A graphic element library management method and system based on data processing

Abstract

The present invention relates to the technical field of element library management, and in particular to a method and system for managing a graphic element library based on data processing. The method comprises the following steps: constructing an element update directed graph according to the log data of the graphic element library to obtain an element update directed graph; designing an element update node tracing structure for the element update directed graph and designing a tracing hierarchical index table to obtain a node tracing hierarchical index table; performing concurrent retrieval attenuation gradient calculation on the node tracing hierarchical index table to obtain concurrent retrieval attenuation gradient data; performing cache hit rate reduction estimation according to the concurrent retrieval attenuation gradient data to obtain cache hit rate reduction estimation data; performing a packaging design graphic element index management strategy design according to the cache hit rate reduction estimation data to obtain a packaging design graphic element index management strategy. The present invention makes the element library management method more perfect by optimizing the element library management method.

Description

Technical Field

[0001] The present invention relates to the technical field of element library management, and in particular to a graphic element library management method and system based on data processing. Background Art

[0002] In the field of modern packaging design, the management and application of graphic elements are crucial. With the intensification of market competition and the diversification of consumer demand, packaging design not only needs innovation, but also needs to be highly repeatable and consistent. As a core component of packaging design, the management method of graphic elements directly affects the efficiency and effect of design. Previous graphic element management methods often rely on manual classification and maintenance, which is difficult to adapt to the ever-changing market needs and design trends. This method may lead to the reuse, failure and waste of resources of design elements. Therefore, it is particularly important to develop an efficient and intelligent graphic element library management method. In this context, the innovation of packaging design graphic element library management methods has become extremely valuable. The new management method needs to be able to support the efficient storage, retrieval and application of graphic elements, and be able to automatically process the classification and update of graphic elements. This requires the use of advanced data management technology and intelligent algorithms to cope with the complexity of large-scale graphic elements. By establishing a standardized graphic element database and introducing functions such as intelligent search, automatic tagging and version control, the efficiency and quality of the design process can be significantly improved. In addition, element usage monitoring and trend prediction based on big data analysis can also provide designers with real-time market feedback and further optimize design strategies. In short, the graphic element library management method of modern packaging design needs to be flexible, intelligent and efficient to cope with the rapidly changing market environment and increasingly complex design requirements. However, the traditional graphic element library management method based on data processing has the problem of being unable to effectively trace the changes of graphic elements and low retrieval efficiency. Summary of the invention

[0003] Based on this, it is necessary to provide a graphic element library management method and system based on data processing to solve at least one of the above technical problems.

[0004] To achieve the above object, a graphic element library management method based on data processing is provided, the method comprising the following steps:

[0005] Step S1: Obtaining graphic element library log data; constructing an element update directed graph according to the graphic element library log data to obtain an element update directed graph;

[0006] Step S2: Designing an element update node tracing structure for the element update directed graph to obtain element update node tracing structure data; designing a tracing level index table for the element update node tracing structure data to obtain a node tracing level index table;

[0007] Step S3: performing concurrent retrieval attenuation gradient calculation on the node tracing level index table to obtain concurrent retrieval attenuation gradient data; performing hierarchical identification of index table performance bottlenecks on the node tracing level index table according to the concurrent retrieval attenuation gradient data to obtain index table hierarchical performance bottleneck data; performing cache hit rate reduction estimation on the index table hierarchical performance bottleneck data to obtain cache hit rate reduction estimation data;

[0008] Step S4: grouping the node tracing hierarchical index table with similar structures according to the index table hierarchical performance bottleneck data and the cache hit rate reduction estimation data to obtain the node tracing hierarchical structure grouping index table; designing a packaging design graphic element index management strategy according to the node tracing hierarchical structure grouping index table to obtain the packaging design graphic element index management strategy; sending the packaging design graphic element index management strategy to the cloud platform to execute the packaging design graphic element library management method.

[0009] The present invention can help improve the accuracy of data by constructing an element update directed graph through the log data of the graphical element library. This is because the directed graph can clearly show the relationship and dependency between elements, which helps to identify and correct errors or inconsistencies in the data. The directed graph can intuitively show the update path and influence relationship between elements. This visualization helps team members understand the specific process and scope of influence of data updates, so as to better plan and execute related operations. The directed graph also supports tracing back historical updates. This is very helpful for finding the origin of specific changes, understanding the evolution process of historical data, and analyzing long-term trends. This historical tracing ability helps to improve the basis and accuracy of decision-making. The node tracing hierarchical index table design can significantly improve the efficiency of data tracing. The index table can quickly locate and retrieve the node path and related information of a specific element update according to the pre-designed structure, avoiding cumbersome full-text search and traversal. By designing the index table, complexity can be effectively managed. For large-scale data sets, the index table can help optimize the performance of queries and operations and improve the system response speed and efficiency. The index table design can make key element update information easier to access and understand. This enhanced information availability helps teams make decisions quickly and respond to changing needs, thereby improving work efficiency and overall business responsiveness. By calculating the concurrent retrieval attenuation gradient, the performance degradation trend of the index table in a high-concurrency environment can be effectively evaluated. This helps identify the weaknesses of the index table when facing a large number of concurrent requests, thus providing an important basis for optimizing the system. Hierarchical identification of performance bottlenecks for the node tracing hierarchical index table can clearly understand the performance bottlenecks at different levels. Through this hierarchical identification, different parts of the index table can be optimized in a targeted manner to improve overall performance and response speed. Estimating the decline in cache hit rate can predict the performance changes of the cache under different load conditions. This helps make adjustments during system design and maintenance to prevent cache bottlenecks from affecting overall performance. Grouping the node tracing hierarchical structure based on hierarchical performance bottleneck data and cache hit rate decline estimation data helps group index nodes of similar structures together. This grouping optimizes the organizational structure of the index table, making retrieval more efficient while reducing query complexity. Designing index management strategies based on the optimized node tracing hierarchical structure grouping can improve index access efficiency and management flexibility. Better index management strategies can effectively reduce data retrieval time and improve the overall performance of the system. Sending the designed index management strategy to the cloud platform can achieve cross-platform data management and dynamic adjustment. The advantage of the cloud platform lies in its scalability and flexibility, which enables the packaging design graphic element library management method to be efficiently executed on a larger scale and quickly adapt to changing needs.Therefore, the present invention is an optimization processing of a traditional graphic element library management method based on data processing, which solves the problems of the traditional graphic element library management method based on data processing that the changes of graphic elements cannot be effectively traced and the retrieval efficiency is low, improves the ability to trace the changes of graphic elements, and improves the retrieval efficiency.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Obtaining graphic element library log data;

[0012] Step S12: performing data cleaning on the graphic element library log data to obtain graphic element library log cleaning data;

[0013] Step S13: extracting update records from the graphic element library log cleaning data to obtain graphic element update record data;

[0014] Step S14: construct an element update directed graph according to the graphic element update record data to obtain the element update directed graph.

[0015] The present invention obtains the log data of the graphic element library, which is the first step to start the element update management process. These data usually contain the operation records and change information of the graphic elements, and are the basis for subsequent data processing and analysis. The acquisition of log data ensures the integrity and reliability of the data. By recording the detailed information of each operation, the change history of the element can be traced back to avoid data loss or inconsistency. Cleaning the log data of the graphic element library helps to remove duplicate, incomplete or erroneous records, thereby improving the quality and accuracy of the data. This cleaning process can standardize the data format, making it easier for subsequent analysis and processing. The cleaned data can better meet the unified data standards and structures, so that data from different sources can be processed and analyzed on a unified platform, which helps to improve the consistency and comparability of the overall data. By extracting the update records, the change history and operation details of each element can be accurately tracked. This accurate record helps to trace the cause and impact of a specific change when necessary, and improves the efficiency of problem location and resolution. The extraction of update records makes it possible to analyze the impact of element changes on the system or data as a whole. This analysis helps to assess the risk and effect of the change, so as to make more informed decisions and plans. Constructing an element update directed graph can intuitively display the update path and dependency between elements. This visualization helps the team understand and analyze the relationship between elements, providing a basis for subsequent data management and optimization. The construction of a directed graph enables decision support and planning based on actual data relationships. The team can optimize the update process, improve the data structure, or perform system optimization based on the information in the graph to improve overall efficiency and quality.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: performing time-series synchronous update increment calculation between different nodes on the element update directed graph to obtain node time-series synchronous update increment data;

[0018] Step S22: performing iterative update identification on the incremental data of the node time series synchronous update to obtain the iterative update data of the node element;

[0019] Step S23: Designing an element update node tracing structure for the element update directed graph according to the node element iterative update data, and obtaining element update node tracing structure data;

[0020] Step S24: Design a tracing level index table for the element update node tracing structure data to obtain a node tracing level index table.

