Blasting effect intelligent evaluation method and device and electronic equipment
Through depth image technology, the gravel distribution fitting curve of the gravel pile after blasting is automatically determined, which solves the problem of low efficiency in blasting effect evaluation in the existing technology, and achieves efficient and accurate blasting effect evaluation.
Patent Information
- Application Number
- CN202411569107.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-05
- Publication Date
- 2025-02-14
AI Technical Summary
In the prior art, the evaluation of blasting effect relies on on-site observation and manual measurement, resulting in inefficiency and difficulty in achieving accurate and efficient evaluation.
By obtaining the depth images of multiple gravel piles after blasting, the gravel distribution fitting curve is automatically determined, and the blasting effect level is evaluated based on the curve, which improves the efficiency and accuracy of blasting effect evaluation.
Quantitative evaluation of the blasting effect is achieved, the efficiency, accuracy and reliability of the evaluation are improved, and the advantages and disadvantages of the blasting effect can be intuitively evaluated.
Smart Images
Figure CN119445295A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of mining engineering, and specifically to a method, device and electronic equipment for intelligent evaluation of blasting effects. Background Art
[0002] With the acceleration of global industrialization, the mining of mineral resources has become particularly important. In mining production, blasting is one of the key steps to obtain ore, and its effect directly affects the utilization rate and mining cost of the ore. Efficient blasting can not only significantly improve the recovery rate of ore, but also reduce damage to the environment. Therefore, how to accurately evaluate the blasting effect and optimize the blasting parameters has become the key to improving the economic benefits of mining and environmental protection.
[0003] At present, the evaluation of blasting effects relies on on-site observation and manual measurement, which are time-consuming, labor-intensive and inefficient.
[0004] Therefore, there is an urgent need for an intelligent blasting effect evaluation method, device and electronic equipment. Summary of the invention
[0005] The present application provides a method, an apparatus and an electronic device, which automatically determine a gravel distribution fitting curve through a depth image, and further evaluate the blasting effect level, thereby improving the efficiency of blasting effect evaluation.
[0006] In a first aspect of the present application, a method for intelligently evaluating blasting effects is provided, the method comprising: acquiring a plurality of depth images of a rubble pile after blasting; determining a target depth image from the plurality of depth images, and performing contour extraction on the target depth image to obtain a rubble contour; calculating the rubble size of each rock fragment contained in the target depth image according to the rubble contour; sorting the rubble size of each rock fragment from small to large to obtain a size sequence; in the size sequence, calculating a size characteristic value according to a preset percentage group, the preset percentage group including a plurality of preset percentages, one preset percentage corresponding to one size characteristic value; determining a rubble distribution fitting curve according to the size characteristic values corresponding to each preset percentage; and determining a blasting effect grade corresponding to the rubble pile according to the rubble distribution fitting curve.
[0007] By adopting the above technical solution, by obtaining multiple depth images of the rubble pile after blasting, the three-dimensional information of the rubble pile can be fully and accurately obtained. The target depth image is determined from multiple depth images, and the contour of the target depth image is extracted to obtain the rubble contour, so that the area of each rock fragment can be accurately identified. The rubble size of each rock fragment is calculated according to the rubble contour, and then the size sequence is obtained by sorting by size, which can quantitatively describe the particle size distribution of the rock rubble. In the size sequence, the size characteristic value is calculated according to the preset percentage group, and the rubble distribution fitting curve is determined according to the size characteristic value corresponding to each preset percentage. A continuous rubble distribution fitting curve can be used to approximately characterize the particle size distribution law of the rubble. Finally, the blasting effect level is determined according to the rubble distribution fitting curve, and the quality of the blasting effect can be intuitively evaluated. This technical solution realizes the quantitative evaluation of the blasting effect through machine vision and data analysis, and improves the efficiency, accuracy and reliability of the evaluation.
[0008] Optionally, in the size sequence, the size characteristic value is calculated according to a preset percentage group, specifically including: determining a target preset percentage, and multiplying the target preset percentage by the total gravel size to obtain a corresponding gravel index, the target preset percentage being any one of a plurality of the preset percentages, and the total gravel size being the sum of the gravel sizes included in the size sequence; in the size sequence, determining a first gravel size and a second gravel size corresponding to the gravel index, the first gravel size and the second gravel size being two adjacent gravel sizes among the plurality of gravel sizes, and the second gravel size being larger than the first gravel size; and taking the second gravel size as the size characteristic value.
[0009] By adopting the above technical solution, the target preset percentage is first determined, and then multiplied by the total gravel size to obtain the gravel index. The corresponding two adjacent gravel sizes are found in the size sequence through the gravel index, and the larger gravel size is taken as the size characteristic value of the target preset percentage. This calculation method fully considers the order of arrangement of the sizes of each gravel particle, and can accurately find the size characteristic value corresponding to each preset percentage, which can better reflect the representative size of the gravel in each preset percentage.
[0010] Optionally, determining the gravel distribution fitting curve according to the size characteristic values corresponding to the preset percentages specifically includes: Using each of the preset percentages as the horizontal axis and the size characteristic value corresponding to each of the preset percentages as the vertical axis, a scatter plot is drawn; using a preset function as a model of the gravel distribution fitting curve, the preset function is ; Wherein, x is the preset percentage, a is the partition coefficient, b is the first fluctuation constant, c is the second fluctuation constant, and x max is the size characteristic value corresponding to the preset percentage being 100%, x 50 is the size characteristic value corresponding to the preset percentage being 50%; Substitute the data points in the scatter plot into the preset function, use the least squares method to fit the preset function, and obtain the optimal partition coefficient, the optimal first fluctuation constant and the optimal second fluctuation constant; substitute the optimal partition coefficient, the optimal first fluctuation constant and the optimal second fluctuation constant into the preset function to obtain the gravel distribution fitting curve.
