Complete-set perforating method for perforation blasting in open pit coal mine mining

Abstract

The invention provides a complete hole opening method for open-pit coal mine mining perforation blasting. The complete hole opening method comprises the steps that image information of an area around a hole opening is obtained, feature extraction is conducted on the image information, feature information of the hole opening is obtained, the feature information is input into a pre-established hole opening position recognition model, and the accurate coordinate position of the hole opening is obtained; the method comprises the following steps: acquiring the filling amount and compactness information of a material in the blocking process, inputting the filling amount and compactness information into a pre-established filling amount-compactness-sealing quality mapping model to obtain real-time control parameters in the material filling process, and controlling the filling speed and the compaction degree of the blocking material according to the real-time control parameters; evaluation indexes such as orifice positioning accuracy, blocking material performance, blocking process control accuracy and sealing quality detection results are obtained, each evaluation index is endowed with a weight, a comprehensive score is calculated through a weighted summation method, a sealing scheme is optimized according to the comprehensive score, and an optimal sealing scheme is obtained.

Description

Technical Field

[0001] The invention relates to the field of information technology, and in particular to a complete hole-opening method for perforation blasting in open-pit coal mining. Background Art

[0002] Problem background: In open-pit coal mining, perforation blasting is a key process. After the hole is opened, the hole needs to be sealed to prevent the explosive gas from escaping and ensure the blasting effect and operation safety. However, the sealing of the hole faces a series of technical difficulties. First, the position of the hole is often difficult to accurately locate, and the presence of gaps and cracks in the hole wall makes it difficult for the plugging material to fully fill the pores, which easily leads to problems of poor sealing. Secondly, the performance of the plugging material itself also affects the sealing effect. Conventional clay and other materials have poor sealing performance, while special materials are expensive and easily affected by ambient temperature and humidity, and the preparation process is complicated. Furthermore, the plugging process lacks effective quality control measures, the amount and density of the plugging material are difficult to accurately control, and manual operation is prone to fluctuations in sealing quality. Finally, the plugged hole is prone to loosening and falling off under the action of external forces such as blasting vibration, causing leakage of explosive gas. Therefore, there is an urgent need for a complete set of orifice sealing methods. Through technical innovations in aspects such as precise orifice positioning, plugging material optimization, plugging process control, and sealing quality inspection, the above-mentioned technical difficulties can be solved, reliable sealing of the orifice can be achieved, and the safe and efficient perforation blasting operations in open-pit coal mining can be ensured. Summary of the invention

[0003] The present invention provides a complete set of hole-opening method for perforation blasting in open-pit coal mining, which mainly comprises: Acquire image information of the area around the orifice, perform feature extraction on the image information to obtain feature information of the orifice, input the feature information into a pre-established orifice position recognition model to obtain the precise coordinate position of the orifice; Acquire the filling amount and density information of the material during the plugging process, input the filling amount and density information into a pre-established filling amount-density-sealing quality mapping model, obtain real-time control parameters of the material filling process, and control the filling speed and compaction strength of the plugging material according to the real-time control parameters; An infrared thermal imaging device is used to obtain an infrared thermal image of the orifice after plugging, and according to a preset temperature abnormality threshold, it is determined whether there is a temperature abnormality area in the infrared thermal image. If there is a temperature abnormality area, it is determined that the plugging body has a sealing defect; if there is no temperature abnormality area, it is determined that the plugging body is sealed intact; The evaluation indicators such as orifice positioning accuracy, plugging material performance, plugging process control accuracy and sealing quality test results are obtained, and each evaluation indicator is weighted. The comprehensive score is calculated by the weighted summation method. The sealing scheme is optimized according to the comprehensive score to obtain the optimal sealing scheme.

[0004] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: The present invention discloses an orifice sealing method. The method first accurately locates the orifice position through image processing and feature extraction technology, and then monitors the material filling amount and density in real time during the plugging process, and dynamically adjusts the filling speed and compaction strength to ensure the best sealing effect. After the sealing is completed, the present invention uses infrared thermal imaging technology to detect the sealing quality, and determines whether there are sealing defects by analyzing the temperature abnormality area. Finally, the present invention comprehensively evaluates multiple indicators such as orifice positioning accuracy, material properties, process control accuracy and sealing quality, and obtains the optimal sealing solution through weighted calculation. This full-process intelligent sealing method not only improves the accuracy and reliability of the seal, but also realizes real-time monitoring and quality evaluation of the sealing process, providing an efficient and reliable solution to the orifice sealing problem in industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 The present invention is a flow chart of a complete set of hole-opening method for perforation blasting in open-pit coal mining.

