Multi-source heterogeneous natural resource data processing method and system

Abstract

The invention relates to a multi-source heterogeneous natural resource data processing method and system. The multi-source heterogeneous natural resource data processing method comprises the following steps: dividing a data coverage area of natural resource data into grids; calculating a basic hash according to the data content of the natural resource data in each grid; analyzing hidden features of the natural resource data in each grid, and calculating an expanded hash according to the hidden features; combining the basic hash and the expanded hash to form a composite hash; and only recording the composite hash. Unified tamper-proof management of data of different sources and structures is achieved, and safety is good.

Description

Technical Field

[0001] The present invention relates to the field of geographic information processing, and particularly to a method and system for processing multi-source heterogeneous natural resource data. Background Art

[0002] In recent years, with the wide application of GIS (Geographic Information System) platforms, remote sensing technologies, and Internet of Things devices, the multi-source heterogeneous characteristics of natural resource data have become increasingly prominent. Natural resource data usually includes multiple modalities, and its sources cover multiple channels such as satellite images, ground sensors, and manual surveys, resulting in diverse data formats, inconsistent spatio-temporal benchmarks, and large scales. For natural resource data, there are relatively high requirements for security and anti-tampering. Traditional data processing methods often require high-cost full-volume data comparison in the face of complex natural resource data scenarios and are difficult to identify data tampering behaviors with strong concealment. Therefore, there is an urgent need for a data processing method that can adapt to the multi-source heterogeneous characteristics and relatively high security of natural resource data to address the deficiencies of the prior art in achieving unified security management for multiple types of data. Summary of the Invention

[0003] The objective of this application is to propose a method and system for processing multi-source heterogeneous natural resource data to improve the security of multi-source heterogeneous natural resource data in terms of anti-tampering.

[0004] The multi-source heterogeneous natural resource data processing method of this application includes:

[0005] Dividing the data coverage area of natural resource data into grids;

[0006] Calculating a basic hash based on the data content of the natural resource data within each of the grids;

[0007] Analyzing the concealment characteristics of the natural resource data within each of the grids and calculating an extended hash based on the concealment characteristics;

[0008] Combining the basic hash and the extended hash to form a composite hash;

[0009] Only recording the composite hash.

[0010] Optionally, the calculating a basic hash based on the data content of the natural resource data within each of the grids includes:

[0011] Calculating sub-basic hashes respectively according to the data content of various data types;

[0012] Concatenating the sub-basic hashes and performing a secondary hash to obtain the basic hash.

[0013] Optionally, the hiding features for analyzing the natural resource data in each of the grids include:

[0014] Analyze the adjacency grid topology relationship types of the natural resource data, where the adjacency grid topology relationship types include adjacent, inclusion, and overlap;

[0015] Convert the adjacency grid topology relationship types into corresponding codes to be used as the hiding features.

[0016] Optionally, calculating the extended hash according to the hiding features includes:

[0017] Calculate sub-extended hashes respectively according to different hiding features;

[0018] After concatenating each of the sub-extended hashes, perform a secondary hash to obtain the extended hash.

[0019] Optionally, after calculating the sub-extended hashes respectively according to different hiding features, it further includes:

[0020] Compare the sub-extended hashes within the same grid for conflict detection;

[0021] When there are contradictions in the sub-extended hashes within a certain grid, mark that grid as a suspicious conflict area.

[0022] Optionally, combining the basic hash and the extended hash to form a composite hash includes:

[0023] Perform an exclusive OR operation to merge consecutive N bits of the basic hash and consecutive N bits of the extended hash, where N is less than the minimum length of the basic hash and the extended hash.

[0024] Optionally, the basic hash and the extended hash have the same length, and N is half of the length of the basic hash and the extended hash.

[0025] Optionally, when performing tampering verification, compare the composite hash;

[0026] When it is found in the comparison of the composite hash that the part of the basic hash that has not been merged with the extended hash has changed, record it as the data content being tampered with;

[0027] When it is found in the comparison of the composite hash that the part of the extended hash that has not been merged with the basic hash has changed, record it as the hiding features being tampered with.

