A method and system for analyzing and processing data from single-stage well drilling and trenching blasting.

Abstract

The application discloses a method and system for analyzing and processing one-time well-forming slot blasting data, and belongs to the technical field of computers. According to the technical scheme provided by the embodiment of the application, structured blasting data, unstructured blasting data and expected blasting effect parameters of a target region to be blasted are acquired. Based on the structured blasting data, the unstructured blasting data and the expected effect parameters, a blasting scheme of the target region is generated, and automatic generation of the blasting scheme is realized. In response to a blasting instruction of the target region, borehole data, stress wave propagation data and laser scanning data of a plurality of boreholes are acquired in real time, and the initial detonation time of a target borehole which has not been detonated is adjusted by using the borehole data, geological data, stress wave propagation data, laser scanning data and expected blasting effect parameters, so that the blasting process is optimized and the blasting effect is improved.

Description

Technical Field

[0001] This application relates to the field of blasting technology, and in particular to a method and system for data analysis and processing of single-stage well-drilling trenching blasting. Background Technology

[0002] In the fields of mining and underground engineering, one-time shaft-forming trenching blasting technology plays a crucial role in the quality of shaft and tunnel formation, blasting efficiency, and safety.

[0003] Traditional methods typically design blasting schemes based on static geological survey data (such as borehole cores and geological profiles), controlling energy release through a fixed detonation sequence. However, a fixed detonation sequence may not be suitable for complex blasting environments, leading to poor blasting results.

[0004] Therefore, there is an urgent need for a blasting data analysis method that can integrate multi-source data, perceive blasting status in real time, and dynamically optimize blasting time to improve well completion quality and blasting effect. Summary of the Invention

[0005] This application provides a method and system for analyzing and processing data from single-stage well drilling and trenching blasting, which can improve well drilling quality and blasting effect. The technical solution is as follows:

[0006] On the one hand, a method for analyzing and processing data from single-stage well drilling and trenching blasting is provided, the method comprising:

[0007] The structured blasting data, unstructured blasting data, and expected blasting effect parameters of the target area to be blasted are obtained. The structured blasting data includes geological data, borehole attributes, and explosive parameters. The unstructured blasting data includes core images and ground-penetrating radar scan point clouds.

[0008] Based on the structured blasting data, the unstructured blasting data, and the expected blasting effect parameters, a blasting plan for the target area is generated. The blasting plan includes the amount of explosive filling in multiple blast holes within the target area and the initial detonation time.

[0009] In response to a blasting command for the target area, the system acquires in real time borehole data, stress wave propagation data, and laser scanning data of multiple boreholes within the target area. The blasting command instructs blasting to be performed according to the initial detonation time of the multiple boreholes. The borehole data includes borehole location, borehole temperature, borehole pressure, and explosive charge.

[0010] Based on the borehole data of the multiple boreholes, the geological data, the stress wave propagation data of the target area, the laser scanning data of the target area, and the expected blasting effect parameters, the initial detonation time of the target boreholes that have not yet been detonated among the multiple boreholes is adjusted.

[0011] On the one hand, a data analysis and processing system for single-stage well drilling and trenching blasting is provided, the system comprising:

[0012] The first acquisition module is used to acquire structured blasting data, unstructured blasting data and expected blasting effect parameters of the target area to be blasted. The structured blasting data includes geological data, borehole attributes and explosive parameters, and the unstructured blasting data includes core images and ground-penetrating radar scan point clouds.

[0013] The blasting scheme generation module is used to generate a blasting scheme for the target area based on the structured blasting data, the unstructured blasting data, and the expected blasting effect parameters. The blasting scheme includes the explosive filling amount and initial detonation time of multiple blast holes in the target area.

[0014] The second acquisition module is used to acquire, in response to the blasting command of the target area, borehole data of multiple boreholes in the target area, stress wave propagation data of the target area, and laser scanning data of the target area in real time. The blasting command is used to instruct blasting to be carried out according to the initial detonation time of the multiple boreholes. The borehole data includes borehole position, borehole temperature, borehole pressure, and explosive filling amount.

[0015] The time adjustment module is used to adjust the initial detonation time of the target boreholes that have not yet been detonated among the multiple boreholes based on the borehole data, the geological data, the stress wave propagation data of the target area, the laser scanning data of the target area, and the expected blasting effect parameters.

[0016] On one hand, a computer device is provided, the computer device including one or more processors and one or more memories, the one or more memories storing at least one computer program, the computer program being loaded and executed by the one or more processors to implement the method for analyzing and processing data from a single well-drilling trenching blasting operation.

[0017] On the one hand, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, the computer program being loaded and executed by a processor to implement the method for analyzing and processing data of single-stage well trenching blasting.

[0018] On the one hand, a computer program product or computer program is provided, which includes program code stored in a computer-readable storage medium. The processor of a computer device reads the program code from the computer-readable storage medium and executes the program code, causing the computer device to perform the above-described method for analyzing and processing data from a single well-drilling trenching blasting operation.

[0019] The technical solution provided in this application acquires structured blasting data, unstructured blasting data, and expected blasting effect parameters of the target area to be blasted. Based on the structured blasting data, unstructured blasting data, and expected effect parameters, a blasting plan for the target area is generated, achieving automatic generation of the blasting plan. In response to the blasting command for the target area, borehole data, stress wave propagation data, and laser scanning data from multiple boreholes are acquired in real time. Using the borehole data, geological data, stress wave propagation data, laser scanning data, and expected blasting effect parameters, the initial detonation time of the target boreholes that have not yet been detonated is adjusted, thereby optimizing the blasting process and improving the blasting effect. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the implementation environment for a method for analyzing and processing data from a single well-drilling trenching blasting operation, as provided in an embodiment of this application.

[0022] Figure 2 This is a flowchart of a method for analyzing and processing data from a single well-drilling trenching blasting operation, provided in an embodiment of this application.

[0023] Figure 3 This is a flowchart of another method for analyzing and processing data from a single well-drilling trenching blasting operation, provided in an embodiment of this application.

[0024] Figure 4 This is a schematic diagram of the structure of a data analysis and processing system for single-stage well drilling and trenching blasting, provided in an embodiment of this application.

[0025] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0027] In this application, the terms "first," "second," etc., are used to distinguish identical or similar items with essentially the same function. It should be understood that there is no logical or temporal dependency between "first," "second," and "nth," nor are there any restrictions on quantity or execution order.

[0028] Artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain better results.

[0029] Machine learning (ML) is a multidisciplinary field involving probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specifically studies how computers can simulate or implement human learning behavior to acquire new knowledge or skills, and reorganize existing knowledge sub-models to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to endow computers with intelligence; its applications span all areas of artificial intelligence.

[0030] Single-stage shaft construction: Single-stage shaft construction refers to a construction method in underground engineering projects such as mines, where a complete shaft or tunnel is formed through a single blasting operation. Single-stage shaft construction technology is typically used to improve construction efficiency and safety, and reduce construction time and costs. It requires precise blasting design and construction techniques to ensure the stability and quality of the shaft or tunnel.

[0031] Slot blasting: Slot blasting is a commonly used blasting technique in mining. It creates a slot-shaped space within the ore body to facilitate subsequent mining operations. Slot blasting is typically used in both open-pit and underground mining. Through precise blasting design and construction, a slot-shaped space is created to facilitate subsequent mining operations.

[0032] Stress waves: Stress waves are mechanical waves that propagate in a solid medium, caused by changes in stress. When stress waves propagate in a solid medium, they cause deformation and vibration of the medium. The propagation speed and attenuation characteristics of stress waves depend on the properties of the medium and the frequency of the stress wave.

[0033] Blast holes: Blast holes are drilled in blasting operations to hold explosives. The arrangement and design of blast holes are crucial to the blasting effect and safety. Parameters such as the diameter, depth, and spacing of blast holes need to be precisely designed based on the nature and requirements of the blasting target.

[0034] Ground-penetrating radar (GPR): GPR is a geophysical exploration device used to detect underground geological structures and objects. GPR detects underground geological structures and objects by emitting high-frequency electromagnetic waves and receiving reflected waves. It can be used to detect geological information such as underground cavities, rock strata interfaces, and groundwater.

