Backup method based on natural resource geological database

Abstract

The invention relates to the technical field of database management, in particular to a backup method based on a natural resource geological database. The method comprises the following steps: acquiring natural resource geological data and performing data priority allocation to obtain priority allocation geological data; performing data slice check code generation on the structured natural resource geological data according to the priority distribution geological data to obtain a data slice check code; obtaining natural resource geological data to be backed up and carrying out verification and comparison to obtain a geological data incremental backup set; performing real-time data increment consistency check according to the geological data increment backup set and the natural geological data slices to obtain a real-time increment data verification report; acquiring natural resource missing geological data; and according to the real-time incremental data verification report, carrying out missing recovery rollback on the natural resource missing geological data to obtain natural resource filling geological data. According to the method, the data backup and recovery cost and time can be remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of database management, and particularly to a backup method based on a natural resources geological database. Background Art

[0002] In the current field of natural resources development and management, geological databases have become indispensable important tools in aspects such as resource development, environmental monitoring, and risk assessment. These databases contain a large amount of geological information, such as key data like mining area distribution, resource composition, mining history, environmental data, and stratigraphic structure. For natural resources geological databases, high requirements are placed on security, availability, and data integrity. Any data loss, damage, or inconsistency may lead to serious consequences such as increased resource development costs, incorrect decisions, and aggravated environmental risks. Therefore, ensuring the efficient backup and recovery of geological databases is particularly important. During the backup and recovery process of natural resources geological databases, data consistency is a crucial factor. Traditional backup methods often have deficiencies in aspects such as data synchronization, version control, and timestamp management. During the recovery operation, it is necessary to manually verify the data consistency between the data source and the target multiple times, which easily leads to problems such as recovery errors, data misalignment, and version mismatches. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a backup method based on a natural resources geological database to solve at least one of the above technical problems.

[0004] To achieve the above object, a backup method based on a natural resources geological database includes the following steps:

[0005] Step S1: Obtain natural resources geological data through the natural resources geological database, and perform data classification structuring on the natural resources geological data to obtain structured natural resources geological data; perform data priority allocation on the structured natural resources geological data to obtain priority-allocated geological data;

[0006] Step S2: Perform structured data slicing on the structured natural resources geological data according to the priority-allocated geological data to obtain natural geological data slices; generate data slice check codes for the natural geological data slices to obtain data slice check codes;

[0007] Step S3: Obtain the natural resources geological data to be backed up, and perform incremental differential processing on the natural resources geological data to be backed up and the structured natural resources geological data to obtain the geological incremental data to be backed up, and verify and compare the geological backup set and the data slice check codes to obtain the geological data incremental backup set;

[0008] Step S4: Perform real-time data incremental consistency verification based on the geological data incremental backup set and the natural geological data slices, thereby obtaining a real-time incremental data verification report and uploading it to the natural resources geological database to execute the data storage task;

[0009] Step S5: Obtain the missing geological data of natural resources through the natural resources geological database; perform missing recovery rollback on the missing geological data of natural resources according to the real-time incremental data verification report, thereby obtaining the filled geological data of natural resources and uploading it to the natural resources geological database to execute the data filling task.

[0010] The present invention optimizes the management and utilization efficiency of the geological database through the classification, structured processing, and priority allocation of natural resource geological data, enabling critical data to be processed and stored preferentially, thereby ensuring the efficient backup and accurate restoration of data. By structuring and slicing geological data and generating slice verification codes, the integrity and accuracy of data during the backup and restoration processes are significantly improved, reducing potential risks caused by data loss or damage. At the same time, the slicing technology makes large-scale data processing more flexible, facilitating subsequent batch verification and restoration operations. The incremental differential processing method effectively reduces the amount of data to be backed up, lowering storage requirements and transmission costs, while ensuring that the backed-up data only includes the changed parts, greatly enhancing the real-time performance of backups and the efficiency of incremental restoration. The verification mechanism for slice verification codes further strengthens the integrity verification ability of the backed-up data. By comparing and verifying the verification codes, data inconsistencies can be quickly identified, providing an accurate basis for subsequent remedial measures. The introduction of real-time data incremental consistency verification enables the quality of the backed-up data to be dynamically monitored and verified. This online verification ability not only improves the reliability of data storage but also effectively guards against systematic problems caused by incorrect data backups. The verification report generated based on the consistency verification can provide clear backup status and data integrity information for natural resource managers, further enhancing the controllability of data management. The rapid rollback and compensation mechanism for missing data achieves precise repair of missing data through an intelligent processing flow, effectively solving the problems of low efficiency and high error rates in traditional manual restoration operations. The missing data filling strategy guided by the real-time verification report can dynamically adjust the restoration priority, ensuring the timely replenishment of critical data and comprehensively enhancing the restoration ability of the geological database in case of emergencies. The entire process realizes the intelligence and automation of the backup and restoration processes, greatly reducing the possibility of manual operations and human errors, and improving the security and stability of the natural resource geological database. The architecture of this method has high scalability and can be flexibly adjusted according to the database requirements of different scales and complexities, suitable for various application scenarios from small resource points to large-scale regional resource development. By comprehensively integrating the classification management, incremental backup, slice verification, real-time verification, and missing data rollback compensation of geological data, this solution not only ensures the consistency of geological data during the backup and restoration processes but also significantly reduces the costs and time of data backup and restoration, providing strong technical support for natural resource development, environmental monitoring, risk assessment, and other work.

[0011] Optionally, step S1 is specifically as follows:

[0012] Step S11: Obtain natural resource geological data from the natural resource geological database and perform data preprocessing on the natural resource geological data to obtain the natural resource geological data to be analyzed;

[0013] Step S12: Perform structured data division on the natural resource geological data to be analyzed, so as to obtain structured geological data and unstructured geological data;

[0014] Step S13: Perform geological data structured conversion on the unstructured geological data, so as to obtain structured conversion geological data;

[0015] Step S14: Perform spatial merging of the structured conversion geological data and the structured geological data to obtain structured natural resource geological data;

[0016] Step S15: Perform data priority allocation on the structured natural resource geological data to obtain priority-allocated geological data.

[0017] Through the comprehensive preprocessing and structured division of natural resource geological data, the present invention improves the accuracy and efficiency of geological data analysis, and provides a reliable data basis for subsequent resource development and management. In the process of structured division of geological data, structured and unstructured data are clearly distinguished, making the processing of different types of data more targeted and laying a foundation for the accurate extraction of complex geological information. The structured conversion of unstructured geological data significantly improves the usability of the data, converting the originally scattered and complex unstructured information into standardized structured data, thus greatly reducing the difficulty of data analysis and improving the efficiency of data integration and utilization. By performing spatial merging on the structured conversion geological data and the existing structured geological data, the integrity and consistency of the geological data are further enhanced. This merging strategy can effectively integrate data resources from different sources and in different formats, providing richer and more accurate data support for comprehensive and in-depth geological research. The data priority allocation mechanism intelligently sorts the structured natural resource geological data according to actual needs, enabling important data to be processed and analyzed first, thus optimizing the utilization efficiency of data resources and improving the execution effect of key tasks.

[0018] Optionally, step S13 is specifically:

[0019] Perform unstructured data classification on the unstructured geological data to obtain text-type geological data, image-type geological data, and geological sensing data;

[0020] Extract geological feature keywords from the text-type geological data to obtain text geological feature data, and perform key-value storage on the text geological feature data to obtain structured text geological data;

[0021] Extract computer vision geological features from the image-type geological data to obtain image geological feature data, and perform spatial coordinate conversion on the image geological feature data to obtain structured image geological data;

[0022] Extract the frequency-domain features and time-domain features of geological sensing data to obtain geological sensing feature data, and convert the geological sensing feature data into a time series format to obtain structured geological sensing data;

[0023] Perform geological feature mapping and annotation based on the structured image geological data, structured geological sensing data, and structured text geological data to obtain structured natural resource geological data.

[0024] Through the classification and refined processing of unstructured geological data, the present invention improves the utilization efficiency and analysis accuracy of the data. In the unstructured data classification stage, the text, image, and sensing data are successfully and clearly divided, and different processing strategies are adopted for different data types, providing a clear path and technical basis for subsequent feature extraction and structured conversion. In the processing of text-type geological data, the accurate extraction of key information is achieved through the extraction of feature keywords, and the key-value storage method is used to further optimize the data organization and query efficiency, thus forming standardized structured text geological data, significantly improving the usability and processing efficiency of text-type data. For image-type geological data, geological features are extracted through computer vision technology, which not only realizes the intelligent recognition of geological information but also effectively avoids the inefficiency and subjective errors of traditional manual annotation. Subsequently, combined with spatial coordinate conversion, the image data is highly matched with the geological spatial information, providing a solid foundation for 3D modeling and spatial analysis in geological research. In the processing of geological sensing data, frequency-domain and time-domain feature extraction technologies are used to efficiently mine the key features in the original sensing data, and at the same time, the data standardization and unified expression of time features are realized through time series format conversion, thus enhancing the usability and analysis ability of sensing data in dynamic geological monitoring. Using the structured text, image, and sensing data for the comprehensive mapping and annotation of geological features, structured natural resource geological data is formed, realizing the organic integration and unified expression of multi-source data. This comprehensive mapping and annotation technology can comprehensively reflect the multi-dimensional features of geological objects, significantly improve the accuracy and integrity of geological data, and thus provide strong data support for geological research, resource management, and environmental protection. It solves the problems of low processing efficiency, inaccurate feature extraction, and difficult data integration of traditional unstructured data, significantly improves the overall performance of the geological data management system, and has broad application value and promotion potential.