[0006] Figure 2 The present invention is a schematic diagram of a complete set of hole-opening method for perforation blasting in open-pit coal mining.

[0007] Figure 3 It is another schematic diagram of a complete hole-opening method for perforation blasting in open-pit coal mining according to the present invention. DETAILED DESCRIPTION

[0008] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are only used to explain the relevant inventions, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0009] like Figure 1-3 In this embodiment, a complete set of hole-opening methods for perforating and blasting in open-pit coal mining may specifically include: Step S101, acquiring image information of the area around the orifice, performing feature extraction on the image information to obtain feature information of the orifice; inputting the feature information into a pre-established orifice position recognition model to obtain the precise coordinate position of the orifice.

[0010] The method comprises the following steps: obtaining an original image including a target orifice, performing denoising and enhancement processing on the original image to obtain a preprocessed image; determining the area around the orifice from the preprocessed image according to a preset orifice area determination rule to obtain an orifice area image; performing feature extraction on the orifice area image using a SIFT algorithm to obtain a SIFT feature vector; inputting the SIFT feature vector into a pre-trained SVM model for position recognition to obtain orifice coordinate information; if the orifice coordinates are within the preset target area, outputting the orifice coordinates; otherwise, outputting a prompt message indicating that the orifice does not exist.

[0011] The SIFT (Scale-Invariant Feature Transform) algorithm is an algorithm used for image feature extraction. The SIFT algorithm is used to extract features from orifice area images. The specific steps are as follows: 1. Establish a scale space by performing Gaussian blurring of different scales on the original image.

[0012] 2. Detect key points, compare pixel differences in scale space, and use the differential Gaussian operator to detect local extreme points.

[0013] 3. Determine the location and scale of key points, accurately locate and remove low-contrast and edge response points.

[0014] 4. Determine the direction of the key points and determine the main direction by calculating the gradient direction histogram of the local area.

[0015] 5. Generate SIFT feature vectors, take the neighborhood around the key point, divide the small area into statistical gradient direction histograms and concatenate them to get the feature vectors. SIFT feature vectors are invariant to scale, rotation and illumination, and can be used for tasks such as image matching and target recognition.

[0016] SIFT algorithm is usually used to extract features from orifice area images to obtain SIFT feature vectors with scale, rotation and illumination invariance. These feature vectors can effectively describe the local feature information of orifice area images.

[0017] Then, these SIFT feature vectors are input into a pre-trained SVM (Support Vector Machine) model. SVM is a machine learning algorithm that uses a large number of SIFT feature vectors of images with known orifice locations for learning during the training phase, and continuously adjusts the model parameters so that the model can accurately predict the orifice location based on the input feature vectors.

[0018] When the SIFT feature vectors of the new orifice area image are input into the trained SVM model, the model will perform position recognition, that is, analyze the image content represented by these feature vectors, and finally output the specific location information of the orifice in the image, usually presented in the form of coordinates, thereby determining the exact position of the orifice in the image.

[0019] Specifically, an original image containing a target orifice is obtained, and the original image is preprocessed, including image denoising and image enhancement, to obtain a preprocessed image. According to a preset orifice area determination rule, the area around the orifice is determined from the preprocessed image, and the rule is formulated based on the shape characteristics and edge characteristics of the orifice. The determined area around the orifice is cut out to obtain an orifice area image. The orifice area image is extracted using a SIFT feature extraction algorithm to extract features from the orifice area image to obtain a SIFT feature vector representing the orifice features. The SIFT feature vector is input into a pre-trained support vector machine SVM model for position recognition to obtain coordinate information representing the orifice position. The SVM model is trained using a large number of orifice image samples with position annotations, and can effectively identify the position of the orifice. According to the coordinate information output by the SVM model, it is determined whether the orifice is located in the preset target area of ​​the image. If the orifice is located in the preset target area, the coordinate information is determined as the precise coordinate position of the orifice and output; if the orifice is not located in the preset target area, it is considered that the orifice does not exist in the current image, and a prompt message indicating that the orifice does not exist is output.

