Portable laser hole inspection equipment, application device and detection method

Abstract

This application discloses a portable laser borehole inspection device, application apparatus, and detection method, relating to the field of mining and blasting technology. The device includes a handle, an RTK positioning module, a lidar module, a temperature measurement module, and a data storage card. The RTK positioning module, lidar module, and temperature measurement module are connected to the data storage card. The RTK positioning module, temperature measurement module, and lidar module are all mounted on the handle. The lidar module is located on the data storage card. The RTK positioning module is used to acquire geographical location and attitude information during inspection. The lidar module is used to acquire point cloud data. The temperature measurement module is used to acquire temperature data. The data storage card is used to store point cloud data, geographical location and attitude information during inspection, and temperature data corresponding to the point cloud data. This application can solve the problems of existing technologies, such as inaccurate acquisition of borehole temperature distribution, inaccurate borehole depth measurement, inability to acquire borehole wall integrity information, low detection efficiency, and lack of digital recording.

Description

Technical Field

[0001] This application relates to the field of mining blasting technology, and in particular to a portable laser borehole inspection device, application apparatus and detection method. Background Technology

[0002] In open-pit mine blasting operations, the quality inspection of blasting holes is a crucial step in ensuring blasting effectiveness and safety. Parameters such as water level, depth, and temperature within the blasting hole directly affect blasting results and safety.

[0003] Currently, the traditional method for detecting blast holes involves using a measuring tape with a stone attached and dropping it into the hole to check for water, followed by measuring the temperature of the blast hole with a temperature gun. Based on this, the existing detection method has the following problems: (1) The operation method is primitive and backward, and it is impossible to accurately determine the specific location of the water level. It can only determine whether there is water in the hole; (2) The temperature measurement can only measure the temperature at the top of the blast hole and cannot obtain the temperature distribution inside the hole, so the temperature measurement accuracy is not high. (3) The lack of accurate hole depth measurement methods affects the calculation of the charge amount; (4) Information on the integrity of the borehole wall cannot be obtained, posing a safety hazard; (5) The detection efficiency is low and manual operation poses safety risks; (6) Lack of digital records is not conducive to quality control and subsequent analysis. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a portable laser hole inspection device, application apparatus, and detection method.

[0005] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a portable laser hole inspection device, including: a handle, an RTK positioning module, a lidar module, a temperature measurement module, and a data storage card; The RTK positioning module, the lidar module, and the temperature measurement module are all connected to the data storage card; the RTK positioning module, the temperature measurement module, and the lidar module are all mounted on the handle; the lidar module is located on the data storage card. The RTK positioning module is used to acquire geographical location and attitude information during detection; the lidar module is used to acquire point cloud data; the temperature measurement module is used to acquire temperature measurement data; and the data storage card is used to store the point cloud data, geographical location and attitude information during detection, and temperature data corresponding to the point cloud data.

[0006] Secondly, this application provides a blast hole detection device, including: a data processing system and the portable laser hole inspection device provided above; The data processing system is connected to the portable laser borehole inspection device; the portable laser borehole inspection device is used to acquire detection data of the blasting borehole; the detection data includes point cloud data, geographical location and attitude information during detection, and temperature data corresponding to the point cloud data; The data processing system is used to read the detection data and generate multidimensional detection results based on the detection data; the multidimensional detection results include the depth of the blast hole, the water level in the blast hole, the temperature of the blast hole, and the morphology of the hole wall.

[0007] Thirdly, this application provides a method for detecting blast holes, applied to the aforementioned blast hole detection device; the method includes: Acquire detection data of the blast hole; the detection data includes point cloud data, geographical location and attitude information at the time of detection, and temperature data corresponding to the point cloud data; Based on the detection data, attitude correction and tilt compensation are performed to obtain corrected point cloud data; Point cloud fusion and coordinate assignment are performed based on the corrected point cloud data to form a three-dimensional point cloud model of the blast hole; the three-dimensional point cloud model includes the hole depth and hole wall morphology of the blast hole. The point cloud reflectance is determined based on the detection data, and the water level in the blast hole is determined based on the point cloud reflectance. The temperature data is calibrated according to the probe depth in the portable laser borehole inspection device. Based on the calibrated temperature data, each depth position is correlated with the point cloud axis coordinates. The temperature at any depth in the blast hole is determined by linear interpolation. The temperature at any depth is then mapped into the three-dimensional point cloud model to form a temperature-spatial joint distribution model.

[0008] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a portable laser borehole inspection device, application apparatus, and detection method. By mounting the RTK positioning module, temperature measurement module, and lidar module on a handle, the portable laser borehole inspection device can easily probe into the blast hole, realizing the detection of point cloud data, geographical location and attitude information during detection, and temperature data corresponding to the point cloud data. This solves the problems of existing technologies where temperature measurement can only measure the temperature at the top of the blast hole, failing to obtain the temperature distribution inside the hole, resulting in insufficient temperature measurement accuracy, inability to obtain hole wall integrity information, and low detection efficiency. By setting up a data storage card and data processing system, the water level, hole depth, and hole wall information can be accurately determined based on the data detected by the portable laser borehole inspection device, improving detection efficiency while solving the problem of lack of digital recording in existing technologies. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in 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.

