Dynamic printing data minimization splitting method based on topological entropy mapping

Abstract

The invention discloses a dynamic printing data minimization splitting method based on topological entropy mapping. The method comprises the steps that S1, a complete data set of a printing task is acquired; s2, establishing a topological network for the divided data blocks; s3, calculating the three-dimensional fractal dimension of each data block in the topological network by using an improved spatial fractal algorithm; s4, recording topological entropy values of the data blocks and priority indexes of the data blocks in the network; s5, calculating the comprehensive priority of the data blocks in a weighted fusion mode; s6, performing dynamic minimization splitting on the data blocks according to the comprehensive priority; s7, dynamically adjusting the transmission sequence and the splitting granularity; s8, carrying out local compensation on missing data generated by splitting; s9, optimizing subsequent transmission and splitting strategies; and S10, performing supplementary transmission on the data blocks marked as low priorities according to the integrity requirement of the task. According to the invention, efficient resource allocation is realized, and the integrity and accuracy of the printing task are ensured.

Description

Technical Field

[0001] The present invention relates to the field of printing technology, and in particular to a dynamic printing data minimization splitting method based on topological entropy mapping. Background Art

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

[0003] At present, most data processing technologies in dynamic printing scenarios adopt a full-volume transmission mode, that is, all data of the printing task is transmitted as a whole 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 transmission and storage problems of a large amount of redundant data. In templated printing and batch printing tasks, the data repetitiveness is high, and bandwidth resources cannot be used efficiently. Secondly, full-volume transmission puts higher requirements on the processing capacity of the printing device. The instantaneous processing of a large amount of data may cause equipment overload, resulting in task delays and reduced printing quality.

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

[0005] The existing technology also has the problem of insufficient adaptation between data processing and device status. During the dynamic printing process, the real-time status of the printing device (cache occupancy, processing power and bandwidth utilization) has an important impact on data transmission and processing efficiency. However, most technologies fail to fully combine the real-time status of the device to optimize the data transmission strategy, making it difficult to achieve efficient use of device resources, which in turn affects the execution efficiency of the overall printing task. Summary of the invention

[0006] One object of the present invention is to propose a dynamic printing data minimization splitting method based on topological entropy mapping, which realizes 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 comprises the following steps:

[0008] S1. Obtain a complete data set of a printing task, divide the complete data set 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. Establish a topological network for the divided data blocks, analyze the correlation and path dependency between the data blocks to generate a topological graph of the data blocks, and the topological graph records the node connection relationship and weight;

[0010] S3. Calculate the three-dimensional fractal dimension of each data block in the topological 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, and calculating the comprehensive priority of the data block by weighted fusion;

[0013] S6. Dynamically minimize the data blocks according to the comprehensive priority, perform fine-grained multi-layer splitting on the data blocks with a priority higher than the threshold to form smaller data units, and only retain the key data related to the current printing task, and mark the data blocks with a priority lower than the threshold as secondary data that can be delayed or ignored;

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

[0015] S8. Reorganize the transmitted data units at the receiving end, restore the data to a complete structure according to the printing task requirements, and partially compensate for the missing data caused by the splitting;

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

[0017] S10. After the transmission of the key data units of the printing task is completed, the data blocks marked as low priority are supplementally transmitted according to the integrity requirements of the task.

[0018] Optionally, the S1 includes the following steps:

[0019] S11. Obtain a complete data set D for the printing task;

[0020] S12. Classify the complete data set 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 The elements in the cluster analysis are clustered to form multiple independent data block sets B:

[0022] B={b1,b2,…,b k};

[0023] Among them, b k is the kth independent data block, containing data elements belonging to the same category, and represents the empty set;

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

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

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

[0027] Data block preliminary correlation parameter R k , used to indicate the initial correlation strength between a data block and other data blocks, the correlation parameter R k The calculation of is based on the relevant characteristics of the elements in the data block, which is expressed as:

[0028]

[0029] Among them, w i is the weight of a single data element within a data block, and the weight is assigned based on its relevance to the task;

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

[0031]

[0032] Optionally, S2 includes the following steps:

[0033] S21. According to each data block b recorded in the association matrix M k Initial attribute information of the data block is extracted to extract the feature vector v 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 correlation between ij :

[0035]

[0036] in, Represents data block b i The transpose of the eigenvector, W is the weight matrix, λ is the balance coefficient, which is used to adjust the influence of the feature difference term, v i,l is the feature vector v i The lth element of , there are d feature dimensions, γ is the adjustment index, ∥v i ∥2 is the Euclidean norm of the eigenvector;