[0028] Optionally, when the data changes, only record the composite hashes in the changed grids to form an incremental hash chain.

[0029] On the other hand, the present application also provides a multi-source heterogeneous natural resource data processing system, including:

[0030] A grid division unit for dividing the data coverage area of the natural resource data into grids;

[0031] A hash calculation unit for calculating a basic hash according to the data content of the natural resource data in each grid; analyzing the concealment characteristics of the natural resource data in each grid, and calculating an extended hash according to the concealment characteristics; combining the basic hash and the extended hash to form a composite hash;

[0032] A recording unit for only recording the composite hash.

[0033] The multi-source heterogeneous natural resource data processing method provided by the present application can calculate a composite hash based on natural resource data of various different structures, so as to maintain a consistent processing logic in the processes of tampering detection, modification traceability, etc. There is no need to process the data contents of various types and sources separately, and the complexity is low. By introducing an extended hash to form a composite hash, the security in the application process of natural resource data is improved. In practical applications, if someone tampers with the data content of the natural resource data itself, the composite hash will change. Since the composite hash formed after tampering with the content is different from the recorded composite hash, it can be prompted that the data has been tampered with. If someone tampers with more concealed information of the natural resource data, for example, modifying other information in the grid without modifying the data content itself may also cause the composite hash to change. It realizes unified anti-tampering management of data from different sources and structures, and has good security. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic flowchart of the basic process of the multi-source heterogeneous natural resource data processing method provided by the embodiment of the present application;

[0035] Figure 2 It is a schematic flowchart of calculating the basic hash provided by the embodiment of the present application;

[0036] Figure 3 It is a schematic flowchart of determining the concealment characteristics provided by the embodiment of the present application;

[0037] Figure 4 It is a schematic flowchart of calculating the extended hash provided by the embodiment of the present application;

[0038] Figure 5 It is a schematic flowchart of detecting a data conflict area based on a sub-extended hash provided by the embodiment of the present application;

[0039] Figure 6 It is a schematic structural diagram of the composite hash provided by the embodiment of the present application;

[0040] Figure 7 This is a schematic structural diagram of a multi-source heterogeneous natural resource data processing system provided by an embodiment of the present application. Specific embodiments

[0041] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0042] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0043] Embodiment 1:

[0044] This embodiment provides a method for processing multi-source heterogeneous natural resource data, which divides the data coverage area of the natural resource data into grids. Calculate a basic hash according to the data content of the natural resource data in each grid. Analyze the hiding characteristics of the natural resource data in each grid, and calculate an extended hash according to the hiding characteristics. Combine the basic hash and the extended hash to form a composite hash, and only record the composite hash. It realizes the unified security management of multi-source heterogeneous natural resource data and can effectively discover some data tampering behaviors with strong concealment.

[0045] Please refer to Figure 1 As shown, the method for processing multi-source heterogeneous natural resource data in this embodiment includes but is not limited to the following steps:

[0046] S101. Divide the data coverage area of the natural resource data into grids;

[0047] In this embodiment, the data coverage area of natural resource data is the geographical area corresponding to each natural resource data. That is, for a series of natural resource data, if it records the situation of natural resources in a certain geographical range, the corresponding data coverage area is the geographical range recorded by the natural resource data. Due to the complexity of multi-source heterogeneous natural resource data, with various data files and databases, some traditional anti-tampering hash calculations generate hash values for the full data of a single data file or a single database (or the eigenvalue calculated from the full data). After detecting tampering, it can only locate the data file or database and cannot locate the specific tampered position. This embodiment can achieve precise tampering location to a specific grid without the need to compare and trace all data.

[0048] The grid can be divided according to a preset rule. For example, the data coverage area can be divided into grids of the same size according to a preset size. In some examples, the divided grids can also be of unequal sizes. For example, multiple different specifications of grids can be divided.

