A method, device and medium for data compression and fusion of digital earth space

Abstract

The present application discloses a data compression and fusion method, device and medium for digital earth space, and relates to the field of digital earth space technology. The method includes: extracting features, classifying and encoding bit vectors stored in the cloud to obtain an index directory set; reading each data unit in a multi-source heterogeneous data stream, and searching for the longest matching string in the index directory set; when the longest matching string is found, outputting the code in the index directory set to obtain a compressed code stream; reading the code stream, and searching for corresponding geospatial data, meteorological data and interaction records in the index directory set; according to the timestamp, fusing the geospatial data, meteorological data and interaction records to obtain multi-source fused data. The present application realizes the construction and use of the index directory set simultaneously during compression and decompression through the above method, without the need to know all data patterns in advance, and effectively reducing the storage space occupied in the system.

Description

Technical Field

[0001] The present application relates to the field of digital earth space technology, and in particular to a method, device and medium for data compression and fusion of digital earth space. Background Art

[0002] Compressed transmission refers to the process of compressing files, data or media content through a series of algorithms and technical means, and decompressing and restoring the original information during transmission. With the popularization of the Internet and the rapid development of information technology, people's requirements for network transmission speed and efficiency are getting higher and higher. However, the resources of Internet bandwidth and storage devices are still limited. In order to make more efficient use of limited resources, data needs to be compressed to reduce transmission and storage costs.

[0003] Digital Earth spatial data includes various Geographic Information System (GIS) data, remote sensing data, three-dimensional terrain models, etc. These data usually have the characteristics of large data volume, high similarity and redundancy in continuous areas of space, and rich data dimensions. In the database system, the data dictionary is a set of metadata used to describe and manage database system objects (such as tables, views, indexes, etc.). However, with the increase in the number of objects in the database system and the expansion of the data dictionary size, the traditional data dictionary storage method will take up a lot of disk space, especially it cannot better handle the compression of digital Earth spatial data.

[0004] Through the above analysis, the problems and defects of the prior art are as follows:

[0005] In the existing technology, the multi-source heterogeneous data for the digital earth are inconsistent and the data volume is huge. It takes up a lot of space during compression and transmission, wastes computing resources, and is difficult to support complex applications. Summary of the invention

[0006] The embodiments of the present application provide a method, device and medium for data compression and fusion of digital earth space, which can solve the problems in the prior art of inconsistency of multi-source heterogeneous data for digital earth, huge data volume, large space occupied during compression and transmission, waste of computing resources, and difficulty in supporting complex applications.

[0007] In a first aspect, an embodiment of the present application provides a data compression and fusion method for a digital earth space, the method comprising: extracting features, classifying, and encoding bit vectors stored in a cloud to obtain an index directory set, wherein the bit vectors include geospatial data, meteorological data, and interaction records between living things and non-living things, and the geospatial data include coordinates, elevation, and image data; reading each data unit in a multi-source heterogeneous data stream, and searching for the longest matching string in the index directory set, wherein the multi-source heterogeneous data stream includes geospatial data, meteorological data, and interaction records; when the longest matching string is found, outputting the code in the index directory set to obtain a compressed code stream to perform online compression on the data stream; reading the code stream, and searching for corresponding geospatial data, meteorological data, and interaction records in the index directory set to perform online decompression on the code stream; and fusing the geospatial data, meteorological data, and interaction records according to timestamps to obtain multi-source fused data.

[0008] In one implementation of the present application, after reading the data stream and searching for the longest matching string in the index directory set, the method further includes: if the longest matching string is not found, adding the current data unit to the index directory set to perform online dynamic compression on the data stream.

[0009] In one implementation of the present application, feature extraction, classification, and encoding are performed on bit vectors stored in the cloud to obtain an index directory set, which specifically includes: preprocessing the bit vectors and extracting the type features of the preprocessed bit vectors; dividing the preprocessed bit vectors according to the type features to obtain the number of each type of bit vectors and the total number of types; extracting the structural features of each type of bit vectors; dividing the bit vectors according to the structural features to obtain the number of each structure of the bit vector and the total number of structures.

