A digital management platform for cultural and creative products

Abstract

The application relates to the field of data processing, in particular to a digital management platform for cultural and creative products, which comprises a data characteristic curve construction module for constructing a data characteristic curve for any type of data of the cultural and creative products; a similar segment acquisition module for dividing the data characteristic curve into similar segments with similar change trends; an optimal strategy evaluation module for taking the similar segments as primitives, arranging and combining all types of the primitives to obtain different combined strategies, and evaluating the combined strategies to screen out optimal strategies; and a data coding module for constructing a Huffman tree according to the optimal strategies, obtaining corresponding Huffman codes, completing data compression, and fully reflecting the differences in character appearance frequency through the Huffman codes obtained by the optimal strategies, so that the efficiency of data compression of the related data of the cultural and creative products by the Huffman codes is improved, and the safety of the data and the overall performance of the system are enhanced.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more particularly to a digital management platform for cultural and creative products. Background Technology

[0002] Digital management platforms for cultural and creative products are tools designed to help manage and promote cultural and creative products, projects, or brand designs. These platforms encompass a vast amount of data of various types, such as: work information and copyright data, market and user data, social and interaction data, security and permissions-related data, etc. These data types play a crucial role in the digital management of cultural and creative products. Therefore, data compression is necessary to save storage space. Data compression can also significantly improve data transmission speed and system performance, especially when multiple users are accessing the system, reducing waiting time and latency, and improving the overall user experience and satisfaction. Furthermore, it can enhance data security and effectively prevent unauthorized data access and theft.

[0003] Existing technologies generally use Huffman coding for data compression. Huffman coding is a lossless compression algorithm that can accurately restore the original data. At the same time, Huffman coding has an efficient encoding method that can significantly reduce the data transmission or storage requirements.

[0004] However, traditional Huffman coding is not effective at compressing characters with relatively fixed frequencies, resulting in a flat Huffman tree structure and the encoding length not being able to fully reflect the differences in the frequency of character occurrence, thus reducing encoding efficiency and compression rate.

[0005] Therefore, how to improve the efficiency of data compression for cultural and creative products using Huffman coding has become an urgent problem to be solved. Summary of the Invention

[0006] In view of this, embodiments of the present invention provide a digital management platform for cultural and creative products to solve the problem of how to improve the efficiency of data compression of relevant data of cultural and creative products using Huffman coding.

[0007] This invention provides a digital management platform for cultural and creative products, comprising: The data feature curve construction module is used to obtain the indicator data corresponding to any type of data of cultural and creative products within a preset time period and construct the data feature curve of the indicator data. The similar segmentation acquisition module is used to acquire trend change feature points on the data feature curve, divide the data feature curve into at least two independent segments based on all trend change feature points, and select independent segments with similar change trends from all independent segments as similar segments. The optimal strategy evaluation module is used to take the first independent segment in each of the similar segments as a primitive, count all types of primitives, obtain at least one combination strategy based on all types of primitives, construct the strategy evaluation coefficient of the combination strategy based on the number of primitives contained in the combination strategy for any combination strategy, and obtain the optimal strategy based on the strategy evaluation coefficient of each combination strategy. The data encoding module is used to construct the Huffman tree of the corresponding indicator data within the preset time period according to the optimal strategy, obtain the corresponding Huffman code, and complete the data compression.

[0008] Furthermore, the similar segmentation acquisition module filters out independent segments with similar changing trends from all independent segments as similar segments, including: For any independent segment, obtain the maximum and minimum values ​​of the curvature of the independent segment at the target point, and calculate the mean between the maximum and minimum values ​​of the curvature at the target point as the average curvature of the independent segment; The degree of segment change trend between each pair of independent segments is obtained based on the average curvature of each independent segment, and independent segments with similar change trends are selected as similar segments based on the degree of segment change trend between each pair of independent segments.

[0009] Furthermore, the similarity segmentation acquisition module obtains the degree of segmentation change trend between every two independent segments based on the average curvature of each independent segment, including: For any independent segment, calculate the tangent slope of all points on the independent segment to obtain the average slope on the independent segment; For any two independent segments, the absolute value of the difference between the average curvatures of the two independent segments is obtained as the first absolute value, and the absolute value of the difference between the average slopes of the two independent segments is obtained as the second absolute value. The first absolute value is inversely normalized to obtain the first variable, and the second absolute value is inversely normalized to obtain the second variable. The first variable and the second variable are weighted and summed, and the corresponding result is taken as the degree of segmentation change trend of the two independent segments.