[0049] S102. Calculate a basic hash based on the data content of the natural resource data in each grid;

[0050] It should be clear that the data content of natural resource data refers to the direct recorded content of these natural resource data, such as text, images, etc. Exemplarily, assume that the area of forest land in a certain grid uploaded by a certain first data source is 90 mu, then the corresponding data content is "forest land area 90 mu". For the reporting source, time, etc. of this natural resource data, if not directly recorded in the data, they do not belong to the data content of natural resource data.

[0051] It can be understood that for common data forms of natural resource data, hash values can be generated through preset hash functions. In this embodiment, the hash value calculated based on the data content of natural resource data is called the basic hash. It can be understood that by comparing whether the hash value changes, it can be known whether the data content has been changed, thus achieving the effect of anti-tampering.

[0052] S103. Analyze the hiding features of the natural resource data in each grid and calculate an extended hash based on the hiding features;

[0053] It should be clear that the concealment features of natural resource data are not directly recorded in the natural resource data, nor are they the data content. However, the concealment features of this embodiment should be information related to the corresponding natural resource or its grid, and cannot be irrelevant information. For example, the concealment features can include the association information between grids. As an example, the concealment features can be the adjacent position relationship with other grids, the natural resource density within the grid (obtained by identifying the image data of the natural resource data), etc.

[0054] In this embodiment, the hash value calculated based on the concealment features of natural resource data is called an extended hash. The hash function for calculating the extended hash can be the same as or different from the one for calculating the basic hash, and the lengths of their hash values can also be the same or different.

[0055] S104. Combine the basic hash and the extended hash to form a composite hash;

[0056] In this embodiment, there is no need to perform complex merging on natural resource data from multiple sources. The natural resource data from each source can be separately calculated to obtain a composite hash. For example, the natural resource data provided by the first data source is calculated to obtain a composite hash based on its data content and concealment features, and the natural resource data provided by the second data source in the same grid is also calculated to obtain a composite hash based on its data content and concealment features.

[0057] It should be clear that the combination mentioned in this embodiment refers to simply concatenating the two hash data at the beginning and end or performing basic logical operations (such as exclusive OR, etc.); that is, no further encryption or secondary hashing and other calculations are introduced during the combination process.

[0058] In this embodiment, by combining the basic hash and the extended hash to form a composite hash, the features of the basic hash and the extended hash can be retained in some implementation processes to achieve the positioning of the tampered object.

[0059] S105. Only record the composite hash;

[0060] It should be noted that only recording the composite hash means that intermediate calculation results such as the basic hash and the extended hash are not recorded, and there is no restriction on the storage of source data such as natural resource data. The recording mentioned in this embodiment can be recording to the server of platforms such as blockchain or authentication and evidence storage. After receiving the natural resource data, it can be calculated as a composite hash and uploaded to the server of platforms such as blockchain or authentication and evidence storage for comparison.

[0061] The above multi-source heterogeneous natural resource data processing method of this embodiment can calculate a composite hash based on natural resource data of various different structures, so as to maintain a consistent processing logic in the processes of tampering detection, modification traceability, etc. There is no need to process the data content of various types and sources separately, and the complexity is low. The introduction of an extended hash to form a composite hash improves the security in the application process of natural resource data. In practical applications, if someone tampers with the data content of natural resource data itself, the composite hash will change. Since the composite hash formed after tampering with the content is different from the recorded composite hash, it can be prompted that the data has been tampered with. If someone tampers with more concealed information of natural resource data, for example, modifying other information in the grid without modifying the data content itself may also cause the composite hash to change.

