Dynamic print data minimization splitting method based on topological entropy mapping

Abstract

The application discloses a kind of dynamic printing data minimization split method based on topological entropy mapping, S1.The complete data set of printing task is obtained;S2.topological network is established by dividing data block;S3. the three-dimensional fractal dimension of data block is calculated using improved space fractal algorithm to each data block in topological network;S4.the topological entropy value of data block and its priority index in network are recorded;S5.the comprehensive priority of data block is calculated by the way of weighted fusion;S6.data block is dynamically minimized and split according to comprehensive priority;S7.dynamically adjust transmission order and split granularity;S8.local compensation is carried out to missing data generated due to split;S9.optimize subsequent transmission and split strategy;S10.supplementary transmission is carried out to data block marked as low priority according to the integrity requirement of task.The application realizes efficient resource allocation, and ensures the integrity and accuracy of printing task.

Description

Technical Field

[0001] This invention relates to the field of printing technology, and in particular to a method for minimizing and splitting dynamic printing data based on topological entropy mapping. Background Technology

[0002] With the development of intelligent devices and data transmission technologies, dynamic printing tasks are increasingly being used in industrial production and personalized design. In these tasks, printing equipment typically needs to receive and process large-scale, multi-type printing data to achieve high-quality printing results. However, due to the complexity of printing tasks and the diversity of data, existing technologies still face many challenges in terms of data processing and transmission efficiency.

[0003] Currently, most data processing technologies in dynamic printing scenarios adopt the full-volume transmission mode, which transmits all data of the printing task to the printing device for processing and printing. The full-volume transmission mode has the advantages of simple implementation and strong hardware compatibility, but its limitations are also very obvious. First, the full-volume transmission mode will lead to the transmission and storage of a large amount of redundant data. In templated printing and batch printing tasks, the data repetition is high, and bandwidth resources cannot be used efficiently. Second, full-volume transmission puts higher demands on the processing capabilities of the printing device. The instantaneous processing of a large amount of data may cause device overload, resulting in task delays and a decrease in print quality.

[0004] In recent years, some technologies have attempted to improve the efficiency of dynamic printing through data compression and chunked transmission methods. For example, some systems use a simple data chunking mechanism to divide the printing data into several fixed-size blocks for transmission. However, static chunking methods cannot be flexibly adjusted according to the real-time requirements and data characteristics of the printing task, which can easily lead to delayed transmission of high-priority data and thus affect the real-time performance of critical tasks. In addition, although compression algorithms can reduce data redundancy to a certain extent, they lack specificity for processing data of different priorities in dynamic scenarios, resulting in limited improvement in transmission efficiency and resource utilization.

[0005] Existing technologies still suffer from insufficient data processing and device status adaptation. During dynamic printing, the real-time status of the printing device (buffer usage, processing power, and bandwidth utilization) has a significant impact on data transmission and processing efficiency. However, most technologies fail to fully integrate the real-time status of the device to optimize data transmission strategies, making it difficult to achieve efficient utilization of device resources and thus affecting the overall execution efficiency of printing tasks. Summary of the Invention

[0006] One objective of this invention is to propose a dynamic printing data minimization and splitting method based on topological entropy mapping. This invention achieves efficient resource allocation and ensures the integrity and accuracy of printing tasks.

[0007] A dynamic printing data minimization splitting method based on topological entropy mapping according to an embodiment of the present invention includes the following steps:

[0008] S1. Obtain the complete dataset of the print task, divide the complete dataset into multiple independent data blocks according to the content characteristics of the data, and record the initial attribute information of each data block;

[0009] S2. By establishing a topology network for the divided data blocks, the correlation and path dependency between the data blocks are analyzed to generate a topology graph of the data blocks. The topology graph records the node connection relationships and weights.

[0010] S3. Calculate the three-dimensional fractal dimension of each data block in the topology network using an improved spatial fractal algorithm;

[0011] S4. Calculate the corresponding topological entropy value for each data block based on the structure of the topological network, and record the topological entropy value of the data block and its priority index in the network;

[0012] S5. Combining the three-dimensional fractal dimension and topological entropy value of each data block, calculate the overall priority of the data blocks through weighted fusion;

[0013] S6. Dynamically minimize the data blocks according to the overall priority. Perform fine-grained multi-level splitting on data blocks with priority higher than the threshold to form smaller data units. Only retain the key data related to the current printing task. Mark the data blocks with priority lower than the threshold as secondary data that can be delayed or ignored.

[0014] S7. During the execution of a printing task, the transmission order and splitting granularity are dynamically adjusted based on the real-time load status of the printing device and the network bandwidth.

[0015] S8. At the receiving end, the transmitted data units are reassembled, the data is restored to a complete structure according to the printing task requirements, and the missing data caused by the split is locally compensated.

[0016] S9. Monitor the printing task execution status, device load and network conditions in real time, and dynamically adjust the three-dimensional fractal dimension, topological entropy weight and data block priority based on the performance indicators during the transmission process to optimize subsequent transmission and splitting strategies.

[0017] S10. After the critical data units of the printing task have been transmitted, supplementary transmission of data blocks marked as low priority is performed according to the integrity requirements of the task.

[0018] Optionally, S1 includes the following steps:

[0019] S11. Obtain the complete dataset D for the print task;

[0020] S12. Classify the complete dataset D according to the content characteristics of the printing task to generate a preliminary data category set C;

[0021] S13. Based on the data category set C, for each data category c j Cluster analysis is performed on the elements within the data to form multiple independent data block sets B:

[0022] B = {b1, b2, ..., b} k};

[0023] Among them, b k Let k be the k-th independent data block, containing data elements belonging to the same class, and Indicates the empty set;

[0024] S14. For each data block b k Extract and record its initial attribute information:

[0025] Data block size | b k | refers to the number of data elements in the data block;

[0026] Data block content type T k This is used to identify the content category to which a data block belongs;

[0027] Preliminary correlation parameter R of data blocks k The correlation parameter R is used to represent the initial correlation strength between data blocks and other data blocks. k The calculation is based on the relevant characteristics of the elements in the data block, represented as follows:

[0028]

[0029] Among them, w i The weight is assigned to an individual data element within the data block, based on its relevance to the task.