[0021] The present invention updates incremental data by calculating the timing synchronization between nodes, ensuring the timing consistency of element updates in multi-node or multi-threaded operations. This calculation can effectively avoid data conflicts and inconsistencies, and improve the stability and reliability of the system. Incremental calculation can effectively reduce the cost of repeated calculations and data transmission, and improve the efficiency of update operations. This is particularly important for large-scale data sets or frequent updates, and can save computing resources and time costs. By identifying iterative updates, a change history record of each element can be established. This record helps to understand and analyze the evolution of elements, and provides support for subsequent version management and backtracking. The identification of iterative updates can help the team quickly locate the source of the problem, trace it back and debug it. This ability is crucial for troubleshooting and performance optimization, and can shorten the time to solve problems. Designing an element update node traceability structure based on iterative update data can optimize the updated relationship map. This optimization can accurately reflect the dependencies and update paths between elements, and help understand and analyze the overall architecture of the system. Designing a clear traceability structure helps team members quickly understand and use data, reducing the possibility of misunderstandings and operational errors. This clarification improves the team's collaboration efficiency and project execution effect. Designing a traceability hierarchical index table can accelerate access to and query of traceability data. This design optimizes the efficiency of data retrieval, especially in large data volumes and complex query scenarios, which can significantly improve the system's response speed. Index table design can also help optimize system performance and resource utilization. Through reasonable data structure and index algorithm, unnecessary computing overhead and storage resource usage can be reduced, thereby improving the overall efficiency of the system.

[0022] Preferably, step S3 comprises the following steps:

[0023] Step S231: extracting the update frequency between different nodes of the node element iterative update data to obtain node update frequency data;

[0024] Step S232: performing iterative level calculation between different nodes on the node element iterative update data according to the node update frequency data to obtain node element iterative level data;

[0025] Step S233: performing node list pointer structure configuration on the node element iteration level data to obtain node list pointer structure data;

[0026] Step S234: Designing an element update node tracing structure for the element update directed graph according to the node linked list pointer structure data to obtain element update node tracing structure data.

[0027] The present invention extracts the update frequency of iterative update data of node elements, and the system can adjust the update strategy according to the update frequency of each node. This optimization can ensure that nodes with high frequency updates are processed in time, reducing system delay and response time. The update frequency data extraction helps to reasonably allocate system resources, such as computing resources and storage space. According to the node update frequency, the allocation of resources can be dynamically adjusted to avoid resource waste and unnecessary load. According to the node update frequency data, the iterative hierarchical data of the node elements are calculated. This hierarchical calculation can optimize the efficiency of data access and processing, and reduce unnecessary repeated calculations and data transmission by establishing an update hierarchy. The calculation of iterative hierarchical data helps to optimize the data structure design and improve the efficiency and response speed of data access. This is particularly important for large-scale data sets and complex systems, and can significantly improve the performance of the overall system. Configuring the node linked list pointer structure helps to manage and maintain the relationship between nodes. This structure can effectively track and connect the update data of each node, and improve the flexibility and efficiency of data operations. The configuration of the node linked list pointer structure supports data tracing and history recording functions. Through the pointer structure, the update history of the node can be easily found and traced back to help solve problems and analyze. According to the node linked list pointer structure data, the traceability structure of the element update node is designed. This design helps to accurately model and manage data dependencies, ensuring that the system maintains data consistency and integrity when updating and operating. The design of the traceability structure optimizes the system's architecture and data management capabilities. It improves the scalability and stability of the system, allowing the system to better adapt to complex business needs and changes.

[0028] Preferably, step S24 includes the following steps:

[0029] Step S241: performing dependency analysis between nodes on the element update node traceability structure data to obtain update node dependency data;

[0030] Step S242: constructing a multi-dimensional node dependency graph based on the updated node dependency relationship data to obtain a multi-dimensional node dependency graph;

[0031] Step S243: performing hierarchical relationship weighted calculation on the multi-dimensional node dependency graph to obtain hierarchical relationship weighted data;

[0032] Step S244: using the B-tree algorithm and designing a tracing hierarchical index table for the element update node tracing structure data according to the hierarchical relationship weighted data, to obtain a node tracing hierarchical index table.

[0033] The present invention can accurately capture the dependency between nodes by analyzing the traceability structure data of the element update node. This helps to clarify the order and dependency of data updates and avoid update conflicts and data inconsistencies. After understanding the dependency between nodes, the system can optimize the update order to ensure that the nodes on the dependency chain are updated in the correct order. This optimization can improve the stability of the system and the efficiency of data processing. Constructing a multidimensional node dependency map can display the dependency between nodes from multiple dimensions. This comprehensive perspective helps system administrators and developers to fully understand and analyze the complex data dependency structure in the system. When a problem occurs in the system, the multidimensional node dependency map can help quickly locate the root cause of the problem. By analyzing the dependency, the nodes and data flow paths that may cause the problem can be quickly traced back. According to the multidimensional node dependency map, a weighted calculation of the hierarchical relationship is performed. This calculation can determine the importance and priority of each node in the dependency chain, thereby adjusting the order and strategy of data processing in a targeted manner. The weighted data of the hierarchical relationship helps to optimize the allocation of system resources. Allocating more resources to high-priority nodes can improve the processing speed of key tasks and ensure that the system updates and processes important data in a timely manner. The node tracing hierarchical index table designed using the B-tree algorithm can effectively manage and optimize the index of large amounts of data. This design enables the system to quickly locate and access relevant data when tracing node data. The design of the index table optimizes data access efficiency and can significantly shorten system response time. Especially in the case of large amounts of data and high concurrency processing, it can ensure the stability and efficiency of the system.

[0034] Preferably, step S3 comprises the following steps:

[0035] Step S31: performing a concurrent simulation test on the node tracing hierarchical index table to obtain hierarchical index concurrent simulation data;

[0036] Step S32: performing concurrent search attenuation gradient calculation on the node tracing hierarchical index table according to the hierarchical index concurrent simulation data to obtain concurrent search attenuation gradient data;

[0037] Step S33: performing hierarchical identification of index table performance bottlenecks on the node tracing hierarchical index table according to the concurrent retrieval attenuation gradient data, and obtaining index table hierarchical performance bottleneck data;

[0038] Step S34: performing cache hit rate reduction estimation on the index table hierarchical performance bottleneck data to obtain cache hit rate reduction estimation data.

[0039] The present invention performs concurrent simulation tests on the node tracing hierarchical index table, and the system can evaluate the performance under multi-user or high concurrency conditions. This test can reveal the responsiveness and concurrent processing capabilities of the system under different loads. The hierarchical index concurrent simulation data provides detailed information about resource utilization and bottlenecks. Based on these data, the system administrator can optimize resource allocation to ensure that stable performance can be maintained under high load. According to the hierarchical index concurrent simulation data, the calculation of concurrent retrieval attenuation gradient data can predict the attenuation trend of system performance under different load conditions. This helps to take measures in advance to avoid or alleviate performance degradation. The gradient data can guide the optimization of query processing algorithms and strategies to ensure that the system can maintain a high query response speed and accuracy even under high load conditions. According to the concurrent retrieval attenuation gradient data, the performance bottlenecks in the index table are identified and identified in layers. This precise performance analysis helps the system administrator locate and solve the key factors affecting the overall performance of the system. After identifying the performance bottleneck, the structure and optimization strategy of the index table can be adjusted in a targeted manner to improve data access efficiency and response speed, thereby improving the overall performance of the system. By estimating the cache hit rate drop of the index table hierarchical performance bottleneck data, the system can monitor and evaluate the performance of the cache system. This helps predict the actual impact of cache hit rate reduction on system performance. Based on the estimated cache hit rate reduction data, cache strategies and algorithms can be adjusted to improve the cache hit rate of key data, reduce frequent access to the database, and further optimize the overall performance of the system.

[0040] Preferably, step S33 includes the following steps:

[0041] Step S331: performing retrieval attenuation-tracing level association mapping on the node tracing level index table according to the concurrent retrieval attenuation gradient data to obtain retrieval attenuation-tracing level association mapping data;

[0042] Step S332: performing retrieval attenuation fluctuation calculation between different levels on the retrieval attenuation-traceability level association mapping data to obtain retrieval attenuation fluctuation data;

[0043] Step S333: performing attenuation fluctuation complexity calculation between different levels on the retrieved attenuation fluctuation data based on the Big O notation to obtain attenuation fluctuation complexity data;

[0044] Step S334: performing handover scheduling overhead load bearing calculation according to the retrieved attenuation fluctuation data and the attenuation fluctuation complexity data to obtain handover scheduling overhead load bearing data;

[0045] Step S335: performing hierarchical identification of index table performance bottlenecks on the node tracing hierarchical index table according to the switching scheduling overhead load bearing data to obtain index table hierarchical performance bottleneck data.