[0020] Step S102, obtaining the filling amount and density information of the material during the plugging process, inputting the filling amount and density information into a pre-established filling amount-density-sealing quality mapping model to obtain real-time control parameters of the material filling process; and controlling the filling speed and compaction strength of the plugging material according to the real-time control parameters.

[0021] The filling amount and density data of the material during the plugging process are obtained and transmitted to the data processing module; for the historical filling process data, a support vector regression algorithm is used to establish a mapping relationship model between the filling amount, density and sealing quality, wherein the input variables are the filling amount and density, and the output variable is the corresponding sealing quality value; if it is determined that there is a risk of quality defects, the optimal control parameter value of the filling process is determined by the optimization algorithm, and the control parameters include the material filling speed and compaction strength.

[0022] Specifically, the filling amount and density data of the material in the blocking process are collected in real time by sensors, and the collected data are transmitted to the data processing module. The data processing module receives the filling amount and density data collected by the sensor and stores it in the database. Then, the stored data is preprocessed, including data cleaning operations such as removing outliers and missing values, and normalizing the data to scale the data to the [0,1] interval to obtain standardized filling amount and density data. Based on the historical filling process data, a mapping relationship model between filling amount, density and sealing quality is established. The model is trained using the support vector regression (SVR) algorithm, with the input variables being the filling amount and density, and the output variable being the corresponding sealing quality value. The hyperparameters of SVR are optimized using the grid search and cross-validation methods, and the prediction performance of the model is evaluated on the test set. The standardized filling amount and density data are input into the trained SVR model to obtain the predicted sealing quality value. The sealing quality threshold is set as the median of the sealing quality in the historical data, and the predicted value is compared with the threshold. If the predicted sealing quality value is lower than the threshold, it is judged that there is a risk of quality defects in the current filling process. When it is judged that there is a risk of quality defects, the optimization algorithm is used to calculate the optimal control parameter values ​​of the filling process, including the material filling speed and compaction intensity. The optimization goal is to make the predicted sealing quality value reach or exceed the threshold, while considering constraints such as equipment capacity limitations. The sequential minimum optimization (SMO) algorithm is used to solve the optimization problem to obtain the adjustment values ​​of the filling speed and compaction intensity. The calculated real-time control parameters are transmitted to the control unit of the plugging material filling equipment. The control unit adjusts the material filling speed and compaction intensity in real time according to the received parameters to optimize the filling process of the plugging material. During the optimized filling process, the filling amount and density data are continuously collected and input into the SVR model for sealing quality prediction. If three consecutive predicted values ​​reach or exceed the sealing quality threshold, the optimization is stopped, and the real-time optimization control of the plugging material filling process is completed.

[0023] Step S103, using infrared thermal imaging equipment to obtain an infrared thermal image of the orifice after blocking, and judging whether there is a temperature abnormality area in the infrared thermal image according to a preset temperature abnormality threshold; if there is a temperature abnormality area, it is judged that there is a sealing defect in the blocking body; if there is no temperature abnormality area, it is judged that the sealing of the blocking body is intact.

[0024] Acquire an infrared thermal imaging image of the blocked orifice, the infrared thermal imaging image being used to determine the sealing performance of the blocking body; preprocess the infrared thermal imaging image to obtain a preprocessed infrared thermal image; compare the preprocessed infrared thermal image with a preset temperature anomaly threshold to determine whether there is an abnormal temperature area in the preprocessed infrared thermal image; if there is an abnormal temperature area in the preprocessed infrared thermal image, it is determined that the blocking body has a sealing defect.