[0037] S23. According to the correlation degree S between data blocks ij Establish topological connections between data blocks and generate a topological network:

[0038] G = (V, E);

[0039] Among them, the node set V represents all data blocks, and the construction rule of the edge set E is when the association degree S ij When it is greater than the preset threshold θ, in data block b i and data block b j Create a directed edge e between ij , the direction of the edge is determined according to the path dependency between data blocks, indicating that 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 S ij and path dependence strength P ij The calculation results are:

[0041] w ij =α·S ij +β·P ij ;

[0042] Among them, α and β are weight coefficients, which are used to balance the importance of relevance and path dependence. The intensity of path dependence P ij Represents data block b i For data block b j Dependence:

[0043] P ij =exp(-η·D ij );

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

[0045] S25. The topological network G and its node connection relationship and weight information are recorded as a topological graph T, which includes 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 topological network, the node coordinates of each data block are extracted to construct a three-dimensional space mapping, and the nodes and edges of the topological network are embedded into the three-dimensional space. k Corresponding three-dimensional coordinates (x k ,y k ,z k ), the three-dimensional coordinates are determined by the task logic relationship, weight distribution and node relevance;

[0048] S32. For each data block b k Construct a covering unit set U based on the geometric relationship 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 that the data block b k The number of covering units q is determined by the data block b. k The number of adjacent nodes and the three-dimensional distance constraints are determined by the coverage unit range 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 of , ∈ is the coverage scale;

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

[0054] S34. Calculate data block b based on the number of coverage 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, f3 represents the three-dimensional fractal, and ∈ represents the coverage scale.

[0057] Optionally, S4 includes the following steps:

[0058] S41. Extract the adjacent data block set N for each data block according to the topological network k ;

[0059] S42. Calculate data block b k The strength of association with adjacent data blocks a 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 weight between them indicates the degree of direct connection between data blocks, 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 the strength of association a kj Calculate data block b using normalization k The local connection probability distribution P k ={p kj};

[0063] S44. Using local connection 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 a data block and the data blocks directly related to it;

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

[0067]

[0068] Among them, C k Represents data block b k The global importance in the entire topological network, with the numerator being the data block b k The sum of the edge weights between all data blocks, and the denominator is the sum of all edge weights in the network;

[0069] S46. Combined with 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] Among them, α1 and β1 are weight coefficients, which are used to balance the influence of local association complexity and global importance on the comprehensive topological entropy value.

[0072] Optionally, S5 includes the following steps:

[0073] S51. Get 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. Three-dimensional fractal dimension D f3 (b k ) and the comprehensive topological entropy value H C (b k ) is normalized to obtain the normalized three-dimensional fractal dimension D′f3 (b k ) and the normalized comprehensive topological entropy value H′ C (b k );

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

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

[0077] Among them, μ and ν are weight coefficients;

[0078] S54. According to the comprehensive priority P T (b k ) Sort all data blocks and get a priority list of data blocks:

[0079]

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

[0081] Optionally, the S6 comprises the following steps:

[0082] S61. Setting a comprehensive priority threshold τ for distinguishing the priority levels of data blocks;

[0083] S62. According to each data block b in the comprehensive priority list L k The overall priority P T (b k ), divide the data blocks 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 data block b with high priority k ∈B H Perform fine-grained multi-layer splitting to determine data block b k The number of split layers l k , calculate the number of split layers according to the complexity of the data block and the printing task requirements:

[0087]

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

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

[0090]

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

[0092] For each sub-block Extract key data elements related to the current printing task using the correlation filtering function To filter:

[0093]

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

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

[0096] S65. Collect all the split sub-data blocks together Update to the comprehensive priority list L, and according to the new comprehensive priority The sub-data blocks are sorted to obtain an updated priority list L'.

[0097] Optionally, the S8 includes the following steps:

[0098] S81. The receiving end receives the transmitted sub-data block according to the priority list L' and storing the received data unit into a temporary cache queue Q;

[0099] S82. Analyze the received temporary cache queue Q and extract each sub-data block A collection of data elements And according to the hierarchical structure of the data block k Reconstruct data block b with the original decomposition relation k Partial structure of

[0100] S83. For the partial data block b′ that has not been completely received k , based on data block splitting rules and associative filtering functions The result of calculating the current data block b k The missing data set Δ k :

[0101]

[0102] Among them, m k For data block b k The total number of sub-data blocks, Δ k Represents the missing data set caused by splitting and incomplete reception;

[0103] S84. Use local compensation strategy to compensate for missing data set Δ k Perform data recovery and compensate for missing data sets The generation rules are:

[0104]

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

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

[0107] S86. All reorganized data blocks are set B′={b k}Merge according to the printing task requirements to generate a complete data set D'.

