3D scanning rock core characteristic parameter identification system and method

Abstract

The invention relates to the technical field of engineering geological exploration, and discloses a 3D scanning rock core characteristic parameter identification system and method, and the system comprises a rock core accommodation module and a 3D scanning module; the method comprises the following steps: separating a complete rock core and a fragmented rock core from a drilling coring sample through self-adaptive cleaning to form a space-attribute binding initial data set; the automatic numerical control platform dynamically adjusts the height and levelness of the 3D laser scanner to generate an optimal scanning path topology network covering all the rock core boxes; scanning the path topology network, feeding back the point cloud coverage rate in real time, dynamically adjusting the rotation angle, and outputting a full-angle high-density rock core point cloud sequence; the full-angle high-density rock core point cloud sequence is subjected to multi-scale feature fusion to generate an automatic drawing histogram, a quantization parameter set and a joint parameter packet. Through automatic and high-precision scanning and data analysis, digital acquisition of the rock core and accurate calculation of geological parameters are realized, and the efficiency and quality of geological exploration are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engineering geological exploration, and particularly relates to a 3D scanning core characteristic parameter identification system and method. BACKGROUND

[0002] Collecting core data in geological drilling has extremely important significance in many fields such as geological exploration, engineering construction and resource development; the core data obtained through drilling exploration can help understand the distribution and properties of underground rock-soil layers and predict possible geological problems in engineering construction; obtaining core through drilling is also an important means of directly obtaining the lithology of the stratum, which has important significance for geological description and engineering design. At present, the engineering drilling data collection mainly relies on manual operation, especially after obtaining the core, the sample needs to be detected and recorded, which includes image acquisition of the core, statistics of data such as hole depth, footage, core sampling length, calculation of data such as sampling rate and RQD, and judgment of joint development, etc.; this step needs to consume a lot of personnel time and energy, and is affected by the subjective experience of the data collection personnel, and human errors are easy to occur in the process of collecting a large amount of data by multiple people.

[0003] Prior art one, Chinese patent, application number: 202510419252.6 discloses a tunnel stratum identification method based on shield tunneling parameter machine learning, which includes data collection, used for constructing a project data set, collecting core samples from drilling cores in the project data set, and confirming a learning sample set based on the core samples; data feature mining, used for obtaining tunneling parameters corresponding to each type of stratum from the learning sample set, and mining a statistical feature set responding to stratum changes based on the tunneling parameters corresponding to each type of stratum, and forming a first feature parameter matrix; data feature screening, used for sensitivity analysis of the first feature parameter matrix, and then screening out a second feature parameter matrix; model construction, used for constructing a stratum identification model based on the learning sample set and the second feature parameter matrix by using K nearest neighbor algorithm; stratum identification, used for identifying a target stratum according to the stratum identification model. Although it has the effect of improving the stratum identification efficiency; but the fixed angle cannot be adjusted, resulting in feature omission caused by fixed angle sampling.

[0004] The prior art two, Chinese patent, application number: 202010878952.9 discloses a lithology identification method, device, system and storage medium based on vibration signal, the method comprises the following steps: obtaining the vibration signal sample of the drill bit when drilling the rock sample of the work area, and extracting the signal characteristic parameter from the vibration signal sample; according to the corresponding relationship between the lithology of the rock sample and the signal characteristic parameter of the vibration signal sample, a probability distribution relationship model between the lithology of the rock in the work area and the signal characteristic parameter of the vibration signal of the drill bit is established; obtain the vibration signal generated when the drill bit breaks the rock during drilling in the work area, and extract the signal characteristic parameter from the vibration signal. Although according to the signal characteristic parameter of the vibration signal, the lithology of the work area formation drilled by the drill bit is inferred by using the probability distribution relationship model between the lithology of the rock in the work area and the signal characteristic parameter of the vibration signal of the drill bit; but relying on the indirect parameters such as vibration signal to calculate the lithology, which leads to the complexity and tediousness of the process of obtaining the lithology of the rock.

[0005] The prior art three, Chinese patent, application number: 201410037682.3 discloses a full well section lithology identification method, comprising: taking out a plurality of core samples from a plurality of depth positions, determining the particle size data and lithology type of each core sample; determining the values of a plurality of logging parameters at a plurality of depth positions, selecting N logging parameters which increase or decrease with the increase of the particle size of the lithology sample as characteristic parameters, N≥2; according to the lithology type of the core sample point at the plurality of depth positions and the values of the N characteristic parameters, the corresponding relationship between each N-dimensional cell and the lithology type in the N-dimensional space of the characteristic parameter coordinates is determined; according to the values of the characteristic parameters at each depth position and the corresponding relationship, the representative lithology type at each depth position is determined. Although the advantages of the traditional geological analysis and logging processing method for identifying lithology type are combined, the interactive multi-dimensional histogram lithology identification method established is accurate and reliable, especially suitable in the case of less core data; but the discrete depth sampling data is large, and batch processing cannot be realized, which leads to the decline of the accuracy of the full well section lithology identification.

[0006] The prior art one, the prior art two and the prior art three have the problems of time-consuming and labor-consuming in the drilling collection process. Therefore, the present application provides a 3D scanning core characteristic parameter identification system and method for drilling in many fields such as geological exploration, engineering construction and resource development. SUMMARY

[0007] The main purpose of the present application is to provide a 3D scanning core characteristic parameter identification system and method to solve the problem of time-consuming and labor-consuming in the drilling collection process in the prior art; the present application can effectively reduce the time and labor cost of drilling collection, and improve the work efficiency and accuracy.

[0008] To achieve the above purpose, the present application provides the following technical scheme: A 3D scanning core feature parameter identification system, the identification system comprises: A core holding module is used for automatically rotating the core by a single rotation fixed angle under the control of an automatic numerical control platform to realize multi-angle scanning; and the core holding module provides a core ordered fixation, controllable rotation and scanning environment optimization; A 3D scanning module is used for scanning and obtaining high-precision point cloud data of the core surface; the point cloud is processed by a data acquisition processor to construct a complete three-dimensional model, automatically calculate key geological parameters, and assist in identifying joints.

[0009] As a further improvement of the present application, the core holding module comprises a core box, a broken core box, a core rotating wheel, a core card and a transmission structure. The transmission structure is connected with the automatic numerical control platform through a control line, and the core rotating wheel is arranged on the core box; the core rotating wheel is arranged below each clamping groove of the core box and connected with the transmission structure through a bolt structure to realize the rotation of the core rotating wheel in the same direction; the transmission structure is connected with the core rotating wheel through the bolt structure to drive the core rotating wheel to rotate by rotation.

[0010] As a further improvement of the present application, one or more core boxes are connected according to requirements to realize the overall scanning of the core.

[0011] As a further improvement of the present application, the core card comprises an anti-wear sleeve, a hardboard card and a clamping groove structure, and is arranged on the core box; the anti-wear sleeve is arranged outside the clamping groove structure, and the hardboard card is arranged at the middle position of the anti-wear sleeve.