[0010] Furthermore, the similar segmentation acquisition module filters out independent segments with similar change trends as similar segments based on the degree of segmentation change trend between every two independent segments, including: For any two independent segments, if the degree of segmentation change between the two independent segments is within a preset range, then the two independent segments are determined to be initial similar segments; For E initial similar segments, detect whether there is at least one identical independent segment among the E initial similar segments. If at least one identical independent segment is detected among the E initial similar segments, then the initial similar segments with identical independent segments among the E initial similar segments are considered as one similar segment.

[0011] Furthermore, the optimal strategy evaluation module obtains at least one combined strategy based on all types of the primitives, including: Select s types of primitives from all the types of primitives to form a frequency statistical primitive, and use each of the remaining primitives as a frequency statistical primitive to obtain... A combination strategy, wherein u is the number of types of the primitive, and s is less than or equal to u.

[0012] Furthermore, the optimal strategy evaluation module constructs the strategy evaluation coefficients of the combined strategy based on the number of primitives contained in the combined strategy, including: The total number of frequency statistical primitives under the combined strategy is counted. Based on the occurrence frequency of each frequency statistical primitive under the combined strategy, the occurrence number of each occurrence frequency is counted, and the occurrence frequency corresponding to the maximum occurrence number is obtained as the target occurrence frequency. The ratio between the frequency of occurrence of the target and the total number is calculated as the evaluation index. The evaluation index is then substituted into an exponential function with the natural constant as the base, and the result is used as the strategy evaluation coefficient of the combined strategy.

[0013] Furthermore, the optimal strategy evaluation module obtains the optimal strategy based on the strategy evaluation coefficients of each combined strategy, including: The strategy evaluation coefficient of each combined strategy is subtracted from the preset value, and the combined strategy with the smallest difference is selected as the optimal strategy.

[0014] Furthermore, after filtering out independent segments with similar changing trends from all independent segments as similar segments in the similar segment acquisition module, the module further includes: The difference between the maximum endpoint value and the minimum endpoint value in each independent segment of the similar segments is calculated as the endpoint difference of the corresponding independent segment. The first independent segment that appears on the data feature curve in the similar segments is taken as the reference segment. For any independent segment in the similar segments other than the reference segment, the ratio error value of the endpoint difference between the reference segment and the independent segment is calculated. All error values ​​obtained from the similarity segmentation are combined into an error sequence.

[0015] Furthermore, after data compression is completed, the data encoding module also includes: The Huffman-coded bitstream is read from the data compressed using the Huffman coding, and the corresponding index data within the preset time period is reconstructed based on the error sequence and the Huffman-coded bitstream.

[0016] Furthermore, the similarity segmentation acquisition module acquires trend change feature points on the data feature curve, including: The maximum and minimum points on the data feature curve are obtained as trend change feature points on the data feature curve.

[0017] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: This invention provides a digital management platform for cultural and creative products, including a data feature curve construction module for acquiring indicator data corresponding to any type of data of cultural and creative products within a preset time period and constructing a data feature curve for the indicator data; a similar segment acquisition module for acquiring trend change feature points on the data feature curve, dividing the data feature curve into at least two independent segments based on all trend change feature points, and selecting independent segments with similar change trends from all independent segments as similar segments; an optimal strategy evaluation module for taking the first independent segment in each similar segment as a primitive, statistically obtaining all types of primitives, obtaining at least one combination strategy based on all types of primitives, constructing a strategy evaluation coefficient for any combination strategy based on the number of primitives contained in the combination strategy, and obtaining an optimal strategy based on the strategy evaluation coefficient of each combination strategy; and a data encoding module for constructing a Huffman tree for the indicator data corresponding to the preset time period based on the optimal strategy, obtaining the corresponding Huffman code, and completing data compression. The data feature curve is divided into multiple independent segments based on trend change characteristic points. By selecting independent segments with similar change trends as primitives, various combination strategies are constructed according to the combination methods of primitive types, and the optimal strategy is evaluated. The Huffman tree of the indicator data is constructed using the primitives under the optimal strategy, resulting in Huffman coding that can fully reflect the differences in the frequency of character occurrence. This greatly reduces the number of characters with relatively fixed frequencies in the indicator data, improves the efficiency of data compression of cultural and creative product-related data using Huffman coding, and enhances data security and overall system performance. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0019] Figure 1 This is a structural block diagram of a digital management platform for cultural and creative products provided in Embodiment 1 of the present invention. Detailed Implementation

