Mine safety risk early warning method based on space-air-ground integration

Abstract

The invention discloses a mine safety risk early warning method based on space-air-ground integration, and relates to the technical field of risk early warning. The method comprises the following steps: respectively acquiring satellite remote sensing data, aviation monitoring data and ground monitoring data; the collected data information is transmitted to a ground data center; carrying out format unification and de-noising processing on the collected data; extracting mine safety risk related features from the preprocessed data; utilizing machine learning to construct a mine risk assessment model; and outputting and judging an early warning level according to the mine risk assessment model, and issuing early warning information. The satellite remote sensing data, the aviation monitoring data and the ground monitoring data are respectively acquired through the space-air-ground technical means, the mine risk assessment model is constructed for the acquired data by utilizing machine learning, real-time safety early warning of the mine from the space and the ground is realized, the mine safety management level is improved, and the probability of mine accidents is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of risk warning, and in particular relates to a mine safety risk warning method based on air-ground-ground integration. Background Art

[0002] With the continuous development of the global mining industry, the scale of mining is constantly expanding, and the operating environment is becoming increasingly complex. Mine safety issues have always been one of the key factors restricting the sustainable development of the industry. Traditional mine safety monitoring and early warning methods mainly rely on limited ground sensor networks and manual inspections, which have many limitations.

[0003] In terms of ground monitoring, although sensors can monitor some key parameters such as mine pressure and harmful gas concentration in real time, the deployment of sensors is limited by the geographical environment and mining layout, making it difficult to achieve comprehensive coverage of the entire mining area. For example, in some mountain mines with complex terrain or large open-pit mines, it is difficult to deploy sensors to all potential dangerous areas, resulting in the inability to detect safety hazards in some areas in a timely manner. Moreover, the data from a single ground sensor can only reflect local and specific types of safety information, and lacks comprehensive perception of the overall geological structure changes of the mine, large-scale surface deformation, and safety risks caused by interactions with the surrounding environment.

[0004] Manual inspections are a traditional auxiliary monitoring method that is inefficient and highly subjective. The professionalism, work experience, physical and mental state of the inspectors will have a significant impact on the test results. In addition, manual inspections are difficult to achieve real-time and continuous monitoring, and there is a large time interval. Safety accidents may occur between two inspections without timely detection and warning.

[0005] Satellite remote sensing technology has been gradually applied to the field of mine monitoring in recent years. It can obtain large-scale mine surface information and conduct macroscopic monitoring of the overall landform, mining scope, waste slag storage, etc. of the mine. However, the resolution of satellite remote sensing data is relatively limited, and it is difficult to accurately capture some detailed information inside the mine, such as the operating status of small equipment and local rock cracks. At the same time, the acquisition of satellite data is affected by factors such as weather and satellite orbit period, and the data update frequency sometimes cannot meet the needs of real-time mine safety warning.

[0006] Aerial monitoring, especially the rise of drone technology, has provided a new means for mine safety monitoring. Drones can fly at low altitudes and carry a variety of high-precision sensors, such as high-resolution cameras, thermal imagers, and lidars, which can obtain more detailed images and data of local areas of mines, making up for some of the shortcomings of satellite remote sensing and ground monitoring. However, drones have limited endurance, a small coverage range for a single flight, and limited flight operations under complex meteorological conditions (such as strong winds, heavy rains, etc.), making it difficult for them to independently undertake long-term and stable safety monitoring tasks for the entire mine.

[0007] In summary, the existing mine safety monitoring methods all have their own limitations and cannot comprehensively, real-timely and accurately warn of mine safety risks. Therefore, there is an urgent need for an innovative method that integrates the advantages of multiple monitoring methods in the air, ground and space to achieve all-round, multi-level and high-efficiency monitoring and early warning of mine safety risks, so as to effectively improve the level of mine safety management, reduce the probability of mine accidents, and ensure the safety of life and property of mine workers and the stability of the ecological environment around mines. Summary of the invention

[0008] The purpose of the present invention is to provide a mine safety risk early warning method based on air-ground integration, which provides early warning of mine safety risks through integrated safety monitoring means of satellite remote sensing technology, aerial monitoring technology and ground monitoring technology, solving the problems of limitations of existing ground limited sensor networks and manual inspections, and the inability to detect and warn in a timely manner.