[0108] The beneficial effects of the present invention are:

[0109] (1) The present invention minimizes the large-scale data in dynamic printing tasks by combining an innovative fusion model of a three-dimensional fractal algorithm and topological entropy mapping, and uses a comprehensive priority calculation method to achieve refined data management and transmission strategy optimization. Based on the quantification of the three-dimensional fractal dimension, the present invention can deeply analyze the spatial complexity of the data, and combine the topological entropy mapping technology to 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 dynamic data splitting and transmission strategy driven by comprehensive priority, and realize dynamic optimization of data transmission and processing flow. In the data blocks with high priority, the present invention further combines the multi-layer splitting mechanism and the association filtering rules to retain only the key data directly related to the current printing task, and adopts a delay or ignore strategy for the data blocks with low priority, thereby realizing efficient resource allocation.

[0111] (3) The present invention realizes accurate recovery and compensation of data blocks through recursive reorganization and local compensation strategy of sub-data blocks during dynamic splitting and transmission. By combining the characteristics of three-dimensional fractal dimension and topological entropy value, it can efficiently compensate for missing data locally. By utilizing association filtering rules and missing data priority compensation strategy, the integrity and accuracy of the printing task are ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0113] Figure 1 A flowchart of a dynamic printing data minimization splitting method based on topological entropy mapping proposed by the present 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 by the present invention. DETAILED DESCRIPTION

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

[0116] refer to Figure 1-Figure 2, a dynamic printing data minimization splitting method based on topological entropy mapping, comprising the following steps:

[0117] S1. Obtain a complete data set of a printing task, divide the complete data set 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. Establish a topological network for the divided data blocks, analyze the correlation and path dependency between the data blocks to generate a topological graph of the data blocks, and the topological graph records the node connection relationship and weight;

[0119] S3. Calculate the three-dimensional fractal dimension of each data block in the topological 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, and calculating the comprehensive priority of the data block by weighted fusion;

[0122] S6. Dynamically minimize the data blocks according to the comprehensive priority, perform fine-grained multi-layer splitting on the data blocks with a priority higher than the threshold to form smaller data units, and only retain the key data related to the current printing task, and mark the data blocks with a priority lower than the threshold as secondary data that can be delayed or ignored;

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

[0124] S8. Reorganize the transmitted data units at the receiving end, restore the data to a complete structure according to the printing task requirements, and partially compensate for the missing data caused by the splitting;

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

[0126] S10. After the transmission of the key data units of the printing task is completed, the data blocks marked as low priority are supplementally transmitted according to the integrity requirements of the task.

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

[0128] S11. Obtain a complete data set D for the printing task;

[0129] S12. Classify the complete data set 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 The elements in the cluster analysis are clustered to form multiple independent data block sets B:

[0131] B={b1,b2,…,b k};

[0132] Among them, b k is the kth independent data block, containing data elements belonging to the same category, and represents the empty set;

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

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

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

[0136] Data block preliminary correlation parameter R k , used to indicate the initial correlation strength between a data block and other data blocks, the correlation parameter R k The calculation of is based on the relevant characteristics of the elements in the data block, which is expressed as:

[0137]

[0138] Among them, w i is the weight of a single data element within a data block, and the weight is assigned based on its relevance to the task;

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

[0140]

[0141] In this embodiment, the logical grouping of print task data and the division of independent data blocks provide a basis for subsequent dynamic optimization. By recording the size, content type and preliminary correlation parameters of the data block, the characteristics of each data block can be accurately expressed, laying a data support for building a topological network and priority sorting. Compared with the traditional overall processing method, the block 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 implementation, S2 includes the following steps:

[0143] S21. According to each data block b recorded in the association matrix M k Initial attribute information of the data block is extracted to extract the feature vector v 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 correlation between ij :

[0145]

[0146] in, Represents data block b i The transpose of the eigenvector, W is the weight matrix, λ is the balance coefficient, which is used to adjust the influence of the feature difference term, v i,l is the feature vector v i The lth element of , there are d feature dimensions, γ is the adjustment index, ∥v i ∥2 is the Euclidean norm of the eigenvector;

[0147] S23. According to the correlation degree S between data blocks ij Establish topological connections between data blocks and generate a topological network:

[0148] G = (V, E);