[0011] By adopting the above technical solution, a scatter plot is drawn with the preset percentage as the horizontal axis and the corresponding size characteristic value as the vertical axis, which intuitively shows the representative size of the gravel pile at different preset percentages. A preset function is used as a model for the gravel distribution fitting curve, and the preset function can describe the general law of gravel distribution. By fitting the preset function by the least squares method, the optimal function parameters can be found, so that the gravel distribution fitting curve has the highest degree of consistency with the actual scatter point distribution. The gravel distribution fitting curve obtained by fitting is used to characterize the gravel distribution law. Compared with scattered scatter points, it has stronger continuity and regularity, can reveal the particle size distribution characteristics of the gravel pile, and provide a more reliable basis for the subsequent evaluation of the blasting effect.
[0012] Optionally, determining the blasting effect level corresponding to the gravel pile according to the gravel distribution fitting curve specifically includes: determining a corresponding median characteristic size according to the gravel distribution fitting curve, the median characteristic size being a size characteristic value corresponding to a preset percentage on the gravel distribution fitting curve of fifty percent; comparing the median characteristic size with a preset blasting effect level judgment range, the preset blasting effect level judgment range including a median characteristic size range corresponding to the blasting effect level; when the median characteristic size is within a target median characteristic size range, determining the blasting effect level corresponding to the target median characteristic size range as the blasting effect level corresponding to the gravel pile, the blasting effect level being one of excellent, good, medium and poor.
[0013] By adopting the above technical solution, the median characteristic size is determined by the gravel distribution fitting curve, that is, the size characteristic value when the preset percentage on the gravel distribution fitting curve is 50%. The median characteristic size represents the central particle size of the gravel pile and reflects the overall size level of the gravel particles. By comparing the median characteristic size with the preset blasting effect grade judgment range, the qualification degree of the overall particle size of the gravel pile can be objectively judged. The grade corresponding to the median characteristic size range in which the median characteristic size is located is determined as the blasting effect grade of the gravel pile, giving an intuitive and qualitative evaluation result. The median characteristic size represents the overall particle size of the gravel pile, which is more representative and comparable. The blasting effect grade is matched by the median characteristic size, thereby accurately reflecting the blasting effect of the entire gravel pile.
[0014] Optionally, determining the target depth image from the multiple depth images specifically includes: performing quality assessment on the multiple depth images to obtain a quality assessment score for each of the depth images; sorting the quality assessment scores from high to low, and determining the depth image with the highest quality assessment score as the target depth image, wherein the quality assessment includes evaluating the image clarity, image integrity and amount of gravel of the depth images.
[0015] By adopting the above technical solution, the quality of multiple depth images is evaluated and the target depth image with the best imaging effect is screened out. By comprehensively judging the clarity, integrity and number of gravel contained in the depth image, the image quality can be comprehensively examined. The depth image with the highest quality evaluation score is selected as the target depth image, which ensures that the data source for subsequent analysis and processing is optimal in imaging quality, laying the foundation for accurately extracting gravel contours and calculating gravel size. Quantifying each indicator into a quality evaluation score facilitates horizontal comparison and sorting and screening of the quality of multiple depth images. The target depth image with the highest quality is selected as the target for subsequent analysis, ensuring the reliability of the solution from the source of the data.
[0016] Optionally, calculating the gravel size of each rock fragment contained in the target depth image according to the gravel contour specifically includes: determining the three-dimensional point cloud data of the gravel pile according to the gravel contour; segmenting the three-dimensional point cloud data to obtain a target point cloud subset corresponding to a target rock fragment, wherein the target rock fragment is any one of a plurality of rock fragments; extracting the three-dimensional spatial coordinates included in the target point cloud subset, and calculating the maximum size of the target point cloud subset in a preset orthogonal direction according to the three-dimensional spatial coordinates, wherein the preset orthogonal direction includes length, width and height; and determining the maximum size as the gravel size of the rock fragment.
[0017] By adopting the above technical solution, the three-dimensional point cloud data of the rubble pile is determined according to the rubble contour, the three-dimensional spatial information of the rubble pile is restored, and it is digitally represented in the form of three-dimensional point cloud data. The three-dimensional point cloud data is segmented to obtain the target point cloud subsets of each rock fragment, and each target point cloud subset describes the three-dimensional shape of a rock fragment. The three-dimensional spatial coordinates of each target point cloud subset are extracted, and its maximum size in the three orthogonal directions of length, width and height is calculated as the rubble size. The rubble size is defined by the maximum size in three-dimensional space, which can reflect the true size of the rock fragment in space. This technical solution makes full use of the three-dimensional information of the depth image, so that the calculation results are accurate and reliable.
[0018] Optionally, performing contour extraction on the target depth image to obtain a gravel contour specifically includes: performing a preprocessing operation on the target depth image, the preprocessing operation including denoising and image enhancement; performing image segmentation on the preprocessed target depth image to obtain a segmented image area; determining a gravel area from the segmented image area according to a preset gravel area judgment rule, wherein the preset gravel area judgment rule includes a depth value range, an area range and a shape feature of the segmented image area; performing contour extraction on the gravel area to obtain the gravel contour.
