A quantitative evaluation method and system for dynamic changes in open-pit mining development

Abstract

The present invention discloses a quantitative evaluation method and system for dynamic changes in the development of open-pit mines, and relates to the technical field of mine monitoring. The original remote sensing image data of the open-pit mine is obtained; the original remote sensing image data is preprocessed to obtain new fused image data and old remote sensing image data; multi-scale segmentation and object-oriented classification methods are used to extract feature categories, and new feature category extraction results and old feature category extraction results are obtained; area change calculations are performed to obtain area change conditions; overlay analysis and Boolean operations are used to calculate the new feature category extraction results and the old feature category extraction results to obtain position change conditions; a quantitative evaluation of dynamic changes in the development of the open-pit mine is performed based on the area change conditions and position change conditions to obtain a comprehensive evaluation result. The present invention realizes a multi-dimensional quantitative comprehensive analysis of the open-pit mine, reduces the difficulty of obtaining information on the open-pit mine, and improves the accuracy of obtaining information on the open-pit mine.

Description

Technical Field

[0001] The present invention relates to the technical field of mining area monitoring, and in particular to a method and system for quantitatively evaluating dynamic changes in open-pit mining area development. Background Art

[0002] Open-pit mining is a common mining method. It involves using specific mining techniques and a specific mining sequence to remove rock and extract ore. Common open-pit minerals include coal, iron ore, nickel ore, and rare earth ore. Open-pit mining areas have significant impacts on the regional economy and ecological environment. On the one hand, mining is often a pillar industry in the local area, making a significant contribution to local economic development. On the other hand, open-pit mining inevitably has negative impacts on the local ecological environment, such as vegetation destruction, soil erosion, and environmental pollution, seriously hindering sustainable development. Therefore, it is necessary to keep abreast of the development of open-pit mining areas, including mining and remediation. This not only helps to understand the local mineral situation and make targeted mineral trade decisions, but also helps to promptly identify ecological and safety risks, facilitating ecological protection and emergency management decisions.

[0003] However, the current state of mineral resource information lags behind, especially in the difficulty and poor accuracy of obtaining information about mining areas, which significantly limits traditional field surveys and statistical methods. Remote sensing technology, unrestricted by geographical location, can provide timely, long-term, and accurate information about mining areas. Applying remote sensing technology to monitoring open-pit mining areas can provide a rapid and effective means of visualizing the current state of mineral mining and the ecological environment. Numerous studies have been conducted on the extraction and classification of remote sensing images of open-pit mining areas, as well as on the analysis of mining area changes. However, most analyses focus solely on changes in the number of mining areas, lacking a comprehensive, multi-dimensional, quantitative analysis. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a method and system for quantitatively evaluating the dynamic changes in the development of open-pit mines, so as to realize multi-dimensional quantitative comprehensive analysis of open-pit mines, reduce the difficulty of obtaining information on open-pit mines, and improve the accuracy of obtaining information on open-pit mines.

[0005] To achieve the above objectives, the present invention provides the following solutions:

[0006] A quantitative evaluation method for dynamic changes in open-pit mine development includes:

[0007] Acquire original remote sensing image data of an open-pit mine area; the original remote sensing image data includes new remote sensing image data and old remote sensing image data;

[0008] Preprocessing the new remote sensing image data to obtain new fused image data; preprocessing the old remote sensing image data to obtain old fused image data; the preprocessing includes: atmospheric correction, geometric correction and image fusion;

[0009] Using a multi-scale segmentation and object-oriented classification method to extract feature categories from the new fused image data, obtaining a new feature category extraction result; using a multi-scale segmentation and object-oriented classification method to extract feature categories from the old fused image data, obtaining an old feature category extraction result;

[0010] Calculating the area change of the new feature category extraction result and the old feature category extraction result to obtain the area change;

[0011] Using overlay analysis and Boolean operations to calculate the new feature category extraction results and the old feature category extraction results to obtain position changes;

[0012] A quantitative evaluation of the dynamic changes in the development of the open-pit mine is performed based on the area changes and the location changes to obtain a comprehensive evaluation result.

[0013] Optionally, performing area change calculation on the new feature category extraction result and the old feature category extraction result to obtain the area change specifically includes:

[0014] Counting the number of pixels of the new feature category extraction result to obtain the number of new pixels; and counting the number of pixels of the old feature category extraction result to obtain the number of old pixels;

[0015] The area of ​​any element category in the new fused image data is obtained by multiplying the number of new pixels by the square of the resolution of the new fused image data; the area of ​​any element category in the old fused image data is obtained by multiplying the number of old pixels by the square of the resolution of the old fused image data;

[0016] The area of ​​any element category in the new fused image data is calculated as a difference from the area of ​​any element category in the corresponding old fused image data to obtain the area change amount of any element category.