[0009] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0010] The present invention is a mine safety risk early warning method based on air-ground integration, comprising the following steps:

[0011] Step S1, data acquisition: obtaining satellite remote sensing data, aerial monitoring data and ground monitoring data respectively;

[0012] Step S2, data transmission: transmitting the collected data information to the ground data center;

[0013] Step S3, data processing: formatting and denoising the collected data;

[0014] Step S4, feature extraction: extracting mine safety risk related features from the preprocessed data;

[0015] Step S5, model construction: constructing a mine risk assessment model using machine learning;

[0016] Step S6, warning release: determine the warning level according to the output of the mine risk assessment model and release the warning information.

[0017] As a preferred technical solution, in step S1, the satellite remote sensing data is a high-definition image of the mining area, which is collected by high-resolution optical satellites and synthetic aperture radar satellites to identify static information such as the mining scope and waste slag storage location of the mine; the aerial monitoring data is the mining operation picture and equipment and facility picture of the mine, which is collected by drones and visible light cameras, thermal imagers and laser radars carried on drones; the visible light camera is used to photograph the mining operation surface, equipment and facilities of the mine, and can promptly detect equipment damage, illegal operations of personnel, etc. Thermal imagers can detect heat sources in mines. For example, in some mines prone to spontaneous combustion, thermal imaging can detect abnormal temperature increases in ore piles in advance, thereby warning of fire risks. Laser radar can build a three-dimensional model of the mine and accurately measure changes in the mine terrain, such as changes in the slope of the slope.

[0018] The ground monitoring data include the slope inclination angle, roof pressure and gas concentration in the mine, which are collected through inclination sensors, pressure sensors and gas sensors installed at different locations in the mine.

[0019] As a preferred technical solution, the satellite remote sensing data is transmitted to the ground data monitoring center via a satellite communication link; the aviation monitoring data is transmitted to the ground data monitoring center in real time via wireless communication; the ground monitoring data is transmitted to the ground data monitoring center via wireless communication technology or a wired network.

[0020] As a preferred technical solution, in step S3, when the data format is unified, a template covering the key elements of the data is pre-set, specifically including the data collection time, data type, geographic location information, and data content, and the data is stored in JSON data format; when the data is denoised, the corresponding image data is processed by mean filtering, and the center pixel value is replaced by the average value of the pixel values ​​in the neighborhood around the pixel; the sensor data is processed by Kalman filtering based on the previous state estimation and the current measurement value to dynamically calculate the optimal estimate and remove noise.

[0021] As a preferred technical solution, in step S4, the specific process of extracting mine safety risk-related features from image data is as follows:

[0022] Step SY41: Selecting a window size and a step size for the preprocessed image data;

[0023] Step SY42: Calculate texture features. For each window, calculate the statistical gray-level co-occurrence matrix according to the selected direction and distance. Suppose the image is , then the pixel The gray value is , then the distance is calculated as GLCM of a pixel ,in , count all windows that meet this condition For the number of occurrences, GLCM is obtained;

[0024] In the texture feature, the contrast calculation formula is as follows:

[0025] ;

[0026] The correlation calculation formula is as follows:

[0027] ;

[0028] In the formula, is the gray level, for The mean of the directions, is the standard deviation;

[0029] Step SY43: for each central pixel, compare it with the surrounding adjacent pixels, and count the frequency of occurrence of different LBP values ​​in the image as texture features;

[0030] Step SY44: Calculate shape features and perform Gaussian filtering. Let the processed Gaussian filtered image be , then the Sobel operator calculates the horizontal gradient and vertical gradient ,direction , perform non-maximum suppression and double threshold detection to obtain the edge;

[0031] Step SY45: Boundary point sequence for extraction , convert it to its plural form , perform discrete Fourier transform , take the front The sequence is used as the Fourier descriptor.

[0032] As a preferred technical solution, in step S4, the specific process of extracting mine safety risk-related features from sensor data is as follows:

[0033] Step SC41: For time series , calculate the mean ,variance , by looping through the data points;

[0034] Step SC42: Calculate the transformation rate based on the data of the adjacent points , by looping through the time series data to calculate the rate of change at each time interval;

[0035] Step SC43: Assume that the sensor vibration signal time series is , the frequency domain signal is calculated ;

[0036] Step SC44: Divide the signal into several segments, perform FFT on each segment, and then calculate the average power spectrum;

[0037] Step SC45: Set signal , the window function is , the window length is The length of the overlapping part is , then the PSD estimation formula for each signal segment is:

[0038] ;

[0039] In the formula, is the normalization factor.

