Intelligent logging method and system for drilling rock core

Abstract

The invention provides a drilling rock core intelligent recording method and system, a cloud end uses project information and drilling design data input by a user to create a project record and a drilling record, and the WEB end obtains the project record and the drilling record of the cloud end and associates and stores the project record, the drilling record and the drilling design data to the cloud end. The mobile terminal obtains corresponding target data from the cloud terminal based on geological identification information and rock core attribute data input by a user, the cloud terminal performs geological calculation by using rock core parameter information input by the user to the mobile terminal, and the mobile terminal obtains a geological calculation result of the cloud terminal and associatively stores the rock core parameter information and the geological calculation result to the cloud terminal; the cloud end processes the original rock core box image to obtain a standard rock core box image, and the mobile end obtains the standard rock core box image of the cloud end and associates and stores related data to the cloud end. By the adoption of the method, the drilling rock core recording quality can be guaranteed, and the drilling rock core recording efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent logging method and system for drilling cores. Background Art

[0002] Cores are the most intuitive and practical materials for studying and understanding underground geology and mineral conditions. Through cores, the age, lithology, sedimentary characteristics of strata, physical and chemical properties of reservoirs, and the conditions of oil, gas, and water content, characteristics of source rocks and source rock indicators, underground structural conditions, etc. can be understood. Drilling core logging is a process of systematically recording and describing the drilling process of cores.

[0003] Under the existing technical conditions, the process of drilling core logging is as follows:

[0004] 1) Preparation: Determine the tools (such as core boxes, measuring tools, rulers, magnifying glasses, writing tools) and materials (such as logging forms, core boxes, record books) required for logging to manually record core information. 2) Fill in the logging form: Fill in the measurement results, observed and described information into the record forms (such as back-rod record forms, geotechnical record forms, water level record forms, dynamic penetration record forms, standard penetration record forms, etc.). 3) Core processing: Number the taken-out cores in the order of drilling depth, and perform preliminary processing (including removing dirt, washing the surface of the cores to remove drilling fluid, marking the starting and ending positions of each core run, etc.), and finally put them into the core box. 4) Core photography: Use a camera to take pictures of each box of cores for recording. 5) Logging measurement: Use measuring tools (such as a scale or a digital measuring instrument) to measure data such as the length and diameter of the cores, and record the measurement results of each core segment. 6) Observation and description: Use a magnifying glass to observe the details of the cores (including characteristics such as the color, structure, texture, and minerals contained in the rocks), and describe them, and record any special structures, fractures, joints, or changes in the cores. 7) Sampling: Select appropriate positions for sampling according to needs and sampling purposes, and record the detailed information of the sampling. 8) Logging data collation: Collate and summarize all logging data and digitize them for subsequent analysis and application. 9) Data archiving: Archive and store the logging data and core samples for future query and research. 10) Report compilation: Compile a detailed core logging report based on the logging data, and the report includes a detailed description of the cores, measurement data, image materials, etc.

[0005] The disadvantages of the existing working method for drilling core logging are as follows: 1) The workload of handwritten logging is large and the work efficiency is low. 2) The standardization degree of handwritten logging is low, the professional quality requirements for logging personnel are high, and the data quality cannot be guaranteed. 3) Paper storage, data maintenance and management are complex, information security is poor, data query and analysis are inconvenient, and sharing and collaboration are difficult. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide an intelligent logging method and system for drilling cores to alleviate the above problems existing in the prior art.

[0007] In a first aspect, an embodiment of the present invention provides an intelligent logging method for drilling cores, which is applied to an intelligent logging system for drilling cores. The intelligent logging system for drilling cores includes a cloud, a mobile terminal, and a WEB terminal. The cloud is respectively connected to the mobile terminal and the WEB terminal. The intelligent logging method for drilling cores includes: the WEB terminal obtains the project record created by the cloud using the project information based on the project information input by the user, and obtains the drilling record created by the cloud using the drilling design data based on the drilling design data input by the user, and then associates and stores the project record, the drilling record, and the drilling design data to the cloud; the mobile terminal obtains the first target data from the cloud based on the geological identification information input by the user, and associates and stores the geological identification information and the first target data to the cloud; the mobile terminal obtains the geological calculation result calculated by the cloud using the core parameter information based on the core parameter information input by the user, and associates and stores the core parameter information and the geological calculation result to the cloud; wherein, the geological calculation result includes at least one of the following: ROD, permeability coefficient, shape coefficient, Lugeon value, P-Q curve type, formation continuity; the mobile terminal obtains the second target data from the cloud based on the core attribute data input by the user, and obtains the standard core box image obtained by the cloud after processing the original core box image, and then associates and stores the core attribute data, the second target data, and the standard core box image to the cloud.

[0008] Second aspect, an embodiment of the present invention further provides an intelligent logging system for borehole cores. The intelligent logging system for borehole cores includes a cloud, a mobile terminal, and a WEB terminal. The cloud is respectively connected to the mobile terminal and the WEB terminal. The WEB terminal is configured to: obtain a project record created by the cloud using the project information based on the project information input by the user, and obtain a borehole record created by the cloud using the borehole design data based on the borehole design data input by the user, and then associate and store the project record, the borehole record, and the borehole design data in the cloud; The mobile terminal is configured to: obtain first target data from the cloud based on the geological identification information input by the user, and associate and store the geological identification information and the first target data in the cloud; The mobile terminal is configured to: obtain a geological calculation result calculated by the cloud using the core parameter information based on the core parameter information input by the user, and associate and store the core parameter information and the geological calculation result in the cloud; wherein, the geological calculation result includes at least one of the following: ROD, permeability coefficient, shape coefficient, Lugeon value, P-Q curve type, formation continuity; The mobile terminal is configured to: obtain second target data from the cloud based on the core attribute data input by the user, and obtain a standard core box image obtained by the cloud after processing the original core box image, and then associate and store the core attribute data, the second target data, and the standard core box image in the cloud.