[0010] Figure 1 This is a schematic diagram of the overall structure of a portable laser hole inspection device provided in an embodiment of this application; Figure 2 A schematic diagram of the operation of a portable laser hole inspection device provided in an embodiment of this application; Figure 3 This is a data processing flowchart of a portable laser hole inspection device provided in an embodiment of this application; Figure 4 A circuit diagram of the power supply system in a portable laser hole inspection device provided in an embodiment of this application; Figure 5 This is a schematic diagram illustrating a typical application scenario provided in an embodiment of this application; Figure 6 This is a flowchart illustrating a method for detecting blast holes according to an embodiment of this application. Figure 7 A schematic diagram of the processing flow of each core algorithm in the data processing system provided in an embodiment of this application. Reference numerals: 1. Handle; 2. Power supply system; 3. RTK positioning module; 4. LiDAR module; 5. Protective cover; 6. Temperature measurement module; 7. Data storage card; 8. Bursting hole; 9. Water level line. Detailed Implementation

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0013] In one exemplary embodiment, this application provides a portable laser hole inspection device (hereinafter referred to as the device). Figure 1 As shown, the device includes: a handle 1, an RTK positioning module 3, a lidar module 4, a temperature measurement module 6, and a data storage card 7.

[0014] RTK positioning module 3, LiDAR module 4, and temperature measurement module 6 are all connected to data storage card 7. RTK positioning module 3, temperature measurement module 6, and LiDAR module 4 are all mounted on handle 1. The LiDAR module 4 is located on data storage card 7.

[0015] RTK positioning module 3 is used to acquire geographical location and attitude information during detection. LiDAR module 4 is used to acquire point cloud data. Temperature measurement module 6 is used to acquire temperature measurement data. Data storage card 7 is used to store point cloud data, geographical location and attitude information during detection, and temperature data corresponding to the point cloud data.

[0016] In practical applications, the lidar module 4 can use the MID-360 lidar, which has a 360° horizontal field of view and a 59° vertical field of view, with a detection range of 0.1-100 meters. The RTK positioning module 3 can provide a high-precision coordinate reference for the lidar point cloud data. The temperature measurement module 6 uses a flexible probe to penetrate deep into the borehole for temperature detection. The data processing system integrates an inclined installation compensation algorithm to achieve multi-sensor data fusion. Based on this, a detailed introduction of each module is as follows: (1) LiDAR module 4.

[0017] The lidar module 4 employs hybrid solid-state technology, with a laser wavelength of 905nm, a spot rate of 200,000 points / second, a minimum detection distance of 0.1 meters, and a reflectivity detection range of 0-255. The lidar module 4 measures 65×65×60mm and weighs 265g. Its built-in IMU can provide 200Hz attitude data.

[0018] A protective cover 5 can be installed inside the LiDAR module 4. For example, the protective cover 5 can be made of transparent polycarbonate material with an IP67 protection rating, completely covering the LiDAR module 4 while ensuring an unobstructed 360° scanning field of view. For example, the protective cover 5, made of transparent polycarbonate material, is fixed to the handle 1 housing via screws connected to the front flange. The outer diameter of the protective cover 5 is 70mm, the length is 80mm, and the thickness is 3mm. The flange has an outer diameter of 75mm and a thickness of 4mm, and is fixed with four M3×8 stainless steel screws. The outer edge of the protective cover 5 has a 2mm thick silicone sealing ring, and the interior has a 10mm wide annular support ring to stabilize the LiDAR module 4 and prevent vibration. A 65mm diameter optical window is provided at the front of the protective cover 5 to ensure an unobstructed 360° scanning field of view for the radar.

[0019] In practical applications, the lidar module 4 can use the DJI MID-360 lidar, installed at a 15° tilt angle, so that the laser beam can effectively irradiate the inner wall of the borehole 8, avoiding the scanning blind zone when installed vertically.

[0020] (2) RTK positioning module 3.

[0021] The RTK positioning module 3 has a positioning accuracy of ±2cm and is time-synchronized with the LiDAR module 4, assigning precise coordinate information to each laser point. The RTK positioning module 3 and LiDAR module 4 achieve high-precision synchronous positioning through a unified time reference and data fusion algorithm. The device can be configured with a main control unit as the system's time core. The RTK positioning module 3 outputs a standard PPS pulse signal per second, providing the device with a microsecond-level time reference. The main control unit uses this as a reference to synchronize the working timing of various sensors (such as the RTK positioning module 3, LiDAR module 4, and temperature measurement module 6). When the LiDAR module 4 begins scanning, the main control unit sends a synchronization trigger signal to it, ensuring that each frame of point cloud data carries a unified timestamp. Simultaneously, the same timestamp is also recorded in the positioning data packet output by the RTK positioning module 3.

