodule uses the unique retrieval hash code of the cross-chain asset as the retrieval anchor point, performs full network broadcast and location extraction in the preset distributed state database, points to the corresponding evidence storage block in the blockchain ledger according to the index of the hash code, extracts the encrypted record associated with the retrieval anchor point from the located block, and generates encrypted historical asset evidence storage feature data. The data decryption and alignment submodule restores the original plaintext data structure of the encrypted record, parses the feature vector dimension information in the plaintext data, performs vector dimension alignment processing on the restored encrypted historical asset evidence feature data, and generates restored historical asset evidence feature data. The exclusive ownership determination submodule maps the restored historical asset evidence feature data and the digital cultural and creative asset feature sequence to the same vector space, calculates the cosine value of the angle between the two, compares it with the preset asset repetition circuit breaker threshold, determines the financial digital asset entries with unique ownership or conflicting ownership, and generates multi-chain asset ownership confirmation risk control verification results.
8. The cultural heritage digital display platform based on multi-chain fusion according to claim 7, characterized in that: The specific process of performing vector dimension alignment processing on the recovered encrypted historical asset evidence feature data is as follows: The total number of feature dimensions in the feature sequence of digital cultural and creative assets is analyzed and set as the standard alignment length. Traverse and reconstruct each feature vector in the historical asset evidence feature data, and detect the dimensional length of the feature vector; If the dimension length of the restored historical asset evidence feature data is less than the standard alignment length, zero values are padded at the end of the vector of the restored historical asset evidence feature data until the dimension length is consistent with the standard alignment length.
9. The digital display platform for cultural heritage based on multi-chain fusion according to claim 1, characterized in that: The collection trading and display module includes: The unique asset extraction submodule extracts financial digital asset entries marked as having unique ownership from the multi-chain asset ownership confirmation risk control verification results, removes financial digital asset entries marked as having conflicting ownership, and generates a unique asset feature data set. The ownership binding pricing submodule calls the asset valuation clustering index to perform financial ownership binding on the unique asset feature data set, assigns the asset entries to the corresponding financial asset categories and performs initial transaction pricing assignment, and generates initial transaction pricing assignment results; The transaction list generation submodule sorts and arranges the contents of the transaction display list based on the initial transaction pricing assignment result, and generates a financial digital collectible transaction display list.
10. A method for digitally displaying cultural heritage based on multi-chain fusion, characterized in that: The implementation of the multi-chain fusion-based cultural heritage digital display platform according to any one of claims 1-9 includes the following steps: S1: Obtain the set of 3D scanned point cloud coordinates and multi-angle texture mapping map of cultural relics, extract the features of the two and perform weighted fusion to construct a feature sequence of digital cultural and creative assets; S2: Obtain the preset standard financial asset value benchmark centroid data, perform feature space projection and dimension alignment based on financial valuation attributes on the digital cultural and creative asset feature sequence, and divide the asset value assessment cluster index. S3: Perform binary quantization encoding on the digital cultural and creative asset feature sequence and asset value assessment clustering index to generate a cross-chain asset uniqueness retrieval hash code; S4: Using the unique retrieval hash code of the cross-chain asset as the retrieval anchor point, perform full network broadcasting and location extraction in the preset distributed state database to generate multi-chain asset ownership confirmation risk control verification results; S5: Extract the corresponding digital cultural and creative asset features from the financial digital asset entries marked as having unique ownership in the multi-chain asset ownership confirmation and risk control verification results, and generate a financial digital collectible transaction display list.