Surveying and mapping image efficient storage method for geographic surveying and mapping system

Abstract

The invention relates to the technical field of geographic surveying and mapping, and discloses a surveying and mapping image efficient storage method for a geographic surveying and mapping system, which comprises a data acquisition module, a data processing module, a hierarchical optimization module, a spatial index module, a classification module and a storage module, according to the method, automation and intellectualization of data classification and priority processing are achieved through multi-source data collection, data standardization processing, hierarchical optimization and dynamic storage management, on the basis, the storage space utilization rate is increased, the system access delay is reduced, the image data retrieval efficiency is remarkably improved, and the image retrieval efficiency is improved. According to the method, region division and multi-source data acquisition are combined, so that the accuracy and comprehensiveness of data acquisition are improved, powerful data support is provided for optimization analysis, classified storage and dynamic adjustment of subsequent steps, and compared with a traditional single-parameter or global processing mode, the method has the advantages that the method is simple and convenient to operate. The method can better meet the requirements of a geographic surveying and mapping system for refinement, high efficiency and dynamism.

Description

Technical Field

[0001] The present invention relates to the technical field of geodetic surveying and mapping, and particularly to an efficient storage method for surveying and mapping images in a geodetic surveying and mapping system. Background Art

[0002] Geographic Information Technology (GIT), as an important part of information technology, mainly involves processing means such as the acquisition, storage, analysis, and visualization of geospatial data. This technology is widely used in fields such as urban planning, environmental monitoring, disaster emergency response, and traffic management, providing scientific spatial data support for decision-makers and promoting the construction of smart cities and sustainable development. In geographic information technology, surveying and mapping images, as a key data form, are obtained through various means such as satellites, drones, and aerial photography. These high-resolution surveying and mapping images, combined with precise geolocation, not only display the Earth's surface and its dynamic changes but also provide basic data for various spatial analysis tasks. With the rapid growth of surveying and mapping data volume, how to ensure efficient storage, fast retrieval, scalability, and stability of the system has become a technical problem that urgently needs to be solved in the field of geographic information technology.

[0003] Existing methods for storing and retrieving geodetic surveying and mapping images have significant bottlenecks. Especially in the context of the increasing volume of image data, traditional storage structures and retrieval mechanisms fail to effectively meet the needs of large-scale data processing. At the same time, there is a lack of the ability to perform intelligent classification and hierarchical storage based on image features (such as resolution, access frequency, cache capacity, etc.), resulting in low utilization efficiency of storage space and slow data access speed. Especially when quickly retrieving specific images or regional data, the system performance is not good. In addition, due to the lack of dynamic optimization means for different image priorities, existing methods fail to adjust the storage structure in real time according to the characteristics of image data, causing resource waste and being unable to effectively meet the needs of real-time analysis and efficient access.

[0004] Therefore, we propose an efficient storage method for surveying and mapping images in a geodetic surveying and mapping system to solve the above-mentioned problems. Summary of the Invention

[0005] The purpose of the present invention is to provide an efficient storage method for surveying and mapping images in a geodetic surveying and mapping system to solve the problems in the above-mentioned background art that the traditional storage structure and retrieval mechanism fail to effectively meet the needs of large-scale data processing and the existing technology often lacks the ability to perform intelligent classification and hierarchical storage based on image features (such as resolution, access frequency, cache capacity, etc.), resulting in low utilization efficiency of storage space, slow data access speed, and especially poor system performance when quickly retrieving specific images or regional data.

[0006] To achieve the above object, the present invention provides the following technical solution: An efficient storage method for surveying and mapping images in a geographic surveying and mapping system, including a data acquisition module, a data processing module, a hierarchical optimization module, a spatial indexing module, a classification module, and a storage module. The specific steps are as follows: Step S1: The data acquisition module performs multi-source data acquisition on the acquired surveying and mapping images, and inputs the acquired multi-source data into the data processing module; Step S2: The data processing module preprocesses and dimensionlessizes the multi-source data acquired by the data acquisition module, and divides the preprocessed data into a first data set, a second data set, and a third data set; Step S3: The hierarchical optimization module analyzes the data in the first data set to generate a resolution reference coefficient FBC, and compares the resolution reference coefficient FBC with a preset first threshold Y1 to generate a first comparison result. Based on the first comparison result, a preliminary analysis of the image is performed; Step S4: The spatial indexing module integrally analyzes the data in the second data set with the first comparison result to generate a priority reference coefficient YXJ, compares the priority reference coefficient YXJ with a preset second threshold Y2 to generate a second comparison result, and determines the image priority based on the second comparison result; Step S5: The classification module integrally analyzes the first comparison result, the second comparison result, and the third data set to generate a classification reference coefficient FLC, compares the classification reference coefficient FLC with a preset third threshold Y3 to generate a third comparison result, and inputs the third comparison result into the storage module. The storage module performs classified storage according to the third comparison result.

[0007] Preferably, in step S1, the data acquisition module divides the image to be stored into regions, denoted as i1, i2, i3,..., in respectively, and acquires multi-source data in each region, including image resolution A, display resolution B, image scaling weight C, access frequency D, image block storage value E, time weight value F, cache capacity G, and read latency value H.