[0025] Optionally, step S15 is specifically as follows:

[0026] Step S151: Extract the regional features of the structured natural resource geological data to obtain the geological information coverage area data, regional information update frequency data, and regional resource density data;

[0027] Step S152: Perform spatial overlay analysis based on the geological information coverage area data and the regional resource density data, so as to obtain the coverage area resource density data;

[0028] Step S153: Estimate the regional resource reserves for the coverage area resource density data, so as to obtain the regional resource reserve estimation data;

[0029] Step S154: Construct a regional geological data priority scoring system according to the regional information update frequency data and the regional resource reserve estimation data;

[0030] Step S155: Allocate data priorities to the structured natural resource geological data through the regional geological data priority scoring system, so as to obtain the priority-allocated geological data.

[0031] Through the refined processing and regional feature analysis of the structured natural resource geological data, the present invention significantly improves the practical application value of geological data in resource development and management. Through the regional feature extraction technology, the scope, update frequency, and resource density of the geological information coverage area are comprehensively identified, making the originally scattered data more systematic and laying a solid foundation for subsequent analysis. This feature extraction method can quickly capture the key geological features in the region, providing efficient support for regional division, resource distribution analysis, and dynamic update. In the process of spatial overlay analysis, by combining the geological information coverage area and the resource density data, the comprehensive processing of multi-dimensional data is realized. The overlay analysis not only improves the accuracy of resource distribution but also provides quantifiable density distribution information for resource utilization planning, optimizing the scientificity and reliability of resource evaluation. Subsequently, through the regional resource reserve estimation technology, the coverage area resource density data is converted into specific reserve estimates, providing an intuitive basis for the feasibility analysis and economic benefit evaluation of natural resource development, and significantly reducing the risk of decision-making errors caused by inaccurate reserve estimation. The construction of the priority scoring system is based on the dual indicators of regional information update frequency and resource reserves, fully considering the regional resource value and dynamic change characteristics. This scoring system not only improves the scientificity of geological data allocation but also provides a flexible and accurate decision-making basis for regional resource management. By allocating data priorities through the scoring system, the data processing efficiency and utilization priority of key resource areas are effectively ensured, optimizing the resource development sequence and avoiding the problems of resource waste and inefficient development. Through the overall process optimization of regional feature extraction, spatial overlay analysis, reserve estimation, and priority scoring, a highly intelligent and systematic geological data processing and allocation method is constructed. This method solves the problems in traditional geological data management such as incomplete feature extraction, inaccurate resource evaluation, and lack of scientific basis for priority allocation, provides a new technical means for natural resource development and management, greatly improves resource utilization efficiency and economic benefits, and has broad potential for popularization and application.

[0032] Optionally, step S2 is specifically as follows:

[0033] Step S21: Divide the priority-assigned geological data into high-priority geological data and low-priority geological data;

[0034] Step S22: Slice the structured natural resource geological data according to the high-priority geological data to obtain high-priority geological data slices; slice the structured natural resource geological data according to the low-priority geological data to obtain low-priority geological data slices;

[0035] Step S23: Integrate the high-priority geological data slices and the low-priority geological data slices in terms of the time sequence of geological data slices to obtain natural geological data slices;

[0036] Step S24: Convert the natural geological data slices into byte streams to obtain natural geological byte streams;

[0037] Step S25: Generate data slice check codes according to the natural geological byte streams to obtain data slice check codes.

[0038] Through the precise division and optimization of geological data with priority allocation, the present invention significantly improves the efficiency and reliability of geological data management. Through the priority division technology, high-priority and low-priority geological data are effectively distinguished to ensure that key data is processed first in subsequent processing. This approach lays the foundation for the hierarchical management of geological data, optimizes the allocation order of data resources, and avoids resource waste and priority conflict problems during the data processing process. In the slicing processing stage, high-priority and low-priority geological data are sliced separately to implement a more refined processing strategy. The slicing of high-priority geological data ensures the fast access and processing capabilities of core data, while the slicing of low-priority geological data provides a flexible management solution for secondary data. This slicing method improves the orderliness of data storage and processing and provides higher flexibility and scalability for subsequent integration operations. Through the chronological integration of geological data slices, not only are the scattered data slices effectively integrated into a complete natural geological data structure, but also the chronological consistency and integrity of the data are enhanced, providing a basic guarantee for the dynamic analysis and traceability of geological information. The chronologically integrated natural geological data slices can more accurately reflect the variation law of data in the time dimension, contributing to the joint utilization of historical data and real-time data in resource development. In the byte stream conversion stage, the natural geological data slices are efficiently compressed into byte stream form, which significantly reduces the costs of data transmission and storage while retaining the integrity and traceability of the data. The byte stream data structure is more suitable for large-scale network transmission and cloud storage, improving the accessibility and sharing efficiency of the data. Through the generation of check codes, the integrity and security of data slices during transmission and storage are further ensured. The data slice check codes can quickly detect and locate data damage or error problems, providing an efficient verification mechanism for the backup and recovery of geological data. This verification method not only improves the verification efficiency of data consistency but also provides a reliable technical guarantee for the full life cycle management of geological data.

[0039] Optionally, step S25 is specifically as follows:

[0040] Perform byte stream priority sharding on the natural geological byte stream to obtain geological byte stream priority shards;

[0041] Calculate the shard hash values for the geological byte stream priority shards to obtain high-priority shard hash values and low-priority shard hash values;

[0042] Generate check codes based on the high-priority shard hash values to obtain high-priority initial check codes, and add redundant check information to the high-priority initial check codes to obtain high-priority check codes;

[0043] Generate check codes based on the low-priority shard hash values to obtain low-priority check codes;

[0044] Summarize the slice verification codes of the natural geological data slices according to the high-priority verification code and the low-priority verification code, so as to obtain the data slice verification code.

[0045] Through multi-step optimization of the priority sharding and subsequent processing of the natural geological byte stream, the present invention realizes an efficient and secure geological data verification system. Through the byte stream priority sharding technology, the geological byte stream is divided into high-priority shards and low-priority shards according to the importance of the data. This sharding method ensures the high-priority processing of key data, while taking into account the storage and verification requirements of secondary data, optimizing the allocation and utilization of resources, and laying a foundation for the differential processing of subsequent data verification. During the calculation of the shard hash value, a unique identifier for each shard is generated, ensuring the clear traceability of the data shards during transmission, storage, and verification. The hash values of the high-priority shards and the low-priority shards are calculated separately, which not only improves the accuracy of data integrity verification but also provides technical support for the independent verification of data with different priorities. The hash value, as the fingerprint information of the data shard, provides a reliable basis for the subsequent generation of the verification code. Through the generation of the high-priority verification code and the addition of redundant verification information, the enhanced protection of high-priority data is realized. The addition of redundant verification information further improves the fault tolerance of high-priority data during the verification process, ensuring that critical data can be quickly recovered even if it is damaged or lost during transmission or storage. This enhanced verification mechanism provides double guarantees for the security and reliability of critical geological data. The generation of the low-priority verification code provides basic verification capabilities for secondary data, meeting the integrity requirements of general data transmission and storage. Combining the comprehensive verification of the high-priority verification code and the low-priority verification code not only improves the overall data verification efficiency but also enhances the hierarchical management ability of the data, ensuring the flexibility and security of data with different priorities. During the slice verification code summarization stage, by integrating the high-priority and low-priority verification codes, the integrity of the natural geological data slices is comprehensively verified. The slice verification code summarization ensures the consistency and integrity of the overall data, providing a reliable guarantee for the subsequent management and application of geological data. This integrated verification method not only reduces the complexity of the verification operation but also provides technical support for the multi-scenario adaptability and efficient management of the data.

[0046] Optionally, step S3 is specifically as follows:

[0047] Step S31: Obtain the natural resource geological data to be backed up, and perform data structured integration on the natural resource geological data to be backed up, so as to obtain the structured natural resource geological data to be backed up;

[0048] Step S32: Perform data incremental difference on the natural resource geological structured data to be backed up and the structured natural resource geological data, so as to obtain the geological incremental data to be backed up;

[0049] Step S33: Based on the geological incremental data to be backed up, perform data slicing and checksum generation, so as to obtain incremental data slices and incremental data checksums;

[0050] Step S34: Verify and compare the incremental data checksum and the data slice checksum, so as to obtain the geological data incremental backup set.