[0020] Embodiments of this disclosure are described in detail below, with examples of these embodiments illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting it.

[0021] It should be noted that the terms "first," "second," etc., used in this disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure.

[0022] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0023] See Figure 1 This is a structural block diagram of a digital management platform for cultural and creative products provided in Embodiment 1 of the present invention, as shown below. Figure 1 As shown, the system may include: The data feature curve construction module 11 is used to obtain the indicator data corresponding to any type of data of cultural and creative products within a preset time period and construct the data feature curve of the indicator data.

[0024] First, based on the data types existing in the digital management platform for cultural and creative products, a data type is determined, such as user activity, page views, visitor volume, conversion rate, etc. Based on the determined data type, all indicator data under this data type within a preset time period are obtained. To ensure the accuracy and consistency of the indicator data, after obtaining all indicator data, data preprocessing is performed to achieve data cleaning. Data preprocessing includes, but is not limited to: handling outliers, identifying and adjusting abnormal data to ensure data accuracy and consistency; handling missing values, filling or deleting missing data points; and data transformation, such as normalizing or standardizing the data to ensure the comparability of different indicators. The data preprocessing techniques are existing technologies and will not be elaborated upon here.

[0025] After preprocessing all indicator data, Matplotlib is used to plot the data feature curves that construct the indicator data, where the x-axis represents time and the y-axis represents the value of the indicator data.

[0026] The similar segment acquisition module 12 is used to acquire trend change feature points on the data feature curve, divide the data feature curve into at least two independent segments based on all trend change feature points, and select independent segments with similar change trends from all independent segments as similar segments.

[0027] Traditional Huffman coding is ineffective at compressing characters with relatively fixed frequencies, resulting in a flat Huffman tree structure whose encoding length cannot adequately reflect the differences in character frequency. Furthermore, the digital management platform for cultural and creative products contains a massive amount of data, including many more characters with relatively fixed frequencies, significantly reducing the efficiency and compression rate of Huffman coding. Therefore, in this embodiment of the invention, it is necessary to construct new frequency statistics primitives to reduce the amount of data containing characters with relatively fixed frequencies, thereby improving the compression efficiency of Huffman coding for the index data of cultural and creative products.

[0028] Because curve segments with similar trends on the data feature curve contain multiple data points with relatively fixed frequencies and continuous arrangement, in this embodiment of the invention, the trend change feature points on the data feature curve are first marked, and the data feature curve is split into independent segments based on the marked trend change feature points. That is, the curve between two adjacent trend change feature points is considered as an independent segment. Then, independent segments with similar trends are selected from all independent segments as similar segments, which are used to integrate the indicator data on the similar segments into a primitive, reduce the number of characters with relatively fixed frequencies in the indicator data, and complete the efficient compression and management of data by Huffman coding.

[0029] The methods for marking trend change feature points on data feature curves include: The maximum and minimum points on the data feature curve are obtained as trend change feature points on the data feature curve.

[0030] In one embodiment, the derivative of the function of the data characteristic curve corresponding to a trend change feature point is 0, and the product of the slope values ​​on both sides of that point is less than 0. That is, the point is a maximum and minimum point of the data characteristic curve. The formula for determining the trend change feature point is as follows:

[0031] in, This represents the value of the first derivative at the i-th point of the data characteristic curve. A derivative value of 0 indicates that the trend of the curve may have changed at that point. This represents the slope value at the (i+1)th point. Let M represent the slope value at point i-1, where M is the slope value. and slope value The product of G and G is always less than 0, indicating that the slope values ​​of the adjacent points on both sides of the i-th point on the data feature curve have different positive and negative attributes.