[0012] As a further improvement of the present application, the 3D scanning module comprises a 3D laser scanner, a scanning fixing structure, a data acquisition processor and an automatic numerical control platform. The 3D laser scanner is used for scanning the model of the core and is controlled by the automatic numerical control platform; the 3D laser scanner is located directly above the core box; the scanning fixing structure is used for fixing the 3D laser scanner and is controlled by the automatic numerical control platform; the data acquisition processor is used for processing the data obtained by the 3D laser scanner; the 3D laser scanner emits a laser beam and receives the light signal reflected from the surface of the object to obtain the three-dimensional coordinate data of the surface of the object by calculating the propagation time and angle of the light; and the automatic numerical control platform is used for controlling the scanning fixing structure and the core rotating wheel, and the number of the core boxes, the interval and the clamping groove interval of the core box are preset to make the 3D laser scanner located directly above the core each time the core is scanned.

[0013] As a further improvement of the present invention, the four corner brackets are fixed to the ground and can extend and retract in height. A level is attached to the top of the four corner brackets. By adjusting the four corner brackets to align with the level, the 3D laser scanner on the top surface of the four corner brackets is kept horizontal. The part of the top surface of the four corner brackets that connects to the 3D laser scanner is connected to the pulley and then locked on the top four corner brackets. The horizontal position of the 3D laser scanner can be adjusted by adjusting the pulley.

[0014] To achieve the above objectives, the present invention also provides the following technical solution: A method for identifying characteristic parameters of 3D scanned rock cores, applied to the aforementioned 3D scanned rock core characteristic parameter identification system, the method comprising: The core samples from the borehole were separated into intact and fragmented cores through adaptive cleaning. The fragmented cores were stored in the fragmented core box, while the intact cores and the core tags were positioned together in the slots of the core box to form an initial dataset with spatial-attribute binding. The core boxes are arranged, and based on point cloud registration, the automated CNC platform dynamically adjusts the height and level of the 3D laser scanner to generate the optimal scanning path topology network covering all core boxes. The scanning path topology network triggers a reinforcement learning control loop, a 3D laser scanner collects point clouds, the core rotating wheel performs angle optimization rotation, the point cloud coverage is fed back in real time, the rotation angle is dynamically adjusted, and a full-angle high-density core point cloud sequence is output. The full-angle high-density core point cloud sequence is processed through multi-scale feature fusion: point cloud filtering and registration are performed to remove noise and align multi-view data; a topologically complete model is generated through 3D reconstruction; and a convolutional neural network is used to identify joints and fractures, generating automatically drawn bar charts, quantized parameter sets, and joint parameter packages.

[0015] As a further improvement of the present invention, the process of generating automatically drawn bar charts, quantified parameter sets, and joint parameter packages includes the following steps: A high-density core point cloud sequence from all angles was obtained. Using the core box slot matrix as the topological skeleton, the multi-angle point clouds were rigidly registered according to the core rotating wheel angle parameters. Spatial markers of the fragmented core box were then fused to generate a mask for the missing area. Joint identification and parameter extraction, slicing along the groove axis to generate virtual core columns, detecting curvature discontinuities within the slice area, and adjusting detection sensitivity based on the lithological record of the core plate; The column chart and parameter set are generated, the virtual core column length is accumulated and matched with the core card footage data, the joint attitude is converted into dip and dip angle parameter packages, and the core extraction length is calculated to be forced to be an integer multiple of the slot spacing. The output includes a triple modeling result: a topologically complete model that preserves the entity of the card slot space topology, a quantified parameter set, and joint parameters including a dip distribution histogram and dip angle confidence intervals.

[0016] As a further improvement of the present application, the process of topologically complete model preserving the entity of the card slot space topology comprises the following steps: Taking the core box card slot matrix as the space grid reference, the point cloud surface is divided into equal length segments according to the card slot spacing, and the end points of each segment are forced to align with the card slot center normal plane; generate a structure-constrained quantization parameter generation; Engineering packaging of joint parameters, inclination and dip angle conversion establishes the occurrence reference system based on the card slot normal plane, and the confidence interval calculation is based on the repeated observation times of the eight-period scanning point cloud; Output triple modeling results, topologically complete model preserves the entity of card slot number and broken core box space marker, and the set of quantization parameters; joint parameters include inclination-dip angle joint distribution matrix.

[0017] As a further improvement of the present application, the process of engineering packaging of joint parameters comprises the following steps: Extract the core box card slot long axis normal vector as the horizontal reference datum, and establish the dip angle zero plane with the vertical bisector plane of the card slot spacing; the angle between the joint surface and the card slot long axis normal vector is defined as the tendency of the angle, and the angle between the joint surface and the vertical bisector plane is directly used as the dip angle; Statistical same joint in eight-period point cloud is independently detected the number of times, when the detection times is not less than the minimum observation number required by core card record lithology, automatically expand the confidence interval of joint parameters to the upper limit of engineering allowed; The row vector is the card slot number index, and the column vector is the joint occurrence data cluster verified by eight periods, and the inclination-dip angle joint distribution matrix is output.

[0018] The application realizes automatic and high-precision collection through the cooperative work of the core storage module and the 3D scanning module: the system can automatically control the fixing and rotation of the core, making it possible to scan at multiple angles, ensuring the automation and efficiency of data collection; at the same time, cooperating with the accurate positioning of the 3D laser scanner, the obtained core surface point cloud data has high precision. Multi-angle scanning: the core rotating wheel and transmission structure in the core storage module can automatically rotate the core to a certain angle, realizing multi-angle scanning, which helps to obtain more comprehensive core feature information. Reduce scanning interference: the black / transparent material designed by the system reduces interference during the scanning process, improving the accuracy of scanning. Optimize the scanning environment: the broken core box provides a solution for independent storage of broken samples, avoiding the impact on scanning during rotation, further optimizing the scanning environment. Three-dimensional modeling and parameter calculation: the 3D scanning module processes point cloud data through the data acquisition processor to construct a complete three-dimensional model and automatically calculate key geological parameters such as hole depth, recovery rate and RQD, which greatly improves the efficiency and accuracy of geological parameter calculation and assists in identifying joints. Geological parameter assisted identification: the system assists in identifying geological features such as joints through three-dimensional modeling analysis, providing an important reference for geological research. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 It is a functional module schematic diagram of the 3D scanning core feature parameter identification system of the application; Figure 2 It is a structural schematic diagram of the 3D scanning core feature parameter identification system of the application; Figure 3 It is a core box structure schematic diagram of the 3D scanning core feature parameter identification system of the application Figure 1 ; Figure 4 It is a core box structure schematic diagram of the 3D scanning core feature parameter identification system of the application Figure 2 ; Figure 5 It is a core box structure schematic diagram of the 3D scanning core feature parameter identification system of the application Figure 3 ; Figure 6 It is a broken core box structure schematic diagram of the 3D scanning core feature parameter identification system of the application Figure 1 ; Figure 7 It is a broken core box structure schematic diagram of the 3D scanning core feature parameter identification system of the application Figure 2 ; Figure 8 It is a broken core box structure schematic diagram of the 3D scanning core feature parameter identification system of the application Figure 3 ; Figure 9Structure diagram of core card of 3D scanning core characteristic parameter identification system of the present application Figure 1 Figure 10 Structure diagram of core card of 3D scanning core characteristic parameter identification system of the present application Figure 2 Figure 11 Structure diagram of transmission structure of 3D scanning core characteristic parameter identification system of the present application Figure 1 Figure 12 Structure diagram of transmission structure of 3D scanning core characteristic parameter identification system of the present application Figure 2 Figure 13 Step flow diagram of one embodiment of 3D scanning core characteristic parameter identification method of the present application Figure 14 Structure diagram of one embodiment of electronic device of the present application Figure 15 Structure diagram of one embodiment of storage medium of the present application DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0021] The terms "first", "second", "third" in the present application are only for descriptive purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", "third" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise specifically limited. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present application are only used to explain the relative position relationship, movement condition, etc. between the components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, the process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units not listed, or optionally includes other steps or units inherent to the process, method, product or device.

