Mineral exploration method, mineral exploration system and mineral exploration robot

Abstract

The present invention provides a mineral exploration method, a mineral exploration system and a mineral exploration robot, which are applied to the mineral exploration robot. The mineral exploration robot is provided with a visual perception device, an analysis device and a sampling tool, and is communicatively connected to an external host computer; the method includes: obtaining an exploration point image of a target exploration point through the visual perception device; inputting the exploration point image into a pre-trained geological type recognition model to output the actual geological type corresponding to the exploration point image; determining a target sampling tool corresponding to the actual geological type based on the actual geological type and the preset corresponding relationship between the geological type and the sampling tool, and collecting samples through the target sampling tool; and analyzing the collected samples in real time through the analysis device to obtain real-time exploration data of the target exploration point. In this way, the geological type can be automatically identified in complex terrains, a suitable sampling tool can be selected, and the sample composition can be analyzed in real time, thereby improving the safety and efficiency of exploration work.

Description

Technical Field

[0001] The present invention relates to the technical field of mineral exploration, and in particular, to a mineral exploration method, a mineral exploration system, and a mineral exploration robot. Background Art

[0002] During the process of mineral resource exploration, exploration personnel usually need to identify geological types and collect samples in the target area to obtain mineral composition and content data. The surveying equipment in the prior art often requires on-site operation by operators, and the operation difficulty and complexity of the surveying equipment are relatively high. While affecting the surveying efficiency, it also makes the surveying operation extremely vulnerable to natural environmental factors, affecting the versatility and environmental adaptability of the surveying operation. Summary of the Invention

[0003] In view of this, an object of the present invention is to provide a mineral exploration method, a mineral exploration system, and a mineral exploration robot, which can automatically identify geological types, select appropriate sampling tools in complex terrains, and analyze sample components in real time, thereby improving the safety and efficiency of exploration work.

[0004] In a first aspect, an embodiment of the present invention provides a mineral exploration method, which is applied to a mineral exploration robot. The mineral exploration robot is provided with a visual perception device, an analysis device, and a sampling tool, and is communicatively connected to an external host computer; the method includes: obtaining an exploration point image of a target exploration point through the visual perception device; inputting the exploration point image into a pre-trained geological type recognition model to output the actual geological type corresponding to the exploration point image; determining a target sampling tool corresponding to the actual geological type based on the actual geological type and a preset correspondence between geological types and sampling tools, and collecting a sample through the target sampling tool; and performing real-time analysis on the collected sample through the analysis device to obtain real-time exploration data of the target exploration point.

[0005] Further, the mineral exploration robot is further provided with a positioning device; before the step of obtaining an exploration point image of a target exploration point through the visual perception device, the method further includes: obtaining a coordinate file sent by the host computer; the coordinate file includes multiple exploration points to be measured, an exploration order, and the longitude and latitude coordinates, elevation data, and geological environment note information corresponding to each exploration point to be measured; obtaining the current position of the mineral exploration robot collected by the positioning device, and setting the current position as the starting point; obtaining the exploration status of each exploration point to be measured, and determining, based on the exploration order, the next exploration point to be measured with an exploration status of uncompleted exploration as the target exploration point; planning a first exploration path from the starting point to the target exploration point based on the longitude and latitude coordinates; and traveling to the target exploration point based on the first exploration path.

[0006] Further, after the step of planning the first exploration path from the starting point to the target exploration point based on the longitude and latitude coordinates, the method further includes: obtaining a road condition image sent by a visual perception device; inputting the road condition image into a pre-trained road condition type recognition model to output a road condition analysis result corresponding to the road condition image; identifying the road condition analysis result; if the road condition analysis result is that there are obstacles, updating the first exploration path based on a preset obstacle avoidance algorithm; if the road condition analysis result is that there is abnormal terrain, controlling the running speed based on a preset adjustment rule.

[0007] Further, after the step of obtaining real-time exploration data of the target exploration point by analyzing the samples collected by the analysis device, the method further includes: S1: after obtaining the real-time exploration data of the target exploration point, associating and saving the target exploration point and the real-time exploration data corresponding to the target exploration point, and uploading them to the host computer; S2: changing the exploration status of the target exploration point to completed exploration; S3: determining, based on the exploration order, the next exploration point to be measured with the exploration status of uncompleted exploration as the next target exploration point; S4: planning a second exploration path from the target exploration point to the next target exploration point based on the longitude and latitude coordinates; and traveling to the next target exploration point based on the second exploration path; S5: repeating steps S1 - S4 until the exploration status of each exploration point to be measured is completed exploration.

[0008] Further, the mineral exploration robot is also provided with a power supply device; the method further includes: identifying real-time battery information collected by the power supply device; the real-time battery information includes battery power and battery health status; if it is identified that the battery power is lower than a preset return threshold, returning to the starting point; if it is identified that the battery health status is not within the preset battery health threshold range, returning to the starting point.

[0009] Further, the geological type recognition model is trained by the following method: obtaining a historical geological image data set; the historical geological image data set includes geological images and geological type annotation information corresponding to the geological images; dividing the historical geological image data set into a training set, a validation set, and a test set according to a preset ratio; training an initial convolutional neural network model based on the training set until reaching a preset training requirement to obtain a first geological type recognition model; optimizing the first geological type recognition model based on a preset validation algorithm to obtain a second geological type recognition model; validating the second geological type recognition model based on the validation set until reaching a preset validation requirement to obtain the geological type recognition model.