[0032] The specific process for selecting independent segments with similar trends from all independent segments as similar segments is as follows: (1) For any independent segment, obtain the maximum and minimum values ​​of the curvature of the independent segment at the target point, and calculate the mean between the maximum and minimum values ​​of the curvature at the target point as the average curvature of the independent segment.

[0033] In one embodiment, a point is randomly selected on the independent segment as the target point, and the maximum and minimum values ​​of the curvature of the independent segment at that target point are obtained. The acquisition of the maximum and minimum values ​​of curvature is prior art and will not be elaborated here. Then, the average of the maximum and minimum values ​​of curvature is calculated as the average curvature of the independent segment. The formula for the average curvature is:

[0034] in, This represents the average curvature of the independent segments. Represents the maximum value of curvature. This represents the minimum value of curvature.

[0035] (2) Based on the average curvature of each independent segment, obtain the degree of segment change trend between each two independent segments, and select independent segments with similar change trends as similar segments based on the degree of segment change trend between each two independent segments.

[0036] Specifically, the degree of segmentation change trend between any two independent segments is obtained based on the average curvature of each independent segment, including: For any independent segment, calculate the tangent slope of all points on the independent segment to obtain the average slope on the independent segment; For any two independent segments, the absolute value of the difference between the average curvatures of the two independent segments is obtained as the first absolute value, and the absolute value of the difference between the average slopes of the two independent segments is obtained as the second absolute value. The first absolute value is inversely normalized to obtain the first variable, and the second absolute value is inversely normalized to obtain the second variable. The first variable and the second variable are weighted and summed, and the corresponding result is taken as the degree of segmentation change trend of the two independent segments.

[0037] In one embodiment, taking independent segments T and M as examples, for any point on independent segment T, the value of the first derivative of that point on independent segment T is calculated, which is the slope of the tangent line at that point on independent segment T. Similarly, the slopes of the tangent lines at all points on independent segment T are calculated, and the average value of the slopes of the tangent lines at all points is taken as the average slope of independent segment T. Similarly, the average slope of independent segment M is calculated. Then, based on the difference in the average curvature and the difference in the average slope between independent segments T and independent segment M, the degree of segmented change trend between independent segments T and independent segment M is obtained. The formula for the degree of segmented change trend is:

[0038] in, Indicates the degree of segmented change trend. and It is a weight value. This represents the inverse proportional normalization function. This represents the difference in the average curvature of independent segments T and M. This represents the difference in the average slope of segment T and segment M. Represents the absolute value symbol.

[0039] It should be noted that, The reference value is 0.7. The reference value is 0.3, which is not limited here. Implementers can set it according to the implementation scenario. Inverse proportional normalization fixes the difference in average curvature and average slope between independent segments T and M in the interval [0, 1]. The smaller the difference in average curvature between independent segments T and M, that is, the smaller the difference in average curvature between them, the better. The smaller the value, the more similar the segmentation trends of independent segments T and M are, and the greater the degree of segmentation trend between them. Similarly, the smaller the difference in the average slope of independent segments T and M, the better. The smaller the value, the more similar the segmentation change trends between independent segments T and M, and the greater the degree of segmentation change trends between independent segments T and M.

[0040] Specifically, based on the degree of segmentation change trend between any two independent segments, independent segments with similar change trends are selected as similar segments, including: For any two independent segments, if the degree of segmentation change between the two independent segments is within a preset range, then the two independent segments are determined to be initial similar segments; For E initial similar segments, detect whether there is at least one identical independent segment among the E initial similar segments. If at least one identical independent segment is detected among the E initial similar segments, then the initial similar segments with identical independent segments among the E initial similar segments are considered as one similar segment.

[0041] In one embodiment, the degree of segmented change trend The closer the value is to 1, the more similar the changing trends of the two independent segments. In this embodiment of the invention, the preset range is set to [0.8, 1], which is not limited here, and implementers can set it according to the implementation scenario. When two independent segments are considered to have similar trends, they are considered as an initial similar segment; otherwise, when... Time, that is, the degree of segmented change trend. If the changes are not within the preset range, it is assumed that the changing trends of the two independent segments are not similar, and these two independent segments do not belong to the initial similar segments.

