Method, system, device and medium for mineral loading control based on mineral moisture content

Abstract

The present application discloses a mineral aggregate loading control method, system, device and medium based on the moisture content of mineral aggregate, relates to the technical field of mineral aggregate loading control, and through the surface reflectivity of newly added mineral aggregate at two adjacent time points in the current detection time window, the real-time moisture content of the newly added mineral aggregate is inverted, the deviation of the real-time moisture content relative to the moisture content of the standard sample is taken as the correction basis, and the actual density of each layer of newly added mineral aggregate is corrected in combination with the standard sample parameters; the carriage is divided into multiple partitions, the cumulative weight of each partition is calculated in combination with the incremental volume of each partition and the overall average density of the mineral aggregate, and a real-time deviation value is output; when the real-time deviation value is within a preset deviation range, the material falling position is adjusted in a timely manner. Through dynamic correction of the mineral aggregate density during loading and synchronous adjustment of the loading deviation, the risk of carriage overturning caused by deviation is reduced, the loss of mineral aggregate and the waste of loading time are reduced, and intelligent mineral aggregate loading control is realized.

Description

Technical Field

[0001] This invention relates to the field of mineral loading control technology, and in particular to mineral loading control methods, systems, equipment and media based on mineral moisture content. Background Technology

[0002] In existing technologies, multi-line lidar is fixedly mounted on the gantry of the loading station to scan and collect three-dimensional coordinate point clouds of passing trains. By segmenting the point clouds of the carriages and the ore, the volume of the ore accumulation is calculated, and the ore pile is segmented along the coordinate axis. The volume difference is used to determine the carriage overloading, and the abnormal loading areas are identified by combining the carriage dimensions. The shortcomings of existing technologies are: After the ore loading is completed, the loading status of the ore is judged. Because the moisture content of the ore changes dynamically in real time due to factors such as rain, snow and storage time, the misjudgment rate of overloading and unbalanced loading is high. Once overloading or unbalanced loading occurs, rework can only be carried out, resulting in low efficiency of ore loading operation. Summary of the Invention

[0003] This invention provides a method, system, equipment, and medium for controlling ore loading based on ore moisture content. This addresses the problem in the prior art where, after ore loading is completed, the loading status of the ore is judged. However, due to the real-time dynamic changes in ore moisture content caused by factors such as rain, snow, and storage time, the misjudgment rate of overloading and off-center loading is high. As a result, overloading or off-center loading can only be reworked, leading to low efficiency in ore loading operations.

[0004] In a first aspect, the present invention provides a mineral loading control method based on the moisture content of the mineral material, the mineral loading control method based on the moisture content of the mineral material includes: Step 100: Obtain the three-dimensional point cloud and surface reflectance of the ore material in the current compartment at various times during the loading process, as well as the standard moisture content and standard density of the standard ore sample. Step 200: Based on the surface reflectance of the ore at each moment within the current detection time window, obtain the surface reflectance of the newly added ore at each moment within the current detection time window relative to the previous moment; based on the surface reflectance of the newly added ore, obtain the moisture content of the newly added ore; based on the moisture content of the newly added ore, the standard moisture content of the standard ore sample, and the standard density of the standard ore sample, obtain the corrected density of the newly added ore; based on the three-dimensional point cloud of the ore at each moment within the current detection time window, obtain the incremental volume of the newly added ore at each moment within the current detection time window relative to the previous moment; based on the incremental volume of the newly added ore, obtain the incremental weight of the newly added ore. Step 300: Based on the incremental volume of the newly added ore at each moment within the current detection time window relative to the previous moment, obtain the total volume of the ore in the current carriage; based on the incremental weight of the newly added ore at each moment within the current detection time window relative to the previous moment, obtain the total weight of the ore in the current carriage; and based on the total volume and the total weight, obtain the average density of the ore in the current carriage. Step 400: Based on the average density and the incremental volume of the ore in each partition area of ​​the current car, obtain the cumulative weight of each partition area of ​​the current car, and determine the real-time off-center load value of the current car based on the cumulative weight of each partition area. Step 500: When the real-time off-center load value of the current carriage is within the first preset off-center load range, adjust the dropping position of the ore.

[0005] Secondly, the present invention provides a mineral loading control system based on mineral moisture content. The mineral loading control system includes a drone and a terminal device, which are wirelessly connected. The terminal device is used to acquire the three-dimensional point cloud and surface reflectance of the mineral material in the loading compartment at various times during the loading process, as well as the standard moisture content and standard density of a standard mineral sample. Based on the surface reflectance of the mineral material at various times within the current detection time window, the surface reflectance of the newly added mineral material at each time within the current detection time window relative to the previous time is obtained. Based on the surface reflectance of the newly added mineral material, the moisture content of the newly added mineral material is obtained. Based on the moisture content of the newly added mineral material, the standard moisture content of the standard mineral sample, and the standard density of the standard mineral sample, the corrected density of the newly added mineral material is obtained. Based on the three-dimensional point cloud of the mineral material at various times within the current detection time window, the incremental volume of the newly added mineral material at each time within the current detection time window relative to the previous time is obtained. Based on the newly added... The incremental volume of the ore is used to obtain the incremental weight of the newly added ore; the total volume of the ore in the current carriage is obtained based on the incremental volume of the newly added ore relative to the previous moment at each moment within the current detection time window; the total weight of the ore in the current carriage is obtained based on the incremental weight of the newly added ore relative to the previous moment at each moment within the current detection time window; the average density of the ore in the current carriage is obtained based on the total volume and the total weight; the cumulative weight of each partition area of ​​the current carriage is obtained based on the average density and the incremental volume of the ore in each partition area of ​​the current carriage; the real-time off-center load value of the current carriage is determined based on the cumulative weight of each partition area; when the real-time off-center load value of the current carriage is within a first preset off-center load range, the dropping position of the ore is adjusted; the UAV is used to scan the reference three-dimensional point cloud of the empty carriage, continuously collect the three-dimensional point cloud and surface reflectance of the ore in the current carriage at each moment during the loading process, and collect the surface reflectance of each standard ore sample.

[0006] Thirdly, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the ore loading control method based on ore moisture content as described in the first aspect.

[0007] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the ore loading control method based on ore moisture content as described in the first aspect.