[0017] Optionally, after performing difference calculation between the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data to obtain the amount of area change of any element category, the method further includes:

[0018] Establishing an area transfer matrix of any element category according to the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data;

[0019] According to the area transfer matrix, the annual average increase intensity G of any element category is calculated. tj and the average annual reduction intensity L of any factor category ti The specific formula is:

[0020]

[0021] Among them, J is the number of feature categories; C tij Y is the area change from category i to category j within time t; t+1 is the starting time of time period t, Y t is the end time of time period t;

[0022] The annual average increase in intensity G tj The corresponding annual average reduction intensity L ti Perform difference calculations to obtain the net increase in intensity for any element category;

[0023] If the net increase intensity is greater than 0, it is determined that the area corresponding to the element category is increasing;

[0024] If the net increase intensity is equal to 0, it is determined that the area corresponding to the element category has a constant trend;

[0025] If the net increase intensity is less than 0, it is determined that the area corresponding to the element category is showing a decreasing trend.

[0026] Optionally, after establishing an area transfer matrix of any element category according to the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data, the method further includes:

[0027] The annual average observed change intensity S(t) is calculated based on the area transfer matrix; the specific formula is:

[0028]

[0029] The annual average increase in intensity G tj , the annual average reduction intensity L ti Calculate the difference between the annual average observed change intensity S(t) and the obtained difference;

[0030] If the difference is greater than 0, it is determined that the area corresponding to the element category has a sharp increase or decrease trend;

[0031] If the difference is equal to 0, it is determined that the trend of increase or decrease of the area corresponding to the element category remains unchanged;

[0032] If the difference is less than 0, it is determined that the trend of increase or decrease of the area corresponding to the element category is stable.

[0033] Optionally, the using of overlay analysis and Boolean operation to calculate the new feature category extraction result and the old feature category extraction result to obtain the position change specifically includes:

[0034] Using overlay analysis and Boolean operations to calculate the new feature category extraction results, to obtain the position corresponding to the new feature category;

[0035] Using overlay analysis and Boolean operations to calculate the old feature category extraction results, to obtain the position corresponding to the old feature category;

[0036] The position corresponding to the new feature category is compared with the position corresponding to the old feature category to obtain the position change.

[0037] To achieve the above objectives, the present invention further provides the following solutions:

[0038] A quantitative evaluation system for dynamic changes in open-pit mine development, including:

[0039] A data acquisition module is used to obtain original remote sensing image data of the open-pit mine area; the original remote sensing image data includes new remote sensing image data and old remote sensing image data;

[0040] Preprocessing module for:

[0041] Preprocessing the new remote sensing image data to obtain new fused image data;

[0042] Preprocessing the old remote sensing image data to obtain old fused image data;

[0043] The preprocessing includes: atmospheric correction, geometric correction and image fusion;

[0044] Feature extraction module, used to:

[0045] extracting element categories from the new fused image data using a multi-scale segmentation and object-oriented classification method to obtain new element category extraction results;

[0046] extracting element categories from the old fused image data using a multi-scale segmentation and object-oriented classification method to obtain an old element category extraction result;

[0047] An area calculation module, configured to calculate area changes between the new feature category extraction result and the old feature category extraction result to obtain area changes;

[0048] A position calculation module is used to calculate the new feature category extraction result and the old feature category extraction result by using overlay analysis and Boolean operation to obtain position change;

[0049] The comprehensive evaluation module is used to quantitatively evaluate the dynamic changes in the development of the open-pit mine area based on the area changes and the location changes to obtain a comprehensive evaluation result.

[0050] Optionally, the area calculation module includes:

[0051] Pixel number statistics unit, used for;

[0052] Counting the number of pixels in the new feature category extraction result to obtain the number of new pixels;

[0053] Counting the number of pixels in the old feature category extraction result to obtain the number of old pixels;

[0054] Multiplication unit, used for:

[0055] Multiplying the number of new pixels by the square of the resolution of the new fused image data to obtain the area of ​​any element category in the new fused image data;

[0056] Multiplying the number of old pixels by the square of the resolution of the old fused image data to obtain the area of ​​any element category in the old fused image data;

[0057] A difference calculation unit, used to:

[0058] The area of ​​any element category in the new fused image data is calculated as a difference from the area of ​​any element category in the corresponding old fused image data to obtain the area change amount of any element category.

[0059] Optionally, the area calculation module further includes:

[0060] Net increase intensity calculation unit, used for:

[0061] Establishing an area transfer matrix of any element category according to the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data;

[0062] According to the area transfer matrix, the annual average increase intensity G of any element category is calculated. tj and the average annual reduction intensity L of any factor category ti The specific formula is:

[0063]

[0064] Among them, J is the number of feature categories; C tij Y is the area change from category i to category j within time t; t+1 is the starting time of time period t, Y t is the end time of time period t;

[0065] The annual average increase in intensity G tj The corresponding annual average reduction intensity L ti Perform difference calculations to obtain the net increase in intensity for any element category;

[0066] If the net increase intensity is greater than 0, it is determined that the area corresponding to the element category is increasing;

[0067] If the net increase intensity is equal to 0, it is determined that the area corresponding to the element category has a constant trend;

[0068] If the net increase intensity is less than 0, it is determined that the area corresponding to the element category is showing a decreasing trend.

