Co-construction sharing method and system based on geological data

Abstract

The invention discloses a co-construction and sharing method and system based on geological data, and the method mainly comprises the steps: data collection: collecting the existing historical data of a region from a plurality of channels; data cleaning: carrying out standardization processing on the multi-source and heterogeneous geological data to ensure the consistency and comparability of the data; data storage: systematically storing the cleaned and standardized structured data in a database in a hierarchical manner; data encryption: the geological data needs to be encrypted after being put in storage, so that data security and user privacy are ensured; a geological information co-construction and sharing platform is established, so that a user can access, purchase, download and use geological data; and data service: providing information service of geological big data. By adopting the mechanism and the method, the potential value of the existing historical geological data can be further explored, and the geological information circulation efficiency and the service capability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of informatization management and service of geological exploration, and particularly relates to a co - construction and sharing method and system based on geological data. Background Art

[0002] With China's vigorous promotion of the "digitalization" strategy, activating and unleashing the potential of data elements, and accelerating the construction of the digital economy, the overall digital transformation drives the transformation of production methods. Especially in the field of geological data management and service, through the co - construction and sharing of geological data, the risks of geological work can be greatly reduced, duplicate work and investment can be reduced, and unnecessary waste can be avoided.

[0003] Geological data is an important basic material in fields such as geological survey, mineral resource exploration, and engineering construction. However, traditional geological data management has problems such as scattered data, difficult retrieval, and low utilization efficiency, making it difficult to meet the needs of the efficient utilization of geological data in modern society. The forms of geological data are mainly drawings and semi - structured multi - source heterogeneous data materials. It is difficult for geological data to be shared and transferred. Therefore, establishing a co - construction and sharing method based on geological data is of great significance for exploring the potential value of existing historical geological data and improving the management level and service ability of geological information.

[0004] However, in actual operation, there are many problems in the co - construction and sharing of geological data. First, due to different sources of geological data, the degree of data consistency is not high. Geological data from different sources and periods may adopt different data standards and formats, which brings difficulties to data integration and sharing. Second, the accuracy of data is not high. When establishing a co - construction and sharing mechanism, it is a challenge to ensure the accuracy, integrity, and reliability of geological data on the digital platform, and a strict data quality control process needs to be established. Finally, there is a potential risk of data leakage in geological data security. During the process of realizing the co - construction and sharing of the digital platform, data encryption processing needs to be done to ensure that sensitive data is not leaked, and at the same time, data privacy regulations need to be complied with. There are also different interest demands. During the process of co - construction and sharing, it is necessary to balance the interests of all parties to ensure that all participants can benefit. There may be differences in interest demands between different organizations, which may affect the willingness and efficiency of data sharing. The present invention innovatively proposes a co - construction and sharing based on geological data, which realizes the efficient sharing and dissemination of data by integrating, managing, and optimizing geological data, and accelerates the innovation and development of the geological discipline. Summary of the Invention

[0005] The present application provides a co - construction and sharing method and system based on geological data to solve the problem of co - construction and sharing of geological data.

[0006] According to the first aspect, in one embodiment, a co - construction and sharing method based on geological data is provided. The method includes:

[0007] Collect geological data and clean and standardize the collected multi-source heterogeneous geological data;

[0008] Store the processed geological data in a database and encrypt the sensitive geological data after storage;

[0009] Build a geological data sharing platform and provide big data-based geological services for users based on the sharing platform.

[0010] Further, collecting geological data specifically includes:

[0011] For the target research area, collect regional geological data in various ways, including geological exploration, remote sensing technology, and laboratory analysis; cover different geological conditions and characteristics to ensure the comprehensiveness and diversity of the data.

[0012] Further, cleaning and standardizing the collected multi-source heterogeneous geological data specifically includes:

[0013] Outlier detection: Identify and process abnormal data, including the interquartile range (IQR) method;

[0014] Standardization processing: Use a unified standard to describe according to the differences in the sources and formats of different data.