[0010] Second aspect, an embodiment of the present invention provides a mineral exploration system, which is applied to a mineral exploration robot. The mineral exploration robot is provided with a visual perception device, an analysis device, a sampling tool and a positioning device, and is communicatively connected to an external host computer; the system includes: an exploration point image acquisition module, configured to acquire an exploration point image of a target exploration point through the visual perception device; a geological type determination module, configured to input the exploration point image into a pre-trained geological type recognition model, and output the actual geological type corresponding to the exploration point image; a sampling module, configured to determine a target sampling tool corresponding to the actual geological type based on the actual geological type and a preset correspondence between the geological type and the sampling tool, and collect a sample through the target sampling tool; an exploration data generation module, configured to perform real-time analysis on the collected sample through the analysis device to obtain real-time exploration data of the target exploration point.

[0011] Third aspect, an embodiment of the present invention provides a mineral exploration robot, including: a carrying platform and a control device, a visual perception device, an analysis device, a sampling tool and a positioning device provided on the carrying platform; the visual perception device, the analysis device, the sampling tool and the positioning device are respectively connected to the control device; it further includes the above-mentioned mineral exploration system, and the mineral exploration system is arranged in the control device.

[0012] Further, the mineral exploration robot further includes a robotic arm and a guiding mechanism; the robotic arm is arranged on one side of the carrying platform; the guiding mechanism is arranged below the carrying platform.

[0013] Further, the sampling tool includes a push shovel, a small drill and a sweeper; the sampling tool, the visual perception device and the analysis device are respectively arranged at the front end of the robotic arm; the analysis device is a laser-induced breakdown spectroscopy analyzer.

[0014] An embodiment of the present invention provides a mineral exploration method, a mineral exploration system and a mineral exploration robot, which are applied to a mineral exploration robot. The mineral exploration robot is provided with a visual perception device, an analysis device and a sampling tool, and is communicatively connected to an external host computer; the method includes: acquiring an exploration point image of a target exploration point through the visual perception device; inputting the exploration point image into a pre-trained geological type recognition model, and outputting the actual geological type corresponding to the exploration point image; determining a target sampling tool corresponding to the actual geological type based on the actual geological type and a preset correspondence between the geological type and the sampling tool, and collecting a sample through the target sampling tool; performing real-time analysis on the collected sample through the analysis device to obtain real-time exploration data of the target exploration point. In this way, it is possible to automatically identify the geological type, select a suitable sampling tool, and perform real-time analysis of the sample composition in complex terrain, thereby improving the safety and efficiency of the exploration work.

[0015] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention are realized and attained by the structure particularly pointed out in the specification, claims and drawings.

[0016] To make the above objectives, features and advantages of the present invention more comprehensible, the following specific preferred embodiments are given, in conjunction with the accompanying drawings, and are described in detail as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 Flowchart of the mineral exploration method provided for Embodiment 1 of the present invention;

[0019] Figure 2 Flowchart of the exploration route planning method for the target exploration point provided for Embodiment 1 of the present invention;

[0020] Figure 3 Flowchart of the obstacle avoidance method provided for Embodiment 1 of the present invention;

[0021] Figure 4 Flowchart of the geological type recognition model training method provided for Embodiment 1 of the present invention;

[0022] Figure 5 Flowchart of the next target exploration point planning method provided for Embodiment 1 of the present invention;

[0023] Figure 6 Flowchart of the power supply equipment detection method provided for Embodiment 1 of the present invention;

[0024] Figure 7 Schematic diagram of the mineral exploration system provided for Embodiment 2 of the present invention;

[0025] Figure 8 Another schematic diagram of the mineral exploration system provided for Embodiment 2 of the present invention;

[0026] Figure 9 Schematic diagram of the mineral exploration robot provided for Embodiment 3 of the present invention.

[0027] Icons: 1 - power supply equipment; 2 - guiding mechanism; 3 - analysis equipment; 4 - sampling tool; 5 - visual perception device; 6 - communication equipment; 7 - positioning equipment; 8 - control equipment; 9 - carrying platform; 10 - robotic arm; 11 - exploration point image acquisition module; 12 - geological type determination module; 13 - sampling module; 14 - exploration data generation module; 15 - battery information acquisition module. Specific Embodiment

[0028] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] For the convenience of understanding this embodiment, the embodiments of the present invention will be introduced in detail below.

[0030] Embodiment 1:

[0031] Figure 1 This is the flowchart of the mineral exploration method provided in Embodiment 1 of the present invention.

[0032] The mineral exploration method is applied to a mineral exploration robot equipped with a visual perception device, an analysis device and a sampling tool, and is communicatively connected to an external host computer. The mineral exploration robot is also equipped with a positioning device.

[0033] The visual perception device includes a camera and a lidar sensor, etc.

[0034] The sampling tool includes a push shovel, a small drill and a sweeper.

[0035] The analysis device is a laser-induced breakdown spectroscopy analyzer.

[0036] The mineral exploration robot is communicatively connected to an external host computer through an antenna.

[0037] Refer to Figure 1 , the mineral exploration method includes:

[0038] Step S101, obtaining an exploration point image of a target exploration point through the visual perception device.

[0039] Here, when the mineral exploration robot travels to the target exploration point, it takes a ground image of the scene through a visual camera installed in the front, and determines that the captured ground image is the exploration point image.

[0040] In one embodiment, refer to Figure 2 , before the steps of Step S101, the method further includes:

[0041] Step S201: Obtain the coordinate file sent by the host computer. The coordinate file includes multiple exploration points to be measured, the exploration order, and the longitude and latitude coordinates, elevation data, and geological environment remarks corresponding to each exploration point to be measured.

[0042] Here, before the exploration task starts, the exploration team determines several target exploration points in the target area through remote sensing images and preliminary geological surveys. The coordinate data of these exploration points are sorted into a coordinate file and uploaded to the host computer. After the mineral exploration robot is started, it obtains the coordinate file sent by the host computer.