[0069] Optionally, the area calculation module further includes:

[0070] The unit for calculating the annual average observed change intensity is used to:

[0071] The annual average observed change intensity S(t) is calculated based on the area transfer matrix; the specific formula is:

[0072]

[0073] The annual average increase in intensity G tj , the annual average reduction intensity L ti Calculate the difference between the annual average observed change intensity S(t) and the obtained difference;

[0074] If the difference is greater than 0, it is determined that the area corresponding to the element category has a sharp increase or decrease trend;

[0075] If the difference is equal to 0, it is determined that the trend of increase or decrease of the area corresponding to the element category remains unchanged;

[0076] If the difference is less than 0, it is determined that the trend of increase or decrease of the area corresponding to the element category is stable.

[0077] Optionally, the position calculation module includes:

[0078] Position calculation unit, used for:

[0079] Using overlay analysis and Boolean operations to calculate the new feature category extraction results, to obtain the position corresponding to the new feature category;

[0080] Using overlay analysis and Boolean operations to calculate the old feature category extraction results, to obtain the position corresponding to the old feature category;

[0081] The position corresponding to the new feature category is compared with the position corresponding to the old feature category to obtain the position change.

[0082] In this embodiment of the present invention, for open-pit mining areas, the size and speed of changes in the area of ​​each element category, the interactive transformation of land elements between different element categories, and the changes in the location of mining areas can all reflect the development status of the mining area. Raw remote sensing image data of the open-pit mining area is obtained; new remote sensing image data is preprocessed to obtain new fused image data; and old remote sensing image data is preprocessed to obtain old remote sensing image data, thereby reducing the difficulty of obtaining information about the open-pit mining area.

[0083] Multi-scale segmentation and object-oriented classification methods were used to extract feature categories from the newly fused image data, resulting in new feature category extraction results. Multi-scale segmentation and object-oriented classification methods were also used to extract feature categories from the old fused image data, resulting in old feature category extraction results. Area changes were calculated between the new and old feature category extraction results to determine area changes. Overlay analysis and Boolean operations were used to calculate the new and old feature category extraction results to determine location changes. Multi-dimensional monitoring of open-pit mining areas was achieved based on area and location changes, improving the accuracy of open-pit mining area information acquisition. A comprehensive evaluation of the dynamic development of open-pit mining areas was conducted based on area and location changes, resulting in a comprehensive evaluation result. This quantitative and comprehensive analysis of open-pit mining areas was achieved to quantitatively evaluate changes in mining activity areas, thereby analyzing the resource development potential and future development trends of mineral deposits and providing intuitive and accurate mining area information for changes in ore supply and production capacity adjustments in my country. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0085] Figure 1 A schematic diagram of a process for quantitatively evaluating dynamic changes in open-pit mine development provided by an embodiment of the present invention;

[0086] Figure 2 A schematic diagram of the original Worldview2 image data from 2013 provided by an embodiment of the present invention;

[0087] Figure 3 A schematic diagram of the original Worldview2 image data for 2020 provided in an embodiment of the present invention;

[0088] Figure 4A schematic diagram of the old feature category extraction results provided by an embodiment of the present invention;

[0089] Figure 5 A schematic diagram of the new feature category extraction results provided by an embodiment of the present invention;

[0090] Figure 6 A schematic diagram of the intensity of the hierarchical changes in various element categories of the Luoliai laterite nickel ore provided by an embodiment of the present invention;

[0091] Figure 7 A schematic diagram of changes in the Luoliai nickel mining area from 2013 to 2020 provided in an embodiment of the present invention;

[0092] Figure 8 A schematic diagram of the structure of a quantitative evaluation system for dynamic changes in open-pit mine development provided by an embodiment of the present invention.

[0093] Explanation of symbols:

[0094] Data acquisition module-1, preprocessing module-2, feature extraction module-3, area calculation module-4, position calculation module-5, comprehensive evaluation module-6. DETAILED DESCRIPTION

[0095] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0096] The purpose of the present invention is to provide a method and system for quantitatively evaluating the dynamic changes of open-pit mine development, so as to solve the problems of one-sided quantitative analysis of open-pit mines, difficulty in obtaining open-pit mine information, and low accuracy in obtaining open-pit mine information.

[0097] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0098] Figure 1 An exemplary process of the above-mentioned quantitative evaluation method for dynamic changes in open-pit mine development is shown. Each step is described in detail below.

[0099] Step 1: Obtain original remote sensing image data of the open-pit mining area; the original remote sensing image data includes new remote sensing image data and old remote sensing image data.

[0100] Step 1 can be specifically performed by the data acquisition module 1.

[0101] In one example, if Figure 2 As shown in the figure, the old remote sensing image data is the original Worldview2 image data of the laterite nickel mine in Loliai, Indonesia in 2013. Figure 3 As shown, the new remote sensing image data is the 2020 Worldview2 original image data of the Loliai laterite nickel mine in Indonesia.

[0102] Step 2: Preprocess the new remote sensing image data to obtain new fused image data; preprocess the old remote sensing image data to obtain old fused image data; preprocessing includes: atmospheric correction, geometric correction and image fusion.