[0019] By adopting the above technical solution, the target depth image is subjected to preprocessing operations such as denoising and image enhancement, which can eliminate noise interference in the target depth image, improve image quality, and provide clearer image data for subsequent segmentation processing. The preprocessed target depth image is segmented to separate the gravel area from the background. The gravel area in the segmentation result is determined according to the preset gravel area judgment rule, and the regional depth value range, area range and shape characteristics are introduced as the preset gravel area judgment rule, so as to extract the gravel area in a targeted manner, eliminate the interference of non-gravel areas, and improve the accuracy of gravel area extraction. The extracted gravel area is contour extracted to obtain the contour information of the rock fragments, which provides a basis for the subsequent calculation of the gravel size.
[0020] In a second aspect of the present application, a blasting effect intelligent evaluation device is provided, the device comprising: an acquisition module and a processing module; the acquisition module is used to acquire multiple depth images of a rubble pile after blasting; the processing module is used to determine a target depth image from the multiple depth images, and perform contour extraction on the target depth image to obtain a rubble contour; the processing module is also used to calculate the rubble size of each rock fragment contained in the target depth image according to the rubble contour; the processing module is also used to sort the rubble size of each rock fragment from small to large to obtain a size sequence; the processing module is also used to calculate the size characteristic value according to a preset percentage group in the size sequence, the preset percentage group includes multiple preset percentages, and one preset percentage corresponds to one size characteristic value; the processing module is also used to determine a rubble distribution fitting curve according to the size characteristic values corresponding to each preset percentage; the processing module is also used to determine the blasting effect level corresponding to the rubble pile according to the rubble distribution fitting curve.
[0021] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.
[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.
[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By acquiring multiple depth images of the gravel pile after blasting, the three-dimensional information of the gravel pile can be fully and accurately obtained. The target depth image is determined from multiple depth images, and the contour of the target depth image is extracted to obtain the gravel contour, so that the area of each rock fragment can be accurately identified. The gravel size of each rock fragment is calculated according to the gravel contour, and then the size sequence is obtained by sorting by size, which can quantitatively describe the particle size distribution of rock gravel. In the size sequence, the size characteristic value is calculated according to the preset percentage group, and the gravel distribution fitting curve is determined according to the size characteristic value corresponding to each preset percentage. A continuous gravel distribution fitting curve can be used to approximately characterize the particle size distribution law of the gravel. Finally, the blasting effect level is determined according to the gravel distribution fitting curve, and the quality of the blasting effect can be intuitively evaluated. This technical solution realizes the quantitative evaluation of the blasting effect through machine vision and data analysis, and improves the efficiency, accuracy and reliability of the evaluation.
[0024] 2. First determine the target preset percentage, multiply it by the total gravel size to get the gravel index, find the corresponding two adjacent gravel sizes in the size sequence through the gravel index, and take the larger gravel size as the size characteristic value of the target preset percentage. This calculation method fully considers the order of the size arrangement of each gravel particle, can accurately find the size characteristic value corresponding to each preset percentage, and can better reflect the representative size of the gravel in each preset percentage.
[0025] 3. Draw a scatter plot with the preset percentage as the horizontal axis and the corresponding size characteristic value as the vertical axis, which intuitively shows the representative size of the gravel pile at different preset percentages. The preset function is used as the model of the gravel distribution fitting curve, and the preset function can describe the general law of gravel distribution. By fitting the preset function by the least squares method, the optimal function parameters can be found, so that the gravel distribution fitting curve is most consistent with the actual scattered point distribution. The gravel distribution fitting curve obtained by fitting is used to characterize the gravel distribution law. Compared with scattered scatter points, it has stronger continuity and regularity, can reveal the particle size distribution characteristics of the gravel pile, and provide a more reliable basis for the subsequent evaluation of the blasting effect. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] Figure 1 It is a flow chart of a method for intelligently evaluating blasting effects disclosed in an embodiment of the present application; Figure 2 It is a module schematic diagram of a blasting effect intelligent evaluation device disclosed in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.
[0027] Explanation of the reference numerals: 201, acquisition module; 202, processing module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION
[0028] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.
[0029] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.
[0030] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.
[0031] This application provides a method for intelligent evaluation of blasting effect. Figure 1 , Figure 1 This is a flow chart of a method for intelligently evaluating blasting effects provided in an embodiment of the present application. The method is applied to a server, which is the hardware support of the method of the present application and integrates data acquisition, image processing, effect evaluation, and human-computer interaction functions. The server can be a single server, or a server cluster composed of multiple servers, or a cloud computing service center. The method includes steps S101 to S107, and the above steps are as follows: Step S101: Acquire multiple depth images of the rubble pile after blasting.
[0032] In step S101, the server controls the camera to collect multiple images of the rubble pile. In the embodiment of the present application, a binocular camera including two cameras is selected, the optical axes of the lenses of the left and right cameras of the binocular camera are parallel, and the camera parameters (such as focal length, baseline distance, etc.) are known. During the collection, the server synchronizes the binocular camera through a trigger signal, and the collected image pairs are transmitted back to the server and temporarily stored in the memory or hard disk.
[0033] Next, the server performs binocular stereo matching on the collected image pairs and calculates the disparity map. Specifically, the server first corrects the left and right images to eliminate the distortion introduced by the camera angle difference. Then the server selects a pixel in the left image (or right image) and searches for its corresponding matching point in the right image (or left image). After finding the matching point, the horizontal coordinate difference between the two corresponding points is the disparity value of the pixel. The server repeats this process to obtain a dense disparity map of the entire image. The larger the disparity value, the closer the pixel is to the camera. The server uses the disparity map to calculate the depth image. In a binocular stereo system, the depth of a pixel is inversely proportional to its disparity value, and the relationship is: d=b*f / d, where b is the camera baseline length, f is the camera focal length, and d is the disparity value. According to this formula, the server converts the disparity map into a depth image and obtains the depth information of each pixel on the surface of the gravel pile.