[0042] After obtaining all initial similar segments, these segments are regrouped. Specifically, for any given initial similar segment, it is checked whether the two independent segments within that initial similar segment form an initial similar segment with other independent segments. If the two independent segments do not form an initial similar segment with other independent segments, then that initial similar segment is considered a similar segment; that is, when E=1, the two independent segments within the initial similar segment are considered similar segments. Conversely, if the two independent segments form an initial similar segment with other independent segments, then all associated initial similar segments are grouped together. Independent segments in the initial similar segment are grouped into a similar segment. For example, if the independent segments T and M in the initial similar segment a1 have similar trends, and the independent segments M and P in the initial similar segment a2 have similar trends, then it can be concluded that the independent segments T and P have similar trends. That is, the independent segments T, M and P in the initial similar segments a1 and a2 are considered as a similar segment. In other words, when E=2, if at least one identical independent segment is detected among the E initial similar segments, then the initial similar segments with identical independent segments among the E initial similar segments are considered as a similar segment.

[0043] Similarly, based on the degree of segmentation change trend between each two independent segments, find all similar segments on the data feature curve.

[0044] After selecting independent segments with similar trends from all independent segments as similar segments, an error sequence is created based on the similar segments to facilitate data reconstruction during decoding. The method for creating the error sequence is as follows: The difference between the maximum endpoint value and the minimum endpoint value in each independent segment of the similar segments is calculated as the endpoint difference of the corresponding independent segment. The first independent segment that appears on the data feature curve in the similar segments is taken as the reference segment. For any independent segment in the similar segments other than the reference segment, the ratio of the endpoint differences between the reference segment and the independent segment is calculated as the error value. All error values ​​obtained from the similarity segmentation are combined into an error sequence.

[0045] In one embodiment, since the degree of segmentation change between any two independent segments in the similarity segment is not a fixed value, each independent segment in the similarity segment is not completely similar but has a certain error. Therefore, the endpoint difference of each independent segment in the similarity segment is calculated, where the endpoint difference is the difference in the ordinate between the two endpoints of the independent segment. Taking the first independent segment in the similarity segment that appears on the data characteristic curve as the reference segment, the ratio of the endpoint difference of the reference segment to the endpoint difference of each other independent segment in the similarity segment is calculated as the error value. The error value formula is as follows:

[0046] in, This represents the error value between the reference segment and the nth independent segment in the similar segmentation. This represents the endpoint difference in the reference segment. This represents the endpoint difference in the nth independent segment.

[0047] Based on the above error value formula, the error value between each independent segment and the reference segment in the similar segment is obtained, thus forming an error sequence.

[0048] The optimal strategy evaluation module 13 is used to take the first independent segment in each of the similar segments as a primitive, count all types of primitives, obtain at least one combination strategy based on all types of primitives, construct the strategy evaluation coefficient of the combination strategy based on the number of primitives contained in the combination strategy for any combination strategy, and obtain the optimal strategy based on the strategy evaluation coefficient of each combination strategy.

[0049] Because similarity segments contain at least two data points with relatively fixed and continuous frequencies, the first independent segment in each similarity segment is integrated into a single primitive. The number of independent segments in a similarity segment represents the frequency of the primitive. Due to the large volume of acquired index data, multiple similarity segments are obtained, meaning each similarity segment corresponds to one primitive, leading to various primitives. All types of primitives are statistically analyzed, and the primitives are arranged and combined according to these types to obtain at least one combination strategy. For any combination strategy, a strategy evaluation coefficient is constructed based on the number of frequency statistical primitives contained in the strategy. Based on the strategy evaluation coefficient of each combination strategy, the optimal strategy is obtained, and the frequency statistical primitives under the optimal strategy are taken as the optimal statistical primitives.

[0050] Among them, arranging and combining primitives according to all types of primitives yields at least one combination strategy, including: Select s types of primitives from all the types of primitives to form a frequency statistical primitive, and then use each of the remaining primitive types as a frequency statistical primitive to obtain... A combination strategy, where u is the number of all types of primitives, and s is less than or equal to u.