[0010] In one implementation of the present application, after dividing the bit vector according to the component structure and obtaining the number of each component structure of the bit vector and the total number of structures, the method also includes: obtaining an initial index directory set according to the type, wherein the total number of types is the number of directories in the initial index directory set; sorting the bit vectors of each type according to the number, and encoding the directories to obtain a directory number code; combining the total number of types and the type characteristics, establishing a mapping relationship between the type and the directory number code to obtain a primary directory.

[0011] In one implementation of the present application, after obtaining a first-level directory by combining the total number of categories and category characteristics to establish a mapping relationship between categories and directory number codes, the method further includes: sorting the bit vectors of each component structure according to quantity; establishing a mapping relationship between the structure and the directory number codes in the initial index directory set by combining the total number of structures and the structure characteristics to obtain a second-level directory; generating encoded data for the bit vectors under the corresponding categories and component structures to obtain an index directory set of the bit vectors.

[0012] In one implementation of the present application, after the remote sensing image data and the meteorological data are fused according to the timestamp, the method also includes: performing surface target recognition on the multi-source fusion data to extract the map and surface targets of the image data; aligning the surface targets according to the geographic coordinates, and using the polygon merging method to splice the surface targets and the map to obtain a continuous image.

[0013] In one implementation of the present application, after aligning the surface targets according to the geographic coordinates and splicing the surface targets and the map sheets using the polygon merging method to obtain a continuous image map, the method also includes: constructing a basic model of the digital earth; creating a digital earth ID of multi-source fusion data and continuous image maps through digital avatar coding, the digital earth ID including a location column and a user identification column; inputting the digital earth ID into the basic model to obtain the digital earth space, and performing a visual display.

[0014] In one implementation of the present application, when the longest matching string is not found, the current data unit is added to the index directory set to perform online dynamic compression on the data stream. The method also includes: based on the access frequency of the multi-source heterogeneous data stream, regularly deleting the index directory set that is lower than the first preset frequency; and storing the index directory set that is higher than the second preset frequency in the cache.

[0015] In a second aspect, an embodiment of the present application also provides a data compression and fusion device for a digital earth space, the device comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: extract features, classify, and encode bit vectors stored in the cloud to obtain an index directory set, wherein the bit vectors include geospatial data, meteorological data, and interaction records between living things and non-living things, and the geospatial data include coordinates, elevation, and image data; read each data unit in a multi-source heterogeneous data stream, and search for the longest matching string in the index directory set, wherein the multi-source heterogeneous data stream includes geospatial data, meteorological data, and interaction records; when the longest matching string is found, output the code in the index directory set to obtain a compressed code stream to perform online compression on the data stream; read the code stream, and search for the corresponding geospatial data, meteorological data, and interaction records in the index directory set to perform online decompression on the code stream; according to the timestamp, fuse the geospatial data, meteorological data, and interaction records to obtain multi-source fused data.

[0016] In a third aspect, an embodiment of the present application also provides a non-volatile computer storage medium for data compression and fusion of a digital earth space, storing computer executable instructions, wherein the computer executable instructions are configured to: extract features, classify, and encode bit vectors stored in the cloud to obtain an index directory set, wherein the bit vectors include geospatial data, meteorological data, and interaction records between living and non-living things, and the geospatial data include coordinates, elevation, and image data; read each data unit in a multi-source heterogeneous data stream, and search for the longest matching string in the index directory set, wherein the multi-source heterogeneous data stream includes geospatial data, meteorological data, and interaction records; when the longest matching string is found, output the code in the index directory set to obtain a compressed code stream to perform online compression on the data stream; read the code stream, and search for corresponding geospatial data, meteorological data, and interaction records in the index directory set to perform online decompression on the code stream; according to the timestamp, fuse the geospatial data, meteorological data, and interaction records to obtain multi-source fused data.