[0034] Step S102: determining a target depth image from a plurality of depth images, and performing contour extraction on the target depth image to obtain a gravel contour.
[0035] In step S102, a target depth image is determined from multiple depth images, and quality assessment is performed on the multiple depth images to obtain a quality assessment score for each depth image; the quality assessment scores are sorted from high to low, and the depth image with the highest quality assessment score is determined as the target depth image, wherein the quality assessment includes evaluating the image clarity, image integrity, and amount of gravel of the depth image.
[0036] Specifically, the server preprocesses each depth image, including image denoising and image enhancement operations. The server performs quality assessment on the preprocessed depth image. The first indicator is image clarity, which reflects the overall quality of the depth image. The server uses the gradient variance method to calculate the clarity score of the depth image. The higher the clarity score, the clearer the depth image. The second indicator is image integrity, which reflects the proportion of missing or invalid areas in the depth image. The server counts the number of pixels whose depth value is 0 or exceeds the preset depth value range, calculates its proportion of the total number of pixels, and obtains the integrity score. The higher the integrity score, the more complete the depth image is and the fewer missing areas are. The third indicator is the number of gravels, which reflects the coverage of the gravel pile by the depth image. The server can use threshold segmentation and connected domain analysis methods to detect the number of gravels in the depth image and obtain the gravel number score. The higher the gravel number score, the more gravels there are in the depth image, which is more beneficial to the evaluation of the blasting effect.
[0037] Combining the above three indicators, the server obtains the quality assessment score of each depth image. In order to balance the importance of different indicators, the server assigns weights to each indicator and calculates the weighted average as the final quality assessment score. For example, the weight of image clarity is 0.5, the weight of image integrity is 0.3, and the weight of the number of gravel is 0.2. The server sorts the quality assessment scores of all depth images from high to low, and the depth image with the highest score is determined as the target depth image for subsequent processing.
[0038] After obtaining the target depth image, the server extracts the gravel contour. The server implements this through image segmentation algorithms, such as threshold segmentation, edge detection, and region growing. The server first divides the target depth image into foreground (gravel) and background (ground) parts according to the depth value, and then performs morphological processing on the foreground area to eliminate small noise areas to obtain the rough outline of the gravel. Then the server uses the Canny edge detection algorithm to extract the precise boundary of the gravel contour. After extracting the gravel contour, the server post-processes the extracted gravel contour to obtain vectorized gravel contour data suitable for subsequent analysis.
[0039] In a possible implementation, contour extraction is performed on a target depth image to obtain a gravel contour, specifically including: performing a preprocessing operation on the target depth image, the preprocessing operation including denoising and image enhancement; performing image segmentation on the preprocessed target depth image to obtain a segmented image area; determining a gravel area from the segmented image area according to a preset gravel area judgment rule, wherein the preset gravel area judgment rule includes a depth value range, an area range, and a shape feature of the segmented image area; and performing contour extraction on the gravel area to obtain a gravel contour.
[0040] Specifically, the server performs preprocessing operations on the target depth image, including denoising and image enhancement. The server performs image segmentation on the preprocessed target depth image. Image segmentation is to divide the image into several non-overlapping areas, and the pixels in each area are consistent in certain features. In the embodiment of the present application, it can be understood that the server divides the target depth image into two categories: gravel area and non-gravel area. For example, the server can apply global threshold segmentation to the depth image, divide pixels with depth values greater than a preset depth threshold into gravel areas, and divide the remaining pixels into non-gravel areas. The selection of the preset depth threshold can be determined based on factors such as the histogram characteristics of the image and prior knowledge, and the present application does not limit this.
[0041] After image segmentation, the server obtains a segmented image, and based on the segmented image, the server further determines the gravel area. Since the segmentation result may contain some misjudged or over-segmented areas, the server will filter out the gravel area from the segmented image area according to the preset gravel area judgment rules.
[0042] The preset gravel area judgment rules include depth value range, area range and shape features. For example, the server can set the following rules: the depth value range indicates that the depth value of the gravel area should be within a reasonable preset depth value range, such as between 0.5m and 1.5m; the area range indicates that the area of the gravel area should be greater than the preset area threshold, such as 100 square pixels, to filter out small noise areas. The shape feature indicates that the shape of the gravel area should be approximately elliptical or polygonal. The server compares each segmented image area with these rules, and the area that meets all the rules is determined to be a gravel area, and the remaining areas are eliminated. Next, the server extracts the contour of the determined gravel area to obtain the gravel contour.
[0043] Step S103: Calculate the gravel size of each rock fragment contained in the target depth image according to the gravel contour.
[0044] In step S103, the three-dimensional point cloud data of the gravel pile is determined according to the gravel contour; the three-dimensional point cloud data is segmented to obtain a target point cloud subset corresponding to the target rock fragment, where the target rock fragment is any one of the multiple rock fragments; the three-dimensional space coordinates included in the target point cloud subset are extracted, and based on the three-dimensional space coordinates, the maximum size of the target point cloud subset in a preset orthogonal direction is calculated, where the preset orthogonal direction includes length, width and height; the maximum size is determined as the gravel size of the rock fragment.