[0040] As a preferred technical solution, in step S5, the specific process of using machine learning to construct a mine risk assessment model is as follows:

[0041] Step S51: Collect the mine data after preprocessing and feature extraction, label these data, and determine the safety risk level corresponding to each data sample; Collect the mine data after preprocessing and feature extraction. These data should contain various features related to safety risks, such as statistical features of sensor data, texture and shape features of image data, etc. At the same time, these data need to be labeled to determine the safety risk level corresponding to each data sample. Risk levels can be divided into low risk, medium risk and high risk, etc. The labeling is usually completed by mine safety experts based on historical accident data, on-site inspection conditions and relevant safety standards. For example, for gas concentration data, if the gas concentration is within the safety threshold range, it is labeled as low risk; if it is close to or exceeds the warning value, it is labeled as medium risk or high risk;

[0042] Step S52: Divide the collected labeled data into training set, validation set and test set; generally speaking, the training set is used to train the model, the validation set is used to adjust the model's hyperparameters, and the test set is used to evaluate the model's final performance. The common division ratio is 70% for training, 15% for validation, and 15% for testing. However, the specific division ratio can be adjusted according to the amount of data and the complexity of the model.

[0043] Step S53: Rotate the support vector machine as a classification model; For classification problems such as mine safety risk assessment (determining risk levels), a variety of machine learning models can be selected. If the relationship between data features and risk levels is complex, a neural network (such as a multi-layer perceptron) is a good choice. It can automatically learn complex nonlinear relationships in data. For example, a neural network with multiple hidden layers can learn complex mappings between different sensor features and image feature combinations and different risk levels.

[0044] Support vector machine (SVM) is also a commonly used classification model, especially when the data feature space dimension is high but the number of samples is relatively small. SVM divides data categories of different risk levels by finding an optimal hyperplane. It works well for linearly separable data. For nonlinear cases, kernel functions (such as Gaussian kernels) can be used to map data to high-dimensional space to make it linearly separable.

[0045] Decision tree models (such as C4.5 and CART) can also be used for mine safety risk assessment. Decision trees classify data through a series of conditional judgments (such as whether the gas concentration is higher than a certain threshold, whether the slope inclination angle exceeds the safety range, etc.). Its advantage is that the model is highly interpretable and can intuitively display the decision-making process of risk assessment;

[0046] Step S54: Initialize the parameters of the model according to the selected model; for example, for a neural network, it is necessary to initialize the connection weights and bias items between neurons. Generally, a random initialization method is adopted, such as randomly generating initial weights and biases within a certain range; different models have different training algorithms. For example, a neural network can use a back-propagation algorithm to update parameters. At the same time, it is necessary to select a suitable loss function to measure the difference between the model prediction result and the true label. For multi-classification problems, a commonly used loss function is the cross-entropy loss function. Taking a neural network as an example, in each training iteration, the prediction result is calculated through forward propagation, and then the value of the loss function is calculated. Then, the model parameters are updated according to the gradient of the loss function through the back-propagation algorithm to gradually reduce the loss function;

[0047] Step S55: Use the back propagation algorithm to update the parameters and select a suitable loss function to measure the difference between the model prediction result and the true label;

[0048] Step S56: Use evaluation indicators to measure the performance of the model on the validation set and the test set;

[0049] Step S57: Optimize model performance by adjusting the model's hyperparameters; Optimize model performance by adjusting the model's hyperparameters. Hyperparameters are parameters that need to be manually set before model training, such as the number of hidden layers of the neural network, the number of neurons in each layer, the learning rate, etc., the penalty parameters and kernel function parameters of the SVM, etc. Grid search, random search, or more advanced Bayesian optimization methods can be used to find the optimal hyperparameter combination. For example, in a grid search, a value range and step size are set for each hyperparameter, and then all possible hyperparameter combinations are trained and evaluated to find the combination with the best performance;

[0050] Step S58: Deploy the evaluated and optimized model to the mine safety risk early warning system. Ensure that the model can receive new data input in real time and output risk assessment results. This may require integrating the model into an existing software system or developing a dedicated interface to achieve data transmission and feedback of results; as the mining environment changes, new data accumulates, and technology develops, the model needs to be updated and maintained regularly. For example, when the mining depth of the mine increases, the mining method changes, or new monitoring equipment is installed, the distribution and characteristics of the data may change. At this time, it is necessary to re-collect data, retrain the model, or fine-tune the existing model to ensure the accuracy and effectiveness of the model.