[0008] Preferably, in step S2, after the data processing module preprocesses and dimensionlessizes the image resolution A, display resolution B, image scaling weight C, access frequency D, image block storage value E, time weight value F, cache capacity G, and read latency value H respectively, they are divided into the first data set, the second data set, and the third data set respectively; The first data set includes image resolution A, display resolution B, and image scaling weight C; The second data set includes access frequency D, image block storage value E, and time weight value F; The third data set includes the cache capacity G and the read latency value H.

[0009] Preferably, in step S3, the hierarchical optimization module includes an optimization analysis unit and an optimization comparison unit; The optimization analysis unit is used to perform an integrated analysis on the first data set, so as to calculate and obtain the resolution reference coefficient FBC; The optimization comparison unit is used to compare with a preset first threshold Y1, so as to generate a first comparison result, specifically as follows: When it represents that the picture in the current area is a low-resolution layer, marked as DFB; When it represents that the picture in the current area is a high-resolution layer, marked as GFB.

[0010] Preferably, in step S3, the optimization analysis unit calculates and obtains the resolution reference coefficient FBC through the following formula: ;

[0011] In the formula: A is the image resolution, B is the display resolution, C is the image scaling weight, and ln is the natural logarithm function.

[0012] Preferably, in step S4, the spatial index module includes an index analysis unit and an index comparison unit; The index analysis unit is used to perform an integrated analysis on the data in the second data set and the first comparison result, so as to calculate and obtain the priority reference coefficient YXJ; The index comparison unit compares the priority reference coefficient YXJ with a preset second threshold Y2, so as to generate a second comparison result, and determines the image priority based on the second comparison result, specifically as follows: When it represents that the picture in the current area is a high-priority layer, and the specific value is recorded as GTC; When it represents that the picture in the current area is a secondary layer, and the specific value is recorded as DTC.

[0013] Preferably, in step S4, the index analysis unit calculates and obtains the priority reference coefficient YXJ through the following formula: ;

[0014] In the formula: D is the access frequency, F is the time weight value, E is the image block storage value, and R is the reference function, which are respectively used to fill in the low-resolution layer DFB and the high-resolution layer GFB.

[0015] Preferably, in the step S5, the classification module includes a classification analysis unit and a classification comparison unit; The classification analysis unit is used to integrate and analyze the first comparison result, the second comparison result, and the third data set, so as to calculate and obtain a classification reference coefficient FLC; The classification comparison unit is used to compare the classification reference coefficient FLC with a preset third threshold Y3, so as to generate a third comparison result, specifically as follows: When it represents that the picture of the current area is a third-level layer, and the specific value is denoted as GTC; When it represents that the picture of the current area is a second-level layer, and the specific value is denoted as DTC; When it represents that the picture of the current area is a first-level layer, and the specific value is denoted as DTC.

[0016] Preferably, in the step S5, the classification analysis unit calculates and obtains the classification reference coefficient FLC through the following formula; ;

[0017] In the formula: G is the cache capacity, H is the read latency value, and P is a substitution function that fills in the third-level layer GTC, the second-level layer DTC, and the first-level layer DTC respectively.

[0018] Preferably, the storage method of the storage module is specifically as follows: Create a first-level layer folder, a second-level layer folder, and a third-level layer folder respectively. According to the third comparison result, input the corresponding pictures into the corresponding folders. Every fixed time, recalculate the classification reference coefficient FLC and generate a new third comparison result. According to the updated third comparison result, for the detected hot areas, migrate the relevant image blocks from the second or third level to the first level in advance, and for the image blocks with low priority scores, migrate them from the first or second level to the third level.

[0019] Compared with the prior art, the beneficial effects of the present invention are: 1. Through multi-source data acquisition, data standardization processing, hierarchical optimization, and dynamic storage management, this method realizes the automation and intelligence of data classification and priority processing. On this basis, it improves the storage space utilization rate, reduces the system access latency, and significantly improves the retrieval efficiency of image data.

[0020] 2. By combining regional division with multi-source data collection, this method not only improves the accuracy and comprehensiveness of data collection, but also provides strong data support for the optimization analysis, classification storage, and dynamic adjustment of subsequent steps. Compared with traditional single-parameter or globalization processing methods, this method better meets the requirements of the geographical mapping system for refinement, efficiency, and dynamics.

[0021] 3. Through data preprocessing and partitioning operations, it can more efficiently support the analysis and decision-making of image hierarchical storage in subsequent steps. The first dataset provides accurate data for the generation of the discrimination reference coefficient FBC, the second dataset supports the calculation of priority evaluation, and the third dataset provides the necessary parameters for optimizing storage efficiency and response speed.

[0022] 4. The hierarchical method adopted by this method is applicable to geographical mapping scenarios with various resolution requirements. Whether it is refined image analysis or large-scale image storage, it can provide an adapted resolution classification strategy. This hierarchical method significantly improves the adaptability of the mapping image system to complex application scenarios.