[0046] The present invention determines the association mapping between retrieval attenuation and tracing level in the node tracing level index table by analyzing the concurrent retrieval attenuation gradient data. This step can reveal the attenuation trend of data query in different level index tables and its relationship in the hierarchical structure. After clarifying the relationship between retrieval attenuation and tracing level, the tracing structure can be optimized in a targeted manner, such as reallocating indexes, adjusting hierarchical relationships, or optimizing data access paths, thereby improving query efficiency and response speed. The retrieval attenuation-tracing level association mapping data is analyzed to calculate the retrieval attenuation fluctuations between different levels. This helps to understand the changes in query performance between different levels and provides data support for subsequent performance optimization. Through fluctuation data analysis, the query performance change trend of different level index tables under high load or specific conditions can be predicted, helping system administrators to pre-adjust resources or optimization strategies. Based on the big O notation, the complexity of the retrieval attenuation fluctuation data is calculated. This step helps to quantify the complexity of query attenuation in different level index tables, thereby more accurately evaluating the performance of the system under different loads. The complexity data provides suggestions for improving the index structure or query algorithm to reduce the complexity of query attenuation, thereby optimizing the overall performance and stability of the system. Based on the retrieval attenuation fluctuation data and complexity data, the load carrying capacity of the switching scheduling overhead is calculated. This helps determine the load that the system can bear when switching different levels of index tables, avoiding performance degradation or system failures caused by frequent switching. Based on the load carrying data, more effective scheduling strategies can be formulated, such as the reasonable allocation of tasks and scheduling resources to optimize the response time and processing efficiency of the system. Based on the switching scheduling overhead load carrying data, the node traceability level index table is hierarchically identified for performance bottlenecks. This can help system administrators identify the performance bottlenecks in each level, so as to formulate optimization strategies and improvement plans in a targeted manner. After identifying the performance bottlenecks, structural adjustments or performance optimizations can be carried out in an orderly manner to improve the stability and reliability of the system under high load environments and ensure user experience and service quality.

[0047] Preferably, step S34 includes the following steps:

[0048] Step S341: performing different levels of index table cache hierarchy analysis on the index table hierarchical performance bottleneck data to obtain index table cache hierarchy data;

[0049] Step S342: performing cache load failure analysis between different levels according to the cache hierarchy data in the index table to obtain cache load failure data;

[0050] Step S343: Calculate cache conflict trigger probabilities between different levels of index table cache hierarchy data according to cache bearer failure data to obtain cache conflict trigger probability data;

[0051] Step S344: performing cache hit rate reduction estimation according to the cache conflict trigger probability data and the cache load failure data to obtain cache hit rate reduction estimation data.

[0052] By analyzing the performance bottleneck data of the index table at different levels, the present invention can identify which levels have a high access frequency to the index table and which levels may become the bottleneck of the system performance. This analysis helps to accurately locate the cache level that needs to be optimized to improve the overall system performance. According to the cache level data of the index table, the cache resources can be reasonably allocated, such as increasing the cache space for the commonly used levels and reducing the resource allocation for the uncommon levels, thereby improving the cache hit rate and the system response speed. By analyzing the cache load failure data between different levels, the probability and frequency of cache failure at different levels can be predicted. This helps system designers understand the impact of cache failure on system performance and then adopt corresponding optimization strategies. High failure rate may cause frequent cache misses, thereby increasing memory access delay or increasing disk access. By analyzing the failure data, the key levels that may cause performance degradation can be identified and optimized in a targeted manner. According to the cache load failure data, the probability of triggering cache conflicts between different levels is calculated. This analysis helps to understand the possibility of cache competition when multiple accesses occur simultaneously, and then design a more effective cache management strategy. By reducing the probability of conflict triggering, data contention and lock contention can be reduced, thereby improving the concurrent processing capability and overall performance of the system. For example, optimize the cache replacement algorithm or implement a more sophisticated lock strategy to reduce the performance loss caused by conflicts. Combining the cache conflict trigger probability and cache load failure data, the cache hit rate reduction at different levels can be estimated. This analysis helps to evaluate the effectiveness of the cache strategy and the optimization direction that may need to be adjusted. Based on the estimated cache hit rate reduction data, decision support can be provided for system optimization. For example, based on the data, a more suitable cache replacement algorithm can be selected or the cache size can be adjusted to maximize the overall system performance and stability.

[0053] Preferably, step S4 comprises the following steps:

[0054] Step S41: grouping the node tracing hierarchical index table with similar structures according to the index table hierarchical performance bottleneck data and cache hit rate reduction estimation data to obtain a node tracing hierarchical structure grouping index table;

[0055] Step S42: performing clustering performance evaluation on the node tracing hierarchical structure grouping index table to obtain grouping index table clustering performance evaluation data;

[0056] Step S43: performing thread priority allocation processing on the node tracing hierarchical structure grouping index table according to the grouping index table clustering performance evaluation data to obtain grouping index thread priority allocation data;

[0057] Step S44: designing a packaging design graphic element index management strategy according to the group index thread priority allocation data and the node tracing hierarchical structure group index table to obtain a packaging design graphic element index management strategy;

[0058] Step S45: Send the packaging design graphic element index management strategy to the cloud platform to execute the packaging design graphic element library management method.

[0059] The present invention groups the index tables of the node tracing hierarchical structure with similar structures according to the index table hierarchical performance bottleneck data and the cache hit rate reduction estimation data. This helps to aggregate index items with similar access patterns together to improve the cache hit rate and access efficiency. Optimizing the index table structure can reduce memory access latency and CPU resource consumption, thereby improving the response speed and overall performance of the system. Clustering performance evaluation is performed on the node tracing hierarchical structure grouping index table. This step can help evaluate the access mode and performance characteristics of different index table groups so as to further optimize and adjust the index management strategy. According to the clustering performance evaluation data, a more effective index table management strategy can be determined, such as selecting a suitable index cache size or optimizing the index data structure to maximize system performance and resource utilization. According to the grouping index table clustering performance evaluation data, thread priority allocation processing is performed on the node tracing hierarchical structure grouping index table. This step helps to adjust the access priority of the index table in a multi-threaded environment to improve the concurrent processing capability and the multi-tasking efficiency of the system. Through thread priority allocation, processor resources and memory access can be allocated more accurately, thread competition and resource contention can be avoided, thereby improving the stability and performance of the system. Design an index management strategy for packaging design graphic elements based on the priority allocation of data and node tracing hierarchical structure grouping index tables by grouping index threads. This includes how to organize and manage the index data of graphic elements to support efficient access and processing. An optimized index management strategy can significantly reduce the loading time and rendering delay of graphic elements, improve user experience and overall performance of the application. Send the designed packaging design graphic element index management strategy to the cloud platform for execution, and use cloud computing resources for efficient index management and data processing. This can save local resources and take advantage of the elasticity and scalability of the cloud platform to cope with large-scale data processing needs. The cloud platform executes the index management method to achieve real-time updates and monitoring of changes and access patterns of index data, so as to adjust the strategy in time to cope with changing application needs and user behaviors.

[0060] Preferably, the present invention further provides a graphic element library management system based on data processing, which is used to execute the graphic element library management method based on data processing as described above, and the graphic element library management system based on data processing includes:

[0061] The element update directed graph module is used to obtain the graphic element library log data; construct the element update directed graph according to the graphic element library log data to obtain the element update directed graph;

[0062] The traceability level index table design module is used to design the element update node traceability structure for the element update directed graph to obtain the element update node traceability structure data; design the traceability level index table for the element update node traceability structure data to obtain the node traceability level index table;

[0063] The index table test module is used to perform concurrent retrieval attenuation gradient calculation on the node tracing level index table to obtain concurrent retrieval attenuation gradient data; perform hierarchical identification of index table performance bottlenecks on the node tracing level index table according to the concurrent retrieval attenuation gradient data to obtain index table hierarchical performance bottleneck data; perform cache hit rate reduction estimation on the index table hierarchical performance bottleneck data to obtain cache hit rate reduction estimation data;

[0064] The management strategy design module is used to group the node traceability hierarchical index table with similar structures according to the index table hierarchical performance bottleneck data and the cache hit rate reduction estimation data to obtain the node traceability hierarchical structure grouping index table; design the packaging design graphic element index management strategy according to the node traceability hierarchical structure grouping index table to obtain the packaging design graphic element index management strategy; send the packaging design graphic element index management strategy to the cloud platform to execute the packaging design graphic element library management method.