[0051] Through multi-step optimizations such as structured integration, incremental difference, slicing verification, and verification comparison of the natural resource geological data to be backed up, the present invention effectively improves the efficiency, integrity, and security of geological data backup. Through data structured integration, the natural resource geological data to be backed up is converted into a structured format, thereby enhancing the accessibility and manageability of the data. Structured data integration helps with efficient storage, fast retrieval, and data consistency maintenance, ensuring higher operability of the data during backup and recovery. In the data incremental difference stage, only the newly added or changed data is backed up, thus significantly reducing the amount of data to be backed up. Incremental difference backup not only saves storage space but also improves the speed of backup and recovery, helping to respond more quickly to data recovery needs. This differential backup technology also reduces resource consumption during the data backup process and reduces the impact on system performance. During the data slicing and checksum generation process, the incremental data is sliced and the corresponding checksums are generated, which helps to ensure the integrity of the data slices during transmission, storage, and verification. The checksum of each slice serves as a data fingerprint to verify whether data corruption or loss has occurred in the slice, thereby enhancing the verifiability and fault tolerance of the data. This not only ensures the independence of the data slices but also improves the security of the backed-up data. By verifying and comparing the incremental data checksum and the data slice checksum, an efficient and reliable geological data incremental backup set can be generated. This verification process ensures the accuracy and consistency of the data between backup and recovery, avoiding data loss or errors caused by data misalignment, version mismatch, etc. In addition, this also improves the recoverability and disaster tolerance of the backup system, ensuring that in the event of system failures, data corruption, or other anomalies, the complete geological data can be quickly restored, thus maximizing the security and reliability of the data.

[0052] Optionally, step S34 is specifically:

[0053] Step S341: Verify and compare the incremental data checksum and the data slice checksum, so as to obtain the successfully verified incremental checksum and the failed verified incremental checksum;

[0054] Step S342: Perform a successful geological incremental data backup on the incremental data slices based on the successfully verified check code, so as to obtain the first geological data incremental backup data;

[0055] Step S343: Extract the failed incremental data of the geological incremental data to be backed up based on the failed verification incremental check code, so as to obtain the failed geological incremental data;

[0056] Step S344: Perform slice reconstruction verification comparison on the failed geological incremental data, so as to obtain the second geological data incremental backup data;

[0057] Step S345: Integrate the first geological data incremental backup data and the second geological data incremental backup data, so as to obtain a geological data incremental backup set.

[0058] Through strict verification and comparison of the incremental data check code and the slice check code, the present invention effectively improves the security, accuracy, and recovery reliability of incremental data backup. By verifying and comparing the incremental data check code and the data slice check code, data with successful verification and failed verification can be distinguished, thus ensuring the integrity and consistency of the backup data. For the data slices with successful verification, incremental data backup can be safely performed to obtain an efficient and error-free data incremental backup set. This not only reduces redundant data storage but also speeds up the backup process and improves the backup efficiency of the system. For the data with failed verification, by extracting and analyzing the failed check code, the incremental data with data errors or losses can be quickly located, thus ensuring that the error data can be effectively identified. The extraction and slice reconstruction operations of the failed verification data help to re-verify and repair the data slices during the recovery process, thus ensuring the high availability and consistency of the backup data. Through slice reconstruction and verification comparison, data errors can be further repaired to minimize the risk of data loss. Integrating the first geological data incremental backup and the second geological data incremental backup realizes the efficient integration and consistency verification of the data. This not only ensures the integrity of the incremental data during storage, transmission, backup, and recovery but also improves the retrievability and manageability of the data. The integration operation can avoid problems such as data misalignment and version mismatch caused by the scattered storage of incremental data, thus ensuring that the data can remain accurate and reliable in all operation links.

[0059] Optionally, step S344 is specifically:

[0060] Divide the priority of the geological incremental data for the failed geological incremental data according to the priority-allocated geological data, so as to obtain the high-priority failed geological incremental data and the low-priority failed geological incremental data;

[0061] Slice and reconstruct the geological incremental data with high - priority verification failure to obtain the high - priority geological incremental reconstructed data slices, and generate check codes for the high - priority geological incremental reconstructed data slices to obtain the high - priority geological incremental reconstructed check codes;

[0062] Verify and compare the high - priority geological incremental reconstructed check codes and the data slice check codes to obtain the successfully verified high - priority reconstructed check codes and the failed - verified high - priority reconstructed check codes;

[0063] Perform a successfully verified geological data incremental backup on the high - priority geological incremental reconstructed data slices according to the successfully verified high - priority reconstructed check codes to obtain the first reconstructed geological data incremental backup data;

[0064] Extract the high - priority geological incremental data with reconstruction verification failure from the high - priority geological incremental data with verification failure according to the failed - verified high - priority reconstructed check codes to obtain the high - priority geological incremental data with reconstruction verification failure;

[0065] Batch - reconstruct and inspect the low - priority geological incremental data with verification failure and the high - priority geological incremental data with reconstruction verification failure to obtain the second reconstructed geological data incremental backup data;

[0066] Integrate the first reconstructed geological data incremental backup data and the second reconstructed geological data incremental backup data for reconstructed incremental backup to obtain the second geological data incremental backup data.

[0067] Through a series of operations such as priority division, slice reconstruction, verification comparison, and backup integration on the geological incremental data with verification failures, the security, accuracy, and reliability of the geological incremental data backup are effectively improved. By dividing the priority of the geological incremental data with verification failures, the data can be divided into high-priority and low-priority, thus ensuring that critical data is processed first. This not only improves the efficiency of data recovery but also reduces unnecessary redundant data storage and ensures the manageability of the data. Slicing and reconstructing the high-priority geological incremental data with verification failures and generating a verification code can maximize the integrity and consistency of the data during the reconstruction process. This operation not only improves the accuracy of data slice reconstruction but also ensures the security of data slices during transmission and storage through verification code verification. The verification comparison operation can distinguish between successful and failed reconstructed verification codes, thus effectively ensuring the consistency and correctness of the data in all links. Based on the successfully verified high-priority reconstructed verification codes, the incremental backup of geological data can be smoothly carried out to ensure the integrity and recoverability of the backup data. For the data with verification failures, by reconstructing, verifying, and extracting the high-priority incremental data with verification failures, the error data can be quickly located and repaired, thus reducing the risk of data loss and incorrect transmission. For the low-priority geological incremental data with verification failures and the high-priority geological incremental data with reconstruction verification failures, through batch reconstruction and verification operations, these data slices can be gradually recovered and verified to ensure that all data can be correctly reconstructed and recovered. This not only improves the controllability of data recovery but also enhances the disaster recovery ability of the system. By integrating the first and second reconstructed geological incremental backup data, efficient integration and consistency verification of the backup data can be achieved. This not only optimizes data storage and management but also ensures the security and availability of the data in all backup, transmission, and recovery links.

[0068] Optionally, the batch reconstruction and verification in step S344 are specifically as follows:

[0069] Integrate the low-priority geological incremental data with verification failures and the high-priority geological incremental data with reconstruction verification failures in a time window to obtain the geological incremental data to be reconstructed;

[0070] Perform small-grained division of the data slices of the geological incremental data to be reconstructed to obtain the incremental data slices to be verified;

[0071] Perform batch data reconstruction on the incremental data slices to be verified to obtain the reconstructed slices of the incremental data to be verified;

[0072] Generate a verification code based on the reconstructed slices of the incremental data to be verified to obtain the verification code of the reconstructed slices;

[0073] Verify and compare the reconstructed slice check code and the data slice check code. If the verification and comparison are successful, perform geological data incremental backup on the corresponding reconstructed slice of the incremental data to be verified, so as to obtain the second reconstructed geological data incremental backup data; if the verification and comparison fail, store the corresponding reconstructed slice of the incremental data to be verified in the form of an exception record.

[0074] Through a series of operations such as time window integration, data slice division, batch reconstruction, and verification comparison of low-priority verification failure geological incremental data and high-priority geological incremental data with reconstruction verification failure, the present invention effectively improves the integrity, accuracy, and recoverability of geological data incremental backup. Through time window integration, data in different time periods can be uniformly processed, so as to better identify and recover continuous data slices, ensuring the consistency and integrity of data in the time dimension. Performing small-grain division on the geological incremental data to be reconstructed can improve the manageability and verification efficiency of data slices, ensuring that each slice can be quickly verified and repaired independently. This not only reduces the risk of error propagation during data reconstruction but also improves the positioning and repair speed of data slices. The batch data reconstruction operation further optimizes the controllability of data recovery, and each group of slices can be verified batch by batch, thereby gradually recovering the incremental data to be verified. When generating the reconstructed slice check code, each slice can be independently verified to verify the data integrity and consistency of each group of slices. Through verification comparison, if the verification comparison is successful, the corresponding slice can be used for geological data incremental backup to ensure the integrity and recoverability of the incremental backup data. If the verification comparison fails, the error slice is stored in the form of an exception record, thereby retaining the error information, which is helpful for subsequent data repair and fault diagnosis. Brief Description of the Drawings

[0075] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, objects, and advantages of the present invention will become more apparent:

[0076] Figure 1 It is a schematic flowchart of the steps of the backup method based on the natural resources geological database of the present invention;

[0077] Figure 2 It is a detailed schematic flowchart of step S1 in the present invention;

[0078] Figure 3 It is a detailed schematic flowchart of step S2 in the present invention;

[0079] The realization, functional characteristics, and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the drawings. Detailed Embodiments

[0080] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0081] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0082] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0083] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a backup method based on a natural resource geological database, and the method includes the following steps:

[0084] Step S1: Obtain natural resource geological data through the natural resource geological database, and perform data classification structuring on the natural resource geological data to obtain structured natural resource geological data; perform data priority allocation on the structured natural resource geological data to obtain priority-allocated geological data;

[0085] In this embodiment, a large amount of natural resource geological data is obtained from the natural resource geological database, and these data include geological attributes, distributions, ore deposit information, etc. The obtained data is subjected to classification structuring processing. For example, the data is divided into categories such as mineral resources, soil components, and geological structures, and stored in a structured natural resource geological data table. Subsequently, priority allocation is performed according to the importance of the data. For example, scarce resources and key ore deposit information are classified as high priority, while general distribution areas are classified as medium and low priority. The priority allocation adopts an algorithm based on criteria such as data scarcity, data reliability, and geological value to allocate each piece of data to the corresponding priority category.