[0017] The embodiments of the present application provide a method, device and medium for data compression and fusion of a digital earth space. During the compression and decompression process, an index directory set is simultaneously constructed and used, so that when the digital earth space is established, it is possible to adapt to changes in the data stream without knowing all possible data modes in advance, which can effectively reduce the storage space occupied in the system, improve system performance and efficiency, reduce hardware costs and maintenance difficulties, and provide better storage management and query performance under limited resources. The real-time compression operation of building a dictionary at the sending end and reproducing the dictionary at the receiving end provides a good idea for the subsequent effective implementation of building a digital earth space while inputting data. The fused and spliced ​​data sets are usually stored in a standardized format, which is easy to be read and processed by various geographic information systems and analysis software, and is convenient for subsequent data analysis and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 A flowchart of a method for data compression and fusion of digital earth space provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the internal structure of a data compression and fusion device for digital earth space provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0022] The embodiments of the present application provide a method, device and medium for data compression and fusion of digital earth space, which solves the problems in the prior art of inconsistency of multi-source heterogeneous data for digital earth, huge data volume, large space occupied during compression and transmission, waste of computing resources, and difficulty in supporting complex applications.

[0023] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0024] Figure 1 A flow chart of a method for data compression and fusion of digital earth space provided in an embodiment of the present application. Figure 1As shown, a method for data compression and fusion of digital earth space provided in an embodiment of the present application specifically includes the following steps:

[0025] Step 10: extracting features, classifying, and encoding the bit vectors stored in the cloud to obtain an index directory set, wherein the bit vectors include geospatial data, meteorological data, and interaction records between living and non-living things, and the geospatial data include coordinates, elevation, and image data;

[0026] In this step, all bit vectors currently stored in the cloud are traversed. The bit vectors may also include terrain features, and the geographic coordinates may also include longitude and latitude. The interaction records between living things and non-living things may be sanitation workers planting trees.

[0027] As an optional embodiment, feature extraction, classification, and encoding are performed on the bit vector stored in the cloud to obtain an index directory set, which may specifically include:

[0028] Step 101: preprocessing the bit vector and extracting the type feature of the preprocessed bit vector;

[0029] In this step, the bit vectors can be properly sorted, such as Z-order curve sorting, to improve spatial similarity and increase compression efficiency. Large-scale data can also be processed in blocks through clustering algorithms such as K-means or DBSCAN, and the total number of types can be evaluated using methods such as the elbow method or silhouette coefficient. The traversal operation for preprocessing the data source is specifically to read the data one by one according to the specified unit size, and the specified unit size can be 8 bits.

[0030] For example, the cloud stores a data set of 1,000 bit vectors, which are generated by different types of geographic data collection devices, that is, historical data collected and saved before. Each bit vector is 32 bits long, of which the first 8 bits can represent geographic coordinates (binary encoding of longitude and latitude), the next 4 bits can represent elevation information, the next 16 bits are features extracted from image data (for example, the hash value of the image or the binary representation of certain image features), and the last 4 bits represent meteorological data (for example, binary encoding of parameters such as temperature and humidity). The specific implementation can be determined according to actual conditions.

[0031] Download bit vector datasets from the cloud. For geographic coordinates, they can be converted from binary codes to longitude and latitude expressed in decimal. For elevation information, they can be converted from binary codes to heights expressed in decimal. For image data features, since they are expressed in binary, they can be used directly as feature vectors or further extracted for more specific image features. For meteorological data, they can be converted from binary codes to meteorological parameters expressed in decimal.

[0032] For example, this 32-bit bit vector can be split into the following parts: Geographic coordinates (first 8 bits): Binary representation: 00011010 00101011 (i.e., the first 8 bits and the last 8 bits of hexadecimal 0x1A2B represent longitude and latitude respectively) Converted to decimal: Longitude: 0001 1010 = 0x1A = 26, Latitude: 0010 1011 = 0x2B = 43, assuming that each decimal number represents a specific latitude and longitude unit, this is just an example, and more complex conversion is required in practice. Elevation information (next 4 bits): Binary representation: 00000001 (i.e., the last 4 bits of hexadecimal 0x0001) Converted to decimal: 0000 0001 = 0x01 = 1. Image data features (next 16 bits): Binary representation: 00010010 00110000 01010110 01111000 (i.e. hexadecimal 0x02035678). Since this is a binary representation of the image feature, we can directly use it as a feature vector, for example, extract certain bits of the hash value as features. Meteorological data (last 4 bits): Binary representation: 11011110 00010010 (take the last 4 bits of hexadecimal 0xDEF01234, but note that we only need the last 4 bits, i.e. 0010, because the previous bits are part of the image data), but in fact, we should get the meteorological data from 0x00001234 (i.e. the 4 bits after the image data): Binary representation: 00000000 00010010, converted to decimal: 0000 0000 0001 0010 = 0x02 = 2.