[0035] Core images: Core images are images of rock samples obtained through core drilling. Core images can be used to analyze geological information such as rock structure, composition, and porosity. Core images are typically obtained using optical or electron microscopes.

[0036] In related technologies, during a well-drilling trenching blasting operation, once the blast hole location is determined, engineers typically analyze relevant data based on experience to decide the explosive charge and detonation time. Once the detonation time is determined, it is usually impossible to adjust, which may result in unsatisfactory blasting effects.

[0037] The technical solution provided in this application allows for automatic analysis of relevant data after the borehole location is determined, enabling the determination of the explosive charge and initial detonation time. Furthermore, it allows for the analysis and processing of real-time collected data during blasting, thereby adjusting the initial detonation time of undetonated boreholes and improving the final blasting effect.

[0038] Figure 1 This is a schematic diagram illustrating the implementation environment of a method for analyzing and processing data from a single well-drilling trenching blasting operation, as provided in an embodiment of this application. (See attached diagram.) Figure 1 The implementation environment includes a data processing device 101 and a blasting controller 102.

[0039] The data processing device 101 is electrically connected to multiple sensors, enabling it to acquire blasting data collected by these sensors. The data processing device has strong data processing capabilities and can process multimodal data quickly.

[0040] The blasting controller 102 is used to control the detonation time of the blast hole, that is, to control the detonation time of the explosives inside the blast hole. The blasting controller 102 is electrically connected to the data processing device 101, and the blasting controller 102 and the data processing device 101 can exchange data. The data processing device 101 can send instructions to the blasting controller to control the detonation time of the blast hole by controlling the blasting controller 102.

[0041] The following describes the application scenarios of the embodiments of this application. The technical solution provided by the embodiments of this application can be applied to the scenario of single-stage well drilling and trenching blasting. Due to the complex blasting environment, using a fixed detonation time may not meet the requirements of single-stage well drilling. The technical solution provided by the embodiments of this application can not only automatically determine the blasting plan before the blasting begins, but also adjust the detonation time of the blast holes in real time during the blasting process, so that the blasting effect is as close as possible to the requirements of single-stage well drilling.

[0042] The following describes the data analysis and processing method for single-stage well drilling and blasting provided in the embodiments of this application. Figure 2 This is a flowchart of a method for analyzing and processing data from a single-stage well drilling and trenching blasting operation, provided in an embodiment of this application. See also... Figure 2 Taking a data processing device as the executing entity as an example, the method includes the following steps.

[0043] 201. The data processing equipment acquires structured blasting data, unstructured blasting data, and expected blasting effect parameters of the target area to be blasted. The structured blasting data includes geological data, borehole attributes, and explosive parameters. The unstructured blasting data includes core images and ground-penetrating radar scan point clouds.

[0044] The target area is the region requiring a single well-drilling blasting operation. This area includes multiple pre-drilled blast holes. Geological data represents the geological conditions of the target area. Hole attributes are the characteristics of the drilled holes, such as location and size. Explosive parameters include the explosive energy, which is the energy produced by a unit amount of explosive detonation and is related to the type of explosive. Core images are images of rock samples obtained through core drilling, used to analyze rock structure, composition, porosity, and other information. Ground-penetrating radar (GPR) point cloud is a point cloud obtained by GPR detection of the target area. Expected blasting effect parameters represent the expected blasting effect on the target area, i.e., a quantitative representation of the expected blasting effect. In some embodiments, these parameters include the expected shape of the blasting area and the expected size of the blasted rocks.

[0045] 202. The data processing equipment generates a blasting plan for the target area based on the structured blasting data, the unstructured blasting data, and the expected blasting effect parameters. The blasting plan includes the amount of explosive filling for multiple blast holes in the target area and the initial detonation time.

[0046] The blasting plan is determined based on structured blasting data, unstructured blasting data, and expected blasting effect parameters. The structured and unstructured blasting data reflect the conditions of the target area, while the expected blasting effect parameters reflect the desired blasting effect. The resulting blasting plan is well-matched with the actual situation of the target area and the expected blasting effect. The explosive charge amount per multiple blast hole refers to the explosive charge amount per blast hole. For any two blast holes, the explosive charge amounts may be different. Correspondingly, the initial detonation time per multiple blast hole refers to the initial detonation time per blast hole. For any two blast holes, the initial detonation times may be different.

[0047] 203. In response to the blasting command for the target area, the data processing equipment acquires in real time the borehole data of multiple boreholes in the target area, the stress wave propagation data of the target area, and the laser scanning data of the target area. The blasting command is used to instruct blasting to be carried out according to the initial detonation time of the multiple boreholes. The borehole data includes borehole temperature, borehole pressure, and explosive filling amount.

[0048] The blasting command instructs the commencement of blasting the target area according to the initial detonation time specified in the blasting plan. If no adjustments are made to the blasting plan generated in step 202, it is assumed that the boreholes have been filled with explosives according to the plan's specified amount. Stress wave propagation data is acquired through stress wave data sensors; for example, it includes the time history curve of the stress wave. Laser scan data is obtained by scanning the target area using a laser detector; laser scan data is also a type of point cloud.

[0049] 204. The data processing equipment adjusts the initial detonation time of the target boreholes that have not yet been detonated among the multiple boreholes based on the borehole data, the geological data, the stress wave propagation data of the target area, the laser scanning data of the target area, and the expected blasting effect parameters.

[0050] The technical solution provided in this application acquires structured blasting data, unstructured blasting data, and expected blasting effect parameters of the target area to be blasted. Based on the structured blasting data, unstructured blasting data, and expected effect parameters, a blasting plan for the target area is generated, achieving automatic generation of the blasting plan. In response to the blasting command for the target area, borehole data, stress wave propagation data, and laser scanning data from multiple boreholes are acquired in real time. Using the borehole data, geological data, stress wave propagation data, laser scanning data, and expected blasting effect parameters, the initial detonation time of the target boreholes that have not yet been detonated is adjusted, thereby optimizing the blasting process and improving the blasting effect.

[0051] Steps 201-204 above are a brief introduction to the data analysis and processing method for single-stage well drilling and trenching blasting provided in the embodiments of this application. The following will provide a clearer explanation of the data analysis and processing method for single-stage well drilling and trenching blasting provided in the embodiments of this application, using some examples. See [link to relevant documentation]. Figure 3 Taking a data processing device as the executing entity as an example, the method includes the following steps.

[0052] 301. The data processing equipment acquires structured blasting data, unstructured blasting data, and expected blasting effect parameters of the target area to be blasted. The structured blasting data includes geological data, borehole attributes, and explosive parameters. The unstructured blasting data includes core images and ground-penetrating radar scan point clouds.

[0053] The target area is the region requiring a single well-drilling blast. This area includes multiple pre-drilled blast holes. Geological data describes the geological conditions of the target area. Typically, this data is obtained through geological surveys conducted by technical personnel before blasting. The geological data is stored in the storage medium of the data processing equipment and can be retrieved using the target area's identifier. Hole attributes describe the characteristics of the drilled holes, such as location and size. Explosive parameters include the explosive energy, which is the energy produced by a unit amount of explosive detonation. Explosive energy is related to the type of explosive and is stored in advance in the data processing equipment's storage medium. Core images are images of rock samples obtained through core drilling, used to analyze rock structure, composition, porosity, and other information. Ground-penetrating radar (GPR) point cloud is a point cloud obtained from GPR detection of the target area. Expected blasting effect parameters represent the expected blasting effect on the target area, i.e., a quantitative representation of the expected blasting effect. In some embodiments, the expected blasting effect parameters include the expected blasting area shape and the expected blasting rock size. The expected blasting area shape refers to the shape of the target area that is expected to be formed after the blasting is completed, and the expected blasting rock size refers to the size of the expected rock after the blasting is completed.

[0054] In one possible implementation, the data processing device uses the area identifier of the target area to query and obtain the geological data of the target area, the borehole attributes of multiple boreholes in the target area, explosive parameters, core images, ground-penetrating radar scan point clouds, and expected blasting effect parameters.