[0103] Step 2 can be specifically performed by the preprocessing module 2.

[0104] In one example, new and old remote sensing image data were preprocessed using atmospheric correction, geometric correction, and image fusion, resulting in two 8-band fused images with a spatial resolution of 0.5 meters. The data included panchromatic and multispectral images with spatial resolutions of 0.5 meters and 2 meters, respectively. The multispectral images included six visible bands and two near-infrared bands. The Loliai laterite nickel deposit is located in the laterite nickel ore cluster in Southeast Sulawesi, Indonesia. The area is rich in basic and ultrabasic rocks, which serve as the mineralization source of the Loliai laterite nickel deposit. Currently, reliable data on the nickel reserves, nickel ore grades, and mining development status of the Loliai laterite nickel deposit is lacking. Therefore, remote sensing technology is urgently needed to quickly and intuitively capture the dynamic changes in mining development. This will provide spatial data reference for understanding laterite nickel development in Indonesia and assessing the export trends of its nickel ore resources.

[0105] Step 3: Use multi-scale segmentation and object-oriented classification methods to extract feature categories from the new fused image data to obtain new feature category extraction results; use multi-scale segmentation and object-oriented classification methods to extract feature categories from the old fused image data to obtain old feature category extraction results.

[0106] Step 3 can be specifically performed by feature extraction module 3.

[0107] In one example, the feature categories include mining area, spoil dump, smelter, bare soil, vegetation, building, road, sump and water body, a total of 9 categories. Figure 4 , the new feature category extraction results are shown in Figure 5 600 test samples were evenly selected in the study area for accuracy testing, and the accuracy of each feature category and the overall accuracy were obtained. The overall accuracy of the old feature category extraction results and the new feature category extraction results were 91.41% and 90.9%, respectively, and the Kappa coefficients were 0.8145 and 0.8407, respectively.

[0108] Step 4: Calculate the area change of the new feature category extraction results and the old feature category extraction results to obtain the area change.

[0109] Step 4 may be specifically performed by the area calculation module 4 .

[0110] The area changes of the new feature category extraction results and the old feature category extraction results are calculated, and the specific area changes include:

[0111] Step 41: Count the number of pixels of the new feature category extraction results to obtain the number of new pixels; count the number of pixels of the old feature category extraction results to obtain the number of old pixels; the pixel is the smallest unit that constitutes the fused image data. The number of pixels of each feature category is shown in Table 1. The number of pixels is directly counted using the spatial analysis software ARCGIS software.

[0112] Table 1

[0113]

[0114] Step 41 may be specifically performed by a pixel quantity counting unit.

[0115] Step 42: Multiply the number of new pixels by the square of the resolution of the new fused image data to obtain the area of ​​any feature category in the new fused image data; multiply the number of old pixels by the square of the resolution of the old fused image data to obtain the area of ​​any feature category in the old fused image data;

[0116] Step 42 may be specifically performed by a multiplication unit.

[0117] In one example, the multiplication unit may be a multiplier. The area of ​​any feature category in the new fused image data corresponds one-to-one to the area of ​​any feature category in the old fused image data;

[0118] Step 43: performing a difference calculation between the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data to obtain the area change of any element category.

[0119] Step 43 may be specifically performed by a difference calculation unit.

[0120] In one example, the area of ​​each feature category between the new fused image data and the old fused image data is subtracted to obtain the area change of each feature category from 2013 to 2020.

[0121] After calculating the difference between the area of ​​any feature category in the new fused image data and the area of ​​any feature category in the corresponding old fused image data to obtain the area change of any feature category, the method further includes:

[0122] Step 44: Establishing an area transfer matrix of any element category according to the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data.

[0123] Step 44 may be specifically performed by a net increase strength calculation unit.

[0124] In one example, the area transfer matrix is ​​shown in Table 2, which shows the changes in the area quantity and transfer matrix of the element categories in the Luoli Ai nickel mine area from 2013 to 2020.

[0125] Table 2

[0126]

[0127]