[0149] Among them, the node set V represents all data blocks, and the construction rule of the edge set E is when the association degree S ij When it is greater than the preset threshold θ, in data block b i and data block b j Create a directed edge e between ij , the direction of the edge is determined according to the path dependency between data blocks, indicating that 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 S ij and path dependence strength P ij The calculation results are:

[0151] w ij =α·S ij +β·P ij ;

[0152] Among them, α and β are weight coefficients, which are used to balance the importance of relevance and path dependence. The intensity of path dependence P ij Represents data block b i For data block b j Dependence:

[0153] P ij =exp(-η·D ij );

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

[0155] S25. The topological network G and its node connection relationship and weight information are recorded as a topological graph T, which includes a node set V, an edge set E, and a weight matrix W=[w ij ].

[0156] This implementation generates a topological network with a logical hierarchy by modeling the relationship between data blocks. The network structure can fully express the correlation and path dependency between data blocks and reflect the importance differences in the transmission and processing processes 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, which provides a mathematical basis for the calculation of topological entropy values ​​and priority sorting, and significantly improves the processing efficiency and accuracy of complex tasks.

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

[0158] S31. Based on the topological network, the node coordinates of each data block are extracted to construct a three-dimensional space mapping, and the nodes and edges of the topological network are embedded into the three-dimensional space. k Corresponding three-dimensional coordinates (x k ,y k ,zk ), the three-dimensional coordinates are determined by the task logic relationship, weight distribution and node relevance;

[0159] S32. For each data block b k Construct a covering unit set U based on the geometric relationship 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 that the data block b k The number of covering units q is determined by the data block b. k The number of adjacent nodes and the three-dimensional distance constraints are determined by the coverage unit range 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 of , ∈ is the coverage scale;

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

[0165] S34. Calculate data block b based on the number of coverage 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, 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 the traditional method of 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 the data and provide 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 implementation, S4 includes the following steps:

[0170] S41. Extract the adjacent data block set N for each data block according to the topological network k ;

[0171] S42. Calculate data block b k The strength of association with adjacent data blocks a 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 weight between them indicates the degree of direct connection between data blocks, 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 the strength of association a kj Calculate data block b using normalization k The local connection probability distribution P k ={p kj};

[0175] S44. Using local connection 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 a data block and the data blocks directly related to it;

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

[0179]

[0180] Among them, C k Represents data block b k The global importance in the entire topological network, with the numerator being the data block b k The sum of the edge weights between all data blocks, and the denominator is the sum of all edge weights in the network;

[0181] S46. Combined with 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] Among them, α1 and β1 are weight coefficients, which are used to balance the influence of local association complexity and global importance on the comprehensive 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 the traditional method of prioritizing data based only on simple rules or fixed weights, the topological entropy value can dynamically adjust the weight distribution based on local and global correlation information to generate a more accurate priority indicator, solving the problem of insufficient expression of correlation between data blocks.

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

[0186] S51. Get 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. Three-dimensional fractal dimension D f3 (b k ) and the comprehensive topological entropy value H C (b k ) is normalized to obtain the normalized three-dimensional fractal dimension D′ f3 (b k ) and the normalized comprehensive topological entropy value H′ C (bk );

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

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

[0190] Among them, μ and ν are weight coefficients;

[0191] S54. According to the comprehensive priority P T (b k ) Sort all data blocks and get a priority list of data blocks:

[0192]

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

[0194] This implementation method 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 associated importance of data blocks. Different from 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 method significantly improves the data transmission efficiency in dynamic task scenarios and ensures the timely delivery and processing of high-priority critical data.

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

[0196] S61. Setting a comprehensive priority threshold τ for distinguishing the priority levels of data blocks;

[0197] S62. According to each data block b in the comprehensive priority list L k The overall priority P T (b k ), divide the data blocks 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 data block b with high priority k ∈B H Perform fine-grained multi-layer splitting to determine data block b k The number of split layers l k , calculate the number of split layers according to the complexity of the data block and the printing task requirements:

[0201]

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

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

[0204]

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

[0206] For each sub-block Extract key data elements related to the current printing task using the correlation filtering function To filter:

[0207]

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

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

[0210] S65. Collect all the split sub-data blocks together Update to the comprehensive priority list L, and according to the new comprehensive priority The sub-data blocks are sorted to obtain an updated priority list L'.