[0055] The geological data, core images, and ground-penetrating radar point clouds were all obtained by technicians through geological exploration of the target area before blasting. Multiple blast holes were drilled by technicians within the target area based on actual conditions; once these holes were formed, their attributes were determined. Explosive parameters are related to the type of explosive selected by the technicians; once the explosives used for blasting are determined, the explosive parameters can be finalized. The expected blasting effect parameters are configured by technicians according to requirements, and this application embodiment does not limit this. In this application embodiment, single-stage well-drilling and trenching blasting is represented by the expected blasting effect parameters. The technical solution provided in this application embodiment aims to closely approximate the actual blasting effect in the target area with these expected blasting effect parameters, thereby improving the blasting effect. In this application embodiment, all data related to blasting belongs to blasting data; for example, the aforementioned structured blasting data, unstructured blasting data, and expected blasting effect parameters all belong to blasting data.

[0056] In this implementation, by querying using the area identifier of the target area, the structured blasting data, unstructured blasting data, and expected blasting effect parameters of the target area can be obtained, resulting in high efficiency in acquiring the structured blasting data, unstructured blasting data, and expected blasting effect parameters.

[0057] In some embodiments, geological data includes P-wave propagation velocity, which is affected by various factors during blasting. For example, P-wave propagation velocity is related to the type of medium, the density of the medium, the elastic modulus of the medium, and the Poisson's ratio of the medium. Generally speaking, the greater the density of the medium, the faster the P-wave propagation velocity; the greater the elastic modulus of the medium, the faster the P-wave propagation velocity; and the smaller the Poisson's ratio of the medium, the faster the P-wave propagation velocity.

[0058] 302. The data processing equipment generates a three-dimensional geological model of the target area based on the core image and the ground-penetrating radar scan point cloud.

[0059] Among them, the three-dimensional geological model is a three-dimensional model that can be visualized. Through the three-dimensional geological model, the target area can be digitally represented. Data processing equipment can use the three-dimensional geological model to simulate the target area and facilitate the processing of data related to the target area.

[0060] In one possible implementation, the data processing device determines fracture information of the target area based on the core image. This fracture information represents the fracture rate, fracture size, and fracture density of the target area. The data processing device processes the ground-penetrating radar scan point cloud to generate an initial three-dimensional geological model of the target area. Based on the fracture information, the data processing device processes this initial three-dimensional geological model to generate a three-dimensional geological model of the target area.

[0061] In this context, "fracture" refers to cracks present in the target area, which can be considered as cracks in the rock mass within that area. "Fracturation ratio" is the ratio of the volume or area of ​​fractures in the rock mass to the total volume or area of ​​the rock mass, usually expressed as a percentage. "Fracturation size" describes the size of the fracture. "Fracturation density" refers to the number of fractures per unit area, usually expressed as fractures per square meter. It is an important indicator describing the density of fracture distribution in the rock mass. A higher fracture surface density indicates a denser fracture mass, and vice versa. It should be noted that multiple core images of the target area are used, and the fracture information is determined based on these multiple core images. "Ground-penetrating radar (GPR) point cloud" refers to the scan data obtained after GPR scans the target area. Processing the GPR point cloud involves performing 3D reconstruction to obtain an initial 3D geological model. Processing the initial 3D geological model based on fracture information aims to add fracture-related information to the initial 3D geological model, thereby improving the accuracy of the generated 3D geological model. Furthermore, in this embodiment, since there is a method for dynamically adjusting the borehole detonation time, the blasting scheme is not dependent on a highly accurate three-dimensional geological model. In actual engineering experiments, a three-dimensional geological model generated from core images and ground-penetrating radar point clouds can improve the blasting effect. Of course, with the development of science and technology and the improvement of data processing equipment capabilities, the accuracy of generating blasting schemes using a more accurate three-dimensional geological model is also higher, but this does not affect the implementation of the technical solution provided in this embodiment. In other words, step 302 above provides a low-cost method for generating a three-dimensional geological model for blasting.

[0062] In this implementation, core images and ground-penetrating radar scan point clouds are used to generate a three-dimensional geological model of the target area. The generation cost of the three-dimensional geological model is low and the efficiency is high.

[0063] To provide a clearer explanation of the above embodiments, the embodiments will be described in several parts below.

[0064] Part 1: The data processing equipment determines the fracture information of the target area based on the core image.

[0065] In one possible implementation, there are multiple core images, each corresponding to a different location within the target area. The data processing device inputs each core image into a fracture information recognition model, extracting features from each image to obtain its core image features. Based on these features, the data processing device uses the fracture information recognition model to determine fracture information at multiple locations within the target area.

[0066] In this embodiment, "location" refers to the location within the coordinate system corresponding to the target area. For example, the locations of the boreholes and the core images mentioned above are both within this coordinate system. The base model of the fracture information recognition model is an image recognition model. This model can identify fractures from core images, thereby determining fracture information by combining the fractures in the core images. This embodiment does not limit the structure and training method of the fracture information recognition model.

[0067] In this implementation, by using a fracture information recognition model to identify multiple core images, fracture information at multiple locations in the target area can be obtained, resulting in high efficiency in acquiring fracture information.

[0068] For example, the data processing equipment inputs multiple core images into a fracture information recognition model. The model performs multiple convolutions on each core image to obtain its core image features. Using this fracture information recognition model, the equipment identifies fractures in each core image. Based on these fractures, the equipment determines the fracture rate, fracture size, and fracture density for each core image.

[0069] It should be noted that since a core image corresponds to a location in the target area, the fracture rate, fracture size, and fracture density corresponding to the obtained core image are actually the fracture rate, fracture size, and fracture density at the corresponding location in the core image.

[0070] The second part involves data processing equipment processing the point cloud scanned by the ground-penetrating radar to generate an initial three-dimensional geological model of the target area.

[0071] In one possible implementation, the data processing device interpolates the ground-penetrating radar scan point cloud to obtain three-dimensional voxel mesh data of the target area. Based on the three-dimensional voxel mesh data of the target area, the data processing device generates an initial three-dimensional geological model of the target area.

[0072] Ground-penetrating radar (GPR) point clouds are typically discrete data. Interpolation of these point clouds transforms the discrete data into continuous data, facilitating the generation of 3D geological models. In some embodiments, interpolation methods include Kriging interpolation or inverse distance weighted interpolation, which are not limited in this application. 3D voxel grid data is a data structure used to represent objects or scenes in 3D space. Similar to pixels in a 2D image, a voxel is the smallest unit in 3D space, representing a small cubic region. These voxels are arranged according to a regular 3D grid to form a voxel grid, used to describe the shape, structure, and properties of an object.

[0073] In this implementation, by interpolating the point cloud of the ground-penetrating radar scan, three-dimensional voxel grid data of the target area can be obtained. The three-dimensional voxel grid data can be used to generate an initial three-dimensional geological model of the target area, and the generation efficiency of the initial three-dimensional geological model is relatively high.

[0074] For example, the data processing equipment denoises and filters the ground-penetrating radar (GPR) point cloud to obtain a reference GPR point cloud. The equipment then transforms the coordinates of the target GPR point cloud from the GPR coordinate system to the target region's coordinate system, obtaining the target GPR point cloud. Next, the equipment interpolates the target GPR point cloud to obtain a three-dimensional voxel mesh of the target region. Finally, the equipment performs three-dimensional reconstruction on this voxel mesh to obtain an initial three-dimensional geological model of the target region.

[0075] Denoising and filtering are used to eliminate outliers in the ground-penetrating radar point cloud. For example, filtering includes longitudinal filtering (such as bandpass filtering and overlay) and lateral filtering (such as background removal and gain compensation). Longitudinal filtering removes high-frequency noise and direct wave interference, while lateral filtering corrects signal attenuation. Three-dimensional reconstruction methods include the Marching Cubes algorithm and the Dual Contouring algorithm.

[0076] Part Three: The data processing equipment processes the initial three-dimensional geological model based on the fracture information to generate a three-dimensional geological model of the target area.

[0077] In one possible implementation, data processing is based on fracture information at multiple locations in the target area, and the corresponding fractures in the initial three-dimensional geological model are corrected to obtain a three-dimensional geological model of the target area.

[0078] The fracture information includes fracture ratio, fracture size, and fracture density. Using this information, fracture-related data can be added to the initial 3D geological model, thereby improving its accuracy. The reason for using fracture information to correct the initial 3D geological model is that the presence of fractures has a significant impact on the blasting effect during the blasting process. Adding fracture-related information to the initial 3D geological model helps improve the accuracy of the subsequently determined blasting plan.