[0062] As a simple example, assume that the data content in a certain natural resource data only records the area and the type of natural resources. If the forest land boundary is tampered with without changing the forest land area, since the data content of this forest land is not directly modified in the natural resource data, the tampering behavior cannot be found by comparing the hash values. In some examples, introducing the adjacent grid topological relationship as a concealed feature, even if the forest land area remains unchanged (the basic hash remains unchanged), but because the forest land boundary changes (for example, the modified boundary invades the adjacent cultivated land), its adjacency relationship with the surrounding grids may change (the extended hash will change). At this time, the comparison of the final composite hash cannot pass, which will reflect that the grid has been tampered with. On the other hand, the extended hash can realize tampering detection independently of the data content itself, and its concealment is high. The specific extended hash generation rule cannot be directly guessed and is not easy to be discovered and avoided by malicious tamperers. It can be seen that this embodiment can accurately locate the tampering situation to the precise position of the grid, and can play a role in preventing and detecting tampering with the data content itself in natural resource data and other more concealed tampering in some implementation processes. Moreover, the introduction of the extended hash increases the final hash complexity, and the probability of hash collision (that is, different inputs form the same hash value) decreases. For example, when two data are similar in content, their concealed features may be different, and the corresponding composite hashes are not easily the same.

[0063] In some implementation manners, the division of the grid can be dynamically determined according to the actual situation. Since natural resource data is usually obtained through various different channels, and the accuracy of natural resource data from each source is different, when processing natural resource data, the mixed processing of high-precision and low-precision data is likely to cause waste of processing resources or data. In this embodiment, the specification of the grid can be determined according to the accuracy of the natural resource data. In practical applications, the accuracy can be determined according to the collection source of the natural resource data. For example, the accuracy of data collected by drones is 0.1m, and the accuracy of satellite data is 10m, etc.

[0064] Based on the accuracy of natural resource data, determine the grid size of the corresponding data coverage area, which can avoid high-precision data being diluted by low-precision networks, and low-precision data will not waste computing power and resources due to excessive grid subdivision.

[0065] As an example, the minimum specification of the grid can also be set to avoid excessive subdivision. Suppose the minimum specification of the grid is set to 0.5m × 0.5m. When the accuracy is higher than 0.5m, the grid is matched to 0.5m × 0.5m. For example, the accuracy of drone collection is 0.1m, the accuracy of satellite data is 10m, and the meteorological sensors are set at intervals of 1km; then the grid within the data coverage area of the natural resource data collected by the drone is automatically matched to 0.5m × 0.5m, the grid within the data coverage area of the satellite data is 10m × 10m, and the grid divided based on the data of the meteorological sensors is 1km × 1km. Of course, if there are multiple sources of data in an area, the grid can be divided according to the highest-precision data among them to ensure that the accuracy of the data is not wasted. Through the above grid division method, larger grids can be formed in areas without high precision, reducing computing power consumption and not diluting high-precision data.

[0066] In some embodiments, as Figure 2 shown, calculating the basic hash based on the data content of the natural resource data in each grid includes:

[0067] S201. Calculate sub-basic hashes respectively according to the data content of various data types;

[0068] It can be understood that the data content in the grid may have various different data types. For example, satellite image data (image raster data), sensor detection data (data with time series), text records (structured text data), etc.

[0069] As an example, assume that there are data contents of three different data types: satellite image data, sensor detection data, and text records. Then calculate the first sub-basic hash based on the data content of the satellite image data; calculate the second sub-basic hash based on the data content of the sensor detection data; calculate the third sub-basic hash based on the data content of the text records.

[0070] When natural resource data has different sources, it can be calculated separately for different sources. For example, the corresponding composite hash is calculated for the natural resource data from the first data source, and the corresponding composite hash is calculated for the natural resource data from the second data source. In the same grid, there may be the composite hashes of the data from the first data source and the composite hashes of the data from the second data source. In some implementation processes, the different composite hashes in the grid can also be compared to verify whether there are data conflicts (data conflicts are not hash conflicts, referring to logical contradictions, semantic contradictions, etc. between different data), etc.

