A digital quality inspection data storage method and system based on automation archiving

Abstract

The application relates to the technical field of data processing, and specifically discloses a digital quality inspection data storage method and system based on automatic archiving, which comprises the following steps: firstly, normalizing the digital quality inspection data stream after feature extraction to generate a standardized quality inspection feature set; selecting dynamic classification dimensions based on the distribution density of each dimension of the features, combining sample quality inspection judgment results, and building a self-adaptive classification code with a hierarchical structure; taking the code as an index stem, embedding two types of identification of product batches and generation time sequences as associated nodes, and completing node cross-association to form a hierarchical association index; referring to the index hierarchy and the coding structure to divide logical storage blocks, automatically archiving quality inspection data according to the index, and synchronously generating an archiving position mapping; relying on the mapping to carry out block storage density measurement, dynamically adjusting the data block distribution according to the measurement results, and finally forming a self-adaptive archiving storage structure of the quality inspection data stream; and the application can improve the whole-process storage management effect of the digital quality inspection data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a digital quality inspection data storage method and system based on automated archiving. Background Technology

[0002] In the fields of intelligent operation and maintenance and digital product quality inspection, quality inspection data exhibits characteristics of multi-dimensionality, high concurrency, and large scale. Traditional data storage methods often employ fixed classification rules and static storage architectures, which cannot adapt to the dynamically changing distribution characteristics and business needs of quality inspection data. Existing storage solutions do not incorporate dynamic classification design based on the distribution density of quality inspection characteristics; the classification dimensions are fixed and singular, making it difficult to achieve accurate hierarchical classification of quality inspection data, resulting in low efficiency in data retrieval and location.

[0003] The current digital quality inspection data archiving and storage process lacks adaptive index building and dynamic optimization mechanisms for logical blocks. Data storage block partitioning relies on a single basis, easily leading to uneven storage density and wasted block resources. Furthermore, archived data cannot be dynamically reorganized based on the actual storage state, resulting in redundant storage structure and a continuous decline in archiving efficiency over the long term, failing to meet the actual needs for efficient, automated archiving and rapid querying of digital quality inspection data. Summary of the Invention

[0004] In view of the above problems, the purpose of this invention is to provide a digital quality inspection data storage method and system based on automated archiving, so as to solve the problems of fixed classification dimensions, inability to adapt to dynamic data distribution characteristics, rigid archiving index construction, unreasonable logical storage block division, uneven storage density and resource waste in existing digital quality inspection data storage, and inability to dynamically reorganize and optimize after archiving, which ultimately lead to low storage and archiving efficiency and slow query and call speed.

[0005] This invention provides a digital quality inspection data storage method based on automated archiving, comprising: Step 1: Normalize the digital quality inspection data stream after feature extraction to obtain a standardized quality inspection feature set of the digital quality inspection data stream; Step 2: Determine the dynamic classification dimension based on the distribution density of features in each dimension of the standardized quality inspection feature set, and construct an adaptive classification code with hierarchical relationship based on the dynamic classification dimension and the quality inspection judgment results in the standardized quality inspection feature set. Step 3: Using adaptive classification coding as the index backbone, embed the product batch identifier and generation time sequence identifier from the standardized quality inspection feature set as related nodes into the index backbone, and perform cross-association on the related nodes to obtain a hierarchical related index; Step 4: Based on the hierarchical structure and node distribution characteristics of the hierarchical associative index, and combined with the hierarchical relationship of the adaptive classification coding, divide the logical storage blocks, and automatically archive the digital quality inspection data stream to the corresponding logical storage blocks according to the hierarchical associative index, and generate the archive location mapping. Step 5: Based on the archive location mapping, perform storage density analysis on the logical storage blocks, and based on the analysis results, dynamically reorganize and optimize the distribution of the digital quality inspection data stream in the logical storage blocks to obtain an adaptive archive storage structure for the digital quality inspection data stream.

[0006] Preferably, the normalization process for the extracted digital quality inspection data stream to obtain a standardized quality inspection feature set is as follows: Extract multi-dimensional quality inspection parameters from the digital quality inspection data stream to obtain the initial quality inspection feature set of the digital quality inspection data stream; The initial quality inspection feature set is dimensionally identified, and grouped according to the differences in the physical dimensions of the quality inspection parameters in each dimension, to obtain the dimensional grouping feature data of the digital quality inspection data stream. By performing intra-group scaling on the dimensional grouped feature data, a dimensional unified feature set for the digital quality inspection data stream is obtained. Aligning the distribution centers of the dimensionless feature set yields the standardized quality inspection feature set of the digital quality inspection data stream.

[0007] Preferably, the process of determining the dynamic classification dimension based on the distribution density of features in each dimension of the standardized quality inspection feature set is as follows: The value distribution of each dimension of the standardized quality inspection feature set is scanned to identify data clustering intervals, and the sample frequency within each data clustering interval is counted to obtain the distribution density information of each dimension of the feature. Based on the distribution density information, the data aggregation and discrimination ability of each dimension feature is evaluated, and the dimension features in each dimension feature that meet the preset hierarchical construction requirements in terms of the number of data aggregation intervals are selected to obtain the candidate classification dimensions of the standardized quality inspection feature set. The feasibility of hierarchical nesting of candidate classification dimensions is verified, and the dimensional features with progressive inclusion relationships between the data aggregation intervals of each candidate classification dimension are mapped to dynamic classification dimensions of standardized quality inspection feature sets.