[0079] 303. Based on the borehole attributes, geological data, and three-dimensional geological model, the data processing equipment determines multiple blasting energy transfer paths in the target area.

[0080] The borehole attributes include its location and size, while the blast energy transfer path refers to the path along which blast energy is transferred during the blast. It should be noted that the multiple blast energy transfer paths determined in step 303 are estimated paths. In the actual blasting process, energy may not be transferred strictly according to these paths; therefore, the detonation time of the borehole will be adjusted subsequently.

[0081] In one possible implementation, the data processing device adds an explosion source to the three-dimensional geological model based on the borehole location and size, obtaining a three-dimensional explosion source distribution model of the target area, where the explosion source is the borehole within the target area. The data processing device identifies the three-dimensional explosion source distribution model to obtain connectivity parameters between multiple boreholes within the target area, which represent the degree of connectivity. Based on the geological data and the connectivity parameters, the data processing device determines the multiple blasting energy transfer paths.

[0082] Since the boreholes are filled with explosives, they can be considered as blast sources. Connectivity parameters are used to indicate the connectivity between boreholes. For example, during the drilling process within the target area, two boreholes may be directly connected. Alternatively, although not directly connected, the presence of fissures within the target area may cause the two boreholes to be effectively or nearly connected. In some embodiments, the connectivity parameter between two boreholes is determined based on the connectivity area. For ease of processing, the connectivity parameter can be expressed as a percentage, with 100% representing complete connectivity and the largest connectivity area, and 0% representing complete non-connectivity. During blasting, the direction of blast energy transmission is related to the connectivity between boreholes and geological data. That is, blast energy will preferentially propagate towards connected boreholes. Furthermore, the direction of blast energy transmission is also influenced by geological properties, preferentially propagating towards weaker locations. Geological properties are represented by geological data.

[0083] In this implementation, based on the location and size of the boreholes, explosion sources are added to the three-dimensional geological model to obtain a three-dimensional explosion source distribution model. The three-dimensional explosion source distribution model is then identified to obtain the connectivity parameters between multiple boreholes within the target area. Using geological data and connectivity parameters, multiple blasting energy transfer paths are determined, thus achieving the estimation of the blasting energy transfer paths.

[0084] To provide a clearer explanation of the above embodiments, the following description is divided into several parts.

[0085] Part 1: The data processing equipment adds explosion sources to the three-dimensional geological model based on the location and size of the blast hole, thereby obtaining a three-dimensional explosion source distribution model of the target area.

[0086] In one possible implementation, the data processing device determines the voxels corresponding to each borehole in the three-dimensional geological model based on the borehole location. The data processing unit then adjusts the voxels corresponding to each borehole in the three-dimensional geological model based on the borehole size to obtain the three-dimensional explosion source distribution model.

[0087] The 3D explosion source distribution model, compared to the 3D geological model, only marks the location and size of the blast holes. Adjusting the voxels corresponding to the blast holes establishes a correlation between the multiple voxels corresponding to the blast holes and the blast holes themselves. Since the voxels corresponding to the blast holes were determined using the blast hole location in the previous step, and the voxels determined using the blast hole location are single voxels, while blast holes actually correspond to multiple voxels due to their size, adjusting the voxels corresponding to the blast holes is to mark all the voxels corresponding to the blast holes.

[0088] The second part involves the data processing equipment identifying the three-dimensional explosion source distribution model and obtaining the connectivity parameters between multiple blast holes within the target area.

[0089] In one possible implementation, the data processing device transforms the 3D explosion source distribution model into a graph network, which includes multiple nodes, each node corresponding to a borehole. The data processing device then divides the 3D explosion source distribution model into multiple sub-models, each sub-model corresponding to a borehole. The data processing device uses the model data corresponding to each sub-model and the borehole positions as node features for each corresponding node to obtain the target graph network. The data processing device performs graph convolution on the target network to obtain the weights between every two nodes, which are the connectivity parameters between the two boreholes.

[0090] The method for segmenting the three-dimensional explosion source distribution model is set by technicians according to the actual situation, and this application embodiment does not limit this. The graph convolution method can be the graph convolution method in related technologies, and this application embodiment does not limit this.

[0091] Part Three: The data processing equipment determines the multiple blasting energy transfer paths based on the geological data and the connectivity parameters.

[0092] In this context, a blast energy transfer path corresponds to a blast hole, and a blast energy transfer path refers to the path of blast energy transfer in a blast hole when only one blast hole explodes.

[0093] In one possible implementation, the geological data includes rock mass density, rock mass elastic modulus, and rock mass Poisson's ratio. For any one of the multiple boreholes, the data processing device determines the blasting energy transfer path of the borehole based on the borehole's connectivity parameters and the rock mass density, rock mass elastic modulus, and rock mass Poisson's ratio of the rock mass where the target borehole is located.

[0094] The energy transfer path is calculated by the simulation software after the data processing device inputs the connectivity parameters of the borehole and the rock mass density, rock mass elastic modulus and rock mass Poisson's ratio of the target borehole into the simulation software. For example, the simulation software can be a general finite element analysis software, such as LS-DYNA or ANSYS Autodyn, etc. This application embodiment does not limit this.

[0095] 304. The data processing equipment generates a blasting plan for the target area based on the borehole attributes, the three-dimensional geological model, geological data, the multiple blasting energy transfer paths, the explosive parameters, and the expected blasting effect parameters. The blasting plan includes the explosive filling amount and initial detonation time of multiple boreholes in the target area.

[0096] The blasting plan is determined based on structured blasting data, unstructured blasting data, and expected blasting effect parameters. The structured and unstructured blasting data reflect the conditions of the target area, while the expected blasting effect parameters reflect the desired blasting effect. The resulting blasting plan is well-matched with the actual situation of the target area and the expected blasting effect. The explosive charge amount per multiple blast hole refers to the explosive charge amount per blast hole. For any two blast holes, the explosive charge amounts may be different. Correspondingly, the initial detonation time per multiple blast hole refers to the initial detonation time per blast hole. For any two blast holes, the initial detonation times may be different.

[0097] In one possible implementation, the explosive parameters include the explosive energy, the expected blasting effect parameters include the expected blasting area shape and the expected blasting rock size, and the borehole attributes include the borehole location and borehole size. The data processing device determines the explosive requirement for the target area based on the three-dimensional geological model, the geological data, the explosive parameters, the expected blasting area shape, and the expected blasting rock size. The data processing device adds the borehole location, the borehole size, and the multiple blasting energy transfer paths to the three-dimensional geological model to obtain a three-dimensional energy transfer model of the target area. Based on the explosive requirement, the three-dimensional energy transfer model, and the expected blasting rock size, the data processing device determines the explosive filling amount for multiple boreholes within the target area. Based on the explosive filling amount for the multiple boreholes, the expected blasting area shape, and the three-dimensional energy transfer model, the data processing device determines the initial detonation time for the multiple boreholes within the target area.

[0098] The explosive demand in the target area refers to the sum of the explosive filling amounts of multiple blast holes in the target area. The three-dimensional energy transfer model is obtained by adding multiple blasting energy transfer paths to the three-dimensional geological model, and the starting point of multiple blasting energy transfer paths is the blast hole.

[0099] To provide a clearer explanation of the above embodiments, the following description is divided into several parts.

[0100] Part 1: The data processing equipment determines the explosive requirements for the target area based on the three-dimensional geological model, the geological data, the explosive parameters, the expected shape of the blasting area, and the expected size of the blasted rocks.

[0101] In one possible implementation, the data processing device determines the target amount of blasting rock in the target area based on the three-dimensional geological model and the shape of the expected blasting zone. Based on the target amount of blasting rock, the geological parameters, and the explosive parameters, the data processing device determines the initial explosive requirement and the initial size of the blasting rocks. Based on the expected size of the blasting rocks and the initial size of the blasting rocks, the data processing device determines a requirement correction factor. The data processing device uses this requirement correction factor to correct the initial explosive requirement, thus obtaining the explosive requirement for the target area.

[0102] The target blasting rock quantity refers to the amount of rock required to blast into the desired shape of the blasting area. The initial blasting rock size refers to the estimated rock size after blasting the target area according to the initial explosive demand. The demand correction factor is used to correct the initial explosive demand to obtain a more accurate explosive demand.