[0009] An intelligent cataloging method and system for drilling cores provided by an embodiment of the present invention. The cloud creates a project record by using the project information input by the user on the WEB side, and creates a drilling record by using the drilling design data input by the user on the WEB side; the WEB side obtains the project record and the drilling record from the cloud, and stores the project record, the drilling record, and the drilling design data in the cloud in an associated manner; the mobile side obtains the first target data from the cloud based on the geological identification information input by the user, and stores the geological identification information and the first target data in the cloud in an associated manner; the cloud performs geological calculations by using the core parameter information input by the user on the mobile side, the mobile side obtains the geological calculation results from the cloud, and stores the core parameter information and the geological calculation results in the cloud in an associated manner; the mobile side obtains the second target data from the cloud based on the core attribute data input by the user, the cloud processes the original core box image to obtain a standard core box image, and the mobile side obtains the standard core box image from the cloud and stores the core attribute data, the second target data, and the standard core box image in the cloud in an associated manner. By adopting the above technology, when cataloging, the user only needs to input the project information and the drilling design data on the WEB side, and the cloud can automatically create the project record and the drilling record. The user only needs to input the geological identification information, the core parameter information, and the core attribute data on the mobile side, and the cloud can automatically obtain the relevant data and perform geological calculations, image processing, and associated storage. The cloud, the mobile side, and the WEB side can be used together for drilling core cataloging, and the degree of automation and standardization is relatively high, which is conducive to ensuring the quality of drilling core cataloging and improving the efficiency of drilling core cataloging.

[0010] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification, the claims, and the drawings.

[0011] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. Description of the Drawings

[0012] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0013] Figure 1 It is a schematic structural diagram of an intelligent cataloging system for drilling cores in an embodiment of the present invention;

[0014] Figure 2Schematic flow diagram of an intelligent logging method for borehole cores in an embodiment of the present invention;

[0015] Figure 3 Example diagram of the implementation principle of intelligent logging of borehole cores in an embodiment of the present invention;

[0016] Figure 4 Effect diagram of the mobile terminal calling the geological knowledge retrieval service in an embodiment of the present invention;

[0017] Figure 5 Effect diagram of the mobile terminal calling the geological calculation service in an embodiment of the present invention;

[0018] Figure 6 Example diagram of the mobile terminal inputting attribute data in an embodiment of the present invention;

[0019] Figure 7 Example diagram of the mobile terminal taking the original photo of the core box in an embodiment of the present invention;

[0020] Figure 8 Effect diagram of the mobile terminal calling the image processing service to identify the core box in an embodiment of the present invention;

[0021] Figure 9 Effect diagram of the mobile terminal calling the image processing service for perspective transformation in an embodiment of the present invention;

[0022] Figure 10 Effect diagram of the mobile terminal associating attribute data with the standard core box picture in an embodiment of the present invention;

[0023] Figure 11 Effect diagram of the core photo without being processed by the image processing service in an embodiment of the present invention;

[0024] Figure 12 Effect diagram of the core photo processed by the image processing service in an embodiment of the present invention;

[0025] Figure 13 Effect diagram of the mobile terminal calling the image processing service to splice core photos in an embodiment of the present invention;

[0026] Figure 14 Effect diagram of the borehole list on the WEB side in an embodiment of the present invention;

[0027] Figure 15 Effect diagram of the mobile terminal sharing core photos in an embodiment of the present invention;

[0028] Figure 16 Effect diagram of loading boreholes on the map in an embodiment of the present invention;

[0029] Figure 17 Effect diagram of rendering the core model on the map and displaying core information in an embodiment of the present invention. Detailed implementation manners

[0030] For the purposes of making the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making any creative effort shall fall within the scope of protection of the present invention.

[0031] Currently, the disadvantages of the drilling core logging working method under the existing technical conditions mainly include: large workload and low work efficiency of handwritten logging; low standardization degree of handwritten logging, high requirements for the professional qualities of logging personnel, and inability to guarantee data quality; paper storage, complex data maintenance and management, poor information security, inconvenient data access and analysis, and difficult sharing and collaboration. Based on this, a drilling core intelligent logging method and system provided by the embodiments of the present invention can alleviate the above problems existing in the prior art.

[0032] For the convenience of understanding this embodiment, first, a drilling core intelligent logging method disclosed in the embodiments of the present invention will be introduced in detail. The drilling core intelligent logging method can be applied to a drilling core intelligent logging system; see Figure 1 As shown, the drilling core intelligent logging system can include a cloud end 200, a mobile end 100, and a WEB end 300. The cloud end 200 is respectively connected to the mobile end 100 and the WEB end 300; see Figure 2 As shown, the drilling core intelligent logging method can include the following steps:

[0033] Step S202, the WEB end 300 obtains the project record created by the cloud end 200 using the project information based on the project information input by the user, and obtains the drilling record created by the cloud end 200 using the drilling design data based on the drilling design data input by the user. Then, the project record, the drilling record, and the drilling design data are associated and stored in the cloud end 200.

[0034] When creating a project, relevant personnel can enter the detailed information of the project manually or by importing a file on the WEB side 300. The WEB side 300 uploads the detailed information of the project to the cloud 200. The cloud 200 automatically creates a project record in the project database using the detailed information of the project and returns the created project record to the WEB side 300. After the project is successfully created, the drilling logging personnel enter the drilling design data in the preset drilling template file and import the template file after entry into the WEB side 300. The WEB side 300 uploads the template file to the cloud 200. The cloud 200 reads the drilling design data in the template file and creates a drilling record in the database using the read data. The cloud 200 returns the created drilling record to the WEB side 300. After both the project and the drilling are successfully created, the WEB side 300 uploads the project record, the drilling record, and the drilling design data to the database of the cloud 200 for associated storage in the form of a data table.

[0035] Step S204: The mobile device 100 obtains first target data from the cloud 200 based on the geological identification information input by the user, and associates and stores the geological identification information and the first target data in the cloud 200.

[0036] Among them, the geological identification information can refer to information that plays an identifying role for the geological information of the borehole (such as geological era, geological age, geological origin, rock and soil name, lithology description, etc.), such as formation number, etc., and is not limited thereto.

[0037] The user can enter the geological identification information on the mobile device 100. The mobile device 100 sends the geological identification information to the cloud 200. The cloud 200 uses the geological identification information for data retrieval and returns the retrieved first target data to the mobile device 100. The mobile device 100 uploads the geological identification information and the first target data to the database of the cloud 200 for associated storage in the form of a data table.

[0038] Step S206: The mobile device 100 obtains the geological calculation result calculated by the cloud 200 using the core parameter information based on the core parameter information input by the user, and associates and stores the core parameter information and the geological calculation result in the cloud 200.