[0211] This implementation achieves flexible adjustment of data block granularity through dynamic minimization splitting and priority-driven transmission strategy, which not only ensures the refined processing of key data but also reduces the transmission cost of low-priority data. Compared with traditional static block or full transmission methods, this implementation adjusts data splitting granularity and transmission order in combination with real-time task requirements, which greatly improves transmission efficiency and flexibility of task completion, and provides a breakthrough solution for data optimization in dynamic printing tasks.

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

[0213] S81. The receiving end receives the transmitted sub-data block according to the priority list L' and storing the received data unit into a temporary cache queue Q;

[0214] S82. Analyze the received temporary cache queue Q and extract each sub-data block A collection of data elements And according to the hierarchical structure of the data block k Reconstruct data block b with the original decomposition relation k Partial structure of

[0215] S83. For the partial data block b′ that has not been completely received k , based on data block splitting rules and associative filtering functions The result of calculating the current data block b k The missing data set Δ k :

[0216]

[0217] Among them, m k For data block b k The total number of sub-data blocks, Δ k Represents the missing data set caused by splitting and incomplete reception;

[0218] S84. Use local compensation strategy to compensate for missing data set Δ k Perform data recovery and compensate for missing data sets The generation rules are:

[0219]

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

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

[0222] S86. All reorganized data blocks are set B′={b k}Merge according to the printing task requirements to generate a complete data set D'.

[0223] Embodiment 1:

[0224] Example 1 is applied in a high-end customized home furnishing manufacturer. At 9 am on November 10, 2024, a high-end customized home furnishing manufacturer received an urgent task to provide customers with 3D printed parts for 100 sets of customized furniture. The printing task is required to be completed within 48 hours, including complex decorative carved parts and supporting components. The total task data volume is 150GB. The printing equipment is distributed in two production workshops, belonging to different regional networks. Each workshop has 20 and 30 printing devices respectively, and the local network bandwidth is 150Mbps and 200Mbps respectively.

[0225] At 9:30 a.m., the engineer obtained the design files provided by the customer and found that the files contained a large amount of repeated template data (support structures and fixed 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 uses full data transmission, the bandwidth utilization of a single workshop will be overloaded and may cause high-priority engraving data to fail to be delivered in time.

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

[0227] At 10:00 a.m., the system generated a priority list L based on the calculation results. The data block with the highest priority mainly contains personalized engraving design data: the complex engraving model of the furniture leg b 120, its priority P T (b 120 ) is as high as 0.95, while the template data block b of the supporting component 10 Lower priority, P T (b 10 ) is only 0.2, the system puts the key data blocks with a priority higher than 0.8 into the high priority set B H , and split these data blocks into fine-grained 120 Split into smaller data units The unit sizes are 2MB, 3MB and 1.5MB respectively to facilitate priority transmission.

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

[0229] During the transmission process, the sub-data block received by the device at a certain moment and Stored in temporary cache queue Q, the system finds Due to network fluctuations, the transmission could not be completed in time, so the local compensation mechanism was triggered and the transmission order was readjusted in combination with the priority list to ensure Can be delivered as soon as possible.

[0230] At 1:00 p.m., the printing equipment in workshop B had completed b 120 The reassembly operation will receive the data unit 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 filter function φ(Δ 120 ) Compensate for missing parts and compensate for data sets Only critical data points related to the engraved edge are included, not redundant areas.

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

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

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

[0234] After the task is completed, the method of the present invention is compared and analyzed in detail with the traditional full transmission method and the static block method. The results are as follows:

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

[0236] In terms of data transmission time, the average transmission time of the traditional full transmission method is 12.5 hours. Due to limited network bandwidth, some high-priority data transmission is delayed. The average transmission time of the present invention is shortened to 8 hours. The system dynamically adjusts the priority to ensure that high-priority data blocks are delivered first at critical moments, and the network utilization rate is increased to 90%.

[0237] In terms of network bandwidth utilization, the average bandwidth utilization of the traditional full transmission method is 75%. Since low-priority data transmission occupies a large amount of bandwidth, the transmission efficiency of key data blocks is limited. The average bandwidth utilization of the present invention is increased to 90%. After the transmission strategy is optimized, high-priority data blocks take up bandwidth resources first.

[0238] In terms of the completion time of the printing task, the traditional full-transmission method takes 30 hours from data transmission to the completion of the printing task, mainly because of the delay in high-priority data transmission, which leads to the delay in the start time of the printing task. The printing task completion time of the present invention is shortened to 22 hours, and the task is completed 8 hours in advance, ensuring that customer needs are delivered on time.