[0071] S202. Concatenate each sub-base hash and perform a second hash to obtain a base hash;

[0072] In this embodiment, the so-called concatenation simply means concatenating the two hashes at the head and tail. As an example, assume that the first sub-base hash is "a3f8b1...c7d2e9", the second sub-base hash is "4e6a80...9f1b3c", and the third sub-base hash is "7d2f9a...b4c8e5", then the concatenated result is:

[0073] "a3f8b1...c7d2e94e6a80...9f1b3c7d2f9a...b4c8e5".

[0074] In this embodiment, the so-called second hash means using the hash value or the new data composed of hash values as input and performing a hash operation again. For example, the data obtained by concatenating the above three sub-base hashes is used as input, and a hash operation is performed again to obtain a base hash. The length of the base hash can be the same as that of a single sub-base hash.

[0075] In some implementation manners, as Figure 3 shown, analyzing the hiding features of the natural resource data in each grid includes the steps of:

[0076] S301. Analyze the topological relationship types of adjacent grids of the natural resource data;

[0077] In this embodiment, the topological relationship types of adjacent grids include adjacent, inclusion, and overlap. In other implementation manners, more topological relationships or other association relationships can also be introduced.

[0078] S302. Convert the topological relationship types of adjacent grids into corresponding codes as hiding features;

[0079] Automated generation of hidden features can be achieved through coding, which requires less computational effort and generates less data. The introduction of the adjacency grid topological relationship can achieve tampering protection for scenarios such as illegally translating the boundary of a plot while keeping the area unchanged. In natural resource data, there may be spatial reference differences in natural resource data from different sources, such as inconsistent coordinate systems. The adjacency grid topological relationship realizes the associated description between data through the relative relationship of uniformly divided grids, without problems such as complex conversions and processing caused by inconsistent references such as coordinate systems.

[0080] In some embodiments, analyzing the hidden features of the natural resource data within each grid includes steps such as analyzing the distribution density of ground objects. As an example, for image data, the pixel ratio occupied by a certain natural resource in the raster data is statistically determined as the density of the natural resource. This density result is used as the hidden feature. If the boundary range of a plot is distorted, although the area may not change, it may cause a change in the density of the natural resources within the range, thereby determining whether it has been tampered with through the distribution density of ground objects.

[0081] In this embodiment, the generation elements of the hidden features can be at least one of the adjacency grid topological relationship, ground object density, natural resource plot shape features, and gradient change of sensor detection values.

[0082] When there is more than one hidden feature, the calculation logic of the extended hash is similar to that of the basic hash. See [[ID=])Figure 4 , calculating the extended hash based on the hidden features includes:

[0083] S401. Calculate sub-extended hashes respectively according to different hidden features;

[0084] S402. Concatenate the sub-extended hashes and perform a second hash to obtain the extended hash;

[0085] As an example, assume there are two different hidden features: the adjacency grid topological relationship and ground object density. Then calculate the first sub-extended hash based on the adjacency grid topological relationship; calculate the second sub-extended hash based on the ground object density. The result of the second hash after concatenating the first sub-extended hash and the second sub-extended hash is used as the final extended hash.

[0086] In some embodiments, see Figure 5 , after calculating the sub-extended hashes respectively according to different hidden features, it further includes:

[0087] S501. Compare the sub-extended hashes within the same grid for conflict detection;

[0088] S502. When there is a contradiction in the sub-extended hashes within a certain grid, mark this grid as a suspicious conflict area;

[0089] Direct comparison between multi-source data is relatively complex and usually requires manually setting up a rule library and processing to achieve the comparison. In this embodiment, after the hidden features are analyzed, the hidden features in the grid should not conflict with each other. Therefore, based on the comparison of sub-expanded hashes, the direct comparison between multi-source data can be avoided, and the analyzed hidden features can be used to detect whether there are conflicts in data from different sources.