[0043] Step S202: Obtain the current position of the mineral exploration robot collected by the positioning device, and set the current position as the starting point.

[0044] Here, the positioning device can adopt GPS (Global Positioning System) and RTK (Real-time kinematic), etc.

[0045] When the task is started, the system will set the current geographical coordinates as the starting point of the exploration task, which is used as the starting point for path planning.

[0046] Step S203: Obtain the exploration status of each exploration point to be measured, and based on the exploration order, determine the next exploration point to be measured with an uncompleted exploration status as the target exploration point.

[0047] Here, the coordinate file also contains the exploration status (completed or uncompleted) of each exploration point to be measured. The mineral exploration robot reads this status information, automatically determines the next exploration point to be measured that needs to be explored according to the preset exploration order, and sets it as the current target exploration point.

[0048] Step S204: Plan the first exploration path from the starting point to the target exploration point based on the longitude and latitude coordinates; and drive to the target exploration point based on the first exploration path.

[0049] Here, the mineral exploration robot converts the obtained starting point environmental data into a raster map, divides the exploration area into several grid cells, and each cell represents different terrain features, such as passable areas, obstacle areas, high-risk areas, etc. Each cell of the raster map is assigned a different weight or cost value to reflect the difficulty of passing different terrains or obstacles. The obstacle area is marked as impassable, and the flat area has a lower cost.

[0050] Based on a preset path planning algorithm, according to the longitude and latitude coordinates of the starting point and the target exploration point, calculate and plan an optimal driving path (based on the shortest distance, safety, obstacle avoidance requirements, etc.) to ensure that the robot can efficiently reach the target location. The path planning algorithms can be the A* search algorithm (A-star algorithm), Dijkstra algorithm, and DWA (Dynamic Window Approach), etc.

[0051] According to the planned path, control the wheels, motors, and navigation system to move along the predetermined route. During the driving process, the mineral exploration robot will use the real-time data sent by the camera and sensors for real-time adjustment to ensure safe arrival at the target exploration point.

[0052] Specifically, in a mineral exploration task in a mountainous area, the exploration team needs to explore multiple points within a specified area.

[0053] When the exploration task is started, the staff of the exploration team send the pre-designed coordinate file to the mineral exploration robot through the host computer. This file contains the longitude and latitude coordinates, elevation data, geological environment note information of 10 exploration points to be measured, and the exploration order of each point.

[0054] The mineral exploration robot activates the positioning device, obtains the longitude and latitude coordinates of the current location (for example, 34.056 degrees north latitude, 118.245 degrees east longitude), and sets this point as the starting point, which is the departure location for the exploration task.

[0055] The mineral exploration robot reads the status of each exploration point in the coordinate file and finds that the status of the first point (Point A) is unfinished. According to the exploration order, the robot automatically sets Point A as the current target exploration point.

[0056] The mineral exploration robot uses the path planning algorithm to calculate the optimal driving path based on the longitude and latitude data of the starting point (current location) and the target point (Point A), considering factors such as obstacles and slopes on the path to ensure safe arrival.

[0057] According to the planned path, the mineral exploration robot starts to drive automatically. During the journey, the visual perception device and the obstacle avoidance camera monitor the road conditions ahead in real time. When encountering an obstacle, the system will automatically adjust the route and continue to move forward after bypassing the obstacle.

[0058] In one embodiment, referring to Figure 3 , after the step of planning the first exploration path from the starting point to the target exploration point based on the longitude and latitude coordinates in step S204, the method further includes:

[0059] Step S301, obtaining the road condition image sent by the visual perception device.

[0060] Here, during the driving process of the mineral exploration robot, the visual perception device (such as a camera or lidar) continuously captures the road conditions ahead and obtains image data in real time. These images contain terrain information, obstacles, road surface conditions, etc. on the current path, serving as the basic data for judging the driving environment.

[0061] Step S302: Input the road condition image into a pre-trained road condition type recognition model, and output the road condition analysis result corresponding to the road condition image.

[0062] Here, the obtained road condition image is input into a pre-trained road condition type recognition model. The road condition type recognition model has been trained with a large amount of road condition image data and can accurately identify various road condition types contained in the image, such as flat areas, obstacles, muddy sections, slopes, etc. After analysis, the model outputs the corresponding road condition analysis result, such as "obstacle exists" or "abnormal terrain".

[0063] Among them, the road condition type recognition model is trained through the following method.

[0064] Obtain a historical road condition image dataset; the historical road condition image dataset includes road condition images and road condition annotation information corresponding to the road condition images.

[0065] Divide the historical road condition image dataset into a training set, a validation set, and a test set according to a preset ratio.

[0066] Based on the training set, train the initial convolutional neural network model until the preset training requirements are met, and obtain the first road condition type recognition model.

[0067] Based on a preset validation algorithm, optimize the first road condition type recognition model to obtain the second road condition type recognition model.

[0068] Based on the validation set, validate the second road condition type recognition model until the preset validation requirements are met, and obtain the road condition type recognition model.

[0069] Among them, during the model training process, the hyperparameters are adjusted through the validation set to prevent overfitting. Finally, the recognition accuracy of the model is evaluated through the test set to ensure its recognition ability for unknown data.

[0070] The trained road condition type recognition model can output the corresponding road condition analysis result in real time according to the input road condition image.

[0071] Step S303: Identify the road condition analysis result.

[0072] Here, the mineral exploration robot analyzes and judges the road condition analysis result output by the model to identify the specific road conditions on the current driving path, such as whether there are obstacles (such as stones, branches, etc.) or special terrains (such as steep slopes, muddy areas).

[0073] Step S304, if the road condition analysis result indicates the existence of an obstacle, update the first exploration path based on a preset obstacle avoidance algorithm.