[0065] The beneficial effect of the present invention is that building an element update directed graph through the log data of the graphical element library can help improve the accuracy of the data. This is because the directed graph can clearly show the relationship and dependency between elements, which helps to identify and correct errors or inconsistencies in the data. The directed graph can intuitively show the update path and impact relationship between elements. This visualization helps team members understand the specific process and scope of influence of data updates, so as to better plan and execute related operations. The directed graph also supports tracing back historical updates. This is very helpful for finding the origin of specific changes, understanding the evolution process of historical data, and analyzing long-term trends. This historical tracing capability helps to improve the basis and accuracy of decision-making. The node tracing hierarchical index table design can significantly improve the efficiency of data tracing. The index table can quickly locate and retrieve the node path and related information of a specific element update according to the pre-designed structure, avoiding cumbersome full-text search and traversal. By designing the index table, complexity can be effectively managed. For large-scale data sets, the index table can help optimize the performance of queries and operations and improve the system response speed and efficiency. The index table design can make key element update information easier to access and understand. This enhanced information availability helps teams make decisions quickly and respond to changing needs, thereby improving work efficiency and overall business responsiveness. By calculating the concurrent retrieval attenuation gradient, the performance degradation trend of the index table in a high-concurrency environment can be effectively evaluated. This helps identify the weaknesses of the index table when facing a large number of concurrent requests, thus providing an important basis for optimizing the system. Hierarchical identification of performance bottlenecks for the node tracing hierarchical index table can clearly understand the performance bottlenecks at different levels. Through this hierarchical identification, different parts of the index table can be optimized in a targeted manner to improve overall performance and response speed. Estimating the decline in cache hit rate can predict the performance changes of the cache under different load conditions. This helps make adjustments during system design and maintenance to prevent cache bottlenecks from affecting overall performance. Grouping the node tracing hierarchical structure based on hierarchical performance bottleneck data and cache hit rate decline estimation data helps group index nodes of similar structures together. This grouping optimizes the organizational structure of the index table, making retrieval more efficient while reducing query complexity. Designing index management strategies based on the optimized node tracing hierarchical structure grouping can improve index access efficiency and management flexibility. Better index management strategies can effectively reduce data retrieval time and improve the overall performance of the system. Sending the designed index management strategy to the cloud platform can achieve cross-platform data management and dynamic adjustment. The advantage of the cloud platform lies in its scalability and flexibility, which enables the packaging design graphic element library management method to be efficiently executed on a larger scale and quickly adapt to changing needs.Therefore, the present invention is an optimization processing of a traditional graphic element library management method based on data processing, which solves the problems of the traditional graphic element library management method based on data processing that the changes of graphic elements cannot be effectively traced and the retrieval efficiency is low, improves the ability to trace the changes of graphic elements, and improves the retrieval efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic diagram of the steps of a graphic element library management method based on data processing;

[0067] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0068] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0069] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0070] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0071] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0072] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0073] To achieve this, please refer to Figures 1 to 3 , a graphic element library management method based on data processing, the method comprising the following steps:

[0074] Step S1: Obtaining graphic element library log data; constructing an element update directed graph according to the graphic element library log data to obtain an element update directed graph;

[0075] Step S2: Designing an element update node tracing structure for the element update directed graph to obtain element update node tracing structure data; designing a tracing level index table for the element update node tracing structure data to obtain a node tracing level index table;

[0076] Step S3: performing concurrent retrieval attenuation gradient calculation on the node tracing level index table to obtain concurrent retrieval attenuation gradient data; performing hierarchical identification of index table performance bottlenecks on the node tracing level index table according to the concurrent retrieval attenuation gradient data to obtain index table hierarchical performance bottleneck data; performing cache hit rate reduction estimation on the index table hierarchical performance bottleneck data to obtain cache hit rate reduction estimation data;

[0077] Step S4: grouping the node tracing hierarchical index table with similar structures according to the index table hierarchical performance bottleneck data and the cache hit rate reduction estimation data to obtain the node tracing hierarchical structure grouping index table; designing a packaging design graphic element index management strategy according to the node tracing hierarchical structure grouping index table to obtain the packaging design graphic element index management strategy; sending the packaging design graphic element index management strategy to the cloud platform to execute the packaging design graphic element library management method.

[0078] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a graphic element library management method based on data processing of the present invention. In this example, the graphic element library management method based on data processing includes the following steps:

[0079] Step S1: Obtaining graphic element library log data; constructing an element update directed graph according to the graphic element library log data to obtain an element update directed graph;

[0080] In an embodiment of the present invention, in this step, log data is first extracted from the graphic element library system. This includes obtaining all log entries that record the creation, modification, and deletion operations of graphic elements in the system. In order to ensure the integrity and accuracy of the log data, the standard data export interface provided by the system is used to export the log data into a CSV format file. The log file should include information such as timestamp, operation type, element ID, and operation details. Next, the exported log file is cleaned and preprocessed using a data parsing tool to remove duplicate entries and format errors to ensure the validity of the log data. The preprocessed log data is structured and analyzed using a data analysis tool, and the operation data is arranged in chronological order to provide basic data for the subsequent construction of an element update directed graph. After completing the data sorting, a graphical tool is used to construct an element update directed graph, and the update relationship between different graphic elements is displayed in the form of a directed graph to form a graphic element update directed graph.

[0081] Step S2: Designing an element update node tracing structure for the element update directed graph to obtain element update node tracing structure data; designing a tracing level index table for the element update node tracing structure data to obtain a node tracing level index table;

[0082] In an embodiment of the present invention, in this step, the constructed element update directed graph is further analyzed to design the traceability structure of the element update node. First, each update node in the directed graph is identified, including the graphic elements associated with each node and their update records. Through the graphical design tool, the basic framework of the traceability structure is constructed to clarify the traceability relationship between the update nodes. Then, based on the dependency relationship between the nodes, the hierarchical model of the traceability structure is designed to determine the parent node and child node of each node to reflect the hierarchical relationship of the graphic element update. After the construction is completed, the element update node traceability structure data is generated, including the traceability path, node information, and the dependency relationship between nodes. Afterwards, the hierarchical index table is designed for the traceability structure data to clearly represent the hierarchy of each node and its related traceability path. Finally, the node traceability hierarchical index table is created and saved to ensure that the update history of each graphic element and its associated nodes can be quickly queried and located.

[0083] Step S3: performing concurrent retrieval attenuation gradient calculation on the node tracing level index table to obtain concurrent retrieval attenuation gradient data; performing hierarchical identification of index table performance bottlenecks on the node tracing level index table according to the concurrent retrieval attenuation gradient data to obtain index table hierarchical performance bottleneck data; performing cache hit rate reduction estimation on the index table hierarchical performance bottleneck data to obtain cache hit rate reduction estimation data;

[0084] In an embodiment of the present invention, in this step, the concurrent retrieval attenuation gradient of the node tracing hierarchical index table is calculated by simulation technology. First, a simulation environment is created to simulate an actual concurrent retrieval scenario. Using a simulation tool, such as a network simulator or a load generator, different numbers of virtual users are configured to perform index retrieval operations simultaneously. The operation of each virtual user should include multiple retrieval requests to simulate real user behavior. Next, the simulation environment is run to record the retrieval response time and system resource usage at different concurrency levels. The simulation tool will generate real-time performance data, including retrieval delay, system load, and resource consumption. These data are processed by statistical analysis tools to calculate the attenuation gradient under each concurrency condition, that is, the rate at which the retrieval performance decreases as the number of concurrent users increases. The concurrent retrieval attenuation gradient data generated by this process will help understand the system performance under high concurrency conditions. Then, based on the obtained attenuation gradient data, the performance bottleneck of the node tracing hierarchical index table is hierarchically identified. Using a performance analysis tool, the simulation data is hierarchically analyzed to identify the performance bottlenecks at different levels, and index table hierarchical performance bottleneck data is generated. This data will show the degree of performance attenuation at different levels, helping to determine which levels have significant performance bottlenecks. Finally, the performance bottleneck data obtained from the simulation is used to estimate the cache hit rate drop. The cache hit rate drop trend is calculated based on the cache access recorded in the simulation. Specifically, the cache hit rate of different levels is tested using a cache simulation tool to generate cache hit rate drop estimation data. This data will be used to evaluate and optimize the cache strategy to improve the overall performance of the system.

[0085] Step S4: grouping the node tracing hierarchical index table with similar structures according to the index table hierarchical performance bottleneck data and the cache hit rate reduction estimation data to obtain the node tracing hierarchical structure grouping index table; designing a packaging design graphic element index management strategy according to the node tracing hierarchical structure grouping index table to obtain the packaging design graphic element index management strategy; sending the packaging design graphic element index management strategy to the cloud platform to execute the packaging design graphic element library management method.

[0086] In the embodiment of the present invention, in this step, the node traceability hierarchical index table is first grouped using a structural similarity analysis tool. By calculating the structural similarity of nodes at each level, nodes with similar structures and performance characteristics are grouped. In the specific implementation, the nodes of the index table are measured for similarity using a graph analysis tool, and the similarity of the nodes is determined by an algorithm such as Jaccard similarity calculation, and similar nodes are grouped into groups to form a node traceability hierarchical structure grouping index table. The output of this step is an optimized structural grouping, which helps to improve the subsequent index management strategy. Subsequently, the design of the packaging design graphic element index management strategy is carried out according to the node traceability hierarchical structure grouping index table. A strategy optimization tool is used to formulate a new index management strategy based on the grouping results. Specifically, it includes setting priorities, cache strategies and retrieval optimization rules for each group of nodes to improve the overall retrieval efficiency and management effect. After the design is completed, the generated packaging design graphic element index management strategy will include grouping indexes, priority rules and cache configurations. Finally, the designed packaging design graphic element index management strategy is sent to the cloud platform. Using the cloud service interface, the strategy data is uploaded to the cloud platform to facilitate the execution of the management method of the packaging design graphic element library in practical applications. The cloud platform will automatically adjust index management and cache configuration according to the uploaded strategy to ensure system performance optimization and improved management effects.