[0090] Exemplarily, regardless of the coordinate systems and data specifications of different data sources, the adjacency relationships between grids should be determined. Suppose there is an overlap between the "forest land" data of the first data source and the "cultivated land" data of the second data source in grid G-123; then the sub-expanded hash of the "forest land" data in grid G-122 may include "bordering the forest land in G-123", and the sub-expanded hash of the "cultivated land" data in grid G-122 may include "bordering the cultivated land in G-123". For the same type of hidden feature (adjacent grid topological relationship), the sub-expanded hashes of the two are significantly different. In grid G-123, there are contradictions in the sub-expanded hashes of data from different sources. In the data of the first data source, G-123 is defined as "forest land", but in the data of the second data source, G-123 is defined as "cultivated land". It can be seen that through the sub-expanded hash, the grid with conflicts can be simply and quickly located without using complex conflict detection means such as a manual rule library.

[0091] In some embodiments, combining the basic hash and the expanded hash to form a composite hash includes: performing an exclusive OR operation on consecutive N bits of the basic hash and consecutive N bits of the expanded hash for combination, where N is less than the minimum length of the basic hash and the expanded hash.

[0092] The lengths of the basic hash and the expanded hash can be the same or different, and the lengths can be set according to conditions in practical applications. For example, both can be 256 bits. When combining, the order of the basic hash and the expanded hash can be arbitrary, and the data that does not perform the exclusive OR operation on them is retained and can be directly concatenated with the data of the exclusive OR operation in order. For example, the basic hash is in the front and the expanded hash is in the back. At this time, the last N bits of the basic hash will be combined with the first N bits of the expanded hash through the exclusive OR operation. As Figure 6As shown, the composite hash in this embodiment may have three segments. The first segment S1 is the low (L1-N) bit hash value of the base hash, the second segment S2 is the part where the base hash and the extended hash are combined, and the third segment S3 is the high (L2-N) bit hash value of the extended hash, where L1 is the length of the base hash and L2 is the length of the extended hash. It can be seen that the composite hash can retain part of the content of the base hash and the extended hash, but its length is reduced by N bits compared to directly storing the base hash and the composite hash. In practical applications, it can also be configured with the extended hash first and the base hash second. At this time, the first N bits of the base hash will be combined with the last N bits of the extended hash through exclusive OR operation, and the achieved effect is the same.

[0093] In some embodiments, the composite hash is calculated separately for the natural resource data from different sources in the same grid. Although the natural resource data from these different sources are usually different in data content, in some cases, the extended hash may be the same. For example, when the extended hash only represents the adjacency grid topology relationship, the association relationships between the natural resource data in the same grid and other surrounding grids are all consistent. It is possible to determine whether there are obvious conflicts by comparing whether the extended hash parts in each composite hash within the grid are the same. Taking the Figure 6 composite hash format described above as an example, whether the third segment S3 of each composite hash corresponding to the same grid is the same may reflect whether there are data conflicts.

[0094] During the anti-tampering verification process, whether the data content or the hidden feature of the natural resource data is tampered with, the composite hash will be changed. Suppose the data content of the natural resource data is tampered with, then the first segment S1 and the second segment S2 of the composite hash will definitely change. Therefore, when only the comparison of the first segment S1 and the second segment S2 of the composite hash fails, it can be determined that the data content of a certain grid has been tampered with. If the first segment S1 of the composite hash does not change and the data comparison of the second segment S2 and the third segment S3 fails, then it can be determined that the hidden feature of a certain grid has been tampered with. If the comparison of each segment of the composite hash fails, it may be that both the data content and the hidden feature have been tampered with, or the tampering of one of them has caused the other to change synchronously.

[0095] During the management process of natural resource data, the quantity of natural resource data is huge. The above method of only recording the composite hash can reduce the storage pressure, and the composite hash itself also reduces the length while retaining the ability to locate the tampered target, further reducing the information that needs to be stored.