[0008] The aforementioned mineral loading control method, system, equipment, and medium based on mineral moisture content extracts the surface reflectance of newly added mineral material at two adjacent moments within the current detection time window. Based on this reflectance, the real-time moisture content of the newly added mineral material is derived. The deviation of the real-time moisture content from the moisture content of the standard sample is used as a correction basis. Combined with the parameters of the standard sample, the actual density of each layer of newly added mineral material is dynamically corrected. This approach can match the real-time fluctuations in the moisture content of the mineral material during loading, eliminating weight calculation errors caused by moisture content and batch variations. Furthermore, the incremental volume calculation avoids errors caused by already compacted material. Repeated calculations of the ore pile improve the accuracy of calculating the total weight of the ore in the wagon. Dividing the wagon into multiple zones, the cumulative weight of each zone is calculated by combining the incremental volume of each zone with the overall average density of the ore. Real-time off-center load values ​​are dynamically output, eliminating the need to wait until the entire wagon is loaded before checking the uniformity of loading. When the real-time off-center load value is within a first preset off-center load range, an instruction to adjust the material placement is promptly issued, and the dynamic trend of the real-time off-center load value is continuously monitored within subsequent detection time windows until the real-time off-center load value falls back to the preset safe off-center load threshold range or the entire wagon is loaded. Compared to existing technologies, this invention dynamically corrects the ore density and synchronously adjusts the loading offset during the loading process, avoiding the need for rework and unloading only after loading is completed due to severe off-center loading. This reduces the risk of wagon overturning caused by off-center loading, minimizes material loss and loading time waste, and achieves intelligent ore loading control with high precision, high real-time performance, and high stability. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. 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 an application environment for the mineral loading control method based on the moisture content of minerals in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a mineral loading control method based on mineral moisture content in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of standard mineral sample data collected by a drone in Embodiment 4 of the present invention; Figure 4 This is a schematic diagram of the data collected by the drone in the current ore compartment in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of the structure of a computer device provided in Embodiment 9 of this application. Detailed Implementation

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

[0012] The mineral loading control method based on mineral moisture content provided in this embodiment of the invention can be applied to, for example, mineral loading control in ... Figure 1 The application environment is shown. Specifically, this ore loading control method based on ore moisture content is applied in an ore loading control system, which includes, as shown in the example... Figure 1 The drones and terminal devices shown communicate via a network to achieve real-time updates in ore loading control. The drones are mobile aerial inspection platforms equipped with LiDAR, GNSS positioning, INS, and wireless data transmission modules. They employ a combination of fixed-wing and multi-rotor designs, combining wide-area rapid inspection with close-range high-precision point scanning capabilities. They can autonomously be released from drone hangars, flexibly fly to any train parking point in the mine, and perform remote-controlled operations. The terminal devices can be implemented using independent controllers equipped with high-performance processors and dedicated algorithm software, or a cluster of multiple controllers.

[0013] In Example 1, as Figure 2 As shown, this embodiment provides a mineral loading control method based on mineral moisture content, which is applied to... Figure 1 Taking the terminal equipment in the example, the mineral loading control method based on the moisture content of the mineral includes: Step 100: Obtain the three-dimensional point cloud and surface reflectance of the ore material in the current compartment at various times during the loading process, as well as the standard moisture content and standard density of the standard ore sample. Here, 3D point cloud refers to the set of coordinates of the mineral material in three-dimensional space (X, Y, and Z dimensions), acquired by a LiDAR (Light Detection and Ranging) system mounted on a UAV, which emits laser beams and receives the echoes. Surface reflectivity refers to the intensity of the echo reflected from the surface of the mineral material after the LiDAR system emits a laser beam. Standard mineral material sample refers to a standard sample with the same type of mineral material as the one currently in the carriage, covering a certain range of moisture content variation. Standard moisture content refers to the baseline moisture content obtained by calibration using standard mineral material samples. Standard density refers to the baseline density of the mineral material at the standard moisture content, obtained by calibration using standard mineral material samples.

[0014] In this embodiment, a gradient sample group is selected that is consistent with the type of ore in the current carriage and covers the actual variation range of moisture content (e.g., 0% to 20%). A certain number of parallel samples (e.g., 3 to 5) are set up in each gradient sample group as standard ore samples. The actual moisture content of each standard ore sample is obtained by manually sampling and drying (ore samples are collected on-site and sent to the laboratory, where the moisture content is calculated by drying and weighing). The density of each standard ore sample is measured, and the arithmetic mean is taken to obtain the standard density of each standard ore sample.

[0015] In this embodiment, as Figure 4 As shown, during the loading process, the drone hovers at a certain height (e.g., 5 to 10 meters) above the ore in the current compartment to perform vertical scanning, collecting the three-dimensional point cloud and surface reflectivity of the ore at various times.

[0016] Step 200: Based on the surface reflectance of the ore at each moment within the current detection time window, obtain the surface reflectance of the newly added ore at each moment within the current detection time window relative to the previous moment; based on the surface reflectance of the newly added ore, obtain the moisture content of the newly added ore; based on the moisture content of the newly added ore, the standard moisture content of the standard ore sample, and the standard density of the standard ore sample, obtain the corrected density of the newly added ore; based on the three-dimensional point cloud of the ore at each moment within the current detection time window, obtain the incremental volume of the newly added ore at each moment within the current detection time window relative to the previous moment; based on the incremental volume of the newly added ore, obtain the incremental weight of the newly added ore. The newly added mineral material refers to the mineral material that newly falls into the current carriage and accumulates on the surface of the existing mineral material within the current detection time window. Corrected density refers to the actual density of the newly added mineral material within the current detection time window, dynamically calculated based on the moisture content of the newly added mineral material, the standard moisture content of the standard mineral material sample, and the standard density of the standard mineral material sample. Incremental volume refers to the spatial volume occupied by the newly added mineral material within the current detection time window. Incremental weight refers to the weight of the newly added mineral material within the current detection time window.

[0017] In this embodiment, since the ore continuously falls from the unloading port of the loading station during the loading process, the three-dimensional point cloud and surface reflectivity of the ore will be continuously updated. The frame difference method or incremental point cloud segmentation is used to separate the newly added three-dimensional point cloud and surface reflectivity of the ore at each moment in the current detection time window (calculated from the second moment) relative to the previous moment, thus avoiding the repeated calculation of the already compacted area.

[0018] Step 300: Based on the incremental volume of the newly added ore at each moment within the current detection time window relative to the previous moment, obtain the total volume of the ore in the current carriage; based on the incremental weight of the newly added ore at each moment within the current detection time window relative to the previous moment, obtain the total weight of the ore in the current carriage; and based on the total volume and the total weight, obtain the average density of the ore in the current carriage. The total volume of ore in the current car refers to the cumulative volume of all loaded ore in the current car from the start of loading to the last moment of the current detection time window. The total weight of ore in the current car refers to the cumulative weight of all loaded ore in the current car from the start of loading to the last moment of the current detection time window. The average density of ore in the current car refers to the overall average bulk density of all loaded ore in the current car.

