Geographic surveying and mapping data security storage system based on block chain encryption technology

Abstract

The invention discloses a geographic surveying and mapping data security storage system based on a block chain encryption technology, and belongs to the technical field of data storage encryption. The geographic surveying and mapping data storage method solves the problems that an existing geographic surveying and mapping data storage mode has many potential safety hazards, including that data are easy to tamper and leak, and data security is difficult to effectively guarantee, encrypts the geographic surveying and mapping data through a hash value function, and uses the non-tampering characteristic of the block chain to improve the security of the geographic surveying and mapping data. The confidentiality of the geographic surveying and mapping data in the storage and transmission process is ensured, tampering is prevented, and the hash value is of a fixed length, so that the storage and transmission efficiency of the geographic surveying and mapping data is higher; the geographic surveying and mapping data and the hash value thereof are encrypted in combination with an asymmetric key encryption algorithm, so that only a user holding a private key can decrypt the geographic surveying and mapping data, and the geographic surveying and mapping data are prevented from being leaked.

Description

Technical Field

[0001] This invention relates to the field of data storage encryption technology, specifically a secure storage system for geographic surveying and mapping data using blockchain encryption technology. Background Technology

[0002] Geographic mapping data refers to geographic information about the Earth's surface and its surrounding space obtained through surveying and mapping techniques. This data has important applications in many fields, including urban planning, land management, environmental monitoring, transportation planning, and national defense and security. With the widespread application of Geographic Information Systems (GIS), the importance of geographic mapping data is becoming increasingly prominent. Geographic mapping data not only includes a large amount of geographic information but may also involve sensitive content such as social security and trade secrets.

[0003] Traditional geographic mapping data storage methods have many security risks, such as the ease with which data can be tampered with or leaked, and the difficulty in effectively ensuring data security when multiple users share and access the data.

[0004] Therefore, to address the shortcomings of existing solutions, we propose a secure storage system for geographic mapping data using blockchain encryption technology. Summary of the Invention

[0005] The purpose of this invention is to provide a secure storage system for geographic surveying and mapping data using blockchain encryption technology. This system encrypts geographic surveying and mapping data through a hash function, leveraging the immutability of blockchain to ensure the confidentiality of the data during storage and transmission, preventing tampering. Furthermore, the fixed length of the hash value enhances the efficiency of data storage and transmission. By combining this system with an asymmetric key encryption algorithm to encrypt both the geographic surveying and mapping data and its hash value, only users holding the private key can decrypt the data, preventing data leakage and addressing the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A secure storage system for geographic mapping data using blockchain encryption technology, the system comprising: a secure storage unit and a verification access unit, wherein the secure storage unit includes a primary encryption module and a secondary encryption module;

[0008] The primary encryption module is configured to perform hash processing on geographic mapping data based on an encryption hash algorithm to generate a hash value of fixed length.

[0009] The secondary encryption module is configured to combine hash values ​​and geographic mapping data, and group the combined geographic mapping data according to the principle of the same number of bits. Each group of geographic mapping data is a encryption unit. Each encryption unit is encrypted using an asymmetric key to form encrypted data and store it in the blockchain.

[0010] The verification access unit is configured to obtain encrypted data and the corresponding hash value from the blockchain, decrypt the asymmetric key to obtain the original geographic mapping data, and verify whether the hash value has changed to determine whether the geographic mapping data has been tampered with during storage and transmission.

[0011] Furthermore, the secondary encryption module includes:

[0012] The feature extraction module is configured to extract geographic location coordinates and altitude from the collected initial mapping data, and perform unit unification, coordinate system transformation and accuracy adjustment to obtain standardized mapping data;

[0013] The format setting module is configured to determine the format and byte size of each data group, and to determine the group length based on the format, byte size, and requirements of the asymmetric key algorithm for each data group.

[0014] Furthermore, the format setting module is used to perform the following steps:

[0015] Retrieve standardized surveying data corresponding to geographic surveying data;

[0016] Retrieve the fixed length of the hash value corresponding to the encryption process performed by the primary encryption module;

[0017] Extract the mapping data precision corresponding to the geographic mapping data;

[0018] The accuracy of the surveying data is standardized so that its value is projected into the interval (1-2), and the accuracy of the standardized surveying data is obtained.

[0019] The fixed length corresponding to the hash value is multiplied by the precision of the standardized surveying data to obtain the product value between the fixed length corresponding to the hash value and the precision of the standardized surveying data.

[0020] Retrieve the spatial dispersion of the geographic mapping data;

[0021] The spatial dispersion is compared with a preset spatial dispersion threshold;

[0022] When the spatial dispersion does not exceed the preset spatial dispersion threshold, the product of the fixed length corresponding to the hash value and the set multiple between the accuracy of the standardized mapping data is used as the group length.