[0051] As a preferred technical solution, in step S6, when issuing warning information, a visual warning of risk anomaly is performed in combination with the three-dimensional model of the mine, and the location information of the warning information is displayed on the three-dimensional model of the mine; the modeling formula of the three-dimensional model of the mine is as follows:

[0052] ;

[0053] In the formula, is the line-of-sight distance between the mine and the satellite, is the angle between the line of sight between the mine and the satellite and the local horizontal plane, is the angle between the line of sight between the mine and the satellite and the satellite’s radial direction, is the effective radius of the Earth, is the satellite altitude, It is the geocentric angle between the subsatellite point and the mine.

[0054] As a preferred technical solution, the coverage area of ​​the three-dimensional mine model is , according to satellite The position at the time in the Earth-fixed coordinate system , the mine's geographical longitude and latitude location , and the elevation angle of the mine Range:

[0055] ;

[0056] In the formula, is the right ascension of the satellite ascending node, S is Greenwich Mean Time.

[0057] The present invention has the following beneficial effects:

[0058] (1) The present invention uses air-space-ground technology to obtain satellite remote sensing data, aerial monitoring data and ground monitoring data respectively, and uses machine learning to construct a mine risk assessment model based on the collected data, thereby achieving real-time safety warning for mines from air-space-ground, improving the level of mine safety management, and reducing the probability of mine accidents.

[0059] (2) The present invention constructs a three-dimensional mine model through satellite remote sensing technology, and displays the collected satellite remote sensing data, aerial monitoring data and ground monitoring data in real time on the three-dimensional mine model. Once the mine risk assessment model outputs judgment warning information and levels, it is displayed at the corresponding position of the three-dimensional mine model, which is convenient for administrators to quickly locate abnormal positions and perform maintenance in time.

[0060] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0062] Figure 1 The present invention is a flow chart of a mine safety risk early warning method based on air-ground integration. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0065] In order to make the purpose, technical solution and advantages of this application clearer, the following Figure 1 The implementation methods of the present application are described in further detail.

[0066] Before introducing the embodiments of the present application, the air-ground-space integration is first described.

[0067] ‌Air-space-ground integration‌ refers to the deep integration of the three dimensions of sky (satellites, drones and other aerial resources), ground (various sensing devices) and space (remote sensing satellites) to form a new multi-level, multi-access fusion architecture. This architecture achieves accurate acquisition, rapid processing and efficient transmission of information through collaborative work, and is widely used in smart city management, unmanned driving, vehicle-road collaboration, urban emergency logistics, emergency firefighting and emergency rescue.

[0068] The specific components of air-ground integration include:

[0069] ‌Sky layer‌: includes low-orbit remote sensing satellites and low-altitude drones. These devices are responsible for data collection and transmission, and through the collaborative work of satellites and drones, real-time monitoring and data collection of ground conditions can be achieved‌.

[0070] ‌Ground layer‌: Mainly composed of various ground sensing devices, such as cameras, sensors, etc. These devices are responsible for data processing and analysis, providing real-time data support for decision-making‌.

[0071] ‌Space layer‌: Mainly remote sensing satellites, which are responsible for acquiring large amounts of geographic information data from space and providing basic data support for the entire system‌.

[0072] Application scenarios of air-space-ground integration:

[0073] Smart city management: Through satellite image comparison and drone on-site inspection, accurate identification and management of illegal buildings in cities can be achieved, improving the efficiency and accuracy of urban management.

[0074] ‌Autonomous driving and vehicle-road collaboration‌: By integrating air and ground data, the environmental perception capabilities of autonomous vehicles can be improved to achieve safer autonomous driving‌.