[0039] Among them, the geological calculation result can include ROD (Rock Quality Designation), permeability coefficient, shape coefficient, Lugeon value, P-Q curve type, formation continuity, etc., and is not limited thereto.

[0040] The user can enter the values ​​of core-related parameters on the mobile terminal 100, and the mobile terminal 100 sends these values ​​to the cloud 200. The cloud 200 uses these values ​​to complete the calculation of ROD, permeability coefficient, shape coefficient, Lu Rong value, PQ curve type, formation continuity, etc. and returns the calculation results to the mobile terminal 100. The mobile terminal 100 uploads these values ​​and calculation results to the database of the cloud 200 for associated storage in the form of a data table.

[0041] In step S208, the mobile terminal 100 obtains the second target data from the cloud 200 based on the core attribute data input by the user, and obtains the standard core box image obtained after the cloud 200 processes the original core box image, and then associates the core attribute data, the second target data and the standard core box image and stores them in the cloud 200.

[0042] The user can enter the necessary core attribute data (such as project, work site, borehole, depth, etc.) on the mobile terminal 100, and the mobile terminal 100 sends this part of the core attribute information to the cloud 200, which uses the core attribute information to perform data retrieval and returns the retrieved second target data to the mobile terminal 100; the user can also use an external camera device or a camera device built into the mobile terminal 100 to take photos of the core box on site, and upload the core box photos to the cloud 200 through the mobile terminal 100; the cloud 200 processes the uploaded core box photos to obtain standard core box photos, and returns the standard core box photos to the mobile terminal 100; the mobile terminal 100 uploads the core attribute data, the second target data and the standard core box photos to the cloud 200 for associated storage. In addition, the cloud 200 can also use the core attribute data and the second target data associated with the standard core box photos to add annotation information to the standard core box photos, and return the standard core box photos with annotation information to the mobile terminal 100. The core box images (ie, core box photos and / or standard core box images) may be stored in the cloud 200 and the mobile terminal 100 at the same time to prevent data loss.

[0043] An embodiment of the present invention provides an intelligent cataloging method for borehole cores. When cataloging, the user only needs to input project information and drilling design data on the WEB side to automatically create project records and drilling records through the cloud. The user only needs to input geological identification information, core parameter information, and core attribute data on the mobile side to automatically obtain relevant data through the cloud and perform geological calculations, image processing, and associated storage. The cloud, mobile, and WEB sides can be used to jointly catalog borehole cores. The degree of automation and standardization are both high, which is conducive to ensuring the quality of borehole core cataloging and improving the efficiency of borehole core cataloging.

[0044] As a possible implementation manner, the drilling design data may include the original drilling coordinate data; based on this, the step of the cloud 200 creating a drilling record by using the drilling design data may include: the cloud 200 converts the original drilling coordinate data into the drilling map coordinate data in a preset map coordinate system, and creates a drilling record by using the drilling design data and the drilling map coordinate data.

[0045] Exemplarily, multiple preset coordinate systems with known parameters (such as a general coordinate system, a local coordinate system, etc.) and a preset coordinate conversion library (that is, a library providing conversion methods between different coordinate systems, such as PROJ.4, etc.) may be stored on the cloud 200, and the preset coordinate conversion library includes the coordinate conversion relationships between each preset coordinate system and the preset map coordinate system; based on this, the step of the cloud 200 converting the original drilling coordinate data into the drilling map coordinate data in the preset map coordinate system may have the following two different operation manners for different situations:

[0046] Operation manner 1: If the original coordinate system where the original drilling coordinate data is located is a preset coordinate system, the cloud 200 uses the preset coordinate conversion library to convert the original drilling coordinate data into the drilling map coordinate data.

[0047] Operation manner 2: If the original coordinate system where the original drilling coordinate data is located is not a preset coordinate system, the cloud 200 uses the Bursa seven-parameter model to convert the original drilling coordinate data into the drilling map coordinate data; among them, the Bursa seven-parameter model includes coordinate conversion parameters calculated by using the reference coordinate values of more than four control points corresponding to the original coordinate system and the preset map coordinate system respectively.

[0048] In the actual application process, a coordinate conversion service can be pre-built for the cloud 200: First, the database of the cloud 200 has a built-in general coordinate system and local coordinate system with known parameters for selection when creating a project; then, a coordinate conversion library is built into the cloud 200 for use during coordinate conversion; if the original coordinates in the drilling design data use the coordinate system built into the database, the cloud 200 can directly use the built-in coordinate conversion library for coordinate conversion; if the original coordinates in the drilling design data use a local coordinate system and the parameters of this coordinate system are unknown, then more than 4 control points with coordinates in the target coordinate system (i.e., the map coordinate system unique to the map used for drilling and core positioning and display) and coordinates in the local coordinate system can be input to the cloud 200, so that the cloud 200 can use the Bursa seven-parameter method to calculate the seven parameters of coordinate conversion. The cloud 200 performs accuracy evaluation on the calculated seven parameters of coordinate conversion and, after the accuracy evaluation result is qualified, takes this part of the seven parameters of coordinate conversion as the coordinate conversion parameters. The cloud 200 uses the Bursa model to convert the original coordinates to the target coordinate system; the above coordinate conversion process can be encoded as an API using a programming language (such as the Python language, etc.), a program framework (such as the Flask framework), and a coordinate conversion library (such as PROJ.4, etc.), and this API is used as the coordinate conversion service of the cloud 200 for calling.

[0049] As a possible implementation, a pre-trained geological knowledge extraction model can be stored on the cloud 200; based on this, the above intelligent core logging method for drilling can further include: the cloud 200 obtains geological text data, and uses the geological knowledge extraction model to perform geological entity extraction and geological entity relationship extraction on the geological text data, and then uses a preset graph database to construct a geological knowledge graph using the extracted geological entities and geological entity relationships.