[0033] In this particular example, each bit vector actually already contains all of these types of information, so a more accurate classification task might be to identify which types of information in each bit vector are salient or dominant.

[0034] To simplify the problem, we can assume that each bit vector mainly represents one type of information and classify it based on the main features of that information. For example, if the longitude and latitude features of a bit vector are very obvious, for example, their values ​​are within a reasonable geographical range and have a higher weight or importance than other features, then we can classify it as a geographical coordinate.

[0035] Step 102: Divide the preprocessed bit vectors according to their category characteristics to obtain the number of each category of bit vectors and the total number of categories;

[0036] In this step, bit vectors of the same kind are classified into the same category. To determine whether two bit vectors belong to the same category, the Hamming distance between them can be calculated. If the Hamming distance is less than a certain threshold, the two bit vectors can be considered to be similar enough in features and therefore can be classified into the same category. Alternatively, specific business rules can be used to determine which bit vectors belong to the same category, splitting large-scale data into smaller blocks and determining the total number of final categories.

[0037] Step 103: extracting the structural features of each type of bit vector; Step 104: dividing the bit vector according to the structural features to obtain the number of each structure of the bit vector and the total number of structures.

[0038] In this step, the preprocessed data blocks are traversed twice, the same category data is subdivided according to different composition structures, and the total number of different structure bit vector data structures and the total number of bit vectors of each type in the same category are determined; for bit vectors from the same geographical area, such as mountains and oceans, but with different data structures: bit distribution, after the initial classification of the bit vectors, it is necessary to further subdivide the bit vectors of each category according to their specific composition structures. Through this subdivision, different types of bit vector data can be obtained. After the subdivision is completed, the rule statistics method or clustering algorithm K-means, DBSCAN method can be used to determine the total number of bit vectors of each category and structure, and count the number of bit vectors of each type and structure.

[0039] For example, the accuracy of longitude and latitude, that is, the number of bits of binary encoding, is selected as the subdivision standard. Assuming that longitude and latitude are represented by 8-bit binary numbers respectively, the bit vector can be subdivided into high-precision and low-precision categories according to the distribution of these 8 bits. The bit vector of the mountain may pay more attention to the changes in elevation and slope, while the bit vector of the ocean may pay more attention to the water depth and the direction of the ocean current; the geographical area to which the bit vector belongs is identified according to the values ​​of longitude, latitude, elevation, and possible other information (such as topographic maps or ocean maps). The binary encoding of the positive elevation value in the bit vector of the mountain is more complex: elevation (4-bit binary): 0100 converted to decimal: 4; while the binary encoding of the water depth (negative elevation value) in the bit vector of the ocean is more complex: elevation (4-bit binary): 1011, converted to decimal: -5. According to the analysis results, the bit vector of the geographic coordinate category is further subdivided into two categories: mountain and ocean, and the number of bit vectors of these two categories is counted respectively. It is assumed that the result is: there are 250 mountain bit vectors and 250 ocean bit vectors.

[0040] As an optional embodiment, after dividing the bit vector according to the component structures to obtain the number of each component structure of the bit vector and the total number of structures, the method may further include:

[0041] Step 105: obtaining an initial index directory set according to the categories, wherein the total number of categories is the number of directories in the initial index directory set;

[0042] In this step, an empty initial index directory set is created. According to the key-value mapping requirements, a 4-digit sequence code can be used as the key, and the total number of different types of bit vector data types is used as the number of directories to create the index directory set.