[0045] Specifically, the server determines the three-dimensional point cloud data of the gravel pile based on the gravel contour. The server extracts pixels from the target depth image, converts the coordinates and depth value of each pixel in the target depth image into points in three-dimensional space, and obtains the three-dimensional coordinates (three-dimensional point cloud data) of the gravel surface. Next, the server segments the three-dimensional point cloud data to distinguish different rock fragments. The server projects each gravel contour into three-dimensional space using the gravel contour information extracted in the previous steps, and surrounds the corresponding three-dimensional point cloud data with a closed surface to form an independent target point cloud subset. In this way, the entire point cloud data is segmented into multiple target point cloud subsets, each target point cloud subset corresponding to a rock fragment.
[0046] In order to calculate the gravel size of the target rock fragment, the server extracts all the three-dimensional coordinate points contained in the target point cloud subset. The three-dimensional coordinate points are stored in the form of triples (x, y, z), which represent the horizontal coordinate, vertical coordinate, and height coordinate of the point in three-dimensional space, respectively. The server performs a statistical analysis on the distribution of the three-dimensional coordinate points to obtain the range of the target point cloud subset in three orthogonal directions. Specifically, the server finds the maximum and minimum values of the point cloud subset in the X-axis, Y-axis, and Z-axis directions, and determines the difference between them as the maximum size of the fragment in the corresponding direction. For example, if the maximum value of the target point cloud subset in the X-axis direction is 50cm and the minimum value is 20cm, the size in this direction is 50-20=30cm. After obtaining the sizes in the three orthogonal directions, the server determines the maximum size among them as the gravel size of the target rock fragment.
[0047] Step S104: sorting the crushed stone sizes of the rock fragments from small to large to obtain a size sequence.
[0048] In step S104, the server uses a sorting algorithm to sort the gravel sizes of the rock fragments and arranges them in ascending order to obtain a size sequence.
[0049] Step S105: in the size sequence, the size characteristic value is calculated according to the preset percentage group, the preset percentage group includes a plurality of preset percentages, and one preset percentage corresponds to one size characteristic value.
[0050] In step S105, a target preset percentage is determined, and the target preset percentage is multiplied by the total gravel size to obtain a corresponding gravel index, where the target preset percentage is any one of a plurality of preset percentages; in the size sequence, a first gravel size and a second gravel size corresponding to the gravel index are determined, where the first gravel size and the second gravel size are two adjacent gravel sizes among a plurality of gravel sizes; if it is determined that the first gravel size is greater than or equal to the second gravel size, the first gravel size is used as a size characteristic value; if it is determined that the first gravel size is less than the second gravel size, the second gravel size is used as a size characteristic value.
[0051] Specifically, the server determines a target preset percentage from a preset percentage group, and the target preset percentage is any preset percentage in the preset percentage group. The preset percentage group is a set consisting of multiple preset percentages. For example, one combination in the preset percentage group is {10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%}. The preset percentages represent different quantiles of the gravel size distribution and reflect the overall particle size characteristics of the gravel pile. The server can set the value of the preset percentage group according to actual needs. After determining the target preset percentage, the server uses it as the basis for calculating the size characteristic value.
[0052] The server calculates the corresponding gravel index according to the target preset percentage. The gravel index is an integer, which indicates the gravel position corresponding to a certain percentage position in the size sequence sorted by size. The specific calculation method is: multiply the target preset percentage by the total gravel size to obtain the gravel index, wherein the total gravel size is the sum of all gravel sizes in the size sequence. For example, if the total gravel size is 1000 and the target preset percentage is 60%, the gravel index is 1000*60%=600. In the sorted size sequence, the server adds the gravel sizes in sequence to obtain the gravel size sum. When the gravel size sum is close to 600, the server determines the first size sum when the gravel size sum is less than or equal to 600 and the second size sum when the gravel size sum is greater than or equal to 600, and further determines the first gravel size corresponding to the first size sum and the second gravel size corresponding to the second size sum, wherein the first gravel size is the largest gravel size among the gravel sizes included in the first size sum, and the second gravel size sum is the largest gravel size among the gravel sizes included in the second gravel size. The server uses the second gravel size as the size characteristic value corresponding to the preset percentage.
[0053] For example, assuming there are n rock fragments, and the crushed stone size of each rock fragment is yi, then the size sequence sorted from small to large according to the crushed stone size is y1, y2, y3, y4, y5..., and the fragment feature value is determined according to the following formula: ; At this time, the size characteristic value x corresponding to the preset percentage of 20% 20 is yk; since the proportion of the cumulative size sum to the total size sum is not necessarily exactly equal to 20%; if there is ; ; At this time, y3 and y4 are the fragment sizes of two adjacent rock fragments, y3 corresponds to the first crushed stone size, y4 corresponds to the second crushed stone size, and the corresponding size characteristic value x when the preset percentage is 20% 20 is y4.
[0054] By repeating the above process, the server can obtain the size characteristic values corresponding to all preset percentages, forming a new sequence that fully describes the particle size distribution characteristics of the gravel pile. This sequence can be used for subsequent analysis and evaluation, such as drawing a histogram of fragment characteristic values and fitting fragment size distribution.
[0055] Step S106: determining a gravel distribution fitting curve according to the size characteristic values corresponding to each preset percentage.