[0008] Preferably, the process of constructing an adaptive classification code with hierarchical relationships based on the quality inspection judgment results of the dynamic classification dimension and the standardized quality inspection feature set is as follows: By analyzing the interval boundaries and central feature values ​​of the data clustering intervals in the dynamic classification dimension, a candidate set of hierarchical nodes for the dynamic classification dimension is obtained. The quality inspection judgment results in the standardized quality inspection feature set are categorized and summarized to identify the type identifiers in the quality inspection judgment results, thus obtaining a set of judgment type identifiers for the quality inspection judgment results. Traverse each level node in the candidate set of level nodes, aggregate the type identifiers corresponding to the quality inspection judgment results that fall into the same data clustering interval, and count the co-occurrence frequency of each data clustering interval and the judgment type identifier to construct a node-type co-occurrence matrix. Based on the relationship between the values ​​of each element in the node-type co-occurrence matrix, the judgment type identifier with the highest co-occurrence frequency is bound to the corresponding hierarchical node, and a hierarchical subordinate relationship is established based on the binding result to obtain the hierarchical topology of the dynamic classification dimension. Each node in the hierarchical topology is assigned a hierarchical prefix code, and the hierarchical prefix code is concatenated with the corresponding judgment type identifier to obtain an adaptive classification code with hierarchical relationship.

[0009] Preferably, the process of using adaptive classification coding as the index backbone, embedding product batch identifiers and generation time sequence identifiers from the standardized quality inspection feature set as associated nodes into the index backbone, and cross-associating the associated nodes to obtain a hierarchical associated index is as follows: The hierarchical structure of the adaptive classification coding is analyzed, and the coding nodes at each level in the adaptive classification coding are used as the main index nodes; Extract product batch identifiers and generate time sequence identifiers from standardized quality inspection feature sets; Based on the correspondence between the digital quality inspection data stream and the index backbone nodes, the product batch identifier and the generation time sequence identifier are written into the corresponding index backbone nodes to obtain the index backbone with the associated nodes. In the index backbone with associated nodes, based on the similarity of product batch identifiers, the same product batch identifiers distributed under different index backbone nodes are linked by index jump, and based on the temporal continuity of the generation time sequence identifiers, adjacent generation time sequence identifiers are linked by index progression to obtain a hierarchical associated index.

[0010] Preferably, the process of dividing logical storage blocks based on the hierarchical structure and node distribution characteristics of the hierarchical associative index, combined with the hierarchical relationship of adaptive classification coding, is as follows: The nesting depth of the main nodes of each level of the hierarchical associative index is analyzed to obtain the index hierarchy structure information; The total number of associated nodes attached to each index backbone node is counted to obtain node distribution density information; Read the nesting depth of each level of the adaptive classification coding to obtain the coding level depth information; Based on the index hierarchy structure information and the encoding hierarchy depth information, the logical storage space capacity corresponding to the index backbone nodes with the same nesting depth is aggregated into candidate storage blocks; Based on the node distribution density information, candidate storage blocks that are in the same density clustering interval are merged to obtain logical storage blocks.

[0011] Preferably, the process of automatically archiving the digital quality inspection data stream to the corresponding logical storage block according to the hierarchical association index and generating the archiving location mapping is as follows: Based on the node-block correspondence between the logical storage block identifiers bound to each index backbone node in the hierarchical association index, the routing of each data unit in the digital quality inspection data stream is directed to the corresponding logical storage block. Write the digital quality inspection data stream to the corresponding logical storage block to obtain the archived quality inspection data of the digital quality inspection data stream; Based on the storage path identifier of the archived quality inspection data in the logical storage block, establish the pointing relationship between each data unit in the digital quality inspection data stream and the storage path identifier, and integrate the pointing relationship into the archive location mapping of the digital quality inspection data stream.

[0012] Preferably, the process of performing storage density analysis on logical storage blocks based on archive location mapping is as follows: Based on the archive location mapping, the storage path distribution information of the archived digital quality inspection data streams in each logical storage block is extracted to obtain the data distribution status within the logical storage block. The capacity occupancy statistics of the data distribution status within the block are performed, and the ratio of the amount of archived data in each logical storage block to the corresponding logical storage space capacity is used as the storage density value of each logical storage block. By comparing the storage density values ​​horizontally, the distribution characteristics of density differences in each logical storage block are identified, and the storage density analysis results of each logical storage block are obtained.

[0013] Preferably, the process of dynamically reorganizing and optimizing the distribution of the digital quality inspection data stream in the logical storage blocks based on the analysis results to obtain an adaptive archiving storage structure for the digital quality inspection data stream is as follows: Based on the density level identifier of each logical storage block in the storage density analysis results, logical storage blocks with a density level in the preset high-density clustering range are marked as source blocks, and logical storage blocks with a density level in the preset low-density clustering range are marked as target blocks. Based on the hierarchical relationship of the adaptive classification codes corresponding to the archived digital quality inspection data streams in the source block, the digital quality inspection data streams with the same hierarchical relationship are migrated to the same target block to obtain the reorganized block data distribution; Based on the reorganized block data distribution, the storage path pointers of each digital quality inspection data stream in the archive location mapping are updated to obtain the adaptive archive storage structure of the digital quality inspection data stream.

[0014] The present invention also provides a digital quality inspection data storage system based on automated archiving, the system comprising: The feature standardization module is used to normalize the digital quality inspection data stream after feature extraction to obtain a standardized quality inspection feature set of the digital quality inspection data stream. The hierarchical coding construction module is used to determine the dynamic classification dimension based on the distribution density of each dimension of features in the standardized quality inspection feature set, and to construct an adaptive classification code with hierarchical relationship based on the dynamic classification dimension and the quality inspection judgment results in the standardized quality inspection feature set. The hierarchical index building module is used to embed the product batch identifier and generation time sequence identifier from the standardized quality inspection feature set as related nodes into the index backbone, using adaptive classification coding as the index backbone, and cross-linking the related nodes to obtain a hierarchical related index. The automatic partitioning and archiving module is used to divide logical storage blocks according to the hierarchical structure and node distribution characteristics of the hierarchical associative index, combined with the hierarchical relationship of adaptive classification coding, and automatically archive the digital quality inspection data stream to the corresponding logical storage blocks according to the hierarchical associative index, and generate an archiving location mapping. The storage adaptive optimization module is used to perform storage density analysis on logical storage blocks based on archive location mapping, and dynamically reorganize and optimize the distribution of digital quality inspection data streams in logical storage blocks according to the analysis results, so as to obtain an adaptive archive storage structure for digital quality inspection data streams.