[0128] From the analysis in Table 2, we can see that the area of ​​mining area, spoil dump and smelter in the mining activity area has shown a significant increase. Among them, the area of ​​mining area has increased the most, from 1.03km in 2013 to 2 Increased to 2.05km in 2020 2 , an increase of about 1.02km 2 , most of which comes from vegetation (0.86km 2 ) and bare soil (0.37km 2 At the same time, the mining area has also been transformed into a spoil dump to varying degrees (0.11km 2 ), bare soil (0.07km 2 ) and vegetation (0.095km 2 ), which also shows that the area of ​​mining area is not simply increasing or decreasing, but is transformed with other element categories. Some old mining areas (tailings areas or waste mining areas) have been transformed into spoil dumps or bare soil, or transformed into vegetation areas due to natural recovery or human restoration. The area of ​​spoil dumps has increased from 0.15km in 2013 to 1.5km in 2014. 2 Increased to 0.49km in 2020 2 , an increase of about 0.34km 2 The main reasons are the transformation of vegetation, bare soil and mining areas (abandoned mines or tailings areas). Compared with the 2013 image, the Luoliai nickel mine has built a new laterite nickel smelter in 2020, covering an area of ​​about 0.18 km 2 , mainly occupied the vegetation area (0.11km 2 ) and bare soil (0.06km2 This also indicates that the Roliai Nickel Mine had the ability to smelt and process laterite nickel ore by at least 2020, forming a mining development system that integrates mining, smelting, and processing. The establishment of the smelter further accelerated the progress and speed of mining area development. The categories above Table 2 correspond to the 2013 categories, and the categories on the left correspond to the 2020 categories. Taking 15.83 as an example, it means that 15.83 square kilometers of land were vegetation from 2013 to 2020; 0.07 means that 0.07 square kilometers of land was a mining area in 2013 and became bare soil in 2020; 0.064 means that 0.064 square kilometers of land was bare soil in 2013 and became a road in 2020.

[0129] Step 45: Calculate the average annual increase in intensity G for any element category based on the area transfer matrix. tj and the average annual reduction intensity L of any factor category ti The specific formula is:

[0130]

[0131] Among them, J is the number of feature categories; C tij Y is the area change from category i to category j within time t; t+1 is the starting time of time period t, Y t is the end time of time period t.

[0132] Step 46: Increase the annual intensity G tj The corresponding annual average reduction intensity L ti Perform difference calculations to obtain the net increase in intensity for any element category.

[0133] If the net increase intensity is greater than 0, it is determined that the area corresponding to the element category is increasing;

[0134] If the net increase intensity is equal to 0, it is determined that the area corresponding to the element category has a constant trend;

[0135] If the net increase intensity is less than 0, it is determined that the area corresponding to the element category is showing a decreasing trend.

[0136] Step 46 may be specifically performed by a net increase strength calculation unit.

[0137] In one example, if Figure 6 As shown in the figure, based on the average annual increase intensity, the average annual increase intensity S of the Luoliai nickel mine from 2013 to 2020 is calculated to be 5.82%. This is used as the benchmark value to judge the change rate of each category.

[0138] Depend on Figure 6It can be seen that the average annual increase intensity of other factor categories except the vegetation area is greater than the baseline value S, indicating that the overall changes in the mining area are relatively drastic; among them, the three factor categories directly related to mining activities, namely mining areas, spoil dumps and buildings (smelters), have a net increase intensity of positive values, indicating that they all show a net increase trend, and the growth momentum is relatively rapid, reflecting that the overall mining of the Luoliai Nickel Mine maintained a relatively fast speed between 2013 and 2020.

[0139] After establishing an area transfer matrix of any element category according to the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data, the method further includes:

[0140] Step 47: Calculate the average annual observed change intensity S(t) based on the area transfer matrix; the specific formula is:

[0141]

[0142] Step 48: Increase the annual average strength G tj , the average annual reduction intensity L ti The difference is calculated with the annual average observed change intensity S(t) to obtain the difference;

[0143] If the difference is greater than 0, it is determined that the area corresponding to the feature category has a sharp increase or decrease trend;

[0144] If the difference is equal to 0, it is determined that the trend of area increase or decrease corresponding to the feature category remains unchanged;

[0145] If the difference is less than 0, it is determined that the trend of increase or decrease in the area corresponding to the feature category is stable.

[0146] Step 48 may be specifically performed by the annual average observed change intensity calculation unit.

[0147] In one example, the difference = the average annual observed change intensity S(t) - the average annual increase intensity G tj - Average annual reduction intensity L ti .

[0148] Step 5: Use overlay analysis and Boolean operations to calculate the new feature category extraction results and the old feature category extraction results to obtain the position change.

[0149] Step 5 may be specifically performed by the position calculation module 5 .

[0150] Overlay analysis and Boolean operations are used to calculate the new feature category extraction results and the old feature category extraction results, and the position changes are as follows:

[0151] Step 51: Use overlay analysis and Boolean operations to calculate the new feature category extraction results to obtain the location corresponding to the new feature category;

[0152] Step 52: Use overlay analysis and Boolean operations to calculate the old feature category extraction results to obtain the location corresponding to the old feature category;

[0153] Step 53: Compare the position corresponding to the new feature category with the position corresponding to the old feature category to obtain the position change.

[0154] In one example, by Figure 7 The mining areas of both the northern and southern mining districts of the Rollie Nickel Mine have increased to varying degrees. The southern mining district has expanded westward and southwestward from its original mining area, primarily occupying large areas of vegetation. The mining area of ​​the northern mining district, which was smaller than the southern mining district in 2013, expanded significantly by 2020, forming a long, nearly east-west strip of mining, primarily occupying bare soil and vegetation. Furthermore, large areas of bare soil remain in the eastern part of the northern mining district, suggesting this will be the primary direction of future mining expansion. The newly constructed smelter is located southeast of both districts and is connected by a major road. Overall, the Rollie Nickel Mine exhibited rapid expansion between 2013 and 2020, with the northern mining district experiencing a greater expansion than the southern.