[0051] In one implementation, due to the large amount of data, multiple primitives are obtained. Therefore, the types of primitives are counted, and all types of primitives are permuted and combined to obtain at least one combination strategy. For example, if there are 4 primitives (a, b, c, d), arbitrarily selecting one primitive as a frequency statistics primitive and using each of the remaining primitive types as a frequency statistics primitive results in 4 combination strategies; arbitrarily selecting 2 primitives as frequency statistics primitives and using each of the remaining primitive types as a frequency statistics primitive results in 6 combination strategies; arbitrarily selecting 3 primitives as frequency statistics primitives and using each of the remaining primitive types as a frequency statistics primitive results in 4 combination strategies; arbitrarily selecting 4 primitives as frequency statistics primitives and using each of the remaining primitive types as a frequency statistics primitive results in 1 combination strategy. Therefore, there are a total of 15 combination strategies among these 4 primitives. The combination strategy formula is:

[0052] Where N represents the number of possible combination strategies, and u represents the number of types of primitives. This indicates that any one of the u primitives can be selected as the frequency statistics primitive. This indicates that any two of the u primitives can be selected as frequency statistics primitives. This indicates that any 3 of the u primitives can be selected as frequency statistics primitives. This means selecting any u-2 primitives from u primitives as frequency statistics primitives. The expression 'u-1' represents selecting any u-1 primitives from u primitives as frequency statistics primitives, and '1' represents selecting u primitives from u primitives as frequency statistics primitives. The combination strategies obtained from each selection method are summed to obtain the final N combination strategies, which is... A combination strategy.

[0053] Furthermore, after determining the combination strategy, in this embodiment of the invention, for any combination strategy, a strategy evaluation coefficient of the combination strategy is constructed according to the number of frequency statistical primitives contained in the combination strategy, and an optimal strategy is obtained based on the strategy evaluation coefficient of each combination strategy.

[0054] The strategy evaluation coefficients of the combined strategy are constructed based on the number of frequency statistical primitives included in the combined strategy, including: The total number of frequency statistical primitives under the combined strategy is counted. Based on the occurrence frequency of each frequency statistical primitive under the combined strategy, the occurrence number of each occurrence frequency is counted, and the occurrence frequency corresponding to the maximum occurrence number is obtained as the target occurrence frequency. The ratio between the frequency of occurrence of the target and the total number is calculated as the evaluation index. The evaluation index is then substituted into an exponential function with the natural constant as the base, and the result is used as the strategy evaluation coefficient of the combined strategy.

[0055] In one implementation, assuming there are 10 primitives (a, b, c, b, c, d, a, d, b, c), of which (a, b, c, d) are four types of primitives, (a, d) is selected as a frequency statistics primitive A. The remaining primitive b is selected as frequency statistics primitive b, and primitive c is selected as frequency statistics primitive c. The primitive sequence then becomes (A, b, c, b, c, A, b, c). At this point, the total number of frequency statistics primitives is 8. The occurrence frequency of frequency statistics primitive A is 2, the occurrence frequency of frequency statistics primitive b is 3, and the occurrence frequency of frequency statistics primitive c is 3. The number of occurrences with a frequency of 2 is 1, and the number of occurrences with a frequency of 3 is 2. Therefore, the occurrence frequency corresponding to the occurrence frequency of 2 is selected as the target occurrence frequency. The target occurrence frequency is 3, and the total number of frequency statistics primitives is 8. The strategy evaluation coefficient formula is:

[0056] in, This represents the ratio of the frequency of frequency statistical primitives that appear with the same frequency under this combination strategy to the total number of frequency statistical primitives. This represents the frequency of frequency statistical primitives that appear with the same frequency under this combined strategy, and m represents the total number of frequency statistical primitives under this combined strategy. denoted by , where e represents the base of the natural logarithm.

[0057] Similarly, the strategy evaluation coefficients for each combined strategy are calculated.

[0058] The optimal strategy is obtained based on the strategy evaluation coefficient of each of the combined strategies, including: The strategy evaluation coefficient of each combined strategy is subtracted from the preset value, and the combined strategy with the smallest difference is selected as the optimal strategy.

