Geological exploration project data management method and system
By constructing an extended inverted index, setting multi-level passwords, and implementing a dynamic distributed lock mechanism, the problems of inaccurate retrieval and low collaboration efficiency in geological exploration project data management were solved, achieving efficient and secure data management and collaboration.
Patent Information
- Application Number
- CN202511026981.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-24
- Publication Date
- 2025-10-28
AI Technical Summary
Existing methods and systems for managing geological exploration project data have low retrieval accuracy and recall rates, low team collaboration efficiency, and are prone to data conflicts, resulting in a poor user experience.
An extended inverted index is constructed by performing semantic similarity analysis on geological data texts, multi-level password encryption is set and permissions are assigned, and a dynamically adjusted distributed lock mechanism is used to restrict collaborative sharing.
It improved the accuracy and recall of data retrieval, enhanced storage security and team collaboration efficiency, ensured data consistency and integrity, and improved user experience and system stability.
Smart Images
Figure CN120849533A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data storage and management technology, specifically to a method and system for managing geological exploration project data. Background Technology
[0002] Geological exploration projects are a highly specialized and practical comprehensive undertaking in the field of earth sciences. They aim to systematically investigate and study the structure, rock types, stratigraphic distribution, and mineral resource occurrence of geological bodies within a specific area using diverse exploration methods and technologies such as remote sensing, geophysical exploration, geochemical analysis, and geological drilling. This process yields detailed information on underground geological structures and mineral resources, providing accurate and comprehensive geological data and scientific decision-making support for subsequent mineral resource development and utilization, site stability assessments for engineering construction, and prevention and control of geological disasters. Furthermore, geological exploration generates a large amount of project data, necessitating a reasonable project data management method and system for managing geological exploration project data.
[0003] While existing methods and systems for managing geological exploration project data utilize inverted indexes to facilitate data retrieval, conventional inverted indexes only match keywords. When users input words with similar meanings, they often struggle to find data containing those keywords, resulting in low accuracy and recall rates. Furthermore, existing methods and systems lack robust mechanisms for collaborative data sharing, hindering simultaneous editing and real-time updates of the same data by different users. This can lead to data conflicts, impacting team collaboration efficiency and resulting in a poor user experience.
[0004] Based on the above, this invention proposes a convenient method and system for managing geological exploration project data. Summary of the Invention
[0005] To overcome the shortcomings of existing geological exploration project data management methods and systems, which, while employing inverted indexes to facilitate data retrieval, only achieve keyword matching. When users input similar terms, they often struggle to find relevant data, resulting in low accuracy and recall. Furthermore, collaborative data sharing suffers from inadequate mechanisms for simultaneous editing and real-time updates, leading to data conflicts, reduced teamwork efficiency, and a poor user experience. Therefore, this invention proposes a user-friendly geological exploration project data management method and system.
[0006] A method for managing geological exploration project data includes the following steps: Data from multiple geological exploration projects are acquired, and the completeness of data from each geological exploration project is quantitatively assessed. Data from each project is then filtered and corrected using data cleaning algorithms to obtain preprocessed data from multiple geological exploration projects. The data from multiple preprocessed geological exploration projects are classified according to geographical location, project stage, and data type, and the classified data from multiple geological exploration projects are then distributed and stored. Geological data texts are extracted from various data of multiple geological exploration projects after preprocessing. The geological data texts are preprocessed, and semantic similarity analysis is performed on the keywords in the geological data texts. An extended inverted index is constructed based on the results of the semantic similarity analysis, and the extended inverted index is updated regularly based on the newly added or modified geological data texts. By setting up multi-level passwords, various data of geological exploration projects are encrypted, and corresponding data management permissions are assigned to each level of password; Collaborative sharing of data is achieved through online links, and reasonable restrictions on collaborative sharing of data are imposed based on a dynamically adjusted distributed lock mechanism.