[0239] Finally, let’s focus on printing accuracy. The printing accuracy error of the traditional full-volume transmission method is 2.2%, which is mainly reflected in the detail processing of the personalized design part. Since some key data failed to arrive in time, the engraving edge is blurred. The printing accuracy error of the present invention is reduced to 1.3%. Through the priority-driven dynamic splitting and reorganization mechanism, the accurate transmission and compensation of key data are ensured, and the final printing effect is accurate and complete.

[0240] In the actual printing process, the traditional full-volume transmission method has high priority data blocks b 120 The delay of the engraving edge accuracy error reaches 0.5 mm, and the method of the present invention preferentially transmits and accurately reconstructs b 120 , reducing the error to 0.2 mm. At the same time, the task completion time was shortened by 6 hours, greatly 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] The present invention minimizes the large-scale data in dynamic printing tasks by combining an innovative fusion model of a three-dimensional fractal algorithm and topological entropy mapping, and realizes refined data management and transmission strategy optimization by using a comprehensive priority calculation method. Based on the quantification of the three-dimensional fractal dimension, the present invention can deeply analyze the spatial complexity of the data, and effectively quantify the relevance and global importance of data blocks by combining topological entropy mapping technology, thereby realizing dynamic block division and priority sorting. Compared with the traditional full-volume transmission or static block division mode, the present invention reduces the transmission amount of redundant data through dynamic splitting and real-time adjustment, and optimizes the network bandwidth utilization. Experimental results show that the transmission efficiency of the present invention in large-scale data scenarios is improved by more than 30%, and data redundancy is reduced by 25%.

[0243] The present invention can adapt to the status of the printing device in real time through a dynamic data splitting and transmission strategy driven by comprehensive priority, and realize dynamic optimization of data transmission and processing flow. In high-priority data blocks, the present invention further combines the multi-layer splitting mechanism and the association 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 realizing efficient resource allocation. Compared with the prior art, the present invention can dynamically adapt to the real-time status of the device, significantly improve the real-time and flexibility of printing tasks, and in distributed printing scenarios, the present invention can effectively avoid the problem of device overload and improve the performance of multi-device collaboration. Experiments show that the response speed of printing tasks is improved by 35% and the equipment utilization rate is improved by 20%.

[0244] The present invention realizes accurate recovery and compensation of data blocks through recursive reorganization and local compensation strategy of sub-data blocks during dynamic splitting and transmission. By combining the characteristics of three-dimensional fractal dimension and topological entropy value, it can efficiently compensate for missing data locally. By utilizing association filtering rules and missing data priority compensation strategy, the integrity and accuracy of printing tasks are ensured. Compared with traditional data recovery methods, the present invention can more accurately identify and recover key missing data when processing complex printing tasks, while effectively reducing the transmission amount of redundant compensation data. Experimental data show that the compensation accuracy of the present 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 specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A dynamic printing data minimization splitting method based on topological entropy mapping, characterized in that: The steps include: S1. Obtain a complete data set of a printing task, divide the complete data set into multiple independent data blocks according to the content characteristics of the data, and record the initial attribute information of each data block; S2. Establish a topological network for the divided data blocks, analyze the correlation and path dependency between the data blocks to generate a topological graph of the data blocks, and the topological graph records the node connection relationship and weight; S3. Calculate the three-dimensional fractal dimension of each data block in the topological network using an improved spatial fractal algorithm; 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, and calculating the comprehensive priority of the data block by weighted fusion; S6. Dynamically minimize the data blocks according to the comprehensive priority, perform fine-grained multi-layer splitting on the data blocks with a priority higher than the threshold to form smaller data units, and only retain the key data related to the current printing task, and mark the data blocks with a priority lower than the threshold as secondary data that can be delayed or ignored; S7. During the execution of the printing task, the transmission order and splitting granularity are dynamically adjusted according to the real-time load status of the printing device and the network bandwidth. S8. Reorganize the transmitted data units at the receiving end, restore the data to a complete structure according to the printing task requirements, and partially compensate for the missing data caused by the splitting; S9. Monitor the execution status of the printing task, equipment load and network status in real time, dynamically adjust the three-dimensional fractal dimension, topological entropy weight and data block priority based on the performance indicators during the transmission process, and optimize the subsequent transmission and splitting strategies; S10. After the transmission of the key data units of the printing task is completed, the data blocks marked as low priority are supplementally transmitted according to the integrity requirements of the task.