[0103] To provide a clearer explanation of the above embodiments, the following description will be divided into several parts.

[0104] A. The data processing equipment determines the target blasting rock quantity in the target area based on the three-dimensional geological model and the expected shape of the blasting area.

[0105] In one possible implementation, the data processing device adds a virtual boundary line to the three-dimensional geological model based on the shape and size of the expected blasting area. The shape enclosed by the virtual boundary line is the shape of the blasting area, and the size of the area enclosed by the virtual boundary line is the shape and size. The data processing device then superimposes the rock volume enclosed by the virtual boundary line to obtain the target blasting rock volume.

[0106] B. The data processing equipment determines the initial explosive demand and the initial blasting rock size based on the target blasting rock quantity, the geological parameters, and the explosive parameters.

[0107] In one possible implementation, the geological parameters include rock mass density, rock mass elastic modulus, and Poisson's ratio. A data processing device concatenates the target blasting rock quantity, the geological parameters, and the explosive parameters to obtain a predicted blasting rock quantity parameter. The data processing device inputs this predicted blasting rock quantity parameter into an explosive demand determination model. The model then extracts features from the predicted blasting rock quantity parameter to obtain a predicted blasting rock quantity feature corresponding to the parameter. The data processing device maps this predicted blasting rock quantity feature to the explosive demand determination model to obtain the initial explosive demand. Finally, the data processing device concatenates the initial explosive demand, the target blasting rock quantity, the geological parameters, and the explosive parameters to obtain a predicted blasting rock size parameter. This parameter is then input into a rock size determination model, which extracts features from the parameter to obtain a predicted blasting rock size feature corresponding to the parameter. The data processing equipment uses the rock size determination model to map the predicted features of the blasted rock size to obtain the initial blasted rock size.

[0108] Both the explosive demand determination model and the stone size determination model are regression models, which can map the input data to the initial explosive demand and the initial blasting stone size, respectively. This application embodiment does not limit the structure and training method of the explosive demand determination model and the stone size determination model.

[0109] For example, the data processing equipment concatenates the target blasting rock quantity, the geological parameters, and the explosive parameters to obtain blasting rock quantity prediction parameters. The data processing equipment inputs these blasting rock quantity prediction parameters into an explosive demand determination model. This model performs multiple full connections on the blasting rock quantity prediction parameters to obtain the corresponding blasting rock quantity prediction features. The data processing equipment then performs full connections and normalization on these blasting rock quantity prediction features using the explosive demand determination model to obtain the initial explosive demand. Finally, the data processing equipment concatenates the initial explosive demand, the target blasting rock quantity, the geological parameters, and the explosive parameters to obtain blasting rock size prediction parameters. This blasting rock size prediction parameter is then input into a rock size determination model. This model performs multiple full connections on the blasting rock size prediction parameters to obtain the corresponding blasting rock size prediction features. The data processing equipment uses the rock size determination model to perform full connection and normalization on the predicted features of the blasted rock size to obtain the initial blasted rock size.

[0110] C. The data processing equipment determines the demand correction factor based on the expected blasting rock size and the initial blasting rock size.

[0111] In one possible implementation, the data processing device subtracts the expected blasted rock size from the initial blasted rock size to obtain a rock size difference. Based on this rock size difference, the data processing device determines the demand correction factor.

[0112] To provide a clearer explanation of the above implementation method, the method for determining the demand correction coefficient based on the difference in stone size in the above implementation method will be explained below.

[0113] In some embodiments, the data processing device substitutes the stone size difference into the first relational data to obtain the demand correction coefficient. Alternatively, the data processing device uses the stone size difference to query the first relational table to obtain the demand correction coefficient.

[0114] The first relational data is a relational function used to represent the correspondence between the piece size difference and the demand correction coefficient. The first relational table stores multiple size difference ranges and the corresponding demand correction coefficients for each size difference range. Both the first relational data and the first relational table are set by technicians according to actual conditions, and this application embodiment does not limit this.

[0115] D. The data processing equipment uses the demand correction coefficient to correct the initial explosive demand to obtain the explosive demand for the target area.

[0116] In one possible implementation, the data processing device adds the demand correction factor to the target value and then corrects it with the initial explosive demand to obtain the explosive demand for the target area.

[0117] The target value is 1.

[0118] The second part involves the data processing equipment adding the location and size of the borehole, as well as the multiple blasting energy transfer paths, to the three-dimensional geological model to obtain a three-dimensional energy transfer model of the target area.

[0119] In one possible implementation, the data processing device adds explosion sources to the three-dimensional geological model based on the borehole location and size, obtaining a three-dimensional explosion source distribution model of the target area. The data processing device then adds multiple blast energy transfer paths to the three-dimensional explosion source distribution model, obtaining a three-dimensional energy transfer model of the target area.

[0120] The method of generating the three-dimensional explosion source distribution model is the same as the method in step 303 above. The implementation process will not be described again. If the three-dimensional explosion source distribution model has been generated in step 303, multiple blast energy transfer paths can be directly added to the three-dimensional explosion source distribution model to obtain the three-dimensional energy transfer model of the target area. There is no need to generate it again.

[0121] Part Three: The data processing equipment determines the amount of explosive to be filled in multiple blast holes within the target area based on the required amount of explosive, the three-dimensional energy transfer model, and the expected size of the blasted rocks.

[0122] In one possible implementation, the data processing device, constrained by the explosive demand and the three-dimensional energy transfer model, performs multiple iterations on the explosive filling amount for multiple boreholes. In each iteration, a predicted blasting rock size is determined based on the determined explosive filling amount. Before the next iteration, the difference between the predicted blasting rock size and the expected blasting rock size from the previous round is used to adjust the explosive demand in the borehole, thereby completing the current iteration.

[0123] The constraints, defined by the required explosive quantity and the three-dimensional energy transfer model, mean that the sum of the explosive filling amounts for multiple boreholes does not exceed the required explosive quantity. Simultaneously, the positions of the multiple boreholes and the blasting energy transfer paths are constrained by the three-dimensional energy transfer model. After one iteration, multiple explosive filling amounts are obtained, each corresponding to a borehole, representing the explosive filling amount determined for that borehole in that round. The data processing equipment uses the explosive filling amounts of the multiple boreholes determined in that round to perform blasting simulation, obtaining the predicted blasted rock size for that round. Based on the difference between the predicted blasted rock size and the expected blasted rock size, the data processing equipment adjusts the explosive filling amounts of the multiple boreholes, thus completing this iteration. This process is similar to the supervised learning process of the model; the explosive filling amounts of the boreholes can be considered as weights during model training. Through multiple iterations, the explosive filling amounts of multiple boreholes within the target area can be obtained.

[0124] Part Four: The data processing equipment determines the initial detonation time of multiple boreholes within the target area based on the explosive charge amount of the multiple boreholes, the expected shape of the blasting area, and the three-dimensional energy transfer model.

[0125] In one possible implementation, the data processing device uses the explosive charge and the three-dimensional energy transfer model as constraints to iterate the detonation time of multiple boreholes in multiple rounds. In each round of iteration, a predicted blasting area shape is determined based on the determined detonation time. Before the next round of iteration, the detonation time of the boreholes is adjusted using the deviation between the predicted blasting area shape and the expected blasting area shape from the previous round, thereby completing the current round of iteration.

[0126] The constraints of the explosive demand and the three-dimensional energy transfer model mean that the explosive filling amount of multiple boreholes remains constant, while the positions of the multiple boreholes and the blast energy transfer paths are constrained by the three-dimensional energy transfer model. After one iteration, multiple detonation times are obtained, each corresponding to a borehole, representing the detonation time of that borehole determined in this round. The data processing equipment uses the detonation times of the multiple boreholes determined in this round to perform blasting simulation and obtain the predicted blasting area shape for this round. Based on the deviation between the predicted blasting area shape and the expected blasting area shape, the data processing equipment adjusts the detonation times of the multiple boreholes to complete this iteration. The above process is similar to the supervised learning process of the model; the detonation time of the boreholes can be regarded as the weights during model training. Through multiple iterations, the detonation times of multiple boreholes within the target area can be obtained.