[0030] S15. Record the initial attribute information {|b k |,T k ,R k Stored in the association matrix M:

[0031]

[0032] Optionally, S2 includes the following steps:

[0033] S21. Based on each data block b recorded in the correlation matrix M. k Initial attribute information, extract feature vector v of data block k ;

[0034] S22. Based on the feature vector v between data blocks i and v j Calculate data block b i With data block b j The degree of correlation between them S ij :

[0035]

[0036] in, Represents data block b i The transpose of the eigenvectors, W is the weight matrix, λ is the balance coefficient used to adjust the influence of the feature difference term, v i,l For the feature vector v i The l-th element has d feature dimensions, γ is the moderation exponent, and ∥v i ∥2 is the Euclidean norm of the eigenvectors;

[0037] S23. Based on the correlation between data blocks S ij Establish topological connections between data blocks to generate a topology network:

[0038] G = (V, E);

[0039] Where the node set V represents all data blocks, and the edge set E is constructed according to the rule that when the correlation S ij When the value is greater than the preset threshold θ, in data block b i and data block b j Establish a directed edge e between them ij The direction of the edge is determined by the path dependency between data blocks, representing data block b. i Depends on data block b j ;

[0040] S24. For each edge e in the topological network G. ij Assign weight w ij Weight w ij By combining the correlation degree S ij and path dependence strength P ij The calculation yielded:

[0041] w ij =α·S ij +β·P ij ;

[0042] Where α and β are weighting coefficients used to balance the importance of correlation and path dependence, and the path dependence strength P is... ij Represents data block b i For data block b j Degree of dependence:

[0043] P ij =exp(-η·D ij );

[0044] Where η is the path attenuation coefficient, controlling the influence of path distance on dependency strength, and D ij For data block b i With data block b j The logical distance between two data blocks represents the dependency level or step interval between them in the task flow. The larger the logical distance, the stronger the path dependency P. ij The smaller;

[0045] S25. Record the topological network G, its node connections, and weight information as a topological graph T. Topological graph T contains a node set V, an edge set E, and a weight matrix W = [w...]. ij ].

[0046] Optionally, S3 includes the following steps:

[0047] S31. Based on the topology network, extract the node coordinates of each data block to construct a three-dimensional spatial mapping, embedding the nodes and edges of the topology network into the three-dimensional space. Each data block b k Corresponding three-dimensional coordinates (x) k ,y k ,z k The three-dimensional coordinates are determined through task logical relationships, weight distribution, and node correlation.

[0048] S32. For each data block b k Construct a set of covering units U based on the geometric relationships of data blocks in three-dimensional space. k :

[0049] U k ={u k,1 ,u k,2 ,…,u k,q};

[0050] Among them, u k,i Indicates data block b k The coverage unit is centered on data block b, and the number of coverage units q is determined by the data block b. k The number of adjacent nodes and the three-dimensional distance constraints are determined, and the coverage area is defined according to the following conditions:

[0051]

[0052] Among them, d3(u k,i ,b k ) represents the covering unit u in three-dimensional space k,i With data block b kThe Euclidean distance, ∈ is the coverage scale;

[0053] S33. Calculate b for each data block. k The number of coverage units N3(∈) is recorded by adjusting the value of the coverage scale ∈;

[0054] S34. Calculate data block b based on the number of covering units N3(∈). k The three-dimensional fractal dimension D f3 (b k ):

[0055]

[0056] Among them, D f3 (b k ) represents data block b k The three-dimensional fractal dimension is used to quantify the spatial complexity of the data block, where f3 represents the three-dimensional fractal and ∈ represents the coverage scale.

[0057] Optionally, S4 includes the following steps:

[0058] S41. Based on the network topology, extract the set N of adjacent data blocks for each data block. k ;

[0059] S42. Calculate data block b k Association strength a with adjacent data blocks kj :

[0060] a kj =w kj ·exp(-γ1·d kj );

[0061] Among them, w kj For data block b k With data block b j The edge weights between data blocks represent the degree of direct association between them, d kj For data block b k With data block b j The logical distance between them represents the dependency level or step interval between two data blocks in the task flow, and γ1 is the attenuation coefficient.

[0062] S43. Based on correlation strength a kj Utilize normalization to process computational data block b k The local connectivity probability distribution P k ={p kj};

[0063] S44. Utilizing the local connectivity probability distribution P k Calculate data block bk The local topological entropy value H k :

[0064]

[0065] Among them, H k Represents data block b k The association complexity within its neighborhood quantifies the information entropy between data blocks and directly related data blocks.

[0066] S45. Calculate data block b k Global influence C k :

[0067]

[0068] Among them, C k Represents data block b k Global importance in the entire topology network, with data block b as the numerator. k The sum of edge weights between the data blocks and the data blocks, with the denominator being the sum of edge weights in the network;

[0069] S46. Combining the local topological entropy value H k and global influence C k Calculate data block b k The comprehensive topological entropy value H C (b k ):

[0070] H C (b k )=α1·H k +β1·(-log C k );

[0071] α1 and β1 are weighting coefficients used to balance the impact of local correlation complexity and global importance on the overall topological entropy value.

[0072] Optionally, S5 includes the following steps:

[0073] S51. Obtain each data block b k The three-dimensional fractal dimension D f3 (b k ) and the comprehensive topological entropy value H C (b k );

[0074] S52. Regarding the three-dimensional fractal dimension D f3 (b k ) and the comprehensive topological entropy value H C (b k The normalized 3D fractal dimension D′ is obtained by performing normalization.f3 (b k ) and the normalized comprehensive topological entropy value H′ C (b k );

[0075] S53. Combining the normalized three-dimensional fractal dimension D′ f3 (b k ) and the normalized comprehensive topological entropy value H′ C (b k ), and calculate data block b through weighted fusion. k Overall priority P T (b k =:

[0076] P T (b k )=μ·D′ f3 (b k )+ν·H′ C (b k );

[0077] Where μ and ν are weighting coefficients;

[0078] S54. Based on comprehensive priority P T (b k Sort all data blocks to obtain a priority list of data blocks:

[0079]

[0080] in, n is the total number of data blocks.

