A method for ore location analysis based on rock drilling and blasting

Abstract

This invention discloses a method for ore dumping location analysis based on rock drilling and blasting. First, the borehole geometry parameters are determined, and the explosive charge and detonation sequence for each blast hole are recorded. Then, multi-source data, including spatial point cloud data, collision acoustic signals, motion attitude data, and environmental dust concentration, are collected. Next, preliminary coordinates are determined based on the aforementioned multi-source data, and the reliability weights of each coordinate are calculated to obtain preliminary fused location coordinates. Subsequently, the preliminary fused location coordinates, along with the borehole geometry parameters and blast hole blasting parameters, are input into a random forest model for secondary correction to obtain the final ore dumping location coordinates. Finally, the obtained coordinates are compared with manually measured values. If the error exceeds a threshold, the model is retrained or the weights are calibrated until the requirements are met. This method, through weighted fusion of multi-source data and secondary correction using a random forest model, significantly reduces ore dumping location errors and effectively improves mine ore extraction efficiency.

Description

Technical Field

[0001] This invention relates to the field of mining engineering technology, and in particular to a method for ore location analysis based on rock drilling and blasting. Background Technology

[0002] Rock drilling and blasting is the core process of underground mining. The spatial distribution, block size characteristics and movement trajectory of the falling ore directly determine the subsequent ore extraction efficiency, equipment wear and tear and resource recovery rate. Existing ore falling location technology mainly relies on manual measurement with the help of instruments such as total station and RTK-GPS, static detection of single sensor or simple multi-source data fusion. These traditional ore falling location technologies have the following defects, which make the mine ore extraction efficiency low and resources wasted: (1) The ore falling location analysis source data is single and cannot effectively explain the cause of ore falling distribution; (2) Underground mines have high dust concentration, large noise interference and insufficient light. Single sensor is prone to data distortion. Traditional fixed weight fusion method lacks differentiated consideration of environmental interference, resulting in positioning error generally exceeding 0.5m, which cannot meet the requirements of accurate ore extraction; (3) The ore falling analysis surface is single and can only obtain the static ore falling position after blasting. It lacks dynamic capture of the ore falling movement trajectory during blasting and cannot reflect the evolution law of ore falling distribution.

[0003] Therefore, a ore-falling positioning analysis method based on rock drilling and blasting is proposed to overcome the above-mentioned technical defects and help improve ore-falling efficiency and resource utilization. Summary of the Invention

[0004] The main technical problem to be solved by this invention is to provide a more accurate and reliable method for ore location analysis based on rock drilling and blasting, so as to improve ore distribution and increase ore extraction efficiency.

[0005] To address the aforementioned technical problems, this invention provides a method for ore location analysis based on rock drilling and blasting, comprising the following steps:

[0006] Step 1: Assign labels to boreholes and determine the geometric parameters of each borehole;

[0007] Step 2: Fill the drill holes with explosives to form blast holes, and record the blasting parameters for each blast hole, including the amount of explosives used and the detonation sequence.

[0008] Step 3: Collect multi-source data, including spatial point clouds of falling ore, collision acoustic signals, motion attitude data, and environmental dust concentration;

[0009] Step 4: Ore Drop Location Analysis, this step includes:

[0010] Step 41: Determine the first preliminary coordinates of the ore deposit based on the spatial point cloud. Determining the second preliminary coordinates of the ore drop based on collision acoustic signals. The third preliminary coordinates of the ore drop were determined based on motion posture data. ;

[0011] Step 42: Calculate the reliability weight of the first preliminary coordinates. Reliability weights of the second preliminary coordinates The reliability weight of the third preliminary coordinates ;

[0012] Step 43: Calculate the preliminary fusion positioning coordinates of the ore drop. The calculation formula is:

[0013]

[0014] Step 44: Calculate the preliminary fused positioning coordinates. The geometric parameters of each borehole and the blasting parameters of each blast hole in the analysis area are input into the random forest model to obtain coordinate correction values, and the final positioning coordinates of the ore fall are calculated. ;

[0015] Step 5: Calculate the final location coordinates of the ore drop. If the error exceeds a preset threshold, the random forest model is retrained and / or the reliability weights of the multi-source data are recalibrated by comparing the coordinates with manually measured ore-fall coordinates. , , Until satisfaction is achieved.

[0016] In a preferred embodiment, in step 42, the reliability weight , , The calculation formulas are as follows:

[0017]

[0018] In the formula, For spatial point cloud density, For environmental dust concentration, The dust impact coefficient is... The signal-to-noise ratio of the acoustic signal. For environmental noise intensity, This is the noise impact factor. For inertial navigation attitude error, This represents the maximum permissible attitude error.

