Blockchain-based geological data secure storage method, system and storage medium

Abstract

The present application relates to the field of data processing technology and discloses a blockchain-based geological data security storage method, system, and storage medium. The method includes: collecting geological environmental data, establishing a standardized data set, storing distributed data blocks through a blockchain network, and constructing a hierarchical access permission structure to achieve fine-grained access control. Polling voting, data verification, and other methods are used to ensure storage consistency and reliability, and abnormal operations are audited through time series analysis, behavioral feature extraction, and other technologies. The present application achieves efficient storage, flexible access, and trusted circulation of geological data through key technologies such as standardized geological data organization, hierarchical network topology construction, distributed data block storage, fine-grained dynamic access control, multi-copy fault-tolerant backup of data, and full-process behavioral security audits.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a blockchain-based geological data secure storage method, system, and storage medium. Background Art

[0002] As geological exploration continues to deepen, the scale and variety of geological data are growing. Traditional centralized geological data storage methods primarily focus on centrally storing data on servers or storage devices in data centers, with data access rights managed through methods such as usernames, passwords, and access control lists. While this approach is simple to operate, it suffers from numerous shortcomings in the distributed storage and secure sharing of massive amounts of heterogeneous data. Furthermore, the increasing application of geophysical exploration, remote sensing, and other technologies in geological surveys has generated vast amounts of unstructured data, such as images and videos, further increasing the heterogeneity and complexity of geological data. How to effectively organize, manage, and securely store this large amount of geological data, while supporting flexible access and trusted sharing for diverse business needs, has become a pressing issue.

[0003] Existing centralized geological data storage methods have the following major shortcomings in dealing with massive heterogeneous data: there is a lack of a unified and standardized data organization form, and geological data from different sources and types vary greatly in format, coordinate system, resolution, etc., making it difficult to effectively integrate them; the centralized storage architecture lacks scalability and cannot effectively cope with the exponential growth of geological data scale, and there are performance bottlenecks and single point failure risks; there is a lack of flexible and fine-grained data access control mechanism, and it mainly relies on static access control policies, which cannot meet differentiated user permission requirements; there is a lack of a trusted verification mechanism for the entire data life cycle, making it difficult to trace the source and evolution process of data, and there is a security risk of data tampering; there is a lack of a trusted data sharing mechanism among multiple parties, and data exchange and value circulation between different stakeholders are difficult to reflect, which affects the realization of data scale effect. Summary of the Invention

[0004] This application provides a blockchain-based geological data security storage method, system and storage medium, which is used to achieve efficient storage, flexible access and trusted circulation of geological data through key technologies such as standardized geological data organization, hierarchical network topology construction, distributed data block storage, fine-grained dynamic access control, multi-copy fault-tolerant backup of data, and full-process behavioral security auditing.

[0005] In the first aspect, the present application provides a method for secure storage of geological data based on blockchain, which includes: collecting data on the topography, mining environment, hydrological environment and soil environment of the study area, establishing a spatiotemporal correlation mapping of the collected data through partitioned geocoding, and standardizing the mapped data by spatial reference conversion to obtain a normalized geological environment data set; performing hierarchical credit evaluation and dynamic load distribution on network nodes based on the normalized geological environment data set, constructing a network topology of the nodes through a minimum spanning tree algorithm to obtain a blockchain basic network; and performing geocoding on the normalized geological environment data set in the blockchain basic network according to a geographic grid. The rows are partitioned into quadtrees, and the partitioned data is stored in proximity blocks through data association evaluation to obtain distributed data blocks; a three-layer access permission structure is constructed based on the distributed data blocks, the permission inheritance relationship is calculated through data sensitivity quantification, and the user permissions are verified through environmental parameter constraints to obtain an access control chain; the nodes are grouped and numbered according to the access control chain, the data consistency is verified through polling voting, and the backup data is backed up through data check codes to obtain verification records; the verification records are analyzed in time series, the operation modes are classified through behavioral feature extraction, and abnormal behaviors are judged through access frequency statistics and spatiotemporal feature matching to obtain an audit report.

[0006] In a second aspect, the present application provides a blockchain-based geological data security storage system, which includes:

[0007] The acquisition module is used to collect data on the topography, mining environment, hydrological environment and soil environment of the study area, establish spatiotemporal correlation mapping of the collected data through zoning geocoding, and standardize the mapped data through spatial reference conversion to obtain a standardized geological environment dataset;

[0008] A construction module is used to perform hierarchical credit assessment and dynamic load distribution on network nodes based on the standardized geological environment dataset, and to construct a network topology of the nodes using a minimum spanning tree algorithm to obtain a blockchain basic network;

[0009] A partitioning module is used to perform quadtree partitioning on the standardized geological environment dataset in the blockchain basic network according to a geographic grid, and to perform proximity group storage on the partitioned data through data association evaluation to obtain distributed data blocks;

[0010] A calculation module is used to build a three-layer access permission structure based on the distributed data block, calculate the permission inheritance relationship through data sensitivity quantification, verify user permissions through environmental parameter constraints, and obtain an access control chain;

[0011] A backup module is used to group and number nodes according to the access control chain, verify data consistency through polling and voting, and back up the data to be backed up through a data check code to obtain a verification record;

[0012] The classification module is used to perform time series analysis on the verification records, classify the operation modes by extracting behavioral features, determine abnormal behaviors through access frequency statistics and spatiotemporal feature matching, and obtain an audit report.

[0013] The third aspect of the present application provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computer, it enables the computer to execute the above-mentioned blockchain-based geological data security storage method.

[0014] In the technical solution provided by this application, data on the topography, mining environment, hydrological environment and soil environment of the study area are collected to establish a standardized geological environment data set, and the distributed data blocks are stored in the blockchain-based network generated by the hierarchical network topology. A three-layer access permission structure is constructed to achieve fine-grained access control, and the consistency and reliability of data storage and backup are guaranteed by means of polling voting, data checksums, etc., and abnormal operation behaviors are audited by combining time series analysis, behavioral feature extraction and other technologies. This method can effectively solve the problem of standardized management of massive heterogeneous geological data, ensure the efficient storage and dynamic expansion of geological data, realize flexible and diverse hierarchical authorized access to geological data, ensure the traceability and tamper-proofing of geological data throughout its life cycle, reduce the security risks of data leakage and illegal use, support cross-domain geological data sharing applications with multiple parties, and lay a solid foundation for the in-depth analysis and mining of geological big data. In addition, the full-process abnormal behavior audit and warning can timely discover and curb the data abuse of internal personnel and improve the security protection capabilities of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 This is a schematic diagram of an embodiment of a method for secure storage of geological data based on blockchain in an embodiment of the present application;

[0017] Figure 2 This is a schematic diagram of an embodiment of a blockchain-based geological data security storage system in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The embodiments of the present application provide a method, system and storage medium for secure storage of geological data based on blockchain. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of a method for securely storing geological data based on blockchain includes:

[0020] Step S101: Collect data on the topography, mining environment, hydrological environment, and soil environment of the study area, establish a spatiotemporal correlation mapping of the collected data through zoning geocoding, and standardize the mapped data through spatial reference conversion to obtain a standardized geological environment dataset;

[0021] Step S102: Perform hierarchical credit assessment and dynamic load distribution on network nodes based on the standardized geological environment dataset, and construct a network topology of the nodes using a minimum spanning tree algorithm to obtain a blockchain basic network;

[0022] Step S103: partition the standardized geological environment dataset in the blockchain infrastructure network into quadtree partitions according to the geographic grid, and perform proximity group storage on the partitioned data through data association evaluation to obtain distributed data blocks;

[0023] Step S104: construct a three-layer access permission structure based on the distributed data blocks, calculate the permission inheritance relationship through data sensitivity quantification, verify the user permissions through environmental parameter constraints, and obtain the access control chain;

[0024] Step S105: Group and number the nodes according to the access control chain, verify data consistency through polling and voting, and back up the data to be backed up using a data check code to obtain a verification record;

[0025] Step S106: Perform time series analysis on the verification records, classify the operation modes through behavioral feature extraction, determine abnormal behaviors through access frequency statistics and spatiotemporal feature matching, and obtain an audit report.

[0026] It is understood that the execution subject of this application can be a blockchain-based geological data security storage system, or a terminal or server, and the specific implementation is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0027] Specifically, the geological environment data of the study area is collected using integrated sky-ground technology. Remote sensing equipment is used to obtain topographic image data, which includes information such as surface morphology, slope, and slope aspect. Field sampling is used to obtain mining environmental parameters, including mine type, geological structure, hydrogeological conditions, and other data. Long-term monitoring is carried out at hydrological monitoring stations to collect water environment parameters such as water quality, water level, water temperature, and flow rate. Soil composition is analyzed through soil sampling to obtain soil environmental indicators such as soil acidity and heavy metal content, forming the original environmental data. During the data collection stage, topographic and geomorphological data mainly include elevation values, slope values, and surface cover types. Mining environmental parameters include information such as lithology, structure, and degree of weathering. Water environment monitoring covers indicators such as pH value, dissolved oxygen, and conductivity. Soil sampling and analysis include parameters such as organic matter content and heavy metal concentration.