[0043] Step 106: sorting the bit vectors of each type according to the quantity, and encoding the directory to obtain the directory number code;

[0044] In this step, the total number of structural bit vectors in the same category is finally sorted from most to least, and an index is assigned to each category.

[0045] Step 107: Based on the total number of categories and the category characteristics, a mapping relationship between the category and the directory number code is established to obtain a first-level directory.

[0046] In this step, a mapping relationship between the number of categories and the index directory set directory number encoding is established based on the total number of final categories and their characteristics. The corresponding 3-bit binary number encoding is generated based on the total number of final categories. The total number of final categories corresponds to the encoding in ascending order.

[0047] As an optional embodiment, after the mapping relationship between the category and the directory number code is established by combining the total number of categories and the category characteristics and obtaining the first-level directory, the method may further include:

[0048] Step 108: sorting the bit vectors of each constituent structure according to quantity;

[0049] Step 109: Combine the total number of structures and the structural features to establish a mapping relationship between the structure and the directory number code in the initial index directory set to obtain a secondary directory;

[0050] In this step, the categories are sorted from most to least according to the total number of bit vectors of each type. Combined with the total number of bit vectors of each type and their characteristics, a mapping relationship between the type and the index directory set code is established in the order of arrangement to form a secondary directory. The directory interval is determined by the total number of bit vectors of each type. The secondary directory further subdivides the different types of bit vectors within each category according to their number to form a more fine-grained classification structure. The corresponding 4-bit binary number code is generated according to the total number of bit vectors of each type. The total number of bit vectors of each type corresponds to the code in order from small to large. Through this directory structure, large-scale geospatial data can be effectively managed and indexed.

[0051] Step 110: Generate encoding data from the bit vectors of the corresponding type and structure to obtain an index directory set of the bit vectors.

[0052] In this step, based on the mapping relationship, the bit vectors of the corresponding types and structures are replaced with encoded data to complete the index directory set of the data (complete the compression of the data); the process of mapping the high-dimensional data bit vector to a low-dimensional space encoded data can be implemented using a hash function to map the input high-dimensional vector to a specific encoding index for easy retrieval and storage.

[0053] Step 20: Read each data unit in the multi-source heterogeneous data stream and search for the longest matching string in the index directory set, wherein the multi-source heterogeneous data stream includes geospatial data, meteorological data, and interaction records;

[0054] In this step, after completing the index directory set as mentioned above, start reading the real-time data stream, or read according to the preset time window, for example, once every 5 minutes or every 24 hours. The multi-source heterogeneous data comes from different data collection devices. First, preprocess the multi-source heterogeneous data stream, and then start compressing the data stream.

[0055] Step 30: When the longest matching string is found, the code in the index directory set is output to obtain a compressed code stream to perform online compression on the data stream;

[0056] As an optional embodiment, after reading the data stream and searching for the longest matching string in the index directory set, the method may further include: Step 30': if the longest matching string is not found, adding the current data unit to the index directory set to perform online dynamic compression on the data stream.

[0057] Step 40: read the encoded stream, search for corresponding geospatial data, meteorological data and interaction records in the index directory set, and decompress the encoded stream online;

[0058] Step 50: According to the timestamp, the geospatial data, meteorological data and interaction records are fused to obtain multi-source fused data.

[0059] As an optional embodiment, after the remote sensing image data and the meteorological data are fused according to the timestamp, the method may further include: Step 60: performing surface target recognition on the multi-source fusion data to extract the map and surface targets of the image data; Step 70: aligning the surface targets according to the geographic coordinates, and using the polygon merging method to splice the surface targets and the map to obtain a continuous image.

[0060] In this step, the image stitching ensures that remote sensing images taken from different sources and at different times can be seamlessly stitched together to form a continuous image map covering a wide area. The surface target stitching integrates data with geospatial relevance: buildings, roads, rivers, etc., to form a comprehensive geospatial information layer. The fused and stitched datasets are usually stored in standardized formats, such as GeoTIFF, Shapefile, etc. These formats are easy to be read and processed by various geographic information systems and analysis software, which facilitates subsequent data analysis and application.