[0155] Step 6: Conduct a quantitative evaluation of the dynamic changes in open-pit mine development based on the changes in area and location to obtain a comprehensive evaluation result.

[0156] Step 6 can be specifically performed by the comprehensive evaluation module 6.

[0157] From 2013 to 2020, the mining scale and intensity of the Luoliai nickel mine showed a rapid expansion trend, with the mining area increasing the most, reaching 1.02 km 2 , and the annual average change intensity is drastic, and the corresponding area of ​​the spoil dump has also increased by about 0.35km 2 In 2013, the Luoli'ai nickel mine was basically in the exploration and mining stage. By 2020, the completion of the laterite nickel ore smelter marked that the Luoli'ai nickel mine had formed a relatively complete integrated mining and smelting industrial chain, and there were multiple trunk roads connecting to the southern coastal ports to facilitate export transportation. The Luoli'ai nickel mine began to enter a stage of rapid expansion of mining development.

[0158] In summary, in this embodiment of the present invention, for open-pit mining areas, the size and speed of changes in the area of ​​each element category, the interactive transformation of land elements between different element categories, and the changes in the location of mining areas can all reflect the development status of the mining area. Acquiring original remote sensing image data of the open-pit mining area; preprocessing the new remote sensing image data to obtain new fused image data; and preprocessing the old remote sensing image data to obtain the old remote sensing image data reduces the difficulty of acquiring information about the open-pit mining area.

[0159] Multi-scale segmentation and object-oriented classification methods were used to extract feature categories from the newly fused image data, resulting in new feature category extraction results. Multi-scale segmentation and object-oriented classification methods were also used to extract feature categories from the old fused image data, resulting in old feature category extraction results. Area changes were calculated between the new and old feature category extraction results to determine area changes. Overlay analysis and Boolean operations were used to calculate the new and old feature category extraction results to determine location changes. Multi-dimensional monitoring of open-pit mining areas was achieved based on area and location changes, improving the accuracy of open-pit mining area information acquisition. A comprehensive evaluation of the dynamic development of open-pit mining areas was conducted based on area and location changes, resulting in a comprehensive evaluation result. This quantitative and comprehensive analysis of open-pit mining areas was achieved to quantitatively evaluate changes in mining activity areas, thereby analyzing the resource development potential and future development trends of mineral deposits and providing intuitive and accurate mining area information for changes in ore supply and production capacity adjustments in my country.

[0160] Based on the remote sensing extraction results of mining activity areas, the transfer matrix of each category was calculated. Combined with the Aldwaik change intensity analysis theory and spatial overlay analysis, a comprehensive evaluation framework with five dimensions including area change and location change was constructed.

[0161] The comprehensive evaluation framework of the present invention can quantitatively determine the dynamic changes in the development of open-pit mines from multiple dimensions, which helps to analyze the resource development potential and future development trends of mineral deposits, and further provides intuitive and accurate spatial information for my country's ore supply decision-making and production capacity adjustment.

[0162] To achieve the above objectives, the present invention further provides the following solutions:

[0163] A quantitative evaluation system for dynamic changes in open-pit mining development, see Figure 8 ,include:

[0164] The data acquisition module 1 is used to obtain original remote sensing image data of the open-pit mine area; the original remote sensing image data includes new remote sensing image data and old remote sensing image data.

[0165] Preprocessing module 2 is used to:

[0166] Preprocessing the new remote sensing image data to obtain new fused image data;

[0167] Preprocessing the old remote sensing image data to obtain old remote sensing image data;

[0168] Preprocessing includes: atmospheric correction, geometric correction and image fusion.

[0169] Feature extraction module 3 is used to:

[0170] Multi-scale segmentation and object-oriented classification methods are used to extract feature categories from the new fused image data to obtain new feature category extraction results;

[0171] Multi-scale segmentation and object-oriented classification methods are used to extract feature categories from old fused image data, and the old feature category extraction results are obtained.

[0172] The area calculation module 4 is used to calculate the area change of the new feature category extraction results and the old feature category extraction results to obtain the area change situation.

[0173] The area calculation module 4 includes:

[0174] The pixel number statistics unit is used for;

[0175] Count the number of pixels in the new feature category extraction results to obtain the number of new pixels;

[0176] Count the number of pixels in the old feature category extraction results to obtain the number of old pixels;

[0177] The multiplication unit is used to:

[0178] Multiply the number of new pixels by the square of the resolution of the new fused image data to obtain the area of ​​any feature category in the new fused image data;

[0179] The area of ​​any feature category in the old fused image data is obtained by multiplying the number of old pixels by the square of the resolution of the old fused image data;

[0180] The area of ​​any feature category in the new fused image data corresponds one-to-one with the area of ​​any feature category in the old fused image data.

[0181] The difference calculation unit is used to:

[0182] The difference between the area of ​​any feature category in the new fused image data and the area of ​​any feature category in the corresponding old fused image data is calculated to obtain the area change of any feature category.