[0086] Step S2: Perform structured data slicing on the structured natural resource geological data according to the priority-allocated geological data to obtain natural geological data slices; generate data slice verification codes for the natural geological data slices to obtain data slice verification codes;

[0087] In this embodiment, according to the priority-allocated data, perform slicing operations on the structured natural resource geological data to divide the data into smaller manageable slices. For example, divide each ore deposit area into multiple small slices, and each small slice includes specific geological attributes, coordinate information, etc. Subsequently, generate data verification codes for each slice, and verify each slice of data through a hashing algorithm (such as SHA-256) to generate corresponding data slice verification codes, ensuring data consistency and integrity for each slice of data during transmission, storage, and subsequent operations.

[0088] Step S3: Obtain the natural resource geological data to be backed up, perform incremental difference processing on the natural resource geological data to be backed up and the structured natural resource geological data to obtain the geological incremental data to be backed up, and verify and compare the geological backup set and the data slice verification codes to obtain a geological data incremental backup set;

[0089] In this embodiment, perform incremental difference processing on the natural resource geological data to be backed up and the structured natural resource geological data. Specifically, only perform difference calculations on the newly added or modified data, without backing up the already stored data, thereby improving the data backup efficiency. The difference calculation uses an incremental difference algorithm, and stores the difference data in an incremental data table. Compare the geological backup set and the data slice verification codes to ensure the consistency and integrity between the data slices and the backup set. For example, if it is detected that the difference data is inconsistent with the existing backup set, perform data repair and re-backup to generate a complete geological data incremental backup set. The difference algorithm compares each geological data with the backup set to generate a difference data set. Then, verify and compare the incremental data with the existing data slice verification codes. For example, if the difference data does not match the slice verification code, immediately perform data repair to ensure consistency. Generating a complete geological data incremental backup set can provide efficient data recovery and access in scenarios such as system failure recovery, data migration, and analysis and retrieval.

[0090] Step S4: Perform real-time data incremental consistency verification based on the geological data incremental backup set and the natural geological data slices to obtain a real-time incremental data verification report, and upload it to the natural resource geological database to execute the data storage task;

[0091] In this embodiment, the geological data slices and the incremental backup set are checked for consistency by real-time synchronization of the preset geological data backup system and the natural resource geological database. The incremental backup is compared with the slice data in real time, and the consistency of each set of data is verified by the data check code. For example, if it is found that there is a check mismatch between some slice data and the incremental backup, it is immediately reported and corrected. The data that has been checked for consistency is uploaded to the natural resource geological database to ensure that the data storage operation is correct. In this way, the data storage operation of the system can be ensured to be carried out in real time, efficiently and safely. The consistency check includes comparing the check codes of each set of incremental slices one by one to verify the integrity of the data in transmission, storage and access. For example, if the system detects that there is a difference between the slice data of a certain mineral deposit and the incremental backup, the data will be reloaded from the backup source and repaired. The data that has been checked for consistency will be uploaded to the database in real time, thereby ensuring that the data storage operation is efficient, reliable and lossless.

[0092] Step S5: Obtain missing geological data of natural resources through the natural resource geological database; perform missing recovery and rollback on the missing geological data of natural resources according to the real-time incremental data verification report, thereby obtaining natural resource filling geological data, and upload it to the natural resource geological database to perform the data filling task.

[0093] In this embodiment, currently missing data is obtained from the natural resource geological database, for example, the data of certain mineral deposit areas is incomplete. The system locates the location of the missing data based on the real-time incremental data verification report generated previously, and rolls back and recovers the missing data. Specifically, the previously backed up incremental data is compared with the checksum, and automatically rolled back to the latest available backup point to supplement the missing data. For example, if the fault information of a certain area is incomplete, the system will automatically restore the fault coordinates, component distribution, depth information, etc. from the backup. For example, if it is found that the attribute data of a mining area is missing, the system will automatically locate the data slice and roll back to the backup point for repair. These restored geological data will be uploaded to the database to ensure that all missing areas are supplemented, thereby performing the data filling task. This ensures that the data of the entire natural resource geological database is complete, accurate, and traceable, and provides efficient data access and analysis services.

[0094] Optionally, step S1 specifically includes:

[0095] Step S11: acquiring natural resource geological data through a natural resource geological database, and performing data preprocessing on the natural resource geological data, thereby obtaining natural resource geological data to be analyzed;

[0096] In this embodiment, a large amount of natural resource geological data is obtained from the natural resource geological database, such as mineral distribution data, soil composition information, stratigraphic structure, fault distribution, etc. Since the data sources are relatively complex, including remote sensing data, survey reports, sensor measurements, geological maps, experimental data, etc., it is necessary to preprocess the data. For example, the system will remove redundant data, fill in missing data, standardize the data format, and filter out noise from the data. For latitude and longitude data, the system will perform spatial correction to ensure the accuracy of geographical locations. For attribute data, unit standardization conversion will be carried out. For example, the depth of the ore deposit will be converted into the standard metric unit, and the percentage of components will be processed by retaining decimal places. These preprocessing operations ensure higher data quality for subsequent analysis and can support more accurate geological analysis.

[0097] Step S12: Divide the natural resource geological data to be analyzed into structured data, so as to obtain structured geological data and unstructured geological data;

[0098] In this embodiment, the natural resource geological data to be analyzed is divided into two categories: structured data and unstructured data. Structured data includes data with a fixed format, such as the latitude and longitude of ore deposit distribution, the proportion of mineral components, the depth of faults, etc. These data can be stored in a database table. Unstructured data includes data with non-fixed formats such as geological reports, remote sensing images, experimental notes, geological maps, image data, etc. For example, preprocess the remote sensing image, convert the image into a pixel matrix format, and extract key information (such as fault boundaries, ore deposit contours). At the same time, the text data in the survey report will be parsed using natural language processing techniques, and key information (such as ore deposit names, stratigraphic descriptions) will be extracted and converted into structured data. The goal of this step is to divide the original data into manageable, analyzable, and storable structured and unstructured parts.

[0099] Step S13: Perform structured conversion of geological data on the unstructured geological data, so as to obtain structured conversion geological data;

[0100] In this embodiment, structured conversion is performed on the unstructured geological data. For example, convert remote sensing image data into analyzable fault boundary information, and convert the descriptive text in the experimental report into a structured data attribute table. For example, for a remote sensing image, use an edge detection algorithm (such as Canny edge detection) to identify the fault boundary, and extract the boundary coordinates into a structured table. For the survey report, use natural language processing techniques (such as the BERT model) to automatically extract key geological information, such as ore deposit components, distribution locations, depth data, etc., and store them in the database. Through these conversions, unstructured data can be integrated with other structured data to achieve deeper data analysis.

[0101] Step S14: Perform spatial merging of the structured transformed geological data and the structured geological data to obtain structured natural resource geological data;

[0102] In this embodiment, the structured transformed geological data and the structured geological data are spatially merged. For example, perform spatial overlay analysis on the ore deposit distribution data on the geological map and the fault boundary information in the remote sensing image. The specific operations include: geometrically matching the ore deposit longitude and latitude with the fault boundary data, performing spatial merging through a spatial database (such as PostGIS), and aligning the two data sets in the same coordinate system. Identify the overlapping areas and perform attribute fusion. For example, merge the fault depth and the ore deposit composition information into a comprehensive data table. This operation can more comprehensively display the geological spatial relationships, helping to better understand the ore deposit distribution, fault characteristics, resource extraction feasibility, etc.

[0103] Step S15: Assign data priorities to the structured natural resource geological data to obtain priority-assigned geological data.