[0096] On the other hand, the generation of the above-mentioned composite hash depends, at least in part, on different bit combinations of the basic hash and the extended hash. For example, the 100th bit of the basic hash and the 28th bit of the extended hash jointly affect the 128th bit (100 + 28) of the composite hash. This makes it so that even local changes in the basic hash or the extended hash will spread to multiple positions of the composite hash through misaligned superposition, reducing the collision probability. The exclusive OR operation has a good balance in information retention, computational efficiency, and conflict suppression. If the same bit of the basic hash and the extended hash both change, the exclusive OR result may remain unchanged. However, through the above-mentioned head-to-tail superposition method, the same bit of the basic hash and the extended hash is misaligned, and the impact of this local conflict will also be greatly diluted. Therefore, the above-described method for forming the composite hash can have good application effects. However, in some embodiments, other logical operations can also be tried to combine the basic hash and the extended hash.

[0097] In practical applications, in order to better locate whether the tampered object is the data content or the hidden feature, the first segment S1 and the third segment S3 of the composite hash should have a certain length to ensure that the corresponding parts form different hash values when the data is tampered with. Exemplarily, the length L1 of the first segment S1 can be set to be not less than one-third of the length of the basic hash, and the length of the third segment S3 is not less than one-third of the length L2 of the extended hash.

[0098] In some embodiments, the basic hash and the extended hash have the same length, and the value of N is half of their length. For example, if the lengths of both the basic hash and the extended hash are 256 bits, then the value of N is 128. At this time, the high 128 bits of the basic hash will be exclusive OR combined with the low 128 bits of the extended hash. The length of the first segment S1 in the formed composite hash is 128 bits, the length of the second segment S2 is 128 bits, and the length of the third segment S3 is 128 bits. Compared with directly storing the basic hash and the extended hash, the composite hash at this time saves 128 bits of length, that is, one-fourth of the space.

[0099] When performing tampering verification, the composite hash is compared. When it is found that the part of the composite hash where the basic hash is not combined with the extended hash has changed, it is recorded that the data content has been tampered with. When it is found that the part of the composite hash where the extended hash is not combined with the basic hash has changed, it is recorded that the hidden feature has been tampered with. Of course, the above two results can be recorded simultaneously, that is, if the entire composite hash has changed, it is recorded that the data content has been tampered with and it is also recorded that the hidden feature has been tampered with.

[0100] In some embodiments, when data changes, only the composite hash in the changed grid is recorded to form an incremental hash chain. The incremental hash chain can clearly show the derivation paths of multiple versions and can be traced back through the hash chain when problems occur, helping to resolve conflicts. In this embodiment, version recording is achieved through the incremental hash chain, realizing unified version management of multi-source heterogeneous natural resource data, avoiding cross-tracing between data from different sources; and being able to record changes in individual grids, making it clearer when tracing back and displaying version changes and being able to locate specific grids.

[0101] Exemplarily, assume the initial version hash chain V1 = Hash(entire grid). When updating data for the first time, assume only grid G-122 is updated, then the hash chain for the first update V2 = Hash(V1 + Hash(new value of G-122)); assume grid G-125 is updated for the second time, then the hash chain for the second update V3 = Hash(V2 + Hash(new value of G-125)). The chained hash storage method enables each new version's hash chain to contain the hash value of the previous version, eliminating the need to store the hash chain completely each time an update occurs and saving storage space.

[0102] This embodiment also provides a multi-source heterogeneous natural resource data processing system 100, as shown in Figure 7 It seems there is a small error in your original text where is missing the closing square bracket in the English translation part. I've corrected it as best as possible. If this was not an error, please let me know. shown, including:

[0103] A grid division unit 101 for dividing the data coverage area of natural resource data into grids.

[0104] A hash calculation unit 102 for calculating a basic hash based on the data content of the natural resource data in each grid; analyzing the concealment characteristics of the natural resource data in each grid and calculating an extended hash based on the concealment characteristics; combining the basic hash and the extended hash to form a composite hash.

[0105] A recording unit 103 for only recording the composite hash.