[0007] As a preferred aspect of the invention, the specific steps for performing a quantitative assessment of the completeness of various data from each geological exploration project and filtering and correcting the data in each data using a data cleaning algorithm to obtain preprocessed data from multiple geological exploration projects are as follows: Obtain the actual number of data items for each geological exploration project. Calculate the ratio of the actual number of data items to the standard number of data items for each geological exploration project to obtain the data completeness score for each project. For geological exploration projects with a data completeness score lower than a preset threshold, data needs to be supplemented until the data completeness score meets the requirements. To handle the missing or vacuous values in the data, use linear interpolation or polynomial interpolation to fill the missing values, or directly use the mean or median of the data in the column to fill the missing values. Outliers that do not conform to the expected pattern in various data are identified by box plots or IQR methods. Outliers are then removed and replaced with the mean or median of the data in that column, or outliers are repaired using interpolation methods, thus obtaining preprocessed data for various geological exploration projects.
[0008] As a preferred aspect of the invention, the specific steps of preprocessing the geological data text, performing semantic similarity analysis on the keywords in the geological data text, and constructing an extended inverted index based on the results of the semantic similarity analysis are as follows: The geological data text is segmented to ensure that each word unit is accurately identified, and stop words in the geological data text are filtered out to reduce noise information, thereby obtaining all the keywords in the geological data text. A vocabulary list is created based on all keywords in the geological data text, where each keyword has a unique identifier. For each keyword, the documents in which it appears and its location information are recorded to obtain the basic inverted index. Each keyword is mapped to a low-dimensional vector space using a trained Word2Vec or GloVe-based word vector model, resulting in a vector representation of each keyword. Keywords with similar semantics have their vector representations close to each other. The semantic similarity between keywords is calculated using the cosine similarity formula, the specific expression of which is:
[0009] in and These represent the vector representations of the two keywords, and Indicates and and The semantic similarity between two corresponding keywords and Then they represent vectors respectively. sum vector The modulus length; For each keyword in the vocabulary, find other keywords whose semantic similarity is higher than a preset threshold. Add an extension part to each keyword in the basic inverted index to record other keywords with similar semantics and their similarity information, thus obtaining the extended inverted index.
[0010] As a preferred aspect of the invention, the specific steps for periodically updating the extended inverted index based on the newly added or modified geological data text are as follows: The geological data text is expanded periodically, that is, newly added or modified geological data text is regularly included in the construction scope of the expanded inverted index to obtain the expanded geological data text and vocabulary. The word vector model based on Word2Vec or GloVe is retrained based on the expanded geological data text to obtain the updated word vector model; Based on the updated word vector model, the semantic similarity between keywords is recalculated using the cosine similarity formula, and the content in the extended inverted index is updated accordingly.
[0011] As a preferred aspect of the invention, the specific steps of encrypting various data of the geological exploration project by setting multi-level passwords and assigning corresponding data management permissions to each level of password are as follows: Data management permissions for geological exploration projects are divided into four levels. The lowest level of data management permission only allows viewing of various data for geological exploration projects. The intermediate level allows viewing and modifying various data for geological exploration projects. The highest level allows viewing, modifying, importing, and exporting various data for geological exploration projects. The highest level of data management permission, in addition to the higher level, also allows deletion and online sharing of various data for geological exploration projects. Each level of data management access is assigned a corresponding password, with different password strengths and complexities for each level. Furthermore, the highest level of data management access allows for the reset and modification of passwords at all levels.
[0012] As a preferred aspect of the invention, the specific steps for reasonably restricting collaborative data sharing based on the dynamically adjusted distributed lock mechanism are as follows: A distributed lock mechanism is adopted. When a user modifies a certain piece of data, a distributed lock is automatically set for that data to prevent other users from modifying the data at the same time and causing conflicts. Using operation queuing technology, user modification operations are temporarily stored in a queue. Once the distributed lock is released, subsequent modification operations are executed sequentially according to the queue order, and the updated data is pushed to other users viewing the data in real time. The release time of the distributed lock is calculated based on the current system load and preset user request priorities using a formula, specifically:
[0013] in Indicates the release time of the distributed lock. This indicates the preset base release time. This indicates the current system load. Indicates the maximum load of the system. Indicates the priority of user requests. This indicates the highest request priority.