[0022] Based on this, during the actual detection of blast holes, the data in data storage card 7 can be transmitted to the data processing system. When receiving point cloud data, the system matches the RTK coordinates and attitude information at the corresponding time based on the timestamp, and converts the point cloud data in the radar coordinate system to the geographic coordinate system through a coordinate transformation matrix, achieving a one-to-one spatial correspondence. The formula for this process is P. geo (t)=R RTK (t)×P lidar (t)+T RTK (t), where R RTK (t) is the attitude rotation matrix, T RTK (t) is the geographic displacement vector, P geo (t) represents the coordinates in the geographic coordinate system, P lidar (t) represents the point cloud coordinates in the radar coordinate system. If the RTK signal experiences a momentary interruption, the device can use IMU data for short-term prediction compensation to ensure that the synchronization error does not exceed 10ms, ultimately achieving a geographic coordinate accuracy of ±2cm for each laser point.

[0023] (3) Temperature measurement module 6.

[0024] The temperature measurement module 6 can employ a flexible temperature probe. The flexible temperature probe consists of multiple sections of bendable stainless steel serpentine sheaths and internal multi-core high-temperature, bend-resistant wires, with an outer layer of waterproof and wear-resistant fluororubber. Through this multi-layered composite structure, the flexible temperature probe can achieve multi-directional bending and deformation while ensuring stable heat conduction and signal transmission, thus adapting to borehole shapes of different depths and angles. Its advantages are: 1. It can conform to the curved shape of the channel, avoiding the rigid probe from getting stuck or damaging the hole wall during insertion.

[0025] 2. Improve the safety and ease of operation of temperature measurement, making it easy for a single person to carry and deploy.

[0026] 3. Even in complex terrain or under inclined borehole conditions, the probe tip can still be kept in contact with the bottom of the borehole or the water surface to achieve continuous sampling of temperature distribution.

[0027] 4. Improves temperature measurement accuracy and data integrity, reducing errors caused by poor local contact with the borehole wall. It can penetrate up to 15 meters into the borehole, with a temperature measurement range of -20°C to 80°C and an accuracy of ±0.5°C.

[0028] Based on the above description, the lidar module 4, RTK positioning module 3, and temperature measurement module 6 respectively undertake three tasks: spatial structure mapping, geographic positioning, and temperature detection. In practical applications, their collaborative work aims to achieve three-dimensional quantitative detection of the spatial morphology, water level depth, and temperature distribution of the blast hole. The lidar module 4 is mainly used to acquire spatial point cloud information inside the blast hole. Through high-speed rotation scanning, it forms raw point cloud data containing hundreds of thousands of three-dimensional coordinate points. Each point records its relative coordinates (X, Y, Z) and reflectivity, thus reflecting the morphology of the blast hole and water surface characteristics. The RTK positioning module 3 provides the device's geographic coordinates (latitude, longitude, elevation) and attitude angles (pitch, roll, azimuth) at the moment of detection, synchronized with the lidar timestamp, to achieve absolute coordinate assignment of the point cloud data, that is, converting the radar relative coordinates into precise position coordinates in the geographic reference coordinate system. The temperature measurement module 6 collects temperature gradient data along the hole depth direction using a flexible probe, providing longitudinal physical parameter support for subsequent temperature distribution modeling.

[0029] In another exemplary embodiment of this application, the portable laser inspection device provided in this application may also be equipped with a power supply system 2. The power supply system 2 may use a lithium battery pack (24V / 5000mAh), with a nominal voltage of 24V, a capacity of 5000mAh, a battery life of ≥1 hour, and overcurrent protection and temperature monitoring functions. Specifically, the power consumption of the lidar module 4 is 8W (normal) / 14W (low temperature self-heating), the power consumption of the RTK positioning module 3 is 2W, the power consumption of the temperature measurement module 6 is 0.5W, and the power consumption of data processing is 3W. The power consumption is ≤15W, and the battery life is ≥1 hour. The power supply system 2 is also equipped with overcurrent, overvoltage, and overtemperature protection functions.

[0030] Power supply system 2 provides a stable power supply to each module. RTK positioning module 3 establishes centimeter-level positioning and outputs a time synchronization signal. LiDAR module 4 performs a 360° scan to acquire point cloud data within the borehole. Temperature measurement module 6 synchronously collects temperature values ​​at different depths. Based on this, in practical applications, the data processing system fuses the point cloud according to RTK coordinates and timestamps, and corrects equipment attitude errors through a tilt compensation algorithm to generate a high-precision geographic point cloud. Subsequently, it automatically identifies the water level position using reflectivity characteristics and interpolates the temperature data with the Z-axis coordinates of the point cloud to achieve temperature distribution mapping. Finally, the system comprehensively outputs results such as borehole depth, water level, temperature, and borehole wall morphology for blasting borehole quality assessment. Protective cover 5 provides dustproof, waterproof, and optical protection throughout the process, ensuring stable operation of the equipment in harsh environments. The circuitry of power supply system 2 is as follows: Figure 4 As shown, power supply system 2 mainly includes a battery pack, a voltage regulator module, a protection circuit, and a power supply interface. The battery pack is a 24V lithium battery module with built-in overcharge, over-discharge, and short-circuit protection units. The voltage regulator module outputs stable 5V, 12V, and 24V voltages to drive the RTK positioning module 3, the LiDAR module 4, and the temperature measurement module 6, respectively. Power supply system 2 uses a power management chip to detect power levels and control power consumption, and can be charged via an external Type-C interface. This circuit structure ensures the reliability and safety of the equipment during long-term outdoor operation.