[0019] In a preferred embodiment, an additional step is included between steps 3 and 4: filtering and denoising the spatial point cloud; performing bandpass filtering and spectrum analysis on the collision acoustic signal to extract feature signals related to ore falling; and smoothing the motion attitude data.

[0020] In a preferred embodiment, in step 1, the geometric parameters of the borehole include the borehole depth. ,inclination and azimuth .

[0021] In a preferred embodiment, in step 1, the geometric parameters of the borehole further include the actual bottom coordinates; the formula for calculating the actual bottom coordinates is:

[0022]

[0023] In the formula, Let be the actual bottom coordinates of the i-th borehole. The coordinates of the i-th borehole are preset in the blasting plan. Let be the depth of the i-th borehole. Let be the inclination angle of the i-th borehole. Let be the azimuth angle of the i-th borehole.

[0024] In a preferred embodiment, in step 3, the spatial point cloud of the falling ore is acquired by lidar; the collision acoustic signal is acquired by an acoustic sensor; the motion attitude data is acquired by an inertial navigation module; and the environmental dust concentration is acquired by a dust sensor.

[0025] In a preferred embodiment, step 44 further includes: collecting a large number of preliminary fusion positioning coordinates under different blasting conditions. The random forest model is trained using a sample set of blasting parameters and manually measured ore-falling coordinates.

[0026] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0027] The method provided by this invention derives preliminary fused positioning coordinates through multi-dimensional weighted calculations of spatial point clouds, collision acoustic signals, and motion posture data. The weighted calculations quantify the impact of uncontrollable environmental factors such as dust and noise on data acquisition. Subsequently, a random forest model is used for secondary correction to obtain the final ore-falling positioning coordinates. This process comprehensively considers variables and employs rigorous analysis and deduction. Furthermore, the random forest model, through continuous training and validation on different sample sets, demonstrates high generalization ability, resulting in high accuracy in ore-falling positioning analysis. In addition, the method simultaneously collects multiple ore-falling positioning data, including borehole geometric parameters, blasting parameters, and spatial point clouds, providing comprehensive and reliable data support for the dynamic reconstruction of ore-falling trajectory and the analysis of ore distribution evolution patterns. Therefore, this method can effectively assist in manual intervention in ore distribution and improve mine ore extraction efficiency. Attached Figure Description

[0028] Figure 1This is a flowchart illustrating the method described in an embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0030] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," "top / bottom," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0031] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installed", "equipped", "sleeved / connected", "connected", etc., should be interpreted broadly. For example, "connection" can be a wall-mounted connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0032] like Figure 1 This invention provides a method for ore location analysis based on rock drilling and blasting, specifically including the following steps:

[0033] Step 1: Drilling Parameter Preprocessing

[0034] The boreholes are assigned numbers, and their geometric parameters—inclination angle, azimuth angle, and depth—are collected. Specifically, a mining spatial coordinate system is established, and a high-precision attitude sensor and a depth measuring instrument are integrated onto the drill rod of the drilling rig. The depth measuring instrument is preferably an ultrasonic / laser depth measuring instrument. The attitude sensor collects the three-dimensional attitude data of the drill rod in real time: roll angle, pitch angle, and yaw angle, and then calculates the borehole inclination angle. and azimuth Ultrasonic / laser hole depth measuring instrument for real-time measurement of borehole depth. .

[0035] Next, the collected actual hole depth data, dip angle, azimuth angle, and the borehole coordinates preset during the blasting scheme design are substituted into the spatial coordinate transformation formula to calculate the actual bottom coordinates of each borehole. The spatial coordinate transformation formula is:

[0036]

[0037] In the formula, Let be the actual bottom coordinates of the i-th borehole. The coordinates of the i-th borehole are preset in the blasting plan. Let be the depth of the i-th borehole. Let be the inclination angle of the i-th borehole. Let be the azimuth angle of the i-th borehole. Then, generate a borehole parameter table corresponding to the borehole number and its actual bottom coordinates. This borehole parameter table includes the borehole number, preset borehole coordinates, inclination angle, azimuth angle, hole depth, and actual bottom coordinates for each borehole.

[0038] Step 2: Acquisition of blasting parameters

[0039] This paper refers to boreholes filled with explosives as blast holes. Each blast hole is assigned a unique identifier and associated with its borehole number in the borehole parameter table. During the charging process, the actual explosive charge for each blast hole is recorded; at the blasting initiation, the initiation time for each blast hole is recorded, and the initiation time difference from the preset benchmark initiation time is calculated as initiation timing data. The borehole number, actual borehole bottom coordinates, explosive charge, initiation time, and initiation time difference are stored together to form a borehole and blasting parameter mapping table.