[0061] As an optional embodiment, after aligning the surface targets according to the geographic coordinates and splicing the surface targets and the map sheets using the polygon merging method to obtain a continuous image map, the method may further include:

[0062] Step 80: Construct a basic model of the digital earth;

[0063] Step 90: Creating a digital earth ID of multi-source fusion data and continuous image maps through digital avatar coding, where the digital earth ID includes a location column and a user identification column;

[0064] In this step, through the hierarchical coding structure, the three levels of geography, industry and user identification can be encoded. For example, the first two bytes are the country code, which complies with the ISO 3166-1 standard; the next byte is the industry code, which complies with the industry code system standard, including industry, agriculture, transportation, medical care, finance, smart cities, data fields, etc.; the next byte is the user identification code. Each user will obtain a unique user code in sequence when applying, which is convenient for the system to manage and identify the user's identity. The coding rules at each level can be flexibly expanded and adjusted as needed to meet the needs of different fields and application scenarios.

[0065] Step 100: Input the digital earth ID into the basic model to obtain the digital earth space and perform visual display.

[0066] In this way, the final digital earth space can show sanitation workers planting trees on a sunny day in real time. In places where no data is obtained, a basic geometric model is temporarily used, and the ID of each area can be clearly known.

[0067] In summary, we acquire massive multi-source heterogeneous data, pre-process and compress the data, so that the multi-source data can be transmitted conveniently and quickly. After transmission to the destination, the data is decompressed and fused, and a digital earth ID is created for the fused data. This realizes the functions of clarifying the framework boundary of the digital earth and uniquely identifying users, allowing users to enter specific virtual scenes and interact with them.

[0068] As an optional embodiment, when the longest matching string is not found, the current data unit is added to the index directory set to perform online dynamic compression on the data stream. The method may also include: based on the access frequency of the multi-source heterogeneous data stream, regularly deleting the index directory set that is lower than a first preset frequency; and storing the index directory set that is higher than a second preset frequency in the cache.

[0069] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a data compression and fusion device for digital earth space, whose structure is as follows: Figure 2 shown.

[0070] Figure 2 This is a schematic diagram of the internal structure of a digital earth space data compression and fusion device provided in an embodiment of the present application. Figure 2 As shown, the device includes:

[0071] at least one processor 201;

[0072] and, a memory 202 communicatively connected to the at least one processor;

[0073] The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 so that the at least one processor 201 can: extract features, classify, and encode bit vectors stored in the cloud to obtain an index directory set, wherein the bit vectors include geospatial data, meteorological data, and interaction records between living things and non-living things, and the geospatial data include coordinates, elevation, and image data; read each data unit in a multi-source heterogeneous data stream, and search for the longest matching string in the index directory set, wherein the multi-source heterogeneous data stream includes geospatial data, meteorological data, and interaction records; when the longest matching string is found, output the code in the index directory set to obtain a compressed code stream to perform online compression on the data stream; read the code stream, and search for the corresponding geospatial data, meteorological data, and interaction records in the index directory set to perform online decompression on the code stream; according to the timestamp, fuse the geospatial data, meteorological data, and interaction records to obtain multi-source fused data.

[0074] Some embodiments of the present application provide corresponding Figure 1A non-volatile computer storage medium for data compression and fusion of digital earth space, storing computer executable instructions, wherein the computer executable instructions are set to: extract features, classify, and encode bit vectors stored in the cloud to obtain an index directory set, wherein the bit vectors include geospatial data, meteorological data, and interaction records between living things and non-living things, and the geospatial data include coordinates, elevation, and image data; read each data unit in a multi-source heterogeneous data stream, and search for the longest matching string in the index directory set, wherein the multi-source heterogeneous data stream includes geospatial data, meteorological data, and interaction records; when the longest matching string is found, output the code in the index directory set to obtain a compressed code stream to perform online compression on the data stream; read the code stream, and search for corresponding geospatial data, meteorological data, and interaction records in the index directory set to perform online decompression on the code stream; according to the timestamp, fuse the geospatial data, meteorological data, and interaction records to obtain multi-source fused data.