[0019] In this embodiment, the formula for calculating the total volume of ore in the current carriage is: , Among them, V total (t i ) represents the total volume of ore in the current car from the start of loading to the last moment within the current detection time window; t i V represents the last moment within the current detection time window. base ΔV represents the volume of ore that may exist in the current car from the start of loading to the start time of the current detection time window (0 if the car is empty); q represents the sequence number of each moment within the current detection time window; i represents the total number of moments within the current detection time window; ΔV q It represents the incremental volume of the newly added ore at time q within the current detection time window relative to the previous time (q-1).

[0020] In this embodiment, the formula for calculating the total weight of the ore in the current carriage is: , Among them, M total (t i ) represents the total weight of the ore in the current wagon from the start of loading to the last moment within the current detection time window; t i The last moment within the current detection time window; M base Δm represents the weight of the ore that may be present in the current car from the start of loading to the start time of the current detection time window (0 if the car is empty); q represents the sequence number of each moment within the current detection time window; i represents the total number of moments within the current detection time window; Δm represents the total number of moments within the current detection time window. q This represents the incremental weight of the newly added ore at time q within the current detection time window relative to the previous time (q-1).

[0021] In this embodiment, the formula for calculating the average density of the ore in the current carriage is: , in, M represents the average density of the ore in the current wagon from the start of loading to the last moment within the current detection time window; total (t i V represents the total weight of the ore in the current wagon from the start of loading to the last moment within the current inspection time window; total (t i ) represents the total volume of ore in the current car from the start of loading to the last moment within the current detection time window; t i This refers to the last moment within the current detection time window.

[0022] Step 400: Based on the average density and the incremental volume of the ore in each partition area of ​​the current car, obtain the cumulative weight of each partition area of ​​the current car, and determine the real-time off-center load value of the current car based on the cumulative weight of each partition area. Among them, the real-time off-center load value of the current carriage is a quantitative indicator used to characterize the degree of uneven weight distribution among the various zones within the current carriage.

[0023] In this embodiment, the plane of the current carriage is divided into N sections along the longitudinal direction (from the front of the carriage to the rear of the carriage) and the transverse direction (from the left of the carriage to the right of the carriage). r The system divides the car into several partitioned regions, discretizing the car's space into a three-dimensional voxel mesh covering the entire car. Each voxel is assigned to a corresponding partitioned region based on its planar coordinates. By counting the number of newly added voxels in each partitioned region, the incremental volume of each partitioned region is calculated. This incremental volume is then converted into incremental weight by combining the average density of the ore in the current car, and accumulated over time to obtain the cumulative weight of each partitioned region.

[0024] In this embodiment, the formula for calculating the cumulative weight of each zone in the current carriage is as follows: , Among them, M r (t i ) represents the cumulative weight of the r-th section of the current carriage; r is the sequence number of each section of the current carriage; t i q represents the last moment within the current detection time window; i represents the total number of moments within the current detection time window; q represents the sequence number of each moment within the current detection time window; p represents the sequence number of each grid cell (a partition contains multiple grid cells) within the r-th partition region of the current carriage; j represents the sequence number of each grid cell within the r-th partition region of the current carriage. rΔm represents the total number of grid cells in the r-th partition region of the current carriage; r q,p Let p be the incremental mass of the p-th grid cell in the r-th partition region of the current carriage at time q relative to the previous time (q-1); ΔV represents the average density of the ore in the current wagon from the start of loading to the last moment within the current detection time window. r q,p Let q be the incremental volume of the p-th grid cell within the r-th partition region of the current carriage at time q relative to the previous time (q-1).

[0025] In this embodiment, the formula for calculating the real-time off-center load value of the current carriage is: , Among them, ULD(t) i ) represents the real-time off-center load value of the current carriage, t i Let i be the last moment within the current detection time window, and let i be the total number of moments within the current detection time window. N represents the maximum cumulative weight of each partition region within the current detection time window, where n is the sequence number of each partition region within the current detection time window. r M represents the total number of partition regions within the current detection time window. n (t i ) represents the cumulative weight of the nth section of the current carriage. This represents the minimum cumulative weight across all partitions within the current detection time window.

[0026] Step 500: When the real-time off-center load value of the current carriage is within the first preset off-center load range, adjust the dropping position of the ore.

[0027] The first preset off-center load range refers to the pre-set warning range threshold for the off-center load state: when the real-time off-center load value of the current car is within this range, the weight distribution of the ore in the current car is uneven, the dropping position of the ore is adjusted and an audible and visual warning is triggered.

[0028] In this embodiment, the first preset off-center load range is: greater than 5% and less than or equal to 8%.

[0029] In this embodiment, the terminal device sends a command to the loading control system (e.g., PLC) to change the planar position of the unloading port (e.g., move it longitudinally or laterally along the carriage), so that the subsequently loaded ore falls into the partition area with a smaller real-time off-center load value.

[0030] This embodiment of the ore loading control method based on ore moisture content extracts the surface reflectance of newly added ore at two adjacent moments within the current detection time window, and inverts the real-time moisture content of the newly added ore based on the reflectance. The deviation of the real-time moisture content from the moisture content of the standard sample is used as the correction basis, and the actual density of each layer of newly added ore is dynamically corrected in combination with the parameters of the standard sample. This can match the characteristics of real-time fluctuation of ore moisture content during loading, eliminate the weight calculation error caused by moisture and batch differences, and the incremental volume calculation further avoids the repeated calculation of the already compacted ore pile, improving the accuracy of the total weight of the ore in the car. The car is divided into multiple zones, and the cumulative weight of each zone is calculated by combining the incremental volume of each zone with the overall average density of the ore. The real-time off-load value is dynamically output, without waiting for the whole car to be loaded before detecting the uniformity of loading. When the real-time off-load value is within the preset off-load threshold range, the instruction to adjust the dropping position is issued immediately, and the dynamic change trend of the real-time off-load value is continuously monitored in the subsequent detection time window until the real-time off-load value falls back to the preset safe off-load threshold range or the whole car is loaded. By dynamically correcting the density of the ore and synchronously adjusting the loading offset during the loading process, the system avoids the need for rework and unloading only after the loading is completed due to severe uneven loading. This reduces the risk of the car overturning caused by uneven loading, minimizes material loss of the ore, and reduces the time wasted in loading. It achieves intelligent ore loading control with high precision, high real-time performance, and high stability.