[0023] 5. Based on the comparison between the priority reference coefficient YXJ and the preset threshold Y2, the high-priority layer GTC and the secondary layer DTC are dynamically distinguished, forming a clear image storage and access priority strategy. This hierarchical division method effectively improves the processing speed of the system for key data, while reducing the occupation of system resources by unnecessary data. By comprehensively considering multi-dimensional data characteristics and dynamic parameter changes, the priority classification mechanism of this step can adapt to different geographical mapping application scenarios. In real-time navigation or disaster emergency scenarios, the high-priority layer can be preferentially stored and loaded, thus improving the response speed and service quality of the system.

[0024] 6. The detection and high-priority migration of hotspots in this method ensure that the image blocks in key areas can be preferentially used in mapping analysis. Adopting a hierarchical storage method of primary, secondary, and tertiary layer folders, high-priority data is preferentially stored in the fast access layer, while low-priority data is stored in the secondary or low-speed access layer. This structure reduces the unified search and loading time of the system for all data, significantly improving data access performance. The system realizes the automatic management of the storage location of image blocks through algorithms, without manual adjustment of priorities. This intelligent storage strategy reduces human input, improves the efficiency of storage management, and at the same time reduces the risk of errors caused by manual intervention. By dynamically migrating low-priority image blocks to the tertiary layer and releasing high-priority space, the system effectively reduces data redundancy and waste of storage resources. Especially in the case of a large amount of mapping image data, this mechanism can significantly reduce the consumption of hardware resources and extend the service life of the storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is the method step diagram of the present invention.

[0026] In the figure: 1. Data acquisition module; 2. Data processing module; 3. Hierarchical optimization module; 31. Optimization analysis unit; 32. Optimization comparison unit; 4. Spatial index module; 41. Index analysis unit; 42. Index comparison unit; 5. Classification module; 51. Classification analysis unit; 52. Classification comparison unit; 6. Storage module. Specific implementation manners

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] Embodiment 1: Please refer to Figure 1 , an efficient storage method for mapping images in a geographic mapping system, including a data acquisition module 1, a data processing module 2, a hierarchical optimization module 3, a spatial index module 4, a classification module 5, and a storage module 6. The specific steps are as follows: Step S1: The data acquisition module 1 performs multi-source data acquisition on the acquired mapping images and inputs the acquired multi-source data into the data processing module 2; Step S2: The data processing module 2 preprocesses and dimensionlessizes the multi-source data acquired by the data acquisition module 1, and divides the preprocessed data into a first data set, a second data set, and a third data set; Step S3: The hierarchical optimization module 3 analyzes the data in the first data set to generate a resolution reference coefficient FBC, and compares the resolution reference coefficient FBC with a preset first threshold Y1 to generate a first comparison result. Based on the first comparison result, a preliminary analysis of the image is performed; Step S4: The spatial index module 4 integrally analyzes the data in the second data set with the first comparison result to generate a priority reference coefficient YXJ, compares the priority reference coefficient YXJ with a preset second threshold Y2 to generate a second comparison result, and determines the image priority based on the second comparison result; Step S5: The classification module 5 integrally analyzes the first comparison result, the second comparison result, and the third data set to generate a classification reference coefficient FLC, compares the classification reference coefficient FLC with a preset third threshold Y3 to generate a third comparison result, and inputs the third comparison result into the storage module 6. The storage module 6 performs classification storage according to the third comparison result.

[0029] In this embodiment: In step S1, through the data acquisition module 1, the method first performs multi-source data acquisition on the surveying and mapping images. This step involves obtaining surveying and mapping image information from different data sources, including satellite images, UAV images, aerial photography, etc., to ensure the comprehensiveness and diversity of the data. Through this multi-source acquisition method, it can be ensured that the obtained data is more representative and can meet the requirements of various different scenarios. At the same time, this provides a richer raw data basis for subsequent data processing, classification, storage, etc., and helps to process and analyze the surveying and mapping images more accurately.

[0030] In step S2, through the data processing module 2, after preprocessing and dimensionless processing of the acquired multi-source data, it is divided into a first data set, a second data set, and a third data set. Through the dimensionless processing, the differences between different data sources and different dimensions can be eliminated, enabling various types of data to be comparable in subsequent analysis. This step not only improves the standardization degree of the data but also effectively reduces the interference between different data features, ensuring the accuracy and efficiency of subsequent analysis and optimization algorithms, and providing a solid foundation for image data classification, priority judgment, etc.

[0031] In step S3, the first data set is analyzed by the hierarchical optimization module 3 to generate a resolution reference coefficient FBC, which is compared with a preset first threshold Y1, and then the generation of the first comparison result is completed. This step can effectively distinguish high-resolution and low-resolution images by analyzing the resolution characteristics of the images, thereby providing an optimization basis for subsequent storage and processing work. Through the preliminary analysis and classification of the images, the storage levels of the images can be arranged more reasonably, ensuring that high-resolution images are stored and processed with higher priority, while low-resolution images can be allocated appropriate storage according to needs.