[0183] The area calculation module 4 also includes:

[0184] The net increase intensity calculation unit is used to:

[0185] Establishing an area transfer matrix of any element category according to the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data;

[0186] According to the area transfer matrix, the annual average increase intensity G of any element category is obtained. tjand the average annual reduction intensity L of any factor category ti The specific formula is:

[0187]

[0188] Among them, J is the number of feature categories; C tij Y is the area change from category i to category j within time t; t+1 is the starting time of time period t, Y t is the end time of time period t;

[0189] Increase the annual intensity by G tj The corresponding annual average reduction intensity L ti Perform difference calculations to obtain the net increase in intensity for any element category;

[0190] If the net increase intensity is greater than 0, it is determined that the area corresponding to this feature category is increasing;

[0191] If the net increase intensity is equal to 0, it is judged that the area corresponding to this feature category has an unchanged trend;

[0192] If the net increase intensity is less than 0, it is determined that the area corresponding to this feature category is showing a decreasing trend.

[0193] The area calculation module 4 also includes:

[0194] Calculation unit of annual average observed change intensity:

[0195] The average annual observed change intensity S(t) is calculated based on the area transfer matrix; the specific formula is:

[0196]

[0197] Increase the annual intensity by G tj , the average annual reduction intensity L ti The difference is calculated with the annual average observed change intensity S(t) to obtain the difference;

[0198] If the difference is greater than 0, it is determined that the area corresponding to the feature category has a sharp increase or decrease trend;

[0199] If the difference is equal to 0, it is determined that the trend of area increase or decrease corresponding to the feature category remains unchanged;

[0200] If the difference is less than 0, it is determined that the trend of increase or decrease in the area corresponding to the feature category is stable.

[0201] The position calculation module 5 is used to calculate the new feature category extraction results and the old feature category extraction results by using overlay analysis and Boolean operations to obtain the position change situation.

[0202] The position calculation module 5 includes:

[0203] The position calculation unit is used to:

[0204] Overlay analysis and Boolean operations are used to calculate the new feature category extraction results to obtain the corresponding position of the new feature category;

[0205] Overlay analysis and Boolean operations are used to calculate the old feature category extraction results to obtain the corresponding positions of the old feature categories;

[0206] Compare the positions corresponding to the new feature categories with those corresponding to the old feature categories to obtain the position changes.

[0207] The comprehensive evaluation module 6 is used to quantitatively evaluate the dynamic changes in the development of the open-pit mine area based on the changes in area and location, and obtain comprehensive evaluation results.

[0208] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0209] This document uses specific examples to illustrate the principles and implementation methods of the embodiments of the present invention. The description of the above embodiments is only intended to help understand the methods and core concepts of the embodiments of the present invention. At the same time, for those skilled in the art, based on the concepts of the embodiments of the present invention, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the embodiments of the present invention.

Claims

1. A quantitative evaluation method for dynamic changes in open-pit mine development, characterized in that: include: Acquire original remote sensing image data of an open-pit mine area; the original remote sensing image data includes new remote sensing image data and old remote sensing image data; Preprocessing the new remote sensing image data to obtain new fused image data; preprocessing the old remote sensing image data to obtain old fused image data; the preprocessing includes: atmospheric correction, geometric correction and image fusion; Using a multi-scale segmentation and object-oriented classification method to extract feature categories from the new fused image data, obtaining a new feature category extraction result; using a multi-scale segmentation and object-oriented classification method to extract feature categories from the old fused image data, obtaining an old feature category extraction result; Calculating the area change of the new feature category extraction result and the old feature category extraction result to obtain the area change; Using overlay analysis and Boolean operations to calculate the new feature category extraction results and the old feature category extraction results to obtain position changes; Conducting a quantitative evaluation of the dynamic changes in the development of the open-pit mine area based on the area changes and the location changes to obtain a comprehensive evaluation result; The area change calculation of the new feature category extraction result and the old feature category extraction result is performed to obtain the area change specifically including: Counting the number of pixels of the new feature category extraction result to obtain the number of new pixels; and counting the number of pixels of the old feature category extraction result to obtain the number of old pixels; The area of ​​any element category in the new fused image data is obtained by multiplying the number of new pixels by the square of the resolution of the new fused image data; the area of ​​any element category in the old fused image data is obtained by multiplying the number of old pixels by the square of the resolution of the old fused image data; performing a difference calculation between the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data to obtain the area change amount of any element category; After calculating the difference between the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data to obtain the area change of any element category, the method further includes: Establishing an area transfer matrix of any element category according to the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data; According to the area transfer matrix, the annual average increase intensity G of any element category is calculated. tj and the average annual reduction intensity L of any factor category ti The specific formula is: Among them, J is the number of feature categories; C tij Y is the area change from category i to category j within time t; t+1 is the starting time of time period t, Y t is the end time of time period t; The annual average increase in intensity G tj The corresponding annual average reduction intensity L ti Perform difference calculations to obtain the net increase in intensity for any element category; If the net increase intensity is greater than 0, it is determined that the area corresponding to the element category is increasing; If the net increase intensity is equal to 0, it is determined that the area corresponding to the element category has a constant trend; If the net increase intensity is less than 0, it is determined that the area corresponding to the element category is in a decreasing trend; After establishing an area transfer matrix of any element category according to the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data, the method further includes: The annual average observed change intensity S(t) is calculated based on the area transfer matrix; the specific formula is: The annual average increase in intensity G tj , the annual average reduction intensity L ti Calculate the difference between the annual average observed change intensity S(t) and the obtained difference; If the difference is greater than 0, it is determined that the area corresponding to the element category has a sharp increase or decrease trend; If the difference is equal to 0, it is determined that the trend of increase or decrease of the area corresponding to the element category remains unchanged; If the difference is less than 0, it is determined that the trend of increase or decrease of the area corresponding to the element category is stable.