[0023] When the spatial dispersion exceeds the preset spatial dispersion threshold, the grouping length is set by combining the product of the fixed length corresponding to the hash value and the accuracy of the standardized mapping data with the spatial dispersion.

[0024] Furthermore, when the spatial dispersion exceeds a preset spatial dispersion threshold, the grouping length is set by combining the product of the fixed length corresponding to the hash value and the accuracy of the standardized mapping data with the spatial dispersion, including:

[0025] When the spatial dispersion exceeds the preset spatial dispersion threshold, the product value between the fixed length corresponding to the hash value and the accuracy of the standardized mapping data and the spatial dispersion are retrieved.

[0026] Extract the geographic survey data and scan the geographic survey data to obtain the data structure type contained in the geographic survey data;

[0027] The data structure types include vector data structure, raster data structure, grid data structure and 3D point cloud data structure;

[0028] Retrieve a table from the database showing the correspondence between data structure types and data structure factors; wherein the value range of the data structure factors is 1-2;

[0029] According to the data structure type and data structure factor correspondence table, obtain the data structure factor corresponding to the data structure type contained in the current geographic mapping data;

[0030] The average value of the data structure factor is obtained based on all data structure types and their corresponding data structure factors contained in the geographic mapping data.

[0031] The grouping length is set by combining the average value of the data structure factors with the fixed length corresponding to the hash value and the product value of the accuracy of the standardized surveying data and the spatial dispersion.

[0032] Furthermore, the secondary encryption module also includes:

[0033] The data grouping module is configured to group geographic mapping data according to the set grouping length, so that each group of data is an independent encrypted unit.

[0034] The grouping verification module is configured to read each group of data sequentially from each encryption unit and determine whether each group of data in each encryption unit is complete; if the data is incomplete, the grouping strategy is adjusted, including:

[0035] If the current group is full, create a new group;

[0036] If the amount of data in the last encryption unit is insufficient, padding data is used to complete the block to a fixed length.

[0037] Furthermore, the secondary encryption module also includes:

[0038] The data encryption module is configured to generate asymmetric key pairs based on asymmetric keys, the asymmetric key pairs including a public key and a private key; the geographic mapping data and its hash value are encrypted using the recipient's public key to form encrypted data;

[0039] The data storage module is configured to store encrypted data in the blockchain, with each block including the hash value of the current data and the hash value of the previous block.

[0040] Furthermore, the secondary encryption module also includes:

[0041] The data optimization module is configured to obtain the total data volume of the geographic surveying data before storing it, select a data compression algorithm to compress the geographic surveying data based on the total data volume, and decompress it after decryption.

[0042] The blockchain interaction module is configured to manage the interaction between various blockchains based on the blockchain interface, including data storage, querying, and verification.

[0043] Further, the verification access unit includes:

[0044] The primary decryption module is configured to decrypt encrypted data using the recipient's private key in the asymmetric key to obtain the original geographic mapping data.

[0045] The secondary decryption module is configured to recalculate the hash value of the original geographic mapping data and compare the recalculated hash value with the hash value stored in the blockchain to determine whether the geographic mapping data has been tampered with during storage and transmission. If the two match, it means that the geographic mapping data has not been tampered with during storage and transmission; if they do not match, it means that the geographic mapping data has been tampered with.

[0046] Furthermore, the verification access unit further includes:

[0047] The tamper alarm module is configured to automatically trigger an alarm and notify the administrator when the secondary decryption module detects a hash value inconsistency.

[0048] The logging module is configured to log access to and operations on geographic mapping data and store the operation logs in the blockchain.

[0049] Furthermore, the system also includes:

[0050] The data collection unit is configured to collect geographic surveying data based on high-precision surveying equipment and to preprocess the geographic surveying data.

[0051] The data backup unit is configured to periodically back up geographic mapping data to multiple nodes based on the distributed storage characteristics of blockchain.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] 1. In this invention, geographic surveying data is encrypted using a hash function. By leveraging the immutability of blockchain, the confidentiality of geographic surveying data during storage and transmission is ensured, preventing tampering. Furthermore, the hash value has a fixed length, resulting in higher efficiency in the storage and transmission of geographic surveying data. Combined with an asymmetric key encryption algorithm, geographic surveying data and its hash value are encrypted, ensuring that only users holding the private key can decrypt the geographic surveying data, thus preventing data leakage.