[0056] In step S106, each preset percentage is used as the horizontal axis, and the size characteristic value corresponding to each preset percentage is used as the vertical axis to draw a scatter plot; a preset function is used as a model of the gravel distribution fitting curve, and the preset function is ; Where x is the preset percentage, a is the partition coefficient, b is the first fluctuation constant, c is the second fluctuation constant, and x max is the size characteristic value corresponding to the preset percentage of 100%, x 50 It is the size characteristic value corresponding to the preset percentage of 50%; Substitute the data points in the scatter plot into the preset function, use the least squares method to fit the preset function, and obtain the optimal partition coefficient, the optimal first fluctuation constant and the optimal second fluctuation constant; substitute the optimal partition coefficient, the optimal first fluctuation constant and the optimal second fluctuation constant into the preset function to obtain the gravel distribution fitting curve.
[0057] Specifically, the server uses the preset percentage as the horizontal axis and the corresponding size characteristic value as the vertical axis to draw a scatter plot in the coordinate system. Each data point represents a preset percentage and its corresponding size characteristic value. For example, if the preset percentage group is {10%, 30%, 50%, 70%, 90%}, and the corresponding size characteristic value is {16cm, 42cm, 65cm, 88cm, 110cm}, the server will draw five points in the coordinate system: (10%, 16cm), (30%, 42cm), (50%, 65cm), (70%, 88cm) and (90%, 110cm). The scatter plot intuitively shows the distribution of gravel piles in different particle size ranges, providing a data basis for subsequent curve fitting.
[0058] Next, the server uses the preset function as the model of the gravel distribution fitting curve. The preset function is ; Where x is the preset percentage, a is the partition coefficient, b is the first fluctuation constant, c is the second fluctuation constant, and x max is the size characteristic value corresponding to the preset percentage of 100%, x 50 is the size characteristic value corresponding to the preset percentage of 50%. The preset function contains three undetermined parameters: a, b and c. Among them, a controls the overall shape of the curve, and b and c control the local fluctuation of the curve. max and x 50 is a known quantity, which can be obtained through the steps in step S105. The server uses the preset function as a fitting model and adjusts the values of a, b, and c to make the preset function as close as possible to the data points in the scatter plot.
[0059] Finally, the server uses the least squares method to fit the preset function to determine the optimal parameter values. Specifically, the server substitutes each data point in the scatter plot into the preset function, calculates the difference between the theoretical value and the actual value, and finds the sum of the squares of these differences. By minimizing the sum of squares, the server can find the optimal partition coefficient a, the optimal first fluctuation constant b, and the optimal second fluctuation constant c.
[0060] For example, for the first data point (10%, 16cm), the server substitutes x=10 into the preset function to obtain an expression for a, b, and c. Similarly, this process is repeated for the other four data points to obtain five expressions. The server uses the sum of the squares of these expressions as the objective function and uses the least squares method to optimize and find the optimal values of a, b, and c.
[0061] Step S107: Determine the blasting effect level corresponding to the gravel pile according to the gravel distribution fitting curve.
[0062] In step S107, the corresponding median characteristic size is determined according to the gravel distribution fitting curve, the median characteristic size being the size characteristic value corresponding to the preset percentage on the gravel distribution fitting curve being 50%; the median characteristic size is compared with the preset blasting effect level judgment range, the preset blasting effect level judgment range includes the median characteristic size range corresponding to the blasting effect level; when the median characteristic size is within the target median characteristic size range, the blasting effect level corresponding to the target median characteristic size range is determined as the blasting effect level corresponding to the gravel pile, and the blasting effect level is one of excellent, good, medium and poor.
[0063] Specifically, the server determines the corresponding median characteristic size based on the gravel distribution fitting curve. The median characteristic size refers to the size characteristic value corresponding to the preset percentage of 50% on the gravel distribution fitting curve. This size characteristic value is the median particle size of the gravel pile and is one of the important indicators for evaluating the blasting effect.
[0064] Specifically, the server can substitute the preset percentage x=50 into the gravel distribution fitting curve obtained in step S106, and calculate the corresponding size characteristic value, that is, the median particle size (median characteristic size), such as 65cm. The median characteristic size indicates that, according to the prediction of the gravel distribution fitting curve, 50% of the gravel in the gravel pile is less than or equal to 65cm in size. The server uses the median characteristic size as a key indicator to measure the blasting effect for subsequent comparison and judgment. Next, the server compares the median characteristic size with the preset blasting effect level judgment range to determine the blasting effect level corresponding to the gravel pile. The blasting effect level is a qualitative evaluation standard, which is divided into four levels: excellent, good, medium, and poor, and each level corresponds to a median characteristic size range.
[0065] For example, the preset blasting effect level judgment range can be: excellent corresponds to a median characteristic size below 40 cm; good corresponds to a median characteristic size between 40 cm and 60 cm; medium corresponds to a median characteristic size between 60 cm and 80 cm; poor corresponds to a median characteristic size above 80 cm.
[0066] The server compares the median characteristic size (65cm) obtained in the above steps with the preset blasting effect level judgment range and determines that 65cm is within the range corresponding to the "medium" level (between 60cm and 80cm). Therefore, the server determines "medium" as the blasting effect level corresponding to the rubble pile.
[0067] The judgment result shows that the blasting effect is average, the overall particle size of the rubble pile is coarse, and there is room for improvement. The server can feed back the result to the blasting engineer to help him analyze the blasting effect and cause.