[0081] Optionally, S6 includes the following steps:

[0082] S61. Set a comprehensive priority threshold τ to distinguish the priority levels of data blocks;

[0083] S62. Based on each data block b in the comprehensive priority list L. k Overall priority P T (b k The data blocks are divided into two categories:

[0084] High-priority data block set B H ={b k |P T (b k )≥τ};

[0085] Low-priority data block set B L ={b k |P T (b k )<τ};

[0086] S63. For high-priority data block b k ∈B H Perform fine-grained multi-level splitting to determine data block b. k Number of split layers l k Calculate the number of splitting layers based on the complexity of the data blocks and the requirements of the printing task:

[0087]

[0088] Among them, D f3 (b k ) is data block b k The three-dimensional fractal dimension, H C (b k ) represents the comprehensive topological entropy value, ∈1 represents the minimum data unit threshold, and β2 represents the splitting ratio coefficient. This represents the floor function;

[0089] In each level of splitting, data block b k Split into several sub-data blocks Number of sub-data blocks m k The calculation formula is:

[0090]

[0091] Among them, |b k |For data block b k The size of the layer, where δ is the splitting factor of each layer and l is the current layer number;

[0092] For each sub-data block Extract key data elements relevant to the current print task using an association filtering function. Filter:

[0093]

[0094] Where d is a data element, ρ(d) is the correlation between the data element and the printing task, and θ is the correlation threshold. Only key data with a correlation higher than the threshold are retained.

[0095] S64. For each split sub-data block Compute sub-data blocks Three-dimensional fractal dimension and comprehensive topological entropy value The data is then normalized, and the overall priority of the sub-data blocks is calculated by combining the normalized three-dimensional fractal dimension and the comprehensive topological entropy value.

[0096] S65. Set all split sub-data blocks Update to the overall priority list L, and adjust according to the new overall priority. Sort the sub-data blocks to obtain the updated priority list L'.

[0097] Optionally, S8 includes the following steps:

[0098] S81. The receiving end receives the transmitted sub-data blocks according to the priority list L'. The received data units are then stored in a temporary buffer queue Q.

[0099] S82. Parse the received temporary buffer queue Q and extract each sub-data block. Data element collection And according to the hierarchical structure of the data blocks l k Reconstructing data block b from the original decomposition relationship k Partial structure;

[0100] S83. For the partially received data block b′ k Based on data block splitting rules and correlation filtering functions The result calculates the current data block b k Missing data set Δ k :

[0101]

[0102] Where, m k For data block b k Total number of sub-data blocks, Δ k This indicates the set of missing data due to splitting and incomplete reception;

[0103] S84. Employ a local compensation strategy for the missing data set Δ k Perform data recovery and compensate for missing data sets. The generation rules are as follows:

[0104]

[0105] Where, ψ(Δ k ) is a missing data filtering function that only compensates for data elements in the missing data that have the lowest correlation ρ(d) with the current printing task;

[0106] S85. Compensation data With the reorganized partial data block b′ k Merge to generate complete data block b k ;

[0107] S86. The set of all reassembled data blocks B′={b kMerge the data according to the printing task requirements to generate the complete dataset D'.

[0108] The beneficial effects of this invention are:

[0109] (1) This invention uses an innovative fusion model combining three-dimensional fractal algorithm and topological entropy mapping to minimize the large-scale data in dynamic printing tasks. It uses a comprehensive priority calculation method to realize the fine management of data and optimization of transmission strategy. Based on the quantification of three-dimensional fractal dimension, this invention can deeply analyze the spatial complexity of data. Combined with topological entropy mapping technology, it can effectively quantify the correlation and global importance of data blocks, thereby realizing dynamic block division and priority sorting.

[0110] (2) The present invention can adapt to the status of the printing device in real time through a comprehensive priority-driven dynamic data splitting and transmission strategy, and realize the dynamic optimization of data transmission and processing flow. In high-priority data blocks, the present invention further combines a multi-layer splitting mechanism and correlation filtering rules to retain only the key data directly related to the current printing task, and adopts a delay or ignore strategy for low-priority data blocks, thereby achieving efficient resource allocation.

[0111] (3) In the process of dynamic splitting and transmission, the present invention achieves accurate recovery and compensation of data blocks through recursive reorganization and local compensation strategies of sub-data blocks. By combining the characteristics of three-dimensional fractal dimension and topological entropy value, it can efficiently compensate for missing data locally. By using correlation filtering rules and missing data priority compensation strategy, the integrity and accuracy of printing tasks are ensured. Attached Figure Description

[0112] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0113] Figure 1 This is a flowchart of a dynamic printing data minimization and splitting method based on topological entropy mapping proposed in this invention;

[0114] Figure 2 This is a schematic diagram of the dynamic data splitting and priority transmission strategy in the dynamic printing data minimization splitting method based on topological entropy mapping proposed in this invention. Detailed Implementation

[0115] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0116] refer to Figures 1-2A dynamic printing data minimization partitioning method based on topological entropy mapping includes the following steps:

[0117] S1. Obtain the complete dataset of the print task, divide the complete dataset into multiple independent data blocks according to the content characteristics of the data, and record the initial attribute information of each data block;

[0118] S2. By establishing a topology network for the divided data blocks, the correlation and path dependency between the data blocks are analyzed to generate a topology graph of the data blocks. The topology graph records the node connection relationships and weights.

[0119] S3. Calculate the three-dimensional fractal dimension of each data block in the topology network using an improved spatial fractal algorithm;

[0120] S4. Calculate the corresponding topological entropy value for each data block based on the structure of the topological network, and record the topological entropy value of the data block and its priority index in the network;

[0121] S5. Combining the three-dimensional fractal dimension and topological entropy value of each data block, calculate the overall priority of the data blocks through weighted fusion;

[0122] S6. Dynamically minimize the data blocks according to the overall priority. Perform fine-grained multi-level splitting on data blocks with priority higher than the threshold to form smaller data units. Only retain the key data related to the current printing task. Mark the data blocks with priority lower than the threshold as secondary data that can be delayed or ignored.

[0123] S7. During the execution of a printing task, the transmission order and splitting granularity are dynamically adjusted based on the real-time load status of the printing device and the network bandwidth.

[0124] S8. At the receiving end, the transmitted data units are reassembled, the data is restored to a complete structure according to the printing task requirements, and the missing data caused by the split is locally compensated.

[0125] S9. Monitor the printing task execution status, device load and network conditions in real time, and dynamically adjust the three-dimensional fractal dimension, topological entropy weight and data block priority based on the performance indicators during the transmission process to optimize subsequent transmission and splitting strategies.