[0054] 2. In this invention, by extracting key information from geographic surveying data and determining the grouping length based on multiple factors, the geographic surveying data is grouped according to the principle of equal number of bits, making each group of data an independent encryption unit. This not only optimizes storage and transmission efficiency and reduces the complexity of encryption operations, but also allows for dynamic adjustment of the grouping strategy based on the amount of data, ensuring the integrity of the data in each encryption unit. This enables the system to flexibly handle geographic surveying data of different sizes, ensuring the integrity and encryption efficiency of the geographic surveying data. Attached Figure Description

[0055] Figure 1 This is a diagram illustrating the components of a secure storage system for geographic surveying and mapping data based on the blockchain encryption technology of this invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] To address the numerous security vulnerabilities inherent in traditional geographic mapping data storage methods, such as data susceptibility to tampering and leakage, and the difficulty in effectively ensuring data security during multi-user sharing and access, please refer to [link to relevant documentation]. Figure 1 This embodiment provides the following technical solution:

[0058] A secure storage system for geographic surveying and mapping data using blockchain encryption technology includes: a data collection unit, a data backup unit, a secure storage unit, and a verification and access unit.

[0059] The data collection unit is configured to collect geographic surveying data using high-precision surveying equipment, such as Global Positioning System (GPS), Geographic Information System (GIS), LiDAR, or total station, to ensure the high accuracy and reliability of the geographic surveying data. The collected geographic surveying data includes geographic coordinates (longitude, latitude, and altitude), topography, and landform features. The unit also performs preprocessing on the geographic surveying data, including removing noise and outliers to ensure accuracy and consistency.

[0060] The data backup unit is configured to periodically back up geographic mapping data to multiple nodes based on the distributed storage characteristics of blockchain. Each node stores a copy of the geographic mapping data, ensuring rapid recovery in the event of data loss or damage, thereby improving the high availability and reliability of the geographic mapping data.

[0061] The secure storage unit includes a primary encryption module and a secondary encryption module.

[0062] The basic encryption module is configured to perform hash processing on geographic mapping data based on a cryptographic hash algorithm to generate a fixed-length hash value; for example, SHA-256 is selected to perform hash calculation on the preprocessed geographic mapping data to generate a fixed-length hash value with a length of 256 bits (32 bytes).

[0063] The secondary encryption module is configured to combine hash values ​​and geographic mapping data, and group the combined geographic mapping data according to the principle of equal bit length. Each group of geographic mapping data serves as an encryption unit, and each encryption unit is encrypted using an asymmetric key to form encrypted data, which is then stored in the blockchain. The secondary encryption module includes:

[0064] The feature extraction module is configured to extract geographic location coordinates and altitude from the collected initial surveying data, and perform unit unification, coordinate system transformation, and accuracy adjustment to obtain standardized surveying data. For example, it converts geographic surveying data into a unified format, such as the WGS-84 coordinate system, for easier subsequent processing and storage; it converts geographic surveying data from different coordinate systems into a standard coordinate system, such as WGS-84 or CGCS2000; and it adjusts the accuracy of the geographic surveying data according to actual needs to ensure that the accuracy of the geographic surveying data meets the application requirements.

[0065] The format setting module is configured to determine the format and byte size of each data group. For example, for coordinate points, each encryption unit includes longitude, latitude, and altitude, in the format "longitude, latitude, altitude". If the longitude, latitude, and altitude of each coordinate point are represented by double-precision floating-point numbers, i.e., each floating-point number is 8 bytes, then the size of each data unit is 24 bytes. Based on the format and byte size of each data group, as well as the requirements of the asymmetric key algorithm, the block length is determined. For example, if the maximum data length allowed by the asymmetric key algorithm is 256 bytes, then each block can include 10 coordinate points, i.e., 24 bytes × 10 = 240 bytes.

[0066] Alternatively, the format setting module can set the group length in the following way, by performing the following steps:

[0067] Retrieve standardized surveying data corresponding to geographic surveying data;

[0068] Retrieve the fixed length of the hash value corresponding to the encryption process performed by the primary encryption module;

[0069] Extract the mapping data precision corresponding to the geographic mapping data;

[0070] The accuracy of the surveying data is standardized so that its value is projected into the interval (1-2), and the accuracy of the standardized surveying data is obtained.

[0071] The fixed length corresponding to the hash value is multiplied by the precision of the standardized surveying data to obtain the product value between the fixed length corresponding to the hash value and the precision of the standardized surveying data.

[0072] Retrieve the spatial dispersion of the geographic mapping data;

[0073] The spatial dispersion is compared with a preset spatial dispersion threshold;

[0074] When the spatial dispersion does not exceed a preset spatial dispersion threshold, the group length is determined by multiplying the fixed length corresponding to the hash value by a set multiple between the standardized mapping data precision and the fixed length corresponding to the hash value. The group length is rounded up, and the set multiple ranges from 0.1 to 0.8. For example, if the fixed length corresponding to the hash value is 256, the standardized mapping data precision is 1.5, and the set multiple is 0.1, then the group length ranges to 39 bytes.