[0050] In the actual application process, the geological knowledge extraction model may include a geological entity information extraction model and a geological entity relationship information extraction model, which are used to extract geological entities and geological entity relationships from geological text data. A geological knowledge retrieval service can be pre-constructed for the cloud 200: perform entity annotation and entity relationship annotation on geological-related text data to obtain the annotated data with entities and entity relationships marked, and then use the annotated data to train the geological entity information extraction model and the geological entity relationship information extraction model respectively (for example, input the annotated data into a deep learning model with a structure of BERT+LSTM+CRF for training to obtain the geological entity information extraction model, and input the annotated data into the BERT model for training to obtain the geological entity relationship information extraction model); then input the text data to be processed (such as data crawled from the Internet, data accumulated by geological exploration departments over the years) into the above-mentioned geological entity information extraction model and geological entity relationship information extraction model respectively, and then the two models can be used to perform entity extraction and relationship extraction on the data by using natural language processing technology, and structured data of geological entity information and structured data of geological entity relationship information can be obtained respectively; then organize the obtained structured data in the form of a knowledge graph in the environment of a graph database (such as Neo4j, etc.) to obtain a geological knowledge graph; the cloud 200 can use a graph database query language (such as Cypher, etc.) to organize query statements to query the data in the graph database, and the query results are returned to the calling end after being formatted. The retrieval query process of geological information can be encoded as an API by using a programming language (such as the Python language, etc.) and a program framework (such as the Flask framework), and this API is used as the geological knowledge retrieval service of the cloud 200 for calling. The geological knowledge retrieval service facilitates the retrieval and query of geological information by constructing a relatively complete knowledge graph in the geological field.

[0051] As a possible implementation manner, the steps for the cloud 200 to process the original core box image may include: the cloud 200 converts the original core box image into a grayscale image, performs edge detection on the grayscale image to obtain the edge image of each object in the grayscale image, then converts each edge image into a corresponding binary image, and uses the standard core box contour information and the binary image to determine the core box contour in the original core box image, and then uses the core box contour to crop the original core box image and perform perspective transformation on the cropped image to obtain a standard core box image.

[0052] In the actual application process, a geological image intelligent processing service can be pre-constructed for the cloud 200:

[0053] The geological image intelligent processing service mainly provides two functions: core box recognition and core box picture stitching;

[0054] Core box recognition: The cloud 200 uses computer vision and machine learning software libraries (such as OpenCV, etc.) to read the original photos of the core boxes taken and convert them into grayscale images. It uses edge detection algorithms (such as the Canny algorithm, etc.) to find the edges of the objects in the original photos, and applies thresholds to convert the edge images into binary images. Then it uses the findContours function to find the contours of each object in the original photo. When finding the contours, the contours of multiple objects will be returned to obtain a contour list. It is necessary to further identify and filter in this contour list to obtain the core box contour; when identifying and filtering the core box contour, it is screened, identified, and processed based on the characteristics of the core box contour (such as perimeter, area, bounding rectangle, etc.) to obtain the core box contour; finally, the original photo is cropped using the exact core box contour to obtain the core box image; when taking the original photo of the core box on-site, due to the limitations of on-site environment and other factors, it is usually impossible to take a strictly standardized frontal photo of the core box, resulting in a distorted shape of the photographed core box. For this reason, the cloud 200 can use the principle of perspective transformation to perform perspective transformation on the cropped core box image to correct the core box image, change the perspective effect of the image, so that the core box images taken from different perspectives can all be presented as a frontal view, making the core box image clearer and more beautiful, and thus obtaining a standard core box picture after the core box image is corrected;

[0055] Core box splicing: The cloud 200 queries all the standard core box pictures of a certain core section from the big data platform and reads the standard core box pictures. After scaling the read standard core box pictures to unify the size, these standard core box pictures are sequentially spliced in the order from shallow to deep depth to form a complete spliced core box image;

[0056] The above geological image processing process of core box recognition and core box splicing can be encoded as an API using programming languages (such as Python language, etc.), program frameworks (such as Flask framework), scientific computing libraries (such as Numpy, etc.), and computer vision and machine learning software libraries, and this API is used as the geological image intelligent processing service of the cloud 200 for calling.

[0057] As a possible implementation manner, the above intelligent core logging method for boreholes may further include:

[0058] Step A1, the cloud 200 associates and stores the borehole design data and borehole map coordinate data.

[0059] Step A2, the WEB end 300 obtains the borehole design data and borehole map coordinate data from the cloud 200, and uses the borehole design data and borehole map coordinate data to generate the borehole information corresponding to each borehole on a preset map.

[0060] Step A3, the WEB terminal 300 obtains the third target data in the borehole design data and the fourth target data in the borehole map coordinate data from the cloud 200 based on the target borehole information selected by the user, and uses the third target data and the fourth target data for rendering to generate a target core model.

[0061] As a possible implementation manner, the target core model may include core segment models of each core segment of the corresponding borehole, and each core segment model is bound with the borehole identifier of the corresponding borehole and the depth information of the corresponding core segment; based on this, the above-mentioned intelligent cataloging method for borehole cores may further include: the WEB terminal 300 obtains target borehole cataloging data from the cloud 200 based on the target core segment model selected by the user, and displays the target borehole cataloging data; wherein, the target borehole cataloging data may include the first target borehole identifier corresponding to the target core segment model, the target depth information, the target standard core box image, etc., and this is not limited.

[0062] As a possible implementation manner, the above-mentioned intelligent cataloging method for borehole cores may further include: the mobile terminal 100 obtains the first spliced image obtained by the cloud 200 after splicing a plurality of first standard core box images corresponding to the first depth parameter information based on the first depth parameter information input by the user.

[0063] As a possible implementation manner, the above-mentioned intelligent cataloging method for borehole cores may further include:

[0064] Step a1, the WEB terminal 300 generates a borehole list by using borehole records; wherein, the borehole list may include the borehole identifiers of each borehole and the second depth parameter information.

[0065] Step a2, the WEB terminal obtains the second spliced image obtained by the cloud 200 after splicing a plurality of second standard core box images corresponding to the second depth parameter information based on the second target borehole identifier selected by the user from the borehole list.

[0066] For the convenience of understanding, the implementation principle of the above-mentioned intelligent cataloging method for borehole cores is described exemplarily as follows with a specific application as an example.

[0067] See Figure 3 As shown, the above-mentioned intelligent cataloging method for borehole cores mainly includes the following steps:

[0068] Step 1, construct a coordinate conversion service, a geological knowledge retrieval service, a geological calculation service, and a geological image processing service (i.e., image processing service, that is, intelligent geological image processing service) in the cloud 200.