[0106] For the specific steps that the modules in the multi-source heterogeneous natural resource data processing system 100 of this embodiment can execute, reference can also be made to the description of the multi-source heterogeneous natural resource data processing method provided above in this embodiment, and this embodiment will not be elaborated herein.

[0107] In addition, although exemplary embodiments have been described herein, their scope includes any and all embodiments based on the present disclosure with equivalent, modified, omitted, combined (e.g., solutions that cross various embodiments), adapted, or changed embodiments. It is not limited to the examples described in this specification or during the implementation of this application, and the examples will be interpreted as non-exclusive.

[0108] The foregoing description is intended to be illustrative and not restrictive. For example, the above examples (or one or more aspects thereof) may be used in combination with each other. For example, those of ordinary skill in the art may use other embodiments when reading the above description.

[0109] The foregoing are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modification, equivalent replacement, improvement, 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 processing multi-source heterogeneous natural resource data, characterized in that, Including: Dividing the data coverage area of natural resource data into grids; Calculating a basic hash according to the data content of the natural resource data in each of the grids; Analyzing the concealment characteristics of the natural resource data in each of the grids, and calculating an extended hash according to the concealment characteristics; Combining the basic hash and the extended hash to form a composite hash; Only recording the composite hash.

2. The multi-source heterogeneous natural resource data processing method according to claim 1, wherein, The calculating a basic hash according to the data content of the natural resource data in each of the grids includes: Calculating sub-basic hashes respectively according to the data content of various data types; Concatenating the sub-basic hashes and then performing a second hash to obtain the basic hash.

3. The multi-source heterogeneous natural resource data processing method according to claim 1, characterized in that, The analyzing the concealment characteristics of the natural resource data in each of the grids includes: Analyzing the topological relationship types of adjacent grids of the natural resource data, where the topological relationship types of adjacent grids include adjacent, inclusion, and overlap; Converting the topological relationship types of adjacent grids into corresponding codes as the concealment characteristics.

4. The multi-source heterogeneous natural resource data processing method according to claim 1, wherein The calculating an extended hash according to the concealment characteristics includes: Calculating sub-extended hashes respectively according to different concealment characteristics; Concatenating the sub-extended hashes and then performing a second hash to obtain the extended hash.

5. The multi-source heterogeneous natural resource data processing method according to claim 4, wherein After calculating the sub-extended hashes respectively according to different concealment characteristics, it further includes: Comparing the sub-extended hashes in the same grid for conflict detection; When there are contradictions in the sub-extended hashes in a certain grid, marking this grid as a suspicious conflict area.

6. The multi-source heterogeneous natural resource data processing method according to claim 1, wherein The combining the basic hash and the extended hash to form a composite hash includes: Performing an exclusive OR operation to merge consecutive N bits of the basic hash and consecutive N bits of the extended hash, where N is less than the minimum length of the basic hash and the extended hash.

7. The multi-source heterogeneous natural resource data processing method according to claim 6, characterized in that, The basic hash and the extended hash have the same length, and N is half of the length of the basic hash and the extended hash.

8. The method for processing multi-source heterogeneous natural resource data according to claim 6, wherein When performing tampering verification, comparing the composite hash; When it is compared that in the composite hash, the part of the basic hash that is not merged with the extended hash has changed, recording that the data content has been tampered with; When it is compared that in the composite hash, the part of the extended hash that is not merged with the basic hash has changed, recording that the concealment characteristics have been tampered with.

9. The multi-source heterogeneous natural resource data processing method according to claim 1, characterized in that When the data changes, only recording the composite hash in the changed grid to form an incremental hash chain.

10. A multi-source heterogeneous natural resource data processing system, characterized in that, Including: A grid division unit for dividing the data coverage area of natural resource data into grids; A hash calculation unit for calculating a basic hash according to the data content of the natural resource data in each of the grids; analyzing the concealment characteristics of the natural resource data in each of the grids, and calculating an extended hash according to the concealment characteristics; combining the basic hash and the extended hash to form a composite hash; A recording unit for only recording the composite hash.

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