[0075] When the spatial dispersion exceeds the preset spatial dispersion threshold, the grouping length is set by combining the product of the fixed length corresponding to the hash value and the accuracy of the standardized mapping data with the spatial dispersion.

[0076] The technical effect of the above solution is as follows: First, retrieve standardized geographic mapping data, a fixed length of primary encrypted hash value, and mapping data precision; calculate the product of the fixed length of hash value and mapping data precision; then retrieve the spatial dispersion and compare it with a threshold. If the threshold is not exceeded, the product value is directly used as the group length; if it is exceeded, the group length is set by combining the product value and the spatial dispersion.

[0077] The basic grouping length is determined by multiplying the fixed length of the hash value by the precision of the surveying data. This links the grouping length to the precision requirements of the surveying data itself, ensuring that the grouping adapts to the precision requirements of the geographic surveying data. This allows the grouped data to accurately match the precision characteristics of the surveying data during encryption and other processing, avoiding the destruction of precision-related information due to improper grouping. Spatial dispersion is introduced and a threshold is set. When the spatial dispersion exceeds the threshold, the grouping length is adjusted accordingly. Grouping can be dynamically optimized based on the spatial distribution dispersion of the geographic surveying data, adapting to the characteristics of complex spatial distribution and high dispersion, improving the adaptability of grouping to spatial characteristics, and ensuring the rationality of data spatial association in subsequent processing (such as encryption and transmission). Based on the comparison results of spatial dispersion and the threshold, the grouping length is set according to different cases, realizing a dynamic adjustment mechanism for the grouping length. This retains the basic adaptation based on precision while handling cases of abnormal spatial dispersion, improving the flexibility and adaptability of the grouping strategy, and making the grouping length more suitable for the diverse characteristics of geographic surveying data. By using a fixed-length initial encryption hash value as a basis for calculating the block length, the block length is linked to the encryption process. This allows for adaptation to subsequent encryption operations during the block formation stage, enhancing the synergy between the block formation and encryption processes. This helps improve encryption effectiveness and efficiency, ensuring the security and integrity of geographic mapping data after encryption. A reasonable block length setting can optimize subsequent processing flows for geographic mapping data (such as storage and transmission). Block formation adapted to data characteristics can reduce data redundancy, improve processing efficiency, avoid performance bottlenecks caused by unreasonable block formation, and ensure the high efficiency and stability of the entire geographic mapping data processing workflow.

[0078] Specifically, when the spatial dispersion exceeds a preset spatial dispersion threshold, the grouping length is set by combining the product of the fixed length corresponding to the hash value and the accuracy of the standardized mapping data with the spatial dispersion, including:

[0079] When the spatial dispersion exceeds the preset spatial dispersion threshold, the product value between the fixed length corresponding to the hash value and the accuracy of the standardized mapping data and the spatial dispersion are retrieved.

[0080] Extract the geographic survey data and scan the geographic survey data to obtain the data structure type contained in the geographic survey data;

[0081] The data structure types include vector data structure, raster data structure, grid data structure and 3D point cloud data structure;

[0082] Retrieve a table from the database showing the correspondence between data structure types and data structure factors; wherein the value range of the data structure factors is 1-2;

[0083] According to the data structure type and data structure factor correspondence table, obtain the data structure factor corresponding to the data structure type contained in the current geographic mapping data;

[0084] The average value of the data structure factor is obtained based on all data structure types and their corresponding data structure factors contained in the geographic mapping data.

[0085] The grouping length is set by combining the average value of the data structure factors with the fixed length corresponding to the hash value and the product value of the accuracy of the standardized surveying data and the spatial dispersion.

[0086] The group length is obtained using the following formula:

[0087]

[0088] Where L represents the grouping length; Q represents the product of the fixed length corresponding to the hash value and the accuracy of the standardized surveying data; σ represents the spatial dispersion of the normalized geographic surveying data; and G represents the average value of the data structure factor. For example: the fixed length corresponding to the hash value is 256, the accuracy of the standardized surveying data is 1.5, the average value of the data structure factor for surveying data accuracy is 1.2, and the spatial dispersion of the geographic surveying data is 0.8; then, the above grouping length is obtained using the following formula:

[0089]

[0090] That is, the current group length is 32 bytes;