[0074] Here, if an obstacle is identified on the path, calculate the optimal path to bypass the obstacle based on a preset obstacle avoidance algorithm, and update the driving route in real time to ensure that the mineral exploration robot can continue to drive safely after avoiding the obstacle. Among them, the preset obstacle avoidance algorithm can be DWA or artificial potential field method, etc.

[0075] Step S305, if the road condition analysis result indicates the existence of abnormal terrain, control the running speed based on a preset adjustment rule.

[0076] Here, if the road condition analysis result shows that there is abnormal terrain ahead, automatically adjust the driving speed based on a preset adjustment rule. For example, reduce the speed before a steep slope to ensure safe climbing, or slow down on a slippery road surface to prevent skidding and losing control.

[0077] Among them, the preset adjustment rule can also include adjusting the center of gravity, etc., to prevent the mineral exploration robot from tipping over by adjusting the center of gravity.

[0078] Specifically, on the way to point A, the visual perception device captures a series of road condition images, showing a rock obstacle and a muddy ground about 5 meters ahead.

[0079] These road condition images are input into the road condition type recognition model in real time, the existence of the rock and the size of the muddy area are recognized, and the results are output as the existence of an obstacle and the existence of abnormal terrain.

[0080] Receive and analyze these analysis results, clearly identify an obstacle (rock) and a special terrain (muddy section) ahead, and corresponding adjustments need to be made in the driving strategy.

[0081] Since the analysis result shows a rock obstacle, calculate the optimal bypass path based on the obstacle avoidance algorithm, update the original driving route, and make the mineral exploration robot deviate 1 meter to the left to bypass the rock while maintaining a safe distance.

[0082] After identifying the muddy section, automatically reduce the speed from the normal 4 km / h to 2 km / h according to the preset adjustment rule to prevent getting stuck in the mud or skidding. At the same time, adjust the torque output of the wheels to improve the passing ability.

[0083] Step S102, input the exploration point image into a pre-trained geological type recognition model, and output the actual geological type corresponding to the exploration point image.

[0084] Here, the actual geological type can be rock or soil or a mixture of rock and soil.

[0085] If the exploration point image shows a mixture of gravel and soil of different sizes, the geological type recognition model recognizes that the image features best match the category of rock and soil mixture in the training data. Therefore, the output result is a rock and soil mixture. If most areas of the exploration point image show a hard and reflective surface, the geological type recognition model recognizes that the image features best match the rock in the training data, and the output result is rock. If the exploration point image presents a loose and irregular granular structure, the geological type recognition model recognizes that the image features best match the soil in the training data, and the output result is soil.

[0086] In one embodiment, with reference to Figure 4 , the geological type recognition model is trained by the following method:

[0087] Step S401, obtain a historical geological image dataset; the historical geological image dataset includes geological images and geological type annotation information corresponding to the geological images.

[0088] Here, the exploration personnel collected a large amount of historical geological image data from multiple geological exploration tasks to form a historical geological image dataset, including geological images of different types such as soil, rock, and mineral mixtures. These geological image data are annotated by geological personnel and classified into different geological type labels, such as "sandstone", "granite", "soil rich in minerals", etc.

[0089] Preprocess the collected geological images, including image size standardization, denoising, contrast enhancement, etc., to improve the efficiency and recognition effect of model training. At the same time, use data augmentation techniques (such as rotation, flipping, brightness adjustment, etc.) to increase the diversity of the dataset and improve the generalization ability of the model.

[0090] Step S402, divide the historical geological image dataset into a training set, a validation set, and a test set according to a preset ratio.

[0091] Here, the preset ratio can be set in advance according to the actual situation, and can be set as training set: validation set: training set = 7:2:1.

[0092] Step S403, train an initial convolutional neural network model based on the training set until the preset training requirements are met, and obtain a first geological type recognition model.

[0093] Here, the model structure of the initial convolutional neural network model includes several convolutional layers, pooling layers, and fully connected layers.

[0094] Input the processed image data into the initial convolutional neural network model, and optimize the model parameters through the backpropagation algorithm so that the initial convolutional neural network model can learn the characteristics of different geological types from the image features. Use the cross-entropy loss function and the Adam optimizer to accelerate convergence.

[0095] Step S404: Optimize the first geological type recognition model based on a preset verification algorithm to obtain a second geological type recognition model.

[0096] Here, during the model training process, adjust the hyperparameters through the validation set to prevent overfitting. Finally, evaluate the recognition accuracy of the model through the test set to ensure its recognition ability for unknown data.

[0097] Step S405: Verify the second geological type recognition model based on the validation set until the preset verification requirements are met to obtain a geological type recognition model.

[0098] Here, the trained geological type recognition model can output the corresponding geological type in real time according to the input geological image.

[0099] Step S103: Based on the actual geological type and the preset correspondence between geological types and sampling tools, determine the target sampling tool corresponding to the actual geological type, and collect samples through the target sampling tool.

[0100] Here, the mineral exploration robot includes a carrying platform and a robotic arm. The robotic arm is arranged on one side of the carrying platform. The sampling tool, visual perception device, and analysis device are respectively arranged at the front end of the robotic arm.

[0101] If the recognized actual geological type is rock, the robotic arm will switch to a small drill to drill samples for in-depth analysis.

[0102] If the recognized actual geological type is soil, the robotic arm switches to a push shovel or sweeper to collect surface samples.

[0103] If the recognized actual geological type is a mixture of rock and soil, the mineral exploration robot will decide whether to use a drill or a push shovel according to the main components of the sample, and at the same time perform surface cleaning for further detection.

[0104] Step S104: Analyze the collected samples in real time through the analysis device to obtain real-time exploration data of the target exploration point.