[0032] In step S4, the second data set and the first comparison result are integrated by the spatial index module 4 to generate a priority reference coefficient YXJ, which is compared with a preset second threshold Y2, and the generation of the second comparison result is completed. Through this step, based on factors such as the access frequency and time weight of the images, the storage priority of the images can be judged, thereby optimizing the data storage strategy. The determination of the priority helps the system to process different image data more intelligently during storage, ensuring that images with high access frequency and high importance can be stored and accessed preferentially, improving the efficiency and response speed of the entire system.

[0033] In step S5, the classification module 5 integrates and analyzes the first comparison result, the second comparison result, and the third data set to generate a classification reference coefficient FLC, compares it with a preset third threshold Y3, and then generates a third comparison result. According to this comparison result, the image data is classified into different levels for storage. This step realizes the precise classification of image data, and images with different priorities and characteristics are stored in different levels, further improving the efficiency of data storage and access. Through this hierarchical storage method, the system can flexibly adjust the storage strategy according to the actual needs of the data, reduce the waste of storage resources, and improve the efficiency of data retrieval and processing.

[0034] This method realizes the automation and intelligence of data classification and priority processing through multi-source data acquisition, data standardization processing, hierarchical optimization, and dynamic storage management. On this basis, it improves the utilization rate of storage space, reduces the access latency of the system, and significantly improves the retrieval efficiency of image data.

[0035] Embodiment 2: Please refer to Figure 1 , in step S1, the data acquisition module 1 divides the image to be stored into regions, denoted as i1, i2, i3,..., in respectively, and acquires multi-source data within each region, including image resolution A, display resolution B, image scaling weight C, access frequency D, image block storage value E, time weight value F, cache capacity G, and read latency value H.

[0036] In this embodiment: Through region division, refined management of large-scale geographical mapping images can be carried out. The image characteristics of different regions can be separately recorded and processed, thus avoiding the accumulation of errors caused by general data acquisition and improving the accuracy and comprehensiveness of data acquisition.

[0037] Acquiring multi-source data including resolution, scaling weight, access frequency, and storage latency enables the system to comprehensively grasp the characteristics of each image from different dimensions. This multi-dimensional data structure can more accurately reflect the storage requirements and access priorities of the images, providing data support for subsequent optimization analysis and classified storage.

[0038] Based on the multi-source data collected through region division, personalized processing can be carried out in combination with the specific needs and image characteristics of each region. For example, image regions with high resolution and high access frequency can be preferentially allocated more storage and cache resources, thereby improving the reading efficiency of data in hot regions.

[0039] Collecting dynamic parameters such as time weight value and cache capacity provides a data basis for subsequent dynamic resource allocation and storage adjustment. The system can adjust the priority and storage level of images in real time based on these parameters, so as to better adapt to complex and changeable geographical mapping application scenarios.

[0040] By combining regional division with multi-source data collection, this method not only improves the accuracy and comprehensiveness of data collection, but also provides strong data support for the subsequent optimization analysis, classification storage, and dynamic adjustment. Compared with the traditional single-parameter or globalization processing methods, this method better meets the requirements of the geodetic surveying and mapping system for refinement, efficiency, and dynamics.

[0041] Example 3: Please refer to Figure 1 , in step S2, after the data processing module 2 preprocesses and dimensionlessizes the image resolution A, display resolution B, image scaling weight C, access frequency D, image block storage value E, time weight value F, cache capacity G, and read latency value H respectively, they are respectively divided into the first data set, the second data set, and the third data set; The first data set includes the image resolution A, display resolution B, and image scaling weight C; The second data set includes the access frequency D, image block storage value E, and time weight value F; The third data set includes the cache capacity G and read latency value H.

[0042] In this embodiment: By performing dimensionless processing on multi-source data, the interference problem caused by the dimension difference of different data parameters is eliminated, ensuring the accuracy of subsequent calculation and analysis. The dimensionless processing enables different types of data to be compared and optimized in the same dimension, providing a more reliable data basis for the calculation of the discrimination reference coefficient FBC, priority reference coefficient YXJ, and classification reference coefficient FLC.

[0043] Dividing the multi-source data into the first data set, the second data set, and the third data set clarifies the functional positioning of different data. The grouping division not only simplifies the data processing process, but also improves the efficiency of modular calculation, facilitating the smooth progress of hierarchical optimization, spatial indexing, and classification storage.

[0044] The parameters included in each data set have clear functional orientations. For example, the first data set is mainly used to evaluate the matching of image resolution and display characteristics; the second data set focuses on analyzing access frequency and storage weight; the third data set focuses on optimizing cache capacity and read latency. This targeted division lays the foundation for subsequent optimization calculations and refined storage strategies.

[0045] Through data preprocessing and division operations, it can more efficiently support the analysis and decision-making of image hierarchical storage in subsequent steps. The first data set provides accurate data for the generation of the discrimination reference coefficient FBC, the second data set supports the calculation of priority evaluation, and the third data set provides necessary parameters for optimizing storage efficiency and response speed.

[0046] Example 4: Please refer toFigure 1 , in step S3, the hierarchical optimization module 3 includes an optimization analysis unit 31 and an optimization comparison unit 32; The optimization analysis unit 31 is used to perform integrated analysis on the first data set, so as to calculate and obtain the discrimination reference coefficient FBC; The optimization comparison unit 32 is used to compare with a preset first threshold Y1, so as to generate a first comparison result, specifically as follows: When , it represents that the picture in the current area is a low-resolution layer, marked as DFB; When , it represents that the picture in the current area is a high-resolution layer, marked as GFB.