[0031] In Embodiment 2, the mineral loading control method based on mineral moisture content further includes: Step 610: Based on the total weight of the ore in the current car, obtain the average loading speed of the ore in the current car; based on the average loading speed and the preset remaining loading time, obtain the estimated weight of the ore. The average loading speed refers to the incremental weight of newly added ore per unit time from the last moment of a detection time window earlier than the current detection time window to the last moment within the current detection time window. The preset remaining loading time refers to the remaining time length between the last moment within the current detection time window and the end of loading. This can be obtained by subtracting the used loading time from the total loading plan time, or by real-time estimation using level gauges or belt scales. The estimated weight of the ore refers to the final predicted total weight of the ore when loading is completed, calculated based on the total weight of the currently loaded ore and the average loading speed within the current detection time window.

[0032] In this embodiment, the formula for calculating the average loading speed in the current carriage is: , in, M represents the average loading speed in the current carriage. total (ti M represents the total weight of the ore in the current wagon from the start of loading to the last moment within the current detection time window; total (t i-s ) represents the total weight of the ore in the current car, calculated from the last moment of s detection time windows earlier than the current detection time window; t i t represents the last moment within the current detection time window. i-s This refers to the last moment of s detection time windows that are earlier than the current detection time window.

[0033] In this embodiment, the formula for calculating the estimated weight of the ore is as follows: , Among them, M pred (t i ) represents the estimated weight of the ore; M total (t i The total weight of the ore in the current wagon is the total weight of the ore from the start of loading to the last moment within the current detection time window. ΔT represents the average loading speed in the current carriage. rem (t i () represents the preset remaining loading time.

[0034] In this embodiment, similarly, the estimated weight of the ore in each zone can be calculated based on the ore weight growth rate of each zone. Based on the estimated weight of the ore in each zone, the real-time off-center load value at the time of loading completion can be further predicted, enabling early prediction and proactive intervention of off-center load.

[0035] Step 620: When the estimated weight of the ore is within the first preset overload range, adjust the loading speed of the ore.

[0036] The first preset overload range refers to the pre-set warning range threshold for overload conditions: when the estimated weight of the ore is within this range, the weight of the ore in the current car is close to or may exceed the rated load, the loading speed of the ore is adjusted and an audible and visual warning is triggered.

[0037] In this embodiment, the first preset overload range is: greater than 0.9 times the rated load of the current carriage and less than or equal to the rated load of the current carriage.

[0038] The ore loading control method based on ore moisture content in this embodiment calculates the short-term average loading speed through a real-time sliding window and predicts the final total loading weight upon completion of loading. When the estimated weight is within a preset two-level overload threshold range, the loading speed is reduced in advance. In the remaining time, the final total loading weight is precisely controlled within the rated load. This method can dynamically control the total amount of material discharged during loading, avoiding overload rework caused by excessively fast material discharge, reducing losses from excess material, and improving loading efficiency. At the same time, it achieves graded management of overload risk, ensuring that loading does not exceed limits without frequent shutdowns, thus balancing loading continuity and safety.

[0039] In Embodiment 3, step 200 includes: Step 201: Based on the surface reflectance of the newly added mineral material at each moment within the current detection time window relative to the previous moment, obtain the average surface reflectance of the newly added mineral material. In this embodiment, the arithmetic mean of the surface reflectance of the newly added mineral material at each moment within the current detection time window relative to the previous moment is taken as the average surface reflectance of the newly added mineral material.

[0040] Step 202: Based on the average surface reflectance of the newly added mineral material and the preset mapping relationship between the moisture content and reflectance of the mineral material, the moisture content of the newly added mineral material is obtained. The pre-defined mapping relationship between the moisture content and reflectivity of the mineral material refers to a mapping model or functional relationship obtained in advance through calibration tests, which reflects the negative correlation between the moisture content and reflectivity of the mineral material.

[0041] In this embodiment, the formula for calculating the moisture content of the newly added mineral material is as follows: , Where c is the sequence number of the current detection time window, w c new f represents the moisture content of newly added ore within the current detection time window. model () represents the preset mapping relationship between the moisture content and reflectivity of the mineral material. This represents the average surface reflectance of newly added mineral materials within the current detection time window.

[0042] Step 203: Based on the moisture content of the newly added mineral material, the standard moisture content of the standard mineral material sample, the standard density of the standard mineral material sample, and the preset mapping relationship between the density and moisture content of the mineral material, the corrected density of the newly added mineral material is obtained. The pre-defined mapping relationship between the density and moisture content of the ore refers to a functional relationship obtained in advance through calibration tests, which reflects the positive correlation between the density and moisture content of the ore.

[0043] Step 204: Based on the three-dimensional point cloud of the ore at each moment within the current detection time window, obtain the incremental volume of the newly added ore at each moment within the current detection time window relative to the previous moment. In this embodiment, based on the three-dimensional point cloud of the ore at each moment within the current detection time window, the three-dimensional point cloud of the newly added ore at each moment within the current detection time window relative to the previous moment is obtained, and based on the three-dimensional point cloud of the newly added ore, the incremental volume of the newly added ore is obtained.

[0044] Step 205: Based on the corrected density of the newly added ore and the incremental volume of the newly added ore, obtain the incremental weight of the newly added ore.

[0045] In this embodiment, the product of the corrected density of the newly added ore and the incremental volume of the newly added ore is the incremental weight of the newly added ore.

[0046] This embodiment of the ore loading control method based on ore moisture content calculates the moisture content of the newly added ore based on the average surface reflectivity of the ore and the mapping relationship between ore moisture content and reflectivity. Then, it performs humidity compensation on the standard density by combining the mapping relationship between ore density and moisture content to obtain a corrected density. Simultaneously, it uses 3D point cloud computing to calculate the incremental volume of the newly added ore, and finally calculates the incremental weight of the newly added ore from the corrected density and the incremental volume. By correcting the ore density in real time based on moisture content, it avoids inaccuracies in ore density caused by material moisture fluctuations due to dynamic fluctuations in moisture content (such as those caused by rain and snow), improving the accuracy of weight calculation based on the 3D point cloud volume method. This provides a data foundation for subsequent early prediction and proactive intervention in overloading and off-center loading.