[0105] Here, the mineral exploration robot collects soil or rock samples at the target exploration point through the corresponding sampling tool. The collected samples are collected in the sample slot or sampling bin of the analysis device for elemental composition analysis.

[0106] After sampling, the laser-induced breakdown spectroscopy analyzer performs rapid and non-destructive elemental composition detection on the sample. The laser-induced breakdown spectroscopy analyzer irradiates the sample surface by emitting high-energy laser pulses, exciting a plasma and generating spectral signals.

[0107] The excited plasma will emit light with specific wavelengths. The laser-induced breakdown spectroscopy analyzer captures these light signals and converts them into electrical signals. These signals are analyzed, and the types and contents of various elements in the sample are identified through a built-in analysis model.

[0108] The laser-induced breakdown spectroscopy analyzer processes the acquired spectral data, matches it with the built-in standard spectral database, and calculates the specific contents of various elements in the sample. The final output results include the real-time exploration data of the target exploration point, such as the types of elements, concentrations, and potential mineral composition information.

[0109] The mineral exploration robot uploads the analyzed real-time exploration data to the host computer for geologists to view and make decisions, assisting geologists in quickly judging the value of the mining area and determining subsequent exploration strategies.

[0110] In one embodiment, after the step of step S104, the method further includes: Figure 5 , the method further includes:

[0111] Step S1: After obtaining the real-time exploration data of the target exploration point, associate and save the target exploration point and the corresponding real-time exploration data, and upload them to the host computer.

[0112] Step S2: Change the exploration status of the target exploration point to completed exploration.

[0113] Here, the status of the target exploration point is automatically updated, changing it from uncompleted exploration to completed exploration. The exploration progress is recorded in real time to avoid repeated operations and ensure that each exploration point to be measured is only detected once.

[0114] Step S3: Based on the exploration order, determine the next exploration point to be measured with an exploration status of uncompleted exploration as the next target exploration point.

[0115] According to the preset exploration order, automatically find the next exploration point to be measured with an exploration status of uncompleted exploration. This point will be set as the target point for the next exploration to ensure that the exploration operation progresses step by step according to the established order, covering all exploration points to be measured.

[0116] Step S4: Based on the latitude and longitude coordinates, plan a second exploration path from the target exploration point to the next target exploration point; and drive to the next target exploration point based on the second exploration path.

[0117] Here, based on the longitude and latitude coordinates of the current target exploration point and the next target exploration point, the optimal driving route is calculated through a path planning algorithm. Factors such as terrain and obstacles are considered during the planning process to ensure safe and efficient driving. After the planning is completed, move along the calculated path to the next target exploration point.

[0118] Step S5: Repeat steps S1 - S4 until the exploration status of each exploration point to be measured is completed exploration.

[0119] Here, after reaching the next target exploration point, continue to execute the steps of sample analysis, data saving and uploading, status update, target point confirmation, and path planning. This loop operation will continue until the exploration status of all exploration points to be measured becomes completed exploration, marking the end of this exploration task.

[0120] In an embodiment, the mineral exploration robot is also provided with a power supply device. Referring to Figure 6 , the method further includes:

[0121] Step S601, identify the real - time battery information collected by the power supply device; the real - time battery information includes battery power and battery health status.

[0122] Here, the initial power of the mineral exploration robot is in a full - charge state, and the current power and the estimated minimum power required for the return journey are automatically recorded. When the mineral exploration robot is driving and performing exploration tasks, it continuously obtains the real - time battery information of the power supply device and reads the battery power every few seconds. At the same time, monitor the battery health status (such as temperature and voltage, etc.) to ensure that the device works within a safe power range.

[0123] Step S602, if it is identified that the battery power is lower than the preset return threshold, return to the starting point.

[0124] Here, the preset return threshold is used to represent the minimum return power. For example, when the power is only 25% left and it is just enough for a safe return, the minimum return power threshold is set to 25%.

[0125] According to the current position, the number of completed exploration points, and the number of remaining task points, dynamically estimate the power required for the return journey. Once it is detected that the current power is close to or lower than the estimated preset return threshold, an alarm will be triggered, and the mineral exploration robot will be controlled to return to the starting point.

[0126] When the battery power is lower than the preset return threshold, the mineral exploration robot aborts the current task, automatically abandons the next exploration point it is going to, plans the nearest route, and starts to return. During the return journey, the robot reduces high - power consumption actions (such as obstacle avoidance and rapid turning, etc.) to save energy.

[0127] During the return journey, continuously monitor the battery power to ensure that the battery power remains within a safe range. If an obstacle or complex terrain is detected during the return journey, the robot automatically adjusts its path to ensure that it returns to the starting point in the most power-saving manner. Finally, the mineral exploration robot successfully returns and docks at the charging platform at the starting point, completing a safe return journey.

[0128] Step S603, if it is recognized that the battery health status is not within the preset battery health threshold range, return to the starting point.

[0129] Here, the battery health threshold range is pre-set according to the actual situation.

[0130] If the battery health status is not within the preset battery health threshold range, the mineral exploration robot aborts the current task, automatically abandons the next exploration point it is going to, plans the shortest route, and starts to return.

[0131] An embodiment of the present invention provides a mineral exploration method, which is applied to a mineral exploration robot. The mineral exploration robot is provided with a visual perception device, an analysis device, and a sampling tool, and is communicatively connected to an external host computer; the method includes: obtaining an exploration point image of a target exploration point through the visual perception device; inputting the exploration point image into a pre-trained geological type recognition model to output the actual geological type corresponding to the exploration point image; determining a target sampling tool corresponding to the actual geological type based on the actual geological type and the preset corresponding relationship between the geological type and the sampling tool, and collecting samples through the target sampling tool; performing real-time analysis on the collected samples through the analysis device to obtain real-time exploration data of the target exploration point. In this way, the geological type can be automatically identified in complex terrain, a suitable sampling tool can be selected, and the sample composition can be analyzed in real time, thereby improving the safety and efficiency of the exploration work.