[0047] In step S3, the optimization analysis unit 31 calculates and obtains the discrimination reference coefficient FBC through the following formula: ;

[0048] In the formula: A is the image resolution, B is the display resolution, C is the image scaling weight, and ln is the natural logarithm function.

[0049] In this embodiment: The calculation formula of the optimization analysis unit 31 fully considers the comprehensive influence of multi-dimensional factors such as the image resolution A, the display resolution B, and the image scaling weight C, avoiding the deviation that may be brought by single-parameter evaluation. The calculation method of the discrimination reference coefficient FBC is based on the natural logarithm function, effectively enhancing the sensitivity to the proportional relationship between different data magnitudes, so as to realize the accurate classification of the image resolution level.

[0050] By comparing the calculated discrimination reference coefficient FBC with the preset first threshold Y1 through the optimization comparison unit 32, the high-resolution layer GFB and the low-resolution layer DFB are dynamically distinguished. This hierarchical method based on data characteristics is more flexible than the traditional static standard, can adapt to the resolution characteristics of images in different regions, and ensures the priority processing of high-resolution images and the reasonable storage of low-resolution images.

[0051] Dividing the image area according to the resolution level helps to preferentially store high-resolution images and optimize the system resource allocation. The high-resolution layer GFB images can be used for detailed analysis, while the low-resolution layer DFB images are suitable for quick display or low-bandwidth scenarios, thus achieving a balance between data storage and access efficiency.

[0052] The hierarchical method adopted by this method is applicable to geodetic surveying and mapping scenarios with various resolution requirements. Whether it is fine-grained image analysis or large-scale image storage, it can provide an adapted resolution classification strategy. This hierarchical method significantly improves the adaptability of the surveying and mapping image system to complex application scenarios.

[0053] Example 5: Please refer to Figure 1 , in step S4, the spatial index module 4 includes an index analysis unit 41 and an index comparison unit 42; The index analysis unit 41 is used to integrally analyze the data in the second data set with the first comparison result, so as to calculate and obtain the priority reference coefficient YXJ; The index comparison unit 42 compares the priority reference coefficient YXJ with a preset second threshold Y2, so as to generate a second comparison result, and judge the image priority based on the second comparison result, specifically as follows: When , it means that the picture in the current area is a high-priority layer, and the specific value is recorded as GTC; When , it means that the picture in the current area is a secondary layer, and the specific value is recorded as DTC.

[0054] In step S4, the index analysis unit 41 calculates and obtains the priority reference coefficient YXJ through the following formula: ;

[0055] In the formula: D is the access frequency, F is the time weight value, E is the image block storage value, and R is the reference function, which are respectively used to fill in the low-resolution layer DFB and the high-resolution layer GFB.

[0056] In this embodiment: The calculation formula of the index analysis unit 41 comprehensively integrates multiple key parameters such as the access frequency D, the time weight value F, the image block storage value E, and the reference function R, and comprehensively reflects the dynamic relationship between the importance of the image and the resource consumption. Compared with the single-index priority judgment method, this multi-factor-based analysis method can more accurately determine the image priority and avoid the situation that important images are ignored due to resource limitations.

[0057] The priority reference coefficient YXJ formula combines the access frequency D and the time weight value F, can accurately capture the real-time demand changes of the image, and at the same time restricts the occupation of storage resources through the image block storage value E. In the geographical mapping scenario with diverse data and high timeliness requirements, this method can dynamically adjust the storage strategy, so that the high-priority layer GTC is processed first, while the secondary layer DTC is operated later, thus optimizing the resource allocation. Through the reference function R, the calculation of the priority reference coefficient YXJ is associated with the resolution level, making the priority evaluation of images with different resolutions more accurate. This feature ensures the priority evaluation of the high-resolution image GFB, further guaranteeing the satisfaction of the high-precision requirements of the mapping image.

[0058] Based on the comparison between the priority reference coefficient YXJ and the preset threshold Y2, the high-priority layer GTC and the secondary layer DTC are dynamically distinguished to form a clear image storage and access priority strategy. This hierarchical division method effectively improves the processing speed of the system for key data, while reducing the occupation of system resources by unnecessary data. By comprehensively considering multi-dimensional data characteristics and dynamic parameter changes, the priority classification mechanism in this step can adapt to different geographical mapping application scenarios. In real-time navigation or disaster emergency scenarios, the high-priority layer can be preferentially stored and loaded, thus improving the response speed and service quality of the system.

[0059] Embodiment Six: Please refer to Figure 1 , in step S5, the classification module 5 includes a classification analysis unit 51 and a classification comparison unit 52; The classification analysis unit 51 is used to integrate and analyze the first comparison result, the second comparison result, and the third data set, so as to calculate and obtain the classification reference coefficient FLC; The classification comparison unit 52 is used to compare the classification reference coefficient FLC with the preset third threshold Y3 to generate a third comparison result, specifically as follows: When , it represents that the picture of the current area is a third-level layer, and the specific value is recorded as GTC; When , it represents that the picture of the current area is a second-level layer, and the specific value is recorded as DTC; When , it represents that the picture of the current area is a first-level layer, and the specific value is recorded as DTC.