[0015] As can be seen from the above technical solution, the present invention provides a digital quality inspection data storage method and system based on automated archiving. By standardizing the quality inspection data, dynamically determining the classification dimensions, and constructing a hierarchical adaptive classification code, it can accurately adapt to the distribution characteristics of digital quality inspection data. Relying on the hierarchical association index, it achieves efficient matching between data and storage blocks. At the same time, it combines storage density analysis to complete the dynamic reorganization and optimization of data distribution, significantly improving the automated archiving efficiency and storage utilization of digital quality inspection data, making data retrieval, querying, and calling faster and more stable, and optimizing the overall storage management effect of digital quality inspection data throughout the entire process. Attached Figure Description

[0016] Other objects and results of the invention will become more apparent and readily understood by referring to the following description taken in conjunction with the accompanying drawings, and with a more complete understanding of the invention. In the drawings: Figure 1 This is a flowchart illustrating a digital quality inspection data storage method based on automated archiving, according to an embodiment of the present invention. Figure 2 This is a functional block diagram of a digital quality inspection data storage system based on automated archiving, according to an embodiment of the present invention. Detailed Implementation

[0017] The existing digital quality inspection data storage has fixed classification dimensions, which cannot adapt to the dynamic distribution characteristics of data. The archive index construction is rigid, the logical storage block division is unreasonable, the storage density is uneven and resources are wasted, and the archive cannot be dynamically reorganized and optimized. Ultimately, this leads to low storage and archiving efficiency and slow query speed.

[0018] To address the aforementioned problems, this invention provides a digital quality inspection data storage method and system based on automated archiving. The specific embodiments of this invention will be described in detail below with reference to the accompanying drawings.

[0019] To illustrate the digital quality inspection data storage method and system based on automated archiving provided by this invention, Figure 1 An exemplary illustration is provided for a digital quality inspection data storage method based on automated archiving according to an embodiment of the present invention; Figure 2 An exemplary illustration is provided for a digital quality inspection data storage system based on automated archiving, according to an embodiment of the present invention.

[0020] The following description of exemplary embodiments is merely illustrative and is in no way intended to limit the invention or its application or use. Techniques and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques and equipment should be considered part of the specification.

[0021] Reference Figure 1 The diagram shown is a flowchart illustrating a digital quality inspection data storage method based on automated archiving, according to an embodiment of the present invention. In this embodiment, the digital quality inspection data storage method based on automated archiving includes: Step 1: Normalize the digital quality inspection data stream after feature extraction to obtain a standardized quality inspection feature set of the digital quality inspection data stream; In this embodiment of the invention, the normalization process for the digital quality inspection data stream after feature extraction to obtain a standardized quality inspection feature set of the digital quality inspection data stream is as follows: Extract multi-dimensional quality inspection parameters from the digital quality inspection data stream to obtain the initial quality inspection feature set of the digital quality inspection data stream; The initial quality inspection feature set is dimensionally identified, and grouped according to the differences in the physical dimensions of the quality inspection parameters in each dimension, to obtain the dimensional grouping feature data of the digital quality inspection data stream. By performing intra-group scaling on the dimensional grouped feature data, a dimensional unified feature set for the digital quality inspection data stream is obtained. Aligning the distribution centers of the dimensionless feature set yields the standardized quality inspection feature set of the digital quality inspection data stream.

[0022] The entire process of digital quality inspection data stream with completed feature extraction is traversed, and all multi-dimensional information that can characterize the product inspection status in the data stream is collected. The information is completely preserved in its original presentation form in the data stream without omitting any valid information. All collected information is aggregated in a fixed order to form an initial quality inspection feature set of digital quality inspection data stream that fully covers all inspection dimensions.

[0023] By examining each piece of information in the initial quality inspection feature set in turn, clarifying the physical dimension attribute corresponding to each piece of information, classifying information with the same physical dimension attribute into the same group, and classifying information with different physical dimension attributes into independent groups, ensuring that each group contains only information of the same dimension type, the resulting digital quality inspection data stream with clear dimensions and distinct dimensions is obtained.

[0024] Each group in the dimensional grouped feature data is processed separately. A unified numerical adjustment rule is set within each group. According to the rule, the range of all information in the group is normalized so that the numerical range of all information in the same group is consistent. This completely eliminates the numerical deviation caused by the different original acquisition scales of the information in the group. After processing, a dimensional unified feature set of digital quality inspection data stream with unified scale and consistent numerical range is obtained.

[0025] First, we statistically analyze the overall distribution of all information in the unified dimension feature set, find the distribution center point that corresponds to all information, and then adjust the position of each piece of information in the unified dimension feature set with this distribution center point as a reference, so that each piece of information maintains a standardized correspondence with the distribution center, making the overall information distribution uniform and orderly. After the adjustment, we obtain a standardized quality inspection feature set of digital quality inspection data stream with unified distribution and standardized features.

[0026] Step 2: Determine the dynamic classification dimension based on the distribution density of features in each dimension of the standardized quality inspection feature set, and construct an adaptive classification code with hierarchical relationship based on the dynamic classification dimension and the quality inspection judgment results in the standardized quality inspection feature set. In this embodiment of the invention, the process of determining the dynamic classification dimension based on the distribution density of features in each dimension of the standardized quality inspection feature set is as follows: The value distribution of each dimension of the standardized quality inspection feature set is scanned to identify data clustering intervals, and the sample frequency within each data clustering interval is counted to obtain the distribution density information of each dimension of the feature. Based on the distribution density information, the data aggregation and discrimination ability of each dimension feature is evaluated, and the dimension features in each dimension feature that meet the preset hierarchical construction requirements in terms of the number of data aggregation intervals are selected to obtain the candidate classification dimensions of the standardized quality inspection feature set. The feasibility of hierarchical nesting of candidate classification dimensions is verified, and the dimensional features with progressive inclusion relationships between the data aggregation intervals of each candidate classification dimension are mapped to dynamic classification dimensions of standardized quality inspection feature sets.