[0028] The collected raw environmental data is divided according to administrative boundaries and evenly divided into geographic grids. Each grid cell is assigned a unique geocode to facilitate data location and retrieval. Geocoding uses a hierarchical coding scheme, consisting of a region code, grid number, and data type identifier. A timestamp is added to each data record to identify the specific time point of data collection and establish a temporal and spatial correspondence between the data. Timestamps are recorded with accuracy to the second to ensure data timeliness and traceability. When data is collected from a monitoring point, the record includes information such as the monitoring point number, sampling time, longitude, latitude, and environmental parameter values. All data items have clear units and value ranges. During the spatial datum conversion process, a seven-parameter conversion method is used to unify data from different coordinate systems into the CGCS2000 coordinate system. The seven parameters include easting, northing, elevation, transverse, longitudinal, and vertical rotation, as well as scale. After the spatial datum conversion, the data format is normalized, the data structure and attribute fields are unified, missing data are supplemented, and outliers are removed. During the data normalization process, the units and decimal places of numerical data are unified, and the encoding and length of character data are standardized to form a standardized geological environment data set.

[0029] Blockchain network nodes are constructed based on a standardized geological environment dataset. Network node capacity is assessed, and node capacity is classified by calculating data flow per unit time to evaluate node processing capabilities. Each node is divided into three capacity levels: high, medium, and low. High-capacity nodes handle large amounts of data and complex computing tasks, medium-capacity nodes handle general data and routine computing tasks, and low-capacity nodes are primarily used for data backup and simple computing tasks. Node trust is assessed based on historical data transmission success rates and node online time, assigning each node a trust score. Task scheduling is performed based on node response time and load balancing factors to ensure network load balancing. A minimum spanning tree algorithm is used to construct the network topology. By calculating inter-node connection weights, the optimal connection scheme is selected. Finally, inter-node communication rules are established to complete the construction of the blockchain network. The standardized geological environment dataset is stored using quadtree partitioning. First, the spatial scope is defined, and the study area is demarcated using geographic coordinates. A geographic grid is used for uniform partitioning, and then the grid is hierarchically subdivided using the quadtree partitioning method. Spatial relationships between partitions are calculated, adjacent partitions are identified, and the degree of correlation between partitions is quantified. Through data content analysis, the association degree of partitioned data is calculated. Similarity is calculated based on the data's spatiotemporal properties and content characteristics, and highly similar data blocks are stored adjacent to each other. Storage nodes are selected through data block size control and load balancing to form distributed data blocks. During data partitioning and storage, data in adjacent areas is preferentially stored on the same or adjacent nodes, reducing the overhead of cross-node access.

[0030] An access rights system is built based on distributed data blocks. Management levels are divided into administrator, business user, and general user levels. Data content is analyzed for sensitivity, with data value assessed by keyword frequency. Sensitivity levels are then assigned based on business type. Different access levels correspond to different data access scopes and operational permissions. The administrator level has the highest operational permissions, the business user level has read and write permissions for specific business data, and the general user level only has read permissions for basic data. Permission inheritance paths and transfer rules are defined to ensure the rationality and security of permission allocation. Environmental parameter testing is performed, and operational time windows and login locations are restricted to prevent unauthorized access and data leakage. Nodes are grouped and numbered, and data consistency is verified. Nodes are grouped based on their service capabilities and network location. Nodes within a group maintain data consistency through a round-robin voting mechanism. Voting is weighted, with higher-trust nodes receiving greater weight. Nodes regularly verify and back up stored data, generate verification codes, and store multiple copies. The backup strategy considers data importance and access frequency, with more frequent backups for important and hotspot data. Detailed operation logs are recorded during the backup process, including backup time, data version, storage location and other information.

[0031] Finally, data access behavior analysis is performed. Each user's data access operation is recorded, including information such as access time, access content, and operation type. Time series analysis is used to identify user operation patterns and calculate access frequency and patterns. Abnormal access behavior is flagged and analyzed, such as frequent access to sensitive data, access at unusual times, and remote logins. User behavior profiles are constructed based on access location and time characteristics for subsequent access control and security audits. When generating audit reports, abnormal behaviors are categorized by risk level and provided with detailed incident descriptions and action recommendations. For example, hydrological monitoring data collected by monitoring stations undergoes spatial benchmark conversion and data normalization before being partitioned and stored according to geographic location. Data access requires permission verification and access logs. Access log analysis can identify data leakage risks, such as a user downloading a large amount of hydrological data in a short period of time or accessing monitoring data in sensitive areas during off-hours. Furthermore, data verification and backup mechanisms ensure data security and reliability. Even if some nodes fail, data can be restored using backup data from other nodes.

[0032] In the embodiment of the present application, data is collected on the topography, mining environment, hydrological environment and soil environment of the study area to establish a standardized geological environment data set, and the distributed data blocks are stored in the blockchain-based network generated by the hierarchical network topology. A three-layer access permission structure is constructed to achieve fine-grained access control, and the consistency and reliability of data storage and backup are guaranteed by means of polling voting, data checksums, etc., and abnormal operation behaviors are audited in combination with time series analysis, behavioral feature extraction and other technologies. This method can effectively solve the problem of standardized management of massive heterogeneous geological data, ensure the efficient storage and dynamic expansion of geological data, realize flexible and diverse hierarchical authorized access to geological data, ensure the traceability and tamper-proofing of geological data throughout its life cycle, reduce the security risks of data leakage and illegal use, support cross-domain geological data sharing applications that are trusted by multiple parties, and lay a solid foundation for the in-depth analysis and mining of geological big data. In addition, the full-process abnormal behavior audit and warning can timely discover and curb the data abuse of internal personnel and improve the security protection capabilities of the entire system.

[0033] In a specific embodiment, the process of executing step S101 may include the following steps:

[0034] (1) Acquire image data of topography and landforms through remote sensing equipment, collect parameters of mine environment through field sampling, conduct long-term monitoring of hydrological environment through hydrological monitoring stations, and analyze soil environment components through soil sampling to obtain original environmental data;

[0035] (2) Based on the original environmental data, the administrative area boundaries are divided, the area is divided according to the geographic grid, and each grid cell is assigned a unique geographic code through coordinate mapping to obtain the partition code data;

[0036] (3) Mark the collection time based on the partition coding data, record the data collection node through the timestamp, establish the time-space correspondence through data association analysis, and obtain time-space correlation data;

[0037] (4) Perform coordinate system conversion on spatiotemporal correlation data, offset the spatial position through easting translation parameters, northing translation parameters, and elevation translation parameters, adjust the direction through horizontal axis rotation parameters, vertical axis rotation parameters, and vertical axis rotation parameters, and unify the scale through scale ratio parameters to obtain benchmark conversion data;

[0038] (5) Normalize the data format according to the benchmark conversion data, unify the data structure by standardizing the attribute fields, and supplement the missing values ​​through data integrity check to obtain standardized data;

[0039] (6) Perform quality inspection on standardized data, eliminate duplicate data through data consistency analysis, mark abnormal data through outlier detection, and obtain a standardized geological environment data set.

[0040] Specifically, integrated air-ground data acquisition technology combines data acquisition methods from three levels: satellite remote sensing, aerial remote sensing, and ground-based monitoring. Remote sensing equipment includes multispectral imagers and hyperspectral imagers, which capture images of topography. Multispectral imagers collect surface reflectance information using different wavelengths, including visible light, near-infrared, and mid-infrared, to capture surface morphological characteristics. Hyperspectral imagers can obtain more detailed spectral information, which can be used to identify the composition of surface materials. For the collection of mining environmental parameters, the layout of field sampling points follows the principle of representativeness. Sampling points are set up in mining areas with different geological units and different mining stages to collect rock and groundwater samples, recording parameters such as geological structural characteristics, rock integrity, and water content. The layout of hydrological monitoring stations takes into account the distribution of water systems. Fixed monitoring stations are set up at the confluence of major rivers and tributaries, as well as in key monitoring areas. Automatic monitoring equipment is used to continuously collect parameters such as water level, flow, and water quality. Soil sampling adopts the grid distribution method, setting sampling points at a certain interval in the study area, collecting surface and profile soil samples, and analyzing indicators such as heavy metal content, organic matter content, and pH value.

[0041] Regional division and coding are performed based on the collected raw environmental data. First, a primary division is performed based on administrative boundaries to ensure consistency between data and administrative management. Based on the administrative divisions, a secondary division is performed using a regular grid. The grid size is determined based on the study area and data density, ensuring that each grid cell contains sufficient sampling points. Geocoding employs a hierarchical structure, with the administrative division code as a prefix, the grid number as an intermediate code, and the data type identifier as a suffix to form a unique geocode. The coordinates of each grid cell are determined by the latitude and longitude of its lower left corner, facilitating spatial location and retrieval. Geocoded data are time-stamped to generate a unified timestamp format. The timestamp contains year, month, day, hour, minute, and second information and is recorded in an international standard time format. For continuous monitoring data, such as data from hydrological monitoring stations, the start and end times of monitoring are recorded; for discrete sampling data, such as soil samples, the sampling time is recorded. Data association analysis establishes connections between data using both temporal and spatial dimensions. Data at the same time but in different spaces reflect spatial distribution characteristics, while data at the same space but in different times reflect temporal evolution patterns. For example, the temporal correspondence between water quality monitoring data and soil monitoring data within a certain grid unit can reveal the mutual influence between surface water and soil environment.