[0104] In this embodiment, priorities are assigned to the structured natural resource geological data. This assignment is based on criteria such as data importance, scarcity, risk level, etc. For example, areas of rare ore deposits with high economic value, such as rare earth ore deposits, will be classified as high-priority data. For fault areas with relatively high geological safety risks, higher priorities will also be assigned. The system will use a data scoring algorithm to score according to indicators such as ore deposit scarcity, economic extraction potential, and formation stability. The data with higher scores will be preferentially stored, analyzed, and backed up. For example, if the data score of a certain ore deposit is as high as 95 points, it will be preferentially processed in backup, retrieval, warning, and other links. This step ensures that operations such as resource analysis, risk assessment, and development planning can be carried out efficiently and accurately, and at the same time, it can provide a higher level of data protection and security management for key resource data.

[0105] Optionally, step S13 is specifically:

[0106] Perform unstructured data classification on the unstructured geological data to obtain text-type geological data, image-type geological data, and geological sensing data;

[0107] In this embodiment, the unstructured geological data obtained from the natural resource geological database is automatically classified. Different types of data are divided into text type, image type, and geological sensing data. For example, the text content in the mine exploration report is classified as text type geological data, including key information such as deposit name, composition, distribution location, etc.; remote sensing images and on-site taken photos are classified as image type geological data, which are used to identify structural features such as faults and deposit outlines; the sensor data collected on-site (such as temperature, stress, vibration, etc.) is classified as geological sensing data. Technologies such as text recognition algorithms (such as OCR technology), image classification algorithms (such as CNN convolutional neural network), and data classification modules are used for automated classification operations.

[0108] Geological feature keywords are extracted from the text type geological data to obtain text geological feature data, and the text geological feature data is stored in key-value form to obtain structured text geological data;

[0109] In this embodiment, automatic feature extraction is performed on the information classified as text type geological data. Through natural language processing algorithms (such as TF-IDF, BERT, etc.), geological feature keywords are extracted from mineral exploration reports, on-site notes, and exploration logs, such as deposit name, composition, distribution area, depth information, etc. These key information will be stored in a structured database, for example, stored as key-value pair data, such as "Deposit Name: Rare Earth Mine", "Composition: REE Ratio", "Distribution Location: Latitude and Longitude", etc. This can efficiently retrieve and query geological data and be used in practical operations such as resource allocation, risk assessment, and mining plan formulation.

[0110] Computer vision geological features are extracted from the image type geological data to obtain image geological feature data, and the image geological feature data is subjected to spatial coordinate transformation to obtain structured image geological data;

[0111] In this embodiment, features are extracted from the images classified as image type geological data. Using computer vision algorithms (such as convolutional neural network CNN), geological features such as fault boundaries, deposit outlines, and rock structures are automatically identified from remote sensing images and on-site taken photos. The extracted geological features will be mapped to the actual geographical coordinates through a spatial coordinate transformation algorithm, for example, using GIS technology for transformation. In this way, geological image data can be integrated with other geographical data to achieve more comprehensive spatial analysis, such as identifying deposit distribution and fault trends.

[0112] Frequency domain features and time domain features are extracted from the geological sensing data to obtain geological sensing feature data, and the geological sensing feature data is subjected to time series format conversion to obtain structured geological sensing data;

[0113] In this embodiment, feature analysis is performed on the collected geological sensing data. Through frequency domain analysis methods such as Fourier transform (FFT), features such as stress distribution and vibration modes in the underground structure are identified. At the same time, the time variation trend is extracted through time domain analysis, such as the peak value, average value, change rate, etc. of continuous stress data. The analysis results are converted into a time series format, recording the data changes of each sensing node at different time points. These time series data can be used for practical applications such as dynamically monitoring construction safety, real-time analyzing stress distribution, and predicting geological risks.

[0114] Geological feature mapping and annotation are performed based on structured image geological data, structured geological sensing data, and structured text geological data, so as to obtain structured natural resource geological data.

[0115] In this embodiment, the information from structured image geological data, structured geological sensing data, and structured text geological data is integrated and mapped. The information such as ore deposit composition information, fault location, stress distribution, etc. distributed in the geographical space is aligned in position. For example, the longitude and latitude, composition distribution, fault strike, etc. of the ore deposit are automatically marked on the geological map. The annotation algorithm is used to integrate key information into the visualization interface, such as 3D geological maps, dynamic fault maps, etc. This not only facilitates explorers to quickly locate and identify geological resources, but also provides more detailed data support for resource extraction, environmental assessment, risk prediction, construction safety, etc.

[0116] Optionally, step S15 is specifically as follows:

[0117] Step S151: Extract regional features from the structured natural resource geological data, so as to obtain geological information coverage area data, regional information update frequency data, and regional resource density data;

[0118] In this embodiment, the geographical information system (GIS technology) and spatial analysis methods are used to extract regional features from the structured natural resource geological data. Specifically, it includes extracting geological information coverage area data, regional information update frequency data, and regional resource density data from the geological data. For example, through remote sensing image analysis, the ore deposit distribution area, rock layer boundary, etc. are identified, and the data coverage range of each area is recorded. The time series comparison method is used to calculate the update frequency of regional data, reflecting the time interval of exploration data collection. The resource density data is obtained through the analysis of the mineral density, ore deposit composition content, etc. of the sample points. Finally, the specific data information of the coverage range, information update rate, and resource density of each geological area can be generated.

[0119] Step S152: Perform spatial overlay analysis based on the geological information coverage area data and the regional resource density data, so as to obtain the coverage area resource density data;

[0120] In this embodiment, superposition analysis technology (GIS spatial analysis tool) is used to perform superposition analysis on the data of the geological information coverage area and the regional resource density data. For example, the coverage area data obtained from different exploration methods (such as drilling, remote sensing images, etc.) is superimposed on the ore deposit resource density data, and the resource density is mapped to the actual geographical space position. In this way, a resource density map of the coverage area can be generated, showing which areas have higher resource density and denser distribution, so as to guide practical operations such as resource exploitation and risk warning.

[0121] Step S153: Estimate the regional resource reserves for the resource density data of the coverage area, so as to obtain the estimated data of the regional resource reserves;

[0122] In this embodiment, methods such as regression analysis, interpolation method, machine learning algorithms (such as random forest or deep learning neural network) are used to estimate the regional resource reserves for the resource density data of the coverage area. For example, by analyzing drilling data, remote sensing image data, and historical resource density distribution information, a regression model is used to predict the total reserves of the ore deposit. For different ore types (such as rare earth, coal, etc.), the reserves are calculated according to parameters such as resource density, component content, and depth distribution, so as to obtain the estimated data of the regional resource reserves for each area, providing an accurate basis for resource exploitation guidance.

[0123] Step S154: Construct a regional geological data priority scoring system according to the regional information update frequency data and the estimated data of the regional resource reserves;

[0124] In this embodiment, the regional information update frequency data and the estimated data of the regional resource reserves are combined to establish a priority scoring system. Specifically, a multi-criteria decision analysis method (such as the analytic hierarchy process AHP) can be used to score each area. For example, data with higher resource density and shorter update frequency has a higher priority. Weight coefficients can be introduced to comprehensively calculate factors such as resource reserves, information update time, and geological stability, and the priority scores of each area are generated through weighted summation or standardized scoring methods. The higher the score of an area, the higher the data reliability and the greater the priority of resource exploitation.

[0125] Step S155: Allocate data priorities to the structured natural resource geological data through the regional geological data priority scoring system, so as to obtain the geological data with priority allocation.

[0126] In this embodiment, according to the constructed priority scoring system for regional geological data, priority is assigned to the data of each geological region. Using data hierarchical storage technology and database index optimization methods, high-priority data is stored in a database partition with faster performance and more efficient access to ensure faster operations such as resource extraction and construction. For example, the data of the ore deposit area with the highest score is stored in a cache database for fast access, medium-priority data is stored in a standard database, and lower-priority data is stored in archival storage. This not only improves data access efficiency but also ensures that resource extraction decisions are based on the most accurate and timely data, enhancing overall operation safety and resource utilization efficiency.

[0127] Optionally, step S2 is specifically as follows:

[0128] Step S21: Divide the priority-assigned geological data into high-priority geological data and low-priority geological data;

[0129] In this embodiment, data classification algorithms (such as decision tree classification, K-means clustering, hierarchical clustering) are used to divide the priority-assigned geological data, thereby dividing the data into high-priority geological data and low-priority geological data. In specific implementation, division can be performed according to indicators such as resource density, data reliability, and information update frequency. For example, by calculating the resource density distribution, ore deposit stability, and historical exploration times of each geological region, data with high resource density and short update time is classified as high-priority geological data, while data with scarce resources and long update intervals is classified as low-priority geological data.

[0130] Step S22: Slice the structured natural resource geological data with high-priority geological data to obtain high-priority geological data slices; slice the structured natural resource geological data with low-priority geological data to obtain low-priority geological data slices;

[0131] In this embodiment, data slicing technology is used to hierarchically cut the structured natural resource geological data. For high-priority geological data, a fast data slicing algorithm (such as a spatial slicing algorithm) can be used to generate high-priority geological data slices. For example, information containing a large amount of resources, dense locations, and rich geological components is extracted from the ore deposit distribution data. For low-priority geological data, areas with more dispersed spatial boundaries and smaller resource densities can be selected for slicing to reduce unnecessary data storage and access overhead. During the slicing process, the format of the data slices can be ensured to be standardized to support subsequent data processing and calculation.