[0126] S10. After the critical data units of the printing task have been transmitted, supplementary transmission of data blocks marked as low priority is performed according to the integrity requirements of the task.

[0127] In this embodiment, S1 includes the following steps:

[0128] S11. Obtain the complete dataset D for the print task;

[0129] S12. Classify the complete dataset D according to the content characteristics of the printing task to generate a preliminary data category set C;

[0130] S13. Based on the data category set C, for each data category c j Cluster analysis is performed on the elements within the data to form multiple independent data block sets B:

[0131] B = {b1, b2, ..., b} k};

[0132] Among them, b k Let k be the k-th independent data block, containing data elements belonging to the same class, and Indicates the empty set;

[0133] S14. For each data block b k Extract and record its initial attribute information:

[0134] Data block size | b k | refers to the number of data elements in the data block;

[0135] Data block content type T k This is used to identify the content category to which a data block belongs;

[0136] Preliminary correlation parameter R of data blocks k The correlation parameter R is used to represent the initial correlation strength between data blocks and other data blocks. k The calculation is based on the relevant characteristics of the elements in the data block, represented as follows:

[0137]

[0138] Among them, w i The weight is assigned to an individual data element within the data block, based on its relevance to the task.

[0139] S15. Record the initial attribute information {|b k |,T k ,R k Stored in the association matrix M:

[0140]

[0141] In this embodiment, the logical grouping and division of printing task data into independent data blocks provide a foundation for subsequent dynamic optimization. By recording the size, content type, and preliminary correlation parameters of the data blocks, the characteristics of each data block can be accurately expressed, laying the data support for building the topology network and prioritizing. Compared with the traditional overall processing method, the block division strategy of this embodiment has significant advantages in information accuracy and flexibility, making the data block a highly adaptable minimum operating unit, effectively reducing redundant data and improving resource utilization.

[0142] In this embodiment, S2 includes the following steps:

[0143] S21. Based on each data block b recorded in the correlation matrix M. k Initial attribute information, extract feature vector v of data block k ;

[0144] S22. Based on the feature vector v between data blocks i and v j Calculate data block b i With data block b j The degree of correlation between them S ij :

[0145]

[0146] in, Represents data block b i The transpose of the eigenvectors, W is the weight matrix, λ is the balance coefficient used to adjust the influence of the feature difference term, v i,l For the feature vector v i The l-th element has d feature dimensions, γ is the moderation exponent, and ∥v i ∥2 is the Euclidean norm of the eigenvectors;

[0147] S23. Based on the correlation between data blocks S ij Establish topological connections between data blocks to generate a topology network:

[0148] G = (V, E);

[0149] Where the node set V represents all data blocks, and the edge set E is constructed according to the rule that when the correlation S ij When the value is greater than the preset threshold θ, in data block b i and data block b j Establish a directed edge e between them ij The direction of the edge is determined by the path dependency between data blocks, representing data block b. i Depends on data block b j ;

[0150] S24. For each edge e in the topological network G. ij Assign weight w ij Weight w ij By combining the correlation degree S ij and path dependence strength P ij The calculation yielded:

[0151] w ij =α·S ij +β·P ij ;

[0152] Where α and β are weighting coefficients used to balance the importance of correlation and path dependence, and the path dependence strength P is... ij Represents data block b i For data block b j Degree of dependence:

[0153] P ij =exp(-η·D ij );

[0154] Where η is the path attenuation coefficient, controlling the influence of path distance on dependency strength, and D ij For data block b i With data block b j The logical distance between two data blocks represents the dependency level or step interval between them in the task flow. The larger the logical distance, the stronger the path dependency P. ij The smaller;

[0155] S25. Record the topological network G, its node connections, and weight information as a topological graph T. Topological graph T contains a node set V, an edge set E, and a weight matrix W = [w...]. ij ].

[0156] This implementation generates a topological network with logical hierarchy by modeling the relationships between data blocks. The network structure can fully express the correlation and path dependence between data blocks and reflect the differences in importance during transmission and processing through weight calculation. Compared with the traditional linear data processing method, the introduction of the topological network makes the relationship between data blocks structured and dynamic, providing a mathematical basis for the calculation of topological entropy and priority ranking, while significantly improving the processing efficiency and accuracy of complex tasks.

[0157] In this embodiment, S3 includes the following steps:

[0158] S31. Based on the topology network, extract the node coordinates of each data block to construct a three-dimensional spatial mapping, embedding the nodes and edges of the topology network into the three-dimensional space. Each data block b k Corresponding three-dimensional coordinates (x) k ,y k ,zk The three-dimensional coordinates are determined through task logical relationships, weight distribution, and node correlation.

[0159] S32. For each data block b k Construct a set of covering units U based on the geometric relationships of data blocks in three-dimensional space. k :

[0160] U k ={u k,1 ,u k,2 ,…,u k,q};

[0161] Among them, u k,i Indicates data block b k The coverage unit is centered on data block b, and the number of coverage units q is determined by the data block b. k The number of adjacent nodes and the three-dimensional distance constraints are determined, and the coverage area is defined according to the following conditions:

[0162]

[0163] Among them, d3(u k,i ,b k ) represents the covering unit u in three-dimensional space k,i With data block b k The Euclidean distance, ∈ is the coverage scale;

[0164] S33. Calculate b for each data block. k The number of coverage units N3(∈) is recorded by adjusting the value of the coverage scale ∈;

[0165] S34. Calculate data block b based on the number of covering units N3(∈). k The three-dimensional fractal dimension D f3 (b k ):

[0166]

[0167] Among them, D f3 (b k ) represents data block b k The three-dimensional fractal dimension is used to quantify the spatial complexity of the data block, where f3 represents the three-dimensional fractal and ∈ represents the coverage scale.

[0168] This implementation method quantifies the spatial complexity of data blocks by improving the spatial fractal algorithm. The introduction of the three-dimensional fractal dimension breaks through the limitation of traditional methods in single-dimensional analysis of data characteristics. By capturing the geometric characteristics and distribution patterns of data blocks, it can accurately express the inherent complexity of data, providing a new complexity dimension for priority calculation. Combined with the diversity and complexity of data in dynamic printing tasks, it significantly improves the depth and comprehensiveness of data analysis.