[0132] Embodiment Two:

[0133] Figure 7 It is a schematic diagram of the mineral exploration system provided by Embodiment Two of the present invention.

[0134] The mineral exploration system is applied to the above-mentioned mineral exploration robot.

[0135] Refer to Figure 7 , the mineral exploration system includes:

[0136] An exploration point image acquisition module 11, configured to obtain an exploration point image of a target exploration point through the visual perception device.

[0137] A geological type determination module 12, configured to input the exploration point image into a pre-trained geological type recognition model to output the actual geological type corresponding to the exploration point image.

[0138] The sampling module 13 is configured to determine a target sampling tool corresponding to the actual geological type based on the actual geological type and the preset correspondence between geological types and sampling tools, and collect samples through the target sampling tool.

[0139] The exploration data generation module 14 is configured to perform real-time analysis on the collected samples through an analysis device to obtain real-time exploration data of the target exploration point.

[0140] In one embodiment, the exploration point image acquisition module 11 is further configured to:

[0141] Obtain a coordinate file sent by a host computer; the coordinate file includes multiple exploration points to be measured, the exploration order, and the longitude and latitude coordinates, elevation data, and geological environment remarks information corresponding to each exploration point to be measured.

[0142] Obtain the current position of the mineral exploration robot collected by a positioning device, and set the current position as the starting point.

[0143] Obtain the exploration status of each exploration point to be measured, and based on the exploration order, determine the next exploration point to be measured with an incomplete exploration status as the target exploration point.

[0144] Based on the longitude and latitude coordinates, plan a first exploration path from the starting point to the target exploration point; and travel to the target exploration point based on the first exploration path.

[0145] In one embodiment, the exploration point image acquisition module 11 is further configured to:

[0146] Obtain a road condition image sent by a visual perception device.

[0147] Input the road condition image into a pre-trained road condition type recognition model, and output a road condition analysis result corresponding to the road condition image.

[0148] Identify the road condition analysis result.

[0149] If the road condition analysis result indicates the existence of an obstacle, update the first exploration path based on a preset obstacle avoidance algorithm.

[0150] If the road condition analysis result indicates the existence of an abnormal terrain, control the running speed based on a preset adjustment rule.

[0151] In one embodiment, the exploration data generation module 14 is further configured to:

[0152] S1: After obtaining the real-time exploration data of the target exploration point, associate and save the target exploration point and the corresponding real-time exploration data, and upload them to the host computer.

[0153] S2: Change the exploration status of the target exploration point to completed exploration.

[0154] S3: Based on the exploration sequence, determine the next exploration point to be explored whose exploration status is incomplete as the next target exploration point.

[0155] S4: Based on the latitude and longitude coordinates, plan the second exploration path from the target exploration point to the next target exploration point; and drive to the next target exploration point based on the second exploration path.

[0156] S5: Repeat steps S1 - S4 until the exploration status of each exploration point to be explored is completed.

[0157] In one embodiment, referring to Figure 8 , the mineral exploration system further includes: a battery information acquisition module 15; the battery information acquisition module 15 is used for:

[0158] Identify the real - time battery information collected by the power supply device; the real - time battery information includes battery power and battery health status.

[0159] If it is identified that the battery power is lower than the preset return threshold, return to the starting point.

[0160] If it is identified that the battery health status is not within the preset battery health threshold range, return to the starting point.

[0161] In one embodiment, the geological type determination module 12 is further used for:

[0162] Obtain a historical geological image data set; the historical geological image data set includes geological images and geological type annotation information corresponding to the geological images.

[0163] Divide the historical geological image data set into a training set, a validation set, and a test set according to a preset ratio.

[0164] Train the initial convolutional neural network model based on the training set until the preset training requirements are met to obtain the first geological type recognition model.

[0165] Optimize the first geological type recognition model based on a preset validation algorithm to obtain the second geological type recognition model.

[0166] Validate the second geological type recognition model based on the validation set until the preset validation requirements are met to obtain the geological type recognition model.

[0167] The embodiment of the present invention provides a mineral exploration system, which is applied to a mineral exploration robot. In this way, it can automatically identify the geological type in complex terrain, select a suitable sampling tool, and analyze the sample composition in real time, thereby improving the safety and efficiency of exploration work.

[0168] Embodiment Three:

[0169] Figure 9Schematic diagram of the mineral exploration robot provided in Embodiment 3 of the present invention.

[0170] Referring to Figure 9 , the mineral exploration robot includes: a carrying platform 9 and a control device 8, a visual perception device 5, an analysis device 3, a sampling tool 4, and a positioning device 7 provided on the carrying platform 9; the visual perception device 5, the analysis device 3, the sampling tool 4, and the positioning device 7 are respectively connected to the control device 8; it further includes the above-mentioned mineral exploration system (not shown in the figure), and the mineral exploration system is arranged in the control device 8.

[0171] In one embodiment, the mineral exploration robot further includes a robotic arm 10 and a guiding mechanism 2; the robotic arm 10 is arranged on one side of the carrying platform 9; the guiding mechanism 2 is arranged below the carrying platform 9.

[0172] The mineral exploration robot further includes a power supply device 1 and a communication device 6. The power supply device 1 and the communication device 6 are arranged on the carrying platform 9. The communication device 6 is used for communication connection with an external host computer.

[0173] In one embodiment, the sampling tool 4 includes a push shovel, a small drill, and a sweeper; the sampling tool 4, the visual perception device 5, and the analysis device 3 are respectively arranged at the front end of the robotic arm 10; the analysis device 3 is a laser-induced breakdown spectroscopy analyzer.