[0015] Further, storing the processed geological data in a database specifically includes:

[0016] Classify the processed geological data, build data indexes, and organize the data.

[0017] Further, encrypting the sensitive geological data after storage specifically includes:

[0018] Perform data encryption using an encryption algorithm.

[0019] Further, performing data encryption using an encryption algorithm specifically includes:

[0020] Key generation: Generate a key using a pseudorandom number generator;

[0021] Plaintext preparation: Format the plaintext data to be encrypted according to the requirements of the encryption algorithm;

[0022] Encryption process: Through multiple rounds of processing, use the key to complete the encryption of the plaintext data. Each round includes the following steps: Apply a non-linear substitution transformation to each byte of the data block; Shift the rows of the data block; Mix the columns of the data block; Perform an exclusive OR operation between the round key and the data block;

[0023] Output ciphertext: After processing all rounds, the final data block obtained is the ciphertext;

[0024] Decryption process: The decryption process is the reverse of the encryption process in terms of the step sequence.

[0025] Furthermore, based on the shared platform, geological services based on big data are provided for users, specifically including:

[0026] Providing geological services based on big data for users, including geological data analysis, model prediction, environmental monitoring and disaster warning, engineering construction and planning services, helping users quickly understand the geological conditions of the area to be studied, and providing a scientific basis for decision-making.

[0027] According to a second aspect, in one embodiment, a co-construction and sharing system based on geological data is provided. The system includes:

[0028] A data processing module, configured to collect geological data and perform cleaning and standardization processing on the collected multi-source heterogeneous geological data;

[0029] A data storage module, configured to store the processed geological data in a database and encrypt the sensitive geological data after storage;

[0030] A platform construction module, configured to construct a geological data sharing platform and provide geological services based on big data for users based on the shared platform.

[0031] According to a third aspect, in one embodiment, an electronic device is provided. The device includes: a processor and a memory;

[0032] The memory is used to store one or more program instructions;

[0033] The processor is configured to run one or more program instructions to execute the steps of a co-construction and sharing method based on geological data as described in any one of the above.

[0034] According to a fourth aspect, in one embodiment, a computer-readable storage medium is provided. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of a co-construction and sharing method based on geological data as described in any one of the above are implemented.

[0035] This application provides a co - construction and sharing method and system based on geological data, which has many remarkable beneficial effects. It not only improves the usability and value of geological data, but also promotes the progress of geology and related fields. The co - construction and sharing mechanism of the present invention provides accurate geological data and in - depth geological analysis, providing strong decision - making support for policymakers and resource managers. This helps to formulate more scientific and reasonable policies and measures, and optimize the exploration, development and protection of resources. By integrating and sharing geological data, this mechanism promotes exchanges and cooperation between different disciplines and different countries. Such interdisciplinary and international cooperation helps to solve global geological problems such as climate change, resource shortage and geological disasters, which is of great significance for global sustainable development. The co - construction and sharing mechanism based on geological data of the present invention, through a series of innovative steps and methods, not only solves the problems in the process of geological data sharing, but also brings a series of positive beneficial effects. These effects not only improve the value and application scope of geological data, but also promote the development of geology and related fields, having a profound impact on scientific research, policy - making, resource management and public education. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flowchart of a co - construction and sharing method based on geological data provided by an embodiment of the present invention;

[0037] Figure 2 It is a specific implementation flowchart of a co - construction and sharing method based on geological data provided by an embodiment of the present invention;

[0038] Figure 3 It is a data encryption flowchart in a co - construction and sharing method based on geological data provided by an embodiment of the present invention;

[0039] Figure 4 It is a schematic diagram of the logical structure of a co - construction and sharing system based on geological data provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The present invention will be further described in detail below in conjunction with the specific embodiments and the accompanying drawings. Similar elements in different embodiments are labeled with related similar element numbers. In the following embodiments, many detailed descriptions are provided to enable a better understanding of the present application. However, those skilled in the art can easily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, or methods. In some cases, some operations related to the present application are not shown or described in the specification, in order to avoid overwhelming the core part of the present application with excessive descriptions. For those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations based on the description in the specification and the general technical knowledge in the art.