[0075] ‌Emergency logistics and emergency rescue‌: When a disaster occurs, the rapid deployment of emergency materials and the efficient execution of rescue operations can be achieved through an integrated air-ground-space rapid response mechanism.‌

[0076] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0077] See also Figure 1As shown, the present invention is a mine safety risk early warning method based on air-ground integration, comprising the following steps:

[0078] Step S1, data acquisition: obtaining satellite remote sensing data, aerial monitoring data and ground monitoring data respectively;

[0079] Step S2, data transmission: transmitting the collected data information to the ground data center;

[0080] Step S3, data processing: formatting and denoising the collected data;

[0081] Step S4, feature extraction: extracting mine safety risk related features from the preprocessed data;

[0082] Step S5, model construction: constructing a mine risk assessment model using machine learning;

[0083] Step S6, warning release: determine the warning level according to the output of the mine risk assessment model and release the warning information.

[0084] In step S1, the satellite remote sensing data is a high-definition image of the mining area, which is collected by high-resolution optical satellites and synthetic aperture radar satellites to identify static information such as the mining range and waste slag pile location of the mine; for example, by analyzing optical images of different periods, it is determined whether the waste slag pile has abnormal sliding signs or whether the mining area exceeds the specified range; synthetic aperture radar satellites are not restricted by weather and lighting conditions and can penetrate clouds to obtain data. It is very effective in monitoring small deformations of the mine surface. Through interferometric measurement technology, it can monitor ground subsidence accurately to the millimeter level, which is extremely important for early warning of surface collapse that may be caused by underground mining;

[0085] The aerial monitoring data is the mining operation pictures and equipment and facilities pictures of the mine, which are collected by drones and visible light cameras, thermal imagers and lidars mounted on drones; visible light cameras are used to photograph the mining operation surface, equipment and facilities of the mine, and can promptly detect equipment damage, illegal operations by personnel, etc. Thermal imagers can detect heat sources in mines. For example, in some mines prone to spontaneous combustion, thermal imaging can detect abnormal temperature increases in ore piles in advance, thereby warning of fire risks. Lidar can build a three-dimensional model of the mine and accurately measure changes in the mine terrain, such as changes in the slope of the slope.

[0086] The ground monitoring data are the slope inclination angle, roof pressure and gas concentration in the mine, which are collected by inclination sensors, pressure sensors and gas sensors installed at different locations in the mine. For example, by installing a inclination sensor on the slope of a mine, the inclination angle of the slope can be monitored in real time. When the inclination angle exceeds the set threshold, it may indicate the risk of slope instability. Pressure sensors can be placed in underground tunnels to monitor roof pressure. Once the pressure changes abnormally, it may indicate that the roof is in danger of falling. In addition, gas sensors are installed in the mine to detect the concentration of harmful gases such as gas and carbon monoxide, and timely alarms are issued when the concentration exceeds the standard.

[0087] Satellite remote sensing data is transmitted to the ground data monitoring center via a satellite communication link. This method can ensure the long-distance transmission of data and achieve effective data return even in remote mining areas; the aerial monitoring data is transmitted to the ground data monitoring center in real time via wireless communication methods such as 4G / 5G networks; the ground monitoring data is transmitted to the ground data monitoring center via wireless communication technologies such as Zigbee, LoRa, etc. or wired networks.

[0088] In step S3, when the data is formatted in a unified manner, a template covering the key elements of the data is pre-set, including data collection time, data type, geographic location information, and data content, and the data is stored in JSON data format, such as {"time":"2024-12-2310:00:00","source":"ground_sensor_001","type":"pressure","location":{"longitude":116.3,"latitude":39.9,"altitude":500},"value":100.5}; of course, specialized data conversion software or programming language libraries can also be used for format conversion. For satellite remote sensing data, many remote sensing image processing software (such as ENVI, ErdasImagine) provide data format conversion functions. For example, the original image data formats of different satellites (such as Landsat's TIFF format and Sentinel's SAFE format) are converted into a common GeoTIFF format, which can well preserve geographic spatial information. In terms of programming, Python's GDAL (Geospatial Data Abstraction Library) library is a powerful tool that can read and convert a variety of remote sensing data formats.

[0089] When the data is denoised, the corresponding image data is processed by mean filtering, and the central pixel value is replaced by the average value of the pixel values ​​in the neighborhood around the pixel; for example, for a 3×3 pixel neighborhood, the 9 pixel values ​​in the neighborhood are added and divided by 9, and the average value is used as the new value of the central pixel. This method can effectively remove salt and pepper noise (black and white point noise in the image);

[0090] The mean filter processing method used above can also use median filtering for denoising, which mainly sorts the pixel values ​​in the pixel neighborhood and then replaces the central pixel value with the median value. This method works well for removing impulse noise (such as isolated bright spots or dark spots in the image). For example, in mining images, if there are isolated abnormal bright spots caused by sensor failure, median filtering can effectively eliminate these noise points while retaining the edge and detail information of the image.