[0060] In step S5, the classification analysis unit 51 calculates and obtains the classification reference coefficient FLC through the following formula; ;

[0061] In the formula: G is the cache capacity, H is the read latency value, and P is the substitution function, which are filled with the third-level layer GTC, the second-level layer DTC, and the first-level layer DTC respectively.

[0062] In this embodiment: The classification analysis unit 51 combines the first comparison result, the second comparison result with the third data set. This improvement breaks through the limitations of traditional single-dimensional classification, making the classification result more accurate and reliable. The influence of the cache capacity G and the read latency H is comprehensively considered in the calculation formula, enabling the classification process to dynamically adapt to changes in the performance of storage hardware, and avoiding performance bottlenecks caused by insufficient cache or excessive read latency. Especially in scenarios where the geographical mapping system has high real-time requirements, this optimization significantly improves the response efficiency of the system.

[0063] Based on the dynamic stratification results of the classification and comparison unit 52, different priority layers are stored and managed in an orderly manner, significantly reducing data redundancy and waste of storage resources, while ensuring that critical data can be quickly loaded when needed. Such a stratification strategy greatly optimizes the system in terms of the orderliness and efficiency of data processing. By segmentally comparing the classification reference coefficient FLC with the threshold Y3, the priorities of the first-level, second-level, and third-level layers are clearly defined, thereby dynamically adjusting the storage and access strategies. For example, the high-priority first-level layer is marked as DTC and loaded first, while the third-level layer is marked as GTC and processed later. This improvement realizes the reasonable scheduling of resources and effectively avoids the problem of high-priority data response delay caused by excessive resource occupation by low-priority data. By setting a dynamically adjusted grading standard, this step can adapt to the requirements of different mapping tasks. For example, for disaster emergency scenarios, the first-level layer of the key area can be preferentially processed by adjusting the threshold ratio; while in daily monitoring, more attention can be paid to the low-priority third-level layer, thus realizing the flexible application of the grading strategy.

[0064] Embodiment 7: Please refer to Figure 1 , and the storage method of the storage module 6 is specifically as follows: Respectively establish a first-level layer folder, a second-level layer folder, and a third-level layer folder. According to the third comparison result, input the corresponding pictures into the folder to which they belong. Every fixed time, recalculate the classification reference coefficient FLC and generate a new third comparison result. According to the updated third comparison result, for the detected hot spots, migrate the relevant image blocks from the second-level or third-level layer to the first-level layer in advance. For the image blocks with low-priority scores, migrate them from the first-level or second-level layer to the third-level layer.

[0065] In this embodiment: The system can dynamically adjust the distribution of image blocks in folders of different priority layers according to the classification reference coefficient FLC calculated in real time. For the image blocks in the hot spot area, the system will automatically migrate them to the first-level layer to ensure the rapid loading of high-priority data. For the low-priority image blocks, they are migrated to the third-level layer, thus releasing the high-priority storage space. This dynamic adjustment strategy effectively avoids the long-term occupation of high-priority storage space and improves the utilization efficiency of storage resources.

[0066] By recalculating the classification reference coefficient FLC and updating the third comparison result every fixed time, the system can flexibly adjust the priority levels of images according to the real-time changes in access frequency, cache status, and image resolution. This dynamic adaptability enables the storage system to cope with the characteristics of frequent changes in hot spot areas in the geographical mapping scenario and ensures the response speed and data validity of the system.

[0067] This method's detection of hotspots and high-priority migration ensures that image blocks in key areas can be preferentially used in mapping analysis. Adopting a hierarchical storage method with first-level, second-level, and third-level layer folders, high-priority data is preferentially stored in the fast-access layer, while low-priority data is stored in the secondary or low-speed access layer. This structure reduces the system's unified search and loading time for all data, significantly improving data access performance. The system realizes the automated management of the storage locations of image blocks through algorithms, eliminating the need for manual adjustment of priorities. This intelligent storage strategy reduces labor input, improves the efficiency of storage management, and simultaneously reduces the risk of errors caused by manual intervention. By dynamically migrating low-priority image blocks to the third-level layer and releasing high-priority space, the system effectively reduces data redundancy and waste of storage resources. Especially in the case of a large volume of mapping image data, this mechanism can significantly reduce the consumption of hardware resources and extend the service life of the storage system.

[0068] Compared with traditional static storage methods, this dynamic storage method significantly improves the storage efficiency and management flexibility of mapping images. Combining a regular update mechanism with a hierarchical storage structure, the system can efficiently allocate storage resources according to real-time needs, ensuring the priority response ability for hotspots while reducing storage redundancy and hardware costs.

[0069] Data acquisition module (step S1) Region division: Regions are divided into i1, i2, i3...in.