[0025] Specifically, an infrared thermal imaging image of the blocked orifice is obtained as raw data for judging the sealing performance of the plugging body. The obtained infrared thermal image is preprocessed, including image denoising and enhancement, to eliminate image noise interference and improve image quality. A temperature abnormality threshold is preset according to the thermal conductivity characteristics of the blocked orifice material and the temperature range during normal operation. The preprocessed infrared thermal image is compared with the threshold to detect whether there is a temperature abnormality area in the image. The temperature abnormality area is defined as a connected area where the temperature exceeds the normal range. If a temperature abnormality area is detected in the infrared thermal image, it is judged that the plugging body has a sealing defect. The area, temperature gradient and other features of the temperature abnormality area are extracted, and the sealing performance of the plugging body is classified using a pre-trained support vector machine model to obtain a quantitative evaluation result of the sealing performance. The support vector machine model is trained using a large number of infrared thermal images of plugging bodies with known sealing performance. If no temperature abnormality area is detected in the infrared thermal image, it is judged that the sealing performance of the plugging body is intact. The quantitative evaluation result of the sealing performance of the plugging body is compared with the preset performance standard, which is determined according to the material, structure and use requirements of the plugging body. If the quantitative evaluation result does not meet the preset standard, it is determined that the blockage needs to be repaired or replaced for sealing performance. While acquiring the infrared thermal imaging image, the number and position coordinates of the blocked orifice are recorded. The test results are associated with the location information of the blocked orifice to generate a test report. The test report includes the location of the blocked orifice, infrared thermal image, temperature abnormality area, quantitative evaluation results and repair suggestions.

[0026] Step S104, obtaining evaluation indicators such as orifice positioning accuracy, plugging material performance, plugging process control accuracy and sealing quality test results, assigning weights to each evaluation indicator, and calculating the comprehensive score by weighted summation method; optimizing the sealing scheme according to the comprehensive score to obtain the optimal sealing scheme.

[0027] Obtain the original score and weight coefficient of each evaluation index, and use the weighted summation model to perform weighted summation of the original score and the weight coefficient to obtain a comprehensive score. Determine the optimal sealing solution based on the comprehensive score and obtain the key parameters of the solution. During the actual sealing construction, use a total station to accurately locate the orifice and control the orifice coordinate deviation within the preset range. After the sealing construction is completed, use an ultrasonic testing instrument to perform non-destructive testing on the sealing part, and judge the sealing quality based on the ultrasonic propagation time and attenuation. If the test result exceeds the threshold, the seal is judged to be unqualified and needs to be reworked.

[0028] Specifically, the original score of each evaluation index is determined according to the orifice positioning accuracy, plugging material performance, plugging process control accuracy and sealing quality test results. The analytic hierarchy process is used to determine the weight coefficient for each evaluation index. A weighted summation model is constructed, and the original score and the weight coefficient are weighted and summed to obtain a comprehensive score: S = Σ(Wi × Si) where S is the comprehensive score, Wi is the weight coefficient of the i-th indicator, and Si is the original score of the i-th indicator. The comprehensive score of each sealing scheme is calculated and sorted according to the score. The scheme with the highest score is the optimal sealing scheme. The key parameters of the optimal sealing scheme are obtained, including the orifice positioning coordinates, the proportion of plugging materials, the grouting pressure, the grouting time, etc. In the actual sealing construction, the orifice is accurately positioned using a total station to ensure that the deviation between the positioning coordinates and the orifice coordinates of the optimal scheme is within ±2mm. A proportion metering system is used to configure the plugging materials strictly according to the material proportion of the optimal scheme, and the deviation of the material dosage is controlled within ±3%. Use pressure sensors to monitor the grouting pressure in real time, and adjust the grouting pressure by controlling the valve, and control the pressure deviation within ±2MPa. After the sealing construction is completed, use ultrasonic testing equipment to perform non-destructive testing on the sealing part to measure the propagation time and attenuation of ultrasonic waves in the sealing material. If the ultrasonic propagation time is greater than the set threshold or the attenuation exceeds the standard value, it is judged that the seal is unqualified and needs to be reworked. At the same time, the sealing part is subjected to a water injection pressure test, and the test pressure is twice the design working pressure, and the pressure is maintained for 30 minutes. If the pressure drop does not exceed 2MPa, the sealing quality is judged to be qualified. If the test finds that the sealing quality is unqualified, the sealing construction is re-performed until the requirements are met.