[0047] In Example 4, step 202, the step of obtaining the preset mapping relationship between the moisture content and reflectivity of the mineral material, includes: Step 2021: Obtain the surface reflectance of each of the standard mineral samples, and perform noise reduction processing on the surface reflectance of each of the standard mineral samples to obtain the pre-processed surface reflectance of each of the standard mineral samples. Among them, noise reduction processing refers to the filtering process performed on the original surface reflectance of the standard mineral sample. The purpose is to eliminate interference factors such as ambient light interference and random noise from the sensor, so as to ensure the accuracy of the surface reflectance data.

[0048] In this embodiment, smoothing algorithms such as Gaussian filtering or median filtering are used to denoise the surface reflectance of each standard mineral sample to obtain the pre-processed surface reflectance of each standard mineral sample.

[0049] In this embodiment, as Figure 3 As shown, the drone hovers at a certain height (e.g., 5 to 10 meters) above each standard mineral sample to perform vertical scanning and collect the surface reflectance of each standard mineral sample.

[0050] Step 2022: Based on the pre-processed surface reflectance of each of the standard mineral samples, obtain the average surface reflectance of each of the standard mineral samples. In this embodiment, the arithmetic mean of the surface reflectance of each standard mineral sample after pretreatment is taken as the average surface reflectance of each standard mineral sample.

[0051] Step 2023: Obtain the moisture content of each of the standard mineral samples, and determine the mapping relationship between the preset moisture content and reflectivity of the mineral based on the moisture content and the average surface reflectivity of each of the standard mineral samples.

[0052] In this embodiment, the moisture content of each standard mineral sample is used as the dependent variable, and the average surface reflectance of each standard mineral sample is used as the independent variable. A multinomial fitting method (with 20 to 50 samples) or a backpropagation neural network (with more than 50 samples) is used to obtain the preset mapping relationship between the moisture content and reflectance of the mineral.

[0053] Taking the acquisition of the preset mapping relationship between the moisture content and reflectivity of mineral materials using polynomial fitting as an example: with the moisture content of each standard mineral material sample as the dependent variable and the average surface reflectivity of each standard mineral material sample as the independent variable, a polynomial model is established. The coefficients of the polynomial model are solved by the least squares method. The optimization objective is to minimize the sum of squared residuals between the predicted moisture content and the measured moisture content. Candidate polynomial models are obtained. The determination coefficient and root mean square error of the candidate polynomial model are calculated. When the determination coefficient of the candidate polynomial model is greater than or equal to 0.95 and the root mean square error is less than or equal to 0.5%, the candidate polynomial model at this time is used as the preset mapping relationship between the moisture content and reflectivity of mineral materials.

[0054] The formula for calculating the coefficient of determination of the candidate polynomial model is as follows: , Among them, R 2 Here, represents the coefficient of determination for the candidate polynomial model, t represents the sequence number of the standard mineral sample, z represents the total number of standard mineral samples, and w represents the coefficient of determination. t Let w be the measured moisture content of the t-th standard mineral sample. t,y Let be the predicted moisture content of the t-th standard mineral sample. It is the arithmetic mean of the measured values ​​of moisture content of standard mineral samples.

[0055] The formula for calculating the root mean square error of the candidate polynomial model is as follows: , Wherein, RMSE is the root mean square error of the candidate polynomial model.

[0056] The mineral loading control method based on mineral moisture content in this embodiment sequentially performs noise reduction and mean calculation on the original surface reflectance of standard mineral samples, eliminating the influence of sensor random noise, ambient light interference, and single-point measurement random errors on the calibration data, significantly improving the stability and reliability of the data. Using the measured value of moisture content as the supervision signal and the coefficient of determination and root mean square error as constraints, the mapping relationship between the moisture content and reflectance of the mineral is fitted, ensuring that the mapping model has good fit and prediction reliability throughout the entire moisture content variation range. This provides an accurate and reliable conversion basis for the inversion of the moisture content of newly added minerals, reducing the density measurement deviation caused by moisture fluctuations from the source, and achieving high-precision measurement of mineral loading weight.

[0057] In Example 5, step 203, the step of obtaining the preset mapping relationship between the density and moisture content of the mineral material, includes: Step 2031: Obtain the measured moisture content and measured density of the M groups of mineral materials, as well as the initial model between the density and moisture content of the mineral materials, where M is a positive integer greater than 1. The expression of the initial model is as follows: ρ = ρ0 × [1 + k × (w - w0)], Wherein, ρ is the measured density of the mineral materials in group M, ρ0 is the standard density of the standard mineral material sample, k is the density correction coefficient of the mineral material to be calibrated, w is the measured moisture content of the mineral materials in group M, and w0 is the standard moisture content of the standard mineral material sample. Among them, the measured moisture content refers to the actual measured value of the moisture content of each group of ore. The measured density refers to the actual measured value of the density of each group of ore.

[0058] Step 2032: Based on the measured moisture content and measured density of the mineral materials in group M, the standard moisture content and standard density of the standard mineral material sample, and the initial model between the density and moisture content of the mineral materials, obtain the density correction coefficient of the calibrated mineral materials. In this embodiment, based on the measured moisture content and measured density of group M mineral materials, the standard moisture content and standard density of standard mineral samples, and the initial model between the density and moisture content of the mineral materials, the least squares method is used to fit the measured moisture content and measured density of group M mineral materials as a whole. The optimization objective is to minimize the sum of squared residuals between the measured density and the predicted density, and the unique density correction coefficient after calibration is obtained.

[0059] Step 2033: Based on the density correction coefficient of the calibrated ore and the initial model between the density and moisture content of the ore, obtain the preset mapping relationship between the density and moisture content of the ore.

[0060] In this embodiment, the initial model between the density and moisture content of the ore is corrected by using the density correction coefficient of the calibrated ore obtained, so as to obtain the preset mapping relationship between the density and moisture content of the ore.

[0061] The mineral loading control method based on mineral moisture content in this embodiment calibrates the density correction coefficient by measuring the actual moisture content and density of multiple sets of minerals. Based on the initial model between mineral density and moisture content, the final mapping relationship between mineral density and moisture content is obtained. This method can accurately quantify the change law of mineral density when the moisture content deviates from the standard value, which facilitates the rapid output of the corrected density after humidity compensation based on the moisture content of the minerals. It effectively eliminates the density deviation caused by rain, snow or uneven moisture content of the minerals, and greatly improves the calculation accuracy of incremental weight. This provides a reliable density correction basis for accurate weighing and overload warning during the mineral loading process.