[0091] Specifically, By using logarithmic transformation, the nonlinear growth of spatial dispersion is converted into a gradual numerical adjustment, avoiding the abnormal shrinkage of the grouping length L caused by the sharp expansion of the denominator when the spatial dispersion is too large. At the same time, the logarithmic function meets the practical requirement that "the effect of spatial dispersion on grouping length diminishes marginally" (when the dispersion is extremely high, the grouping length does not need to shrink indefinitely). Using 8 as the base (to adapt to the binary characteristics of computer data storage such as bytes / bits), a normalization constraint term related to spatial dispersion is constructed to make the grouping length adapt to the spatial distribution characteristics of geographic mapping data, and to avoid unreasonable grouping caused by abnormal spatial dispersion. The basic grouping length, adapted to accuracy and spatial characteristics, is obtained by dividing the "accuracy-encryption base value" by the "spatial dispersion constraint value". The basic grouping length is calibrated by incorporating the data structure characteristics of geographic mapping data (differences in grouping requirements for vector / raster / point cloud structures, etc.). Different data structures (e.g., compact vector structures, loose point cloud structures) adjust the grouping length via G to ensure that the grouping adapts to the data's storage and processing structure. Existing technologies often ignore the spatial dispersion differences in geographic mapping data or simply linearly correlate dispersion with grouping length, easily leading to "groups that are too short when dispersion is extremely high (damaging data integrity) or groups that are too long when dispersion is extremely low (redundancy and waste)". This formula, through logarithmic transformation, makes the impact of spatial dispersion on grouping length "marginally decreasing", accurately matching the "reasonable constraint requirements of spatial dispersion on grouping" in real-world scenarios, avoiding extreme value interference, and improving the grouping adaptability of spatially complex data. Traditional grouping strategies often consider only accuracy or data volume, ignoring the impact of spatial dispersion and data structure. This formula synergistically integrates Q to correlate accuracy and encryption requirements, ensuring data security and detail preservation; log2(σ+1) adapts to spatial distribution characteristics, balancing the impact of dispersion on grouping; G incorporates differences in data structures, matching the storage and processing needs of different structures. Compared to single-dimensional decision-making, it more comprehensively covers the multi-characteristic needs of geographic mapping data, improving the rationality of grouping. Using 8 as the denominator (matching the byte unit of computer binary storage) makes the grouping length naturally adapt to the underlying logic of data storage and transmission. Compared to arbitrary radix settings, it reduces compatibility conflicts between grouping and storage systems, improves the smoothness of geographic mapping data processing in computer systems, and reduces performance losses caused by incompatibility between grouping and storage units. In extreme scenarios with abnormally high / low spatial dispersion (such as locally dense point clouds or large-scale uniform raster), logarithmic transformation and radix constraints can effectively suppress abnormal fluctuations in grouping length: when dispersion is extremely high, the growth of the logarithmic term slows down, preventing excessively short groups; when dispersion is extremely low, the logarithmic term approaches 1, ensuring that the grouping length does not excessively expand. Compared to traditional linear models, it significantly improves the robustness of grouping in extreme scenarios and reduces the risk of data processing failures.

[0092] The technical effect of the above solution is as follows: When the spatial dispersion exceeds the threshold, the product value (the result of multiplying the fixed length of the hash value by the precision of the surveying data) and the spatial dispersion are first retrieved. The geographic surveying data is scanned to obtain the data structure type. According to the corresponding table, the data structure factors of each type are taken and the average value is calculated. Finally, the product value, spatial dispersion, and average value of the data structure factors are substituted into the formula to calculate the grouping length. By identifying the data structure type of geographic surveying data (vector, raster, etc.) and matching the corresponding data structure factors, the grouping length calculation is integrated into the characteristics of the data structure. Different data structures (such as compact vector data structures and dispersed point cloud data structures) have different grouping requirements. Factor intervention can accurately adapt to these requirements, ensuring the compatibility of data processing (encryption, storage) and data structure after grouping, and improving data processing efficiency and accuracy. When the spatial dispersion exceeds the threshold, it is included in the grouping length formula calculation, and the grouping length is dynamically adjusted in combination with the product value. For geographic mapping data with significant spatial distribution differences (such as point clouds of dense urban buildings and raster of open terrain), the system can flexibly optimize grouping based on the degree of spatial dispersion, avoiding unreasonable grouping due to differences in spatial characteristics, ensuring that the spatial correlation of data is not destroyed after grouping, and improving the adaptability to complex spatial distribution data. It integrates multiple factors to calculate grouping length, including product values ​​(correlation hash, precision), spatial dispersion (correlation spatial distribution), and average data structure factors (correlation data structure), breaking the limitations of relying on a single or few factors to determine grouping. Multi-factor collaboration considers multiple dimensions such as precision requirements, spatial characteristics, and structural characteristics, making the grouping length more closely match the comprehensive attributes of geographic mapping data, improving the scientific and comprehensive nature of the grouping strategy. Reasonable and suitable data grouping lays the foundation for subsequent processes such as encryption, transmission, and storage of geographic mapping data. Grouping adapted to data structure and spatial characteristics can reduce data redundancy, lower processing complexity, avoid data errors or performance bottlenecks caused by improper grouping, ensure the efficiency, stability, and accuracy of geographic mapping data processing throughout its entire lifecycle, and help fully realize the value of geographic mapping data. The group length calculation is deeply correlated with the initial encryption hash length, mapping accuracy, spatial dispersion, and data structure, enabling deep collaboration between the grouping process and processes such as encryption and data characteristic analysis. Adapting to subsequent processes during the grouping stage reduces adaptation costs and conflicts between stages, improves the synergy and integrity of the entire geographic surveying and mapping data processing workflow, and optimizes overall system performance.