[0075] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0076] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0077] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0078] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0081] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0082] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0083] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0084] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0085] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for data compression and fusion in digital earth space, characterized in that: The method comprises: Extracting features, classifying, and encoding bit vectors stored in the cloud to obtain an index directory set, wherein the bit vectors include geospatial data, meteorological data, and interaction records between living and non-living things, and the geospatial data include coordinates, elevation, and image data; Reading each data unit in a multi-source heterogeneous data stream, and searching for the longest matching string in the index directory set, wherein the multi-source heterogeneous data stream includes geospatial data, meteorological data, and interaction records; When the longest matching string is found, the code in the index directory set is output to obtain a compressed code stream, so as to perform online compression on the data stream; Reading the encoded stream, searching for corresponding geospatial data, meteorological data and interaction records in the index directory set, so as to decompress the encoded stream online; According to the timestamp, the geospatial data, meteorological data and interaction records are fused to obtain multi-source fused data; Performing surface target recognition on the multi-source fusion data to extract the image data and surface targets; According to the coordinates, the surface targets are aligned, and the surface targets and the map sheets are spliced ​​by a polygon merging method to obtain a continuous image map; Construct the basic model of digital earth; Creating a digital earth ID of the multi-source fusion data and the continuous image map by digital avatar coding, wherein the digital earth ID includes a position column and a user identification column; The digital earth ID is input into the basic model to obtain the digital earth space and perform visual display.

2. The method for data compression and fusion of digital earth space according to claim 1, characterized in that: After reading the data stream and searching for the longest matching string in the index directory set, the method further includes: If the longest matching string is not found, the current data unit is added to the index directory set to perform online dynamic compression on the data stream.

3. The method for data compression and fusion of digital earth space according to claim 1, characterized in that: Feature extraction, classification, and encoding are performed on the bit vectors stored in the cloud to obtain an index directory set, including: Preprocessing the bit vector and extracting the type feature of the preprocessed bit vector; Dividing the preprocessed bit vectors according to the category characteristics to obtain the number of each category of the bit vectors and the total number of categories; Extracting structural features of each type of bit vector; The bit vector is divided according to the structural features to obtain the number of each structure of the bit vector and the total number of structures.

4. The method for data compression and fusion of digital earth space according to claim 3, characterized in that: After dividing the bit vector according to the component structures to obtain the number of each component structure of the bit vector and the total number of structures, the method further includes: Obtaining an initial index directory set according to the categories, wherein the total number of categories is the number of directories in the initial index directory set; sorting the bit vectors of each type according to the quantity, and encoding the directory to obtain a directory number code; In combination with the total number of categories and the category characteristics, a mapping relationship between the categories and the directory number codes is established to obtain a first-level directory.

5. The method for data compression and fusion of digital earth space according to claim 4, characterized in that: After establishing a mapping relationship between the categories and the directory number codes in combination with the total number of categories and the category characteristics to obtain a primary directory, the method further includes: sorting the bit vectors of each constituent structure according to the quantity; Combine the total number of structures and the structural features, establish a mapping relationship between the structure and the directory number code in the initial index directory set, and obtain a secondary directory; The bit vectors of the corresponding types and composition structures are used to generate coded data to obtain an index directory set of the bit vectors.

6. The method for data compression and fusion of digital earth space according to claim 2, characterized in that: If the longest matching string is not found, after adding the current data unit to the index directory set to perform online dynamic compression on the data stream, the method further includes: Based on the access frequency of the multi-source heterogeneous data stream, regularly deleting the index directory set that is lower than a first preset frequency; The index directory set having a frequency higher than a second preset frequency is stored in the cache.

7. A data compression and fusion device for digital earth space, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Execute any process of the digital earth space data compression and fusion method as described in claims 1-6.

8. A non-volatile computer storage medium for data compression and fusion of digital earth space, storing computer executable instructions, characterized in that: The computer executable instructions are configured to execute any process of the digital earth space data compression and fusion method as described in claims 1-6.

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