[0014] A geological exploration project data management system includes: The data preprocessing module is used to acquire various data from multiple geological exploration projects, perform a quantitative assessment of the completeness of each data from each geological exploration project, and use data cleaning algorithms to filter and correct the data in each data to obtain preprocessed data from multiple geological exploration projects. The classification storage module is used to classify various data from multiple preprocessed geological exploration projects according to geographical location, project stage, and data type, and to distribute the classified data from multiple geological exploration projects. The index building module is used to extract geological data text from various data of multiple geological exploration projects after preprocessing, preprocess the geological data text, perform semantic similarity analysis on the keywords in the geological data text, build an extended inverted index based on the results of the semantic similarity analysis, and update the extended inverted index regularly based on the newly added or modified geological data text. The data encryption module is used to encrypt various data of geological exploration projects by setting multi-level passwords, and to assign corresponding data management permissions to each level of password; The collaboration and sharing module is used to enable collaborative sharing of materials through online links, and to reasonably restrict the collaborative sharing of materials based on a dynamically adjusted distributed lock mechanism.
[0015] The present invention has the following advantages: 1. This invention performs semantic similarity analysis on keywords in geological data texts and constructs an extended inverted index based on the results. This allows staff to search for geological data using keywords, not only finding data containing those keywords but also data containing semantically similar words. This improves the accuracy and recall of retrieval and breaks through the limitations of simple keyword matching. It not only enhances retrieval precision but also helps users discover relevant data that might have been missed due to word differences, making search results more relevant to actual needs. This improves the efficiency of project data retrieval and user experience, enhancing the ease of use and practicality of this geological exploration project data management method and system.
[0016] 2. This invention encrypts various data in geological exploration projects by setting multi-level passwords and assigning corresponding data management permissions to each password level. This not only greatly enhances the storage security of project data and effectively prevents unauthorized access and information leakage, but also provides staff with fine-grained permission management, ensuring that each user can only perform necessary operations. This improves staff efficiency and reduces the risk of data corruption due to misoperation. In addition, multi-level password management helps track and audit data usage. By recording the operations performed by each user using different password levels, the flow and changes of data can be better monitored, facilitating subsequent responsibility identification and problem investigation. This enhances the ease of use and practicality of this geological exploration project data management method and system.
[0017] 3. This invention enables collaborative data sharing through online links and reasonably restricts data sharing based on a dynamically adjusted distributed lock mechanism. This not only effectively prevents conflicts caused by multiple users simultaneously modifying the same data, ensuring data consistency and integrity and enhancing system stability, but also utilizes operation queue technology to temporarily store editing requests sequentially, executing them in turn after the lock is released. This ensures the orderliness of operations, improves the overall system efficiency, and avoids chaos caused by concurrent operations. Furthermore, real-time push of updated data to other viewers ensures that team members receive the latest information, thereby improving team collaboration efficiency. In addition, dynamically adjusting the release time of the distributed lock according to system load and user priority optimizes resource allocation and improves system response speed and user experience. Especially in high-concurrency environments, it effectively alleviates system pressure, ensures that critical operations are executed first, and enhances the ease of use and practicality of this geological exploration project data management method and system. Attached Figure Description
[0018] Figure 1 This is a flowchart illustrating a geological exploration project data management method used in an embodiment of the present invention.
[0019] Figure 2 This is a schematic diagram of the structure of a geological exploration project data management system used in an embodiment of the present invention. Detailed Implementation
[0020] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.
[0021] Example 1: A method for managing geological exploration project data, such as... Figure 1 As shown, it includes the following steps: Data from multiple geological exploration projects are acquired, and the completeness of data from each geological exploration project is quantitatively assessed. Data from each project is then filtered and corrected using data cleaning algorithms to obtain preprocessed data from multiple geological exploration projects. The data from multiple preprocessed geological exploration projects are classified according to geographical location, project stage, and data type, and the classified data from multiple geological exploration projects are then distributed and stored. Geological data texts are extracted from various data of multiple geological exploration projects after preprocessing. The geological data texts are preprocessed, and semantic similarity analysis is performed on the keywords in the geological data texts. An extended inverted index is constructed based on the results of the semantic similarity analysis, and the extended inverted index is updated regularly based on the newly added or modified geological data texts. By setting up multi-level passwords, various data of geological exploration projects are encrypted, and corresponding data management permissions are assigned to each level of password; Collaborative sharing of data is achieved through online links, and reasonable restrictions on collaborative sharing of data are imposed based on a dynamically adjusted distributed lock mechanism.