[0031] In another exemplary embodiment of this application, the modules can be centrally arranged at the front end of the handle 1 to achieve unified power supply and signal transmission. The overall center of gravity is close to the center of the handle 1, facilitating one-handed operation. For example, the overall length of the handle 1 is 400mm, the diameter is 45mm, and the length of the front mounting area is approximately 100mm. Based on this, a sensor module mounting area is provided at the front end of the handle 1, wherein one end (lower part) of the handle 1 is provided with a mounting interface for mounting the temperature measuring module 6. For example, the mounting interface provided at the lower part is a threaded interface with a diameter of M12×1.5 and a length of 15mm. Mounting bases are layered on the handle 1 at a predetermined distance (set according to actual detection needs) from one end of the handle 1. The first layer of mounting base (i.e., the RTK positioning module 3 mounting platform) is used to mount the RTK positioning module 3. For example, the RTK positioning module 3 mounting platform is 50mm wide and 25mm high, and is fixed to the handle 1 with M4 screws. The second-layer mounting base (i.e., the mounting bracket for lidar module 4) is used to set lidar module 4. For example, the bracket thickness is 8mm, the bracket width is the same as handle 1, which is 65mm, and the installation depth is 10mm.

[0032] The TK positioning module can be fixed to the aluminum alloy mounting base with screws. The mounting base is connected to the front end of the handle 1 via an internal thread, with an installation depth of 12mm. The lidar module 4 is installed on the second-layer mounting base using a snap-fit ​​structure, with a snap-fit ​​depth of 10mm and a clamping force of approximately 15N. The temperature measurement module 6 is fixed to the lower front end of the handle 1 via a threaded interface with a thread length of 15mm. It is equipped with a 2mm thick silicone sealing ring and a limiting groove to ensure dustproof, waterproof, and shockproof performance. The spacing between the fixed positions of each module is approximately the first predetermined length (e.g., 20mm) to prevent signal interference and vibration coupling.

[0033] The modules are arranged in a layered, parallel configuration at the front end of the handle 1. The RTK positioning module 3 is located above the lidar module 4, with their central axes parallel and signal directions aligned. The temperature measurement module 6 is located below the lidar module 4, with its probe outlet perpendicular to the scanning plane of the lidar module 4 and extending along the borehole axis. A second predetermined gap (e.g., 2mm) is maintained between the outer shells of each module to prevent vibration transmission. The overall front-end mounting area is 100mm long, accounting for 1 / 4 of the total length of the handle 1.

[0034] Based on the above description, in practical applications, the handle 1 can have a total length of 400mm, a diameter of 45mm, and an internal hollow diameter of 35mm for wiring. The front end has a three-layer mounting area: the upper layer is the RTK positioning module 3, measuring 60×50×25mm; the middle layer is the lidar module 4, measuring 65×65×60mm; and the lower layer is the temperature measurement module 6, measuring 50×30×30mm, with a probe diameter of 8mm and a length of 15mm. The central axes of all modules are parallel, with a spacing of 20mm. A protective cover covers the middle lidar module 4, with a cover diameter of 70mm and a length of 80mm, and is fixed to the handle 1 housing via a flange, achieving IP67 protection.

[0035] In one exemplary embodiment, this application also provides a blast hole detection device, including: a data processing system and the portable laser hole inspection device provided above.

[0036] The data processing system is connected to a portable laser borehole inspection device. This device is used to acquire inspection data for blast holes. The inspection data includes point cloud data, geographical location and attitude information during inspection, and temperature data corresponding to the point cloud data.

[0037] The data processing system is used to read the detection data and generate multidimensional detection results based on the data. The multidimensional detection results include the depth of the blast hole, the water level in the blast hole, the temperature of the blast hole, and the morphology of the hole wall.

[0038] In practical applications, the data processing system integrates multi-sensor data fusion algorithms. Its core algorithm includes six steps: data acquisition, attitude correction, point cloud fusion, water level identification, temperature matching, comprehensive analysis, and quality assessment. These steps are sequentially dependent and have information fusion relationships, forming an integrated digital detection system. Specifically, the attitude correction uses a tilt compensation algorithm. When the device's tilt angle is ≤30°, it can automatically correct the point cloud data through a coordinate transformation matrix, ensuring measurement accuracy. Based on this, such as... Figure 7 As shown, the processing methods of each core algorithm include: (1) Data collection.

[0039] After the detection begins, the LiDAR module 4, RTK positioning module 3, and temperature measurement module 6 start simultaneously. LiDAR module 4 completes a 360° scan of the borehole interior, outputting point cloud data including coordinates (X, Y, Z), reflectivity (intensity), and a timestamp. RTK positioning module 3 provides the device's geographic coordinates and attitude information during detection. Temperature measurement module 6 acquires multi-point temperature data along the borehole depth. All sensor data is recorded with a unified timestamp to ensure subsequent fusion accuracy.