[0132] Step S23: Perform temporal integration of high-priority geological data slices and low-priority geological data slices to obtain natural geological data slices;

[0133] In this embodiment, temporal integration of high-priority geological data slices and low-priority geological data slices is performed through a time window integration algorithm (such as a time series merging algorithm). For example, exploration data, remote sensing images, drilling data, etc. can be merged in the time dimension to ensure data consistency at each time node. The specific operations may include: automatically merging data with a short time interval into a newer data window, or performing weighted averaging on historical updated data, so as to integrate efficient, continuous, and unified natural geological data slices in the spatio-temporal dimension.

[0134] Step S24: Perform byte stream conversion on the natural geological data slices to obtain natural geological byte streams;

[0135] In this embodiment, the integrated natural geological data slices are converted into byte stream format to improve storage efficiency and data transmission speed. Data serialization technologies (such as Protocol Buffers, Apache Thrift, MessagePack) are used for byte stream conversion. For example, information such as ore deposit components, spatial coordinates, and resource density in the slice data is binary encoded, thereby reducing the storage space occupied by the data and at the same time increasing the reading and transmission speed. This can ensure that data flows quickly and efficiently between databases, servers, and remote clients.

[0136] Step S25: Generate data slice check codes based on the natural geological byte streams to obtain data slice check codes.

[0137] In this embodiment, a check algorithm (such as SHA-256, MD5) ** is used to generate check codes for the data slices converted into byte streams. For example, SHA-256 hashing calculations can be performed on the data of each slice to generate unique check codes. Through this method, it can be verified whether the data slices have been damaged or modified during storage and transmission. The check codes will be stored in the database and verified against the data of each slice together, so as to ensure data integrity and consistency. This not only improves the security of data storage and access, but also enables efficient error detection and repair during data transmission.

[0138] Optionally, step S25 is specifically:

[0139] Perform byte stream priority sharding on the natural geological byte streams to obtain geological byte stream priority shards;

[0140] In this embodiment, the byte stream sharding algorithm (e.g., fixed-size sharding, dynamic sharding, sliding window sharding) is adopted to divide the natural geological byte stream into multiple priority shards. Specifically, the priorities can be divided according to data element information (such as resource density, data update frequency, historical exploration times). For example, data with a higher resource density and a shorter update interval is divided into high-priority shards, while data with a lower resource density and a longer data update interval is divided into low-priority shards. This helps to ensure that higher resource allocation and access priorities are given to important data during storage and transmission.

[0141] Calculate the shard hash values for the geological byte stream priority shards to obtain high-priority shard hash values and low-priority shard hash values;

[0142] In this embodiment, the hash algorithm (such as SHA-256) is used to calculate the hash value for each shard. For example, for high-priority shards, the data of each shard is input into the SHA-256 algorithm for hash calculation to generate a unique high-priority shard hash value; the same SHA-256 hash calculation is also performed on low-priority shards to obtain low-priority shard hash values. These hash values can be used to verify whether any damage or tampering has occurred to the shard data during storage and transmission, ensuring the integrity and consistency of the data.

[0143] Generate a check code based on the high-priority shard hash value to obtain a high-priority initial check code, and add redundant check information to the high-priority initial check code to obtain a high-priority check code;

[0144] In this embodiment, the high-priority shard hash value is input into a check code generation algorithm (such as ECC coding, Hamming coding) to generate an initial check code. Then, to improve data security and fault tolerance, redundant check information is added to the initial check code, such as using error-correcting code techniques (such as Reed-Solomon coding, BCH coding), to generate a high-priority check code. This can effectively detect and repair data errors during storage and transmission, while enhancing the security of the data in an unreliable environment.

[0145] Generate a check code based on the low-priority shard hash value to obtain a low-priority check code;

[0146] In this embodiment, for low-priority shards, the same check code generation algorithm as for high-priority shards (such as SHA-256 check, ECC coding, etc.) is used to calculate each low-priority shard hash value to generate independent low-priority check codes. These check codes are used to verify the integrity and correctness of the data slices, ensuring that no tampering or damage has occurred to the data during storage and transmission.

[0147] Summarize the slice verification codes of the natural geological data slices according to the high-priority verification code and the low-priority verification code, so as to obtain the data slice verification code.

[0148] In this embodiment, the high-priority verification code and the low-priority verification code are summarized and calculated (for example: exclusive OR operation, sum check). After merging the two verification codes, a comprehensive slice verification code is generated and stored in the meta-information of the data slice. This can be used as the unique identifier of the slice for verification, so as to perform consistency check and integrity verification on each slice during data access, transmission, backup, etc., ensuring the security, reliability and stability of the data throughout the storage and transmission link.

[0149] Optionally, step S3 is specifically as follows:

[0150] Step S31: Obtain the natural resource geological data to be backed up, and perform data structure integration on the natural resource geological data to be backed up, so as to obtain the structured natural resource geological data to be backed up;

[0151] In this embodiment, the data to be backed up is extracted from the natural resource geological database, including information from various sources such as geological exploration data, measurement data, sensor data, and image data. Then, using data integration technologies (such as ETL tools, data mapping technologies), the data from different sources is subjected to structured integration such as format conversion, attribute mapping, time synchronization, and spatial alignment. Specifically, spatial database technologies (such as PostGIS), data warehouse tools, timestamp alignment algorithms, etc. can be used to integrate all geological data into a standardized table structure, attribute information, and time series format, so as to form the structured natural resource geological data to be backed up that is easy to store and analyze.

[0152] Step S32: Perform data incremental difference on the structured natural resource geological data to be backed up and the structured natural resource geological data, so as to obtain the geological incremental data to be backed up;

[0153] In this embodiment, an incremental difference is made between the structured natural resource geological data to be backed up and the existing structured natural resource geological data. Through time difference comparison, spatial difference calculation, and attribute difference algorithm, only the information newly added or modified in the current data is retained. The specific implementation includes timestamp comparison, spatial index algorithms (such as R-tree) for comparing geographical location data, and attribute value comparison. If the attribute changes, the updated data is retained. This will generate an incremental data set containing the newly added and modified information, thus saving storage space and improving backup efficiency.

[0154] Step S33: Generate data slices and verification codes based on the geological incremental data to be backed up, so as to obtain incremental data slices and incremental data verification codes;

[0155] In this embodiment, data slicing is performed based on the incremental data to be backed up, and the data is divided into slices of fixed or dynamic size, and a checksum is generated for each slice using a hashing algorithm such as SHA-256. The sharding can be automatically adjusted according to the data density and size, and redundant check information (such as ECC error correction code) is added when necessary to improve the fault tolerance of the data. This ensures that each data slice remains consistent and complete during storage, transmission, and recovery.

[0156] Step S34: Verify and compare the incremental data checksum and the data slice checksum to obtain the geological data incremental backup set.

[0157] In this embodiment, the checksum of the incremental data slice is verified and compared with the existing checksum in the database, and the checksum of each shard is checked one by one. If a checksum mismatch is found, the redundant information in the ECC code is used for error repair. Finally, all the data slices that pass the verification are merged into a complete geological data incremental backup set and stored in the natural resources database, thus ensuring the integrity and recoverability of data backup and recovery, and at the same time improving the utilization efficiency of storage space.

[0158] Optionally, step S34 is specifically as follows:

[0159] Step S341: Verify and compare the incremental data checksum and the data slice checksum to obtain the successfully verified incremental checksum and the failed verified incremental checksum;

[0160] In this embodiment, the incremental data checksum is compared with the data slice checksum one by one. The SHA-256 hashing verification algorithm is used to calculate the checksum of each slice and compare it with the pre-stored checksum. If the calculation result is consistent with the pre-stored checksum, it is identified as the successfully verified incremental checksum; if not, it is identified as the failed verified incremental checksum. Specifically, a distributed computing framework (such as Apache Spark) can be used to perform parallel comparison on big data to improve the checksum comparison efficiency, and the comparison results, including the information of the successfully and failed data slices, are recorded.

[0161] Step S342: Perform a successfully verified geological incremental data backup on the incremental data slice according to the successfully verified checksum to obtain the first geological data incremental backup data;

[0162] In this embodiment, according to the increment check code with successful verification, the data slices that pass the verification are extracted from the increment data slices and backed up to the natural resources geological database. This requires the use of efficient IO operation techniques, such as batch data writing and column storage format conversion (such as Parquet format), to improve the data writing speed. In addition, data redundancy storage techniques can be used to distribute and back up the data slices among multiple storage nodes to ensure that the data can still be recovered in case of node failure, thereby obtaining the first geological data increment backup data.

[0163] Step S343: Extract the increment data with verification failure from the geological increment data to be backed up according to the increment check code with verification failure, so as to obtain the geological increment data with verification failure;

[0164] In this embodiment, according to the increment check code with verification failure, the data slices corresponding to the failed check code are extracted from the geological increment data to be backed up. This process uses a distributed data retrieval algorithm, locates through the check code index, and retrieves the relevant slices from the storage. Specifically, data indexing techniques (such as Elasticsearch) can be used to accelerate the location, and data query languages (such as SQL or NoSQL queries) can be used to ensure the efficient extraction of the failed data slices. This will generate the geological increment data with verification failure.