[0022] The specific steps for conducting a quantitative assessment of the completeness of various data from each geological exploration project and filtering and correcting the data using data cleaning algorithms to obtain preprocessed data from multiple geological exploration projects are as follows: Obtain the actual number of data items for each geological exploration project. Calculate the ratio of the actual number of data items to the standard number of data items for each geological exploration project to obtain the data completeness score for each project. For geological exploration projects with a data completeness score lower than a preset threshold, data needs to be supplemented until the data completeness score meets the requirements. To handle the missing or vacuous values in the data, use linear interpolation or polynomial interpolation to fill the missing values, or directly use the mean or median of the data in the column to fill the missing values. Outliers that do not conform to the expected pattern in various data are identified by box plots or IQR methods. Outliers are then removed and replaced with the mean or median of the data in that column, or outliers are repaired using interpolation methods, thus obtaining preprocessed data for various geological exploration projects.
[0023] The specific steps for preprocessing geological data text, performing semantic similarity analysis on keywords in the geological data text, and constructing an extended inverted index based on the results of the semantic similarity analysis are as follows: The geological data text is segmented to ensure that each word unit is accurately identified. For example, a professional geological vocabulary dictionary is used to process technical terms such as "granite", "fault" and "fold", and stop words in the geological data text are filtered out to reduce noise information and obtain all the keywords in the geological data text. Stop words usually include "of", "is", "in" and "and". A vocabulary list is created based on all keywords in the geological data text, where each keyword has a unique identifier. For each keyword, the documents in which it appears and its position information are recorded to obtain the basic inverted index. For example, the keyword "geology" appears in the 2nd and 5th positions of document 1 and the 1st position of document 3, and the keyword "structure" appears in the 3rd position of document 1 and the 4th and 7th positions of document 2. Each keyword is mapped to a low-dimensional vector space using a trained Word2Vec or GloVe-based word vector model, resulting in a vector representation of each keyword. Keywords with similar semantics have their vector representations close to each other. The semantic similarity between keywords is calculated using the cosine similarity formula, the specific expression of which is:
[0024] in and These represent the vector representations of the two keywords, and Indicates and and The semantic similarity between two corresponding keywords and Then they represent vectors respectively. sum vector If two vectors are similar in direction in the semantic space, then a larger dot product and a smaller product of their magnitudes indicate higher semantic similarity. For each keyword in the vocabulary, other keywords with semantic similarity higher than a preset threshold are found. For example, for the keyword "geology", keywords with similarity higher than the preset threshold may include "earth", "geology", and "petrology". An extension part is added to each keyword in the basic inverted index to record other keywords with semantic similarity and their similarity information, resulting in an extended inverted index. For example, the basic index entry is: keyword "geology" → document 1 (positions 2 and 5) and document 3 (position 1), and the extended index entry is: keyword "geology" → similar keywords "earth" (semantic similarity of 0.75), "geology" (semantic similarity of 0.8), and "petrology" (semantic similarity of 0.65).
[0025] It should be noted that the specific process of data retrieval based on the above extended inverted index is as follows: User query preprocessing performs the same preprocessing steps as text data on the user-input query statement, including word segmentation, stop word removal, and stemming. The query term expansion uses the expanded inverted index to find keywords that are semantically similar to the user's query term. For example, if the user queries "geological structure", after expansion, similar keywords such as "earth structure", "geological structure" and "rock structure" are obtained. Matching and sorting utilize the expanded keyword set to search for relevant documents in the inverted index and sort them according to semantic similarity. For example, when a user queries "geological structure", in addition to matching documents containing "geological structure", it will also match documents containing similar keywords such as "earth structure" and "geological structure". Furthermore, all matched documents will be automatically sorted according to semantic similarity, allowing users to obtain information with high semantic relevance to their query first.