[0062] In Example 6, step 204 includes: Step 2041: Based on the three-dimensional point cloud of the ore at each moment within the current detection time window, obtain the three-dimensional point cloud of the newly added ore at each moment within the current detection time window relative to the previous moment. In this embodiment, before loading begins, the drone hovers in position and uses LiDAR to perform a baseline scan of the empty wagon, acquiring the internal baseline 3D point cloud of the empty wagon for subsequent removal of structural interference. Using frame difference or incremental point cloud segmentation, the 3D point cloud of newly added ore at each moment within the current detection time window (calculated starting from the second moment) relative to the previous moment is separated from the 3D point cloud of the ore at each moment within the current detection time window, avoiding redundant calculations of already compacted areas.

[0063] Step 2042: Perform three-dimensional reconstruction processing on the three-dimensional point cloud of the newly added ore to obtain a three-dimensional model of the newly added ore. Among them, 3D reconstruction processing refers to the process of spatial interpolation and surface fitting of discrete, newly added 3D point clouds of mineral materials, transforming them into continuous, measurable 3D geometric models. A 3D model is a continuous 3D digital model generated after 3D reconstruction processing, used to describe the geometric shape of the newly added mineral materials.

[0064] Step 2043: Based on the three-dimensional model of the newly added ore, obtain the incremental volume of the newly added ore at each moment within the current detection time window relative to the previous moment.

[0065] In this embodiment, the three-dimensional model is either a Delaunay triangulation model or a voxel-based three-dimensional model.

[0066] Taking the Delaunay triangulation model as an example: An irregular triangular mesh (TIN) is constructed from the 3D point cloud of the newly added ore. The closed volume enclosed by the 3D point cloud of the newly added ore relative to the bottom surface of the carriage (or the surface of the stockpile at the previous moment) is calculated. The specific calculation formula is as follows: , Where, Δv i new N represents the incremental volume of newly added ore at each moment within the current detection time window relative to the previous moment. tri S represents the number of triangular facets formed by the 3D point cloud of the newly added ore, j represents the sequence number of the triangular facets formed by the 3D point cloud of the newly added ore, and S represents the number of the triangular facets formed by the 3D point cloud of the newly added ore. j Let h be the base area of ​​the j-th triangular facet. j Let be the height of the centroid of the j-th triangular facet from the bottom surface (or reference surface) of the train car.

[0067] Taking a voxel-based 3D model as an example (more suitable for dynamic scenes with high real-time requirements): The current carriage space is divided into a fixed-size voxel grid (e.g., 5cm×5cm×5cm). The newly filled voxels in each voxel grid are the incremental volume, and the specific calculation formula is as follows: Δv i =n new ×δ 3 , Where, Δv i new n represents the incremental volume of newly added ore at each moment within the current detection time window relative to the previous moment. new δ represents the number of newly filled voxels at each moment within the current detection time window relative to the previous moment, and δ represents the side length of the newly filled voxels at each moment within the current detection time window relative to the previous moment.

[0068] The ore loading control method based on ore moisture content in this embodiment first acquires a baseline 3D point cloud of an empty car, then uses frame difference or incremental point cloud segmentation to extract the 3D point cloud of newly added ore between two adjacent time points, eliminating duplicate interference from the car structure and previously stockpiled materials; then, the discrete 3D point cloud is reconstructed by spatial interpolation and surface fitting, and the incremental volume of the newly added ore is accurately calculated based on the obtained 3D model. The entire process only counts the volume of ore generated by a single loading, avoiding repeated accumulation of stockpiled ore that could cause measurement overestimation and cumulative errors, balancing volume calculation accuracy and real-time computing efficiency, and providing a reliable data foundation for calculating the incremental weight of ore.

[0069] In Example 7, the mineral loading control method based on mineral moisture content further includes: Step 710: When the estimated weight of the ore is within the second preset overload range and the real-time off-center load value of the current car is within the second preset off-center load range, maintain the current loading speed of the ore. In this embodiment, the second preset overload range is less than or equal to 0.9 times the rated load of the current car, and the second preset off-center load range is less than or equal to 5%. When the estimated weight of the ore is within the second preset overload range and the real-time off-center load value of the current car is within the second preset off-center load range, the current loading speed of the ore is maintained.

[0070] Step 720: When the estimated weight of the ore is within the third preset overload range, or when the real-time off-center load value of the current car is within the third preset off-center load range, loading shall be immediately suspended and manual verification shall be performed.

[0071] In this embodiment, the third preset overload range is: greater than the rated load of the current car, and the third preset off-center load range is: greater than 8%. When the estimated weight of the ore is within the third preset overload range, or when the real-time off-center load value of the current car is within the third preset off-center load range, loading is immediately suspended and manual verification is performed.

[0072] The ore loading control method based on ore moisture content in this embodiment maintains uniform feeding speed within a safe range where the estimated weight of the ore does not reach 90% of the rated load and the current real-time off-center load value of the current carriage does not exceed 5%, ensuring continuous and efficient loading operations. When the estimated weight of the ore reaches the rated load or the current real-time off-center load value of the current carriage exceeds 8%, the machine is immediately stopped and manual verification is initiated, realizing early intervention against overloading and off-center loading. This method balances the efficiency of ore loading operations and avoids transportation safety risks caused by vehicle overloading or center of gravity imbalance from the source, achieving dual safety control of overloading and off-center loading during the loading process and improving the intelligence level of ore loading control.

[0073] In Embodiment 8, a mineral loading control system based on mineral moisture content is provided, comprising a drone and a terminal device. The drone and the terminal device are wirelessly connected. The terminal device is used to acquire the three-dimensional point cloud and surface reflectance of the mineral material in the loading compartment at various times during the loading process, as well as the standard moisture content and standard density of a standard mineral sample. Based on the surface reflectance of the mineral material at various times within the current detection time window, the surface reflectance of the newly added mineral material at each time point within the current detection time window relative to the previous time point is obtained. Based on the surface reflectance of the newly added mineral material, the moisture content of the newly added mineral material is obtained. Based on the moisture content of the newly added mineral material, the standard moisture content of the standard mineral sample, and the standard density of the standard mineral sample, the corrected density of the newly added mineral material is obtained. Based on the three-dimensional point cloud of the mineral material at various times within the current detection time window, the incremental volume of the newly added mineral material at each time point within the current detection time window relative to the previous time point is obtained. Based on the incremental volume of the newly added mineral material, the... The incremental weight of the newly added ore is calculated; based on the incremental volume of the newly added ore relative to the previous moment at each moment within the current detection time window, the total volume of the ore in the current carriage is obtained; based on the incremental weight of the newly added ore relative to the previous moment at each moment within the current detection time window, the total weight of the ore in the current carriage is obtained; based on the total volume and the total weight, the average density of the ore in the current carriage is obtained; based on the average density and the incremental volume of the ore in each partition area of ​​the current carriage, the cumulative weight of each partition area of ​​the current carriage is obtained; based on the cumulative weight of each partition area, the real-time off-center load value of the current carriage is determined; when the real-time off-center load value of the current carriage is within a first preset off-center load range, the dropping position of the ore is adjusted; the UAV is used to scan the reference three-dimensional point cloud of the empty carriage, continuously collect the three-dimensional point cloud and surface reflectance of the ore in the current carriage at each moment during the loading process, and collect the surface reflectance of each standard ore sample.