[0059] In one implementation, since the range of the strategy evaluation coefficient is [1, e], the closer the strategy evaluation coefficient is to 1, the smaller the frequency of the frequency statistical primitives that appear with the same frequency under the combined strategy, that is, the smaller the number of characters with fixed frequency under the strategy. Therefore, the strategy evaluation coefficient of each combined strategy is subtracted from the constant 1, and the combined strategy with the smallest difference is selected as the optimal strategy, and the frequency statistical primitives under the optimal strategy are selected as the optimal statistical primitives.

[0060] The data encoding module 14 is used to construct a Huffman tree of the corresponding indicator data within the preset time period based on the optimal statistical primitives, obtain the corresponding Huffman code, and complete the data compression.

[0061] The frequency of each optimal statistical primitive is used as the weight, and the type of the optimal statistical primitive is used as the leaf node of the Huffman tree. Based on the weight and leaf node, the corresponding Huffman tree of the indicator data within the preset time period is constructed. Based on the constructed Huffman tree, starting from the root node of the Huffman tree, the left subtree is 0 and the right subtree is 1. The 0s and 1s encountered along the way are accumulated to obtain the corresponding Huffman code, thus completing the data compression of cultural and creative products. The construction of the Huffman tree is an existing technology and will not be elaborated here.

[0062] After data compression is completed, the process also includes: The Huffman-coded bitstream is read from the data compressed using the Huffman coding, and the corresponding index data within the preset time period is reconstructed based on the error sequence and the Huffman-coded bitstream.

[0063] Thus, the Huffman tree was constructed based on the optimal strategy, and the corresponding Huffman code was obtained, enabling efficient compression and management of relevant indicator data for cultural and creative products.

[0064] In summary, this invention provides a digital management platform for cultural and creative products, comprising: a data feature curve construction module, used to acquire indicator data corresponding to any type of data of cultural and creative products within a preset time period, and construct a data feature curve for the indicator data; a similar segmentation acquisition module, used to acquire trend change feature points on the data feature curve, divide the data feature curve into at least two independent segments based on all trend change feature points, and select independent segments with similar change trends from all independent segments as similar segments; an optimal strategy evaluation module, used to take the first independent segment in each similar segment as a primitive, statistically obtain all types of primitives, obtain at least one combination strategy based on all types of primitives, construct a strategy evaluation coefficient for any combination strategy based on the number of primitives contained in the combination strategy, and obtain an optimal strategy based on the strategy evaluation coefficient of each combination strategy; and a data encoding module, used to construct a Huffman tree for the indicator data corresponding to the preset time period based on the optimal strategy, obtain the corresponding Huffman code, and complete data compression. The data feature curve is divided into multiple independent segments based on trend change characteristic points. By selecting independent segments with similar change trends as primitives, various combination strategies are constructed according to the combination methods of primitive types, and the optimal strategy is evaluated. The Huffman tree of the indicator data is constructed using the primitives under the optimal strategy, resulting in Huffman coding that can fully reflect the differences in the frequency of character occurrence. This greatly reduces the number of characters with relatively fixed frequencies in the indicator data, improves the efficiency of data compression of cultural and creative product-related data using Huffman coding, and enhances data security and overall system performance.

[0065] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A digital management platform for cultural and creative products, characterized in that, The digital management platform for cultural and creative products includes: The data feature curve construction module is used to obtain the indicator data corresponding to any type of data of cultural and creative products within a preset time period and construct the data feature curve of the indicator data. The similar segmentation acquisition module is used to acquire trend change feature points on the data feature curve, divide the data feature curve into at least two independent segments based on all trend change feature points, and select independent segments with similar change trends from all independent segments as similar segments. The optimal strategy evaluation module is used to take the first independent segment in each of the similar segments as a primitive, count all types of primitives, obtain at least one combination strategy based on all types of primitives, construct the strategy evaluation coefficient of the combination strategy based on the number of primitives contained in the combination strategy for any combination strategy, and obtain the optimal strategy based on the strategy evaluation coefficient of each combination strategy. The data encoding module is used to construct the Huffman tree of the corresponding indicator data within the preset time period according to the optimal strategy, obtain the corresponding Huffman code, and complete the data compression.