[0040] (2) Posture correction and tilt compensation.

[0041] The data processing system calls IMU attitude data to determine the device tilt angle θ. When the tilt angle is ≤30°, a tilt compensation algorithm is used to perform a spatial rotation transformation on the point cloud to obtain a corrected point cloud (X′, Y′, Z′) in the ground coordinate system. The output of this step is a high-precision spatial point cloud after attitude correction, which provides a geometric basis for subsequent water level identification and temperature mapping.

[0042] The implementation process of the tilt compensation algorithm is as follows: When the device is installed at an angle θ, the point cloud coordinate transformation formula is: .

[0043] In the formula, (X, Y, Z) are the original point cloud coordinates in the coordinate system of the portable laser borehole inspection device. (X', Y', Z') are the transformed point cloud coordinates. θ is the tilt angle of the portable laser borehole inspection device.

[0044] The final output of the tilt compensation algorithm is point cloud spatial coordinate data after attitude correction. Its purpose is to eliminate measurement errors caused by tilted installation or uneven ground, ensuring that each laser ranging point has a true spatial position in the geographic coordinate system. Specifically, the data processing system uses the built-in IMU sensor of the RTK positioning module 3 to acquire the device's tilt angle θ in real time. When pitch or roll deviation is detected, a rotation matrix transformation is performed on the original point cloud coordinates (X, Y, Z) according to the coordinate transformation formula, outputting the corrected coordinates (X′, Y′, Z′) in the ground coordinate system. This compensated point cloud data becomes the basis for subsequent water level identification and temperature fusion.

[0045] (3) Point cloud fusion and coordinate assignment.

[0046] Based on the latitude, longitude, and attitude angle information provided by RTK positioning module 3, the data processing system corrects the point cloud and geographic coordinate system. Through a timestamp matching mechanism, each point cloud data is fused with its corresponding RTK coordinates to form a 3D point cloud model with geographic reference attributes, thereby achieving realistic spatial reconstruction of the borehole.

[0047] (4) Water level identification.

[0048] In the geographic coordinate system, the data processing system performs statistical analysis on the reflectivity of point clouds. When the reflectivity of a continuous set of points is greater than a set threshold (intensity>200) and the height difference between adjacent points is less than 0.05m, it is determined to be a water surface area. By fitting a plane of high reflectivity points, the average height Z′ of the water level is calculated. w Based on this, the dry borehole section and the water-bearing section are distinguished, and the water level depth data is output.

[0049] The water level identification algorithm analyzes the height Z′ and reflectivity of the laser point in this coordinate system to identify clusters of strongly reflective points to determine the water surface position. Without tilt compensation, the fitted plane of the water surface will be tilted, leading to misjudgments of the water level. The water level identification algorithm includes: Water surface is identified based on laser reflectivity characteristics. The water surface reflectivity threshold is intensity>200. When the height difference between adjacent points is <0.05m, continuous water surface points are determined, and the final result is: water level height = orifice elevation - mean Z coordinate of water surface points.

[0050] (5) Temperature data matching and interpolation fusion.

[0051] The temperature data output by the temperature measurement module 6 is calibrated according to the probe depth. The system maps each depth position to the Z′ axis coordinate of the point cloud, calculates the temperature at any depth using linear interpolation, and maps the temperature value to the point cloud model to form a temperature-spatial joint distribution model, which is used to analyze the temperature gradient change inside the hole.

[0052] The temperature compensation section utilizes the same ground coordinate system to match and interpolate the temperature data collected by the flexible temperature probe along the depth direction with the Z′ axis coordinate of the point cloud, thereby achieving the corresponding fusion of temperature and spatial distribution.

[0053] In this temperature data fusion algorithm, the temperature at any depth obtained by interpolating the temperature data to the point cloud according to depth is represented as follows: .

[0054] In the formula, For depth The temperature at that location For depth The measured temperature at that location For depth The actual measured temperature at the location.

[0055] (6) Comprehensive analysis and quality assessment The data processing system performs statistical calculations on the fused point cloud, temperature, and water level data to achieve comprehensive analysis and quality assessment.

[0056] Based on the above description, the tilt compensation algorithm first outputs a point cloud with corrected spatial geometric accuracy, providing a true spatial reference for water level identification and a vertical depth coordinate reference for temperature distribution mapping. After water level identification determines the location of the liquid interface within the borehole, the temperature data is corrected in layers based on this depth interface. Finally, the data processing system integrates and outputs multi-dimensional detection results, including borehole depth, water level, temperature, and borehole wall morphology, achieving integrated spatial-physical information analysis throughout the entire process.