[0074] The terminal device is also used to obtain the average loading speed of the ore in the current car based on the total weight of the ore in the current car, and to obtain the estimated weight of the ore based on the average loading speed and the preset remaining loading time; when the estimated weight of the ore is within a first preset overload range, the loading speed of the ore is adjusted.

[0075] The terminal device is further configured to: obtain the average surface reflectance of the newly added mineral material based on the surface reflectance of the newly added mineral material at each moment within the current detection time window relative to the previous moment; obtain the moisture content of the newly added mineral material based on the average surface reflectance of the newly added mineral material and a preset mapping relationship between the moisture content and reflectance of the mineral material; obtain the corrected density of the newly added mineral material based on the moisture content of the newly added mineral material, the standard moisture content of the standard mineral material sample, the standard density of the standard mineral material sample, and a preset mapping relationship between the density and moisture content of the mineral material; obtain the incremental volume of the newly added mineral material at each moment within the current detection time window relative to the previous moment based on the three-dimensional point cloud of the mineral material at each moment within the current detection time window; and obtain the incremental weight of the newly added mineral material based on the corrected density and the incremental volume of the newly added mineral material.

[0076] The terminal device is further configured to acquire the surface reflectance of each of the standard mineral samples, perform noise reduction processing on the surface reflectance of each of the standard mineral samples to obtain the pre-processed surface reflectance of each of the standard mineral samples; obtain the average surface reflectance of each of the standard mineral samples based on the pre-processed surface reflectance of each of the standard mineral samples; acquire the moisture content of each of the standard mineral samples, and determine the mapping relationship between the preset moisture content and reflectance of the mineral based on the moisture content and the average surface reflectance of each of the standard mineral samples.

[0077] The terminal device is also used to acquire the measured moisture content and measured density of M sets of the mineral materials, as well as an initial model between the density and moisture content of the mineral materials, where M is a positive integer greater than 1, and the expression of the initial model is as follows: ρ = ρ0 × [1 + k × (w - w0)], Wherein, ρ is the measured density of the M group of mineral materials, ρ0 is the standard density of the standard mineral material sample, k is the density correction coefficient of the mineral material to be calibrated, w is the measured moisture content of the M group of mineral materials, and w0 is the standard moisture content of the standard mineral material sample; based on the measured moisture content and measured density of the M group of mineral materials, the standard moisture content and standard density of the standard mineral material sample, and the initial model between the density and moisture content of the mineral materials, the density correction coefficient of the calibrated mineral materials is obtained; based on the density correction coefficient of the calibrated mineral materials and the initial model between the density and moisture content of the mineral materials, the preset mapping relationship between the density and moisture content of the mineral materials is obtained.

[0078] The terminal device is further configured to obtain the three-dimensional point cloud of the newly added ore relative to the previous moment at each moment within the current detection time window based on the three-dimensional point cloud of the ore at each moment within the current detection time window; perform three-dimensional reconstruction processing on the three-dimensional point cloud of the newly added ore to obtain the three-dimensional model of the newly added ore; and obtain the incremental volume of the newly added ore relative to the previous moment at each moment within the current detection time window based on the three-dimensional model of the newly added ore.

[0079] The terminal device is also used to maintain the current loading speed of the ore when the estimated weight of the ore is within the second preset overload range and the real-time off-center load value of the current car is within the second preset off-center load range; and to immediately suspend loading and perform manual verification when the estimated weight of the ore is within the third preset overload range or the real-time off-center load value of the current car is within the third preset off-center load range.

[0080] The terminal equipment is also used to determine the final moisture content, final corrected density, final total volume, final total weight, final off-center load condition, and final overload condition of the ore after the loading process is completed, and to generate a loading inspection report.

[0081] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0082] In embodiment nine, a terminal device is provided, such as... Figure 5 As shown, it includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the ore loading control method based on ore moisture content described in any of the embodiments one to seven above, for example... Figure 2 Steps 100 to 500 shown are omitted here to avoid repetition.

[0083] In Embodiment 10, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the ore loading control method based on ore moisture content described in any of Embodiments 1 to 7 above, for example... Figure 2 Steps 100 to 500 shown are omitted here to avoid repetition.

[0084] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0085] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0086] While specific embodiments of the invention have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention. The scope of this invention is defined by the appended claims.

Claims

1. A method for controlling ore loading based on ore moisture content, characterized in that, The mineral loading control method based on mineral moisture content includes: Step 100: Obtain the three-dimensional point cloud and surface reflectance of the ore material in the current compartment at various times during the loading process, as well as the standard moisture content and standard density of the standard ore sample. Step 200: Based on the surface reflectance of the ore at each moment within the current detection time window, obtain the surface reflectance of the newly added ore at each moment within the current detection time window relative to the previous moment; based on the surface reflectance of the newly added ore, obtain the moisture content of the newly added ore; based on the moisture content of the newly added ore, the standard moisture content of the standard ore sample, and the standard density of the standard ore sample, obtain the corrected density of the newly added ore; based on the three-dimensional point cloud of the ore at each moment within the current detection time window, obtain the incremental volume of the newly added ore at each moment within the current detection time window relative to the previous moment; based on the incremental volume of the newly added ore, obtain the incremental weight of the newly added ore. Step 300: Based on the incremental volume of the newly added ore at each moment within the current detection time window relative to the previous moment, obtain the total volume of the ore in the current carriage; based on the incremental weight of the newly added ore at each moment within the current detection time window relative to the previous moment, obtain the total weight of the ore in the current carriage; and based on the total volume and the total weight, obtain the average density of the ore in the current carriage. Step 400: Based on the average density and the incremental volume of the ore in each partition area of ​​the current car, obtain the cumulative weight of each partition area of ​​the current car; and determine the real-time off-center load value of the current car based on the cumulative weight of each partition area. Step 500: When the real-time off-center load value of the current carriage is within the first preset off-center load range, adjust the dropping position of the ore.