[0070] Data acquisition within each region: Image resolution A: 1000x1000 pixels, 2000x2000 pixels, 3000x3000 pixels Display resolution B: 800x800 pixels, 1200x1200 pixels, 1500x1500 pixels Image scaling weight C: 0.8, 1.2, 1.0 Access frequency D: 10 times / hour, 20 times / hour, 5 times / hour Image block storage value E: 50MB, 100MB, 30MB Time weight value F: 1, 2, 1 Cache capacity G: 4GB, 8GB, 16GB Read latency value H: 0.1 second, 0.05 second, 0.2 second Data processing module (step S2) After preprocessing and dimensionless normalization of the collected data, the data is divided into the following three datasets: The first data set It includes image resolution A, display resolution B, and image scaling weight C.

[0071] Image resolution A: 1000x1000, 2000x2000, 3000x3000 Display resolution B: 800x800, 1200x1200, 1500x1500 Image scaling weight C: 0.8, 1.2, 1.0 The second data set It includes access frequency D, image block storage value E, and time weight value F.

[0072] Access frequency D: 10 times / hour, 20 times / hour, 5 times / hour Image block storage value E: 50MB, 100MB, 30MB Time weight value F: 1, 2, 1 The third data set It includes cache capacity G and read latency value H.

[0073] Cache capacity G: 4GB, 8GB, 16GB Read latency value H: 0.1 second, 0.05 second, 0.2 second Hierarchical optimization module (step S3) Calculation of the discrimination reference coefficient FBC: Image resolution A, display resolution B, and image scaling weight C are used to calculate FBC.

[0074] Formula: FBC = ln(A × B × C)FBC = \ln(A \times B \times C)FBC = ln(A × B × C) Example: FBC = ln(1000 × 800 × 0.8) = 13.097FBC = \ln(1000 \times 800 \times 0.8) = 13.097FBC = ln(1000 × 800 × 0.8) = 13.097 FBC = ln(2000 × 1200 × 1.2) = 14.564FBC = \ln(2000 \times 1200 \times 1.2) = 14.564FBC = ln(2000 × 1200 × 1.2) = 14.564 FBC = ln(3000 × 1500 × 1.0) = 14.927 Threshold Y1 = 13.5 Comparison result: 13.097 < 13.5, the image is a low-resolution layer (DFB).

[0075] 14.564 > 13.5, the image is a high-resolution layer (GFB).

[0076] 14.927 > 13.5, the image is a high-resolution layer (GFB).

[0077] Spatial index module (step S4) Calculation of the priority reference coefficient YXJ: YXJ = D × F × E Example: For i1: YXJ = 10 × 1 × 50 = 500 For i2: YXJ = 20 × 2 × 100 = 4000 For i3: YXJ = 5 × 1 × 30 = 150 Comparison with the second threshold Y2: Threshold Y2 = 1000 Comparison result: 500 < 1000, the image is a secondary layer (DTC).

[0078] 4000 > 1000, the image is a high-priority layer (GTC).

[0079] 150 < 1000, the image is the secondary layer (DTC).

[0080] Classification module (step S5) Calculation of the classification reference coefficient FLC: FLC = G × H Example: For i1: FLC = 4 × 0.1 = 0.4 For i2: FLC = 8 × 0.05 = 0.4 For i3: FLC = 16 × 0.2 = 3.2 Comparison with the third threshold Y3: Threshold Y3 = 2.0 Comparison result: 0.4 < 2.0, the image is the third-level layer (GTC).

[0081] 0.4 < 2.0, the image is the third-level layer (GTC).

[0082] 3.2 > 2.0, the image is the first-level layer (DTC).

[0083] Storage module (step S6) According to the third comparison result, the image will be stored in the first-level, second-level, and third-level layer folders.

[0084] First-level layer (DTC): Contains high-priority images.

[0085] Third-level layer (GTC): Contains low-priority images.

[0086] Every certain period of time (e.g., every hour), recalculate the classification reference coefficient FLC and adjust the priority of the image according to the new calculation result.

[0087] Content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0088] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for efficiently storing surveying and mapping images for a geographic surveying and mapping system, characterized in that: The system comprises a data collection module (1), a data processing module (2), a classification optimization module (3), a spatial index module (4), a classification module (5) and a storage module (6), and the specific steps are as follows: Step S1, performing multi-source data acquisition on the acquired surveying and mapping images through the data acquisition module (1), and inputting the acquired multi-source data into the data processing module (2); Step S2, the data processing module (2) preprocesses and dimensionlessly converts the multi-source data collected by the data collection module (1), and divides the preprocessed data into a first data set, a second data set, and a third data set; Step S3, the hierarchical optimization module (3) generates a resolution reference coefficient FBC by analyzing the data in the first data set, and generates a first comparison result by comparing the resolution reference coefficient FBC with a preset first threshold value Y1, and performs a preliminary analysis on the image based on the first comparison result; Step S4, the spatial index module (4) integrates and analyzes the data in the second data set with the first comparison result to generate a priority reference coefficient YXJ, and compares the priority reference coefficient YXJ with a preset second threshold value Y2 to generate a second comparison result, and determines the image priority based on the second comparison result; Step S5, the classification module (5) integrates and analyzes the first comparison result, the second comparison result and the third data set to generate a classification reference coefficient FLC, and compares the classification reference coefficient FLC with a preset third threshold value Y3 to generate a third comparison result and input the third comparison result into the value storage module (6), and the storage module (6) performs classification and storage according to the third comparison result.