[0026] The above steps involve performing semantic similarity analysis on keywords in geological data texts and constructing an extended inverted index based on the results. This allows staff to search for geological data using keywords, not only finding data containing those keywords but also data containing semantically similar words. This improves retrieval accuracy and recall, and breaks through the limitations of simple keyword matching. It not only enhances retrieval precision but also helps users discover relevant data that might have been missed due to word differences, making search results more relevant to actual needs. This improves the efficiency of project data retrieval and user experience, enhancing the ease of use and practicality of this geological exploration project data management method and system.
[0027] The specific steps for periodically updating the extended inverted index based on newly added or modified geological data text are as follows: The geological data text is expanded periodically, that is, newly added or modified geological data text is regularly included in the construction scope of the expanded inverted index to obtain the expanded geological data text and vocabulary. The word vector model based on Word2Vec or GloVe is retrained based on the expanded geological data text to obtain the updated word vector model; Based on the updated word vector model, the semantic similarity between keywords is recalculated using the cosine similarity formula, and the content in the extended inverted index is updated accordingly.
[0028] The specific steps for encrypting various data of the geological exploration project by setting multi-level passwords and assigning corresponding data management permissions to each level of password are as follows: Data management permissions for geological exploration projects are divided into four levels. The lowest level of data management permission only allows viewing of various data for geological exploration projects. The intermediate level allows viewing and modifying various data for geological exploration projects. The highest level allows viewing, modifying, importing, and exporting various data for geological exploration projects. The highest level of data management permission, in addition to the higher level, also allows deletion and online sharing of various data for geological exploration projects. Each level of data management access is assigned a corresponding password, with different password strengths and complexities for each level. Furthermore, the highest level of data management access allows for the reset and modification of passwords at all levels.
[0029] It should be noted that the specific procedure for users to operate on various data of geological exploration projects using a password is as follows: Password input: After the user enters their password, the system verifies its validity. Access control is performed by retrieving the corresponding data management permission level based on the password's hash value. Operation control: If the user's requested operation permission is less than or equal to the current password's data management permission level, then the operation is allowed; otherwise, the operation is denied and an insufficient permission message is displayed.
[0030] The above steps encrypt various data of the geological exploration project by setting multi-level passwords and assigning corresponding data management permissions to each level of password. This not only greatly enhances the storage security of project data and effectively prevents unauthorized access and information leakage, but also provides staff with fine-grained permission management, ensuring that each user can only perform necessary operations. This improves staff efficiency and reduces the risk of data corruption due to misoperation. In addition, multi-level password management helps to track and audit data usage. By recording the operations performed by each user using different levels of passwords, the flow and changes of data can be better monitored, facilitating subsequent responsibility identification and problem investigation. This enhances the ease of use and practicality of the geological exploration project data management method and system.
[0031] The specific steps of the dynamically adjusted distributed lock mechanism to reasonably restrict collaborative data sharing are as follows: A distributed lock mechanism is adopted. When a user modifies a certain piece of data, a distributed lock is automatically set for that data to prevent other users from modifying the data at the same time and causing conflicts. Using operation queuing technology, user modification operations are temporarily stored in a queue. Once the distributed lock is released, subsequent modification operations are executed sequentially according to the queue order, and the updated data is pushed to other users viewing the data in real time. The release time of the distributed lock is calculated based on the current system load and preset user request priorities using a formula, specifically:
[0032] in Indicates the release time of the distributed lock. This indicates the preset base release time. This indicates the current system load. Indicates the maximum load of the system. Indicates the priority of user requests. This indicates the highest request priority.
[0033] The above steps enable collaborative data sharing through online links and reasonably restrict data sharing based on a dynamically adjusted distributed lock mechanism. This not only effectively prevents conflicts caused by multiple users simultaneously modifying the same data, ensuring data consistency and integrity and enhancing system stability, but also utilizes operation queue technology to temporarily store editing requests sequentially, executing them one by one after the lock is released. This ensures the orderliness of operations, improves the overall system efficiency, and avoids chaos caused by concurrent operations. Furthermore, real-time push of updated data to other viewers ensures that team members receive the latest information, thereby improving team collaboration efficiency. In addition, dynamically adjusting the release time of the distributed lock according to system load and user priority optimizes resource allocation and improves system response speed and user experience. Especially in high-concurrency environments, it effectively alleviates system pressure, ensures that critical operations are executed first, and enhances the ease of use and practicality of the geological exploration project data management method and system.