[0091] Kalman filtering is used to dynamically calculate the optimal estimate based on the previous state estimate and the current measurement value of the sensor data to remove noise. Kalman filtering is a more complex but more effective filtering method based on the state space model and probability statistics theory. For the pressure sensor data in the underground mine tunnel, due to the presence of various interference factors, Kalman filtering can dynamically calculate the optimal estimate based on the previous state estimate and the current measurement value, thereby effectively removing noise, while also predicting and correcting the data.

[0092] In step S4, the specific process of extracting mine safety risk related features from image data is as follows:

[0093] Step SY41: Select the window size and step size for the preprocessed image data; for satellite remote sensing images or drone visible light images of mines, GLCM can be used to describe the spatial distribution relationship of pixel gray levels in the image. To calculate GLCM, you need to first determine the direction (such as 0°, 45°, 90°, 135°) and distance (pixel interval). For example, calculate the GLCM with a direction of 0° and a distance of 1 pixel, and count the frequency of each gray value pair at this direction and distance. Texture features such as contrast, correlation, energy, and entropy can be extracted from GLCM. Contrast reflects the severity of local brightness changes in the image. In mine images, high-contrast areas may indicate undulating changes in terrain or the boundaries between mined and unmined areas, which are related to slope stability and changes in mining range; correlation reflects the linear relationship between gray values ​​in space, which is helpful for analyzing the arrangement and structural integrity of mining facilities;

[0094] Step SY42: Calculate texture features. For each window, calculate the statistical gray-level co-occurrence matrix according to the selected direction and distance. Suppose the image is , then the pixel The gray value is , then the distance is calculated as GLCM of a pixel ,in , count all windows that meet this condition For the number of occurrences, GLCM is obtained;

[0095] In the texture feature, the contrast calculation formula is as follows:

[0096] ;

[0097] The correlation calculation formula is as follows:

[0098] ;

[0099] In the formula, is the gray level, for The mean of the directions, is the standard deviation;

[0100] Step SY43: For each central pixel, compare it with the surrounding adjacent pixels, and count the frequency of different LBP values ​​in the image as texture features; LBP is an operator used to describe the local texture features of an image. It generates a binary code to represent the local texture pattern by comparing the central pixel with its neighboring pixels. In mining images, LBP can be used to identify specific texture changes on the surface of a mine, such as changes in the shape of an ore pile or damage to the road surface. For example, when the shape of an ore pile changes due to natural factors or mining activities, the LBP features on its surface will change, and this change can be discovered by comparing the LBP features before and after;

[0101] Such as Canny edge detection. Its specific algorithm steps include Gaussian filtering (for removing noise), calculating image gradient (through Sobel operator, etc.), non-maximum suppression (thinning edges) and double threshold detection (determining the true edge);

[0102] Step SY44: Calculate shape features and perform Gaussian filtering. Let the processed Gaussian filtered image be , then the Sobel operator calculates the horizontal gradient and vertical gradient ,direction , perform non-maximum suppression and double threshold detection to obtain the edge; for some objects with obvious shapes in the mine (such as slag piles, buildings, etc.), their boundaries must be extracted first. Edge detection algorithms such as the Canny edge detection algorithm can be used. The Canny algorithm determines the edge of the object by looking for pixels with drastic changes in grayscale intensity in the image. After extracting the boundary, the shape descriptor can be used to describe the shape characteristics of the object. For example, the Fourier descriptor is used to represent the shape of the object boundary. It regards the boundary curve as a periodic function and converts it to the frequency domain through Fourier transform. The low-frequency part represents the overall shape of the object, and the high-frequency part represents the details of the shape. For monitoring the stability of the slag pile, changes in shape (such as from an approximately circular shape to an irregular shape) may indicate the risk of sliding or collapse of the slag pile;

[0103] Step SY45: Boundary point sequence for extraction , convert it to its plural form , perform discrete Fourier transform , take the front The sequence is used as the Fourier descriptor.