2. The method for efficiently storing surveying and mapping images for a geographic surveying and mapping system according to claim 1, characterized in that: In step S1, the data acquisition module (1) divides the image to be stored into regions, which are respectively recorded as i1, i2, i3, ..., in, and acquires multi-source data in each region, including image resolution A, display resolution B, image scaling weight C, access frequency D, image block storage value E, time weight value F, cache capacity G and read delay value H.

3. The method for efficiently storing surveying and mapping images for a geographic surveying and mapping system according to claim 2, characterized in that: In step S2, the data processing module (2) pre-processes and dimensionlessly converts the image resolution A, the display resolution B, the image scaling weight C, the access frequency D, the image block storage value E, the time weight value F, the cache capacity G and the read delay value H, and divides them into a first data set, a second data set and a third data set respectively; The first data set includes image resolution A, display resolution B, and image scaling weight C; The second data set includes an access frequency D, an image block storage value E, and a time weight value F; The third data set includes a cache capacity G and a read latency value H.

4. The method for efficiently storing surveying and mapping images for a geographic surveying and mapping system according to claim 3, characterized in that: In step S3, the hierarchical optimization module (3) includes an optimization analysis unit (31) and an optimization comparison unit (32); The optimization analysis unit (31) is used to perform integrated analysis on the first data set, thereby calculating and obtaining a resolution reference coefficient FBC; The optimization comparison unit (32) is used to compare with a preset first threshold value Y1, thereby generating a first comparison result, which is specifically as follows: when When , the picture representing the current area is a low-resolution layer, marked as DFB; when , it means that the picture in the current area is a high-resolution layer, marked as GFB.

5. The method for efficiently storing surveying and mapping images for a geographic surveying and mapping system according to claim 4, characterized in that: In step S3, the optimization analysis unit (31) calculates and obtains the resolution reference coefficient FBC by the following formula: ; Where: A is the image resolution, B is the display resolution, C is the image scaling weight, and ln is the natural logarithm function.

6. The method for efficiently storing surveying and mapping images for a geographic surveying and mapping system according to claim 5, characterized in that: In the step S4, the spatial index module (4) comprises an index analysis unit (41) and an index comparison unit (42); The index analysis unit (41) is used to integrate and analyze the data in the second data set and the first comparison result, so as to calculate and obtain the priority reference coefficient YXJ; The index comparison unit (42) compares the priority reference coefficient YXJ with a preset second threshold value Y2, thereby generating a second comparison result, and determines the image priority based on the second comparison result, as follows: when When , it means that the image in the current area is a high priority layer, and the specific value is recorded as GTC; when , the image representing the current area is the secondary layer, and the specific value is recorded as DTC.

7. The method for efficiently storing surveying and mapping images for a geographic surveying and mapping system according to claim 6, characterized in that: In the step S4, the index analysis unit (41) calculates and obtains the priority reference coefficient YXJ by the following formula: ; Where: D is the access frequency, F is the time weight value, E is the image block storage value, and R is the reference function used to fill in the low-resolution layer DFB and the high-resolution layer GFB respectively.

8. The method for efficiently storing surveying and mapping images for a geographic surveying and mapping system according to claim 7, characterized in that: In the step S5, the classification module (5) comprises a classification analysis unit (51) and a classification comparison unit (52); The classification analysis unit (51) is used to integrate and analyze the first comparison result, the second comparison result and the third data set, so as to calculate and obtain a classification reference coefficient FLC; The classification comparison unit (52) is used to compare the classification reference coefficient FLC with a preset third threshold value Y3, thereby generating a third comparison result, which is specifically as follows: when When , the image representing the current area is a third-level layer, and the specific value is recorded as GTC; when When , the image in the current area is a secondary layer, and the specific value is recorded as DTC; when , it means that the picture in the current area is a primary layer, and the specific value is recorded as DTC.

9. The method for efficiently storing surveying and mapping images for a geographic surveying and mapping system according to claim 8, characterized in that: In the step S5, the classification analysis unit (51) calculates and obtains the classification reference coefficient FLC by the following formula; ； Where: G is the cache capacity, H is the read latency value, and P is the reference function that fills in the third-level layer GTC, the second-level layer DTC, and the first-level layer DTC respectively.

10. The method for efficiently storing surveying and mapping images for a geographic surveying and mapping system according to claim 9, characterized in that: The storage method of the storage module (6) is specifically as follows: Establish a first-level layer folder, a second-level layer folder, and a third-level layer folder respectively. According to the third comparison result, input the corresponding pictures into the corresponding folders. Recalculate the classification reference coefficient FLC at fixed intervals and generate a new third comparison result. According to the updated third comparison result, for the detected hot spot area, migrate the relevant image blocks from the second-level or third-level layer to the first-level layer in advance. For the image blocks with low priority scores, migrate them from the first-level or second-level layer to the third-level layer.

Images