[0068] Reference Figure 2 The present application also provides a blasting effect intelligent evaluation device, which is a server, and the server includes an acquisition module 201 and a processing module 202, wherein: the acquisition module 201 is used to acquire multiple depth images of the rubble pile after blasting; the processing module 202 is used to determine the target depth image from the multiple depth images, and perform contour extraction on the target depth image to obtain the rubble contour; the processing module 202 is also used to calculate the rubble size of each rock fragment contained in the target depth image according to the rubble contour; the processing module 202 is also used to sort the rubble size of each rock fragment from small to large to obtain a size sequence; the processing module 202 is also used to calculate the size characteristic value according to the preset percentage group in the size sequence, the preset percentage group includes multiple preset percentages, and one preset percentage corresponds to one size characteristic value; the processing module 202 is also used to determine the rubble distribution fitting curve according to the size characteristic value corresponding to each preset percentage; the processing module 202 is also used to determine the blasting effect level corresponding to the rubble pile according to the rubble distribution fitting curve.
[0069] In a possible implementation, the processing module 202 calculates the size characteristic value according to the preset percentage group in the size sequence, specifically including: the processing module 202 determines the target preset percentage, and multiplies the target preset percentage by the total gravel size to obtain a corresponding gravel index, the target preset percentage is any one of multiple preset percentages, and the total gravel size is the sum of the gravel sizes included in the size sequence; the processing module 202 determines the first gravel size and the second gravel size corresponding to the gravel index in the size sequence, the first gravel size and the second gravel size are two adjacent gravel sizes among multiple gravel sizes, and the second gravel size is larger than the first gravel size; the processing module 202 uses the second gravel size as the size characteristic value.
[0070] In a possible implementation, the processing module 202 determines the gravel distribution fitting curve according to the size characteristic values corresponding to each preset percentage, specifically including: the processing module 202 uses each preset percentage as the horizontal coordinate and the size characteristic values corresponding to each preset percentage as the vertical coordinate to draw a scatter plot; the processing module 202 uses a preset function as a model of the gravel distribution fitting curve, and the preset function is ; Where x is the preset percentage, a is the partition coefficient, b is the first fluctuation constant, c is the second fluctuation constant, and x max is the size characteristic value corresponding to the preset percentage of 100%, x 50 It is the size characteristic value corresponding to the preset percentage of 50%; The processing module 202 substitutes the data points in the scatter plot into a preset function, and uses the least squares method to fit the preset function to obtain the optimal partition coefficient, the optimal first fluctuation constant, and the optimal second fluctuation constant; the processing module 202 substitutes the optimal partition coefficient, the optimal first fluctuation constant, and the optimal second fluctuation constant into the preset function to obtain the gravel distribution fitting curve.
[0071] In a possible implementation, the processing module 202 determines the blasting effect level corresponding to the gravel pile according to the gravel distribution fitting curve, specifically including: the processing module 202 determines the corresponding median characteristic size according to the gravel distribution fitting curve, the median characteristic size is the size characteristic value corresponding to the preset percentage on the gravel distribution fitting curve of 50%; the processing module 202 compares the median characteristic size with a preset blasting effect level judgment range, the preset blasting effect level judgment range includes the median characteristic size range corresponding to the blasting effect level; when the median characteristic size is within the target median characteristic size range, the processing module 202 determines the blasting effect level corresponding to the target median characteristic size range as the blasting effect level corresponding to the gravel pile, and the blasting effect level is one of excellent, good, medium and poor.
[0072] In a possible implementation, the processing module 202 determines a target depth image from multiple depth images, specifically including: the processing module 202 performs quality assessment on the multiple depth images to obtain a quality assessment score for each depth image; the processing module 202 sorts the quality assessment scores from high to low, and determines the depth image with the highest quality assessment score as the target depth image, wherein the quality assessment includes assessing the image clarity, image integrity, and amount of gravel of the depth image.
[0073] In a possible implementation, the processing module 202 calculates the gravel size of each rock fragment contained in the target depth image according to the gravel contour, specifically including: the processing module 202 determines the three-dimensional point cloud data of the gravel pile according to the gravel contour; the processing module 202 segments the three-dimensional point cloud data to obtain a target point cloud subset corresponding to the target rock fragment, the target rock fragment is any one of the multiple rock fragments; the processing module 202 extracts the three-dimensional space coordinates included in the target point cloud subset, and calculates the maximum size of the target point cloud subset in a preset orthogonal direction according to the three-dimensional space coordinates, the preset orthogonal direction includes length, width and height; the processing module 202 determines the maximum size as the gravel size of the rock fragment.
[0074] In a possible implementation, the processing module 202 performs contour extraction on the target depth image to obtain a gravel contour, specifically including: the processing module 202 performs a preprocessing operation on the target depth image, the preprocessing operation includes denoising and image enhancement; the processing module 202 performs image segmentation on the preprocessed target depth image to obtain a segmented image area; the processing module 202 determines a gravel area from the segmented image area according to a preset gravel area judgment rule, wherein the preset gravel area judgment rule includes a depth value range, an area range, and a shape feature of the segmented image area; the processing module 202 performs contour extraction on the gravel area to obtain a gravel contour.
[0075] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.
[0076] The present application also provides an electronic device. Figure 3 , Figure 3The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .
[0077] The communication bus 302 is used to realize the connection and communication between these components.
[0078] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.
[0079] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).
[0080] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.
[0081] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a blasting effect intelligent evaluation method.
[0082] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call an application program storing a method for intelligent evaluation of blasting effects in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.
[0083] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments.
[0084] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0085] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.
[0086] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0087] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0088] If the integrated unit 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 memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.
[0089] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.
[0090] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.