[0069] Building a coordinate conversion service: First, the cloud 200 uses built-in general coordinate systems in the database (such as Beijing 54 coordinate system, Xi'an 80 coordinate system, CGCS2000 coordinate system, etc.) and some local coordinate systems with known parameters (such as Guangzhou 2000 coordinate system, etc.) for selection when creating a project; the cloud 200 also has PROJ.4 built in as a coordinate conversion library; if the original coordinates use the built-in coordinate system in the database, the coordinate conversion is directly performed using PROJ.4; if the original coordinates use a local coordinate system and the coordinate system parameters are unknown, more than 4 control points with coordinates in the target coordinate system (i.e., the map coordinate system) and coordinates in the local coordinate system are input to calculate the seven-parameter coordinate conversion through the Bursa model and use the seven-parameter coordinate conversion to convert the original coordinates into the target coordinate system, so as to subsequently convert the coordinates of drill holes, core models, etc. into coordinates that can be correctly located on the maps of the mobile terminal 100 and the WEB terminal 300. The above coordinate conversion process can be encoded into an API providing a coordinate conversion service for calling using the Python language, the Flask framework, and the coordinate conversion library PROJ.4.

[0070] Building a geological knowledge retrieval service: Entities and entity relationships are annotated for geological-related texts, and the annotated data is respectively input into deep learning models structured as BERT+LSTM+CRF and the BERT model for training to obtain a geological entity information extraction model and a geological entity relationship information extraction model respectively; then, text data (such as unstructured data) to be processed is obtained by means of web scraping, obtaining from geological exploration departments, etc., and input into the above two models for entity extraction and entity relationship extraction, and the structured data containing geological entity information and geological entity relationship information obtained is input into Neo4j for building a geological knowledge graph. After the geological knowledge retrieval service is called, it is organized into a query statement using Cypher to query the data in Neo4j, and the query results are formatted and returned to the calling end. By building a relatively complete knowledge graph in the geological field, it is convenient to retrieve and query geological information. The retrieval and query process of geological information is encoded into an API (i.e., the geological knowledge retrieval service) for calling using the Python language and the Flask framework.

[0071] Building a geological calculation service: Using the Python language, the Flask framework, the scientific computing library Numpy, and the general calculation formulas in geological logging, the calculation processes such as RQD, permeability coefficient, shape coefficient, Lugeon value, PQ curve type, and formation continuity judgment are encoded into an API (geological calculation service) for calling.

[0072] Intelligent Geological Image Processing Service: It includes the above-mentioned core box recognition function and the above-mentioned core box splicing function. The core box recognition function uses OpenCV to read the original photo of the core box and convert it into a grayscale image, uses the Canny algorithm to find the edges of the objects in the original photo, and applies a threshold to convert the edge image into a binary image. Then, the findContours function is used to find the contours of the objects in the original photo, and screening, recognition, and processing are performed based on the features such as the perimeter, area, area-to-perimeter ratio, and aspect ratio of the circumscribed rectangle of the core box contour to obtain the core box contour. After that, the original photo is cropped using the core box contour to obtain the core box image, and the cropped core box image is corrected through a perspective transformation method to obtain a standard core box image; during the perspective transformation process, at least four corresponding control points (such as the corner points, center point, and midpoints of the sides of a rectangle) need to be defined in the source image and the target image respectively, and a perspective transformation matrix is determined using the control points to map each pixel point in the source image to a new pixel position in the target image through this perspective transformation matrix. The core box splicing function queries and reads all the standard core box images of a single core segment from the big data platform, scales the core box images to a unified size, and then splices the images of the unified size into a complete spliced core box image in the order of shallower to deeper depth. The above geological image processing process is encoded into an API (i.e., the intelligent geological image processing service) for calling using the Python language, Flask framework, OpenCV, and Numpy.

[0073] Step 2: The WEB terminal 300 creates a project and imports the borehole to automatically complete the borehole creation.

[0074] When creating a project, relevant personnel enter the detailed information of the project on the WEB terminal 300, select the coordinate system adopted by the project, and the cloud 200 automatically creates a project record in the project database and returns it to the WEB terminal 300. After the project is successfully created, the borehole logging personnel enter the design data of the borehole (including the original coordinate data) in a pre-prepared excel file and then import the entered excel file into the WEB terminal 300. The cloud 200 reads the borehole design data in the excel file to create a borehole record in the database and returns it to the WEB terminal 300. When creating a borehole, the WEB terminal 300 automatically calls the coordinate conversion service of the cloud 200 to convert the original coordinates in the borehole design data into the coordinates adopted by the map and returns this part of the coordinates to the WEB terminal 300. The above project creation process and the above borehole creation process are encoded into an API for calling using the Python language and Flask framework.

[0075] In the third step, the mobile device 100 invokes the geological knowledge retrieval service and enters core information by means of drop-down selection (i.e., after the user clicks the drop-down button, an option list pops up and the user clicks to select the corresponding option in the option list); the mobile device 100 invokes the geological calculation service to automatically complete geological calculations such as RQD, permeability coefficient, shape coefficient, Lugeon value, PQ curve type, and formation continuity judgment.

[0076] After the coordinate transformation service is invoked in the second step to perform coordinate transformation and obtain the coordinate points in the map coordinate system, the mobile device 100 reads the borehole data of the cloud 200, and the boreholes can be loaded and displayed on the respective maps of the mobile device 100 and the WEB terminal 300.

[0077] See Figure 4 As shown, after the user clicks the drop-down button on the mobile device 100, an option list pops up. Then, after clicking to select an option, the geological knowledge retrieval service of the cloud 200 is immediately invoked. The cloud 200 retrieves relevant geological knowledge and returns it to the mobile device 100, and the mobile device 100 automatically writes the returned geological information into the corresponding attribute fields. For example Figure 4 As shown, the user clicks the drop-down button on the right side of "stratum number" on the mobile device 100 to trigger the mobile device 100 to pop up an option list. After selecting the option corresponding to a certain stratum (for example, the stratum with the stratum number "1-3s") in the option list, the mobile device 100 invokes the geological knowledge retrieval service of the cloud 200 to retrieve the geological information associated with the stratum. The cloud 200 returns the retrieved geological information to the mobile device 100 in JSON format. The mobile device 100 automatically completes the entry of attributes such as "geological age", "rock and soil name", and "lithological second description" by means of key-value pair matching (such as using the attribute field name as the key and the attribute field value as the value) with the geological information; the user can also associate and save the entered information in the form of a template description file on the local of the mobile device 100 by clicking the corresponding button and upload it to the cloud 200 for associated storage.