[0087] Preferably, step S1 comprises the following steps:

[0088] Step S11: Obtaining graphic element library log data;

[0089] Step S12: performing data cleaning on the graphic element library log data to obtain graphic element library log cleaning data;

[0090] Step S13: extracting update records from the graphic element library log cleaning data to obtain graphic element update record data;

[0091] Step S14: construct an element update directed graph according to the graphic element update record data to obtain the element update directed graph.

[0092] In an embodiment of the present invention, log data is first extracted from a graphic element library system. This process involves accessing a log database of a graphic element library system, and using a database query tool (such as a SQL client) to perform a query operation to export log data containing graphic element operation records. Log records should include operation time, operation type, element ID, operation details, etc. The exported log data is usually in CSV or Excel format for subsequent processing. After exporting, a data extraction tool is used to perform a preliminary check on the log data to ensure that all relevant operation records have been accurately exported, so as to prepare for data cleaning and analysis. The data cleaning work includes formatting and denoising the exported graphic element library log data. First, a data cleaning tool (such as Python's data processing library Pandas) is used to verify the format of the exported log data and remove records that do not meet the format requirements. Next, duplicate log entries and irrelevant operation records are identified and deleted. For data with incorrect format, correction and repair are performed. In this process, missing values ​​and abnormal data must also be processed to ensure the integrity and accuracy of the data. The cleaned data will form the graphic element library log cleaning data, ready for further record extraction. The cleaned log data is extracted using a data processing tool to update the records. Using the data screening algorithm, all records related to the update of graphic elements are filtered out based on the operation type field (such as "create", "modify", and "delete"). These records are further classified to extract key fields in each record, such as element ID, update time, and specific update content. In order to ensure the accuracy of the records, regular expressions can be used to parse the operation content and extract useful information from it. The final generated graphic element update record data will contain all detailed information related to the graphic element update operation for subsequent construction of the directed graph. Use graphical tools or graph databases to construct the element update directed graph for the graphic element update record data. First, based on the extracted update record data, the update relationship between elements is established. Each node of the directed graph represents a graphic element, and each directed edge represents an update operation from one element to another. Use a graph database (such as Neo4j) or a graph drawing tool to visualize these nodes and edges and create an element update directed graph. In the graph database, graph query languages ​​(such as Cypher) can be used to define and store nodes and edges to ensure that the update relationship of graphic elements is accurately recorded and displayed. The element update directed graph finally generated will show the update process of the graph elements and their relationships, providing structured graph data for subsequent analysis.

[0093] Preferably, step S2 comprises the following steps:

[0094] Step S21: performing time-series synchronous update increment calculation between different nodes on the element update directed graph to obtain node time-series synchronous update increment data;

[0095] Step S22: performing iterative update identification on the incremental data of the node time series synchronous update to obtain the iterative update data of the node element;

[0096] Step S23: Designing an element update node tracing structure for the element update directed graph according to the node element iterative update data, and obtaining element update node tracing structure data;

[0097] Step S24: Design a tracing level index table for the element update node tracing structure data to obtain a node tracing level index table.

[0098] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0099] Step S21: performing time-series synchronous update increment calculation between different nodes on the element update directed graph to obtain node time-series synchronous update increment data;

[0100] In an embodiment of the present invention, firstly, the timestamp data in the graph database is used to calculate the time synchronization update increment of the element update directed graph. The timestamp information of each node in the directed graph is extracted using a graph database management tool (such as Neo4j). Then, a data analysis tool (such as the Pandas library in Python) is used to process these timestamps and calculate the time difference between the nodes. By comparing the update time of adjacent nodes, the synchronization increment between each pair of nodes is calculated. This increment reflects the time difference and update synchronization status of the updates between the nodes. The node time synchronization update increment data finally generated will provide detailed information on the synchronization of the node update time, helping to identify consistency issues in the update time.

[0101] Step S22: performing iterative update identification on the incremental data of the node time series synchronous update to obtain the iterative update data of the node element;

[0102] In an embodiment of the present invention, a statistical analysis tool is used to identify iterative updates of incremental data of node time series synchronization updates. Time series analysis methods, such as moving average method or periodic analysis, are applied to identify iterative patterns in node update data. By analyzing the update increments of the nodes, the periodicity and change trends of their updates are determined. The specific operation includes inputting the incremental data into the time series analysis model to extract the frequency and iterative regularity of node updates. The iterative cycle and update frequency of each node are organized into node element iterative update data. This data provides basic information on the update pattern for the subsequent traceability structure design.

[0103] Step S23: Designing an element update node tracing structure for the element update directed graph according to the node element iterative update data, and obtaining element update node tracing structure data;

[0104] In an embodiment of the present invention, the design of the element update node traceability structure is performed based on the node element iterative update data. Use a graphical modeling tool (such as Graphviz) or a graph database management tool to map the update cycle and iteration information of the node element to a directed graph. By adding the update history and iteration phase of the node in the directed graph, a node traceability structure is designed. The traceability path of each node should reflect all its historical update operations to ensure that each update of the node can be tracked. The generated element update node traceability structure data will display the update trajectory of each node and its relationship chain, facilitating subsequent data management and query.

[0105] Step S24: Design a tracing level index table for the element update node tracing structure data to obtain a node tracing level index table.

[0106] In an embodiment of the present invention, a database tool (such as SQL database or Excel) is used to design a traceability hierarchical index table for element update node traceability structure data. First, the structure of the index table is defined according to the node traceability structure data, including the hierarchical relationship and the traceability path. Each row of records should contain the node ID, hierarchical information, the traceability path and its update record. Use a data sorting tool to import the traceability structure data into the index table to ensure that the traceability level and update history of each node are accurate. The final generated node traceability hierarchical index table will contain detailed hierarchical information and traceability records, providing support for efficient index management and data retrieval.

[0107] Preferably, step S3 comprises the following steps:

[0108] Step S231: extracting the update frequency between different nodes of the node element iterative update data to obtain node update frequency data;

[0109] Step S232: performing iterative level calculation between different nodes on the node element iterative update data according to the node update frequency data to obtain node element iterative level data;

[0110] Step S233: performing node list pointer structure configuration on the node element iteration level data to obtain node list pointer structure data;

[0111] Step S234: Designing an element update node tracing structure for the element update directed graph according to the node linked list pointer structure data to obtain element update node tracing structure data.

[0112] In an embodiment of the present invention, a data processing tool is used to extract the update frequency of the node element iterative update data. First, a database query tool (such as an SQL database) or a data analysis tool (such as Python's Pandas library) is used to extract the update record of each node from the node element iterative update data. The number of updates of each node in a specified time period is counted to calculate the update frequency. The specific operation includes sorting the update records of each node by timestamp and calculating the number of updates in the time interval. The node update frequency data finally generated will show the update frequency of each node, providing a data basis for subsequent hierarchical calculations. The node update frequency data is used to calculate the iterative hierarchy. According to the update frequency of the node, the nodes are divided into different hierarchies. The nodes are grouped using a hierarchical clustering algorithm to determine the iterative hierarchy of each node. First, the nodes are sorted from high to low according to the update frequency and mapped to the hierarchical model. Each hierarchy represents an update frequency range, thereby determining the iterative hierarchy of the node. Through these calculations, the generated node element iterative hierarchy data will assign a hierarchy value to each node, indicating its relative position in the iterative process. The node linked list pointer structure is configured according to the node element iterative hierarchy data. First, use a linked list data structure tool (such as Python's linked list implementation library) to create a linked list, in which the pointer of each node points to its next node in the iteration level. In the process of creating the linked list, it is necessary to connect each node according to its iteration level to ensure that the pointer of the linked list reflects the hierarchical relationship of the nodes. The specific operation includes creating a linked list node object for each node and setting its pointer to the pointer of the next node in the same level. The node linked list pointer structure data finally generated will include each node and its connection relationship in the hierarchical structure, providing structural support for subsequent data tracing. The node linked list pointer structure data is used to design the node tracing structure of the element update directed graph. First, use a graphical modeling tool (such as Graphviz) or a graph database (such as Neo4j) to map the node linked list pointer structure to a directed graph. According to the pointer relationship in the linked list, the directed edges between the nodes are constructed to represent the traceability path of the node during the update process. Ensure that the traceability structure of each node can reflect its position in the iteration level and its relationship with other nodes. The generated element update node traceability structure data will display the update path and traceability relationship between nodes, providing structured information for data management and analysis.