[0104] In step S4, the specific process of extracting mine safety risk-related features from sensor data is as follows:

[0105] Step SC41: For time series , calculate the mean ,variance , by looping through the data points;

[0106] Step SC42: Calculate the transformation rate based on the data of the adjacent points , by looping through the time series data to calculate the rate of change at each time interval;

[0107] Step SC43: Assume that the sensor vibration signal time series is , the frequency domain signal is calculated ;

[0108] Step SC44: Divide the signal into several segments, perform FFT on each segment, and then calculate the average power spectrum;

[0109] Step SC45: Set signal , the window function is , the window length is The length of the overlapping part is , then the PSD estimation formula for each signal segment is:

[0110] ;

[0111] In the formula, is the normalization factor, which is related to the window function. Finally, the PSD estimates of all segments are averaged to obtain the final PSD. In actual calculations, functions in the signal processing library (such as the scipy.signal.welch function in Python) can also be used.

[0112] In step S5, the specific process of using machine learning to build a mine risk assessment model is as follows:

[0113] Step S51: collecting the pre-processed and feature-extracted mining data, labeling the data, and determining the safety risk level corresponding to each data sample;

[0114] Step S52: Divide the collected labeled data into a training set, a validation set, and a test set;

[0115] Step S53: rotating the support vector machine as a classification model;

[0116] Step S54: Initializing the parameters of the model according to the selected model;

[0117] Step S55: Use the back propagation algorithm to update the parameters and select a suitable loss function to measure the difference between the model prediction result and the true label;

[0118] Step S56: Use evaluation indicators to measure the performance of the model on the validation set and the test set;

[0119] Step S57: Optimizing model performance by adjusting the hyperparameters of the model;

[0120] Step S58: Deploy the evaluated and optimized model into the mine safety risk early warning system.

[0121] In step S6, when issuing warning information, a visual warning of risk anomaly is performed in combination with the three-dimensional mine model, and the location information of the warning information is displayed on the three-dimensional mine model; the modeling formula of the three-dimensional mine model is as follows:

[0122] ;

[0123] In the formula, is the line-of-sight distance between the mine and the satellite, is the angle between the line of sight between the mine and the satellite and the local horizontal plane, is the angle between the line of sight between the mine and the satellite and the satellite’s radial direction, is the effective radius of the Earth, is the satellite altitude, It is the geocentric angle between the subsatellite point and the mine.

[0124] As a preferred technical solution, the coverage area of ​​the three-dimensional mine model is , according to satellite The position at the time in the Earth-fixed coordinate system , the mine's geographical longitude and latitude location , and the elevation angle of the mine Range:

[0125] ;

[0126] In the formula, is the right ascension of the satellite ascending node, S is Greenwich Mean Time.

[0127] It is worth noting that in the above system embodiment, the various units included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0128] In addition, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiments can be completed by instructing related hardware through a program, and the corresponding program can be stored in a computer-readable storage medium.

[0129] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A mine safety risk early warning method based on air-ground integration, characterized in that: The steps include: Step S1, data acquisition: obtaining satellite remote sensing data, aerial monitoring data and ground monitoring data respectively; Step S2, data transmission: transmitting the collected data information to the ground data center; Step S3, data processing: formatting and denoising the collected data; Step S4, feature extraction: extracting mine safety risk related features from the preprocessed data; Step S5, model construction: constructing a mine risk assessment model using machine learning; Step S6, warning release: determine the warning level according to the output of the mine risk assessment model and release the warning information.

2. The mine safety risk early warning method based on air-ground integration according to claim 1 is characterized in that: In step S1, the satellite remote sensing data is a high-definition image of the mining area, which is collected by high-resolution optical satellites and synthetic aperture radar satellites; the aerial monitoring data is the mining operation images and equipment facilities images of the mine, which are collected by drones and visible light cameras, thermal imagers and lidars carried by drones; the ground monitoring data is the slope inclination angle, roof pressure and gas concentration in the mine, which are collected by tilt sensors, pressure sensors and gas sensors installed at different positions in the mine.

3. The mine safety risk early warning method based on air-ground integration according to claim 1 is characterized in that: In step S2, the satellite remote sensing data is transmitted to the ground data monitoring center via a satellite communication link; the aviation monitoring data is transmitted to the ground data monitoring center in real time via wireless communication; the ground monitoring data is transmitted to the ground data monitoring center via wireless communication technology or a wired network.