[0165] Step S344: Perform slice reconstruction verification and comparison on the geological increment data with verification failure, so as to obtain the second geological data increment backup data;

[0166] In this embodiment, for the geological increment data with verification failure, slice reconstruction verification and comparison are performed, and the slices are re-integrated according to the spatial and temporal attributes. Spatial data merging algorithms (such as K-Nearest Neighbors, KNN) and time series analysis techniques are used to correct and reorganize the data of the failed slices. Specifically, machine learning algorithms (such as regression models) can be introduced to predict and fill in the attributes, so as to restore the integrity of the data. Finally, this will generate the second geological data increment backup data, ensuring that the error slices reach the standardized format after re-verification.

[0167] Step S345: Integrate the first geological data increment backup data and the second geological data increment backup data to obtain the geological data increment backup set.

[0168] In this embodiment, the first geological data incremental backup data and the second geological data incremental backup data are integrated. Using data merging and deduplication algorithms, the two backup data sets are spatially and attributively aligned. Specifically, a data merging tool (such as the Data Lake merging engine) can be used for batch merging, and at the same time, a deduplication algorithm (such as BloomFilter) is adopted to ensure that there is no duplicate data. The integrated data backup set is finally stored in the natural resources geological database, and multi-node backup is performed through a distributed replica storage technology (such as HDFS) to ensure data availability and fault tolerance, thus forming a complete and reliable geological data incremental backup set.

[0169] Optionally, step S344 is specifically as follows:

[0170] According to the priority assignment geological data, the geological incremental data with verification failure is divided into high-priority and low-priority geological incremental data with verification failure;

[0171] In this embodiment, the geological incremental data with verification failure is classified by an attribute analysis algorithm, and the data is divided into two categories: high-priority and low-priority according to characteristics such as resource density, spatial distribution, and timestamp. In specific implementation, a machine learning classification model (such as Support Vector Machine SVM) can be used, and the data is divided into priorities according to the predefined priority assignment geological data, and the high-priority and low-priority data are stored separately after division. In this way, the efficiency of the data with priority division in subsequent processing can be guaranteed.

[0172] The high-priority geological incremental data with verification failure is sliced and reconstructed to obtain a high-priority geological incremental reconstructed data slice, and a check code is generated for the high-priority geological incremental reconstructed data slice to obtain a high-priority geological incremental reconstructed check code;

[0173] In this embodiment, for the high-priority data slices with verification failure, a spatial data interpolation algorithm (such as Kriging interpolation method) is used for spatial reconstruction. Then, the SHA-256 algorithm is used to generate a unique check code for each slice to ensure the integrity and correctness of the data slice after reconstruction. This process can be processed in parallel for big data using a distributed computing framework (such as Apache Spark), so as to accelerate the slice reconstruction and check code generation.

[0174] The high-priority geological incremental reconstructed check code and the data slice check code are verified and compared to obtain a successfully verified high-priority reconstructed check code and a failed verified high-priority reconstructed check code;

[0175] In this embodiment, the generated high-priority reconstruction check codes are compared one by one with the pre-stored check codes of data slices. In specific implementation, a distributed hash comparison algorithm can be adopted to improve the efficiency of check comparison through parallel computing. A data check comparison tool is used to check each slice. If the match is successful, it is marked as verified successfully; if not, it is marked as verified failed.

[0176] Based on the high-priority reconstruction check codes with successful verification, a successful-geology data increment backup is performed on the high-priority geological increment reconstruction data slices, so as to obtain the first reconstructed geological data increment backup data;

[0177] In this embodiment, for the data slices corresponding to the high-priority reconstruction check codes with successful verification, they are written into an efficient distributed storage system (such as HDFS), and redundant backups are performed to ensure data recoverability. In addition, batch data writing technology can be adopted to upload the data in batches to a distributed database (such as Apache Cassandra) to ensure the stability and scalability during the writing process of the backup data.

[0178] Based on the high-priority reconstruction check codes with failed verification, high-priority geological increment data with failed reconstruction verification is extracted from the high-priority geological increment data with failed verification, so as to obtain high-priority geological increment data with failed reconstruction verification;

[0179] In this embodiment, the data slices related to the failed check codes are quickly located through an index positioning algorithm (such as inverted index technology). Then, the failed slices are extracted from the distributed storage through data retrieval query. A data error correction algorithm (such as Reed-Solomon coding) is used to correct and reconstruct the extracted data to ensure that even if some data is missing, the integrity can be restored.

[0180] The low-priority geological increment data with failed verification and the high-priority geological increment data with failed reconstruction verification are reconstructed and inspected in batches, so as to obtain the second reconstructed geological data increment backup data;

[0181] In this embodiment, batch data reconstruction technology is adopted to process the low-priority and high-priority data with failed verification in batches. The data is reconstructed and inspected through a parallel computing framework. Each batch of data is subjected to integrity verification, and data redundancy storage technology is used to distribute it to multiple nodes to ensure that the data is still recoverable in case of node failure.

[0182] The first reconstructed geological data increment backup data and the second reconstructed geological data increment backup data are integrated for reconstructed increment backup, so as to obtain the second geological data increment backup data.

[0183] In this embodiment, the first batch and the second batch of backup data are merged in a distributed manner using a merging tool (such as Apache NiFi), and duplicate data is removed. A spatial data merging algorithm is used to align the attributes of the backup data to ensure the consistency of the data in terms of space and time. Finally, the merged data is backed up to the main distributed storage system, and a checksum verification is performed to ensure the integrity and reliability of the data backup.

[0184] Optionally, the batch reconstruction and verification in step S344 are specifically as follows:

[0185] Integrate the geological incremental data with failed low-priority verification and the geological incremental data with failed high-priority reconstruction verification in a time window to obtain the geological incremental data to be reconstructed;

[0186] In this embodiment, the geological incremental data with failed low-priority verification and the data with failed high-priority reconstruction verification are merged based on the timestamp attribute. Using the time window integration technology (such as the sliding window algorithm), the data is divided into fixed-size windows according to time continuity. For example, each window can be set to 10 minutes or 1 hour, and the data within the window is aligned and integrated according to the timestamps. This can be performed using a distributed data processing platform (such as Apache Spark) for batch time window merging operations to obtain the geological incremental data to be reconstructed.

[0187] Perform small-grained partitioning of the data slices of the geological incremental data to be reconstructed to obtain the incremental data slices to be verified;

[0188] In this embodiment, based on the integrated geological incremental data, a spatial partitioning algorithm (such as the quadtree partitioning method) is used to divide the data slices into small-grained slices, each slice being approximately 1MB. During the specific partitioning, it can be divided according to the geographical distribution attributes, density, and spatial distribution rules of the data. A distributed slicing tool is used to automatically distribute the data to multiple nodes to ensure the balance of data distribution, thereby improving the parallel efficiency of subsequent verification and reconstruction.

[0189] Perform batch data reconstruction on the incremental data slices to be verified to obtain the reconstructed slices of the incremental data to be verified;

[0190] In this embodiment, under a distributed computing framework (such as Apache Hadoop), a batch data reconstruction technology is used to perform spatial and attribute reconstruction on each batch of incremental data slices. Each batch of data contains approximately 50 to 100 small slices. Through parallel computing, each batch of slices is gradually merged and spatial interpolation reconstruction (such as Kriging interpolation) is performed, and at the same time, an attribute matching algorithm (such as nearest neighbor matching) is used to ensure the consistency of the data in terms of space and attributes. Finally, the reconstructed slices are stored in a distributed database (such as Apache Cassandra).

[0191] Generate a checksum for the reconstructed slice by reconstructing the slice based on the incremental data to be verified, thereby obtaining the reconstructed slice checksum;

[0192] In this embodiment, the SHA-256 encryption algorithm is used to generate a checksum for each reconstructed slice. In a specific implementation, the slice data can be pre-compressed and then input into the checksum generation module. Each slice checksum will be stored together with the location and timestamp attributes of the slice in the distributed storage. In this way, it can be ensured that the check information of each slice is associated with the location data, so that the data consistency can be quickly located and verified in subsequent verification.

[0193] Verify and compare the reconstructed slice checksum and the data slice checksum. If the verification and comparison are successful, the corresponding reconstructed slice of the incremental data to be verified is used for geological data incremental backup, thereby obtaining the second reconstructed geological data incremental backup data; if the verification and comparison fail, the corresponding reconstructed slice of the incremental data to be verified is stored in the form of an exception record.

[0194] In this embodiment, the reconstructed slice checksum is compared with the pre-stored data slice checksum one by one. Use a distributed data comparison tool (such as Apache Zeppelin) to perform parallel verification operations. If all slices match successfully, the data is written to a highly available distributed storage system (such as Ceph). For slices that fail the comparison, the system will automatically generate an error report and store these error slices in a dedicated fault record database in the form of a detailed exception log. This includes information such as timestamps, error types, spatial locations, and reasons for checksum failure, for subsequent fault diagnosis and data recovery.