[0022] ​​​​Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase "in an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily all directed to the same embodiment, or to a single alternative embodiment. One of skill in the art will understand that embodiments described herein can be combined with other embodiments.

[0023] As shown in Figure 1 The present embodiment provides an embodiment of a 3D scanning core feature parameter identification system, which specifically comprises a core storage module 1 and a 3D scanning module 2 in the present embodiment. The core storage module 1 is used to fix the core and the core card 6 through the core box 3 clamping groove, and integrates the core rotating wheel 5 and the transmission structure 7, and automatically rotates the core single rotation fixed angle under the control of the automatic numerical control platform 11 to realize multi-angle scanning; the black / translucent material design minimizes scanning interference, and the broken core box 4 independently stores the broken samples to avoid the influence of rotation; it provides ordered core fixation, controllable rotation, and scanning environment optimization. The 3D scanning module 2 is used to automatically control the 3D laser scanner 8 to be accurately positioned above the core through the automatic numerical control platform 11, and to scan and obtain high-precision point cloud data of the core surface; the point cloud is processed by the data acquisition processor 10 to construct a complete three-dimensional model, automatically calculate key geological parameters such as hole depth, recovery rate, and RQD, and assist in identifying joints; it is responsible for automatic and accurate scanning and three-dimensional modeling analysis. The core storage module 1 and the 3D scanning module 2 cooperatively realize full-automatic and high-precision core digitization acquisition and geological parameter calculation.

[0024] Preferably, the cooperation of the core storage module 1 and the 3D scanning module 2 in this embodiment realizes automatic and high-precision acquisition: the system can automatically control the fixing and rotation of the core, making it possible to scan at multiple angles and ensuring the automation and efficiency of data acquisition; at the same time, combined with the precise positioning of the 3D laser scanner 8, the obtained core surface point cloud data has high precision. Multi-angle scanning: the core rotating wheel 5 and the transmission structure 7 in the core storage module 1 can automatically rotate the core by a certain angle, realizing multi-angle scanning and helping to obtain more comprehensive core feature information. Reduce scanning interference: the black / translucent material designed in the system reduces interference during scanning, improving the accuracy of scanning. Optimize the scanning environment: the broken core box 4 provides a solution for independent storage of broken samples, avoiding the impact on scanning during rotation and further optimizing the scanning environment. Three-dimensional modeling and parameter calculation: the 3D scanning module 2 processes point cloud data through the data acquisition processor 10, constructs a complete three-dimensional model, and automatically calculates key geological parameters such as hole depth, recovery rate, and RQD, which greatly improves the efficiency and accuracy of geological parameter calculation and assists in identifying joints. Geological parameter assisted identification: the system assists in identifying geological features such as joints through three-dimensional modeling analysis, providing an important reference for geological research.

[0025] In summary, this embodiment realizes digital core acquisition and accurate calculation of geological parameters through automatic and high-precision scanning and data analysis, improving the efficiency and quality of geological exploration.

[0026] Further, as shown in Figures 2-11 , the core storage module 1 specifically includes: a core box 3, a broken core box 4, a core rotating wheel 5, a core card 6, and a transmission structure 7.

[0027] The core box 3 has a transmission structure 7 installed on one side, the transmission structure 7 is connected to the automatic numerical control platform 11 through a control line, and the core rotating wheel 5 is placed on the core box 3 and mainly controlled by the automatic numerical control platform 11. The core rotating wheel 5 needs to be placed under each clamping groove of the core box 3 and connected to the transmission structure 7 through the bolt structure 15 to realize the rotation of the core rotating wheel 5 in the same direction, so that the core also rotates synchronously, thereby scanning the core at each angle and realizing the scanning of the whole core. The transmission structure 7 is connected to the core rotating wheel 5 through the bolt structure 15 and drives the core rotating wheel 5 to rotate by rotating, and one or more core boxes can be connected according to the demand, thereby realizing the whole scanning of the core.

[0028] The core card 6 includes an anti-wear sleeve 12, a hardboard card 13, and a clamping groove structure 14 and is placed on the core box 3; the anti-wear sleeve 12 is arranged outside the clamping groove structure 14, and the hardboard card 13 is arranged at the middle position of the anti-wear sleeve 12.

[0029] Wherein, the core box 3 is a cuboid structure with length a+2t, width b+2t, height h, and edge thickness t. The upper side is open, and parallel to the long end of the box, there is an equal-interval slot structure 14, which divides the cuboid structure b with equal interval d. The slot structure 14 is used to place the core, and the core diameter is usually smaller than the interval d of the slot structure 14. The core box height h is usually greater than the interval d of the slot structure 14. The core box 3 needs to be made of black light-absorbing material or transparent light-transmitting material to ensure that almost no point cloud data is generated during scanning, improving the scanning quality. The broken core box 4 is a half-cylinder with a diameter slightly smaller than d and a length less than a. It is hung on the slot structure 14 of the core box 3 through the hooks on the long axis side of the half-cylinder. It is mainly used to place the scattered and broken cores that cannot be spliced completely, so that they do not need to rotate with the complete core.

[0030] The core card 6 is placed on the slot structure 14 according to the needs. Its structure can be combined with the slot structure 14 to ensure that the position of the core card 6 does not change, to avoid the loss of the core card 6, and to reduce the problem that the core card 6 is clamped in the core and not easy to find during logging. At the same time, its structure is usually thin, and because the core box 3 height h is usually greater than the interval d of the slot structure 14, the core card 6 almost does not hinder the rotation of the core. The core card 6 needs to contain information such as sample number, hole depth, footage, core number, core length, recovery rate, time, and recording personnel. The core card 6 needs to be made of black light-absorbing material or transparent light-transmitting and corrosion-resistant material to ensure that almost no point cloud data is generated during scanning, improve the scanning quality, and at the same time ensure the preservation time of the core card.