[0040] Step 3: Multi-source data acquisition

[0041] Multi-source sensor arrays are deployed at key locations around the blasting area, such as the top and sidewalls of the tunnel and the safe area behind the working face. The sensors used specifically include:

[0042] LiDAR is used to quickly acquire spatial point cloud data of the mining area after blasting in order to identify the spatial distribution and shape of the fallen ore.

[0043] Acoustic sensors are used to capture the acoustic signals generated by the collision of falling ore and the fracturing of rocks during blasting, in order to help determine the approximate location and movement of the falling ore.

[0044] An inertial navigation module, integrated at a fixed monitoring point or a small unmanned mobile platform near the blasting area, provides high-precision attitude and position references by collecting motion attitude data to assist lidar and acoustic sensors in data registration and moving target tracking.

[0045] Dust sensors monitor the concentration of environmental dust in the mining area in real time after blasting.

[0046] After the data collected by the lidar, acoustic sensor, inertial navigation module and dust sensor are synchronized in time, multi-source data are obtained: spatial point cloud of falling ore, collision acoustic signal, motion attitude data and environmental dust concentration.

[0047] The multi-source data was then preprocessed, specifically: the spatial point cloud was filtered and denoised; the collision acoustic signal was bandpass filtered and spectral analyzed to extract feature signals related to ore falling; and the motion attitude data was smoothed to improve the stability of attitude and position estimation.

[0048] Step 4: Coupling Analysis of Ore Drop Location

[0049] This step includes:

[0050] Step 41: Obtain the preliminary coordinates of the ore fall based on the preprocessed multi-source data. Specifically:

[0051] Mining point clusters are segmented and identified from the preprocessed spatial point cloud, and the first preliminary coordinates based on the spatial point cloud are obtained. ;

[0052] The initial three-dimensional coordinates of the ore drop are estimated by using the time difference of arrival of the impact acoustic signals, which serve as the second initial coordinates calculated based on the impact acoustic signals. ;

[0053] An inertial navigation module is used to calculate the motion attitude data to obtain the third preliminary coordinates based on the motion attitude data. ;

[0054] Step 42: Dynamically calculate the reliability weights of multi-source data. The specific calculation formulas are as follows:

[0055]

[0056] In the formula, The weights of the initial coordinates of spatial points in cloud computing. For spatial point cloud density, For environmental dust concentration, The dust impact coefficient is... Calculate the weights of the initial coordinates for the collision acoustic signal. The signal-to-noise ratio of the acoustic signal. For environmental noise intensity, This is the noise impact factor. Calculate the weights of the initial coordinates for the motion posture data. For inertial navigation attitude error, This represents the maximum permissible attitude error. , and The actual value is calibrated through experiments under specific mining conditions.

[0057] Step 43: Calculate the preliminary fusion positioning coordinates of the ore drop. The calculation formula is:

[0058]

[0059] Step 44: Extract the explosive dosage and detonation time difference related to the current analysis area from the borehole and blasting parameter mapping table. Input the preliminary fused positioning coordinates with the corresponding explosive dosage and detonation time difference, as well as the estimated deviations that may occur during the preliminary coordinate positioning process, into a pre-trained random forest model. The random forest model outputs coordinate correction values, and then calculates the final ore-falling positioning coordinates. The calculation formula is:

[0060]

[0061] In the formula, This is the coordinate correction value.

[0062] For the random forest model, it is trained by collecting a large sample set of preliminary fusion positioning coordinates, blasting parameters, and manually measured ore-falling coordinates under different blasting conditions. The different blasting conditions include different explosive charges, different detonation sequences, and different geological conditions. Cross-validation is used to evaluate the calibration accuracy of the random forest model to ensure its generalization ability.

[0063] Step 5: Verify and optimize

[0064] The calculated final location coordinates of the ore drop If the error exceeds a preset threshold, the random forest model is retrained and / or the reliability weights of the multi-source data are recalibrated by comparing the coordinates with manually measured ore-fall coordinates. , , Specifically, after the blasting operation is completed safely, technicians enter the mining area and use high-precision total stations or RTK-GPS to sample and measure some of the fallen ore blocks to obtain the actual ore coordinates. The manually measured ore coordinates are compared with the final ore location coordinates to calculate the positioning error. When the average or maximum error of the sampling points exceeds a preset threshold, the current random forest model or reliability weights are considered to need adjustment. If the error mainly originates from the reliability assessment of sensor data, recalibration is performed. , and To re-acquire reliability weights; if the error mainly comes from the influence of blasting parameters such as explosive dosage, detonation time, and detonation time difference on positioning, then new data containing current error information is used to update the random forest model.