[0127] 305. In response to the blasting command for the target area, the data processing equipment acquires in real time the borehole data of multiple boreholes in the target area, the stress wave propagation data of the target area, and the laser scanning data of the target area. The blasting command is used to instruct blasting to be carried out according to the initial detonation time of the multiple boreholes. The borehole data includes borehole location, borehole temperature, borehole pressure, and explosive filling amount.

[0128] The blasting command instructs the commencement of blasting the target area according to the initial detonation time specified in the blasting plan. If no adjustments are made to the blasting plan generated in step 302, it is assumed that the boreholes have been filled with explosives according to the plan's specified amount. Stress wave propagation data is acquired through a stress wave data sensor; for example, it includes the stress wave's time history curve. Laser scan data is obtained by scanning the target area using a laser detector; laser scan data is also a type of point cloud.

[0129] In one possible implementation, a temperature sensor and a pressure sensor are installed inside the borehole to determine the borehole temperature and pressure in real time. The explosive charge amount in the borehole has already been determined in step 304 above and can be used directly. A laser detector continuously scans the target area to acquire laser scanning data. In addition, a stress wave data sensor is also installed inside the borehole, which can directly collect stress wave propagation data.

[0130] 306. Based on the stress wave propagation data and the geological data, the data processing equipment determines the remaining energy transferred from the blasted area within the target region to the target borehole.

[0131] Among them, the target blast hole is the blast hole that has not yet been detonated among multiple blast holes.

[0132] In one possible implementation, the stress wave propagation data includes a stress wave time history curve. A data processing device integrates the stress wave time history curve to obtain the initial energy of the blasted area. Based on the distance between the target borehole and the blasted area and the geological data, the data processing device determines the energy attenuation coefficient corresponding to the target borehole. Based on the initial energy and the energy attenuation coefficient, the data processing device determines the remaining energy transferred from the blasted area within the target region to the target borehole.

[0133] Stress waves are the propagation form of stress and strain disturbances, such as the wave phenomenon generated in a medium under dynamic loads like explosions, impacts, or earthquakes. Time history curves record the changes in dynamic parameters (such as velocity and acceleration) of stress waves over time using sensors (such as accelerometers or speedometers), forming a continuous waveform diagram.

[0134] For example, geological data includes rock mass density, rock mass elastic modulus, and Poisson's ratio. The data processing device integrates the stress wave time history curve using the following formula (1) to obtain the initial energy of the blasted area. The data processing device substitutes the distance between the target borehole and the blasted area and the geological data into the second relational data to obtain the energy attenuation coefficient corresponding to the target borehole. The data processing device multiplies the initial energy by the difference between the target value and the energy attenuation coefficient to obtain the remaining energy transferred from the blasted area within the target region to the target borehole.

[0135] The target value is 1, and the formula for determining the remaining energy can be simplified to initial energy - (1 - energy decay coefficient). The second relational data is a relational function used to represent the correspondence between distance and geological data and the energy decay coefficient. This second relational data is set by technicians according to the actual situation, and this application embodiment does not limit it.

[0136] in, Represents the initial energy. This represents the function representing the change in stress versus time in the stress wave time history curve. This represents the function representing the change in stress wave propagation velocity over time in the stress wave time history curve. Indicates the current time.

[0137] 307. The data processing equipment determines the rock displacement field of the target area based on the laser scanning data.

[0138] In one possible implementation, the data processing device extracts key points from the laser scan data to obtain multiple key points. Based on the initial and current positions of these multiple key points, the data processing device determines the rock displacement field corresponding to the target borehole.

[0139] The initial position of the key point is its location before blasting begins, while the current position is its location at the current time after blasting begins. The rock stratum displacement field is the collection of displacement vectors at different points within the rock mass at different times, characterizing the deformation state of the rock mass after being subjected to external forces or changes in internal stress. For example, when underground mining causes rock strata to bend, fracture, or collapse, the displacement vectors at different depths and in different regions will form a dynamically changing displacement field.

[0140] 308. The data processing equipment adjusts the initial detonation time of the target boreholes that have not yet been detonated among the multiple boreholes based on the borehole data, geological data, remaining energy, rock displacement field, and expected blasting effect parameters.

[0141] In one possible implementation, the geological data includes the P-wave propagation velocity, and the data processing device determines the strain tensor of the target borehole based on the rock displacement field. The data processing device then determines the initiation time variation based on the strain tensor, the P-wave propagation velocity, the borehole data, the remaining energy, and the expected blasting effect parameters. Finally, the data processing device adds the initial initiation time of the target borehole to the initiation time variation to obtain the target initiation time of the target borehole.

[0142] To provide a clearer explanation of the above embodiments, the following description is divided into several parts.

[0143] Part 1: The data processing equipment determines the strain tensor of the target borehole based on the displacement field of the rock strata.

[0144] Here, the strain tensor is a second-order symmetric tensor, whose components are formed by the symmetric parts of the displacement gradient tensor. For any point in the rock strata, if the displacement vector is... Then the components of the strain tensor are defined as follows: This expression reflects the normal strain (diagonal component) and shear strain (non-diagonal component) of a material element along different directions during deformation.

[0145] Furthermore, the rock strata displacement is directly related to the strain tensor through the displacement gradient tensor. The displacement gradient can be decomposed into a symmetric part (strain tensor). and antisymmetric part (rigid body rotation tensor) In this context, the strain tensor reflects only the pure deformation of the material, while rigid body rotation does not affect the strain. The spatial distribution of rock stratum displacement (displacement field) directly determines the distribution of the strain tensor. For example, during blasting, the inhomogeneity of the displacement field can lead to tensile, compressive, or shear strains within the rock strata, thereby forming delamination fissures or fracture zones.

[0146] The second part describes how the data processing equipment determines the change in detonation time based on the strain tensor, the longitudinal wave propagation velocity, the borehole data, the remaining energy, and the expected blasting effect parameters.

[0147] In one possible implementation, the data processing device determines the expected strain tensor and expected residual energy of the target borehole based on the expected blasting effect parameters and the borehole location in the borehole data. The data processing device determines the initial initiation time variation based on the tensor deviation between the strain tensor and the expected strain tensor, and the energy deviation between the residual energy and the expected residual energy. The data processing device corrects the initial initiation time variation using the longitudinal wave propagation velocity, borehole temperature, borehole pressure, and explosive charge amount to obtain the final initiation time variation.

[0148] To provide a clearer explanation of the above embodiments, the following description will be divided into several parts.

[0149] A. Based on the expected blasting effect parameters and the borehole location in the borehole data, the data processing equipment determines the expected strain tensor and expected residual energy of the target borehole.

[0150] In one possible implementation, the data processing device queries the expected blasting effect parameters and the borehole location to obtain the expected strain tensor and expected residual energy of the target borehole.

[0151] In this process, the expected strain tensor and expected residual energy of multiple blast holes when blasted with the expected blasting effect parameters are stored in advance by technicians in a storage medium. By querying the expected blasting effect parameters and the location of the blast hole, the expected strain tensor and expected residual energy of the target blast hole can be obtained.

[0152] B. The data processing equipment determines the change in initial detonation time based on the tensor deviation between the strain tensor and the expected strain tensor, and the energy deviation between the remaining energy and the expected remaining energy.

[0153] In one possible implementation, the data processing device substitutes the tensor deviation and the energy deviation into the third relational data to obtain the change in the initial detonation time.

[0154] The third relational data is a relational function used to represent the correspondence between the tensor deviation and the energy deviation and the change in the initial detonation time. The third relational data is set by technicians according to the actual situation, and this application embodiment does not limit it.

[0155] C. The data processing equipment uses the longitudinal wave propagation velocity, borehole temperature, borehole pressure, and explosive charge to correct the change in the initial detonation time, thus obtaining the change in detonation time.

[0156] In one possible implementation, the data processing device concatenates the P-wave propagation velocity, borehole temperature, borehole pressure, and explosive charge amount into a time correction coefficient determination parameter. The data processing device substitutes this time correction coefficient determination parameter into a time correction coefficient determination model, and extracts features from the parameter using this model to obtain the time correction coefficient determination feature. The data processing device then maps this feature to the time correction coefficient determination model to obtain the detonation time correction coefficient. Finally, the data processing device uses this detonation time correction coefficient to correct the initial detonation time change, obtaining the detonation time change.