[0031] Preferably, the core storage module 1 of the present embodiment provides an efficient and accurate core scanning and storage system. The design of the module integrates multiple functions aimed at achieving the automated processing, storage and information recording of cores. First, the core box 3 is designed as a cuboid structure with an open top and built-in equally spaced card slot structures 14 for placing cores; such a design facilitates both the storage of cores and the scanning work; the core box 3 is made of black light-absorbing material or transparent light-transmitting material, reducing the interference of point cloud data in the scanning process and improving the scanning quality. Second, the setting of the core rotating wheel 5 allows the cores to rotate synchronously in the core box 3, cooperating with the control of the automated numerical control platform 11, to achieve scanning of the cores at various angles; the rotating mechanism is conducive to obtaining comprehensive three-dimensional information of the cores, providing more detailed data for subsequent geological analysis. Third, the transmission structure 7 is connected with the core rotating wheel 5 through the latch structure 15, allowing one or more core boxes to be connected according to needs to achieve the overall scanning of multiple cores, improving work efficiency. The design of the core plate 6 takes into account the problem of wear resistance and point cloud data interference during scanning, using black light-absorbing material or transparent light-transmitting wear-resistant material, while containing important information such as core sample number and hole depth, facilitating the recording and inquiry of detailed core information. The design of the fragmented core box 4 takes into account the storage problem of scattered and broken cores that cannot be spliced, through the design of a semi-cylindrical shape and a hook mechanism, allowing these cores to be stored in an orderly manner, facilitating management and subsequent processing.

[0032] In summary, the design of the core storage module 1 of the present embodiment makes the storage, scanning and information recording of cores more efficient and accurate, improving the automation level of core processing work and providing strong technical support for geological exploration and research.

[0033] Further, as shown in Figure 2 , the 3D scanning module 1 specifically includes: a 3D laser scanner 8, a scanning fixed structure 9, a data acquisition processor 10, and an automated numerical control platform 11.

[0034] The 3D laser scanner 8 is used to scan the model of the core, and is controlled by the automatic numerical control platform 11. The 3D laser scanner 8 needs to be located directly above the core box 3 during scanning. The scanning fixed structure 9 is used to fix the 3D laser scanner 8 and is controlled by the automatic numerical control platform 11. The scanning fixed structure 9 is fixed on the ground by a four-corner support. The four-corner support is telescopic in height. The top of the four-corner support is provided with a level. The 3D laser scanner 8 on the top surface of the four-corner support is kept horizontal by adjusting the level. The part of the four-corner support connecting the 3D laser scanner 8 is clamped on the top surface of the four-corner support after being connected with a pulley. The horizontal position of the 3D laser scanner 8 is adjusted by the four-corner support. The data acquisition processor 10 is used to process the data obtained by the 3D laser scanner 8. The 3D laser scanner 8 emits a laser beam and receives the light signal reflected from the surface of the object. The three-dimensional coordinate data of the surface of the object is obtained by calculating the propagation time and angle of the light. The data usually exists in the form of point cloud, and contains the position, reflectivity and texture information of each point. The collected point cloud data needs to be processed in a series of processes, including filtering, registration and feature extraction. The data obtained by repeated scanning at different angles is combined into a complete three-dimensional model. The three-dimensional model can accurately reflect the shape, size and surface features of the object, and is used to calculate the data such as core depth, footage, core length, and to assist in identifying joints and other conditions. The automatic numerical control platform 11 is used to control the scanning fixed structure 9 and the core rotating wheel 5. The number, interval and slot interval of the core box 3 are preset, so that the 3D laser scanner 8 is located directly above the core each time the core is scanned, and the core rotating wheel 5 is rotated each time. The automatic cycle of scanning-rotation-re-scanning-re-rotation is realized, and the same core box 3 is scanned for eight cycles. Then the position of the 3D laser scanner 8 is adjusted automatically and the next core box 3 is scanned.

[0035] Preferably, the present embodiment constitutes an automated 3D scanning system as a whole, which is significant for efficient and accurate scanning and data acquisition of core models. Specifically: the automatic numerical control platform 11 controls the entire scanning process, including the fixed structure 9 and the core rotating wheel 5, reducing manual operation and improving scanning efficiency; through preset parameters, the system can automatically complete the scanning-rotation-re-scanning cycle to realize continuous operation. The three-dimensional coordinate data obtained by the 3D laser scanner 8 can accurately reflect the shape, size and surface characteristics of the core after being processed by the data acquisition processor 10. The processing and feature extraction of point cloud data provide accurate basic data for subsequent analysis. Through multiple cycles of scanning, the system can obtain core data from different angles and ultimately merge into a complete three-dimensional model to fully reflect the geometric and physical properties of the core. The data acquisition processor 10 not only processes the original point cloud data, but also includes filtering, registration and feature extraction, etc., providing support for subsequent statistical analysis. For example, statistical data such as hole depth, footage, core sampling length, calculation of sampling rate, RQD and other indicators, and auxiliary identification of joint conditions. The design of the scanning fixed structure 9 allows it to be adjusted to the appropriate position and kept level through the four corner supports, adapting to different sizes and shapes of core boxes 3, increasing the adaptability and flexibility of the system.

[0036] In summary, the present embodiment improves the efficiency and accuracy of core analysis through automated and accurate scanning and data processing, providing strong technical support for geological exploration and research.

[0037] As shown in Figure 4 The present embodiment also provides an embodiment of a method for identifying 3D scanned core feature parameters, which is applied to the 3D scanning core feature parameter identification system as described in the above embodiment. The method comprises the following steps: S1: The drilling core sample separates the complete and fragmented cores through adaptive cleaning, and the fragmented cores are stored in the fragmented core box. The complete core and the core card are positioned together to the card slot of the core box, forming the initial data set of space-attribute binding; S2: Arrange the core box, based on point cloud registration, the automatic numerical control platform dynamically adjusts the height and levelness of the 3D laser scanner, and generates an optimal scanning path topology network covering all core boxes; S3: Scanning path topology network, triggering reinforcement learning control cycle, 3D laser scanner collects point cloud, core rotating wheel performs angle optimization rotation, real-time feedback of point cloud coverage rate, dynamic adjustment of rotation angle, output of full-angle high-density core point cloud sequence; S4: Full-angle high-density core point cloud sequence through multi-scale feature fusion: point cloud filtering and registration, remove noise, align multi-view data; three-dimensional reconstruction generates a topologically complete model; convolutional neural network identifies joints and cracks, generates automatic columnar chart, quantifies parameter set and joint parameter package.