[0057] like Figure 3 The data processing flow shown illustrates how different types of data obtained from the various modules (LiDAR module 4, RTK positioning module 3, and temperature measurement module 6) of the portable laser borehole inspection equipment are fused and calculated after entering the data processing system. First, based on the attitude and displacement data output by RTK positioning module 3, the data processing system constructs a coordinate transformation matrix, performs coordinate system transformation and tilt compensation on the radar point cloud, transforming the original relative point cloud data into a global geographic coordinate point set. Subsequently, the data processing system identifies characteristic points of the water level surface within the borehole using a reflectivity analysis algorithm, and calculates the effective borehole depth and water level depth accordingly. Temperature data is interpolated and matched with the Z-axis coordinates of the point cloud according to the depth calibration of the flexible temperature probe, forming a depth-temperature distribution curve, achieving the corresponding fusion of temperature data and spatial structure. Finally, the data processing system outputs a comprehensive inspection report including borehole depth, water level height, borehole wall geometry, and temperature distribution, providing a digital basis for adjusting blasting parameters, calculating explosive charges, and assessing safety and quality.

[0058] Based on the same inventive concept, this application also provides a method for detecting blast holes applied to the aforementioned blast hole detection device. The solution provided by this method is similar to the solution described in the aforementioned device; therefore, the specific limitations in one or more embodiments of the blast hole detection method provided below can be found in the limitations on the data processing procedure of the blast hole detection device described above, and will not be repeated here.

[0059] In another exemplary embodiment of this application, such as Figure 2 As shown, when the device is working, the lidar module 4 emits a laser beam to form a scanning cone. When it encounters the water surface, the laser reflectivity is significantly enhanced due to the water's reflectivity, which is represented by a high intensity value in the point cloud data. By analyzing continuous high reflectivity points, the position of the water level line 9 can be accurately determined.

[0060] The core of the data processing algorithm is tilt compensation technology. Because the equipment is installed at an angle, the original point cloud coordinates need to be transformed. The equipment's built-in IMU sensor monitors the equipment's attitude in real time, and the data processing system automatically performs coordinate correction based on the tilt angle.

[0061] The fusion of temperature data and point cloud data is achieved through depth interpolation. This fusion is primarily based on depth coordinate matching and linear interpolation calculation. The core objective is to correlate the discrete temperature data collected by the flexible temperature probe along the borehole depth direction with the continuous point cloud spatial coordinates (Z-axis direction) acquired by the lidar module 4, thereby constructing a temperature distribution model at any depth within the borehole. Based on this, we have: (1) The temperature measurement data output by temperature measurement module 6 for each temperature measurement point includes the depth value Z. i With corresponding temperature T i The coordinates of each point in the point cloud data are (X, Y, X). j ,Y j Z j First, the Z-axis of the point cloud data is normalized so that the orifice position is defined as Z=0 and the orifice bottom as Z=D, thus unifying the scale with the temperature measurement depth coordinates.

[0062] (2) Divide the depth of the borehole into segments according to the temperature measurement points: [z0,z1],[z1,z2],…,[z n−1 ,z n There are two measured temperature points T within each interval. i ,T i+1 The point cloud data is distributed according to the Z coordinate within this interval.

[0063] (3) For any depth z∈[z i ,z i+1 Temperature is calculated using linear interpolation. .

[0064] (4) For each point cloud point P j (X j ,Y j Z j Based on the interval where its Z coordinate lies, calculate the corresponding T(Z). j The system adds a temperature attribute to this point, thus fusing spatial coordinates with temperature information. The output format is as follows: .

[0065] (5) The interpolated point cloud can represent the temperature field distribution inside the hole in three-dimensional space. The data processing system can then process the data based on the temperature gradient d. T / d Z Calculating thermal anomaly areas helps determine whether there is water seepage, gas accumulation, or uneven heating within the borehole, providing a basis for borehole stability and blasting safety.

[0066] Depth reference and spatiotemporal alignment: based on orifice elevation The zero point or unified reference is used. Attitude / coordinates have been determined by the tilt compensation and RTK positioning module 3, and the point cloud is taken in the ground coordinate system (X', Y', Z'). The depth of the i-th point in the point cloud is defined as... : .

[0067] In the formula, Let be the coordinates of the i-th point after transformation.

[0068] Flexible temperature probe records discrete samples (Timestamps are included, aligned frame-by-frame with the point cloud). If multiple temperature measurements are taken at the same depth, average them within the time window first.

[0069] For any depth d (e.g., points in a point cloud) Find the interval d k ≤d≤d k+1 ,according to The temperature is continuously mapped to this depth.

[0070] The data processing system establishes a temperature-depth function based on the temperature probe's measurements at different depths, and then maps the temperature information onto the point cloud at the corresponding depth. The system also establishes a continuous temperature-depth function based on the measured temperature data from the temperature probe at different depths to describe the temperature variation with depth inside the borehole. The process of establishing this function is as follows: (1) Original data format.

[0071] Temperature measurement module 6 outputs a discrete data point set: .

[0072] (2) Function creation method.

[0073] The data processing system uses piecewise linear interpolation or spline fitting methods to construct continuous functions.

[0074] When the number of measurement points is small (n≤10), linear interpolation is used, and: .

[0075] When there are many measuring points and they are evenly distributed, the system automatically switches to cubic spline fitting to ensure the continuity of the temperature gradient, as follows: .