[0034] Example 2: A geological exploration project data management system, such as... Figure 2 As shown, it includes: The data preprocessing module is used to acquire various data from multiple geological exploration projects, perform a quantitative assessment of the completeness of each data from each geological exploration project, and use data cleaning algorithms to filter and correct the data in each data to obtain preprocessed data from multiple geological exploration projects. The classification storage module is used to classify various data from multiple preprocessed geological exploration projects according to geographical location, project stage, and data type, and to distribute the classified data from multiple geological exploration projects. The index building module is used to extract geological data text from various data of multiple geological exploration projects after preprocessing, preprocess the geological data text, perform semantic similarity analysis on the keywords in the geological data text, build an extended inverted index based on the results of the semantic similarity analysis, and update the extended inverted index regularly based on the newly added or modified geological data text. The data encryption module is used to encrypt various data of geological exploration projects by setting multi-level passwords, and to assign corresponding data management permissions to each level of password; The collaboration and sharing module is used to enable collaborative sharing of materials through online links, and to reasonably restrict the collaborative sharing of materials based on a dynamically adjusted distributed lock mechanism.
[0035] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Parts not described in detail in this specification are prior art known to those skilled in the art.
Claims
1. A method for managing geological exploration project data, characterized in that, Includes the following steps: Data from multiple geological exploration projects are acquired, and the completeness of data from each geological exploration project is quantitatively assessed. Data from each project is then filtered and corrected using data cleaning algorithms to obtain preprocessed data from multiple geological exploration projects. The data from multiple preprocessed geological exploration projects are classified according to geographical location, project stage, and data type, and the classified data from multiple geological exploration projects are then distributed and stored. Geological data texts are extracted from various data of multiple geological exploration projects after preprocessing. The geological data texts are preprocessed, and semantic similarity analysis is performed on the keywords in the geological data texts. An extended inverted index is constructed based on the results of the semantic similarity analysis, and the extended inverted index is updated regularly based on the newly added or modified geological data texts. By setting up multi-level passwords, various data of geological exploration projects are encrypted, and corresponding data management permissions are assigned to each level of password; Collaborative sharing of data is achieved through online links, and reasonable restrictions on collaborative sharing of data are imposed based on a dynamically adjusted distributed lock mechanism.
2. The method for managing geological exploration project data according to claim 1, characterized in that, The specific steps for conducting a quantitative assessment of the completeness of various data from each geological exploration project and filtering and correcting the data using data cleaning algorithms to obtain preprocessed data from multiple geological exploration projects are as follows: Obtain the actual number of data items for each geological exploration project. Calculate the ratio of the actual number of data items to the standard number of data items for each geological exploration project to obtain the data completeness score for each project. For geological exploration projects with a data completeness score lower than a preset threshold, data needs to be supplemented until the data completeness score meets the requirements. To handle the missing or vacuous values in the data, use linear interpolation or polynomial interpolation to fill the missing values, or directly use the mean or median of the data in the column to fill the missing values. Outliers that do not conform to the expected pattern in various data are identified by box plots or IQR methods. Outliers are then removed and replaced with the mean or median of the data in that column, or outliers are repaired using interpolation methods, thus obtaining preprocessed data for various geological exploration projects.