[0038] Preferably, the adaptive cleaning of the present embodiment processes the core sample, effectively separates the intact and fragmented cores, provides a high-quality initial data set for subsequent accurate scanning and analysis, and ensures the accurate association of the spatial position and attribute information of the core sample through the space-attribute binding technology; through point cloud registration and automatic numerical control platform, dynamic adjustment of the height and levelness of the scanner is realized, which not only optimizes the scanning efficiency, but also ensures the generation of an optimal path topology network covering all cores, laying a foundation for obtaining high-quality 3D scanning data; using reinforcement learning control cycle, combined with 3D laser scanner to collect point cloud data and optimized rotation of core rotating wheel, real-time feedback of point cloud coverage rate, dynamic adjustment of rotation angle, ensures the generation of full-angle high-density core point cloud sequence, improves the integrity and accuracy of data acquisition; multi-scale feature fusion is adopted to filter and register the point cloud, effectively remove noise and align multi-view data, realize high-precision three-dimensional reconstruction of the core, and provide a structured data basis for subsequent feature parameter identification; through deep learning techniques such as convolutional neural network, joints / cracks in the core are identified, and columnar charts, parameter sets and joint parameter packages are automatically generated, realizing automatic identification and quantitative analysis of core feature parameters, greatly improving the automation level and efficiency of core feature analysis.

[0039] In summary, the present embodiment integrates adaptive cleaning, point cloud registration, reinforcement learning, multi-scale feature fusion and deep learning technologies, realizes the full-process automation from core sample preprocessing to three-dimensional reconstruction to automatic identification of feature parameters, and greatly improves the accuracy and efficiency of core feature parameter identification.

[0040] Further, the process of separating intact and fragmented cores of the drilling core sample through adaptive cleaning in step S1 specifically includes the following steps: S11: Obtain the initial core stream containing surface covering layer debris and potential broken segments, take the slot spacing as the reference scale, construct a core axial continuity detection template, fuse the preloaded hole depth data of the core card, and mark the suspicious area of lithology mutation; S12: Dynamic fragmentation criterion generation, activate debris removal when the measured extension length in the suspicious area is <0.8d, trigger the broken mark when the curvature gradient between adjacent slots exceeds the reconstruction threshold of the subsequent scanning link; S13: Self-organizing separation execution, intact core forced matching slot space vector, fragmented unit directional import box and inheritance of source slot topology coordinates.

[0041] Preferably, the embodiment realizes intelligent sorting of core samples; the axial continuity detection template established based on the slot spacing realizes spatial reference calibration of core structure integrity by fusing preloaded hole depth data, solves the misjudgment problem caused by lack of unified reference system in traditional manual sorting, and provides a standardized spatial coordinate system for subsequent processing; the joint determination of the extension length threshold (0.8d) and the curvature gradient threshold establishes a hierarchical recognition system of the broken features, the former is for macroscopic fragmentation characteristics, and the latter captures microscopic structural distortion, and both of them realize accurate detection from millimeter-level debris to centimeter-level broken sections. The slot space vector matching of the complete core guarantees the geometric integrity of the sample, and the fragmented unit maintains the traceability of the original stratigraphic information through topological coordinates, which realizes physical separation while completely retaining the spatial relationship data of the core.

[0042] In summary, the embodiment establishes a core three-dimensional structure analysis system based on machine vision, realizes automatic identification and classification of broken cores through the technical chain of spatial reference calibration-dynamic threshold determination-topological preservation separation, and ensures the spatial integrity of geological information. The system output meets the dual needs of sample integrity for laboratory analysis and spatial correlation for field geological survey.

[0043] Further, the process of generating the optimal scanning path topology network covering all core boxes in step S2 specifically includes the following steps: S21: Construct a point cloud registration reference constrained by the core box structure according to the space-attribute binding initial data set, take the slot matrix of the core box as a rigid frame, and exclude non-scanning areas according to the topological coordinates of the fragmented core box; S22: Dynamic scanning parameter optimization, get the laser incidence angle tolerance threshold based on the slot spacing, and link the level meter reading of the fixed structure to compensate the inclination error in real time; S23: Numerical control platform executes topological mapping, converts slot coordinate clusters into three-dimensional space path nodes, and generates the minimum energy scanning trajectory by fusing horizontal compensation parameters; S24: Output the optimal scanning path topology network, the node density matches the hole depth gradient recorded in the core card, and the trajectory envelope covers the rotation space domain of all effective slots.

[0044] Preferably, the present embodiment realizes the global optimization of the core box scanning path through multi-step cooperation; based on the rigid frame constructed based on the core box card slot matrix, combined with the topological coordinates of the fragmented core box, the accurate spatial constraints of the scanning area are established, the invalid scanning area is excluded, and the scanning path is ensured to cover only the effective card slot, reducing redundant calculation. The laser incidence angle tolerance threshold is dynamically adjusted based on the card slot spacing and is linked with the level meter reading to correct the inclination error in real time; the geometric consistency of the scanning data is ensured, and the measurement distortion caused by equipment vibration or assembly deviation is avoided. The card slot coordinate cluster is converted into three-dimensional path nodes, and after fusion with the level compensation parameters, the optimal scanning trajectory is calculated, which minimizes the mechanical arm movement energy consumption while meeting the coverage, thereby improving the scanning efficiency. The node density of the optimal scanning path is adaptively matched with the hole depth gradient of the core card record, ensuring that high-resolution scanning focuses on the key area, and the trajectory envelope covers the rotation space domain of all effective card slots, ensuring no missed data collection.

[0045] In summary, the present embodiment constructs a scanning path planning system based on three-dimensional spatial constraints and dynamic parameter optimization, and realizes efficient and high-precision scanning of the core box through the technical chain of rigid frame registration-real-time error compensation-minimum energy trajectory generation-gradient matching node density. While ensuring data integrity, the motion efficiency of the scanning device is optimized, and the automatic detection demand of complex geological samples is met.

[0046] Further, the process of outputting the optimal scanning path topology network in step S24 specifically includes the following steps: S241: Obtain the minimum energy scanning trajectory, perform node encryption driven by the hole depth gradient, extract the hole depth mutation point of the core card record, and multiply the path node density by the hole depth change rate in the corresponding section of the trajectory; S242: Card slot rotation space envelope construction, taking the center of the effective card slot as the origin, generating a rotating envelope body according to the rotation radius of the core rotation wheel, and excluding the space domain occupied by the fragmented core box; S243: Topology network synthesis, mapping the encrypted nodes into the envelope body, and associating the vertical path with the stretching height extreme constraint of the scanning fixed structure; S244: Output the optimal scanning path topology network, the node distribution reflects the change of the core card geological characteristics, and the envelope body boundary ensures the full-angle collision-free scanning of the core rotation wheel.