[0041] In addition, the features, operations, or characteristics described in the specification can be combined in any appropriate manner to form various embodiments. At the same time, the steps or actions in the method description can also be reordered or adjusted in a manner obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for clearly describing a certain embodiment, and do not mean that they are the necessary sequences, unless it is stated that a certain sequence must be followed.

[0042] A co - construction and sharing method based on geological data provided by the first embodiment of the present invention will be described in detail below in conjunction with Figure 1 and Figure 2 as follows.

[0043] Based on the problems existing in the co - construction and sharing of geological data, the present invention proposes a co - construction and sharing mechanism based on geological data, which includes the following six steps: data collection, collecting existing historical data of the region from multiple channels; data cleaning, standardizing multi - source and heterogeneous geological data to ensure data consistency and comparability; data warehousing, systematically storing the cleaned and standardized structured data in hierarchical levels in a database; data encryption, encrypting the geological data after it is warehoused to ensure data security and user privacy; establishing a co - construction and sharing platform for geological information to provide users with access to, purchase, download, and use geological data; data service, providing information services for geological big data. By integrating, managing, and optimizing geological data, more efficient and extensive data sharing can be achieved, thereby promoting the sharing and dissemination of geological data, accelerating the innovation and development of the geological discipline. By implementing this co - construction and sharing mechanism, valuable data support can also be provided for research in related fields, and further, promoting the progress of geological science and technology, and ultimately making contributions to the sustainable development of social economy. The specific content is as follows:

[0044] As Figure 1As shown, in step S100, geological data is collected, and the collected multi-source heterogeneous geological data is cleaned and standardized.

[0045] S110, Data collection: Collecting geological data is the starting point of the entire mechanism. For a certain research area, regional geological data, drilling data, mapping data, geophysical exploration data, laboratory test data, etc. are collected, covering different geological conditions and characteristics to ensure the comprehensiveness and diversity of the data.

[0046] The data collection in step S110 is the basis for constructing a geological data sharing mechanism. The goal of this stage is to collect as comprehensive and diverse data as possible to ensure that various geological conditions and characteristics can be covered. Geological exploration, as a way of data collection, can provide direct information on the underground structure and composition. This usually involves drilling, geophysical and geochemical exploration techniques, which can reveal the distribution of underground rocks, soils and groundwater. Remote sensing technology uses sensors carried by satellites or aircraft to obtain surface data, which can cover a vast area and provide information such as terrain, vegetation, and hydrology. Laboratory analysis is more refined. Through physical, chemical and mineralogical tests, the specific composition and properties of rocks and minerals can be determined. In the process of data collection, quality control is crucial. This includes ensuring the accuracy, reliability and representativeness of the data. The accuracy of the data can be achieved by using precise measurement tools and standardized acquisition procedures. Reliability requires detailed records and supervision for every step in the data collection process. Representativeness means that the collected data can reflect the real situation of the geological environment, which usually requires using statistical sampling or other scientific methods to select sample points. In addition, environmental factors and sustainability also need to be considered in data collection.

[0047] S120, Data cleaning: Since the data may come from different units and departments, and the data formats, classifications, etc. are not unified, standardization processing is required to ensure the consistency and comparability of the data. This includes using relevant technologies to perform corresponding data cleaning on the data, removing error and redundant information, data conversion, and converting heterogeneous data into a unified format.