3. The method for managing geological exploration project data according to claim 2, characterized in that, The specific steps for preprocessing geological data text, performing semantic similarity analysis on keywords in the geological data text, and constructing an extended inverted index based on the results of the semantic similarity analysis are as follows: The geological data text is segmented to ensure that each word unit is accurately identified, and stop words in the geological data text are filtered out to reduce noise information, thereby obtaining all the keywords in the geological data text. A vocabulary list is created based on all keywords in the geological data text, where each keyword has a unique identifier. For each keyword, the documents in which it appears and its location information are recorded to obtain the basic inverted index. Each keyword is mapped to a low-dimensional vector space using a trained Word2Vec or GloVe-based word vector model, resulting in a vector representation of each keyword. Keywords with similar semantics have their vector representations close to each other. The semantic similarity between keywords is calculated using the cosine similarity formula, the specific expression of which is: in and These represent the vector representations of the two keywords, and Indicates and and The semantic similarity between two corresponding keywords and Then they represent vectors respectively. sum vector The modulus length; For each keyword in the vocabulary, find other keywords whose semantic similarity is higher than a preset threshold. Add an extension part to each keyword in the basic inverted index to record other keywords with similar semantics and their similarity information, thus obtaining the extended inverted index.
4. The geological exploration project data management method according to claim 3, characterized in that, The specific steps for periodically updating the extended inverted index based on newly added or modified geological data text are as follows: The geological data text is expanded periodically, that is, newly added or modified geological data text is regularly included in the construction scope of the expanded inverted index to obtain the expanded geological data text and vocabulary. The word vector model based on Word2Vec or GloVe is retrained based on the expanded geological data text to obtain the updated word vector model; Based on the updated word vector model, the semantic similarity between keywords is recalculated using the cosine similarity formula, and the content in the extended inverted index is updated accordingly.
5. The geological exploration project data management method according to claim 4, characterized in that, The specific steps for encrypting various data of the geological exploration project by setting multi-level passwords and assigning corresponding data management permissions to each level of password are as follows: Data management permissions for geological exploration projects are divided into four levels. The lowest level of data management permission only allows viewing of various data for geological exploration projects. The intermediate level allows viewing and modifying various data for geological exploration projects. The highest level allows viewing, modifying, importing, and exporting various data for geological exploration projects. The highest level of data management permission, in addition to the higher level, also allows deletion and online sharing of various data for geological exploration projects. Each level of data management access is assigned a corresponding password, with different password strengths and complexities for each level. Furthermore, the highest level of data management access allows for the reset and modification of passwords at all levels.
6. The method for managing geological exploration project data according to claim 5, characterized in that, The specific steps of the dynamically adjusted distributed lock mechanism to reasonably restrict collaborative data sharing are as follows: A distributed lock mechanism is adopted. When a user modifies a certain piece of data, a distributed lock is automatically set for that data to prevent other users from modifying the data at the same time and causing conflicts. Using operation queuing technology, user modification operations are temporarily stored in a queue. Once the distributed lock is released, subsequent modification operations are executed sequentially according to the queue order, and the updated data is pushed to other users viewing the data in real time. The release time of the distributed lock is calculated based on the current system load and preset user request priorities using a formula, specifically: in Indicates the release time of the distributed lock. This indicates the preset base release time. This indicates the current system load. Indicates the maximum load of the system. Indicates the priority of user requests. This indicates the highest request priority.
7. A geological exploration project data management system, applied to the geological exploration project data management method described in any one of claims 1-6, characterized in that, Including: The data preprocessing module is used to acquire various data from multiple geological exploration projects, perform a quantitative assessment of the completeness of each data from each geological exploration project, and use data cleaning algorithms to filter and correct the data in each data to obtain preprocessed data from multiple geological exploration projects. The classification storage module is used to classify various data from multiple preprocessed geological exploration projects according to geographical location, project stage, and data type, and to distribute the classified data from multiple geological exploration projects. The index building module is used to extract geological data text from various data of multiple geological exploration projects after preprocessing, preprocess the geological data text, perform semantic similarity analysis on the keywords in the geological data text, build an extended inverted index based on the results of the semantic similarity analysis, and update the extended inverted index regularly based on the newly added or modified geological data text. The data encryption module is used to encrypt various data of geological exploration projects by setting multi-level passwords, and to assign corresponding data management permissions to each level of password; The collaboration and sharing module is used to enable collaborative sharing of materials through online links, and to reasonably restrict the collaborative sharing of materials based on a dynamically adjusted distributed lock mechanism.