2. The mineral loading control method based on mineral moisture content according to claim 1, characterized in that, The mineral loading control method based on mineral moisture content also includes: Step 610: Based on the total weight of the ore in the current car, obtain the average loading speed of the ore in the current car; based on the average loading speed and the preset remaining loading time, obtain the estimated weight of the ore. Step 620: When the estimated weight of the ore is within the first preset overload range, adjust the loading speed of the ore.

3. The mineral loading control method based on mineral moisture content according to claim 1, characterized in that, Step 200 includes: Step 201: Based on the surface reflectance of the newly added mineral material at each moment within the current detection time window relative to the previous moment, obtain the average surface reflectance of the newly added mineral material. Step 202: Based on the average surface reflectance of the newly added mineral material and the preset mapping relationship between the moisture content and reflectance of the mineral material, the moisture content of the newly added mineral material is obtained. Step 203: Based on the moisture content of the newly added mineral material, the standard moisture content of the standard mineral material sample, the standard density of the standard mineral material sample, and the preset mapping relationship between the density and moisture content of the mineral material, the corrected density of the newly added mineral material is obtained. Step 204: Based on the three-dimensional point cloud of the ore at each moment within the current detection time window, obtain the incremental volume of the newly added ore at each moment within the current detection time window relative to the previous moment. Step 205: Based on the corrected density of the newly added ore and the incremental volume of the newly added ore, obtain the incremental weight of the newly added ore.

4. The mineral loading control method based on mineral moisture content according to claim 1, characterized in that, Step 202, the step of obtaining the preset mapping relationship between the moisture content and reflectivity of the mineral material includes: Step 2021: Obtain the surface reflectance of each of the standard mineral samples, and perform noise reduction processing on the surface reflectance of each of the standard mineral samples to obtain the pre-processed surface reflectance of each of the standard mineral samples. Step 2022: Based on the pre-processed surface reflectance of each of the standard mineral samples, obtain the average surface reflectance of each of the standard mineral samples. Step 2023: Obtain the moisture content of each of the standard mineral samples, and determine the mapping relationship between the preset moisture content and reflectivity of the mineral based on the moisture content and the average surface reflectivity of each of the standard mineral samples.

5. The mineral loading control method based on mineral moisture content according to claim 1, characterized in that, Step 203, the step of obtaining the preset mapping relationship between the density and moisture content of the mineral material, includes: Step 2031: Obtain the measured moisture content and measured density of the M groups of mineral materials, as well as the initial model between the density and moisture content of the mineral materials, where M is a positive integer greater than 1. The expression of the initial model is as follows: ρ = ρ0 × [1 + k × (w - w0)], Wherein, ρ is the measured density of the mineral materials in group M, ρ0 is the standard density of the standard mineral material sample, k is the density correction coefficient of the mineral material to be calibrated, w is the measured moisture content of the mineral materials in group M, and w0 is the standard moisture content of the standard mineral material sample. Step 2032: Based on the measured moisture content and measured density of the mineral materials in group M, the standard moisture content and standard density of the standard mineral material sample, and the initial model between the density and moisture content of the mineral materials, obtain the density correction coefficient of the calibrated mineral materials. Step 2033: Based on the density correction coefficient of the calibrated ore and the initial model between the density and moisture content of the ore, obtain the preset mapping relationship between the density and moisture content of the ore.

6. The mineral loading control method based on mineral moisture content according to claim 2, characterized in that, Step 204 includes: Step 2041: Based on the three-dimensional point cloud of the ore at each moment within the current detection time window, obtain the three-dimensional point cloud of the newly added ore at each moment within the current detection time window relative to the previous moment. Step 2042: Perform three-dimensional reconstruction processing on the three-dimensional point cloud of the newly added ore to obtain a three-dimensional model of the newly added ore. Step 2043: Based on the three-dimensional model of the newly added ore, obtain the incremental volume of the newly added ore at each moment within the current detection time window relative to the previous moment.

7. The mineral loading control method based on mineral moisture content according to claim 2, characterized in that, The mineral loading control method based on mineral moisture content also includes: Step 710: When the estimated weight of the ore is within the second preset overload range and the real-time off-center load value of the current car is within the second preset off-center load range, maintain the current loading speed of the ore. Step 720: When the estimated weight of the ore is within the third preset overload range, or when the real-time off-center load value of the current car is within the third preset off-center load range, loading shall be immediately suspended and manual verification shall be performed.

8. A mineral loading control system based on the moisture content of minerals, characterized in that, The mineral loading control system based on mineral moisture content includes a drone and a terminal device. The drone and the terminal device are wirelessly connected. The terminal device is used to acquire the three-dimensional point cloud and surface reflectance of the mineral material in the current compartment at various times during the loading process, as well as the standard moisture content and standard density of the standard mineral sample. Based on the surface reflectance of the ore at each moment within the current detection time window, the surface reflectance of the newly added ore at each moment within the current detection time window relative to the previous moment is obtained. Based on the surface reflectance of the newly added ore, the moisture content of the newly added ore is obtained. Based on the moisture content of the newly added ore, the standard moisture content of the standard ore sample, and the standard density of the standard ore sample, the corrected density of the newly added ore is obtained. Based on the three-dimensional point cloud of the ore at each moment within the current detection time window, the incremental volume of the newly added ore at each moment within the current detection time window relative to the previous moment is obtained. Based on the incremental volume of the newly added ore, the... The incremental weight of the newly added ore; based on the incremental volume of the newly added ore relative to the previous moment at each moment within the current detection time window, the total volume of the ore in the current car is obtained; based on the incremental weight of the newly added ore relative to the previous moment at each moment within the current detection time window, the total weight of the ore in the current car is obtained; based on the total volume and the total weight, the average density of the ore in the current car is obtained; based on the average density and the incremental volume of the ore in each partition area of ​​the current car, the cumulative weight of each partition area of ​​the current car is obtained; based on the cumulative weight of each partition area, the real-time off-center load value of the current car is determined. When the real-time off-center load value of the current carriage is within the first preset off-center load range, the dropping position of the ore is adjusted; the drone is used to scan the reference three-dimensional point cloud of the empty carriage, continuously collect the three-dimensional point cloud and surface reflectance of the ore in the current carriage at various times during the loading process, and collect the surface reflectance of each standard ore sample.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the mineral loading control method based on the moisture content of the mineral as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the mineral loading control method based on the moisture content of the mineral as described in any one of claims 1 to 7.

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