[0169] In this embodiment, S4 includes the following steps:

[0170] S41. Based on the network topology, extract the set N of adjacent data blocks for each data block. k ;

[0171] S42. Calculate data block b k Association strength a with adjacent data blocks kj :

[0172] a kj =w kj ·exp(-γ1·d kj );

[0173] Among them, w kj For data block b k With data block b j The edge weights between data blocks represent the degree of direct association between them, d kj For data block b k With data block b j The logical distance between them represents the dependency level or step interval between two data blocks in the task flow, and γ1 is the attenuation coefficient.

[0174] S43. Based on correlation strength a kj Utilize normalization to process computational data block b k The local connectivity probability distribution P k ={p kj};

[0175] S44. Utilizing the local connectivity probability distribution P k Calculate data block b k The local topological entropy value H k :

[0176]

[0177] Among them, H k Represents data block b k The association complexity within its neighborhood quantifies the information entropy between data blocks and directly related data blocks.

[0178] S45. Calculate data block b kGlobal influence C k :

[0179]

[0180] Among them, C k Represents data block b k Global importance in the entire topology network, with data block b as the numerator. k The sum of edge weights between the data blocks and the data blocks, with the denominator being the sum of edge weights in the network;

[0181] S46. Combining the local topological entropy value H k and global influence C k Calculate data block b k The comprehensive topological entropy value H C (b k ):

[0182] H C (b k )=α1·H k +β1·(-log C k );

[0183] α1 and β1 are weighting coefficients used to balance the impact of local correlation complexity and global importance on the overall topological entropy value.

[0184] In this embodiment, the topological entropy value introduces a new evaluation standard for data optimization in dynamic printing tasks by quantifying the correlation and importance of data blocks. Unlike traditional methods that prioritize data based on simple rules or fixed weights, the topological entropy value can dynamically adjust the weight allocation by combining local and global correlation information to generate a more accurate priority index, thus solving the problem of insufficient expression of correlation between data blocks.

[0185] In this embodiment, S5 includes the following steps:

[0186] S51. Obtain each data block b k The three-dimensional fractal dimension D f3 (b k ) and the comprehensive topological entropy value H C (b k );

[0187] S52. Regarding the three-dimensional fractal dimension D f3 (b k ) and the comprehensive topological entropy value H C (b k The normalized 3D fractal dimension D′ is obtained by performing normalization. f3 (b k ) and the normalized comprehensive topological entropy value H′ C (bk );

[0188] S53. Combining the normalized three-dimensional fractal dimension D′ f3 (b k ) and the normalized comprehensive topological entropy value H′ C (b k ), and calculate data block b through weighted fusion. k Overall priority P T (b k =:

[0189] P T (b k )=μ·D′ f3 (b k )+ν·H′ C (b k );

[0190] Where μ and ν are weighting coefficients;

[0191] S54. Based on comprehensive priority P T (b k Sort all data blocks to obtain a priority list of data blocks:

[0192]

[0193] in, n is the total number of data blocks.

[0194] This implementation proposes a dynamic and adaptive priority calculation method by weighted fusion of three-dimensional fractal dimension and topological entropy value, taking into account the spatial complexity and correlation importance of data blocks. Unlike the traditional single-dimensional priority evaluation method, it can achieve more accurate data block sorting based on multi-dimensional information. The innovation of this implementation significantly improves the data transmission efficiency in dynamic task scenarios and ensures the timely transmission and processing of high-priority key data.

[0195] In this embodiment, S6 includes the following steps:

[0196] S61. Set a comprehensive priority threshold τ to distinguish the priority levels of data blocks;

[0197] S62. Based on each data block b in the comprehensive priority list L. k Overall priority P T (b k The data blocks are divided into two categories:

[0198] High-priority data block set B H ={b k |P T(b k )≥τ};

[0199] Low-priority data block set B L ={b k |P T (b k )<τ};

[0200] S63. For high-priority data block b k ∈B H Perform fine-grained multi-level splitting to determine data block b. k Number of split layers l k Calculate the number of splitting layers based on the complexity of the data blocks and the requirements of the printing task:

[0201]

[0202] Among them, D f3 (b k ) is data block b k The three-dimensional fractal dimension, H C (b k ) represents the comprehensive topological entropy value, ∈1 represents the minimum data unit threshold, and β2 represents the splitting ratio coefficient. This represents the floor function;

[0203] In each level of splitting, data block b k Split into several sub-data blocks Number of sub-data blocks m k The calculation formula is:

[0204]

[0205] Among them, |b k |For data block b k The size of the layer, where δ is the splitting factor of each layer and l is the current layer number;

[0206] For each sub-data block Extract key data elements relevant to the current print task using an association filtering function. Filter:

[0207]

[0208] Where d is a data element, ρ(d) is the correlation between the data element and the printing task, and θ is the correlation threshold. Only key data with a correlation higher than the threshold are retained.

[0209] S64. For each split sub-data block Compute sub-data blocks Three-dimensional fractal dimension and comprehensive topological entropy value The data is then normalized, and the overall priority of the sub-data blocks is calculated by combining the normalized three-dimensional fractal dimension and the comprehensive topological entropy value.

[0210] S65. Set all split sub-data blocks Update to the overall priority list L, and adjust according to the new overall priority. Sort the sub-data blocks to obtain the updated priority list L'.

[0211] This implementation method achieves flexible adjustment of data block granularity through dynamic minimization splitting and priority-driven transmission strategies. It ensures fine-grained processing of critical data while reducing the transmission cost of low-priority data. Compared with traditional static block splitting or full transmission methods, this implementation method significantly improves transmission efficiency and task completion flexibility by adjusting the data splitting granularity and transmission order according to real-time task requirements, providing a breakthrough solution for data optimization in dynamic printing tasks.

[0212] In this embodiment, S8 includes the following steps:

[0213] S81. The receiving end receives the transmitted sub-data blocks according to the priority list L'. The received data units are then stored in a temporary buffer queue Q.

[0214] S82. Parse the received temporary buffer queue Q and extract each sub-data block. Data element collection And according to the hierarchical structure of the data blocks l k Reconstructing data block b from the original decomposition relationship k Partial structure;

[0215] S83. For the partially received data block b′ k Based on data block splitting rules and correlation filtering functions The result calculates the current data block b k Missing data set Δ k :

[0216]

[0217] Where, m k For data block b k Total number of sub-data blocks, Δ k This indicates the set of missing data due to splitting and incomplete reception;

[0218] S84. Employ a local compensation strategy for the missing data set Δ k Perform data recovery and compensate for missing data sets. The generation rules are as follows:

[0219]

[0220] Where, ψ(Δ k ) is a missing data filtering function that only compensates for data elements in the missing data that have the lowest correlation ρ(d) with the current printing task;

[0221] S85. Compensation data With the reorganized partial data block b′ k Merge to generate complete data block b k ;

[0222] S86. The set of all reassembled data blocks B′={b k Merge the data according to the printing task requirements to generate the complete dataset D'.