[0113] Preferably, step S24 comprises the following steps:

[0114] Step S241: performing dependency analysis between nodes on the element update node traceability structure data to obtain update node dependency data;

[0115] Step S242: constructing a multi-dimensional node dependency graph based on the updated node dependency relationship data to obtain a multi-dimensional node dependency graph;

[0116] Step S243: performing hierarchical relationship weighted calculation on the multi-dimensional node dependency graph to obtain hierarchical relationship weighted data;

[0117] Step S244: using the B-tree algorithm and designing a tracing hierarchical index table for the element update node tracing structure data according to the hierarchical relationship weighted data, to obtain a node tracing hierarchical index table.

[0118] In an embodiment of the present invention, a data analysis tool is used to analyze the dependency relationship between nodes of the element update node traceability structure data. First, the dependency relationship between nodes is extracted using a graph analysis tool (such as Neo4j's graph query language). For each node, by analyzing its input and output relationship, the other nodes it depends on are identified. The dependency relationship is usually manifested as the update of node A requires the state of node B as a prerequisite. Use a dependency matrix or a network diagram to display the dependency path between nodes, and organize these relationships into update node dependency data. The data will be displayed in the form of node pair dependencies to help understand the dependency logic between each node. A multidimensional node dependency graph is constructed based on the update node dependency data. First, the dependency data is input into a graph construction tool (such as Gephi or Cytoscape). By mapping nodes and their dependencies into a graph, a multidimensional view of the node dependency graph is generated. Each node is connected in the graph through its dependency relationship to form a complex network structure. Using the layout algorithm in the graph construction tool, the visualization effect of the graph is optimized to make the dependency relationship clearly visible. The generated multidimensional node dependency graph will display the complex dependency network between nodes and provide a structured data view for subsequent analysis. The hierarchical relationship of the multidimensional node dependency graph is weighted. First, the weighted factors of each level in the graph are determined, such as the dependency strength and update frequency of the node. The hierarchical weighted algorithm (such as the PageRank algorithm) is applied to the nodes for weighted calculation, and the weight is assigned according to the position of the node in the graph and the strength of its dependency relationship. The weighted calculation can be performed using a computing tool (such as MATLAB or Python's NumPy library), and the hierarchical relationship and influence of each node are determined through a weight matrix. The generated hierarchical relationship weighted data will reflect the relative importance and influence range of the node in the multidimensional dependency graph. The B-tree algorithm is used to design the traceability hierarchical index table for the element update node traceability structure data. First, a B-tree structure is constructed based on the hierarchical relationship weighted data. B-tree is a self-balancing tree structure suitable for efficient indexing and retrieval. According to the weights in the hierarchical relationship weighted data, the nodes and subnodes of the B-tree are constructed to ensure that the nodes are sorted according to the weights. Each node stores the traceability information related to it in the B-tree. By mapping the B-tree structure to the element update node traceability structure data, a traceability hierarchical index table is generated. Ultimately, the designed index table will support efficient data query and hierarchical relationship management.

[0119] Preferably, step S3 comprises the following steps:

[0120] Step S31: performing a concurrent simulation test on the node tracing hierarchical index table to obtain hierarchical index concurrent simulation data;

[0121] Step S32: performing concurrent search attenuation gradient calculation on the node tracing hierarchical index table according to the hierarchical index concurrent simulation data to obtain concurrent search attenuation gradient data;

[0122] Step S33: performing hierarchical identification of index table performance bottlenecks on the node tracing hierarchical index table according to the concurrent retrieval attenuation gradient data, and obtaining index table hierarchical performance bottleneck data;

[0123] Step S34: performing cache hit rate reduction estimation on the index table hierarchical performance bottleneck data to obtain cache hit rate reduction estimation data.

[0124] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0125] Step S31: performing a concurrent simulation test on the node tracing hierarchical index table to obtain hierarchical index concurrent simulation data;

[0126] In an embodiment of the present invention, data of a node tracing hierarchical index table is first prepared, and a concurrent test is performed on the index table through a concurrent simulation tool (such as Apache JMeter or a custom test script). The process of concurrent simulation testing includes defining the number of concurrent users and test scenarios, such as simulating multiple threads to simultaneously access and query the node tracing hierarchical index table. The test tool will generate a large number of concurrent requests and record the response time, success rate, and error information of each request. By collecting and analyzing these data, hierarchical index concurrent simulation data is generated. These data will demonstrate the response performance of the index table under different concurrent loads and help identify potential performance issues.

[0127] Step S32: performing concurrent search attenuation gradient calculation on the node tracing hierarchical index table according to the hierarchical index concurrent simulation data to obtain concurrent search attenuation gradient data;

[0128] In an embodiment of the present invention, hierarchical index concurrent simulation data is used to calculate the concurrent retrieval attenuation gradient. First, the attenuation of the retrieval performance is calculated by counting the response time and request success rate of each concurrent request. A mathematical tool (such as the SciPy library in MATLAB or Python) is used to perform regression analysis on the simulation data to extract the value of the attenuation gradient. These gradients represent the rate of decline of the index table performance when the concurrent load is increased. The retrieval response time and error rate under different concurrency levels must be considered during the calculation process, and the performance attenuation trend is determined by fitting the model. The generated concurrent retrieval attenuation gradient data will reveal the performance of the index table in a high concurrency environment.

[0129] Step S33: performing hierarchical identification of index table performance bottlenecks on the node tracing hierarchical index table according to the concurrent retrieval attenuation gradient data, and obtaining index table hierarchical performance bottleneck data;

[0130] In an embodiment of the present invention, concurrent retrieval attenuation gradient data is used to perform hierarchical identification of performance bottlenecks in the node traceability hierarchical index table. First, the identification criteria for performance bottlenecks are defined, such as response time thresholds and error rate criteria. Performance analysis tools (such as Splunk or Elasticsearch) are used to analyze the attenuation gradient data and divide different performance bottleneck levels. Each level is marked according to the attenuation degree and impact range of the performance data. The generated index table hierarchical performance bottleneck data will display the different levels of the index table and their corresponding performance bottlenecks, helping to locate specific areas for performance optimization.

[0131] Step S34: performing cache hit rate reduction estimation on the index table hierarchical performance bottleneck data to obtain cache hit rate reduction estimation data.

[0132] In an embodiment of the present invention, the decrease in cache hit rate is estimated based on the hierarchical performance bottleneck data of the index table. First, a calculation formula for the cache hit rate is defined, and a performance monitoring tool (such as Prometheus or Grafana) is used to collect actual cache hit rate data. The performance bottleneck data is compared with the actual cache hit rate to estimate the decrease in hit rate due to the performance bottleneck. By simulating different loads and bottleneck conditions, the expected decrease in cache hit rate is calculated. The cache hit rate decrease estimation data finally generated will show the changes in cache hit rate under different performance bottleneck conditions, helping to optimize the cache strategy.

[0133] Preferably, step S33 includes the following steps:

[0134] Step S331: performing retrieval attenuation-tracing level association mapping on the node tracing level index table according to the concurrent retrieval attenuation gradient data to obtain retrieval attenuation-tracing level association mapping data;

[0135] Step S332: performing retrieval attenuation fluctuation calculation between different levels on the retrieval attenuation-traceability level association mapping data to obtain retrieval attenuation fluctuation data;

[0136] Step S333: performing attenuation fluctuation complexity calculation between different levels on the retrieved attenuation fluctuation data based on the Big O notation to obtain attenuation fluctuation complexity data;

[0137] Step S334: performing handover scheduling overhead load bearing calculation according to the retrieved attenuation fluctuation data and the attenuation fluctuation complexity data to obtain handover scheduling overhead load bearing data;

[0138] Step S335: performing hierarchical identification of index table performance bottlenecks on the node tracing hierarchical index table according to the switching scheduling overhead load bearing data to obtain index table hierarchical performance bottleneck data.