[0093] The data grouping module is configured to initialize a grouping list based on a set grouping length, group standardized geographic mapping data, ensure that each group contains a complete data unit, and make each group of data an independent encrypted unit.

[0094] The grouping verification module is configured to read each group of data sequentially from each encrypted unit and determine whether each group of data in each encrypted unit is complete. Each data unit should contain complete geographical coordinates and altitude information to ensure the integrity of the data unit. If the data is incomplete, the grouping strategy is adjusted, including:

[0095] If the current group is full, a new group is created; if the data in the last encrypted unit is insufficient, padding is used, such as zero-padding, to fill the group to a fixed length; repeat the above steps until all data units are grouped and pass the verification; by using a reasonable group length, storage and transmission efficiency is optimized, the complexity of encryption operations is reduced, the integrity of data in each encrypted unit is ensured, and information loss due to data segmentation is avoided; and each encrypted unit is encrypted independently, which improves the security of geographic mapping data, effectively enhancing the security and integrity of geographic mapping data, and ensuring the security and availability of geographic mapping data throughout its entire lifecycle.

[0096] The beneficial effects achieved by the above are as follows: By extracting key information from geographic surveying data, determining the grouping length based on multiple factors, and grouping the geographic surveying data according to the principle of equal number of bits, each group of data can be treated as an independent encrypted unit. This not only optimizes storage and transmission efficiency and reduces the complexity of encryption operations, but also allows for dynamic adjustment of the grouping strategy based on the amount of data, ensuring the integrity of the data in each encrypted unit. This enables the system to flexibly handle geographic surveying data of different sizes, ensuring the integrity and encryption efficiency of the geographic surveying data.

[0097] The data encryption module is configured to generate asymmetric key pairs based on asymmetric keys. It uses a secure random number generator to generate asymmetric key pairs including a public key and a private key. The public key is stored in the system and used for encrypting data; it can be publicly disclosed. The private key is securely stored at the recipient's location and used for decrypting data; it must be kept confidential and only held by the recipient. The recipient's public key is used to encrypt the geographic mapping data and its hash value, forming encrypted data. Specifically, the geographic mapping data and its hash value are combined to form the data to be encrypted. The RSA asymmetric encryption algorithm is selected, and the recipient's public key is used to encrypt the data to be encrypted, generating encrypted data. Through the asymmetric encryption algorithm, the confidentiality of geographic mapping data during storage and transmission is ensured, guaranteeing that only the recipient holding the private key can decrypt the data, preventing the leakage of geographic mapping data, effectively protecting the security and integrity of geographic mapping data, and ensuring the security and reliability of geographic mapping data throughout its entire lifecycle.

[0098] The data storage module is configured to combine encrypted geographic mapping data and its hash value to form encrypted data to be stored, ensuring that the data format conforms to the requirements of the blockchain; create new blocks, obtain the hash value of the currently stored encrypted data and the hash value of the previous block from the blockchain, store the encrypted data to be stored, the current hash value, and the hash value of the previous block in the created block, and store the created block in the blockchain, ensuring that the block data is correctly written to the blockchain. Each block includes the hash value of the current data and the hash value of the previous block, forming an immutable chain structure. Each block includes: a block header: containing the hash value of the current block, the hash value of the previous block, timestamps, and other metadata; and a block body: containing the encrypted geographic mapping data and its hash value. During the storage process, the integrity of the data is verified through the hash value, ensuring that the hash value of each block is correctly linked with the hash value of the previous block, forming a complete blockchain.

[0099] The data optimization module is configured to obtain the total data volume of the geographic surveying data before storing it, to assess the data size and determine whether compression is necessary. Based on the total data volume, it selects a data compression algorithm to compress the geographic surveying data and stores the compressed data in the blockchain, reducing data storage space and improving transmission efficiency. After decompression, it decompresses the compressed data to restore the original data, ensuring consistency between the decompressed and original geographic surveying data. Through the data compression algorithm, the storage space of the geographic surveying data is significantly reduced, lowering storage costs. Furthermore, the compressed data consumes less bandwidth during transmission, improving transmission efficiency and overall system operating efficiency.

[0100] The blockchain interaction module is configured to manage the interaction between various blockchains based on the blockchain interface, including data storage, query and verification, enabling data uploading and offloading operations, and ensuring data security and immutability.