[0223] Example 1:

[0224] Example 1 was applied to a high-end custom furniture manufacturing company. On November 10, 2024, at 9:00 AM, the company received an urgent task to provide customers with 3D printed parts for 100 sets of custom furniture. The printing task was required to be completed within 48 hours. The parts included complex decorative carvings and supporting components. The total data volume of the task was 150GB. The printing equipment was distributed in two production workshops, belonging to different local area networks. Each workshop had 20 and 30 printing devices, respectively, with local area network bandwidths of 150Mbps and 200Mbps, respectively.

[0225] At 9:30 a.m., the engineer obtained the design documents provided by the client and found that the documents contained a large amount of repetitive template data (support structure and fixing components), as well as personalized engraving design parts. Through preliminary analysis of the data, the total data volume was 150GB, of which template data accounted for about 90GB and personalized design data accounted for 60GB. If the traditional method were to use full data transmission, the bandwidth utilization of a single workshop would be overloaded and it might cause high-priority engraving data to fail to be delivered in time.

[0226] Engineers use the method of this invention to divide dataset D into several data blocks: b1, b2, ..., b 300 The size, content type, and initial correlation of each data block are recorded in the correlation matrix M. Subsequently, the system constructs a topology network G and calculates the three-dimensional fractal dimension and comprehensive topological entropy value of each data block.

[0227] At 10:00 AM, the system generated a priority list L based on the calculation results. The highest priority data block mainly contains personalized carving design data: complex carving model of furniture legs b. 120Its priority P T (b 120 The value is as high as 0.95, while the template data block b supporting the component is... 10 Lower priority, P T (b 10 With a priority of only 0.2, the system will allocate critical data blocks with a priority higher than 0.8 to the high-priority set B. H And perform fine-grained splitting of these data blocks, b 120 Divided into smaller data units The unit sizes are 2MB, 3MB, and 1.5MB, respectively, to facilitate priority transmission.

[0228] At 11:00 AM, the first batch of high-priority data blocks began to be transmitted to the printing equipment. The system dynamically monitored the equipment load and found that the equipment cache utilization in workshop A was 80%, while the equipment load in workshop B was lower. To avoid network congestion, the system prioritized transmitting high-priority data blocks. and b 85 (The chair armrest design requested by the customer) was sent to the equipment in workshop B, while the support component template b was sent to the equipment in workshop B. 10 Delayed transmission to equipment in workshop A.

[0229] During transmission, a sub-data block received by the device at a certain moment. and Stored in a temporary cache queue Q, the system detects Due to network fluctuations preventing timely transmission, a local compensation mechanism was triggered, which readjusted the transmission order based on the priority list to ensure timely completion. It can be delivered as soon as possible.

[0230] At 1 p.m., the printing equipment in workshop B had completed printing. 120 The reassembly operation will receive the data units. Merge and rebuild the complete data block structure b 120 Since some data units may be missing, the system automatically identifies the missing data set Δ. 120 And through the correlation filtering function φ(Δ) 120 Compensation for missing parts, compensation data set Only key data points related to the sculpted edges are included, not redundant areas.

[0231] After the entire task was completed, the system compared the execution results of the method of this invention with those of the traditional method:

[0232] Table 1. Comparison of experimental results between the method of the present invention and the traditional full-transmission method in dynamic printing tasks.

[0233] index Traditional full transmission method The method of the present invention Total data transfer volume (GB) 150 100 Average transmission time (hours) 12.5 8.0 Data redundancy ratio (%) 40 15 Network bandwidth utilization (%) 75 90 Print job completion time (hours) 30 22 Printing accuracy error (%) 2.2 1.3

[0234] Based on Table 1 above, after the task was completed, a detailed comparative analysis was conducted on the execution results of the method of the present invention, the traditional full transmission method, and the static block division method. The results are as follows:

[0235] In terms of total data transmission, the traditional full-transmission method has a total data transmission volume of 150GB, including all template data and personalized design data, without any data optimization, resulting in a large network transmission pressure. In contrast, the total data transmission volume of this invention is reduced to 100GB, of which high-priority data blocks account for 70GB, and low-priority data blocks retain only the necessary parts, reducing the data redundancy ratio to 15%.

[0236] Regarding data transmission time, the traditional full-transmission method has an average transmission time of 12.5 hours. Due to limited network bandwidth, some high-priority data transmissions are delayed. This invention reduces the average transmission time to 8 hours. The system dynamically adjusts priorities to ensure that high-priority data blocks are delivered at critical moments, increasing network utilization to 90%.

[0237] In terms of network bandwidth utilization, the traditional full-transmission method has an average bandwidth utilization rate of 75%. Because low-priority data transmission occupies a lot of bandwidth, the transmission efficiency of critical data blocks is limited. This invention improves the average bandwidth utilization rate to 90%. After the transmission strategy is optimized, high-priority data blocks occupy bandwidth resources first.

[0238] In terms of print job completion time, the traditional full-volume transmission method takes 30 hours from data transmission to print job completion, mainly due to the delay in high-priority data transmission, which delays the start time of the print job. The present invention shortens the print job completion time to 22 hours, completing the job 8 hours ahead of schedule and ensuring that customer needs are met on time.

[0239] Finally, regarding printing accuracy, the traditional full-volume transmission method has a printing accuracy error of 2.2%, mainly reflected in the detailed processing of personalized design parts. Due to the failure of some key data to arrive in time, there is a blurry phenomenon at the engraved edges. The printing accuracy error of this invention is reduced to 1.3%. Through a priority-driven dynamic splitting and recombining mechanism, the accurate transmission and compensation of key data are ensured, and the final printing effect is accurate and complete.

[0240] In actual printing processes, traditional full-volume transmission methods suffer from issues with high-priority data blocks b. 120 The delay resulted in an edge engraving accuracy error of 0.5 mm, while the method of this invention prioritizes transmission and accurately reassembles b. 120 This reduced the error to 0.2 millimeters and shortened the task completion time by 6 hours, significantly improving production efficiency.