[0076] Among them, coefficient , , , From the boundary condition T′ The continuity condition of temperature and first derivative in adjacent intervals is obtained by solving the problem.

[0077] (3) Function characteristics and output results.

[0078] The resulting function T(z) is a continuous temperature distribution model within the borehole, which can be used to calculate the temperature value at any depth z. (Calculation of temperature gradient) Identify thermal anomaly areas. Match temperature values ​​with the Z-axis coordinates of the point cloud to achieve spatial-thermal distribution fusion.

[0079] (4) Data fusion output format.

[0080] After each laser point cloud is assigned a corresponding temperature attribute, the output format is as follows: .

[0081] This leads to a temperature-space joint model, enabling three-dimensional visualization analysis of the internal structure and thermal state of the pores.

[0082] Power supply system 2 adopts a modular design to provide a 24V main power supply, and provides the required voltage to each module through a DC-DC converter. The RTK module and temperature measurement module 6 use 5V power supply, the lidar uses 12V power supply, and the data processing unit uses 3.3V power supply.

[0083] The protective cover 5 is specially designed to protect the lidar from dust and impacts while ensuring that laser transmission is not affected. It is made of high-transmittance polycarbonate with an anti-reflective coating.

[0084] Data storage uses a high-speed SD card, supporting the storage of 100,000 point cloud data points in a single detection. Each data point contains X, Y, and Z coordinates, reflectivity, timestamp, and temperature information, with a single detection data volume of approximately 50MB.

[0085] In practical applications, the operator holds the device and moves it to location 8 of the blast hole. After starting the detection program, the device automatically completes RTK positioning, laser scanning, and temperature measurement. The entire detection process takes approximately 30 seconds, and the output results include: accurate hole depth, water level location, temperature distribution curve, hole wall integrity assessment, and precise coordinate information.

[0086] When the borehole is filled with water, the strong reflection characteristics produced by the water surface are obvious, with a reflectivity typically exceeding 200 (out of a maximum of 255). Simultaneously, the effective depth of the borehole is shortened by the water level. These characteristics provide an accurate basis for adjusting blasting parameters.

[0087] In this embodiment, the device operates normally in an ambient temperature range of -20°C to 60°C. When the ambient temperature is below 0°C, the lidar automatically activates the heating mode, increasing power consumption to 14W, at which point the battery life is approximately 45 minutes.

[0088] Based on the above description, the organic combination of lidar, RTK positioning, and temperature measurement modules has enabled the digitalization, precision, and safety of borehole detection, providing advanced technical equipment for modern mine blasting operations.

[0089] Based on the above description, such as Figure 5 As shown, the portable laser borehole inspection device provided in this application is applicable to geological exploration, hydrological monitoring, and engineering inspection. The operator holds the device and inserts the probe into the borehole or blast hole, transmitting measurement information in real time via a wireless data link. The data processing system (i.e., the system terminal) can display the borehole wall morphology, water level, and temperature changes on-site. Due to its small size and light weight, the device can be operated flexibly in confined spaces or complex terrain, exhibiting excellent portability and practicality. Based on this, compared to existing technologies, this application also has at least the following advantages: (1) Significantly improved detection accuracy: The accuracy of the lidar reaches the centimeter level, the RTK positioning accuracy is ±2cm, and the temperature detection accuracy is ±0.5°C. Compared with traditional methods, the accuracy of this application can be improved by more than 10 times.

[0090] (2) Comprehensive detection content: It can simultaneously obtain multi-dimensional information such as borehole depth, water level, temperature, coordinates, and borehole wall integrity, while traditional methods can only obtain partial information.

[0091] (3) Digital storage of data: Automatically records test data, which facilitates quality control and subsequent analysis and supports batch testing of multiple wells.

[0092] (4) Strong environmental adaptability: IP67 protection level, working temperature -20°C to 80°C, adaptable to harsh mining environment.

[0093] (5) Significant cost-effectiveness: It avoids secondary operations caused by inaccurate hole positions, and significantly improves overall efficiency.

[0094] (6) Multi-sensor fusion innovation: For the first time, lidar, RTK positioning and temperature detection are integrated for borehole detection to achieve multi-dimensional data synchronous acquisition.

[0095] (7) Inclined installation compensation algorithm: The inclined installation compensation algorithm is specifically designed for the needs of borehole detection and is suitable for complex installation conditions.

[0096] (8) Water level identification technology: Automatic water level identification technology based on laser reflectivity characteristics solves the problem that traditional methods cannot accurately locate water levels.

[0097] (9) Integrated portable design: The complex multi-sensor system is integrated into a portable device to meet the needs of field operations.

[0098] (10) Digital testing process: A complete digital testing process from data collection to result output has been established to improve the level of testing standardization.

[0099] In one exemplary embodiment, such as Figure 6 As shown, a method for detecting blast holes is provided, including: Step 100: Obtain the detection data of the blast hole. The detection data includes point cloud data, geographical location and attitude information at the time of detection, and temperature data corresponding to the point cloud data.

[0100] Step 101: Perform attitude correction and tilt compensation based on the detection data to obtain corrected point cloud data.