[0048] The data cleaning in step S120 is a crucial step to ensure data quality and consistency. Geological data may come from different sources, with different formats and standards, which requires standardization. The first step in standardization is to identify errors and redundant information in the data. Errors may be caused by measurement errors, data entry errors, or mistakes in the data processing process. Redundant information may be duplicate data records or unnecessary details. Removing these errors and redundant information can improve the accuracy and usability of the data. Next is data conversion, that is, converting data in different formats into a unified format, which may involve unit conversion, data structure adjustment, or coding standardization. The body can perform the data cleaning process according to the following steps:

[0049] S121, outlier detection techniques, such as the IQR (Interquartile Range) method, to identify and process abnormal data. Outliers can be defined as data points below Q1 - 1.5 * IQR or above Q3 + 1.5 * IQR, where Q1 and Q3 are the first and third quartiles of the data respectively.

[0050] Example: According to indoor tests, 10 groups of standard rock samples were taken from a tunnel to conduct uniaxial saturated compressive strength tests. The dataset of uniaxial saturated compressive strength tests is [50, 92, 18, 55, 53, 50, 60, 51, 58, 48], with the unit of Mpa;

[0051] Perform outlier data detection: First, sort the data in ascending order, [18, 48, 50, 50, 51, 53, 55, 58, 60, 92]. The first quartile (Q1 = 49): that is, the value at the 25% position in the dataset. The third quartile (Q3 = 59): the value at the 75% position in the dataset;

[0052] Secondly, calculate the IQR value, (IQR = Q3 - Q1 = 10);

[0053] Finally, calculate the lower limit Q1 - 1.5 * IQR = 34, and the upper limit Q3 + 1.5 * IQR = 74;

[0054] According to the threshold, 18 and 92 in the dataset of uniaxial saturated compressive strength tests of rocks will be identified as outliers and thus removed.

[0055] S122, in the process of data conversion, adopt a unified standard for description according to the differences in the sources and formats of different data. For example, it can be adopted:

[0056]

[0057] where, z i is the standardized value, x iThe original data is denoted as x, the mean of the data is u, and the standard deviation corresponding to the data is σ.

[0058] As Figure 1 shown, in step S200, the processed geological data is stored in the database, and the sensitive geological data after storage is encrypted.

[0059] S210, Data storage: The data after cleaning and standardization needs to be systematically stored in the database. This process involves data classification, indexing, and organization for future retrieval and analysis.

[0060] The data storage in step S210 is the process of systematically storing the cleaned and standardized data in the database. This step is crucial for ensuring data retrievability and analyzability. The database design needs to consider the structured and unstructured characteristics of the data.

[0061] Structured data, such as geological measurement results and indoor test analysis data, is usually stored in relational databases, which organize data through tables and fields; for unstructured data, such as images, videos, reports, etc., it may need to be stored in a more flexible database system.

[0062] Database indexing and organization are also important considerations in the data storage process. Indexing can improve the efficiency of data retrieval. By creating indexes for data fields, users can quickly find specific data records.

[0063] Organization involves data classification and stratification, which helps users browse data according to specific geological conditions or characteristics. Security and backup mechanisms are aspects that cannot be ignored in the database establishment process. Data security can be achieved through access control, encryption, and network security measures. The backup mechanism ensures that data can be restored in case of hardware failures, data loss, or other unexpected situations. Regular backups and disaster recovery plans are the keys to maintaining database stability and reliability.

[0064] The specific implementation methods are as follows:

[0065] S211, Data classification. Data classification can be achieved by constructing a decision tree, which is a commonly used classification method.

[0066] Assume there are n feature data with k i possible values. The construction of the decision tree can be achieved by minimizing entropy or Gini impurity. First, the calculation of the entropy value is as follows: where H(S) is the entropy value of set S, and Pi is the probability that the sample belongs to a certain category under the condition of selecting the i-th feature. Secondly, in the calculation of information gain:

[0067]

[0068] Among them, IG(S,A) is the information gain of feature A with respect to set S, and S V is the subset of S where the A feature value is v.

[0069] Example: Dataset, using the integrity and hardness of rocks to predict the core recovery rate. The set of all core data. S = {(high, soft, high), (low, soft, low), (high, hard, high), (low, hard, low), (high, soft, high), (low, hard, low)}

[0070] Calculate the entropy of the original dataset:

[0071] Step 1: Calculate the entropy of the original dataset

[0072] There are 6 samples in the entire set S, among which 4 have a high recovery rate and 2 have a low recovery rate.