[0195] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be included in the present invention.

[0196] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A backup method based on a natural resource geological database, characterized in that: The following steps are involved: Step S1: obtaining natural resource geological data through a natural resource geological database, and classifying and structuring the natural resource geological data, thereby obtaining structured natural resource geological data; Assigning data priorities to structured natural resource geological data, thereby obtaining priority assigned geological data; Step S2: performing structured data slicing on the structured natural resource geological data according to the priority allocation geological data, thereby obtaining natural geological data slices; Generating data slice check codes for natural geological data slices, thereby obtaining data slice check codes; Step S3: obtaining the natural resource geological data to be backed up, and performing incremental differential processing on the natural resource geological data to be backed up and the structured natural resource geological data, so as to obtain the geological incremental data to be backed up, and verifying and comparing the geological backup set and the data slice check code, so as to obtain the geological data incremental backup set; Step S4: Perform real-time data increment consistency check based on the geological data incremental backup set and the natural geological data slice, so as to obtain a real-time incremental data verification report, and upload it to the natural resource geological database to perform the data storage task; Step S5: Obtain missing geological data of natural resources through the natural resource geological database; perform missing recovery and rollback on the missing geological data of natural resources according to the real-time incremental data verification report, thereby obtaining natural resource filling geological data, and upload it to the natural resource geological database to perform the data filling task.

2. The backup method based on the natural resource geological database according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: acquiring natural resource geological data through a natural resource geological database, and performing data preprocessing on the natural resource geological data, thereby obtaining natural resource geological data to be analyzed; Step S12: performing structured data division on the natural resource geological data to be analyzed, thereby obtaining structured geological data and unstructured geological data; Step S13: performing geological data structured conversion on the unstructured geological data, thereby obtaining structured converted geological data; Step S14: spatially merging structured geological data according to the structured converted geological data and the structured geological data, thereby obtaining structured natural resource geological data; Step S15: assigning data priorities to the structured natural resource geological data, thereby obtaining priority assigned geological data.

3. The backup method based on the natural resource geological database according to claim 2 is characterized in that: Step S13 is specifically as follows: Classify unstructured geological data to obtain text geological data, image geological data and geological sensor data; Extract geological feature keywords from text geological data to obtain text geological feature data, and perform key-value storage on the text geological feature data to obtain structured text geological data; Perform computer vision geological feature extraction on image geological data to obtain image geological feature data, and perform spatial coordinate transformation on the image geological feature data to obtain structured image geological data; Perform frequency domain feature extraction and time domain feature extraction on geological sensor data to obtain geological sensor feature data, and perform time series format conversion on the geological sensor feature data to obtain structured geological sensor data; Geological feature mapping and annotation are performed based on structured image geological data, structured geological sensor data and structured text geological data to obtain structured natural resource geological data.

4. The backup method based on the natural resource geological database according to claim 2 is characterized in that: Step S15 is specifically as follows: Step S151: extracting regional features from structured natural resource geological data, thereby obtaining geological information coverage area data, regional information update frequency data, and regional resource density data; Step S152: performing spatial overlay analysis based on the geological information coverage area data and the regional resource density data, thereby obtaining the coverage area resource density data; Step S153: performing regional resource reserve estimation on the resource density data of the covered area, thereby obtaining regional resource reserve estimation data; Step S154: constructing a regional geological data priority scoring system based on regional information update frequency data and regional resource reserve estimation data; Step S155: assigning data priority to the structured natural resource geological data through the regional geological data priority scoring system, thereby obtaining priority assigned geological data.

5. The backup method based on the natural resource geological database according to claim 1 is characterized in that: Step S2 is specifically as follows: Step S21: performing geological data priority classification on the priority-assigned geological data, thereby obtaining high-priority geological data and low-priority geological data; Step S22: Slicing the structured natural resource geological data into high-priority geological data according to the high-priority geological data, thereby obtaining high-priority geological data slices; Slicing the structured natural resource geological data with low priority geological data according to the low priority geological data, thereby obtaining low priority geological data slices; Step S23: performing geological data slice time-series integration on the high-priority geological data slices and the low-priority geological data slices, thereby obtaining natural geological data slices; Step S24: performing byte stream conversion on the natural geological data slices to obtain a natural geological byte stream; Step S25: Generate a data slice check code according to the natural geological byte stream, thereby obtaining a data slice check code.

6. The backup method based on the natural resource geological database according to claim 5 is characterized in that: Step S25 is specifically as follows: Performing byte stream priority slicing on the natural geological byte stream, thereby obtaining geological byte stream priority slicing; Calculate the shard hash value of the geological byte stream priority shard, so as to obtain the high priority shard hash value and the low priority shard hash value; Generate a check code according to the high-priority shard hash value to obtain a high-priority initial check code, and add redundant check information to the high-priority initial check code to obtain a high-priority check code; Generate a checksum based on the hash value of the low-priority shard to obtain a low-priority checksum; The slice check codes of natural geological data slices are summarized according to the high priority check codes and the low priority check codes, so as to obtain the data slice check codes.

7. The backup method based on the natural resource geological database according to claim 1 is characterized in that: Step S3 is specifically as follows: Step S31: obtaining the natural resource geological data to be backed up, and performing data structured integration on the natural resource geological data to be backed up, thereby obtaining the natural resource geological structured data to be backed up; Step S32: performing data incremental difference on the natural resource geological structured data to be backed up and the structured natural resource geological data, thereby obtaining the geological incremental data to be backed up; Step S33: performing data slicing and verification code generation based on the incremental geological data to be backed up, thereby obtaining incremental data slicing and incremental data verification codes; Step S34: verify, compare and back up the incremental data check code and the data slice check code, so as to obtain a geological data incremental backup set.

8. The backup method based on the natural resource geological database according to claim 7 is characterized in that: Step S34 is specifically as follows: Step S341: verify and compare the incremental data check code and the data slice check code, so as to obtain the verification success incremental check code and the verification failure incremental check code; Step S342: verify the success of the geological incremental data backup on the incremental data slice according to the successful verification check code, thereby obtaining the first geological data incremental backup data; Step S343: extracting the failed verification incremental data from the backup geological incremental data according to the failed verification incremental check code, thereby obtaining the failed verification incremental data; Step S344: Slice reconstruction, verification and comparison are performed on the geological incremental data that failed verification, so as to obtain the second geological data incremental backup data; Step S345: integrating the first geological data incremental backup data and the second geological data incremental backup data to obtain a geological data incremental backup set.

9. The backup method based on the natural resource geological database according to claim 8 is characterized in that: Step S344 is specifically as follows: Prioritize the failed verification geological incremental data according to the priority allocation geological data, so as to obtain high-priority failed verification geological incremental data and low-priority failed verification geological incremental data; Reconstructing data slices for high-priority geological incremental data that failed verification, thereby obtaining high-priority geological incremental reconstructed data slices, and generating verification codes for the high-priority geological incremental reconstructed data slices, thereby obtaining high-priority geological incremental reconstruction verification codes; Verify and compare the high-priority geological incremental reconstruction check code and the data slice check code, so as to obtain the high-priority reconstruction check code of successful verification and the high-priority reconstruction check code of failed verification; Verify the successful geological data incremental backup of the high-priority geological incremental reconstruction data slice according to the successful verification high-priority reconstruction check code, thereby obtaining the first reconstructed geological data incremental backup data; Reconstruct the high-priority geological incremental data that failed verification according to the high-priority reconstruction check code of the verification failure, and extract the high-priority geological incremental data that failed verification, so as to obtain the reconstructed high-priority geological incremental data that failed verification; Reconstructing and verifying the low-priority geological incremental data that failed verification and the high-priority geological incremental data that failed reconstruction verification in batches, thereby obtaining the second reconstructed geological data incremental backup data; The first reconstructed geological data incremental backup data and the second reconstructed geological data incremental backup data are reconstructed and integrated to obtain the second geological data incremental backup data.

10. The backup method based on the natural resource geological database according to claim 9 is characterized in that: The batch reconstruction and verification in step S344 are specifically as follows: Performing time window integration on low-priority geological incremental data that failed verification and high-priority geological incremental data that failed reconstruction verification, thereby obtaining geological incremental data to be reconstructed; The incremental geological data to be reconstructed is divided into small-scale data slices to obtain incremental data slices to be verified; Reconstruct the incremental data slices to be verified in batches, so as to obtain the reconstructed slices of the incremental data to be verified; Reconstruct the slice according to the incremental data to be verified to generate a check code, thereby obtaining a reconstructed slice check code; Verify and compare the reconstructed slice check code and the data slice check code. If the verification and comparison is successful, perform geological data incremental backup on the corresponding reconstructed slice of the incremental data to be verified, thereby obtaining second reconstructed geological data incremental backup data; If the verification and comparison fails, the corresponding incremental data to be verified will be reconstructed and sliced ​​and stored in the form of an exception record.

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