[0027] The process of constructing an adaptive classification code with hierarchical relationships based on the quality inspection judgment results of the dynamic classification dimension and the standardized quality inspection feature set is as follows: By analyzing the interval boundaries and central feature values ​​of the data clustering intervals in the dynamic classification dimension, a candidate set of hierarchical nodes for the dynamic classification dimension is obtained. The quality inspection judgment results in the standardized quality inspection feature set are categorized and summarized to identify the type identifiers in the quality inspection judgment results, thus obtaining a set of judgment type identifiers for the quality inspection judgment results. Traverse each level node in the candidate set of level nodes, aggregate the type identifiers corresponding to the quality inspection judgment results that fall into the same data clustering interval, and count the co-occurrence frequency of each data clustering interval and the judgment type identifier to construct a node-type co-occurrence matrix. Based on the relationship between the values ​​of each element in the node-type co-occurrence matrix, the judgment type identifier with the highest co-occurrence frequency is bound to the corresponding hierarchical node, and a hierarchical subordinate relationship is established based on the binding result to obtain the hierarchical topology of the dynamic classification dimension. Each node in the hierarchical topology is assigned a hierarchical prefix code, and the hierarchical prefix code is concatenated with the corresponding judgment type identifier to obtain an adaptive classification code with hierarchical relationship.

[0028] A comprehensive value distribution scan is performed on each dimension of the standardized quality inspection feature set. The arrangement of all values ​​under each dimension is completely sorted out according to the continuous change trajectory of the values. By observing the concentrated occurrence of values, the continuous range of highly concentrated data is accurately identified as the data cluster interval. Then, the number of samples contained in each determined data cluster interval is counted and accumulated. The sample number corresponding to each data cluster interval is combined with the interval information to obtain complete and accurate distribution density information of each dimension feature.

[0029] Based on the distribution density information of each dimension feature that has been acquired, the ability of each dimension feature to distinguish different data sets is evaluated item by item. By observing the clarity and independence of the data cluster intervals, it is determined whether the dimension feature can effectively divide different data groups. Then, the number of data cluster intervals of each dimension feature is compared with the number of intervals required for the pre-set hierarchical construction standard. Dimension features whose number of intervals fully meets the standard are retained. These selected dimension features together form the candidate classification dimensions of the standardized quality inspection feature set.

[0030] For all the candidate classification dimensions selected, hierarchical nesting feasibility verification is carried out in sequence. The inclusion status of each data cluster interval under each candidate classification dimension is analyzed one by one. It is checked whether there is a progressive inclusion form in which the outer interval completely wraps the inner interval. The candidate classification dimensions with stable progressive inclusion relationship between data cluster intervals are formally determined and mapped to the dynamic classification dimensions of the standardized quality inspection feature set.

[0031] For the finally determined dynamic classification dimension, the starting and ending values ​​of each data cluster interval are analyzed to clarify the interval boundaries. At the same time, the central feature value of each data cluster interval is determined. The interval boundary information and central feature value information of all data cluster intervals are summarized and organized to form a complete hierarchical node candidate set for the dynamic classification dimension.

[0032] A systematic classification of all quality inspection judgment results in the standardized quality inspection feature set is carried out. Quality inspection judgment results with consistent content and meaning are merged into the same category. From each category of sorted quality inspection judgment results, a type identifier that can uniquely represent the category is extracted. All extracted type identifiers are collected and gathered to form a complete set of judgment type identifiers for quality inspection judgment results.

[0033] In order, each level node in the candidate set of level nodes is traversed one by one. All the type identifiers corresponding to the quality inspection judgment results belonging to the same data clustering interval are collected and aggregated. Then, the number of times each data clustering interval and each judgment type identifier appear at the same time is statistically recorded. Based on these statistical results, a complete node type co-occurrence matrix is ​​constructed.

[0034] The data corresponding to each group within the node type co-occurrence matrix are compared to determine the largest value among all the judgment type identifiers corresponding to each level node. The judgment type identifier with the largest value is then firmly bound to the corresponding level node. Based on the binding results of all nodes, a clear hierarchical relationship is built from top to bottom, ultimately forming a hierarchical topology structure with a clear dynamic classification dimension.

[0035] Following the hierarchical arrangement of the topological structure, a unique corresponding hierarchical prefix code is assigned to each level node from the highest level to the lowest level. Then, the hierarchical prefix code assigned to each level node is sequentially concatenated with the judgment type identifier bound to that node to generate an adaptive classification code with a clear hierarchical relationship.

[0036] Step 3: Using adaptive classification coding as the index backbone, embed the product batch identifier and generation time sequence identifier from the standardized quality inspection feature set as related nodes into the index backbone, and perform cross-association on the related nodes to obtain a hierarchical related index; In this embodiment of the invention, the process of using adaptive classification coding as the index backbone, embedding product batch identifiers and generation time sequence identifiers from the standardized quality inspection feature set as associated nodes into the index backbone, and cross-associating the associated nodes to obtain a hierarchical associated index is as follows: The hierarchical structure of the adaptive classification coding is analyzed, and the coding nodes at each level in the adaptive classification coding are used as the main index nodes; Extract product batch identifiers and generate time sequence identifiers from standardized quality inspection feature sets; Based on the correspondence between the digital quality inspection data stream and the index backbone nodes, the product batch identifier and the generation time sequence identifier are written into the corresponding index backbone nodes to obtain the index backbone with the associated nodes. In the index backbone with associated nodes, based on the similarity of product batch identifiers, the same product batch identifiers distributed under different index backbone nodes are linked by index jump, and based on the temporal continuity of the generation time sequence identifiers, adjacent generation time sequence identifiers are linked by index progression to obtain a hierarchical associated index.