[0073] P(high) = 2 / 3; P(low) = 1 / 3

[0074] The entropy H(S) of the original dataset is calculated as follows:

[0075]

[0076] Step 2: Calculate the information gain of each feature

[0077] The characteristic situation of the rock integrity is:

[0078] Set S 高 : The rock integrity is high. S_high = {(high, soft, high), (high, hard, high), (high, soft, high)}, S_low = {(low, soft, low), (low, hard, low), (low, hard, low)},

[0079] Calculate H(S_high) and H(S_low) = 0

[0080] H(S_high) = 0

[0081] H(S_low) = 0

[0082] Therefore, the information gain of the rock integrity is:

[0083]

[0084] The situation of the characteristic of the rock hardness is:

[0085] Set S_soft: The rock hardness is soft. S_soft = {(high, soft, high), (low, soft, low), (high, soft, high)}; S_hard The rock hardness is hard.

[0086] S_hard = {(low, hard, low), (high, hard, high), (low, hard, low)}

[0087] H(S_hard) = 0

[0088] H(S_soft) = 0

[0089] Therefore, the information gain of the rock hardness level:

[0090]

[0091] Step 3: Select the feature with the maximum information gain

[0092] In this example, both the rock integrity level and the rock hardness level have the same information gain H(S). We can choose either one as the root node. Suppose we choose "rock hardness level".

[0093] Step 4: Construct the decision tree

[0094] Based on "rock hardness level", the following split datasets are obtained::

[0095] Set S_soft: The rock hardness level is soft.

[0096] S_soft = {(high, soft, high), (low, soft, low), (high, soft, high)}

[0097] Set S_hard: The rock hardness level is hard.

[0098] S_hard = {(low, hard, low), (high, hard, high), (low, hard, low)}

[0099] For "soft", further use "rock integrity level" to split:[[]]

[0100] Set S_high_soft: The rock integrity level is high and the hardness level is soft.

[0101] S_high_soft = {(high, soft, high), (high, soft, high)}

[0102] Set S_low_soft: The rock integrity level is low and the hardness level is soft.

[0103] S_low_soft = {(low, soft, low)}

[0104] For "hard", further use "rock integrity level" to split:[[]]

[0105] Set S_high_hard: The rock integrity level is high and the hardness level is hard.

[0106] S_high_hard = {(high, hard, high)}

[0107] Set S_low_hard: The rock integrity level is low and the hardness level is hard.

[0108] S low hardness = {(low, hard, low), (low, hard, low)}

[0109] Therefore, a decision tree can be obtained based on the above content.

[0110] S212, data indexing. In this embodiment, the B-tree is adopted to implement data indexing. This data structure can efficiently handle range queries and sequential access. First, the balance factor of the B-tree can be where BF is the balance factor of the node, and h left and h right are the heights of the left and right subtrees of the node respectively.

[0111] S213, data organization. In this patent, the K-means clustering algorithm is adopted to implement it. The clustering algorithm groups data points into multiple clusters, making the data points within the clusters highly similar and the data points between the clusters less similar. The iterative formula in K-means clustering is where is the center point of the j-th cluster after the t-th iteration, is the set of data points belonging to the j-th cluster in the t-th iteration.

[0112] Through the above methods, the classification, indexing, and organization processes during data warehousing can be described more precisely, thereby optimizing the storage and retrieval efficiency of data, ensuring the retrievability and analyzability of data, and providing support for subsequent processes.

[0113] S220, data encryption: To protect data security and user privacy, geological data needs to be encrypted after being warehoused. At the same time, geological data is transformed into geological products, such as geological section diagrams, borehole core identification tables, geological reports, statistical information tables, etc., which can be provided to different user groups to meet their specific needs.