[0078] See Figure 5 As shown, after the user enters the values of core-related parameters on the mobile device 100, the mobile device 100 automatically invokes the geological calculation service of the cloud 200 to complete calculations such as RQD, permeability coefficient, shape coefficient, Lugeon value, PQ curve type, and formation continuity judgment. The cloud 200 returns the calculation results to the mobile device 100, and the mobile device 100 automatically writes the returned calculation results into the corresponding attribute fields. For example Figure 5As shown, the user inputs the respective values of "starting hole depth", "ending hole depth", "flow rate 1", "flow rate 2", "flow rate 3", "flow rate 4", "flow rate 5", "water level", "number of sub - couplings", and "total drill pipe length" on the mobile device 100. Then the user selects "pressure stage", and then the mobile device 100 carries the input values and calls the geological calculation service of the cloud 200 to calculate the respective values of "test section length", "PQ curve type", and "Lugeon value". The cloud 200 returns the calculation results to the mobile device 100 in JSON format. The mobile device 100 uses the calculation results to automatically complete the attribute entry of "test section length", "PQ curve type", and "Lugeon value" through the key - value pair matching method; after the relevant data entry is completed, the user can also, on the mobile device 100, by clicking the corresponding button (such as Figure 5 the "Save and Add" button in

[0079] associate and save the entered data locally on the mobile device 100 and upload it to the cloud 200 for associated storage.

[0080] See Figure 6 As shown, before the user takes a photo of the core box on the mobile device 100, necessary attribute data such as starting depth, ending depth, recording time, logging personnel, project name, etc. need to be entered.

[0081] See Figures 7 to 10 As shown, the mobile device 100 calls the camera of the mobile device to take a raw photo of the core box (as shown in Figure 7 ). After taking the photo, the mobile device 100 immediately automatically calls the geological image processing service (core box recognition) of the cloud 200 and uploads the raw photo of the core box to the cloud 200 at the same time; when the mobile device 100 uploads the core box photo to the cloud 200, it will also upload the attribute data entered by the user (such as Figure 6 the starting depth, ending depth, recording time, logging personnel, project name in Figure 8 ) to the cloud 200 for storage; the cloud 200 processes the uploaded raw photo through the core box recognition function of the geological image processing service, automatically recognizes the core box in the raw photo to obtain the core box image (such as Figure 8After calculating the perspective transformation matrix using the four corner points and the midpoints of the four side lines of the blue box in the image as eight control points, and performing perspective transformation on the core box image using this perspective transformation matrix, a clear and beautiful standard core box picture is obtained (as shown in Figure 9 ). The mobile device 100 can also associate and save the standard core box picture with the attribute data entered by the user (as shown in Figure 10 ). In addition, the mobile device 100 can also upload the standard core box picture to the cloud 200 for storage.

[0082] The cloud 200 can also retrieve the associated core-related data (such as project, work site, borehole, depth, etc.) according to the attribute data entered by the user, and selectively mark these attribute data and the retrieved core-related data on the standard core box picture, and then return the marked standard core box picture to the mobile device 100. Refer to Figure 11 and Figure 12 . As shown, the core photos that have not been processed by the image processing service (i.e., geological image processing service) are compared with the core photos that have been processed by the image processing service. It can be seen that the core box in Figure 11 is deformed, while the shape of the core box in Figure 12 is a standard rectangle, and necessary core information (such as the project name "XXXXXXX Project" located at the top of the core box and the work site information "Dam Site Area", borehole number "BPZK27", and depth information "42 - 48" located at the bottom of the core box in Figure 12 ) is marked in the white border around the core box (such as the top and bottom).

[0083] Refer to Figure 13 . As shown, after the user enters the depth parameters (i.e., start depth, end depth) on the mobile device 100, the mobile device 100 calls the geological image processing service (core box splicing) of the cloud 200. The cloud 200 automatically searches for the standard core box pictures marked with necessary core information within the corresponding depth range according to the depth parameters, and stitches these found standard core box pictures into a complete image according to a unified size and format (as shown in Figure 13 ), and then returns this image to the mobile device 100 for display.

[0084] The WEB terminal 300 can also display and manage boreholes through a borehole list (as shown in Figure 14 ). After the user selects a certain borehole in the borehole list on the WEB terminal 300, the WEB terminal 300 can also call the cloud 200 to automatically search for the standard core box pictures marked with necessary core information within the corresponding depth range of this borehole, and call the geological image processing service of the cloud 200 for core box splicing (not elaborated here). The WEB terminal 300 downloads the stitched image from the cloud 200 to the local for display and storage.

[0085] The core box pictures can be stored in both the cloud 200 and the mobile device 100 simultaneously to ensure data security and prevent loss.

[0086] During the actual application process, the images obtained after core box splicing by invoking the geological image processing service of the cloud 200 do not need to be stored because the geological image processing service can be invoked at any time for core box splicing. After the mobile device 100 obtains the images spliced from the core boxes by the cloud 200, users can also share the images on the mobile device 100 (such as sharing on platforms like WeChat and Enterprise WeChat). For example Figure 15 As shown, users can trigger the sharing of the spliced images on the mobile device 100 by clicking the "Share" button displayed on the mobile device 100.

[0087] Since the physical core boxes are not convenient for long-term preservation and transportation, in the absence of physical core boxes, the core box pictures become key data for geological problem analysis. When conducting geological problem analysis, the core box splicing function can facilitate users to select and view standard core box pictures within a specified depth range, thus facilitating users to conduct targeted geological problem analysis.

[0088] Step 5, the WEB terminal 300 reads the core data of the cloud 200 and performs comprehensive and three-dimensional visual display and management on the map.

[0089] See Figure 3 and Figure 16 As shown, the WEB terminal 300 reads the borehole data from the cloud 200 and loads it on the map. When the WEB terminal 300 loads the borehole data on the map, it will add borehole icons corresponding to each borehole on the preset map (for example Figure 16 the circular icons on the map in, with the corresponding borehole numbers marked near each circular icon, and the status of each borehole is identified and distinguished by the color of the circular icon), and each borehole icon is bound with corresponding borehole data.