[0101] The beneficial effects achieved by the above are as follows: Encrypting geographic mapping data using a hash function, combined with the immutability of blockchain, ensures the confidentiality of geographic mapping data during storage and transmission, preventing tampering; leveraging the distributed storage characteristics of blockchain ensures high availability and reliability of the data. Even if some node data is lost or damaged, other nodes can still provide complete data copies; and the fixed length of the hash value makes the storage and transmission efficiency of geographic mapping data higher; encrypting geographic mapping data and its hash value using an asymmetric key encryption algorithm ensures that only users holding the private key can decrypt the geographic mapping data, preventing data leakage.

[0102] The access verification unit is configured to retrieve encrypted data and the corresponding hash value from the blockchain, decrypt the asymmetric key to obtain the original geographic mapping data, and verify whether the hash value has changed to determine whether the geographic mapping data has been tampered with during storage and transmission. The access verification unit includes:

[0103] The primary decryption module is configured to retrieve encrypted data and the corresponding hash value from the blockchain when accessing geographic mapping data, and decrypt the encrypted data using the recipient's private key in the asymmetric key to obtain the original geographic mapping data and the corresponding hash value.

[0104] The secondary decryption module is configured to recalculate the hash value of the original geographic surveying data and compare the recalculated hash value with the hash value stored in the blockchain to determine whether the geographic surveying data has been tampered with during storage and transmission. If the two match, it means that the geographic surveying data has not been tampered with during storage and transmission; if they do not match, it means that the geographic surveying data has been tampered with, and an alarm is triggered.

[0105] The tampering alarm module is configured to automatically trigger an alarm and notify the administrator when the secondary decryption module detects a hash value inconsistency, including by sending alarm information via email, SMS, or system notification.

[0106] The logging module is configured to log access to and operations on geographic mapping data, including decryption requests and verification results; and to store the operation logs in the blockchain to ensure the logs are immutable and traceable for auditing and tracking.

[0107] The beneficial effects achieved by the above are as follows: First, by using asymmetric encryption and hash value verification, the security and integrity of geographic mapping data are ensured during storage and transmission. Second, by verifying hash values, the system automatically detects whether geographic mapping data has been tampered with and automatically triggers alarms to notify administrators in a timely manner, reducing manual intervention and improving system operating efficiency, security, and reliability. Third, by storing operation logs through blockchain, the system ensures the transparency and traceability of all operations, facilitating auditing and tracking.

[0108] Working principle: Geographic mapping data is encrypted using a hash function to form fixed-length encrypted data and corresponding hash values. Based on the length of the encrypted data, it is grouped into separate encryption units, each encrypted using an asymmetric key method to ensure the security and integrity of the geographic mapping data storage. Upon subsequent access, the original geographic mapping data is decrypted using an asymmetric key method, and the hash value is recalculated to check if it matches the pre-stored hash value. This determines whether the geographic mapping data has been tampered with during storage and transmission, further ensuring the security of geographic mapping data storage and improving the system's feasibility.

[0109] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "possessing," or any other variations thereof are intended to cover non-exclusive possession, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0110] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that variations, modifications, substitutions and alterations may be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A secure storage system for geographic surveying and mapping data using blockchain encryption technology, characterized in that: The system includes: a secure storage unit and a verification access unit, wherein the secure storage unit includes a primary encryption module and a secondary encryption module; The primary encryption module is configured to perform hash processing on geographic mapping data based on an encryption hash algorithm to generate a hash value of fixed length. The secondary encryption module is configured to combine hash values ​​and geographic mapping data, and group the combined geographic mapping data according to the principle of the same number of bits. Each group of geographic mapping data is a encryption unit. Each encryption unit is encrypted using an asymmetric key to form encrypted data and store it in the blockchain. The verification access unit is configured to obtain encrypted data and the corresponding hash value from the blockchain, decrypt the asymmetric key to obtain the original geographic mapping data, and verify whether the hash value has changed to determine whether the geographic mapping data has been tampered with during storage and transmission.

2. The secure storage system for geographic surveying and mapping data using blockchain encryption technology according to claim 1, characterized in that: The secondary encryption module includes: The feature extraction module is configured to extract geographic location coordinates and altitude from the collected initial mapping data, and perform unit unification, coordinate system transformation and accuracy adjustment to obtain standardized mapping data; The format setting module is configured to determine the format and byte size of each data group, and to determine the group length based on the format, byte size, and requirements of the asymmetric key algorithm for each data group.