[0114] The data encryption in step S220 is an important means to protect data security and user privacy. After data is warehoused, sensitive data needs to be encrypted. The choice of encryption technology should be based on the sensitivity of the data and access requirements. For the encryption method, this patent adopts the Advanced Encryption Standard (AES) algorithm, such as Figure 3 shown below are the steps of the relevant method:

[0115] S221. Key generation, using a cryptographically secure pseudorandom number generator to generate a key with a length of 128 bits.

[0116] S222. Plaintext Preparation: Format the data to be encrypted (plaintext) according to the requirements of the AES encryption algorithm (the size of the data block is 128 bits (16 bytes)).

[0117] S223. Encryption Process: The AES encryption process consists of multiple rounds, and each round includes the following four steps: SubBytes: Apply a non - linear substitution transformation to each byte of the data block; ShiftRows: Shift the rows of the data block; MixColumns: Mix the columns of the data block, which is not applicable to the last round; AddRoundKey: Perform an exclusive - OR operation between the round key and the data block. At the beginning of the encryption process, perform an AddRoundKey operation between the initial key and the data block. Then, perform several loops of the above four steps. After each round, except for the last round, an AddRoundKey operation is required.

[0118] S224. In each round of encryption, a round key needs to be generated based on the current key. This is usually achieved by expanding the initial key into multiple round keys.

[0119] S225. Output Ciphertext: After processing all rounds, the final data block is the ciphertext.

[0120] S226. Decryption Process: The decryption process is similar to the encryption process, but the order of the steps is reversed. AES decryption includes inverse SubBytes, inverse ShiftRows, inverse MixColumns, and inverse AddRoundKey operations.

[0121] As Figure 1 shown, in step S300, build a geological data sharing platform and provide users with big - data - based geological services based on the sharing platform.

[0122] S310. Establish a Data Platform: Establish a geological information sharing platform that allows users to access, purchase, download, and use geological data. This platform should have a user - friendly interface, a powerful search function, and efficient data transmission capabilities.

[0123] Step S310 of establishing a data platform is the core of realizing geological data sharing. The design of this platform needs to consider user - friendliness so that users can easily access and use geological data. The user interface should be intuitive, providing clear navigation and search functions. The search function should be powerful, supporting keyword search, advanced queries, and geospatial search. The efficiency of data transmission is also an important factor to consider in platform design, which may involve optimizing the data loading and rendering processes, as well as using efficient data transmission protocols. In addition, the platform also needs to support batch downloading and real - time access of data to meet the needs of different users.

[0124] S330, Provide data services: Provide users with big data-based geological services, including geological data analysis, model prediction, environmental monitoring and disaster warning, engineering construction and planning, etc. These services can help users quickly understand the geological conditions of the area to be studied and provide a scientific basis for decision-making.

[0125] Step S330 providing users with big data-based geological services is the ultimate goal of the co-construction and sharing mechanism, including geological data analysis, model prediction, environmental monitoring and disaster warning, engineering construction and planning, etc.

[0126] Data analysis can help users identify patterns and trends in geological data. For example, through statistical analysis, the correlation between certain geological features and specific geological events can be discovered.

[0127] Pattern recognition technologies, such as machine learning and artificial intelligence algorithms, can be used to automatically identify complex patterns in geological data. These technologies can help users identify the characteristics of geological phenomena faster and improve the efficiency of research.

[0128] Model prediction can then predict future geological changes based on existing data, which has important application value in fields such as resource exploration, disaster prevention, and environmental management. By establishing mathematical models and using computer simulations, the occurrence probability and impact range of natural disasters such as earthquakes, floods, or landslides can be predicted.

[0129] For example, principal component analysis (PCA) is used for dimensionality reduction, projecting high-dimensional data onto a low-dimensional subspace to maximize data variation, and support vector machine (SVM) is used for prediction problems. Machine learning-based methods, such as artificial neural networks, with input nodes including coordinate points, rainfall, susceptibility levels, etc., are used to judge the likelihood of collapse and landslide disasters. Among them, the calculation of svm is as follows:

[0130] The purpose of SVM is to find a fitting function:

[0131]

[0132] where λ i and μ i are correlation coefficients, k(x1,x2) is the kernel function, b is the intercept, and x i is the i-th feature.