[0174] Specifically, the power supply device is located at the upper part of the carrying platform 9, provides power for the entire mineral exploration robot, ensures the normal operation of all devices (such as the robotic arm, the control device, the analysis device, etc.), and monitors the battery power and battery health status to achieve the automatic return function.

[0175] The guiding mechanism 2 is installed at the bottom of the carrying platform 9, includes a plurality of wheels, is responsible for supporting the carrying platform 9 and providing stable moving ability. The guiding mechanism 2 cooperates to control the mineral exploration robot to achieve navigation and path planning, ensuring that the mineral exploration robot can drive safely in complex terrains.

[0176] The analysis device 3 is fixed at the end of the robotic arm 10, and its main function is to perform real-time analysis on the collected samples. Common analysis devices such as laser-induced breakdown spectroscopy analyzers can quickly identify the elemental composition in the samples, providing support for the acquisition of real-time exploration data.

[0177] The sampling tool 4 is connected to the robotic arm 10, is used for collecting geological samples, and can switch tools (such as drill bits, push shovels, sweepers, etc.) according to different geological types to ensure the efficiency and accuracy of sampling.

[0178] The visual perception device 5 is installed at the front end of the robotic arm 10, responsible for acquiring image data of the exploration points, supporting functions such as geological type identification, road condition monitoring, and obstacle detection, and providing data support for subsequent analysis and path planning.

[0179] The communication device 6 is located at the base of the robotic arm 10 and is responsible for wireless communication with the upper computer. It can be an antenna, enabling real-time upload of exploration data and reception of remote control commands to ensure the timeliness of information interaction.

[0180] The positioning device 7 is installed at the base of the robotic arm 10 and is used to obtain real-time position information, supporting path planning, navigation, and target point confirmation, ensuring the precise positioning and efficient exploration of the mineral exploration robot in complex environments.

[0181] The control device 8 is installed below the power supply device 1 and is responsible for controlling the operation of each device, including robotic arm operation, path planning, sampling, and analysis, etc.

[0182] The bearing platform 9 provides an installation foundation for all devices. It has a strong structure and a reasonable design, enabling stable connection and coordinated operation among the devices, and ensuring the operation stability of the robot in various environments.

[0183] The robotic arm 10 is fixed on one side of the bearing platform 9. The flexible multi-axis design allows the robotic arm 10 to perform sampling and analysis operations in different orientations, and it can accurately position and execute complex sampling and detection tasks.

[0184] The embodiment of the present invention provides a mineral exploration robot, which realizes the automation, high efficiency, and intelligence of multi-point exploration by integrating functions such as visual perception, real-time analysis, path planning, and data upload. At the same time, it can autonomously identify geological types, accurately sample, and perform real-time element analysis, generate real-time data of the exploration points and upload them to the upper computer, ensuring the timeliness and integrity of the exploration data. At the same time, it improves the efficiency and safety of mineral exploration and provides strong decision-making support for geological personnel.

[0185] The computer program product provided by the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, reference can be made to the method embodiments, which will not be elaborated here.

[0186] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems and devices described above can refer to the corresponding processes in the foregoing method embodiments, which will not be elaborated here.

[0187] In addition, in the description of the embodiments of the present invention, unless otherwise clearly defined and limited, the terms "installed", "connected", and "coupled" shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0188] If the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0189] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0190] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any technician familiar with the technical field of the present invention can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims described.

Claims

1. A mineral exploration method, characterized in that, Applied to a mineral exploration robot, the mineral exploration robot is provided with a visual perception device, an analysis device and a sampling tool, and is communicatively connected with a host computer of an external device; the mineral exploration robot comprises a carrying platform and a mechanical arm, and the mechanical arm is arranged on one side of the carrying platform; The visual perception device, the analysis device and the sampling tool are respectively arranged at the front end of the mechanical arm; the method comprises: Acquire an exploration point image of a target exploration point through a visual perception device; Inputting the exploration point image into a pre-trained geological type recognition model, and outputting the actual geological type corresponding to the exploration point image; Based on the actual geological type and the preset correspondence between the geological type and the sampling tool, determining a target sampling tool corresponding to the actual geological type, and collecting samples through the target sampling tool; The collected samples are analyzed in real time by the analysis equipment to obtain real-time exploration data of the target exploration point; wherein the real-time exploration data includes the type and concentration of the element; The step of determining a target sampling tool corresponding to the actual geological type based on the actual geological type and the preset correspondence between the geological type and the sampling tool, and collecting samples through the target sampling tool comprises: If the actual geological type is identified as rock, the robotic arm will switch to a small drilling rig; If the actual geological type is identified as soil, the robotic arm switches to a dozer or a sweeper; If the actual geological type is identified as a mixture of rock and soil, the mechanical arm switches to a drilling machine or the bulldozer; The mineral exploration robot is also provided with a positioning device; Before the step of acquiring the exploration point image of the target exploration point by a visual perception device, the method further includes: Obtaining a coordinate file sent by the host computer; the coordinate file includes a plurality of exploration points to be measured, an exploration sequence, and the longitude and latitude coordinates, elevation data, and geological environment notes corresponding to each of the exploration points to be measured; Acquire the current position of the mineral exploration robot collected by the positioning device, and set the current position as the starting point; Acquire the exploration status of each of the to-be-tested exploration points, and determine, based on the exploration sequence, the next to-be-tested exploration point whose exploration status is that the exploration is not completed as the target exploration point; Based on the longitude and latitude coordinates, planning a first exploration path from the starting point to the target exploration point; and driving to the target exploration point based on the first exploration path; After the step of performing real-time analysis on the collected samples by the analysis device to obtain real-time exploration data of the target exploration point, the method further includes: S1: after acquiring the real-time exploration data of the target exploration point, the target exploration point and the real-time exploration data corresponding to the target exploration point are associated and saved, and uploaded to the host computer; S2: changing the exploration status of the target exploration point to completed exploration; S3: Based on the exploration sequence, determine the next exploration point to be measured whose exploration status is unfinished as the next target exploration point; S4: Based on the longitude and latitude coordinates, plan a second exploration path from the target exploration point to the next target exploration point; and drive to the next target exploration point based on the second exploration path; S5: Repeat steps S1 - S4 until the exploration status of each of the to-be-explored exploration points is completed exploration.