[0090] See Figure 3 、 Figure 16 and Figure 17 As shown, when the user clicks on the borehole icon on the WEB terminal 300 to trigger the WEB terminal 300 to pop up a menu, and then the user clicks the "Core Model" button in the menu, the WEB terminal 300 reads the core data of the cloud 200 and renders a three-dimensional core model using a three-dimensional visualization framework (such as the Cesium WebGIS framework) according to the core data ( Figure 17As shown. When the WEB terminal 300 renders the core model, it needs to first obtain the position (i.e., coordinates) of the core, as well as the starting depth, ending depth, lithology, and size of each core section of the core. For example, the WEB terminal 300 renders the core model at the position with coordinates (112, 26). The core size (i.e., length) is 6 meters. From top to bottom, the core has a limestone section from 0 - 2 meters and a siltstone section from 2 - 6 meters. It is rendered through Cesium to obtain the core model, and the size (i.e., diameter) of the core model is 20 cm.

[0091] See Figure 17 As shown, the core model uses colors to distinguish the lithology of the strata and can also display labels to annotate relevant information (such as the depth and lithology of the strata) for each core section. When the user clicks on a certain section of the core model on the WEB terminal 300, the WEB terminal 300 will display the detailed information of this section of the core through a pop-up box, including attribute descriptions (such as Figure 17 the top depth and bottom depth in Figure 17 ), pictures (such as

[0092] After obtaining the coordinate points in the map coordinate system after calling the coordinate conversion service for coordinate conversion in the second step, the boreholes and core models can be located and displayed on the map through the mobile terminal 100 and the WEB terminal 300. The operation methods of the mobile terminal 100 to locate and display the boreholes and core models are similar to those of the WEB terminal 300, which will not be elaborated here. In addition to being used to locate the boreholes and core models on the map, the coordinate points in the map coordinate system can also be used for range query (for example, a rectangular box is first drawn on the map to define a geographical range, and the boreholes and cores whose coordinates are within this geographical range are selected).

[0093] See Figure 3 As shown, the above-mentioned intelligent core logging method for boreholes can also query, manage, statistically analyze, and visually display various core logging data such as opening records, ending records, sealing records, template descriptions, geotechnical records, water level records, hydrogeology, dynamic penetration records, sampling records, standard penetration records, borehole diameter structures, integrity, injection tests, water pressure tests, core photography, and on-site photography through the mobile terminal 100 and the WEB terminal 300.

[0094] The above-mentioned intelligent logging method for drilling cores uses the cloud, mobile, and WEB terminals to jointly complete the logging and data management of drilling cores. The cloud provides geological knowledge retrieval services, geological calculation services, and intelligent geological image processing services, and at the same time performs big data storage of core data. The mobile terminal calls the services provided by the cloud to perform intelligent core logging, and the WEB terminal reads the core data from the cloud and performs comprehensive and three-dimensional visual display and management.

[0095] The advantages of the above-mentioned intelligent logging method for drilling cores mainly include: using mobile devices for drilling core logging, with high automation and standardization levels and high work efficiency; by calling the geological knowledge retrieval service, only selecting list items and manually inputting a small amount of text are required to complete the input of core information during logging; by calling the geological calculation service, logging personnel can perform calculation and analysis of relevant geological data without manual calculation on-site; by calling the intelligent geological image processing service, the core box can be automatically recognized and perspective transformation correction can be performed to obtain clear and beautiful standard core box pictures, and at the same time, automatic splicing is performed to obtain high-quality pictures without post-processing. By constructing a coordinate conversion service, core data in any coordinate system can be loaded and displayed on the map; at the same time, the three-dimensional solid model, core information, and core photos of the core are centrally and uniformly displayed, making the viewing of core-related information more comprehensive, three-dimensional, and complete.

[0096] Based on the above-mentioned intelligent logging method for drilling cores, an embodiment of the present invention further provides an intelligent logging system for drilling cores. Refer to Figure 1 and Figure 2 As shown, the intelligent logging system for drilling cores may include a cloud 200, a mobile terminal 100, and a WEB terminal 300. The cloud 200 is respectively connected to the mobile terminal 100 and the WEB terminal 300;

[0097] The WEB terminal 300 may be used to: obtain a project record created by the cloud 200 using the project information based on the project information input by the user, and obtain a drilling record created by the cloud 200 using the drilling design data based on the drilling design data input by the user, and then associate and store the project record, the drilling record, and the drilling design data in the cloud 200;

[0098] The mobile terminal 100 may be used to: obtain first target data from the cloud 200 based on the geological identification information input by the user, and associate and store the geological identification information and the first target data in the cloud 200;

[0099] The mobile device 100 can be used to: obtain the geological calculation results calculated by the cloud 200 based on the core parameter information input by the user, and associatively store the core parameter information and the geological calculation results in the cloud 200; wherein, the geological calculation results include at least one of the following: ROD, permeability coefficient, shape coefficient, Lugeon value, P-Q curve type, formation continuity;

[0100] The mobile device 100 can be used to: obtain second target data from the cloud 200 based on the core attribute data input by the user, and obtain the standard core box image obtained by the cloud 200 after processing the original core box image, and then associatively store the core attribute data, the second target data and the standard core box image in the cloud 200.

[0101] With the above-mentioned intelligent core logging system for boreholes, when logging, the user only needs to input project information and borehole design data on the WEB side, and the cloud can automatically create project records and borehole records. The user only needs to input geological identification information, core parameter information, and core attribute data on the mobile side, and the cloud can automatically obtain relevant data and perform geological calculations, image processing, and associative storage. The cloud, mobile side, and WEB side can be used together for borehole core logging, with relatively high automation and standardization levels, which is conducive to ensuring the quality of borehole core logging and improving the efficiency of borehole core logging.

[0102] For the intelligent core logging system for boreholes provided in the embodiments of the present invention, its implementation principle and the technical effects generated are the same as those in the embodiments of the foregoing intelligent core logging method for boreholes. For the sake of brief description, for the parts not mentioned in the embodiments of the intelligent core logging system for boreholes, reference can be made to the corresponding content in the embodiments of the foregoing intelligent core logging method for boreholes.