2. According to the method of claim 1, the method is characterized in that: The S1 comprises the following steps: S11. Obtain a complete data set D for the printing task; S12. Classify the complete data set 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 c j The elements in the cluster analysis are clustered to form multiple independent data block sets B: B={b1,b2,…,b k }; Among them, b k is the kth independent data block, containing data elements belonging to the same category, and ∪ k b k =D, represents the empty set; S14. For each data block b k Extract and record its initial attribute information: Data block size | b k |, the number of data elements in the data block; Data block content type T k , used to identify the content category to which the data block belongs; Data block preliminary correlation parameter R k , used to indicate the initial correlation strength between a data block and other data blocks, the correlation parameter R k The calculation of is based on the relevant characteristics of the elements in the data block, which is expressed as: Among them, w i is the weight of a single data element within a data block, and the weight is assigned based on its relevance to the task; S15. Record the initial attribute information {|b k |,T k ,R k } is stored in the association matrix M:

3. The method for minimizing the dynamic printing data splitting based on topological entropy mapping according to claim 1 is characterized in that: The S2 comprises the following steps: S21. According to each data block b recorded in the association matrix M k Initial attribute information of the data block is extracted to extract the feature vector v k ; 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 correlation between ij : in, Represents data block b i The transpose of the eigenvector, W is the weight matrix, λ is the balance coefficient, which is used to adjust the influence of the feature difference term, v i,l is the feature vector v i The lth element of , there are d feature dimensions, γ is the adjustment index, ∥v i ∥2 is the Euclidean norm of the eigenvector; S23. According to the correlation degree S between data blocks ij Establish topological connections between data blocks and generate a topological network: G = (V, E); Among them, the node set V represents all data blocks, and the construction rule of the edge set E is when the association degree S ij When it is greater than the preset threshold θ, in data block b i and data block b j Create a directed edge e between ij , the direction of the edge is determined according to the path dependency between data blocks, indicating that data block b i Depends on data block b j ; S24. For each edge e in the topological network G ij Assign weight w ij , weight w ij By combining the correlation S ij and path dependence strength P ij The calculation results are: w ij =α·S ij +β·P ij ; Among them, α and β are weight coefficients, which are used to balance the importance of relevance and path dependence. The intensity of path dependence P ij Represents data block b i For data block b j Dependence: P ij =exp(-η·D ij ); Where η is the path attenuation coefficient, which controls the effect of path distance on the dependency intensity, D ij For data block b i With data block b j The logical distance between them is expressed as the dependency level or step interval between two data blocks in the task flow. The larger the logical distance, the stronger the path dependency strength P. ij The smaller; S25. The topological network G and its node connection relationship and weight information are recorded as a topological graph T, which includes a node set V, an edge set E, and a weight matrix W = [w ij ].

4. The method for minimizing the dynamic printing data splitting based on topological entropy mapping according to claim 1, characterized in that: The S3 comprises the following steps: S31. Based on the topological network, the node coordinates of each data block are extracted to construct a three-dimensional space mapping, and the nodes and edges of the topological network are embedded into the three-dimensional space. k Corresponding to the three-dimensional coordinates (x k ,y k ,z k ), the three-dimensional coordinates are determined by the task logic relationship, weight distribution and node relevance; S32. For each data block b k Construct a covering unit set U based on the geometric relationship of data blocks in three-dimensional space k : IN k ={in k,1 ,in k,2 ,…,in k,q }; Among them, u k,i Indicates that the data block b k The number of covering units q is determined by the data block b. k The number of adjacent nodes and the three-dimensional distance constraints are determined by the coverage unit range according to the following conditions: 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 of , ∈ is the coverage scale; S33. Calculate each data block b k The number of coverage units N3(∈) is recorded by adjusting the value of coverage scale ∈; S34. Calculate data block b based on the number of coverage units N3(∈) k The three-dimensional fractal dimension D f3 (b k ): 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, f3 represents the three-dimensional fractal, and ∈ represents the coverage scale.

5. The method for minimizing the dynamic printing data splitting based on topological entropy mapping according to claim 1, characterized in that: The S4 comprises the following steps: S41. Extract the adjacent data block set N for each data block according to the topological network k ; S42. Calculate data block b k The strength of association with adjacent data blocks a kj : a kj =w kj ·exp(-γ1·d kj ); Among them, w kj For data block b k With data block b j The edge weight between them indicates the degree of direct connection between data blocks, 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; S43. Based on the strength of association a kj Calculate data block b using normalization k The local connection probability distribution P k ={p kj }; S44. Using local connection probability distribution P k Calculate data block b k The local topological entropy value H k : Among them, H k Represents data block b k The association complexity within its neighborhood quantifies the information entropy between a data block and the data blocks directly related to it; S45. Calculate data block b k Global influence C k : Among them, C k Represents data block b k The global importance in the entire topological network, with the numerator being the data block b k The sum of the edge weights between all data blocks, and the denominator is the sum of all edge weights in the network; S46. Combined with 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 ): H C (b k )=α1·H k +β1·(-log C k ); Among them, α1 and β1 are weight coefficients, which are used to balance the influence of local association complexity and global importance on the comprehensive topological entropy value.