[0047] Preferably, the embodiment realizes the fine construction of the optimal scanning path topology network through multi-step cooperation; by extracting the hole depth mutation points recorded in the core card, the node density is multiplied according to the hole depth change rate in the corresponding section of the track, the automatic matching of the scanning resolution and the change degree of the geological features is realized, and the key geological interface is ensured to obtain higher-precision data acquisition. The rotation envelope body constructed with the effective slot center as the origin, combined with the spatial domain exclusion mechanism of the fragmented core box, establishes a complete non-collision scanning space model; it not only guarantees the freedom degree of the scanning equipment, but also avoids the spatial interference with the sample container. The mapping process of the encrypted nodes into the envelope body, while associating the extreme value of the extension height of the scanning fixed structure, realizes the feasibility verification of the path nodes in the three-dimensional space, and ensures that the scanning track meets the horizontal and vertical movement constraints at the same time. The final output of the topology network node distribution directly reflects the change of the core card geological features, and the envelope body boundary completely covers the full-angle scanning requirement of the core rotating wheel, realizing the unification of the geological significance and mechanical feasibility of the scanning path.

[0048] In summary, the embodiment forms a scanning path optimization system based on geological feature-mechanical constraint dual driving, through the technical chain of hole depth gradient node encryption-non-collision space modeling-three-dimensional path constraint integration-geological feature mapping, while ensuring the safety of scanning, it realizes high-resolution data acquisition in key geological areas. It is especially suitable for automatic detection of core samples with complex geological features.

[0049] Further, the process of outputting the full-angle high-density core point cloud sequence in step S3 specifically includes the following steps: S31: Taking the network nodes of the optimal scanning path topology network as the positioning reference of the 3D laser scanner, and setting the initial rotation angle base according to the radius of the core rotating wheel; the current slot point cloud void rate is obtained in real time after single scanning; When the void rate is greater than the lithology allowed threshold recorded in the core card, the next cycle rotation angle is dynamically corrected, and the angle base is multiplied by the void rate compensation coefficient; S32: Each core box performs eight scanning-rotation cycles, and terminates in advance when the cumulative rotation angle meets the core circumference coverage requirement; S33: The void rate of the single-box point cloud after superposition and fusion is not greater than the subsequent three-dimensional reconstruction error tolerance, and the sequence space coordinates strictly match the slot topology network, and the full-angle high-density core point cloud sequence is output.

[0050] Preferably, the embodiment realizes efficient acquisition and quality control of full-angle high-density core point cloud sequence through multi-step cooperation; based on the nodes of the optimal scanning path topological network, the rotation angle base is dynamically adjusted according to the real-time acquired slot point cloud void ratio, so as to ensure the automatic improvement of the scanning density in the complex lithology area; the adaptive optimization of the scanning resolution is realized through the void ratio compensation coefficient, so as to avoid the data loss caused by fixed angle scanning. In eight scanning-rotation cycles, if the cumulative rotation angle reaches the core circumference coverage requirement, the scanning is terminated in advance, the scanning efficiency and data integrity are balanced, the redundant scanning is avoided, and the full-angle coverage of the key area is ensured. After the single box point cloud is superimposed and fused, the void ratio is not more than the three-dimensional reconstruction error tolerance, and the sequence coordinates strictly match the slot topological network, so as to ensure the consistency of the geometric accuracy of the point cloud data and the topological structure, and provide high-fidelity input for subsequent three-dimensional reconstruction.

[0051] In summary, the embodiment constructs a dynamic scanning system based on real-time feedback, realizes efficient acquisition and quality controllable of core point cloud through the technical chain of path reference positioning-void ratio driven angle correction-cycle termination condition judgment-point cloud quality verification, optimizes the scanning efficiency while ensuring full-angle coverage, and ensures that the point cloud data meets the accuracy requirements of three-dimensional reconstruction.

[0052] Further, the process of generating the automatically drawn column chart, the quantitative parameter set and the joint parameter package in step S4 specifically includes the following steps: S41: acquiring the full-angle high-density core point cloud sequence, taking the core box slot matrix as the topological skeleton, rigidly registering the multi-angle point cloud according to the core rotation wheel angle parameter, and fusing the broken core box space marker to generate a missing area mask; S42: joint identification and parameter extraction, generating a virtual core column by slicing along the slot axis, detecting the curvature discontinuity zone in the slice domain, and modulating the detection sensitivity according to the core card lithology record; S43: column chart and parameter set generation, matching the core card footage data by adding the length of the virtual core column, converting the joint occurrence into a dip direction and dip angle parameter package, and calculating the core length to force the alignment of the slot spacing by an integer multiple; S44: outputting the three-dimensional modeling results, the topologically complete model retaining the entity of the slot space topology, the quantitative parameter set, and the joint parameter including the dip direction distribution histogram and the dip angle confidence interval.

[0053] Preferably, the present embodiment realizes the three-dimensional digital reconstruction of the core and the automatic extraction of joint parameters through multi-modal data fusion and structured processing; the spatial positioning problem of fragmented cores is solved through multi-angle point cloud rigid registration based on the topological skeleton constraint of the core box card slot matrix; the topological integrity of the model is ensured by the missing area mask generation; the discrete point cloud is converted into structured axial slices by the virtual core column generation technology, which establishes a standardized data carrier for subsequent parameter extraction; the curvature discontinuity band detection algorithm operates in the slice domain, and the sensitivity parameter modulated by the lithology record realizes the lithology-related joint identification; the axial slice strategy maintains the spatial correspondence between the joint surface and the original core, ensuring the geometric authenticity of the occurrence conversion; the length accumulation algorithm is matched with the footage data, eliminating the dimensional error between the scanning data and the field record; the joint parameter package realizes the mathematical standardized description of the structural surface occurrence through the dual parameter (dip / dip angle) representation; the integer multiple alignment mechanism ensures the output data to be compatible with the physical card slot; the topological solid model maintains the spatial relationship of the original card slot, meeting the geometric authenticity requirements of geological interpretation; the quantitative parameter set provides a structured data interface readable by machines; the dip histogram and the dip angle confidence interval constitute a dual verification mechanism for joint statistics.

[0054] In summary, the present embodiment realizes the closed-loop conversion from unstructured point cloud to standardized geological parameters through the three-layer verification mechanism of geometric constraint, parameter conversion, and data alignment, and solves the key technical contradiction between topological preservation and parameter extraction in the digitalization process of fragmented cores.

[0055] Further, the process of the topologically complete model retaining the entity of the card slot spatial topology in step S44 specifically includes the following steps: S441: Taking the core box card slot matrix as a spatial grid reference, the point cloud surface is divided into equal-length segments according to the card slot spacing, and the endpoints of each segment are forced to align with the card slot center normal plane; a structured quantitative parameter generation is generated; Wherein, the core length calculation: the number of effective segments in the card slot x spacing d; the recovery rate calculation: core length ÷ core card footage data x 100%; RQD value calculation: cumulative length of complete segments with length ≥10 cm ÷ core length; S442: Joint parameter engineering encapsulation, dip and dip angle conversion based on card slot normal plane to establish occurrence reference system, confidence interval calculation based on eight-cycle scanning point cloud repeated observation times; S443: Output triple modeling results, topologically complete model retains the entity of the card slot number and the fragmented core box space marker, quantitative parameter set; joint parameters include dip-dip angle joint distribution matrix.