[0157] The time correction coefficient determination model is a regression model. This model can map the time correction coefficient determination parameters to the detonation time correction coefficient. The reason why this time correction coefficient can be used to correct the change in the initial detonation time is that the energy transfer during the blasting process is also affected by the longitudinal wave propagation velocity, borehole temperature, borehole pressure, and explosive filling amount. The accuracy of the detonation time change obtained by using this time correction coefficient to correct the change in the initial detonation time is relatively high.

[0158] For example, the data processing equipment concatenates the P-wave propagation velocity, borehole temperature, borehole pressure, and explosive charge amount into parameters for determining the time correction coefficient. The equipment then substitutes these parameters into a time correction coefficient determination model, performing multiple full connections on the model to obtain the time correction coefficient determination characteristics. Using this model, the equipment performs full ranking and normalization on these characteristics to obtain the detonation time correction coefficient. Finally, the equipment adds this detonation time correction coefficient to the target value and multiplies it by the initial detonation time change to obtain the detonation time change.

[0159] The target value is 1.

[0160] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0161] The technical solution provided in this application acquires structured blasting data, unstructured blasting data, and expected blasting effect parameters of the target area to be blasted. Based on the structured blasting data, unstructured blasting data, and expected effect parameters, a blasting plan for the target area is generated, achieving automatic generation of the blasting plan. In response to the blasting command for the target area, borehole data, stress wave propagation data, and laser scanning data from multiple boreholes are acquired in real time. Using the borehole data, geological data, stress wave propagation data, laser scanning data, and expected blasting effect parameters, the initial detonation time of the target boreholes that have not yet been detonated is adjusted, thereby optimizing the blasting process and improving the blasting effect.

[0162] Figure 4 This is a schematic diagram of a data analysis and processing system for single-stage well drilling and trenching blasting, provided in an embodiment of this application. See also... Figure 4 The system includes: a first acquisition module 401, a blasting scheme generation module 402, a second acquisition module 403, and a time adjustment module 404.

[0163] The first acquisition module 401 is used to acquire structured blasting data, unstructured blasting data and expected blasting effect parameters of the target area to be blasted. The structured blasting data includes geological data, borehole attributes and explosive parameters, and the unstructured blasting data includes core images and ground-penetrating radar scan point clouds.

[0164] The blasting scheme generation module 402 is used to generate a blasting scheme for the target area based on the structured blasting data, the unstructured blasting data, and the expected blasting effect parameters. The blasting scheme includes the amount of explosive filling for multiple blast holes in the target area and the initial detonation time.

[0165] The second acquisition module 403 is used to acquire, in real time, borehole data of multiple boreholes in the target area, stress wave propagation data of the target area, and laser scanning data of the target area in response to the blasting command for the target area. The blasting command is used to instruct blasting to be carried out according to the initial detonation time of the multiple boreholes. The borehole data includes borehole location, borehole temperature, borehole pressure, and explosive filling amount.

[0166] The time adjustment module 404 is used to adjust the initial detonation time of the target boreholes that have not yet been detonated among the multiple boreholes based on the borehole data, the geological data, the stress wave propagation data of the target area, the laser scan data of the target area, and the expected blasting effect parameters.

[0167] In one possible implementation, the blasting scheme generation module 402 is used to generate a three-dimensional geological model of the target area based on the core image and the ground-penetrating radar scan point cloud. Based on the borehole attributes, the geological data, and the three-dimensional geological model, multiple blasting energy transfer paths for the target area are determined. Based on the borehole attributes, the three-dimensional geological model, the geological data, the multiple blasting energy transfer paths, the explosive parameters, and the expected blasting effect parameters, a blasting scheme for the target area is generated.

[0168] In one possible implementation, the blasting scheme generation module 402 is used to determine the fracture information of the target area based on the core image. This fracture information represents the fracture rate, fracture size, and fracture density of the target area. The ground-penetrating radar scan point cloud is processed to generate an initial three-dimensional geological model of the target area. Based on the fracture information, this initial three-dimensional geological model is further processed to generate a three-dimensional geological model of the target area.

[0169] In one possible implementation, the borehole attributes include borehole location and borehole size. The blasting scheme generation module 402 is used to add explosion sources to the three-dimensional geological model based on the borehole location and borehole size, obtaining a three-dimensional explosion source distribution model of the target area, where the explosion sources are the boreholes within the target area. The three-dimensional explosion source distribution model is then identified to obtain connectivity parameters between multiple boreholes within the target area, which represent the degree of connectivity. Based on the geological data and the connectivity parameters, the multiple blasting energy transfer paths are determined.

[0170] In one possible implementation, the explosive parameters include explosive energy, the expected blasting effect parameters include the expected blasting area shape and the expected blasting rock size, and the borehole attributes include borehole location and borehole size. The blasting scheme generation module 402 is used to determine the explosive requirement for the target area based on the three-dimensional geological model, the geological data, the explosive parameters, the expected blasting area shape, and the expected blasting rock size. The borehole location, borehole size, and multiple blasting energy transfer paths are added to the three-dimensional geological model to obtain a three-dimensional energy transfer model of the target area. Based on the explosive requirement, the three-dimensional energy transfer model, and the expected blasting rock size, the explosive filling amount for multiple boreholes within the target area is determined. Based on the explosive filling amount for multiple boreholes, the expected blasting area shape, and the three-dimensional energy transfer model, the initial detonation time for multiple boreholes within the target area is determined.

[0171] In one possible implementation, the time adjustment module 404 is used to determine, based on the stress wave propagation data and the geological data, the remaining energy transferred from the blasted area within the target region to the target borehole. Based on the laser scanning data, it determines the rock strata displacement field of the target region. Based on the borehole data, the geological data, the remaining energy, the rock strata displacement field, and the expected blasting effect parameters, it adjusts the initial detonation time of the target borehole.

[0172] In one possible implementation, the stress wave propagation data includes a stress wave time history curve. The time adjustment module 404 is used to integrate the stress wave time history curve to obtain the initial energy of the blasted area. Based on the distance between the target borehole and the blasted area and the geological data, the energy attenuation coefficient corresponding to the target borehole is determined. Based on the initial energy and the energy attenuation coefficient, the remaining energy transferred from the blasted area within the target region to the target borehole is determined.

[0173] In one possible implementation, the time adjustment module 404 is used to extract key points from the laser scanning data to obtain multiple key points in the laser scanning data. Based on the initial and current positions of these multiple key points, the rock displacement field corresponding to the target borehole is determined.

[0174] In one possible implementation, the geological data includes the P-wave propagation velocity. The time adjustment module 404 is used to determine the strain tensor of the target borehole based on the rock displacement field. Based on the strain tensor, the P-wave propagation velocity, the borehole data, the remaining energy, and the expected blasting effect parameters, the initiation time variation is determined. The initial initiation time of the target borehole is added to the initiation time variation to obtain the target initiation time of the target borehole.

[0175] It should be noted that the above-described embodiment of the data analysis and processing system for single-stage well trenching blasting is only illustrated by the division of the functional modules described above. In practical applications, the functions can be assigned to different functional modules as needed, that is, the internal structure of the computer equipment can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the data analysis and processing system for single-stage well trenching blasting provided in the above embodiment and the method embodiment for data analysis and processing of single-stage well trenching blasting belong to the same concept, and their specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0176] The technical solution provided in this application acquires structured blasting data, unstructured blasting data, and expected blasting effect parameters of the target area to be blasted. Based on the structured blasting data, unstructured blasting data, and expected effect parameters, a blasting plan for the target area is generated, achieving automatic generation of the blasting plan. In response to the blasting command for the target area, borehole data, stress wave propagation data, and laser scanning data from multiple boreholes are acquired in real time. Using the borehole data, geological data, stress wave propagation data, laser scanning data, and expected blasting effect parameters, the initial detonation time of the target boreholes that have not yet been detonated is adjusted, thereby optimizing the blasting process and improving the blasting effect.

[0177] Figure 5 This is a schematic diagram of a computer device provided in an embodiment of this application. This computer device can be implemented as the aforementioned data processing device. The computer device 500 can vary significantly due to different configurations or performance. It may include one or more Central Processing Units (CPUs) 501 and one or more memories 502. The one or more memories 502 store at least one computer program, which is loaded and executed by the one or more processors 501 to implement the methods provided in the various method embodiments described above. Of course, the computer device 500 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The computer device 500 may also include other components for implementing device functions, which will not be elaborated here.