[0103] Unless otherwise specifically stated, the relative steps, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0104] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0105] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0106] Finally, it should be noted that the above-mentioned embodiments are only specific embodiments of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting them. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions described in the foregoing embodiments or easily conceive of changes, or make equivalent replacements for some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for intelligent logging of drill cores, characterized in that: Applied to a drilling core intelligent cataloging system, the drilling core intelligent cataloging system includes a cloud, a mobile terminal and a WEB terminal, the cloud is connected to the mobile terminal and the WEB terminal respectively; the drilling core intelligent cataloging method includes: The WEB end obtains the project record created by the cloud using the project information based on the project information input by the user, and obtains the drilling record created by the cloud using the drilling design data based on the drilling design data input by the user, and then associates and stores the project record, the drilling record and the drilling design data in the cloud; The mobile terminal obtains first target data from the cloud based on the geological identification information input by the user, and associates the geological identification information with the first target data and stores them in the cloud; The mobile terminal obtains the geological calculation results calculated by the cloud using the core parameter information based on the core parameter information input by the user, and associates the core parameter information and the geological calculation results and stores them in the cloud; wherein the geological calculation results include at least one of the following: ROD, permeability coefficient, shape coefficient, Lu Rong value, PQ curve type, and formation continuity; The mobile terminal obtains the second target data from the cloud based on the core attribute data input by the user, and obtains the standard core box image obtained by the cloud after processing the original core box image, and then associates the core attribute data, the second target data and the standard core box image and stores them in the cloud.

2. The method for intelligent logging of drilling core according to claim 1, characterized in that: The drilling design data includes original drilling coordinate data; The cloud creates the drilling record using the drilling design data, including: The cloud converts the original drilling coordinate data into drilling map coordinate data in a preset map coordinate system, and creates the drilling record using the drilling design data and the drilling map coordinate data.

3. The method for intelligent logging of drilling cores according to claim 2, characterized in that: The method for intelligent logging of drill cores also includes: The cloud stores the drilling design data and the drilling map coordinate data in an associated manner; The WEB end obtains the drilling design data and the drilling map coordinate data from the cloud, and generates drilling information corresponding to each drilling hole on a preset map using the drilling design data and the drilling map coordinate data; The WEB end obtains the third target data in the drilling design data and the fourth target data in the drilling map coordinate data from the cloud based on the target drilling information selected by the user, and uses the third target data and the fourth target data for rendering to generate a target core model.

4. The method for intelligent logging of drilling core according to claim 3, characterized in that: The target core model includes core segment models of each core segment of the corresponding borehole, and each core segment model is bound to a borehole identifier of the corresponding borehole and depth information of the corresponding core segment; the borehole core intelligent cataloging method also includes: The WEB end obtains target drilling catalog data from the cloud based on the target core segment model selected by the user, and displays the target drilling catalog data; wherein the target drilling catalog data includes a first target drilling identifier, target depth information and a target standard core box image corresponding to the target core segment model.

5. The method for intelligent logging of drilling core according to claim 1, characterized in that: The method for intelligent logging of drill cores also includes: The mobile terminal obtains, based on the first depth parameter information input by the user, a first stitched image obtained by stitching a plurality of first standard core box images corresponding to the first depth parameter information on the cloud.

6. The method for intelligent logging of drilling cores according to claim 1, characterized in that: The method for intelligent logging of drill cores also includes: The WEB end generates a drilling list using the drilling record; wherein the drilling list includes a drilling identifier and second depth parameter information of each drilling hole; The WEB end obtains a second stitched image obtained by stitching a plurality of second standard core box images corresponding to the second depth parameter information on the cloud end based on a second target drilling identifier selected by the user for the drilling list.

7. The method for intelligent logging of drilling cores according to claim 2, characterized in that: The cloud stores a plurality of preset coordinate systems with known parameters and a preset coordinate conversion library, wherein the preset coordinate conversion library includes a coordinate conversion relationship between each preset coordinate system and the preset map coordinate system; The cloud converts the original drilling coordinate data into drilling map coordinate data in a preset map coordinate system, including: If the original coordinate system where the original coordinate data of the drilling hole is located is a preset coordinate system, the cloud uses the preset coordinate conversion library to convert the original coordinate data of the drilling hole into the coordinate data of the drilling map; If the original coordinate system where the original coordinate data of the drilling hole is located is not the preset coordinate system, the cloud uses the Bursa seven-parameter model to convert the original coordinate data of the drilling hole into the drilling map coordinate data; wherein, the Bursa seven-parameter model includes coordinate conversion parameters calculated using reference coordinate values ​​of more than four control points corresponding to the original coordinate system and the preset map coordinate system.

8. The method for intelligent logging of drilling cores according to claim 1, characterized in that: The cloud stores a pre-trained geological knowledge extraction model; the drilling core intelligent cataloging method also includes: The cloud obtains geological text data, and uses the geological knowledge extraction model to extract geological entities and geological entity relationships from the geological text data, and then uses a preset graph database to construct a geological knowledge graph using the extracted geological entities and geological entity relationships.

9. The method for intelligent logging of drilling cores according to claim 1, characterized in that: The cloud processes the original core box image, including: The cloud converts the original core box image into a grayscale image, and performs edge detection on the grayscale image to obtain an edge image of each object in the grayscale image, then converts each edge image into a corresponding binary image, and uses standard core box contour information and the binary image to determine the core box contour in the original core box image, then uses the core box contour to crop the original core box image, and performs perspective transformation on the cropped image to obtain the standard core box image.

10. An intelligent logging system for drilling cores, characterized in that: The drilling core intelligent cataloging system includes a cloud terminal, a mobile terminal and a WEB terminal, and the cloud terminal is connected to the mobile terminal and the WEB terminal respectively; The WEB end is used to: obtain a project record created by the cloud using the project information based on the project information input by the user, and obtain a drilling record created by the cloud using the drilling design data based on the drilling design data input by the user, and then associate the project record, the drilling record and the drilling design data and store them in the cloud; The mobile terminal is used to: obtain first target data from the cloud based on geological identification information input by a user, and associate the geological identification information with the first target data and store them in the cloud; The mobile terminal is used to: obtain the geological calculation results calculated by the cloud using the core parameter information based on the core parameter information input by the user, and associate the core parameter information with the geological calculation results and store them in the cloud; wherein the geological calculation results include at least one of the following: ROD, permeability coefficient, shape coefficient, Lu Rong value, PQ curve type, and formation continuity; The mobile terminal is used to obtain second target data from the cloud based on the core attribute data input by the user, and obtain a standard core box image obtained by processing the original core box image on the cloud, and then associate the core attribute data, the second target data and the standard core box image and store them in the cloud.

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