[0037] The overall hierarchical structure of the adaptive classification coding is completely decomposed and meticulously analyzed from top to bottom. The hierarchical position, hierarchical relationship and independent structural form of each level of coding in the overall structure are clearly defined. The coding nodes with independent classification functions at each level of the adaptive classification coding are completely extracted. These extracted coding nodes at each level directly constitute the core basic framework for index construction and form the main index nodes for subsequent index building.

[0038] From all the information contained in the standardized quality inspection feature set, information that can uniquely identify the production batch of the product is specifically screened and extracted. At the same time, information that can clearly reflect the generation sequence of the digital quality inspection data stream is specifically screened and extracted. During the extraction process, the correspondence between the two types of identifiers and the original quality inspection data is fully preserved, without changing the original content and expression of any identifier.

[0039] Based on the clear attribution and correspondence between the digital quality inspection data stream and each index backbone node, the extracted product batch identifier is accurately placed in the preset position of the index backbone node that corresponds to it, and the extracted generation time sequence identifier is accurately placed in the preset position of the index backbone node that corresponds to it. This allows the two types of identifiers to be stably attached to the corresponding index backbone nodes. After completing all identifier mounting operations, a structurally complete index backbone of the associated nodes is obtained.

[0040] Within the overall structure of the index backbone where associated nodes have been mounted, the product batch identifiers under all index backbone nodes are fully traversed. The batch identifier content is compared one by one, and completely identical identifier items are locked. The same product batch identifiers under different index backbone nodes are established with direct jumpable index connections. At the same time, the generation time sequence identifiers under all index backbone nodes are fully traversed, and adjacent generation time sequence identifiers are established with progressively advancing index connections according to the continuous order of time. After all index links are established, a hierarchical associated index with a complete structure and clear associations is formed.

[0041] Step 4: Based on the hierarchical structure and node distribution characteristics of the hierarchical associative index, and combined with the hierarchical relationship of the adaptive classification coding, divide the logical storage blocks, and automatically archive the digital quality inspection data stream to the corresponding logical storage blocks according to the hierarchical associative index, and generate the archive location mapping. In this embodiment of the invention, the process of dividing logical storage blocks based on the hierarchical structure and node distribution characteristics of the hierarchical associative index, combined with the hierarchical relationship of adaptive classification coding, is as follows: The nesting depth of the main nodes of each level of the hierarchical associative index is analyzed to obtain the index hierarchy structure information; The total number of associated nodes attached to each index backbone node is counted to obtain node distribution density information; Read the nesting depth of each level of the adaptive classification coding to obtain the coding level depth information; Based on the index hierarchy structure information and the encoding hierarchy depth information, the logical storage space capacity corresponding to the index backbone nodes with the same nesting depth is aggregated into candidate storage blocks; Based on the node distribution density information, candidate storage blocks that are in the same density clustering interval are merged to obtain logical storage blocks.

[0042] The process of automatically archiving the digital quality inspection data stream to the corresponding logical storage block and generating an archive location mapping based on the hierarchical association index is as follows: Based on the node-block correspondence between the logical storage block identifiers bound to each index backbone node in the hierarchical association index, the routing of each data unit in the digital quality inspection data stream is directed to the corresponding logical storage block. Write the digital quality inspection data stream to the corresponding logical storage block to obtain the archived quality inspection data of the digital quality inspection data stream; Based on the storage path identifier of the archived quality inspection data in the logical storage block, establish the pointing relationship between each data unit in the digital quality inspection data stream and the storage path identifier, and integrate the pointing relationship into the archive location mapping of the digital quality inspection data stream.

[0043] Starting from the top node of the hierarchical associative index, we expand downwards layer by layer along the wrapping relationship of the nodes. When we reach each index trunk node, we observe the actual number of layers it is wrapped by the upper-level nodes. We determine the nesting depth of the node directly through the wrapping status of the hierarchy. We record the nesting depth of each index trunk node in order from top to bottom on a fixed carrier. After the nesting depth of all index trunk nodes is recorded, we integrate them to form a complete and directly viewable index hierarchy structure information.

[0044] For each independent index backbone node in the hierarchical associated index, all associated nodes attached to that node are fully expanded and presented. Each associated node is touched and counted one by one to ensure that there are no omissions or duplicates. The specific count is recorded next to the information of the corresponding index backbone node. After completing the counting and recording of associated nodes of all index backbone nodes, all quantity information is summarized to form complete node distribution density information.

[0045] Starting from the beginning of the adaptive classification coding, the structure of each level of coding is read segment by segment. For each level of coding identified, the actual number of layers it is wrapped by the previous level of coding is determined. The nesting depth is directly determined by the arrangement and wrapping status of the coding. The nesting level of each level of coding is recorded one by one. After all the nesting levels of coding are recorded, they are standardized and organized to form clear and complete coding level depth information.

[0046] The nesting depth of each index backbone node recorded in the index hierarchy structure information is compared item by item with the nesting depth of each level of encoding recorded in the encoding hierarchy depth information. All index backbone nodes with the same nesting depth value are selected. The logical storage space capacity allocated to each of these index backbone nodes is collected and aggregated, so that these scattered capacities form a continuous whole and constitute independent candidate storage blocks.

[0047] Based on the density of nodes within each candidate storage block as presented by the node distribution density information, all candidate storage blocks are compared in terms of density, and intervals with the same density are divided. The boundaries of candidate storage blocks in the same density interval are aligned, the dividing lines between blocks are removed, and multiple small blocks are integrated into a complete large storage space, ultimately forming a stable and unified logical storage block.

[0048] Each index backbone node in the hierarchical associative index is fixedly paired with the identifier of the divided logical storage block, and the logical storage block corresponding to each index backbone node is clearly defined. According to this fixed pairing relationship, the travel path is determined for each data unit in the digital quality inspection data stream, so that each data unit points to its corresponding logical storage block along the path.