2. The digital management platform for cultural and creative products according to claim 1, characterized in that, The similarity segmentation acquisition module filters out independent segments with similar changing trends from all independent segments as similar segments, including: For any independent segment, obtain the maximum and minimum values ​​of the curvature of the independent segment at the target point, and calculate the mean between the maximum and minimum values ​​of the curvature at the target point as the average curvature of the independent segment; The degree of segment change trend between each pair of independent segments is obtained based on the average curvature of each independent segment, and independent segments with similar change trends are selected as similar segments based on the degree of segment change trend between each pair of independent segments.

3. The digital management platform for cultural and creative products according to claim 2, characterized in that, The similarity segmentation acquisition module obtains the degree of segmentation change trend between every two independent segments based on the average curvature of each independent segment, including: For any independent segment, calculate the tangent slope of all points on the independent segment to obtain the average slope on the independent segment; For any two independent segments, the absolute value of the difference between the average curvatures of the two independent segments is obtained as the first absolute value, and the absolute value of the difference between the average slopes of the two independent segments is obtained as the second absolute value. The first absolute value is inversely normalized to obtain the first variable, and the second absolute value is inversely normalized to obtain the second variable. The first variable and the second variable are weighted and summed, and the corresponding result is taken as the degree of segmentation change trend of the two independent segments.

4. The digital management platform for cultural and creative products according to claim 2, characterized in that, The similarity segmentation acquisition module filters out independent segments with similar change trends based on the degree of segmentation change trend between every two independent segments, including: For any two independent segments, if the degree of segmentation change trend between the two independent segments is within a preset range, then the two independent segments are determined to be initial similar segments; For E initial similar segments, detect whether there is at least one identical independent segment among the E initial similar segments. If at least one identical independent segment is detected among the E initial similar segments, then the initial similar segments with identical independent segments among the E initial similar segments are considered as one similar segment.

5. The digital management platform for cultural and creative products according to claim 1, characterized in that, The optimal strategy evaluation module obtains at least one combination strategy based on all types of primitives, including: selecting s primitives from all types of primitives to combine into a frequency statistical primitive, and using each of the remaining primitives as a frequency statistical primitive, thus obtaining... A combination strategy, wherein u is the number of types of the primitive, and s is less than or equal to u.

6. The digital management platform for cultural and creative products according to claim 5, characterized in that, The optimal strategy evaluation module constructs the strategy evaluation coefficients of the combined strategy based on the number of primitives contained in the combined strategy, including: The total number of frequency statistical primitives under the combined strategy is counted. Based on the occurrence frequency of each frequency statistical primitive under the combined strategy, the occurrence number of each occurrence frequency is counted, and the occurrence frequency corresponding to the maximum occurrence number is obtained as the target occurrence frequency. The ratio between the frequency of occurrence of the target and the total number is calculated as the evaluation index. The evaluation index is then substituted into an exponential function with the natural constant as the base, and the result is used as the strategy evaluation coefficient of the combined strategy.

7. The digital management platform for cultural and creative products according to claim 1, characterized in that, The optimal strategy evaluation module obtains the optimal strategy based on the strategy evaluation coefficients of each combined strategy, including: The strategy evaluation coefficient of each combined strategy is subtracted from the preset value, and the combined strategy with the smallest difference is selected as the optimal strategy.

8. The digital management platform for cultural and creative products according to claim 1, characterized in that, After filtering out independent segments with similar changing trends from all independent segments as similar segments in the similar segment acquisition module, the module further includes: The difference between the maximum endpoint value and the minimum endpoint value in each independent segment of the similar segments is calculated as the endpoint difference of the corresponding independent segment. The first independent segment that appears on the data feature curve in the similar segments is taken as the reference segment. For any independent segment in the similar segments other than the reference segment, the ratio error value of the endpoint difference between the reference segment and the independent segment is calculated. All error values ​​obtained from the similarity segmentation are combined into an error sequence.

9. A digital management platform for cultural and creative products according to claim 8, characterized in that, After data compression is completed in the data encoding module, the following is also included: The Huffman-coded bitstream is read from the data compressed using the Huffman coding, and the corresponding index data within the preset time period is reconstructed based on the error sequence and the Huffman-coded bitstream.

10. A digital management platform for cultural and creative products according to claim 1, characterized in that, The similarity segmentation acquisition module acquires trend change feature points on the data feature curve, including: The maximum and minimum points on the data feature curve are obtained as trend change feature points on the data feature curve.

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