[0042] The three translation parameters—easting, northing, and elevation—adjust the position of the coordinate origin. The three rotation parameters—horizontal, vertical, and vertical—adjust the orientation of the coordinate axes. The scale parameter unifies the scales of different coordinate systems. Through the combined transformation of these seven parameters, data from different sources are converted to a unified coordinate system. The conversion process first identifies common control points with known coordinates in both coordinate systems. The conversion parameters are then calculated, and finally, all data points are uniformly converted. Standardization focuses on unifying data structures and formats. Attribute field standardization includes standardizing field names, field types, and field value ranges. For example, for water quality monitoring data, pH values ​​are uniformly rounded to one decimal place, conductivity is uniformly measured in μS / cm, and dissolved oxygen is uniformly measured in mg / L. Data integrity checks supplement missing values ​​using contextual information, such as interpolation using data from adjacent time points or estimation using data from adjacent spatial points. Data duplication often occurs during the integration of multi-source data. Duplicate records are identified by comparing timestamps and spatial locations. Outlier detection uses statistical methods to calculate the mean and standard deviation of the data. Values ​​that deviate from the mean by more than three standard deviations are marked as suspected anomalies. Professional knowledge is then used to determine whether they are true outliers. This quality-tested data forms a standardized geological environment dataset, which serves as the foundation for subsequent blockchain storage.

[0043] For example: The monitoring station continuously collects water quality data, and each monitoring point records parameters such as water temperature, pH value, and dissolved oxygen every ten minutes. These raw data are first coded according to the administrative region and grid unit where the monitoring station is located. For example, monitoring point A is located in the 25th grid in a certain area and is coded as "area code-025-W" (W represents hydrological data). Each record has a precise timestamp, such as "2024-01-0610:30:00". The spatial coordinates of the monitoring station are converted and unified into the CGCS2000 coordinate system. After the data format is standardized, the water temperature is uniformly retained to one decimal place and the pH value is retained to two decimal places. Abnormal data where the pH value remains above 9.5 for twelve consecutive hours is marked, and the abnormal value is confirmed by checking the instrument calibration record to determine whether it is caused by equipment failure.

[0044] In a specific embodiment, the process of executing step S102 may include the following steps:

[0045] (1) Statistical analysis of the data storage volume in the standardized geological environment dataset was performed, network nodes were classified according to the data flow per unit time, and node processing capabilities were evaluated according to resource occupancy rates to obtain node capacity data;

[0046] (2) Calculate the node trust score based on the node capacity data, evaluate the node stability based on the historical data transmission success rate, and quantify the node reliability based on the node online time to obtain hierarchical credit assessment data;

[0047] (3) Node task allocation and scheduling are performed based on hierarchical credit assessment data, task priorities are sorted according to node response time, resource allocation is adjusted according to load balancing factors, and dynamic load distribution data is obtained;

[0048] (4) Calculate the connection weights between nodes based on the dynamic load distribution data, quantify the communication overhead between nodes according to the distance matrix, estimate the connection cost according to the communication quality index, and obtain the node connection data;

[0049] (5) Calculate the minimum cost based on the node connection data, optimize the node connection order according to the minimum spanning tree algorithm, select the connection scheme according to the path cost, and obtain the network topology data;

[0050] (6) Establish inter-node communication rules based on network topology data, standardize the data transmission method according to the communication protocol, configure the block generation rules through the node consensus mechanism, and obtain the blockchain basic network.

[0051] Specifically, a capacity assessment is conducted on the standardized geological environment dataset. The processing requirements of network nodes are determined by statistical data storage capacity, including indicators such as data file size, number of data records, and data update frequency. The data flow per unit time is calculated using the following formula:

[0052]

[0053] Among them, F represents the data flow per unit time, V i Indicates the size of the i-th data file, T i represents the data transmission time, n represents the total number of data files, α is the data redundancy coefficient, D r Indicates the data update rate. The node processing capacity is quantified by computing resource utilization:

[0054]

[0055] Among them, C represents the node processing capacity index, M u and M t are used memory and total memory respectively, P u and P t are CPU usage and total processing capacity, S u and S t are the used storage space and total storage space respectively, and β1, β2, and β3 are weight coefficients.

[0056] Node trust is calculated based on node capacity data, taking into account historical data transmission success rates, node online time, and node response time. The data transmission success rate reflects node stability, and the calculation records the total number of data transmissions and the number of successful data transmissions for each node. Node online time statistics include continuous online time and cumulative online time, and a weighted average is used to calculate the node reliability index. Node trust scores are evaluated cumulatively. Newly added nodes have a low initial trust level, which gradually increases with the number of successful interactions. The load distribution process first prioritizes tasks based on node response time, using hierarchical trust evaluation data as a foundation. Task prioritization considers data importance, processing time limits, and resource requirements, assigning high-priority tasks to nodes with high trust and performance. The load balancing factor is used to evenly distribute tasks, preventing some nodes from being overloaded while others remain idle. The load distribution process dynamically adjusts, updating the task allocation strategy as node performance or network status changes.

[0057] When determining the connectivity between nodes, it is necessary to calculate the connection weights between them. This weight takes into account factors such as the physical distance between nodes, network bandwidth, and communication latency. A distance matrix records the number of network hops between nodes; fewer hops indicate lower communication overhead. Communication quality metrics include network latency, packet loss rate, and bandwidth utilization, which together determine the connection cost. Calculating the connection cost requires balancing multiple factors, ensuring communication efficiency while also considering the rational use of network resources. When constructing a network topology based on node connectivity data, a minimum spanning tree algorithm is used for optimization. The minimum spanning tree algorithm begins by selecting edges with the lowest cost and gradually constructs a loop-free network structure connecting all nodes. During execution, the algorithm first selects the edge with the lowest cost to add to the spanning tree. Then, among the remaining edges, edges with the lowest cost that do not form loops are selected until all nodes are connected. The resulting network topology ensures network connectivity while minimizing overall communication overhead.

[0058] Once the network topology is determined, communication rules between nodes need to be established. The communication protocol specifies the format, process, and verification methods for data transmission, ensuring its reliability and security. A node consensus mechanism is used to maintain data consistency. When a new data block needs to be added to the blockchain, the consensus mechanism ensures that all nodes reach agreement on the validity of the data.

[0059] For example, in a geological environment monitoring network, four monitoring nodes are responsible for data collection and processing in different areas. First, the data processing performance of each node is analyzed. Node A primarily processes topographic data, which has a large data volume but low update frequency. Node B is responsible for hydrological data, which has a medium data volume and requires real-time updates. Node C processes soil data, which has a smaller data volume but requires frequent sampling. Node D, as a backup node, needs to store important data from other nodes. Based on these characteristics, the nodes are classified for capacity and evaluated for trustworthiness. The nodes are connected using a minimum spanning tree structure. Taking into account the physical location and network conditions of the nodes, an ABDC backbone connection is formed, with Node B acting as the central node and directly connected to other nodes. During data transmission, the real-time performance of hydrological data is highly required, so Node B's tasks are given higher priority. When Node B detects water quality anomalies, it can quickly transmit the alarm information to other nodes, enabling a timely response.

[0060] The communication rules between nodes dictate the data block generation and verification process. When a node collects new monitoring data, it first packages the data into a standard format, adds a timestamp and digital signature, and then broadcasts it to other nodes for verification. After receiving the data, other nodes verify its integrity and validity. Only when more than two-thirds of the nodes confirm that the data is valid is the new data block added to the blockchain. The blockchain network established in this way not only ensures the secure storage of geological environment monitoring data, but also enables efficient data sharing and processing. The network structure has good scalability. When new monitoring nodes need to be added, the network topology can be dynamically adjusted through the minimum spanning tree algorithm, allowing the new node to smoothly connect to the existing network. At the same time, the node-based trust evaluation and load balancing mechanism ensure the stability and efficiency of network operation.