Claims
1. A method for intelligent evaluation of blasting effect, characterized in that: The method comprises: Acquire multiple depth images of the rubble pile after blasting; Determining a target depth image from the plurality of depth images, and performing contour extraction on the target depth image to obtain a gravel contour; Calculating the gravel size of each rock fragment contained in the target depth image according to the gravel contour; Sorting the crushed stone sizes of the rock fragments from small to large to obtain a size sequence; In the size sequence, the size characteristic value is calculated according to a preset percentage group, the preset percentage group includes a plurality of preset percentages, and one preset percentage corresponds to one size characteristic value; Determining a gravel distribution fitting curve according to the size characteristic values corresponding to the preset percentages; The blasting effect level corresponding to the gravel pile is determined according to the gravel distribution fitting curve.
2. The method according to claim 1, characterized in that The calculating of the size characteristic value in the size sequence according to the preset percentage group specifically includes: Determine a target preset percentage, and multiply the target preset percentage by the total size of the gravel to obtain a corresponding gravel index, wherein the target preset percentage is any one of the plurality of preset percentages, and the total size of the gravel is the sum of the sizes of the gravel included in the size sequence; In the size sequence, determining a first crushed stone size and a second crushed stone size corresponding to the crushed stone index, wherein the first crushed stone size and the second crushed stone size are two adjacent crushed stone sizes among the plurality of crushed stone sizes, and the second crushed stone size is larger than the first crushed stone size; The second crushed stone size is used as the size characteristic value.
3. The method according to claim 1, characterized in that Determining the gravel distribution fitting curve according to the size characteristic values corresponding to the preset percentages specifically includes: Using each of the preset percentages as the horizontal axis and the size characteristic value corresponding to each of the preset percentages as the vertical axis, a scatter plot is drawn; A preset function is used as the model of the gravel distribution fitting curve, and the preset function is ； Wherein, x is the preset percentage, a is the partition coefficient, b is the first fluctuation constant, c is the second fluctuation constant, and x max is the size characteristic value corresponding to the preset percentage being 100%, x 50 is the size characteristic value corresponding to the preset percentage being 50%; Substituting the data points in the scatter plot into the preset function, fitting the preset function using the least squares method, and obtaining an optimal partition coefficient, an optimal first fluctuation constant, and an optimal second fluctuation constant; The optimal partition coefficient, the optimal first fluctuation constant and the optimal second fluctuation constant are substituted into the preset function to obtain the gravel distribution fitting curve.
4. The method according to claim 1, characterized in that: Determining the blasting effect level corresponding to the gravel pile according to the gravel distribution fitting curve specifically includes: Determine a corresponding median characteristic size according to the gravel distribution fitting curve, wherein the median characteristic size is a size characteristic value corresponding to a preset percentage of 50% on the gravel distribution fitting curve; comparing the median characteristic size with a preset blasting effect level judgment range, wherein the preset blasting effect level judgment range includes a median characteristic size range corresponding to the blasting effect level; When the median characteristic size is within the target median characteristic size range, the blasting effect level corresponding to the target median characteristic size range is determined as the blasting effect level corresponding to the rubble pile, and the blasting effect level is one of excellent, good, medium and poor.
5. The method according to claim 1, characterized in that The determining of the target depth image from the plurality of depth images specifically includes: Performing quality assessment on the plurality of depth images to obtain a quality assessment score for each of the depth images; The quality assessment scores are sorted from high to low, and the depth image with the highest quality assessment score is determined as the target depth image, wherein the quality assessment includes assessing the image clarity, image integrity, and the amount of gravel of the depth image.
6. The method according to claim 1, characterized in that The step of calculating the gravel size of each rock fragment contained in the target depth image according to the gravel contour specifically includes: Determining three-dimensional point cloud data of the rubble pile according to the rubble contour; Segmenting the three-dimensional point cloud data to obtain a target point cloud subset corresponding to a target rock fragment, wherein the target rock fragment is any one of the multiple rock fragments; Extracting the three-dimensional spatial coordinates of the target point cloud subset, and calculating the maximum size of the target point cloud subset in a preset orthogonal direction according to the three-dimensional spatial coordinates, wherein the preset orthogonal direction includes length, width and height; The maximum size is determined as the crushed stone size of the rock fragments.
7. The method according to claim 1, characterized in that The extracting the contour of the target depth image to obtain the gravel contour specifically includes: Performing a preprocessing operation on the target depth image, wherein the preprocessing operation includes denoising and image enhancement; Perform image segmentation on the preprocessed target depth image to obtain a segmented image area; Determine the gravel area from the segmented image area according to a preset gravel area determination rule, wherein the preset gravel area determination rule includes a depth value range, an area range, and a shape feature of the segmented image area; The contour of the gravel area is extracted to obtain the gravel contour.
8. A blasting effect intelligent evaluation device, characterized in that: The device comprises an acquisition module (201) and a processing module (202), wherein: The acquisition module (201) is used to acquire a plurality of depth images of the rubble pile after blasting; The processing module (202) is used to determine a target depth image from the plurality of depth images, and perform contour extraction on the target depth image to obtain a gravel contour; The processing module (202) is further used to calculate the gravel size of each rock fragment contained in the target depth image according to the gravel contour; The processing module (202) is further used to sort the crushed stone sizes of each of the rock fragments from small to large to obtain a size sequence; The processing module (202) is further used to calculate the size characteristic value in the size sequence according to a preset percentage group, wherein the preset percentage group includes a plurality of preset percentages, and one preset percentage corresponds to one size characteristic value; The processing module (202) is further used to determine a gravel distribution fitting curve according to the size characteristic values corresponding to each of the preset percentages; The processing module (202) is further used to determine the blasting effect level corresponding to the gravel pile according to the gravel distribution fitting curve.
9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.