[0049] Following the predetermined path of the data units, each data unit in the digital quality inspection data stream is smoothly sent into the corresponding logical storage block, ensuring that each data unit is placed in a fixed position within the logical storage block without displacement or misalignment. Once all data units are placed, the archived quality inspection data of the digital quality inspection data stream is formed and stored completely in the logical storage block.

[0050] Locate the specific storage location of each archived quality inspection data within the logical storage block, extract the storage path identifier corresponding to that location, establish a unique fixed connection between each data unit in the digital quality inspection data stream and the storage path identifier of its own storage location, and centrally organize the connection relationships between all data units and storage path identifiers to form a complete and traceable archiving location mapping of the digital quality inspection data stream.

[0051] Step 5: Based on the archive location mapping, perform storage density analysis on the logical storage blocks, and based on the analysis results, dynamically reorganize and optimize the distribution of the digital quality inspection data stream in the logical storage blocks to obtain an adaptive archive storage structure for the digital quality inspection data stream.

[0052] In this embodiment of the invention, the process of performing storage density analysis on logical storage blocks based on archive location mapping is as follows: Based on the archive location mapping, the storage path distribution information of the archived digital quality inspection data streams in each logical storage block is extracted to obtain the data distribution status within the logical storage block. The capacity occupancy statistics of the data distribution status within the block are performed, and the ratio of the amount of archived data in each logical storage block to the corresponding logical storage space capacity is used as the storage density value of each logical storage block. By comparing the storage density values ​​horizontally, the distribution characteristics of density differences in each logical storage block are identified, and the storage density analysis results of each logical storage block are obtained.

[0053] Based on the analysis results, the distribution of the digital quality inspection data stream in the logical storage blocks is dynamically reorganized and optimized to obtain an adaptive archiving storage structure for the digital quality inspection data stream. The process is as follows: Based on the density level identifier of each logical storage block in the storage density analysis results, logical storage blocks with a density level in the preset high-density clustering range are marked as source blocks, and logical storage blocks with a density level in the preset low-density clustering range are marked as target blocks. Based on the hierarchical relationship of the adaptive classification codes corresponding to the archived digital quality inspection data streams in the source block, the digital quality inspection data streams with the same hierarchical relationship are migrated to the same target block to obtain the reorganized block data distribution; Based on the reorganized block data distribution, the storage path pointers of each digital quality inspection data stream in the archive location mapping are updated to obtain the adaptive archive storage structure of the digital quality inspection data stream.

[0054] Based on the complete correspondence between data units and storage paths recorded in the archive location mapping, each logical storage block is opened one by one. The specific storage path information corresponding to the archived digital quality inspection data stream is extracted from the block one by one. This path information is then centrally collected according to the logical storage block in which it is located, and the specific distribution location, arrangement and coverage of the data within each logical storage block are fully presented. The actual arrangement of the data within the block is clearly shown, and the data distribution status within the logical storage block is obtained.

[0055] For each logical storage block with a clearly defined data distribution status, the actual space occupied by the archived data within the block is statistically analyzed segment by segment. The total space occupied by all archived data within the block is fully recorded. At the same time, the total storage space pre-allocated to the logical storage block is determined. The total space occupied by the archived data within the block is directly compared with the total storage space of the block. This comparison result of actual occupancy and total capacity is used as the storage density value of the logical storage block.

[0056] The storage density values ​​calculated from all logical storage blocks are placed on the same plane for horizontal comparison one by one. The differences in storage density of each value are identified one by one. The differences in storage density between different logical storage blocks, the distribution patterns of high and low values, and the overall differences are recorded in detail. All the differences obtained from the comparison are summarized and organized in a standardized manner to obtain the storage density analysis results of each logical storage block.

[0057] Based on the density level identifier marked for each logical storage block in the storage density analysis results, all logical storage blocks with density levels within the pre-defined high-density clustering range are uniformly marked and identified as source blocks for data migration. At the same time, all logical storage blocks with density levels within the pre-defined low-density clustering range are uniformly marked and identified as target blocks for data migration.

[0058] The adaptive classification codes corresponding to the archived digital quality inspection data streams within the source block are read one by one to clarify the hierarchical relationship of each data stream. All digital quality inspection data streams with identical adaptive classification code hierarchical relationships are filtered out. These data streams with the same hierarchical relationship are moved out of the source block as a whole and uniformly migrated into the same target block for storage. After completing the classification and migration of all data at the same level, a rearranged block data distribution is formed, resulting in the reorganized block data distribution.

[0059] Based on the latest data storage location shown by the reorganized block data distribution, the original storage path of each digital quality inspection data stream in the archive location mapping is adjusted one by one, and the path is replaced with the latest storage location after data migration. This ensures that the path of each data stream is completely consistent with the actual storage location. After all path pointers are updated and replaced, an adaptive archive storage structure for digital quality inspection data streams with a reasonable structure and balanced distribution is formed.

[0060] As can be seen from the above embodiments, the digital quality inspection data storage method based on automated archiving provided by the present invention can accurately adapt to the distribution characteristics of digital quality inspection data by standardizing quality inspection data, dynamically determining classification dimensions and constructing hierarchical adaptive classification codes. It can achieve efficient matching between data and storage blocks by relying on hierarchical association indexes, and at the same time, complete the dynamic reorganization and optimization of data distribution by combining storage density analysis. This significantly improves the automated archiving efficiency and storage utilization of digital quality inspection data, making data retrieval, querying and calling faster and more stable, and optimizing the overall storage management effect of digital quality inspection data.

[0061] like Figure 2 The diagram shown is a functional block diagram of a digital quality inspection data storage system 100 based on automated archiving provided in an embodiment of the present invention, including a feature standardization module 101, a hierarchical encoding construction module 102, a hierarchical index construction module 103, a partitioned automatic archiving module 104, and a storage adaptive optimization module 105.