[0061] In a specific embodiment, the process of executing step S103 may include the following steps:

[0062] (1) Defining the spatial scope of the standardized geological environment dataset in the blockchain infrastructure network, demarcating the boundaries of the study area according to geographic coordinates, and dividing the regional geomorphic units according to topographic features to obtain regional scope data;

[0063] (2) Gridding the study area based on the regional range data, evenly dividing the area through the geographic grid, and hierarchically subdividing the geographic grid according to the quadtree partitioning method to obtain quadtree partitioning data;

[0064] (3) Calculate the spatial relationship of partitions through quadtree partition data, identify adjacent partitions according to their spatial positions, quantify the degree of association between partitions based on geographic distance, and obtain spatial relationship data;

[0065] (4) Analyze the partition data content based on spatial relationship data, calculate the partition content similarity through data correlation evaluation, quantify the data correlation according to the attribute correlation coefficient, and obtain correlation evaluation data;

[0066] (5) The partitioned data is merged into blocks based on the correlation evaluation data, the block range is defined by the proximity threshold, and the size of the block is controlled according to the data block size to obtain the block storage data;

[0067] (6) Allocate nodes for block storage data, select storage nodes through load balancing, and determine backup nodes through data redundancy strategy to obtain distributed data blocks.

[0068] Specifically, demarcating the boundaries of the standardized geological environment dataset within the blockchain infrastructure requires first determining the geographic coordinate range of the study area, including the coordinates of the four vertices of the minimum bounding rectangle. Terrain characterization considers the integrity of geomorphic units, dividing the study area into distinct geomorphic units such as mountains, hills, and plains. Each geomorphic unit possesses similar topographic and environmental characteristics. The division of geomorphic units requires consideration of factors such as elevation, slope, and land cover to ensure internal consistency and external differentiation.

[0069] For data within a defined area, a gridding method is used for spatial subdivision. A geographic grid is a regular spatial partitioning method that evenly divides the study area into grid cells of equal size. The quadtree partitioning method is an adaptive spatial partitioning strategy that recursively subdivides the grid based on data density and spatial characteristics. Starting from the root node, when the amount of data or data heterogeneity within a grid cell exceeds a threshold, the grid is divided into four subgrids. This partitioning method can form a finer grid in data-dense areas while maintaining a larger grid size in data-sparse areas.

[0070] Spatial relationship calculation involves the location relationship and degree of association between partitions. The spatial relationship of partitions is calculated using the following formula:

[0071]

[0072] Among them, R ij represents the spatial correlation between partition i and partition j, Q ij is the shared boundary length, L ij is the distance between the centers of the two partitions, H ij is the elevation difference, E max is the maximum elevation difference, G ij is the landform type similarity, B max is the maximum similarity, ω1, ω2, ω3 are weight coefficients.

[0073] Based on spatial relationship data, similarity analysis is performed on partitioned data content. Content similarity considers data attribute characteristics, such as geological parameters and environmental indicators. The more similar the data content of adjacent partitions, the greater the likelihood of merging. For each partition's data attributes, an attribute correlation coefficient is calculated to quantify the degree of correlation between the data. This correlation assessment considers the data's time series characteristics, spatial distribution characteristics, and attribute value distribution characteristics.

[0074] During the block merging process, a proximity threshold is set to control the merging range. When the spatial relationship and data similarity of adjacent partitions exceed the threshold, these partitions are merged into a single block. Block size control ensures that the merged data block is neither too large to affect processing efficiency nor too small to cause storage fragmentation. For the node allocation of block storage data, the load balancing calculation formula is:

[0075]

[0076] Among them, N k is the load balancing index of node k, Y k is the current storage capacity, X total is the total storage capacity, W k For processing power, Z max is the maximum processing capacity, U k is the current task number, J max is the maximum number of tasks, γ1, γ2, and γ3 are balance factors.

[0077] For example, in a mining area environmental monitoring project, the study area encompassed an open-pit mine, a spoil dump, and a surrounding buffer zone. First, the study area boundaries were determined based on satellite imagery and topographic data, and the area was divided into three primary geomorphic units: the mining area, the waste storage area, and the ecological restoration area. A uniform geographic grid with an initial mesh size of 500 meters was used, followed by quadtree partitioning based on data characteristics. In areas of intensive mining activity, the mesh size was subdivided to 125 meters; in areas of intense environmental fluctuations, the mesh size was subdivided to 62.5 meters; and in the buffer zone, the mesh size was maintained at a larger size. Spatial relationship calculations revealed that the sub-areas within the mining area had a high degree of spatial correlation, due to shared topographical features and environmental influences. Data content analysis revealed that adjacent monitoring points often exhibited similar pollutant concentration trends. Based on these characteristics, spatially adjacent sub-areas with similar data were merged into data chunks. The resulting data chunks reflected the spatial distribution of environmental impacts, such as the extent of pollutant diffusion and groundwater flow. During node allocation, these data chunks were assigned to high-performance nodes, given the frequent data updates and high processing requirements of mining areas. At the same time, to ensure data security, data copies are stored on geographically dispersed nodes. This distributed storage method not only ensures efficient data access but also provides reliable disaster recovery backup.

[0078] For each data chunk, the node allocation strategy is further refined. Load balancing considers not only the storage capacity and processing power of nodes but also their geographic distribution. Data chunks from adjacent regions are preferentially stored on nodes with closer network proximity to reduce data access latency. A data redundancy strategy utilizes a multi-replication mechanism, with at least three copies of important environmental monitoring data stored in different physical locations. The spatial distribution of geological environmental data exhibits distinct clustering characteristics. For example, groundwater monitoring data around mining areas often exhibit strong correlations between water levels and water quality data from adjacent monitoring wells. This spatial correlation directly influences the degree of data partitioning refinement. Areas with strong correlations require finer gridding to capture data variation. Furthermore, the temporal resolution of the data also influences the storage strategy. Different storage solutions are used for real-time monitoring data and periodic sampling data. Controlling the size of data chunks requires balancing multiple factors. Excessively large chunks increase the storage pressure and processing burden on individual nodes, while too small chunks increase management overhead and network communication costs. In practice, it has been found that keeping the chunk size within a range that supports efficient querying and analysis is optimal. The physical size and logical size of a data block need to be considered separately. The physical size affects storage efficiency, while the logical size affects query performance.

[0079] After node allocation is complete, a mapping relationship between data blocks and nodes must be established. This mapping relationship is not static but dynamically adjusted as data volume and node status change. When a node is overloaded, the system triggers data block migration, moving some blocks to less-loaded nodes. Similarly, when new nodes join the network, data blocks are redistributed to optimize storage balance across the network. A distributed data block network offers excellent scalability and fault tolerance. Through appropriate data partitioning and node allocation, data access efficiency is ensured while maintaining data integrity and security. If a node fails, replicas on other nodes ensure service continuity, and the system automatically initiates a data recovery process to reconstruct the lost data on a new node. This distributed storage architecture is particularly well-suited for processing data with distinct spatial characteristics, such as those found in geological environmental monitoring. It effectively supports spatial queries and analysis, such as identifying trends in environmental parameters within a specific area or analyzing the spread of pollutants. Furthermore, the spatial distribution of data provides optimization opportunities for data compression and indexing. Highly similar data blocks can be compressed using more efficient algorithms, while highly correlated data blocks can be indexed using spatial indices to accelerate queries.

[0080] In a specific embodiment, the process of executing step S104 may include the following steps:

[0081] (1) Based on the distributed data blocks, the management levels are divided, the access rights are graded according to the importance of the data, and the user groups are divided according to the permission levels, thereby obtaining a three-layer access permission structure; wherein the three-layer access permission structure includes the administrator permission layer, the business user permission layer, and the general user permission layer;

[0082] (2) Analyze the data content in the three-tier access permission structure, assess the data value based on the frequency of keyword occurrence, and classify the data sensitivity level by business type to obtain sensitivity quantitative data;

[0083] (3) The permission transfer relationship is sorted out through the sensitive quantitative data, the permission inheritance path is set through the user group level, and the permission transfer rules are limited according to the parent-child node relationship to obtain the permission inheritance data;

[0084] (4) Calculate the permission group to which the user belongs based on the permission inheritance data, determine the access level based on the user identity type, and constrain the user's operation scope based on the permissions within the group to obtain permission calculation data;

[0085] (5) Perform environmental detection on the permission calculation data, limit the operation window according to the access time, and constrain the login scope according to the access location to obtain environmental constraint data;

[0086] (6) The user's permission status is judged based on the environmental constraint data, the user's legitimacy is verified through identity authentication, and the access request is authorized through permission evaluation to obtain the access control chain.

[0087] Specifically, distributed data blocks are divided into management hierarchies. Data is categorized according to its importance into three categories: core data, business data, and basic data. Core data includes critical information such as geological hazard warnings and important mineral resource data; business data includes work data such as daily monitoring data and analysis reports; and basic data includes general information such as historical records and reference materials. Based on this data categorization, a three-tier access permission structure is established: the administrator level is responsible for overall system management and maintenance, with the highest data access and operation permissions; the business user level is primarily for professional technicians, with access and processing permissions for specific business data; and the general user level is primarily for querying and using basic data. After determining the three-tier access permission structure, in-depth analysis of the data content is required. Keyword frequency analysis is used to determine the data's value. Keywords include professional terms such as "fault," "displacement," and "water content." The higher the frequency of these terms, the more specialized and important the data. Furthermore, data sensitivity is categorized based on the business type. For example, data related to geological hazard warnings has the highest sensitivity level, requiring real-time updates and strict confidentiality; whereas routine geological parameter measurement data has a relatively lower sensitivity level and allows for wider access and sharing.