[0139] In an embodiment of the present invention, the retrieval performance decay information of each node is first extracted from the concurrent retrieval decay gradient data, and this information is associated with the node traceability level index table. Data integration and mapping are performed using data processing tools (such as Python's Pandas library). The decay gradient of each node is mapped to the corresponding traceability level to generate retrieval decay-traceability level association mapping data. By calculating the performance decay of the node at different traceability levels, the decay characteristics of each level can be clarified. The generated mapping data helps to identify the decay mode in different levels and provides a basis for subsequent analysis. Using the retrieval decay-traceability level association mapping data, the retrieval decay fluctuation between different levels is calculated. First, the mapping data is processed using a statistical analysis tool (such as R language or Python's SciPy library) to calculate the decay fluctuation amount of each level. The fluctuation calculation includes the analysis of the decay difference between levels and the determination of the fluctuation range. The generated retrieval decay fluctuation data will show the performance fluctuation at different levels. This data can reveal the performance unevenness between levels and help determine the optimization direction of the hierarchical structure. The complexity of the retrieval decay fluctuation data is calculated using the big O notation. First, define the complexity model of attenuation fluctuation, such as logarithmic complexity or linear complexity model. Then, use computational complexity analysis tools (such as MATLAB or Python's NumPy library) to process attenuation fluctuation data at different levels and calculate complexity indicators. According to the size and fluctuation range of attenuation fluctuation data, calculate its complexity between different levels. The generated attenuation fluctuation complexity data will reflect the difficulty of attenuation processing at different levels of the system, which is helpful to evaluate the performance carrying capacity of the system. The overhead load carrying of switching scheduling is calculated by retrieving attenuation fluctuation data and attenuation fluctuation complexity data. First, define the calculation model of switching scheduling overhead, including the influence of scheduling frequency and load carrying. Use data modeling tools (such as MATLAB or Python's SimPy library) to model and simulate the data. Calculate the overhead load carrying of each level when scheduling switching. By simulating the switching scheduling overhead under different load conditions, generate switching scheduling overhead load carrying data. These data can show the load carrying capacity at different levels and its impact on performance. Use the switching scheduling overhead load carrying data to hierarchically identify performance bottlenecks in the node traceability level index table. First, define the identification criteria for performance bottlenecks, such as overhead thresholds and load carrying capacity. Use data analysis tools (such as Python's Scikit-learn library) to process the overhead load data, map it to different levels, and identify bottlenecks. The generated index table hierarchical performance bottleneck data will show the bottleneck areas and their impacts at different levels. Through this identification, the structure of the index table and the scheduling strategy can be optimized to improve the overall performance of the system.

[0140] Preferably, step S34 includes the following steps:

[0141] Step S341: performing different levels of index table cache hierarchy analysis on the index table hierarchical performance bottleneck data to obtain index table cache hierarchy data;

[0142] Step S342: performing cache load failure analysis between different levels according to the cache hierarchy data in the index table to obtain cache load failure data;

[0143] Step S343: Calculate cache conflict trigger probabilities between different levels of index table cache hierarchy data according to cache bearer failure data to obtain cache conflict trigger probability data;

[0144] Step S344: performing cache hit rate reduction estimation based on the cache conflict trigger probability data and the cache load failure data to obtain cache hit rate reduction estimation data.

[0145] In an embodiment of the present invention, a detailed analysis of the cache hierarchy is first performed based on the index table hierarchical performance bottleneck data. The cache hierarchy of the index table at different levels is evaluated using a cache analysis tool (such as a cache simulator tool or a dedicated hardware performance monitoring tool). The specific steps include: extracting the cache access mode and performance indicators of each level from the performance bottleneck data. The cache hierarchy is modeled using a tool (such as MATLAB's Simulink module or Python's SciPy library) to analyze the performance and bottleneck conditions of the cache at each level. According to the analysis results, cache hierarchy data is generated, including information such as cache hit rate, cache capacity, and access delay at each level. This data is used to determine the effectiveness and optimization direction of the cache. The cache load failure of different levels is analyzed using the index table cache hierarchy data. The specific steps include: defining evaluation indicators for cache load failure, such as cache failure rate and access delay. Using a statistical analysis tool (such as Python's Pandas library or R language) to process the cache hierarchy data, the cache failure rate of different levels is calculated. According to the calculation results, cache load failure data between different levels is obtained. This data reflects the stability of caches at each level and their impact on system performance. Determine a computational model for the probability of triggering a cache conflict, such as a model based on a cache replacement strategy (such as LRU or FIFO). Use a probability calculation tool (such as MATLAB's Probability Toolbox or Python's NumPy library) to calculate cache hierarchy data and determine the probability of triggering a conflict at each level. Obtain cache conflict trigger probability data for each level, showing the conflict situation of each cache level. These data are used to analyze the effectiveness of cache design and optimize cache strategies. Establish an estimation model for cache hit rate reduction that combines cache conflict trigger probability and failure data. Use mathematical modeling tools (such as MATLAB's Simulink or Python's SymPy library) to calculate cache hit rates, considering the impact of conflict probability and failure on hit rates. Based on the calculation results, generate cache hit rate reduction estimation data. This data provides a comprehensive evaluation of cache performance and helps identify directions for optimizing cache strategies.

[0146] Preferably, step S4 comprises the following steps:

[0147] Step S41: grouping the node tracing hierarchical index table with similar structures according to the index table hierarchical performance bottleneck data and cache hit rate reduction estimation data to obtain a node tracing hierarchical structure grouping index table;

[0148] Step S42: performing a grouping index table clustering performance evaluation on the node tracing hierarchical structure grouping index table to obtain grouping index table clustering performance evaluation data;

[0149] Step S43: performing thread priority allocation processing on the node tracing hierarchical structure grouping index table according to the grouping index table clustering performance evaluation data to obtain grouping index thread priority allocation data;

[0150] Step S44: designing a packaging design graphic element index management strategy according to the group index thread priority allocation data and the node tracing hierarchical structure group index table to obtain a packaging design graphic element index management strategy;

[0151] Step S45: Send the packaging design graphic element index management strategy to the cloud platform to execute the packaging design graphic element library management method.

[0152] In an embodiment of the present invention, the node tracing hierarchical index table is grouped with similar structures according to the index table hierarchical performance bottleneck data and the cache hit rate reduction estimation data to obtain the node tracing hierarchical structure grouping index table. First, the hierarchical analysis tool is used to classify the index table hierarchical performance bottleneck data in detail to identify the performance bottleneck points of each level. Then, by analyzing the cache hit rate reduction estimation data, the cache access mode and similarity between different levels are calculated. Using these data, the nodes with similar structures are grouped by clustering analysis methods (such as K-means or hierarchical clustering algorithms). This process requires detailed recording of the grouping rules of each level and ensuring that the node tracing hierarchical structure after grouping can effectively reflect the update frequency and cache requirements of the nodes. The node tracing hierarchical structure grouping index table finally output contains the grouping information of each node, which can optimize subsequent index query and management. The node tracing hierarchical structure grouping index table is evaluated for clustering performance of the grouping index table to obtain the grouping index table clustering performance evaluation data. First, a performance evaluation tool (such as a Benchmark suite) is used to perform detailed performance tests on the grouped index table, including indicators such as access speed, query efficiency, and cache utilization. By comparing the performance data before and after grouping, the processing efficiency, cache hit rate, and data retrieval time of each grouping index table are calculated. During the evaluation process, statistical analysis methods are used to process the test data to identify performance bottlenecks and optimization points. The grouping index table clustering performance evaluation data generated in the end will show the impact of different grouping strategies on the overall system performance and provide a basis for further optimization. According to the grouping index table clustering performance evaluation data, the node tracing hierarchical structure grouping index table is processed with thread priority allocation to obtain the grouping index thread priority allocation data. First, the performance indicators (such as query speed and data access frequency) of each group in the grouping index table clustering performance evaluation data are analyzed to determine the processing priority of each group. The multi-thread scheduling algorithm (such as the priority scheduling algorithm) is used to allocate threads to the data in the node tracing hierarchical structure grouping index table to ensure that the group with high performance obtains a higher thread priority. This process requires configuring the thread pool and allocating appropriate resources to each thread to achieve efficient data processing. The grouping index thread priority allocation data generated in the end includes the priority and resource allocation scheme allocated to each thread, which is used to optimize the concurrency and efficiency of data processing. According to the priority allocation data of grouped index threads and the grouped index table of node tracing hierarchical structure, the index management strategy of packaging design graphic elements is designed, and the index management strategy of packaging design graphic elements is obtained. Firstly, the priority allocation data of grouped index threads is applied to the grouped index table of node tracing hierarchical structure, and an index management strategy that conforms to the priority allocation is designed. This involves setting appropriate index cache strategies and access control rules for each graphic element to improve the query efficiency and cache hit rate of the index table.The strategy design includes defining the index path, cache level and update rules of each graphic element to ensure that the packaging design graphic element library can be effectively managed and accessed in practical applications. The final generated packaging design graphic element index management strategy will guide how to manage and access graphic elements under different threads and cache levels. The packaging design graphic element index management strategy is sent to the cloud platform to execute the packaging design graphic element library management method. First, the designed packaging design graphic element index management strategy is uploaded to the cloud platform through a secure network protocol (such as HTTPS). This process needs to ensure the integrity and confidentiality of the policy data and use encryption technology to transmit the data. After the upload is completed, the cloud platform automatically configures and adjusts the management method of the packaging design graphic element library according to the received policy data. This includes applying policy rules, optimizing data indexing and cache management on the cloud platform, and real-time monitoring and adjustment of the storage and access strategies of graphic elements. After the cloud platform is executed, efficient management and dynamic adjustment of the graphic element library can be achieved, improving the performance and reliability of the system.