[0062] In this embodiment, the functions of each module are as follows: The feature standardization module 101 is used to normalize the digital quality inspection data stream after feature extraction to obtain a standardized quality inspection feature set of the digital quality inspection data stream. The hierarchical coding construction module 102 is used to determine the dynamic classification dimension based on the distribution density of each dimension feature in the standardized quality inspection feature set, and to construct an adaptive classification code with hierarchical relationship based on the dynamic classification dimension and the quality inspection judgment results in the standardized quality inspection feature set. The hierarchical index building module 103 is used to embed the product batch identifier and generation time sequence identifier in the standardized quality inspection feature set as related nodes into the index backbone, using adaptive classification coding as the index backbone, and cross-associating the related nodes to obtain a hierarchical association index. The partition automatic archiving module 104 is used to divide logical storage blocks according to the hierarchical structure and node distribution characteristics of the hierarchical association index, combined with the hierarchical relationship of adaptive classification coding, and automatically archive the digital quality inspection data stream to the corresponding logical storage blocks according to the hierarchical association index, and generate an archiving location mapping. The storage adaptive optimization module 105 is used to perform storage density analysis on logical storage blocks based on archive location mapping, and dynamically reorganize and optimize the distribution of digital quality inspection data stream in logical storage blocks according to the analysis results, so as to obtain an adaptive archive storage structure for digital quality inspection data stream.

[0063] As can be seen from the above embodiments, the digital quality inspection data storage system based on automated archiving provided by the present invention can accurately adapt to the distribution characteristics of digital quality inspection data by standardizing quality inspection data, dynamically determining classification dimensions and constructing hierarchical adaptive classification codes. It can achieve efficient matching of data and storage blocks by relying on hierarchical association indexes, and at the same time, complete the dynamic reorganization and optimization of data distribution by combining storage density analysis. This significantly improves the automated archiving efficiency and storage utilization of digital quality inspection data, making data retrieval, querying and calling faster and more stable, and optimizing the overall storage management effect of digital quality inspection data.

[0064] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A digital quality inspection data storage method based on automated archiving, characterized in that, The method includes: Step 1: Normalize the digital quality inspection data stream after feature extraction to obtain a standardized quality inspection feature set of the digital quality inspection data stream; Step 2: Determine the dynamic classification dimension based on the distribution density of features in each dimension of the standardized quality inspection feature set, and construct an adaptive classification code with hierarchical relationship based on the dynamic classification dimension and the quality inspection judgment results in the standardized quality inspection feature set. Step 3: Using adaptive classification coding as the index backbone, embed the product batch identifier and generation time sequence identifier from the standardized quality inspection feature set as related nodes into the index backbone, and perform cross-association on the related nodes to obtain a hierarchical related index; Step 4: Based on the hierarchical structure and node distribution characteristics of the hierarchical associative index, and combined with the hierarchical relationship of the adaptive classification coding, divide the logical storage blocks, and automatically archive the digital quality inspection data stream to the corresponding logical storage blocks according to the hierarchical associative index, and generate the archive location mapping. Step 5: Based on the archive location mapping, perform storage density analysis on the logical storage blocks, and based on the analysis results, dynamically reorganize and optimize the distribution of the digital quality inspection data stream in the logical storage blocks to obtain an adaptive archive storage structure for the digital quality inspection data stream.

2. The digital quality inspection data storage method based on automated archiving as described in claim 1, characterized in that, The process of normalizing the digital quality inspection data stream after feature extraction to obtain a standardized quality inspection feature set is as follows: Extract multi-dimensional quality inspection parameters from the digital quality inspection data stream to obtain the initial quality inspection feature set of the digital quality inspection data stream; The initial quality inspection feature set is dimensionally identified, and grouped according to the differences in the physical dimensions of the quality inspection parameters in each dimension, to obtain the dimensional grouping feature data of the digital quality inspection data stream. By performing intra-group scaling on the dimensional grouped feature data, a dimensional unified feature set for the digital quality inspection data stream is obtained. Aligning the distribution centers of the dimensionless feature set yields the standardized quality inspection feature set of the digital quality inspection data stream.

3. The digital quality inspection data storage method based on automated archiving as described in claim 1, characterized in that, The process of determining the dynamic classification dimension based on the distribution density of features in each dimension of the standardized quality inspection feature set is as follows: The value distribution of each dimension of the standardized quality inspection feature set is scanned to identify data clustering intervals, and the sample frequency within each data clustering interval is counted to obtain the distribution density information of each dimension of the feature. Based on the distribution density information, the data aggregation and discrimination ability of each dimension feature is evaluated, and the dimension features in each dimension feature that meet the preset hierarchical construction requirements in terms of the number of data aggregation intervals are selected to obtain the candidate classification dimensions of the standardized quality inspection feature set. The feasibility of hierarchical nesting of candidate classification dimensions is verified, and the dimensional features with progressive inclusion relationships between the data aggregation intervals of each candidate classification dimension are mapped to dynamic classification dimensions of standardized quality inspection feature sets.

4. The digital quality inspection data storage method based on automated archiving as described in claim 3, characterized in that, The process of constructing an adaptive classification code with hierarchical relationships based on the quality inspection judgment results of the dynamic classification dimension and the standardized quality inspection feature set is as follows: By analyzing the interval boundaries and central feature values ​​of the data clustering intervals in the dynamic classification dimension, a candidate set of hierarchical nodes for the dynamic classification dimension is obtained. The quality inspection judgment results in the standardized quality inspection feature set are categorized and summarized to identify the type identifiers in the quality inspection judgment results, thus obtaining a set of judgment type identifiers for the quality inspection judgment results. Traverse each level node in the candidate set of level nodes, aggregate the type identifiers corresponding to the quality inspection judgment results that fall into the same data clustering interval, and count the co-occurrence frequency of each data clustering interval and the judgment type identifier to construct a node-type co-occurrence matrix. Based on the relationship between the values ​​of each element in the node-type co-occurrence matrix, the judgment type identifier with the highest co-occurrence frequency is bound to the corresponding hierarchical node, and a hierarchical subordinate relationship is established based on the binding result to obtain the hierarchical topology of the dynamic classification dimension. Each node in the hierarchical topology is assigned a hierarchical prefix code, and the hierarchical prefix code is concatenated with the corresponding judgment type identifier to obtain an adaptive classification code with hierarchical relationship.