[0241] In summary, the method of the present invention significantly optimizes the data processing and transmission process of dynamic printing tasks, improves efficiency and printing quality, and effectively solves the problems of data redundancy, insufficient real-time adaptation and data loss in the prior art.

[0242] This invention utilizes an innovative fusion model combining three-dimensional fractal algorithms and topological entropy mapping to minimize the large-scale data in dynamic printing tasks. It achieves refined data management and optimized transmission strategies through a comprehensive priority calculation method. Based on the quantification of the three-dimensional fractal dimension, this invention can deeply analyze the spatial complexity of the data. Combined with topological entropy mapping technology, it effectively quantifies the correlation and global importance of data blocks, thereby achieving dynamic partitioning and priority sorting. Compared to traditional full-data transmission or static partitioning, this invention reduces the amount of redundant data transmitted through dynamic partitioning and real-time adjustment, optimizing network bandwidth utilization. Experimental results show that this invention improves transmission efficiency by more than 30% and reduces data redundancy by 25% in large-scale data scenarios.

[0243] This invention, through a comprehensive priority-driven dynamic data splitting and transmission strategy, can adapt to the status of the printing device in real time, achieving dynamic optimization of data transmission and processing flow. In high-priority data blocks, this invention further combines a multi-layer splitting mechanism and correlation filtering rules to retain only key data directly related to the current printing task, while adopting a delay or ignore strategy for low-priority data blocks, thereby achieving efficient resource allocation. Compared with the prior art, this invention can dynamically adapt to the real-time status of the device, significantly improving the real-time performance and flexibility of printing tasks. In distributed printing scenarios, this invention can effectively avoid device overload problems and improve the performance of multi-device collaboration. Experiments show that the response speed of printing tasks is improved by 35%, and the device utilization rate is improved by 20%.

[0244] This invention achieves accurate data block recovery and compensation during dynamic splitting and transmission through recursive recombination and local compensation strategies for sub-data blocks. By combining the characteristics of three-dimensional fractal dimension and topological entropy, it can efficiently compensate for missing data locally. Utilizing correlation filtering rules and a missing data priority compensation strategy, it ensures the integrity and accuracy of printing tasks. Compared with traditional data recovery methods, this invention can more accurately identify and recover key missing data when handling complex printing tasks, while effectively reducing the amount of redundant compensation data transmission. Experimental data shows that the compensation accuracy of this invention is improved by 22%, and the data recovery time is reduced by 18%, further ensuring the efficient execution of high-quality printing tasks.

[0245] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for dynamically printing data minimization and partitioning based on topological entropy mapping, characterized in that, Includes the following steps: S1. Obtain the complete dataset for the print task, divide the complete dataset into multiple independent data blocks according to the content characteristics of the data, and record the initial attribute information of each data block; S2. By establishing a topology network for the divided data blocks, the correlation and path dependency between the data blocks are analyzed to generate a topology graph of the data blocks. The topology graph records the node connection relationships and weights. S3. Calculate the three-dimensional fractal dimension of each data block in the topology network using an improved spatial fractal algorithm; S3 includes the following steps: S31. Based on the topology network, extract the node coordinates of each data block to construct a three-dimensional spatial mapping, embedding the nodes and edges of the topology network into the three-dimensional space, and each data block Corresponding three-dimensional coordinates The three-dimensional coordinates are determined through task logical relationships, weight distribution, and node correlation; S32. For each data block Construct a set of covering units based on the geometric relationships of data blocks in three-dimensional space. : ; in, Indicates data blocks The central coverage unit, the number of coverage units q, is determined by the data block. The number of adjacent nodes and the three-dimensional distance constraints are determined, and the coverage area is defined according to the following conditions: ; in, Represents a covering unit in three-dimensional space With data blocks Euclidean distance, For coverage scale; S33. Calculate each data block Number of coverage units By adjusting the coverage scale The value records the number of coverage units at different coverage scales; S34. Based on the number of coverage units Compute data blocks Three-dimensional fractal dimension : ; in, Represents data block The three-dimensional fractal dimension is used to quantify the spatial complexity of a data block; f3 represents the three-dimensional fractal. Indicates the coverage scale; S4. Calculate the corresponding topological entropy value for each data block based on the structure of the topological network, and record the topological entropy value of the data block and its priority index in the network; S5. Combining the three-dimensional fractal dimension and topological entropy value of each data block, calculate the overall priority of the data blocks through weighted fusion; S6. Dynamically minimize the data blocks according to the overall priority. Perform fine-grained multi-level splitting on data blocks with priority higher than the threshold to form smaller data units. Only retain the key data related to the current printing task. Mark the data blocks with priority lower than the threshold as secondary data that can be delayed or ignored. S7. During the execution of a printing task, the transmission order and splitting granularity are dynamically adjusted based on the real-time load status of the printing device and the network bandwidth. S8. At the receiving end, the transmitted data units are reassembled, the data is restored to a complete structure according to the printing task requirements, and the missing data caused by the split is locally compensated. S9. Monitor the printing task execution status, device load and network conditions in real time, and dynamically adjust the three-dimensional fractal dimension, topological entropy weight and data block priority based on the performance indicators during the transmission process to optimize subsequent transmission and splitting strategies. S10. After the critical data units of the printing task have been transmitted, supplementary transmission of data blocks marked as low priority shall be performed according to the integrity requirements of the task.

2. The dynamic printing data minimization and splitting method based on topological entropy mapping according to claim 1, characterized in that, S1 includes the following steps: S11. Obtain the complete dataset D for the print task; S12. Classify the complete dataset D according to the content characteristics of the printing task to generate a preliminary data category set C; S13. Based on the data category set C, for each data category... Cluster analysis is performed on the elements within the data to form multiple independent data block sets B: ; in, Let k be the k-th independent data block, containing data elements belonging to the same class, and , , Represents the empty set; S14. For each data block Extract and record its initial attribute information: Data block size , which is the number of data elements in the data block; Data block content type This is used to identify the content category to which a data block belongs; Preliminary correlation parameters of data blocks This parameter, used to represent the initial association strength between data blocks and other data blocks, is called the association parameter. The calculation is based on the relevant characteristics of the elements in the data block, represented as follows: ; Among them, w i The weight is assigned to an individual data element within the data block, based on its relevance to the task. S15. Record the initial attribute information Stored in the association matrix M: .