[0153] Preferably, the present invention further provides a graphic element library management system based on data processing, which is used to execute the graphic element library management method based on data processing as described above, and the graphic element library management system based on data processing includes:

[0154] The element update directed graph module is used to obtain the graphic element library log data; construct the element update directed graph according to the graphic element library log data to obtain the element update directed graph;

[0155] The traceability level index table design module is used to design the element update node traceability structure for the element update directed graph to obtain the element update node traceability structure data; design the traceability level index table for the element update node traceability structure data to obtain the node traceability level index table;

[0156] The index table test module is used to perform concurrent retrieval attenuation gradient calculation on the node tracing level index table to obtain concurrent retrieval attenuation gradient data; perform hierarchical identification of index table performance bottlenecks on the node tracing level index table according to the concurrent retrieval attenuation gradient data to obtain index table hierarchical performance bottleneck data; perform cache hit rate reduction estimation on the index table hierarchical performance bottleneck data to obtain cache hit rate reduction estimation data;

[0157] The management strategy design module is used to group the node traceability hierarchical index table with similar structures according to the index table hierarchical performance bottleneck data and the cache hit rate reduction estimation data to obtain the node traceability hierarchical structure grouping index table; design the packaging design graphic element index management strategy according to the node traceability hierarchical structure grouping index table to obtain the packaging design graphic element index management strategy; send the packaging design graphic element index management strategy to the cloud platform to execute the packaging design graphic element library management method.

[0158] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0159] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A graphic element library management method based on data processing, characterized in that: The following steps are involved: Step S1: Obtaining graphic element library log data; An element update directed graph is constructed according to the log data of the graphic element library to obtain an element update directed graph; Step S2: Designing an element update node tracing structure for the element update directed graph to obtain element update node tracing structure data; Design a traceability level index table for the element update node traceability structure data to obtain a node traceability level index table; Step S2 includes the following steps: Step S21: performing time-series synchronous update increment calculation between different nodes on the element update directed graph to obtain node time-series synchronous update increment data; Step S22: performing iterative update identification on the incremental data of the node time series synchronous update to obtain the iterative update data of the node element; Step S23: Designing an element update node tracing structure for the element update directed graph according to the node element iterative update data to obtain element update node tracing structure data; Step S23 includes the following steps: Step S231: extracting the update frequency between different nodes of the node element iterative update data to obtain node update frequency data; Step S232: performing iterative level calculation between different nodes on the node element iterative update data according to the node update frequency data to obtain node element iterative level data; Step S233: performing node list pointer structure configuration on the node element iteration level data to obtain node list pointer structure data; Step S234: Designing an element update node tracing structure for the element update directed graph according to the node link list pointer structure data to obtain element update node tracing structure data; Step S24: Design a tracing hierarchical index table for the element update node tracing structure data to obtain a node tracing hierarchical index table; Step S24 includes the following steps: Step S241: performing dependency analysis between nodes on the element update node traceability structure data to obtain update node dependency data; Step S242: constructing a multi-dimensional node dependency graph based on the updated node dependency relationship data to obtain a multi-dimensional node dependency graph; Step S243: performing hierarchical relationship weighted calculation on the multi-dimensional node dependency graph to obtain hierarchical relationship weighted data; Step S244: using the B-tree algorithm and designing a tracing hierarchical index table for the element update node tracing structure data according to the hierarchical relationship weighted data, to obtain a node tracing hierarchical index table; Step S3: performing concurrent retrieval attenuation gradient calculation on the node tracing level index table to obtain concurrent retrieval attenuation gradient data; performing hierarchical identification of index table performance bottlenecks on the node tracing level index table according to the concurrent retrieval attenuation gradient data to obtain index table hierarchical performance bottleneck data; performing cache hit rate reduction estimation on the index table hierarchical performance bottleneck data to obtain cache hit rate reduction estimation data; Step S4: grouping the node tracing hierarchical index table with similar structures according to the index table hierarchical performance bottleneck data and the cache hit rate reduction estimation data to obtain the node tracing hierarchical structure grouping index table; designing a packaging design graphic element index management strategy according to the node tracing hierarchical structure grouping index table to obtain the packaging design graphic element index management strategy; sending the packaging design graphic element index management strategy to the cloud platform to execute the packaging design graphic element library management method.

2. The method for managing a graphic element library based on data processing according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtaining graphic element library log data; Step S12: performing data cleaning on the graphic element library log data to obtain graphic element library log cleaning data; Step S13: extracting update records from the graphic element library log cleaning data to obtain graphic element update record data; Step S14: construct an element update directed graph according to the graphic element update record data to obtain the element update directed graph.

3. The method for managing a graphic element library based on data processing according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing a concurrent simulation test on the node tracing hierarchical index table to obtain hierarchical index concurrent simulation data; Step S32: performing concurrent search attenuation gradient calculation on the node tracing hierarchical index table according to the hierarchical index concurrent simulation data to obtain concurrent search attenuation gradient data; Step S33: performing hierarchical identification of index table performance bottlenecks on the node tracing hierarchical index table according to the concurrent retrieval attenuation gradient data, and obtaining index table hierarchical performance bottleneck data; Step S34: performing cache hit rate reduction estimation on the index table hierarchical performance bottleneck data to obtain cache hit rate reduction estimation data.

4. The method for managing a graphic element library based on data processing according to claim 3, characterized in that: Step S33 includes the following steps: Step S331: performing retrieval attenuation-tracing level association mapping on the node tracing level index table according to the concurrent retrieval attenuation gradient data to obtain retrieval attenuation-tracing level association mapping data; Step S332: performing retrieval attenuation fluctuation calculation between different levels on the retrieval attenuation-traceability level association mapping data to obtain retrieval attenuation fluctuation data; Step S333: performing attenuation fluctuation complexity calculation between different levels on the retrieved attenuation fluctuation data based on the Big O notation to obtain attenuation fluctuation complexity data; Step S334: performing handover scheduling overhead load bearing calculation according to the retrieved attenuation fluctuation data and the attenuation fluctuation complexity data to obtain handover scheduling overhead load bearing data; Step S335: performing hierarchical identification of index table performance bottlenecks on the node tracing hierarchical index table according to the switching scheduling overhead load bearing data to obtain index table hierarchical performance bottleneck data.

5. The method for managing a graphic element library based on data processing according to claim 4, characterized in that: Step S34 includes the following steps: Step S341: performing different levels of index table cache hierarchy analysis on the index table hierarchical performance bottleneck data to obtain index table cache hierarchy data; Step S342: performing cache load failure analysis between different levels according to the cache hierarchy data in the index table to obtain cache load failure data; Step S343: Calculate cache conflict trigger probabilities between different levels of index table cache hierarchy data according to cache bearer failure data to obtain cache conflict trigger probability data; Step S344: performing cache hit rate reduction estimation based on the cache conflict trigger probability data and the cache load failure data to obtain cache hit rate reduction estimation data.

6. The method for managing a graphic element library based on data processing according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: grouping the node tracing hierarchical index table with similar structures according to the index table hierarchical performance bottleneck data and cache hit rate reduction estimation data to obtain a node tracing hierarchical structure grouping index table; Step S42: performing a grouping index table clustering performance evaluation on the node tracing hierarchical structure grouping index table to obtain grouping index table clustering performance evaluation data; Step S43: performing thread priority allocation processing on the node tracing hierarchical structure grouping index table according to the grouping index table clustering performance evaluation data to obtain grouping index thread priority allocation data; Step S44: designing a packaging design graphic element index management strategy according to the group index thread priority allocation data and the node tracing hierarchical structure group index table to obtain a packaging design graphic element index management strategy; Step S45: Send the packaging design graphic element index management strategy to the cloud platform to execute the packaging design graphic element library management method.

7. A graphic element library management system based on data processing, characterized in that: Used to execute the graphic element library management method based on data processing as claimed in claim 1, the graphic element library management system based on data processing comprises: The element update directed graph module is used to obtain the graphic element library log data; construct the element update directed graph according to the graphic element library log data to obtain the element update directed graph; The traceability level index table design module is used to design the element update node traceability structure for the element update directed graph to obtain the element update node traceability structure data; design the traceability level index table for the element update node traceability structure data to obtain the node traceability level index table; The index table test module is used to perform concurrent retrieval attenuation gradient calculation on the node tracing level index table to obtain concurrent retrieval attenuation gradient data; perform hierarchical identification of index table performance bottlenecks on the node tracing level index table according to the concurrent retrieval attenuation gradient data to obtain index table hierarchical performance bottleneck data; perform cache hit rate reduction estimation on the index table hierarchical performance bottleneck data to obtain cache hit rate reduction estimation data; The management strategy design module is used to group the node traceability hierarchical index table with similar structures according to the index table hierarchical performance bottleneck data and the cache hit rate reduction estimation data to obtain the node traceability hierarchical structure grouping index table; design the packaging design graphic element index management strategy according to the node traceability hierarchical structure grouping index table to obtain the packaging design graphic element index management strategy; send the packaging design graphic element index management strategy to the cloud platform to execute the packaging design graphic element library management method.

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