5. The digital quality inspection data storage method based on automated archiving as described in claim 1, characterized in that, The process of using adaptive classification coding as the index backbone, embedding product batch identifiers and generation time sequence identifiers from the standardized quality inspection feature set as associated nodes into the index backbone, and cross-associating the associated nodes to obtain a hierarchical associated index is as follows: The hierarchical structure of the adaptive classification coding is analyzed, and the coding nodes at each level in the adaptive classification coding are used as the main index nodes; Extract product batch identifiers and generate time sequence identifiers from standardized quality inspection feature sets; Based on the correspondence between the digital quality inspection data stream and the index backbone nodes, the product batch identifier and the generation time sequence identifier are written into the corresponding index backbone nodes to obtain the index backbone with the associated nodes. In the index backbone with associated nodes, based on the similarity of product batch identifiers, the same product batch identifiers distributed under different index backbone nodes are linked by index jump, and based on the temporal continuity of the generation time sequence identifiers, adjacent generation time sequence identifiers are linked by index progression to obtain a hierarchical associated index.

6. The digital quality inspection data storage method based on automated archiving as described in claim 1, characterized in that, The process of dividing logical storage blocks based on the hierarchical structure and node distribution characteristics of the hierarchical associative index, combined with the hierarchical relationship of adaptive classification coding, is as follows: The nesting depth of the main nodes of each level of the hierarchical associative index is analyzed to obtain the index hierarchy structure information; The total number of associated nodes attached to each index backbone node is counted to obtain node distribution density information; Read the nesting depth of each level of the adaptive classification coding to obtain the coding level depth information; Based on the index hierarchy structure information and the encoding hierarchy depth information, the logical storage space capacity corresponding to the index backbone nodes with the same nesting depth is aggregated into candidate storage blocks; Based on the node distribution density information, candidate storage blocks that are in the same density clustering interval are merged to obtain logical storage blocks.

7. A digital quality inspection data storage method based on automated archiving as described in claim 6, characterized in that, The process of automatically archiving the digital quality inspection data stream to the corresponding logical storage block and generating an archive location mapping based on the hierarchical association index is as follows: Based on the node-block correspondence between the logical storage block identifiers bound to each index backbone node in the hierarchical association index, the routing of each data unit in the digital quality inspection data stream is directed to the corresponding logical storage block. Write the digital quality inspection data stream to the corresponding logical storage block to obtain the archived quality inspection data of the digital quality inspection data stream; Based on the storage path identifier of the archived quality inspection data in the logical storage block, establish the pointing relationship between each data unit in the digital quality inspection data stream and the storage path identifier, and integrate the pointing relationship into the archive location mapping of the digital quality inspection data stream.

8. A digital quality inspection data storage method based on automated archiving as described in claim 1, characterized in that, The process of performing storage density analysis on logical storage blocks based on archive location mapping is as follows: Based on the archive location mapping, the storage path distribution information of the archived digital quality inspection data streams in each logical storage block is extracted to obtain the data distribution status within the logical storage block. The capacity occupancy statistics of the data distribution status within the block are performed, and the ratio of the amount of archived data in each logical storage block to the corresponding logical storage space capacity is used as the storage density value of each logical storage block. By comparing the storage density values ​​horizontally, the distribution characteristics of density differences in each logical storage block are identified, and the storage density analysis results of each logical storage block are obtained.

9. A digital quality inspection data storage method based on automated archiving as described in claim 8, characterized in that, Based on the analysis results, the distribution of the digital quality inspection data stream in the logical storage blocks is dynamically reorganized and optimized to obtain an adaptive archiving storage structure for the digital quality inspection data stream. The process is as follows: Based on the density level identifier of each logical storage block in the storage density analysis results, logical storage blocks with a density level in the preset high-density clustering range are marked as source blocks, and logical storage blocks with a density level in the preset low-density clustering range are marked as target blocks. Based on the hierarchical relationship of the adaptive classification codes corresponding to the archived digital quality inspection data streams in the source block, the digital quality inspection data streams with the same hierarchical relationship are migrated to the same target block to obtain the reorganized block data distribution; Based on the reorganized block data distribution, the storage path pointers of each digital quality inspection data stream in the archive location mapping are updated to obtain the adaptive archive storage structure of the digital quality inspection data stream.

10. A digital quality inspection data storage system based on automated archiving, characterized in that, The system for implementing the digital quality inspection data storage method based on automated archiving as described in any one of claims 1-9 includes: The feature standardization module is used to normalize the digital quality inspection data stream after feature extraction to obtain a standardized quality inspection feature set of the digital quality inspection data stream. The hierarchical coding construction module is used to determine the dynamic classification dimension based on the distribution density of each dimension of features in the standardized quality inspection feature set, and to construct an adaptive classification code with hierarchical relationship based on the dynamic classification dimension and the quality inspection judgment results in the standardized quality inspection feature set. The hierarchical index building module is used to embed the product batch identifier and generation time sequence identifier from the standardized quality inspection feature set as related nodes into the index backbone, using adaptive classification coding as the index backbone, and cross-linking the related nodes to obtain a hierarchical related index. The automatic partitioning and archiving module is used to divide logical storage blocks according to the hierarchical structure and node distribution characteristics of the hierarchical associative index, combined with the hierarchical relationship of adaptive classification coding, and automatically archive the digital quality inspection data stream to the corresponding logical storage blocks according to the hierarchical associative index, and generate an archiving location mapping. The storage adaptive optimization module is used to perform storage density analysis on logical storage blocks based on archive location mapping, and dynamically reorganize and optimize the distribution of digital quality inspection data streams in logical storage blocks according to the analysis results, so as to obtain an adaptive archive storage structure for digital quality inspection data streams.

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