[0088] The analysis of permission transfer relationships is a crucial step based on sensitive quantitative data. User groups are divided into system management, business processing, and data query groups based on responsibilities. Each group can be further divided into subgroups. Permission inheritance is implemented from top to bottom. The permissions of a parent group include all permissions of subordinate groups, but subordinate groups cannot obtain the special permissions of a parent group. Permission transfer between parent and child nodes adheres to the principle of least privilege, ensuring that each user group only has access to the minimum set of permissions necessary to complete their work.

[0089] The calculation of the permission group to which a user belongs is based on permission inheritance data. First, the basic access level of the user is determined based on the user's identity type, such as system administrator, business specialist, ordinary user, etc. Then, based on the user's role and responsibilities in the organization, the specific permission group to which the user belongs is determined. Each permission group has a clearly defined scope of operation, including the type of data that can be accessed, the type of operation allowed (such as reading, modifying, deleting, etc.), and the time limit for operation. Environmental detection is a dynamic permission control process. Environmental parameters are detected for each access request, including access time, login location, device characteristics, etc. The limitation of the operation time window ensures that sensitive operations can only be performed within the specified time period. For example, the modification of certain important data can only be performed during working hours. The constraint of the login location is implemented through IP address and physical location verification to prevent illegal access from other places.

[0090] Determining permission status is the final security control step. Multi-factor authentication verifies the legitimacy of the user's identity, using methods such as password verification, digital certificates, and biometrics. Permission assessment comprehensively considers user identity, environmental conditions, and the requested operation to determine whether to grant access. This entire process forms a complete access control chain, and each access request must undergo verification before being authorized.

[0091] For example, monitoring projects involve multiple aspects, including surface deformation monitoring, groundwater monitoring, and soil environmental monitoring. Surface deformation monitoring data, including GPS and tilt measurement data, are core data directly related to geological disaster early warning. Groundwater monitoring data, including parameters such as water level and water quality, are operational data. Soil environmental monitoring data, including conventional physical and chemical indicators, are basic data.

[0092] Analyze the keyword frequency of each data item. Taking surface deformation monitoring reports as an example, keywords such as "deformation," "displacement," and "settlement" appear far more frequently than other terms, indicating that this data is closely related to geological hazard early warning. Through text analysis of a large number of historical monitoring reports, a keyword frequency table was established, serving as an important basis for data value assessment. Furthermore, sensitivity levels were further refined based on the timeliness requirements and update frequency of the data. Real-time monitoring data has a higher sensitivity level than regular monitoring data, and abnormal data has a higher sensitivity level than normal data. The permission inheritance path is set up in a tree structure. At the administrator permission level, a system management group and a security audit group are set up; at the business user permission level, surface monitoring groups, hydrological monitoring groups, and environmental monitoring groups are set up according to monitoring type; and at the general user permission level, data query groups and report statistics groups are set up. The permission inheritance relationship between groups is clear and unambiguous. For example, the surface monitoring group inherits all permissions of the data query group and also has permission to process surface monitoring data.

[0093] When assigning user permissions, work needs and security requirements are taken into consideration. The person in charge of the monitoring project is assigned to the corresponding business group and has data processing and analysis permissions; field technicians are assigned to specific monitoring teams and have data collection and entry permissions; project managers are assigned to management groups and have global management and supervision permissions. Refined management of permissions ensures the controllability of data access. The setting of environmental constraints takes into account the actual work scenario. For monitoring data collected on-site, data upload must be carried out within the specified monitoring point location range; for data analysis and processing, it must be carried out in a specified office network environment; for system maintenance operations, it must be performed on a specific management terminal. These environmental constraints are closely integrated with the workflow, ensuring data security without affecting normal work.

[0094] In practice, every access request undergoes a complete control chain verification process. For example, when a technician from the surface monitoring team needs to modify monitoring data, their digital certificate is first verified to confirm their identity; then, they are checked to see if they are within the designated working hours and location; and finally, their permission group is determined to determine if they have data modification permissions. Only after all verifications are passed can the data modification operation be carried out.

[0095] In a specific embodiment, the process of executing step S105 may include the following steps:

[0096] (1) Evaluate the node service capability based on the access control chain, group the nodes by processing performance, and sort the grouped nodes by number according to the network distance to obtain node group data;

[0097] (2) Voting rules are set based on node grouping data, voting order is arranged based on node response time, and node decision importance is differentiated based on voting weights to obtain round-robin voting data;

[0098] (3) Compare the storage content of each node based on the polling voting data, verify the data content through the data block feature value, mark the data differences according to the consistency judgment rules, and obtain consistency verification data;

[0099] (4) Screen the data to be backed up using the consistency verification data, sort the backup priorities by data importance, confirm the backup content according to data integrity, and obtain a list of data to be backed up;

[0100] (5) Redundantly encode the data in the backup list, divide the backup data into blocks through data sharding, calculate the data check code through check bit generation, and obtain data check data;

[0101] (6) The data backup operation is recorded through data verification data, the data version is identified by the backup time, and the data copy is located according to the backup location to obtain a verification record.

[0102] Specifically, a comprehensive assessment of the node's service capabilities is conducted based on the access control chain. This assessment includes three key metrics: processing performance, storage capacity, and network bandwidth. Processing performance is measured by CPU utilization and memory usage, storage capacity includes available space and read / write speeds, and network bandwidth considers both uplink and downlink rates. Based on these metrics, nodes are divided into high-performance, medium-performance, and basic performance groups. Within each performance group, nodes are numbered and sorted according to the network distance between them, which is calculated using inter-node communication latency and the number of hops.

[0103] Specific voting rules are set for the data after node grouping and sorting. The voting process uses a hierarchical voting mechanism, with high-performance nodes receiving higher voting weight due to their superior data processing capabilities and stability. Voting is arranged according to node response time, with nodes with faster response times receiving priority. The importance of a node's decision is related to its historical performance, including metrics such as the number of successfully processed transactions and data synchronization accuracy. This voting mechanism, based on performance and reputation, ensures the reliability of voting results.

[0104] During consistency verification, the data stored on each node is compared. Data block feature values ​​are calculated using a multi-hash algorithm, combining information such as data content, timestamp, and version number. Consistency determination rules define the acceptable range for data discrepancies, and discrepancies outside this range are flagged. This flag includes the location, type, and severity of the discrepancy, which is used for subsequent data repair.

[0105] Based on the consistency verification results, data requiring backup is screened and prioritized. Data importance is assessed based on frequency of use, update time, and business relevance. For example, real-time monitoring data is backed up with higher priority than historical archived data, and abnormal data is backed up with higher priority than normal data. Data integrity checks include both structural and content integrity to ensure that the data being backed up is not damaged or missing.

[0106] Redundancy encoding is performed on the selected data to be backed up. Data sharding uses a dynamic sharding strategy, with the number and size of shards determined based on the size and importance of the data. A corresponding checksum is generated for each data shard using erasure coding technology, which can detect data errors and, to a certain extent, repair damaged data. The checksum is stored together with the original data shard to form a complete backup unit.

[0107] Finally, data backup operations are recorded in detail. Each backup operation records the backup time, data version number, and backup location. The backup time is used to track historical data versions, the version number is used to distinguish data states at different times, and the backup location information is used to quickly locate and restore data. These records form a complete verification chain, ensuring the traceability and recoverability of data backups.

[0108] During implementation, the backup and verification of geological environment monitoring data from a mining area is used as an example to illustrate the entire process. Monitoring data includes three core categories: surface deformation monitoring, groundwater monitoring, and soil environment monitoring. The nodes responsible for storing this data are evaluated for performance, collecting node performance metrics. Surface deformation monitoring data, due to its real-time processing requirements, is assigned to high-performance nodes. These nodes maintain low CPU usage, have ample memory and storage space, and sufficient network bandwidth to support real-time data transmission. Groundwater monitoring data and soil environment monitoring data are assigned to medium-performance nodes, which can handle regular data processing.

[0109] After determining the node groupings, the data consistency verification process begins. Verification begins with the high-performance group nodes, as these nodes store the most critical real-time monitoring data. Each data block generates an eigenvalue, which contains a comprehensive encoding of information such as the data content, acquisition time, and monitoring point location. By comparing the eigenvalues ​​of the same data block on different nodes, data inconsistencies are detected. For example, if the data for a surface deformation monitoring point differs between different nodes, a voting mechanism is required to determine the correct data version.

[0110] During the voting process, high-performance nodes receive greater voting weight because they provide more up-to-date data and higher data reliability. When data inconsistencies are discovered, the node with the fastest response time initiates the vote, followed by other nodes in order of their response time. For each disputed data block, multiple rounds of voting determine the final correct version. The voting results are recorded in the block, forming an immutable proof of decision.