2. The method for quantitatively evaluating dynamic changes in open-pit mine development according to claim 1, characterized in that: The use of overlay analysis and Boolean operations to calculate the new feature category extraction results and the old feature category extraction results to obtain position change information specifically includes: Using overlay analysis and Boolean operations to calculate the new feature category extraction results, to obtain the position corresponding to the new feature category; Using overlay analysis and Boolean operations to calculate the old feature category extraction results, to obtain the position corresponding to the old feature category; The position corresponding to the new feature category is compared with the position corresponding to the old feature category to obtain the position change.

3. A quantitative evaluation system for dynamic changes in open-pit mining development, characterized in that: include: A data acquisition module is used to obtain original remote sensing image data of the open-pit mine area; the original remote sensing image data includes new remote sensing image data and old remote sensing image data; Preprocessing module for: Preprocessing the new remote sensing image data to obtain new fused image data; Preprocessing the old remote sensing image data to obtain old fused image data; The preprocessing includes: atmospheric correction, geometric correction and image fusion; Feature extraction module, used to: extracting element categories from the new fused image data using a multi-scale segmentation and object-oriented classification method to obtain new element category extraction results; extracting element categories from the old fused image data using a multi-scale segmentation and object-oriented classification method to obtain an old element category extraction result; An area calculation module, configured to calculate area changes between the new feature category extraction result and the old feature category extraction result to obtain area changes; A position calculation module is used to calculate the new feature category extraction result and the old feature category extraction result by using overlay analysis and Boolean operation to obtain position change; A comprehensive evaluation module is used to quantitatively evaluate the dynamic changes in the development of the open-pit mine area based on the area change and the location change, and obtain a comprehensive evaluation result; The area calculation module includes: Pixel number statistics unit, used for; Counting the number of pixels in the new feature category extraction result to obtain the number of new pixels; Counting the number of pixels in the old feature category extraction result to obtain the number of old pixels; Multiplication unit, used for: Multiplying the number of new pixels by the square of the resolution of the new fused image data to obtain the area of ​​any element category in the new fused image data; Multiplying the number of old pixels by the square of the resolution of the old fused image data to obtain the area of ​​any element category in the old fused image data; A difference calculation unit, used to: performing a difference calculation between the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data to obtain the area change amount of any element category; Net increase intensity calculation unit, used for: Establishing an area transfer matrix of any element category according to the area of ​​any element category in the new fused image data and the area of ​​any element category in the corresponding old fused image data; According to the area transfer matrix, the annual average increase intensity G of any element category is calculated. tj and the average annual reduction intensity L of any factor category ti The specific formula is: Among them, J is the number of feature categories; C tij Y is the area change from category i to category j within time t; t+1 is the starting time of time period t, Y t is the end time of time period t; The annual average increase in intensity G tj The corresponding annual average reduction intensity L ti Perform difference calculations to obtain the net increase in intensity for any element category; If the net increase intensity is greater than 0, it is determined that the area corresponding to the element category is increasing; If the net increase intensity is equal to 0, it is determined that the area corresponding to the element category has a constant trend; If the net increase intensity is less than 0, it is determined that the area corresponding to the element category is in a decreasing trend; The unit for calculating the annual average observed change intensity is used to: The annual average observed change intensity S(t) is calculated based on the area transfer matrix; the specific formula is: The annual average increase in intensity G tj , the annual average reduction intensity L ti Calculate the difference between the annual average observed change intensity S(t) and the obtained difference; If the difference is greater than 0, it is determined that the area corresponding to the element category has a sharp increase or decrease trend; If the difference is equal to 0, it is determined that the trend of increase or decrease of the area corresponding to the element category remains unchanged; If the difference is less than 0, it is determined that the trend of increase or decrease of the area corresponding to the element category is stable.

4. The quantitative evaluation system for dynamic changes in open-pit mine development according to claim 3 is characterized in that: The position calculation module includes: Position calculation unit, used for: Using overlay analysis and Boolean operations to calculate the new feature category extraction results, to obtain the position corresponding to the new feature category; Using overlay analysis and Boolean operations to calculate the old feature category extraction results, to obtain the position corresponding to the old feature category; The position corresponding to the new feature category is compared with the position corresponding to the old feature category to obtain the position change.

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