[0101] Step 102: Based on the corrected point cloud data, perform point cloud fusion and coordinate assignment to form a 3D point cloud model of the blast hole. The 3D point cloud model includes the hole depth and hole wall morphology.

[0102] Step 103: Determine the point cloud reflectivity based on the detection data, and determine the water level in the blast hole based on the point cloud reflectivity.

[0103] Step 104: Calibrate the temperature data according to the probe depth in the portable laser borehole inspection device. Based on the calibrated temperature data, map each depth position to the point cloud axis coordinates, use linear interpolation to determine the temperature at any depth in the blast hole, and map the temperature at any depth to the three-dimensional point cloud model to form a temperature-spatial joint distribution model.

[0104] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0105] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0106] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0107] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (RRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0108] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0109] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A portable laser hole inspection device, characterized in that, include: Handle, RTK positioning module, LiDAR module, temperature measurement module, and data storage card; The RTK positioning module, the lidar module, and the temperature measurement module are all connected to the data storage card; the RTK positioning module, the temperature measurement module, and the lidar module are all mounted on the handle; the lidar module is located on the data storage card. The RTK positioning module is used to acquire geographical location and attitude information during detection; the lidar module is used to acquire point cloud data; the temperature measurement module is used to acquire temperature measurement data; and the data storage card is used to store the point cloud data, geographical location and attitude information during detection, and temperature data corresponding to the point cloud data.

2. The portable laser hole inspection device according to claim 1, characterized in that, One end of the handle is provided with an installation interface; the handle is provided with mounting bases in layers at a predetermined distance from one end of the handle; the installation interface is used to install the temperature measurement module; the first layer of mounting base is used to install the RTK positioning module; the second layer of mounting base is used to install the lidar module.

3. The portable laser hole inspection device according to claim 2, characterized in that, The RTK positioning module is located above the lidar module, and the central axes of the RTK positioning module and the lidar module are parallel; the temperature measurement module is located below the lidar module, and the probe outlet of the temperature measurement module is perpendicular to the scanning plane of the lidar module and extends along the hole axis.

4. The portable laser hole inspection device according to claim 2, characterized in that, The vertical distance between the RTK positioning module, the lidar module, and the temperature measurement module is less than or equal to a first predetermined length; a second predetermined length of gap is left between the outer shells of the RTK positioning module, the lidar module, and the temperature measurement module.

5. The portable laser hole inspection device according to claim 2, characterized in that, The length of the installation area formed by the RTK positioning module, the lidar module, and the temperature measurement module accounts for 1 / 4 of the total length of the handle.

6. The portable laser hole inspection device according to claim 1, characterized in that, The temperature measurement module uses a flexible temperature probe; the flexible temperature probe consists of multiple sections of bendable stainless steel serpentine sheath and multi-core wires; the stainless steel serpentine sheath is covered with a fluororubber protective layer.

7. A device for detecting blast holes, characterized in that, include: The data processing system and the portable laser hole inspection device as described in any one of claims 1-6; The data processing system is connected to the portable laser borehole inspection device; the portable laser borehole inspection device is used to acquire detection data of the blasting borehole; the detection data includes point cloud data, geographical location and attitude information during detection, and temperature data corresponding to the point cloud data; The data processing system is used to read the detection data and generate multidimensional detection results based on the detection data; the multidimensional detection results include the depth of the blast hole, the water level in the blast hole, the temperature of the blast hole, and the morphology of the hole wall.

8. A method for detecting blast holes, characterized in that, Applied to a blast hole detection device as described in claim 7; the method includes: Acquire detection data of the blast hole; the detection data includes point cloud data, geographical location and attitude information at the time of detection, and temperature data corresponding to the point cloud data; Based on the detection data, attitude correction and tilt compensation are performed to obtain corrected point cloud data; Point cloud fusion and coordinate assignment are performed based on the corrected point cloud data to form a three-dimensional point cloud model of the blast hole; the three-dimensional point cloud model includes the hole depth and hole wall morphology of the blast hole. The point cloud reflectance is determined based on the detection data, and the water level in the blast hole is determined based on the point cloud reflectance. The temperature data is calibrated according to the probe depth in the portable laser borehole inspection device. Based on the calibrated temperature data, each depth position is correlated with the point cloud axis coordinates. The temperature at any depth in the blast hole is determined by linear interpolation. The temperature at any depth is then mapped into the three-dimensional point cloud model to form a temperature-spatial joint distribution model.

9. The method for detecting blast holes according to claim 8, characterized in that, In the process of obtaining corrected point cloud data through attitude correction and tilt compensation based on the detected data, the point cloud coordinate transformation formula used is: ； In the formula, (X, Y, Z) are the original point cloud coordinates in the coordinate system of the portable laser hole inspection device; (X', Y', Z') are the transformed point cloud coordinates; and θ is the tilt angle of the portable laser hole inspection device.

10. The method for detecting blast holes according to claim 8, characterized in that, The formula for determining the temperature at any depth in a blast hole is: ； In the formula, For depth The temperature at that location For depth The measured temperature at that location For depth The actual measured temperature at the location.

Images