[0133] By solving the following quadratic programming problem, the optimal λ i and:

[0134]

[0135] x i ≥0, ζi ≥ 0, i = 1, 2, ... n

[0136] where C > 0 is the penalty factor, ε is the tolerable error, x i and ζ i are slack variables, λ i and μ i are Lagrange multipliers, w is the regression coefficient vector,

[0137]

[0138] The optimal solution can be found through C, ε, σ.

[0139] These services can not only provide research support for geologists, but also provide decision-making basis for policymakers and resource managers. By providing in-depth geological data analysis and prediction, they can help them better understand geological phenomena, formulate reasonable policies and measures to address geological risks and utilize geological resources.

[0140] Establishing a co-construction and sharing mechanism based on geological data through the above steps is a complete process involving multiple links. This process systematically collects data from data collection to providing data services, promotes the sharing and dissemination of geological data, and thus promotes the development of geological disciplines and the effective utilization of geological resources.

[0141] A co-construction and sharing method based on geological data proposed by the present invention solves the problems existing in the traditional geological data sharing process and provides a series of advantages and conveniences. Through multi-source data integration, the integration of various data sources such as geological exploration, remote sensing technology, and laboratory analysis is realized. This integration not only improves the comprehensiveness and diversity of data, but also ensures that the data can cover different geological conditions and characteristics, providing users with a comprehensive data perspective. By identifying and removing error and redundant information, and converting heterogeneous data into a unified format, the present invention ensures the quality and consistency of data. Through the classification, indexing, and organization of the database, the retrieval and analysis of data are greatly facilitated. Through methods such as data analysis, pattern recognition, and prediction modeling, the present invention not only helps users deeply understand geological phenomena, but also provides a scientific decision-making basis for policy formulation and resource management. The co-construction and sharing mechanism based on geological data not only improves the quality and usability of geological data, but also provides users with an unprecedented geological data sharing and management experience by establishing a secure and efficient data platform and providing geological services based on big data, greatly promoting the development of geological disciplines and related fields.

[0142] Corresponding to the above-disclosed co-construction and sharing method based on geological data, an embodiment of the present invention also discloses a co-construction and sharing system based on geological data, as Figure 4 shown, which specifically includes:

[0143] A data processing module, configured to collect geological data and perform cleaning and standardization processing on the collected multi-source heterogeneous geological data;

[0144] A data storage module, configured to store the processed geological data in a database and encrypt the sensitive geological data after storage;

[0145] A platform construction module, configured to construct a geological data sharing platform and provide geological services based on big data for users based on the sharing platform.

[0146] It should be noted that for the detailed description of a co-construction and sharing system based on geological data provided in an embodiment of the present invention, reference can be made to the relevant description of a co-construction and sharing method based on geological data provided in an embodiment of the present application, which will not be elaborated here.

[0147] In addition, an embodiment of the present invention further provides an electronic device, which includes: a processor and a memory; the memory is used to store one or more program instructions; the processor is used to run one or more program instructions to execute the steps of a co-construction and sharing method based on geological data as described in any one of the above.

[0148] It should be noted that for the detailed description of an electronic device provided in an embodiment of the present invention, reference can be made to the relevant description of a co-construction and sharing method based on geological data provided in an embodiment of the present application, which will not be elaborated here.

[0149] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of a co-construction and sharing method based on geological data as described in any one of the above are implemented.

[0150] It should be noted that for the detailed description of a computer-readable storage medium provided in an embodiment of the present invention, reference can be made to the relevant description of a co-construction and sharing method based on geological data provided in an embodiment of the present application, which will not be elaborated here.