[0178] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including a computer program that can be executed by a processor to perform the data analysis and processing method for single-stage well trenching blasting in the above embodiments. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, or optical data storage device, etc.

[0179] In an exemplary embodiment, a computer program product or computer program is also provided, which includes program code stored in a computer-readable storage medium. The processor of a computer device reads the program code from the computer-readable storage medium and executes the program code, causing the computer device to perform the above-described method for analyzing and processing data from a single-well trenching blasting operation.

[0180] In some embodiments, the computer program involved in the present application embodiments may be deployed and executed on a computer device, or executed on multiple computer devices located in one location, or executed on multiple computer devices distributed in multiple locations and interconnected through a communication network. Multiple computer devices distributed in multiple locations and interconnected through a communication network may constitute a blockchain system.

[0181] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0182] The above are merely optional embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for analyzing and processing data of a once-in-a-while well slotting blasting, characterized in that, The method comprises: obtaining structured blasting data, unstructured blasting data and expected blasting effect parameters of a target region to be blasted, the structured blasting data comprising geological data, blast hole attributes of blast holes and explosive parameters, the unstructured blasting data comprising core images and geological radar scanning point clouds; generating a blasting scheme of the target region based on the structured blasting data, the unstructured blasting data and the expected blasting effect parameters, the blasting scheme comprising explosive filling amounts of a plurality of blast holes in the target region and initial initiation times of the blast holes; in response to a blasting instruction for the target region, obtaining blast hole data of the plurality of blast holes in the target region, stress wave propagation data of the target region and laser scanning data of the target region in real time, the blasting instruction being used to instruct blasting according to the initial initiation times of the plurality of blast holes, the blast hole data comprising blast hole positions, blast hole temperatures, blast hole pressures and explosive filling amounts; adjusting the initial initiation times of target blast holes that have not been initiated among the plurality of blast holes based on the blast hole data of the plurality of blast holes, the geological data, the stress wave propagation data of the target region, the laser scanning data of the target region and the expected blasting effect parameters.

2. The method of claim 1, wherein, The generating of the blasting scheme of the target region based on the structured blasting data, the unstructured blasting data and the expected blasting effect parameters comprises: generating a three-dimensional geological model of the target region based on the core images and the geological radar scanning point clouds; determining a plurality of blasting energy transmission paths of the target region based on the blast hole attributes, the geological data and the three-dimensional geological model; generating the blasting scheme of the target region based on the blast hole attributes, the three-dimensional geological model, the geological data, the plurality of blasting energy transmission paths, the explosive parameters and the expected blasting effect parameters.

3. The method of claim 2, wherein, The generating of the three-dimensional geological model of the target region based on the core images and the geological radar scanning point clouds comprises: determining fissure information of the target region based on the core images, the fissure information being used to represent fissure rates, fissure sizes and fissure densities of the target region; processing the geological radar scanning point clouds to generate an initial three-dimensional geological model of the target region; processing the initial three-dimensional geological model based on the fissure information to generate the three-dimensional geological model of the target region.

4. The method of claim 2, wherein, The blast hole attributes comprise blast hole positions and blast hole sizes, and the determining of the plurality of blasting energy transmission paths of the target region based on the blast hole attributes, the geological data and the three-dimensional geological model comprises: adding explosion sources in the three-dimensional geological model based on the blast hole positions and the blast hole sizes to obtain a three-dimensional explosion source distribution model of the target region, the explosion sources being the blast holes in the target region; identifying the three-dimensional explosion source distribution model to obtain connectivity parameters between the plurality of blast holes in the target region, the connectivity parameters being used to represent connectivity degrees; determining the plurality of blasting energy transmission paths based on the geological data and the connectivity parameters.

5. The method of claim 2, wherein, The explosive parameters include explosive explosion energy, the expected blasting effect parameters include expected blasting area shape and expected blasting rock size, the blast hole attributes include blast hole position and blast hole size, the blasting scheme of the target area is generated based on the blast hole attributes, the three-dimensional geological model, the geological data, the plurality of blasting energy transmission paths, the explosive parameters and the expected blasting effect parameters, including: Based on the three-dimensional geological model, the geological data, the explosive parameters, the expected blasting area shape and the expected blasting rock size, determine the explosive demand of the target area; Add the blast hole position, the blast hole size and the plurality of blasting energy transmission paths to the three-dimensional geological model to obtain a three-dimensional energy transmission model of the target area; Based on the explosive demand, the three-dimensional energy transmission model and the expected blasting rock size, determine the explosive filling amount of the plurality of blast holes in the target area; Based on the explosive filling amount of the plurality of blast holes, the expected blasting area shape and the three-dimensional energy transmission model, determine the initial detonation time of the plurality of blast holes in the target area.

6. The method of claim 1, wherein, The initial detonation time of the target blast hole which has not been detonated in the plurality of blast holes is adjusted based on the blast hole data of the plurality of blast holes, the geological data, the stress wave propagation data of the target area, the laser scanning data of the target area and the expected blasting effect parameters, including: Based on the stress wave propagation data and the geological data, determine the residual energy of the energy transmission from the blasted area in the target area to the target blast hole; Based on the laser scanning data, determine the rock displacement field of the target area; Based on the blast hole data of the target blast hole, the geological data, the residual energy, the rock displacement field and the expected blasting effect parameters, adjust the initial detonation time of the target blast hole.

7. The method of claim 6, wherein, The stress wave propagation data includes stress wave time history curve, and the residual energy of the energy transmission from the blasted area in the target area to the target blast hole is determined based on the stress wave propagation data and the geological data, including: Integrate the stress wave time history curve to obtain the initial energy of the blasted area; Based on the distance between the target blast hole and the blasted area and the geological data, determine the energy attenuation coefficient corresponding to the target blast hole; Based on the initial energy and the energy attenuation coefficient, determine the residual energy of the energy transmission from the blasted area in the target area to the target blast hole.

8. The method of claim 6, wherein, Based on the laser scanning data, determine the rock displacement field corresponding to the target blast hole, including: Key point extraction is performed on the laser scanning data to obtain a plurality of key points in the laser scanning data; Based on the initial position and the current position of the plurality of key points, determine the rock displacement field corresponding to the target blast hole.

9. The method of claim 6, wherein, The geological data includes P-wave propagation velocity, and the initial detonation time of the target blast hole is adjusted based on the blast hole data of the target blast hole, the geological data, the residual energy, the rock displacement field and the expected blasting effect parameters, including: determine a strain tensor of a target blast hole based on the rock stratum displacement field; determine a change in a detonation time of the target blast hole based on the strain tensor, the longitudinal wave propagation velocity, the blast hole data, the residual energy, and the expected blast effect parameter; add the initial detonation time of the target blast hole and the change in the detonation time to obtain a target detonation time of the target blast hole.

10. A system for analyzing and processing data of a one-time well hole and slot blasting, comprising: a first obtaining module configured to obtain structured blasting data, unstructured blasting data, and an expected blast effect parameter of a target region to be blasted, wherein the structured blasting data comprises geological data, blast hole attributes of blast holes, and explosive parameters, and the unstructured blasting data comprises core images and geological radar scanning point clouds; a blasting scheme generation module configured to generate a blasting scheme of the target region based on the structured blasting data, the unstructured blasting data, and the expected blast effect parameter, wherein the blasting scheme comprises explosive filling amounts and initial detonation times of a plurality of blast holes in the target region; a second obtaining module configured to, in response to a blasting instruction of the target region, obtain blast hole data of the plurality of blast holes, stress wave propagation data of the target region, and laser scanning data of the target region in real time, wherein the blasting instruction is used to instruct blasting according to the initial detonation times of the plurality of blast holes, and the blast hole data comprises blast hole positions, blast hole temperatures, blast hole pressures, and explosive filling amounts; a time adjustment module configured to adjust the initial detonation time of a target blast hole that has not been detonated among the plurality of blast holes based on the blast hole data of the plurality of blast holes, the geological data, the stress wave propagation data of the target region, the laser scanning data of the target region, and the expected blast effect parameter.

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