3. The secure storage system for geographic surveying and mapping data using blockchain encryption technology according to claim 2, characterized in that: The format setting module is used to perform the following steps: Retrieve standardized surveying data corresponding to geographic surveying data; Retrieve the fixed length of the hash value corresponding to the encryption process performed by the primary encryption module; Extract the mapping data precision corresponding to the geographic mapping data; The accuracy of the surveying data is standardized so that its value is projected into the interval (1-2), and the accuracy of the standardized surveying data is obtained. The fixed length corresponding to the hash value is multiplied by the precision of the standardized surveying data to obtain the product value between the fixed length corresponding to the hash value and the precision of the standardized surveying data. Retrieve the spatial dispersion of the geographic mapping data; The spatial dispersion is compared with a preset spatial dispersion threshold; When the spatial dispersion does not exceed the preset spatial dispersion threshold, the product of the fixed length corresponding to the hash value and the set multiple between the accuracy of the standardized mapping data is used as the group length. When the spatial dispersion exceeds the preset spatial dispersion threshold, the grouping length is set by combining the product of the fixed length corresponding to the hash value and the accuracy of the standardized mapping data with the spatial dispersion.

4. The secure storage system for geographic surveying and mapping data using blockchain encryption technology according to claim 3, characterized in that: When the spatial dispersion exceeds a preset spatial dispersion threshold, the grouping length is set by combining the product of the fixed length corresponding to the hash value and the accuracy of the standardized mapping data with the spatial dispersion, including: When the spatial dispersion exceeds the preset spatial dispersion threshold, the product value between the fixed length corresponding to the hash value and the accuracy of the standardized mapping data and the spatial dispersion are retrieved. Extract the geographic survey data and scan the geographic survey data to obtain the data structure type contained in the geographic survey data; The data structure types include vector data structure, raster data structure, grid data structure and 3D point cloud data structure; Retrieve a table from the database showing the correspondence between data structure types and data structure factors; wherein the value range of the data structure factors is 1-2; According to the data structure type and data structure factor correspondence table, obtain the data structure factor corresponding to the data structure type contained in the current geographic mapping data; The average value of the data structure factor is obtained based on all data structure types and their corresponding data structure factors contained in the geographic mapping data. The grouping length is set by combining the average value of the data structure factors with the fixed length corresponding to the hash value and the product value of the accuracy of the standardized surveying data and the spatial dispersion.

5. The secure storage system for geographic surveying and mapping data using blockchain encryption technology according to claim 2, characterized in that: The secondary encryption module further includes: The data grouping module is configured to group geographic mapping data according to the set grouping length, so that each group of data is an independent encrypted unit. The grouping verification module is configured to read each group of data sequentially from each encryption unit and determine whether each group of data in each encryption unit is complete; if the data is incomplete, the grouping strategy is adjusted, including: If the current group is full, create a new group; If the amount of data in the last encryption unit is insufficient, padding data is used to complete the block to a fixed length.

6. The secure storage system for geographic surveying and mapping data using blockchain encryption technology according to claim 5, characterized in that: The secondary encryption module further includes: The data encryption module is configured to generate asymmetric key pairs based on asymmetric keys, the asymmetric key pairs including a public key and a private key; the geographic mapping data and its hash value are encrypted using the recipient's public key to form encrypted data; The data storage module is configured to store encrypted data in the blockchain, with each block including the hash value of the current data and the hash value of the previous block.

7. The secure storage system for geographic surveying and mapping data using blockchain encryption technology according to claim 6, characterized in that: The secondary encryption module further includes: The data optimization module is configured to obtain the total data volume of the geographic surveying data before storing it, select a data compression algorithm to compress the geographic surveying data based on the total data volume, and decompress it after decryption. The blockchain interaction module is configured to manage the interaction between various blockchains based on the blockchain interface, including data storage, querying, and verification.

8. The secure storage system for geographic surveying and mapping data using blockchain encryption technology according to claim 1, characterized in that: The verification access unit includes: The primary decryption module is configured to decrypt encrypted data using the recipient's private key in the asymmetric key to obtain the original geographic mapping data. The secondary decryption module is configured to recalculate the hash value of the original geographic mapping data and compare the recalculated hash value with the hash value stored in the blockchain to determine whether the geographic mapping data has been tampered with during storage and transmission. If the two match, it means that the geographic mapping data has not been tampered with during storage and transmission; if they do not match, it means that the geographic mapping data has been tampered with.

9. The secure storage system for geographic surveying and mapping data using blockchain encryption technology according to claim 8, characterized in that: The verification access unit further includes: The tamper alarm module is configured to automatically trigger an alarm and notify the administrator when the secondary decryption module detects a hash value inconsistency. The logging module is configured to log access to and operations on geographic mapping data and store the operation logs in the blockchain.

10. The secure storage system for geographic mapping data using blockchain encryption technology according to claim 1, characterized in that: The system also includes: The data collection unit is configured to collect geographic surveying data based on high-precision surveying equipment and to preprocess the geographic surveying data. The data backup unit is configured to periodically back up geographic mapping data to multiple nodes based on the distributed storage characteristics of blockchain.

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