4. The mine safety risk early warning method based on air-ground integration according to claim 1 is characterized in that: In the step S3, when the data format is unified, a template covering the key elements of the data is pre-set, specifically including the data collection time, data type, geographic location information, and data content, and the data is stored in the JSON data format; when the data is denoised, the corresponding image data is processed by mean filtering, and the center pixel value is replaced by the average value of the pixel values ​​in the neighborhood around the pixel; the sensor data is processed by Kalman filtering based on the previous state estimation and the current measurement value to dynamically calculate the optimal estimate and remove noise.

5. The mine safety risk early warning method based on air-ground integration according to claim 1 is characterized in that: In step S4, the specific process of extracting mine safety risk-related features from image data is as follows: Step SY41: Selecting a window size and a step size for the preprocessed image data; Step SY42: Calculate texture features. For each window, calculate the statistical gray-level co-occurrence matrix according to the selected direction and distance. Suppose the image is , then the pixel The gray value is , then the distance is calculated as GLCM of a pixel ,in , count all windows that meet this condition For the number of occurrences, GLCM is obtained; In the texture feature, the contrast calculation formula is as follows: ； The correlation calculation formula is as follows: ； In the formula, is the gray level, for The mean of the directions, is the standard deviation; Step SY43: for each central pixel, compare it with the surrounding adjacent pixels, and count the frequency of occurrence of different LBP values ​​in the image as texture features; Step SY44: Calculate shape features and perform Gaussian filtering. Let the processed Gaussian filtered image be , then the Sobel operator calculates the horizontal gradient and vertical gradient ,direction , perform non-maximum suppression and double threshold detection to obtain the edge; Step SY45: Boundary point sequence for extraction , convert it to its plural form , perform discrete Fourier transform , take the front The sequence is used as the Fourier descriptor.

6. The mine safety risk early warning method based on air-ground integration according to claim 1 is characterized in that: In step S4, the specific process of extracting mine safety risk-related features from sensor data is as follows: Step SC41: For time series , calculate the mean ,variance , by looping through the data points; Step SC42: Calculate the transformation rate based on the data of the adjacent points , by looping through the time series data to calculate the rate of change at each time interval; Step SC43: Assume that the sensor vibration signal time series is , the frequency domain signal is calculated ; Step SC44: Divide the signal into several segments, perform FFT on each segment, and then calculate the average power spectrum; Step SC45: Set signal , the window function is , the window length is The length of the overlapping part is , then the PSD estimation formula for each signal segment is: ； In the formula, is the normalization factor.

7. The mine safety risk early warning method based on air-ground integration according to claim 1 is characterized in that: In step S5, the specific process of using machine learning to build a mine risk assessment model is as follows: Step S51: collecting the pre-processed and feature-extracted mining data, labeling the data, and determining the safety risk level corresponding to each data sample; Step S52: Divide the collected labeled data into a training set, a validation set, and a test set; Step S53: rotating the support vector machine as a classification model; Step S54: Initializing the parameters of the model according to the selected model; Step S55: Use the back propagation algorithm to update the parameters and select a suitable loss function to measure the difference between the model prediction result and the true label; Step S56: Use evaluation indicators to measure the performance of the model on the validation set and the test set; Step S57: Optimizing model performance by adjusting the hyperparameters of the model; Step S58: Deploy the evaluated and optimized model into the mine safety risk early warning system.

8. The mine safety risk early warning method based on air-ground integration according to claim 1 is characterized in that: In step S6, when issuing warning information, a visual warning of risk anomaly is performed in combination with the three-dimensional mine model, and the location information of the warning information is displayed on the three-dimensional mine model; the modeling formula of the three-dimensional mine model is as follows: ； In the formula, is the line-of-sight distance between the mine and the satellite, is the angle between the line of sight between the mine and the satellite and the local horizontal plane, is the angle between the line of sight between the mine and the satellite and the satellite’s radial direction, is the effective radius of the Earth, is the satellite altitude, It is the geocentric angle between the subsatellite point and the mine.

9. The mine safety risk early warning method based on air-ground integration according to claim 8 is characterized in that: The coverage area of ​​the three-dimensional mine model is , according to satellite The position at the time in the Earth-fixed coordinate system , the mine's geographical longitude and latitude location , and the elevation angle of the mine Range: ； In the formula, is the right ascension of the satellite ascending node, S is Greenwich Mean Time.

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