2. The mineral exploration method according to claim 1, characterized in that After the step of planning a first exploration path from the starting point to the target exploration point based on the longitude and latitude coordinates, the method further includes: Obtain a road condition image sent by the visual perception device; Input the road condition image into a pre-trained road condition type recognition model to output a road condition analysis result corresponding to the road condition image; Identify the road condition analysis result; If the road condition analysis result is that there are obstacles, update the first exploration path based on a preset obstacle avoidance algorithm; If the road condition analysis result is that there is abnormal terrain, control the running speed based on a preset adjustment rule.

3. The mineral exploration method according to claim 1, wherein The mineral exploration robot is also provided with a power supply device; the method further includes: Identify real-time battery information collected by the power supply device; the real-time battery information includes battery power and battery health status; If it is identified that the battery power is lower than a preset return threshold, return to the starting point; If it is identified that the battery health status is not within the preset battery health threshold range, return to the starting point.

4. The mineral exploration method according to claim 1, characterized in that The geological type recognition model is trained by the following method: Obtain a historical geological image data set; the historical geological image data set includes geological images and geological type annotation information corresponding to the geological images; Divide the historical geological image data set into a training set, a validation set, and a test set according to a preset ratio; Train an initial convolutional neural network model based on the training set until reaching a preset training requirement to obtain a first geological type recognition model; Optimize the first geological type recognition model based on a preset validation algorithm to obtain a second geological type recognition model; Validate the second geological type recognition model based on the validation set until reaching a preset validation requirement to obtain a geological type recognition model.

5. A mineral exploration system, characterized in that, Applied to a mineral exploration robot, the mineral exploration robot is provided with a visual perception device, an analysis device, and a sampling tool, and is communicatively connected to an external upper computer; the mineral exploration robot includes a carrying platform and a robotic arm, and the robotic arm is arranged on one side of the carrying platform; The visual perception device, the analysis device, and the sampling tool are respectively arranged at the front end of the robotic arm; the system includes: An exploration point image acquisition module, configured to obtain an exploration point image of a target exploration point through a visual perception device; A geological type determination module, configured to input the exploration point image into a pre-trained geological type recognition model to output an actual geological type corresponding to the exploration point image; A sampling module, configured to determine a target sampling tool corresponding to the actual geological type based on the actual geological type and a preset correspondence between geological types and sampling tools, and collect samples through the target sampling tool; An exploration data generation module, configured to perform real-time analysis on the collected samples through the analysis device to obtain real-time exploration data of the target exploration point; wherein, the real-time exploration data includes the types and concentrations of elements; The sampling module is further configured to: if the identified actual geological type is rock, the robotic arm will switch to a small drill; if the identified actual geological type is soil, the robotic arm will switch to a push shovel or a sweeper; if the identified actual geological type is a mixture of rock and soil, the robotic arm will switch to a drill or the push shovel; The exploration point image acquisition module is further configured to obtain the coordinate file sent by the host computer; the coordinate file includes multiple exploration points to be measured, the exploration sequence, and the longitude and latitude coordinates, elevation data, and geological environment note information corresponding to each exploration point to be measured; obtain the current position of the mineral exploration robot collected by the positioning device, and set the current position as the starting point; obtain the exploration status of each exploration point to be measured, and based on the exploration sequence, determine the next exploration point to be measured with the exploration status of uncompleted exploration as the target exploration point; plan a first exploration path from the starting point to the target exploration point based on the longitude and latitude coordinates; and travel to the target exploration point based on the first exploration path; The exploration data generation module is further configured to perform step S1: after obtaining the real-time exploration data of the target exploration point, associate and save the target exploration point and the corresponding real-time exploration data, and upload them to the host computer; perform step S2: change the exploration status of the target exploration point to completed exploration; perform step S3: based on the exploration sequence, determine the next exploration point to be measured with the exploration status of uncompleted exploration as the next target exploration point; perform step S4: plan a second exploration path from the target exploration point to the next target exploration point based on the longitude and latitude coordinates; and travel to the next target exploration point based on the second exploration path; perform step S5: repeat steps S1-S4 until the exploration status of each exploration point to be measured is completed exploration.

6. A mineral exploration robot, characterized in that, Including: A bearing platform and a control device, a visual perception device, an analysis device, a sampling tool, and a positioning device arranged on the bearing platform; the visual perception device, the analysis device, the sampling tool, and the positioning device are respectively connected to the control device; the mineral exploration system described in claim 5 is further included, and the mineral exploration system is arranged in the control device.

7. The mineral exploration robot according to claim 6, wherein The mineral exploration robot further includes a robotic arm and a guiding mechanism; the robotic arm is arranged on one side of the bearing platform; the guiding mechanism is arranged below the bearing platform.

8. The mineral exploration robot according to claim 7, characterized in that, The sampling tool includes a push shovel, a small drill, and a sweeper; the sampling tool, the visual perception device, and the analysis device are respectively arranged at the front end of the robotic arm; the analysis device is a laser-induced breakdown spectroscopy analyzer.

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