3. The dynamic printing data minimization and splitting method based on topological entropy mapping according to claim 1, characterized in that, S2 includes the following steps: S21. Based on each data block recorded in the correlation matrix M. Initial attribute information, extract feature vectors from data blocks ; S22. Based on feature vectors between data blocks and Compute data blocks With data blocks correlation between : ; in, Represents data block The transpose of the eigenvectors, where W is the weight matrix and λ is the balancing coefficient used to adjust the influence of the feature difference terms. For feature vectors The l-th element has d feature dimensions, and γ is the moderation index. Let be the Euclidean norm of the eigenvectors; S23. Based on the correlation between data blocks Establish topological connections between data blocks to generate a topological network: G = (V, E); Where the node set V represents all data blocks, and the edge set E is constructed according to the following rule: when the correlation degree... When the value is greater than the preset threshold θ, in the data block With data blocks Establish a directed edge between them The direction of the edge is determined by the path dependency between data blocks, representing the data block. Depends on data blocks ; S24. For each edge in the topological network G Assign weights Weight By combining correlation and path dependence strength The calculation yielded: ; Where α and β are weighting coefficients used to balance the importance of correlation and path dependence, and the strength of path dependence. Represents data block For data blocks Degree of dependence: ; Where η is the path attenuation coefficient, which controls the influence of path distance on dependency strength. For data blocks With data blocks The logical distance between two data blocks represents the dependency level or step interval between them in the task flow. The larger the logical distance, the stronger the path dependency. The smaller; S25. Record the topological network G, its node connections, and weight information as a topological graph T. Topological graph T contains a node set V, an edge set E, and a weight matrix. .

4. The dynamic printing data minimization and splitting method based on topological entropy mapping according to claim 1, characterized in that, S4 includes the following steps: S41. Extract the set of adjacent data blocks for each data block based on the network topology. ; S42. Calculate the data block Association strength with adjacent data blocks : ; in, For data blocks With data blocks The edge weights represent the degree of direct association between data blocks. For data blocks With data blocks The logical distance between them represents the dependency level or step interval between two data blocks in the task flow. The attenuation coefficient; S43. Based on correlation strength Utilize normalization to process computational data blocks Local connectivity probability distribution ; S44. Utilizing the probability distribution of local connectivity Compute data blocks Local topological entropy value : ; in, Represents data block The association complexity within its neighborhood quantifies the information entropy between data blocks and directly related data blocks. S45. Calculate the data block global influence : ; in, Represents data block Global importance in the entire topology network, with the numerator being data blocks. The sum of edge weights between the data blocks and the data blocks, with the denominator being the sum of edge weights in the network; S46. Combining local topological entropy values and global influence Calculate data blocks Comprehensive topological entropy value : ; in, and is a weighting coefficient used to balance the impact of local correlation complexity and global importance on the overall topological entropy value.

5. The dynamic printing data minimization and splitting method based on topological entropy mapping according to claim 1, characterized in that, S5 includes the following steps: S51. Obtain each data block Three-dimensional fractal dimension and comprehensive topological entropy value ; S52. On the three-dimensional fractal dimension and comprehensive topological entropy value Normalization is performed to obtain the normalized three-dimensional fractal dimension. and normalized comprehensive topological entropy value ; S53. Combining normalized three-dimensional fractal dimensions and normalized comprehensive topological entropy value Data blocks are calculated through weighted fusion. Overall priority : ; Where μ and ν are weighting coefficients; S54. Based on overall priority Sort all data blocks to obtain a priority list of data blocks: ; in, , where n is the total number of data blocks.

6. The dynamic printing data minimization and splitting method based on topological entropy mapping according to claim 1, characterized in that, S6 includes the following steps: S61. Set the overall priority threshold This is used to distinguish the priority level of data blocks; S62. Based on each data block in the comprehensive priority list L Overall priority The data blocks are divided into two categories: High-priority data block set ; low-priority data block set ; S63. For high-priority data blocks Perform fine-grained multi-level splitting to determine data blocks. Number of split layers Calculate the number of splitting layers based on the complexity of the data blocks and the requirements of the printing task: ; in, For data blocks The three-dimensional fractal dimension, To calculate the comprehensive topological entropy value, The threshold for the smallest data unit. To split the ratio coefficient, This represents the floor function; Data blocks in each level of splitting Split into several sub-data blocks Number of sub-data blocks The calculation formula is: ; in, For data blocks Size, Let l be the splitting factor for each layer, and l be the current layer number; For each sub-data block Extract key data elements relevant to the current print task using an association filtering function. Filter: ; Where d is a data element. The correlation between data elements and printing tasks is θ, where θ is the correlation threshold. Only key data with a correlation higher than the threshold is retained. S64. For each split sub-data block Compute sub-data blocks Three-dimensional fractal dimension and comprehensive topological entropy value The data is then normalized, and the overall priority of the sub-data blocks is calculated by combining the normalized three-dimensional fractal dimension and the comprehensive topological entropy value. ; S65. Set all split sub-data blocks Update to the overall priority list L, and adjust according to the new overall priority. Sort the sub-data blocks to obtain the updated priority list L'.

7. The dynamic printing data minimization and splitting method based on topological entropy mapping according to claim 1, characterized in that, S8 includes the following steps: S81. The receiving end receives the transmitted sub-data blocks according to the priority list L'. The received data units are then stored in a temporary buffer queue Q. S82. Parse the received temporary buffer queue Q and extract each sub-data block. Data element collection And according to the hierarchical structure of the data blocks Reconstructing data blocks from the original decomposition relationship Partial structure; S83. For data blocks that were not fully received Based on data block splitting rules and correlation filtering functions The result calculates the current data block missing data set : ; in, For data blocks The total number of sub-data blocks, This indicates the set of missing data due to splitting and incomplete reception; S84. Employ a local compensation strategy for missing data sets. Perform data recovery and compensate for missing data sets. The generation rules are as follows: ; in, This is a missing data filtering function that only compensates for missing data that has the lowest relevance to the current printing task. Data elements; S85. Compensation data With reorganized partial data blocks Merge to generate complete data blocks ; S86. Set of all reassembled data blocks Merge according to the printing task requirements to generate a complete dataset. .

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