[0151] Those skilled in the art can understand that all or part of the functions of the above-mentioned implementation methods can be realized in the form of hardware or in the form of computer programs. When all or part of the functions in the above-mentioned implementation methods are realized in the form of computer programs, the programs can be stored in a computer-readable storage medium, which can include: read-only memory, random access memory, magnetic disks, optical disks, hard disks, etc. The above functions can be realized by a computer executing these programs. For example, the program is stored in the memory of the device, and when the processor executes the program in the memory, all or part of the above functions can be realized. In addition, when all or part of the functions in the above-mentioned implementation methods are realized in the form of computer programs, the programs can also be stored in storage media such as servers, other computers, magnetic disks, optical disks, flash drives or external hard drives, and are saved to the memory of the local device by downloading or copying, or the system of the local device is updated. When the processor executes the program in the memory, all or part of the functions in the above-mentioned implementation methods can be realized.

[0152] The above uses specific examples to illustrate the present invention, which is only for helping to understand the present invention and is not intended to limit the present invention. For those skilled in the art of the present invention, according to the idea of the present invention, several simple deductions, deformations or substitutions can also be made.

Claims

1. A method for co-building and sharing based on geological data, characterized in that: The method comprises: Collect geological data, and clean and standardize the collected multi-source heterogeneous geological data; The processed geological data is stored in the database, and the sensitive geological data is encrypted after storage; Build a geological data sharing platform and provide users with geological services based on big data based on the sharing platform.

2. A method for co-building and sharing based on geological data as claimed in claim 1, characterized in that: Collect geological data, including: For the target research area, regional geological data are collected using a variety of methods, including geological exploration, remote sensing technology, and laboratory analysis; by covering different geological conditions and characteristics, the comprehensiveness and diversity of the data are ensured.

3. A method for co-building and sharing based on geological data as claimed in claim 1, characterized in that: Clean and standardize the collected multi-source heterogeneous geological data, including: Outlier detection: identifying and processing abnormal data; Standardization processing: Use unified standards to describe different data sources and formats.

4. A method for co-building and sharing based on geological data as claimed in claim 1, characterized in that: The processed geological data is stored in the database, including: The processed geological data are classified, data indexes are constructed, and data are organized.

5. A method for co-building and sharing based on geological data as claimed in claim 1, characterized in that: The sensitive geological data after storage is encrypted, including: Use encryption algorithm to encrypt data.

6. A method for co-building and sharing based on geological data as claimed in claim 5, characterized in that: The encryption algorithm is used to encrypt data, including: Key generation: Generate a key using a pseudo-random number generator; Plaintext preparation: format the plaintext data to be encrypted according to the requirements of the encryption algorithm; Encryption process: The plaintext data is encrypted using a key through multiple rounds of processing, where each round includes the following steps: applying a nonlinear substitution transformation to each byte of the data block; shifting the rows of the data block; mixing the columns of the data block; performing an XOR operation on the round key and the data block; Output ciphertext: After all rounds of processing, the final data block is the ciphertext; Decryption process: The decryption process is the reverse order of the encryption process.

7. A method for co-building and sharing based on geological data as claimed in claim 1, characterized in that: Based on the sharing platform, geological services based on big data are provided to users, including: Provide users with geological services based on big data, including geological data analysis, model prediction, environmental monitoring and disaster warning, engineering construction and planning services, to help users quickly understand the geological conditions of the proposed study area and provide a scientific basis for decision-making.

8. A co-construction and sharing system based on geological data, characterized in that: The system comprises: Data processing module, used to collect geological data, and clean and standardize the collected multi-source heterogeneous geological data; The data storage module is used to store the processed geological data in the storage and encrypt the sensitive geological data after storage; The platform construction module is used to construct a geological data sharing platform and provide users with geological services based on big data based on the sharing platform.

9. An electronic device, characterized in that: The device comprises: a processor and a memory; The memory is used to store one or more program instructions; The processor is used to run one or more program instructions to execute the steps of a method for co-construction and sharing based on geological data as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a method for co-construction and sharing based on geological data as described in any one of claims 1 to 7 are implemented.

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