[0111] Data backup prioritization fully considers the business value of the data. For example, data points detecting abnormal surface deformation receive the highest backup priority, as this data is directly relevant to geological disaster warnings. Similarly, data from monitoring points detecting abnormal water quality also receive a higher backup priority. Data integrity checks not only verify the integrity of the data itself, but also the integrity of related metadata, such as monitoring time, monitoring point location, and instrument parameters.

[0112] Data sharding strategies are flexibly adjusted based on data characteristics. For monitoring data with strong time series characteristics, sharding is performed based on the time dimension to facilitate subsequent data recovery by time period. For data with strong spatial correlation, sharding is performed based on spatial location to facilitate regional data recovery. Each shard generates a corresponding checksum. The checksum design takes into account the characteristics of the data and employs stronger error correction capabilities for important fields.

[0113] Backup records contain complete data traceability information. Each record clearly documents the data's source, processing, and storage location. For example, for surface deformation monitoring data, records include timestamps such as the original acquisition time, data processing time, data verification time, and backup time, as well as the data's storage location on each node. These records form a complete data lifecycle chain, through which any data anomalies can be traced back to their source.

[0114] In a specific embodiment, the process of executing step S106 may include the following steps:

[0115] (1) Sort the verification records by timestamp, divide the verification records into intervals by time window, and segment the data sequence according to the continuity of the operation to obtain time series analysis data;

[0116] (2) Extract user operation behaviors based on time series analysis data, classify the behavior sequences according to the operation types, describe the operation process through the data access path, and obtain behavior feature data;

[0117] (3) Clustering user behavior patterns based on behavioral feature data, summarizing behavioral patterns according to the order of operations, and grouping operation patterns through similarity calculation to obtain operation pattern data;

[0118] (4) Counting access behaviors based on operation mode data, counting the number of visits per unit time, and analyzing the operation frequency according to the access target to obtain access frequency data;

[0119] (5) The user operation location is distributed and counted based on the access frequency data, the access location is marked by geographic coordinates, and the spatiotemporal characteristics are associated with the access period to obtain spatiotemporal characteristic data;

[0120] (6) Perform anomaly comparison on spatiotemporal feature data, identify abnormal operations through normal behavior templates, classify abnormal behaviors according to risk levels, and obtain an audit report.

[0121] Specifically, verification records are processed in a time series manner. Each verification record includes a timestamp accurate to the millisecond. Time windows are divided based on the characteristics of business operations. For example, a day is divided into multiple operation periods: morning shift (8:00-16:00), evening shift (16:00-24:00), and night shift (0:00-8:00). Within each time window, the data sequence is segmented based on the continuity of operations. Continuity is determined by the operation time interval, the relevance of the operation objects, and the association of the operation types. In-depth processing of time series analysis data focuses on the characteristics of user operation behavior. Operation types include basic operations such as data query, data modification, and data deletion, as well as complex operations such as data analysis and report generation. The data access path records the complete operation chain of a user from login to logout, including information such as the type of data accessed, the order of access, and the duration of stay. By analyzing this information, a characteristic description of user operation behavior is formed.

[0122] Behavioral clustering analysis uses a time-series-based clustering algorithm. First, the operation sequence is converted into a feature vector. The feature dimensions include operation type, operation frequency, operation duration, etc. The analysis of the operation sequence focuses on the dependency relationship between operations. For example, data viewing operations usually precede data modification operations. Similarity calculation uses a dynamic time warping algorithm, which can handle operation sequences of different lengths and consider the elasticity of operation timing. When performing access behavior statistics based on operation mode data, the focus is on the operation density and operation distribution per unit time. The statistical window can be hours, shifts, or days, and different statistical granularities are set for different types of operations. The analysis of access targets includes data type distribution, access level distribution, and operation permission distribution. These statistical results reflect the user's job responsibilities and operating habits.

[0123] Analyzing the geographic distribution of user operations is an important tool for detecting abnormal access. Each access request carries geographic coordinates, which are converted to actual locations through geocoding. Correlation analysis of spatiotemporal features combines access time and location to form a spatiotemporal trajectory of user activity. For example, access requests occurring in different locations within a short period of time often indicate potential security risks.

[0124] Abnormal behavior identification is based on the construction of normal behavior templates. These templates are extracted from historical access records and include features such as typical operation patterns, common access times and locations, and reasonable operation frequencies. For example, the normal operation pattern of geological data collectors involves uploading data from fixed monitoring points, concentrated during work hours, and primarily involving data upload and simple data verification. The normal operation pattern of data analysts involves more data query and analysis operations, typically performed in a fixed office space. Risk levels are determined based on multiple dimensions of abnormal characteristics. The highest risk level corresponds to clear violations of security policies, such as unauthorized data modification and bulk downloads of sensitive data. The medium risk level corresponds to behavior that deviates from the normal pattern but does not pose a direct threat, such as data access at unusual times and login requests from unusual locations. The low risk level corresponds to minor anomalies, such as small fluctuations in operation frequency and atypical operation sequences.

[0125] For example, a geological environment monitoring project involves data collection and analysis at multiple monitoring points, with users including field monitoring personnel, data analysts, and system administrators. A time series analysis of verification records over a week reveals that field monitoring personnel regularly upload data between 9:00 AM and 3:00 PM daily, with an approximately two-hour data upload interval for each monitoring point. This constitutes a basic operational time series signature.

[0126] When analyzing user behavior, we found that the typical sequence for monitors is: logging in - selecting a monitoring point - uploading data - verifying data - and then logging out. The entire process takes between 5 and 10 minutes. A typical sequence for data analysts includes: logging in - querying data - analyzing data - generating reports - and exporting data. This process takes longer, potentially lasting several hours. These sequences constitute the behavioral characteristics of different user roles.

[0127] Cluster analysis of these behavioral characteristics revealed three primary operational modes: data collection mode, data analysis mode, and system management mode. Each mode has its own specific operational sequence and timing characteristics. For example, operations in the data collection mode are typically linear, short, and repetitive; while operations in the data analysis mode are more complex, involving multiple iterations of query and analysis.

[0128] Access frequency statistics reveal clear temporal patterns. Weekday visits are significantly higher than weekend visits, and there are also significant differences in access patterns between morning and evening shifts. Analysis of access targets reveals data access trends among different user groups: monitors primarily access raw data, analysts access statistical and historical data more frequently, and managers' access is distributed across various data types.

[0129] The spatiotemporal feature analysis pays special attention to off-site access and cross-regional access. Under normal circumstances, the access location of the monitoring personnel should be consistent with the location of the monitoring point, and the access location of the data analyst should be within the office area. Any access that deviates from this pattern will be marked as a potential anomaly. For example, the data upload location of the monitoring personnel is too far away from the monitoring point, or the analyst accesses sensitive data from an abnormal location during non-working hours. The generated audit report records in detail the abnormal behavior discovered and its risk assessment results. For high-risk anomalies, the report gives the specific time, location, operation content and violation type; for medium and low-risk anomalies, the report provides relevant background information and suggested improvement measures.

[0130] In a specific embodiment, the process of clustering user behavior patterns using behavior feature data may include the following steps:

[0131] (1) Sequence segmentation of behavioral feature data, dividing behavioral segments by operation time intervals, marking behavioral sequences according to minimum operation units, and obtaining behavioral sequence data;

[0132] (2) Calculate the operation correlation through the behavior sequence data, perform correlation analysis on the continuous behavior through the operation combination, quantify the behavior relationship according to the operation co-occurrence frequency, and obtain the behavior correlation data;

[0133] (3) Constructing a behavioral distance matrix based on behavioral association data, evaluating the similarity of behavioral sequences by calculating the edit distance, and merging behavioral patterns according to the distance threshold to obtain behavioral distance data;

[0134] (4) Perform hierarchical clustering on the behavioral distance data, divide the behavioral categories by the inter-cluster distance, and classify the user behavior patterns according to the cluster center to obtain behavioral cluster data;

[0135] (5) Extracting behavioral feature vectors based on behavioral clustering data, describing behavioral patterns by operating transition probabilities, and characterizing behavioral patterns according to temporal relationships to obtain behavioral pattern data;

[0136] (6) The operation modes are grouped according to the behavioral regularity data, similar behaviors are merged through the similarity threshold, and the operation modes are identified according to the behavioral characteristics to obtain the operation mode data.

[0137] Specifically, behavior sequences are segmented based on the time interval between operations. When the time interval between adjacent operations exceeds a preset threshold, the interval between the two operations is considered a segmentation point for a behavior segment. The minimum operation unit is an indivisible basic operation, such as data query, data modification, and data upload. Each minimum operation unit contains basic attributes such as the operation type, operation object, and operation time. Behavior sequences are marked using string encoding, with each operation type corresponding to a unique character code. The complete behavior sequence description is formed by string concatenation.

[0138] Operation correlation calculation focuses on the relationship between consecutive operations. Operation combination analysis considers the order and time interval of operations. For example, "data query - data modification - data upload" constitutes a typical operation combination. Operation co-occurrence frequency indicates the probability that two operations co-occur within the same behavior segment. The higher the frequency, the stronger the correlation between the two operations. By constructing an operation correlation matrix, the correlation strength between all pairs of operations is recorded.