[0056] Preferably, the present embodiment realizes the engineering application of the core topological model through space constraint and parameter standardization packaging; the matrix of card slots is taken as the rigid grid reference, the endpoints of the point cloud segment are aligned with the center method plane of the card slot, and the spatial consistency of the digitized model and the physical core box is ensured; the broken core box vacancy label is inherited to the topological entity model, and the geometric representation of the original sampling missing state is maintained; the sampling length calculation eliminates the cumulative error of the scanning data and the physical card slot through the integer multiple relationship between the card slot spacing d and the effective segment number; the sampling rate and the RQD value are calculated based on the same spatial reference, so that the three types of parameters (sampling length / sampling rate / RQD) have self-consistent metrological logic; the inclination / dip angle conversion binds the card slot method plane reference system, and converts the geological occurrence into an engineering coordinate system that can be identified in construction; the repeated observation data of eight cycles support the confidence interval calculation, and improve the statistical significance of the occurrence parameters; the topological entity model retains the card slot numbering system, realizes the reverse tracing of the digitized model and the field record, and replaces the traditional histogram with the inclination-dip angle joint distribution matrix to reveal the spatial coupling relationship of the structural plane parameters.

[0057] In summary, the present embodiment establishes a deterministic mapping relationship from point cloud data to constructible geological parameters through three layers of processing of spatial grid constraint, parameter calculation normalization and engineering coordinate system conversion, and solves the problem of the unity of geometric authenticity and engineering applicability in the broken core digitized model.

[0058] Further, the process of engineering packaging of joint parameters in step S442 specifically includes the following steps: S4421: Extract the core box card slot long axis normal vector as the horizontal reference reference, and establish the dip angle zero position plane with the vertical bisector plane of the card slot spacing; the angle between the joint surface and the card slot long axis normal vector is defined as the inclination angle, and the angle between the joint surface and the vertical bisector plane is directly taken as the dip angle; S4422: Count the number of times the same joint is independently detected in the eight-cycle point cloud, and when the detection number is greater than or equal to the minimum number of observations required by the core label to record the lithology, automatically expand the confidence interval of the joint parameters to the upper limit allowed in engineering; S4423: The row vector is the card slot number index, and the column vector is the joint occurrence data cluster verified by eight cycles, and the inclination-dip angle joint distribution matrix is output.

[0059] Preferably, the technical features of the joint parameter engineering package of the present embodiment are combined to form a standardized joint parameter generation system based on spatial reference constraints and statistical verification; the long-axis normal vector of the card slot is used as the horizontal reference, and the dip angle zero plane is defined by combining the vertical bisector plane, ensuring that the measurement reference of the joint occurrence parameters (dip direction and dip angle) is strictly aligned with the engineering coordinate system, eliminating the conversion error between the traditional geological occurrence and the construction coordinate system; the reliability of the joint parameters is verified by the eight-cycle point cloud independent detection frequency, and when the observation frequency reaches the minimum sample number required by the lithology record, the confidence interval is automatically relaxed to the upper limit of the engineering tolerance, balancing data precision and engineering applicability; the card slot number is used as the row vector index, and the eight-cycle verification data cluster is used as the column vector to construct a matrix joint occurrence database, supporting subsequent spatial distribution analysis of parameters (such as the generation of a dip direction-dip angle joint distribution matrix).

[0060] In summary, the present embodiment realizes efficient conversion from original point cloud joint detection to occurrence parameters that can be directly used for engineering design through the integration of three technologies: reference unification, statistical verification standardization, and data storage structuring.

[0061] Further, the process of establishing a dip angle zero plane with the vertical bisector plane of the card slot spacing in step S4421 specifically includes the following steps: S44211: Obtain the physical characteristics of the core box card slot matrix, determine the normal vector confidence domain based on the parallelism tolerance of the long side of the card slot, and compensate for the assembly tilt by fusing the historical readings of the scanning fixed structure level; S44212: Vertical bisector plane generation, taking the center line of adjacent card slots as the reference line, and making the vertical plane of the short axis of the card slot through the midpoint of the reference line, the intersection of the vertical plane and the bottom surface of the card slot is defined as the zero dip angle baseline; S44213: Double-reference solidification output, horizontal reference baseline long-axis normal vector compensation value, and dip angle zero plane zero dip angle baseline plane.

[0062] Preferably, the technical features of the present embodiment for establishing a dip angle zero plane form a high-precision and anti-interference spatial reference definition system, which has the following technical effects: based on the parallelism tolerance of the long side of the card slot, the normal vector confidence domain is limited, the historical data of the level are fused to compensate for the assembly error, and the engineering applicability of the horizontal reference baseline (long-axis normal vector) is ensured; by constructing a short-axis vertical plane through the midpoint of the center line of adjacent card slots, the intersection is physically solidified as a zero dip angle baseline, making the dip angle zero plane have traceable geometric generation logic; the horizontal reference baseline (long-axis normal vector) and the dip angle zero plane (zero dip angle baseline plane) form an orthogonal spatial coordinate system, and they are dynamically related through compensation values, jointly resisting local disturbances of scanning data.

[0063] In summary, the embodiment realizes the unification of the physical interpretability and the engineering robustness of the tilt angle measurement reference by means of the triple technical means of the machining tolerance constraint, the geometric configuration certainty and the dual-reference dynamic coupling.

[0064] As shown in Figure 5 The electronic device 4 includes a processor 161 and a memory 162 coupled to the processor 161.

[0065] The memory 162 stores program instructions for implementing the 3D scanning core feature parameter identification method of any of the above embodiments.

[0066] The processor 161 is configured to execute the program instructions stored in the memory 162 to perform the 3D scanning core feature parameter identification.

[0067] The processor 161 can also be referred to as a CPU (Central Processing Unit). The processor 161 can be an integrated circuit chip with processing capability. The processor 161 can also be a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0068] Further, Figure 6 The storage medium 17 of the embodiment of the present application stores program instructions 171 capable of implementing all the above methods. The program instructions 171 can be stored in the above storage medium in the form of a software product, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a ROM (Read-Only Memory), a RAM (Random Access Memory), a magnetic disk or an optical disk, and various media capable of storing program codes, or a computer, a server, a mobile phone, a tablet, and other terminal devices.

[0069] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative, for example, the division of units is only a logical function division, and actual implementation can have other division manners, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0070] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit. The above is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation using the content of the present application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

[0071] The specific embodiments of the application are described in detail above, but they are only examples. The present application is not limited to the specific embodiments described above. Any equivalent modification or substitution made by those skilled in the art to the present application is also within the scope of the present application, and therefore, any equivalent transformation, modification, improvement, etc. made without departing from the spirit and principle range of the present application should be included in the scope of the present application.