[0065] As an extension, based on the ore drop location analysis results, areas where ore drop is too concentrated or too dispersed can be identified. Then, combining the drilling and blasting parameter mapping table, the original blasting parameters corresponding to these areas are analyzed: hole location, explosive dosage, and detonation sequence. Based on the analysis results, blasting optimization adjustments can be made, such as increasing drilling density or explosive dosage in areas with insufficient ore drop; optimizing the detonation sequence in areas with excessively high block ratios to improve fragmentation effects; and adjusting the drilling angle to optimize the ore drop throwing direction.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the patent scope of the present invention. Any technically equivalent modifications made based on the content of this specification shall fall within the protection scope of the present invention.

Claims

1. A method for ore location analysis based on rock drilling and blasting, characterized in that: Includes the following steps: Step 1: Assign labels to boreholes and determine the geometric parameters of each borehole; Step 2: Fill the drill holes with explosives to form blast holes, and record the blasting parameters for each blast hole, including the amount of explosives used and the detonation sequence. Step 3: Collect multi-source data, including spatial point clouds of falling ore, collision acoustic signals, motion attitude data, and environmental dust concentration; Step 4: Ore Drop Location Analysis, this step includes: Step 41: Determine the first preliminary coordinates of the ore deposit based on the spatial point cloud. Determining the second preliminary coordinates of the ore drop based on collision acoustic signals. The third preliminary coordinates of the ore drop were determined based on motion posture data. ; Step 42: Calculate the reliability weight of the first preliminary coordinates. Reliability weights of the second preliminary coordinates The reliability weight of the third preliminary coordinates ; Step 43: Calculate the preliminary fusion positioning coordinates of the ore drop. The calculation formula is: Step 44: Calculate the preliminary fused positioning coordinates. The geometric parameters of each borehole and the blasting parameters of each blast hole in the analysis area are input into the random forest model to obtain coordinate correction values, and the final positioning coordinates of the ore fall are calculated. ; Step 5: Calculate the final location coordinates of the ore drop. If the error exceeds a preset threshold, the random forest model is retrained and / or the reliability weights of the multi-source data are recalibrated by comparing the coordinates with manually measured ore-fall coordinates. , , Until satisfaction is achieved.

2. The ore-falling location analysis method based on rock drilling and blasting according to claim 1, characterized in that: In step 42, reliability weight , , The calculation formulas are as follows: In the formula, For spatial point cloud density, For environmental dust concentration, The dust impact coefficient is... The signal-to-noise ratio of the acoustic signal. For environmental noise intensity, This is the noise impact factor. For inertial navigation attitude error, This represents the maximum permissible attitude error.

3. The ore-falling location analysis method based on rock drilling and blasting according to claim 1, characterized in that: Between steps 3 and 4, there are additional steps: filtering and denoising the spatial point cloud; performing bandpass filtering and spectrum analysis on the collision acoustic signal to extract feature signals related to ore falling; and smoothing the motion attitude data.

4. The method for ore location analysis based on rock drilling and blasting according to claim 1, characterized in that: In step 1, the geometric parameters of the borehole include the hole depth. ,inclination and azimuth .

5. The ore-falling location analysis method based on rock drilling and blasting according to claim 4, characterized in that: In step 1, the geometric parameters of the borehole also include the actual bottom coordinates; the formula for calculating the actual bottom coordinates is: In the formula, Let be the actual bottom coordinates of the i-th borehole. The coordinates of the i-th borehole are preset in the blasting plan. Let be the depth of the i-th borehole. Let be the inclination angle of the i-th borehole. Let be the azimuth angle of the i-th borehole.

6. The ore-falling location analysis method based on rock drilling and blasting according to claim 1, characterized in that: In step 3, the spatial point cloud of the ore falling is acquired by lidar; the collision acoustic signal is acquired by acoustic sensor; the motion attitude data is acquired by inertial navigation module; and the environmental dust concentration is acquired by dust sensor.

7. The ore-falling location analysis method based on rock drilling and blasting according to claim 1, characterized in that: Step 44 further includes: collecting a large number of preliminary fusion positioning coordinates under different blasting conditions. The random forest model is trained using a sample set of blasting parameters and manually measured ore-falling coordinates.

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