[0139] The behavior distance matrix is ​​constructed using the edit distance calculation method. The edit distance is the minimum number of operations required to transform one behavior sequence into another, including insertions, deletions, and substitutions. By calculating the edit distance between two behavior sequences, we can quantify the degree of similarity between them. The distance threshold is set based on specific business needs. When the edit distance between two behavior sequences is less than the threshold, the two sequences are considered to belong to the same behavior pattern.

[0140] Hierarchical cluster analysis uses a bottom-up aggregation strategy. Each behavioral sequence is first treated as a separate cluster. The closest clusters are then merged until a preset number of clusters is reached or the inter-cluster distance exceeds a threshold. Inter-cluster distances are calculated using the average linkage method, which averages the distances between all pairs of sequences within two clusters. The cluster center is the sequence with the smallest average distance to other sequences within the same cluster. The cluster center represents the typical characteristics of the behavioral pattern.

[0141] The extraction of behavioral feature vectors focuses on the statistical and structural characteristics of the sequence. Operation transition probabilities describe the transition relationships between different operations and are represented by constructing a Markov transition matrix. For example, in geological data processing, data query operations are more likely to be followed by data analysis operations, while data modification operations are often followed by data verification operations. The characterization of temporal relationships considers not only the order of operations but also the distribution of their time intervals.

[0142] When grouping operational patterns, a reasonable similarity threshold must be set. Similarity calculations comprehensively consider multiple dimensions, including the edit distance of the behavioral sequence, the probability of operational transitions, and temporal characteristics. When merging similar behaviors, the most representative features are retained to form a standard pattern for that type of behavior. Behavioral feature identification includes information such as pattern number, typical sequence, and key features.

[0143] Taking geological environmental monitoring at a mining area as an example, the entire analysis process is illustrated: Monitoring work includes multiple steps, including on-site data collection, data processing, and data analysis. Analysis of a single day's operation logs revealed that the typical operation sequence for data collectors includes: logging in (L) - selecting a monitoring point (S) - data collection (C) - data upload (U) - data verification (V) - exiting the system (E). This sequence can be represented as "LSCUVE." When the time interval between two collection operations exceeds 30 minutes, it is classified as a distinct behavior segment.

[0144] When calculating operation correlation, we found that pairs like "data collection - data upload" and "data upload - data verification" co-occur frequently, while pairs like "data collection - data analysis" rarely co-occur. This correlation reflects the rationality of the business process. By calculating the edit distance, we can identify similar operation patterns. For example, collection operations at different monitoring points may differ only in the point selection step.

[0145] As cluster analysis progresses, the operation sequences gradually form distinct categories. Each category has its own unique characteristics. For example, operation sequences for data collection are typically short and regular, while operation sequences for data analysis are longer and involve multiple iterations. The cluster centers represent the most typical operation sequences for each category, and these sequences can serve as reference standards for behavioral pattern recognition.

[0146] When extracting behavioral patterns, we pay special attention to the temporal characteristics of operations. For example, data collection operations typically occur at fixed time points, while data analysis operations are more dispersed over time. These temporal characteristics, together with the operation transition probabilities, form a complete description of the behavioral pattern. Finally, by setting an appropriate similarity threshold, behavioral sequences with similar characteristics are grouped into the same operational pattern, forming a behavioral classification system.

[0147] The above describes the geological data security storage method based on blockchain in the embodiment of the present application. The following describes the geological data security storage system based on blockchain in the embodiment of the present application. Figure 2 In the embodiments of the present application, an embodiment of a blockchain-based geological data security storage system includes:

[0148] The acquisition module is used to collect data on the topography, mining environment, hydrological environment and soil environment of the study area, establish spatiotemporal correlation mapping of the collected data through zoning geocoding, and standardize the mapped data through spatial reference conversion to obtain a standardized geological environment dataset;

[0149] The construction module is used to perform hierarchical credit assessment and dynamic load distribution on network nodes based on the standardized geological environment dataset, and to construct the network topology of the nodes using the minimum spanning tree algorithm to obtain the blockchain basic network;

[0150] A partitioning module is used to perform quadtree partitioning on the standardized geological environment dataset in the blockchain basic network according to a geographic grid, and to perform proximity group storage on the partitioned data through data association evaluation to obtain distributed data blocks;

[0151] A calculation module is used to build a three-layer access permission structure based on the distributed data block, calculate the permission inheritance relationship through data sensitivity quantification, verify user permissions through environmental parameter constraints, and obtain an access control chain;

[0152] A backup module is used to group and number nodes according to the access control chain, verify data consistency through polling and voting, and back up the data to be backed up through a data check code to obtain a verification record;

[0153] The classification module is used to perform time series analysis on the verification records, classify the operation modes by extracting behavioral features, determine abnormal behaviors through access frequency statistics and spatiotemporal feature matching, and obtain an audit report.

[0154] Through the collaborative efforts of these components, a standardized geological environment dataset is established by collecting data on the topography, mining environment, hydrological environment, and soil environment of the study area. A blockchain-based network is generated using a hierarchical network topology to store distributed data blocks. A three-tiered access permission structure is constructed to implement fine-grained access control. Round-robin voting and data checksums are used to ensure the consistency and reliability of data storage and backup. Furthermore, time series analysis and behavioral feature extraction techniques are combined to audit abnormal operations. This method effectively addresses the challenges of standardized management of massive amounts of heterogeneous geological data, ensuring efficient storage and dynamic expansion of geological data. It implements flexible and diverse hierarchical authorized access to geological data, ensures traceability and tamper-proofing throughout its lifecycle, reduces the security risks of data leakage and illegal use, supports trusted cross-domain geological data sharing by multiple parties, and lays a solid foundation for in-depth analysis and mining of geological big data. Furthermore, full-process abnormal behavior auditing and alerting can promptly detect and curb data abuse by internal personnel, improving the security protection capabilities of the entire system.

[0155] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the blockchain-based geological data secure storage method.

[0156] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0157] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0158] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for secure storage of geological data based on blockchain, characterized in that: The blockchain-based geological data security storage method includes: Data on the topography, mining environment, hydrological environment, and soil environment of the study area were collected. Spatiotemporal correlation mapping was established for the collected data through zoning geocoding, and the mapped data was standardized by spatial reference conversion to obtain a standardized geological environment dataset. Performing hierarchical credit assessment and dynamic load distribution on network nodes based on the standardized geological environment dataset, and constructing a network topology of the nodes using a minimum spanning tree algorithm to obtain a blockchain basic network; The standardized geological environment dataset in the blockchain basic network is partitioned into quadtrees according to a geographic grid, and the partitioned data is stored in proximity blocks through data association evaluation to obtain distributed data blocks; A three-layer access permission structure is constructed based on the distributed data block, the permission inheritance relationship is calculated through data sensitivity quantification, and the user permission is verified through environmental parameter constraints to obtain an access control chain; The nodes are grouped and numbered according to the access control chain, data consistency is verified through polling and voting, and the data to be backed up is backed up through a data check code to obtain a verification record; Performing time series analysis on the verification records, classifying the operation modes by extracting behavioral features, determining abnormal behaviors by access frequency statistics and spatiotemporal feature matching, and obtaining an audit report; The three-layer access permission structure is constructed based on the distributed data block, the permission inheritance relationship is calculated by quantifying the data sensitivity, and the user permission is verified through the environmental parameter constraint to obtain the access control chain, including: Divide the management levels based on the distributed data blocks, grade the access rights according to the importance of the data, and divide the user groups according to the permission levels to obtain a three-layer access rights structure; wherein the three-layer access rights structure includes an administrator permission layer, a business user permission layer, and a general user permission layer; Analyze the data content in the three-tier access permission structure, assess the data value based on the frequency of keyword occurrence, and classify the data sensitivity level by business type to obtain sensitivity quantitative data; The permission transfer relationship is sorted out through the sensitivity quantification data, the permission inheritance path is set by the user group level, and the permission transfer rules are limited according to the parent-child node relationship to obtain the permission inheritance data; Calculate the permission group to which the user belongs based on the permission inheritance data, determine the access level based on the user identity type, and constrain the user's operation scope based on the permissions within the group to obtain permission calculation data; Performing environmental detection on the authority calculation data, limiting the operation window according to the access time, and restricting the login range according to the access location to obtain environmental constraint data; The user authority status is judged according to the environmental constraint data, the user legitimacy is verified through identity authentication, and the access request is authorized through authority evaluation to obtain the access control chain.