Claims

1. A system for identifying characteristic parameters of 3D scanned rock cores, characterized in that, The identification system includes: The core collection module is used to automatically rotate the core at a fixed angle in a single rotation under the control of an automated CNC platform, enabling multi-angle scanning; it provides orderly core fixation, controllable rotation, and optimized scanning environment. The 3D scanning module is used to scan and acquire high-precision point cloud data of the rock core surface; the data acquisition processor processes the point cloud to construct a complete three-dimensional model, automatically calculates key geological parameters, and assists in the identification of joints.

2. The 3D scanning core feature parameter identification system according to claim 1, characterized in that, The core collection module includes: a core box, a fragmented core container, a core rotating wheel, a core label, and a transmission structure; The core box has a transmission structure installed on one side, which is connected to the automated CNC platform via a control line. The core rotating wheel is placed on the core box. The core rotating wheel is placed under each slot of the core box and is connected to the transmission structure via a pin structure, so that the core rotating wheel rotates in the same direction. The transmission structure is connected to the core rotating wheel via a pin structure, and drives the core rotating wheel to rotate by rotating.

3. The 3D scanning core feature parameter identification system according to claim 2, characterized in that, Connect one or more core boxes as needed to achieve overall core scanning.

4. The 3D scanning core feature parameter identification system according to claim 2, characterized in that, The core plate consists of an abrasion-resistant outer jacket, a cardboard plate, and a slot structure, and is placed on the core box. The abrasion-resistant outer jacket is located on the outside of the slot structure, and the cardboard plate is located in the middle of the abrasion-resistant outer jacket.

5. The 3D scanning core feature parameter identification system according to claim 1, characterized in that, The 3D scanning module consists of a 3D laser scanner, a scanning fixture, a data acquisition processor, and an automated numerical control platform. The system comprises a 3D laser scanner for scanning the core model, controlled by an automated CNC platform, positioned directly above the core box; a scanning fixing structure for securing the 3D laser scanner, also controlled by the automated CNC platform; a data acquisition processor for processing the data obtained by the 3D laser scanner, which emits a laser beam and receives light signals reflected from the object's surface, calculating the propagation time and angle information of the light to obtain the three-dimensional coordinate data of the object's surface; and an automated CNC platform for controlling the scanning fixing structure and the core rotating wheel, ensuring that the 3D laser scanner is positioned directly above the core each time it scans, by pre-setting the number and spacing of the core boxes and the slot spacing of the core boxes.

6. The 3D scanning core feature parameter identification system according to claim 5, characterized in that, The scanner is fixed to the ground by four corner brackets, which are telescopic in height. A level is attached to the top of each corner bracket. By adjusting the corner brackets to align with the level, the 3D laser scanner on the top of the corner brackets is kept horizontal. The part of the top of the corner brackets that connects to the 3D laser scanner is connected to pulleys and then locked onto the top corner brackets. The horizontal position of the 3D laser scanner can be adjusted by these pulleys.

7. A method for identifying characteristic parameters of 3D scanned rock cores, applied to the 3D scanned rock core characteristic parameter identification system as described in any one of claims 1 to 6, characterized in that, The method for identifying the characteristic parameters of the 3D scanned rock core includes: The core samples from the borehole were separated into intact and fragmented cores through adaptive cleaning. The fragmented cores were stored in the fragmented core box, while the intact cores and the core tags were positioned together in the slots of the core box to form an initial dataset with spatial-attribute binding. The core boxes are arranged, and based on point cloud registration, the automated CNC platform dynamically adjusts the height and level of the 3D laser scanner to generate the optimal scanning path topology network covering all core boxes. The scanning path topology network triggers a reinforcement learning control loop, a 3D laser scanner collects point clouds, the core rotating wheel performs angle optimization rotation, the point cloud coverage is fed back in real time, the rotation angle is dynamically adjusted, and a full-angle high-density core point cloud sequence is output. The full-angle high-density core point cloud sequence is processed through multi-scale feature fusion: point cloud filtering and registration are performed to remove noise and align multi-view data; a topologically complete model is generated through 3D reconstruction; and a convolutional neural network is used to identify joints and fractures, generating automatically drawn bar charts, quantized parameter sets, and joint parameter packages.

8. The method for identifying characteristic parameters of 3D scanned rock cores according to claim 7, characterized in that, The process of generating automatically drawn bar charts, quantization parameter sets, and joint parameter packages includes the following steps: A high-density core point cloud sequence from all angles was obtained. Using the core box slot matrix as the topological skeleton, the multi-angle point clouds were rigidly registered according to the core rotating wheel angle parameters. Spatial markers of the fragmented core box were then fused to generate a mask for the missing area. Joint identification and parameter extraction, slicing along the groove axis to generate virtual core columns, detecting curvature discontinuities within the slice area, and adjusting detection sensitivity based on the lithological record of the core plate; The column chart and parameter set are generated, the virtual core column length is accumulated and matched with the core card footage data, the joint attitude is converted into dip and dip angle parameter packages, and the core extraction length is calculated to be forced to be an integer multiple of the slot spacing. The output includes a triple modeling result: a topologically complete model that preserves the entity of the card slot space topology, a quantified parameter set, and joint parameters including a dip distribution histogram and dip angle confidence intervals.

9. The method for identifying characteristic parameters of 3D scanned rock cores according to claim 8, characterized in that, The process of preserving the entity of the card slot space topology in the topological complete model includes the following steps: Using the core box slot matrix as the spatial grid reference, the point cloud surface is divided into equal-length segments according to the slot spacing, and the endpoints of each segment are forced to align with the center normal plane of the slot; the quantitative parameters for generating structural constraints are generated. Joint parameters are encapsulated in an engineering manner; dip and tilt angle conversions are based on establishing an attitude reference system on the card slot method plane; confidence intervals are calculated based on the number of repeated observations of point cloud during eight-cycle scanning. The output includes a triple modeling result: a topologically complete model that retains the entities of the slot number and the void marker of the fractured core box, and a quantified parameter set; and joint parameters that include a dip-angle joint distribution matrix.

10. The method for identifying characteristic parameters of 3D scanned rock cores according to claim 9, characterized in that, The process of engineering encapsulating joint parameters includes the following steps: The normal vector of the major axis of the core box slot is extracted as a horizontal reference datum, and the dip zero plane is established with the vertical bisector of the slot spacing; the angle between the joint surface and the normal vector of the major axis of the slot is defined as the dip angle, and the angle between the joint surface and the vertical bisector is directly used as the dip angle; The number of times the same joint is independently detected in the eight-cycle point cloud is counted. When the number of detections is not less than the minimum number of observations required for the lithology to be recorded in the core, the confidence interval of the joint parameter is automatically expanded to the upper limit allowed by the project. The row vector is the slot number index, the column vector is the joint attitude data cluster that has passed eight-period verification, and the output is the dip-pitch joint distribution matrix.

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