[0029] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A complete set of hole-opening method for perforation blasting in open-pit coal mining, characterized in that: The method comprises: Acquire image information of the area around the orifice, perform feature extraction on the image information to obtain feature information of the orifice, input the feature information into a pre-established orifice position recognition model to obtain the precise coordinate position of the orifice; Acquire the filling amount and density information of the material during the plugging process, input the filling amount and density information into a pre-established filling amount-density-sealing quality mapping model, obtain real-time control parameters of the material filling process, and control the filling speed and compaction strength of the plugging material according to the real-time control parameters; An infrared thermal imaging device is used to obtain an infrared thermal image of the orifice after plugging, and according to a preset temperature abnormality threshold, it is determined whether there is a temperature abnormality area in the infrared thermal image. If there is a temperature abnormality area, it is determined that the plugging body has a sealing defect; if there is no temperature abnormality area, it is determined that the plugging body is sealed intact; The evaluation indicators such as orifice positioning accuracy, plugging material performance, plugging process control accuracy and sealing quality test results are obtained, and each evaluation indicator is weighted. The comprehensive score is calculated by the weighted summation method. The sealing scheme is optimized according to the comprehensive score to obtain the optimal sealing scheme.

2. The method according to claim 1, characterized in that The method of acquiring image information of the area around the orifice, extracting features from the image information, and obtaining feature information of the orifice; and inputting the feature information into a pre-established orifice position recognition model to obtain the precise coordinate position of the orifice includes: Acquire an original image containing a target orifice, and perform denoising and enhancement processing on the original image to obtain a preprocessed image; According to a preset orifice region determination rule, the orifice surrounding region is determined from the preprocessed image to obtain an orifice region image; Using SIFT algorithm to extract features from the orifice area image to obtain a SIFT feature vector; Inputting the SIFT feature vector into a pre-trained SVM model for position recognition to obtain orifice coordinate information; If the orifice coordinates are within the preset target area, outputting the orifice coordinates; Otherwise, there is no prompt information for the output port.

3. The method according to claim 1, characterized in that The filling amount and density information of the material during the plugging process are obtained, and the filling amount and density information are input into a pre-established filling amount-density-sealing quality mapping model to obtain real-time control parameters of the material filling process; According to the real-time control parameters, the filling speed and compaction strength of the plugging material are controlled, including: Obtain the filling amount and density data of the material during the plugging process, and transmit the data to the data processing module; Based on the historical filling process data, the support vector regression algorithm is used to establish the mapping relationship model between filling amount, density and sealing quality, where the input variables are filling amount and density, and the output variable is the corresponding sealing quality value; If it is determined that there is a risk of quality defects, the optimal control parameter values ​​of the filling process are determined through an optimization algorithm, and the control parameters include material filling speed and compaction strength.

4. The method according to claim 1, characterized in that The method of using an infrared thermal imaging device to obtain an infrared thermal image of the blocked orifice, and judging whether there is a temperature abnormality area in the infrared thermal image according to a preset temperature abnormality threshold; if there is a temperature abnormality area, judging that the blocking body has a sealing defect; if there is no temperature abnormality area, judging that the blocking body is sealed intact, includes: Acquire an infrared thermal imaging image of the blocked hole, wherein the infrared thermal imaging image is used to determine the sealing performance of the blocking body; Preprocessing the infrared thermal imaging image to obtain a preprocessed infrared thermal image; Comparing the preprocessed infrared thermal image with a preset temperature anomaly threshold to determine whether there is a temperature anomaly area in the preprocessed infrared thermal image; If there is an abnormal temperature area in the pre-processed infrared thermal image, it is determined that the plugging body has a sealing defect.

5. The method according to claim 1, characterized in that The method comprises obtaining evaluation indicators such as orifice positioning accuracy, plugging material performance, plugging process control accuracy and sealing quality test results, assigning weights to each evaluation indicator, and calculating a comprehensive score by a weighted summation method; optimizing the sealing scheme according to the comprehensive score to obtain the optimal sealing scheme, including: Obtain the original score and weight coefficient of each evaluation indicator, and use the weighted summation model to perform weighted summation on the original score and the weight coefficient to obtain a comprehensive score; Determine the optimal sealing solution based on the comprehensive score and obtain the key parameters of the solution; During the actual sealing construction, the total station is used to accurately locate the orifice and control the orifice coordinate deviation within the preset range; After the sealing construction is completed, an ultrasonic testing instrument is used to perform non-destructive testing on the sealing part. The sealing quality is judged based on the ultrasonic propagation time and attenuation. If the test result exceeds the threshold, the seal is judged to be unqualified and needs to be reworked.

Images