6. The method for minimizing the dynamic printing data splitting based on topological entropy mapping according to claim 1, characterized in that: The S5 comprises the following steps: S51. Get each data block b k The three-dimensional fractal dimension D f3 (b k ) and the comprehensive topological entropy value H C (b k ); S52. Three-dimensional fractal dimension D f3 (b k ) and the comprehensive topological entropy value H C (b k ) is normalized to obtain the normalized three-dimensional fractal dimension D ′ f3 (b k ) and the normalized comprehensive topological entropy value H ′ C (b k ); S53. Combined with the normalized three-dimensional fractal dimension D ′ f3 (b k ) and the normalized comprehensive topological entropy value H ′ C (b k ), calculate the data block b by weighted fusion k The overall priority P T (b k )=: P T (b) k )=μ·D ′ f3 (b) k )+ν·H ′ C (b) k ); Among them, μ and ν are weight coefficients; S54. According to the comprehensive priority P T (b k ) Sort all data blocks and get a priority list of data blocks: in, n is the total number of data blocks.

7. The method for minimizing the dynamic printing data splitting based on topological entropy mapping according to claim 1 is characterized in that: The S6 comprises the following steps: S61. Setting a comprehensive priority threshold τ for distinguishing the priority levels of data blocks; S62. According to each data block b in the comprehensive priority list L k The overall priority P T (b k ), divide the data blocks into two categories: High priority data block set B H = {b k ∣P T (b k )≥τ}; Low priority data block set B L = {b k ∣P T (b k )<τ}; S63. For data block b with high priority k ∈B H Perform fine-grained multi-layer splitting to determine data block b k The number of split layers l k , calculate the number of split layers according to the complexity of the data block and the printing task requirements: Among them, D f3 (b k ) is data block b k The three-dimensional fractal dimension, H C (b k ) is the comprehensive topological entropy value, ∈1 is the minimum data unit threshold, β2 is the split ratio coefficient, represents the ceiling function; In each layer of splitting, the data block b k Split into several sub-blocks The number of sub-blocks m k The calculation formula is: Among them, |b k | is data block b k The size of , δ is the split factor of each layer, and l is the current number of layers; For each sub-block Extract key data elements related to the current printing task using the correlation filtering function To filter: Where d is the data element, ρ(d) is the correlation between the data element and the printing task, θ is the correlation threshold, and only the key data with a correlation higher than the threshold is retained; S64. For each split sub-data block Calculate sub-blocks The three-dimensional fractal dimension and comprehensive topological entropy And normalization is performed, and the comprehensive priority of the sub-data block is calculated by combining the normalized three-dimensional fractal dimension and the comprehensive topological entropy value S65. Collect all the split sub-data blocks together Update to the comprehensive priority list L, and according to the new comprehensive priority The sub-data blocks are sorted to obtain an updated priority list L'.

8. The method for minimizing the dynamic printing data splitting based on topological entropy mapping according to claim 1 is characterized in that: The S8 comprises the following steps: S81. The receiving end receives the transmitted sub-data block according to the priority list L' and storing the received data unit into a temporary cache queue Q; S82. Analyze the received temporary cache queue Q and extract each sub-data block A collection of data elements And according to the hierarchical structure of the data block k Reconstruct data block b with the original decomposition relation k Partial structure; S83. For the partial data block b that has not been completely received ′ k , based on data block splitting rules and associative filtering functions The result of calculating the current data block b k The missing data set Δ k : Among them, m k For data block b k The total number of sub-data blocks, Δ k Represents the missing data set caused by splitting and incomplete reception; S84. Use local compensation strategy to compensate for missing data set Δ k Perform data recovery and compensate for missing data sets The generation rules are: Among them, ψ(Δ k ) is a missing data filtering function, which only compensates for the data elements in the missing data that have the lowest correlation ρ(d) with the current printing task; S85. Compensation data and reassembled partial data block b ′ k Merge to generate complete data block b k ; S86. Collect all reorganized data blocks B ′ = {b k }Merge according to the printing task requirements to generate a complete data set D'.

Images