2. The method for secure storage of geological data based on blockchain according to claim 1, characterized in that: The data of the topography, mining environment, hydrological environment and soil environment of the study area are collected, and a spatiotemporal correlation mapping is established for the collected data through zoning geocoding. The mapped data is standardized by spatial reference conversion to obtain a standardized geological environment dataset, including: The image data of the topography is acquired through remote sensing equipment, and the parameters of the mine environment are collected through field sampling. The hydrological environment is monitored long-term through the hydrological monitoring station, and the soil environment is analyzed through soil sampling to obtain the original environmental data. Dividing administrative area boundaries according to the original environmental data, dividing the area according to geographic grids, assigning a unique geographic code to each grid cell through coordinate mapping, and obtaining zoning code data; Marking the collection time based on the partition coding data, recording the data collection node through the timestamp, establishing the time-space correspondence through data association analysis, and obtaining time-space correlation data; Performing coordinate system conversion on the spatiotemporal correlation data, offsetting the spatial position by using easting translation parameters, northing translation parameters, and elevation translation parameters, adjusting the direction by using horizontal axis rotation parameters, vertical axis rotation parameters, and vertical axis rotation parameters, and unifying the scale by using scale ratio parameters to obtain benchmark conversion data; Normalizing the data format according to the benchmark conversion data, unifying the data structure by standardizing the attribute fields, and supplementing the missing values ​​through data integrity checks to obtain standardized data; The standardized data is quality-checked, duplicate data is eliminated through data consistency analysis, and abnormal data is marked through outlier detection to obtain the standardized geological environment data set.

3. The method for secure storage of geological data based on blockchain according to claim 1, characterized in that: The method performs hierarchical credit assessment and dynamic load distribution on network nodes based on the standardized geological environment dataset, and constructs a network topology of the nodes using a minimum spanning tree algorithm to obtain a blockchain basic network, including: Statistics are collected on the data storage volume in the standardized geological environment dataset, network nodes are classified by capacity according to the data flow per unit time, and node processing capabilities are evaluated according to resource occupancy rates to obtain node capacity data; The node trust score is calculated based on the node capacity data, the node stability is evaluated based on the historical data transmission success rate, and the node reliability is quantified based on the node online time to obtain hierarchical credit assessment data; Perform node task allocation and scheduling based on the hierarchical credit assessment data, sort task priorities according to node response time, and adjust resource allocation based on load balancing factors to obtain dynamic load distribution data; Calculate the connection weights between nodes based on the dynamic load distribution data, quantify the communication overhead between nodes according to the distance matrix, estimate the connection cost according to the communication quality index, and obtain node connection data; Performing minimum cost calculation on the node connection data, optimizing the node connection sequence according to the minimum spanning tree algorithm, selecting the connection scheme according to the path cost, and obtaining network topology data; The inter-node communication rules are established based on the network topology data, the data transmission method is standardized according to the communication protocol, and the block generation rules are configured through the node consensus mechanism to obtain the blockchain basic network.

4. The method for secure storage of geological data based on blockchain according to claim 1, characterized in that: The standardized geological environment dataset in the blockchain basic network is partitioned into quadtrees according to a geographic grid, and the partitioned data is stored in proximity blocks through data association evaluation to obtain distributed data blocks, including: Defining the spatial scope of the standardized geological environment dataset in the blockchain-based network, demarcating the boundaries of the study area according to geographic coordinates, and dividing regional geomorphic units according to topographic features to obtain regional scope data; Gridding the study area according to the regional range data, evenly dividing the area through the geographic grid, hierarchically subdividing the geographic grid according to the quadtree partitioning method, and obtaining quadtree partitioning data; Calculating the spatial relationship of partitions using the quadtree partition data, identifying adjacent partitions according to their spatial locations, and quantifying the degree of association between partitions based on geographic distances to obtain spatial relationship data; Analyzing the partition data content based on the spatial relationship data, calculating the partition content similarity through data correlation evaluation, quantifying the data correlation according to the attribute correlation coefficient, and obtaining correlation evaluation data; Merging the partitioned data into blocks based on the association evaluation data, defining the block range based on a proximity threshold, and controlling the size of the blocks based on the data block size to obtain block storage data; Node allocation is performed on the block storage data, storage nodes are selected through load balancing, and backup nodes are determined through a data redundancy strategy to obtain the distributed data block.

5. The method for secure storage of geological data based on blockchain according to claim 1, characterized in that: The nodes are grouped and numbered according to the access control chain, data consistency is verified by polling and voting, and the backup data is backed up by a data check code to obtain a verification record, including: Evaluate the node service capability according to the access control chain, group the nodes by processing performance, and sort the grouped nodes by number according to network distance to obtain node group data; The voting rules are set by the node grouping data, the voting order is arranged by the node response time, the node decision importance is distinguished according to the voting weight, and the polling voting data is obtained; Compare the storage content of each node based on the polling voting data, verify the data content through the data block feature value, mark the data differences according to the consistency judgment rules, and obtain consistency verification data; The consistency verification data is used to screen the data to be backed up, the backup priority is sorted by the importance of the data, the backup content is confirmed according to the data integrity, and a list of data to be backed up is obtained; Redundantly encode the data in the to-be-backed-up list, divide the backup data into blocks by data sharding, calculate the data check code by check bit generation, and obtain data check data; The data backup operation is recorded via the data verification data, the data version is identified by the backup time, and the data copy is located according to the backup location to obtain the verification record.

6. The method for secure storage of geological data based on blockchain according to claim 1, characterized in that: The verification records are analyzed in time series, the operation modes are classified by extracting behavioral features, abnormal behaviors are determined by access frequency statistics and spatiotemporal feature matching, and an audit report is obtained, including: The verification records are sorted according to timestamps, the verification records are divided into intervals by time windows, and the data sequence is segmented according to the continuity of operations to obtain time series analysis data; Extracting user operation behaviors based on the time series analysis data, classifying the behavior sequences according to the operation types, describing the operation processes through the data access paths, and obtaining behavior feature data; Clustering user behavior patterns based on the behavior feature data, summarizing behavior rules according to the operation sequence, grouping the operation patterns by similarity calculation, and obtaining operation pattern data; Collect statistics on access behaviors based on the operation mode data, count the number of accesses per unit time, analyze the operation frequency according to the access target, and obtain access frequency data; The user operation locations are distributed and counted based on the access frequency data, the access locations are marked using geographic coordinates, and the spatiotemporal features are associated with the access periods to obtain spatiotemporal feature data; The spatiotemporal feature data is compared for abnormalities, abnormal operations are identified through normal behavior templates, abnormal behaviors are graded according to risk levels, and the audit report is obtained.

7. The method for secure storage of geological data based on blockchain according to claim 6, characterized in that: The user behavior patterns are clustered using the behavior feature data, the behavior rules are summarized according to the operation sequence, and the operation patterns are grouped by similarity calculation to obtain the operation pattern data, including: Sequence segmentation is performed on the behavior feature data, the behavior segments are divided according to the operation time interval, and the behavior sequence is marked according to the minimum operation unit to obtain behavior sequence data; Calculating the operation correlation degree through the behavior sequence data, performing correlation analysis on the continuous behaviors through the operation combination, quantifying the behavior relationship according to the operation co-occurrence frequency, and obtaining behavior correlation data; Constructing a behavior distance matrix based on the behavior association data, evaluating the similarity of behavior sequences by editing distance calculation, merging behavior patterns according to a distance threshold, and obtaining behavior distance data; Performing hierarchical clustering on the behavior distance data, dividing the behavior categories by inter-cluster distance, and classifying the user behavior patterns according to the cluster centers to obtain behavior cluster data; Extracting behavioral feature vectors based on the behavioral clustering data, describing behavioral regularities through operating transition probabilities, and characterizing behavioral patterns according to temporal relationships to obtain behavioral regularity data; The operation modes are grouped according to the behavior regularity data, similar behaviors are merged according to a similarity threshold, and the operation modes are identified according to the behavior characteristics to obtain the operation mode data.

8. A blockchain-based geological data security storage system, used to implement the blockchain-based geological data security storage method according to any one of claims 1 to 7, characterized in that: The blockchain-based geological data security storage system includes: The acquisition module is used to collect data on the topography, mining environment, hydrological environment and soil environment of the study area, establish spatiotemporal correlation mapping of the collected data through zoning geocoding, and standardize the mapped data through spatial reference conversion to obtain a standardized geological environment dataset; A construction module is used to perform hierarchical credit assessment and dynamic load distribution on network nodes based on the standardized geological environment dataset, and to construct a network topology of the nodes using a minimum spanning tree algorithm to obtain a blockchain basic network; A partitioning module is used to perform quadtree partitioning on the standardized geological environment dataset in the blockchain basic network according to a geographic grid, and to perform proximity group storage on the partitioned data through data association evaluation to obtain distributed data blocks; A calculation module is used to build a three-layer access permission structure based on the distributed data block, calculate the permission inheritance relationship through data sensitivity quantification, verify user permissions through environmental parameter constraints, and obtain an access control chain; A backup module is used to group and number nodes according to the access control chain, verify data consistency through polling and voting, and back up the data to be backed up through a data check code to obtain a verification record; The classification module is used to perform time series analysis on the verification records, classify the operation modes by extracting behavioral features, determine abnormal behaviors through access frequency statistics and spatiotemporal feature matching, and obtain an audit report.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the blockchain-based geological data secure storage method as described in any one of claims 1 to 7 is implemented.

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