Ecological regreening effect detection system and method

Abstract

The invention relates to the field of ecological regreening detection, and particularly discloses an ecological regreening effect detection system and method.The system comprises an information acquisition module, an image analysis module, a regreening effect judgment module, a regreening analysis module and a regreening early warning module; the image analysis module determines a target regreening area and divides the target regreening area into a plurality of regreening analysis areas, the vertical distribution image extracts regreening analysis features of each area, and the regreening effect judgment module determines the effect judgment type of each regreening analysis area and judges whether dominant anomaly exists or not. The regreening analysis module further judges whether ecological regreening abnormity exists or not, the regreening early warning module sends out an early warning signal according to the abnormal judgment result, the accuracy of regreening effect detection is improved, and data support is provided for improving regreening efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological restoration detection, and particularly to an ecological restoration effect detection system and method. Background Art

[0002] At present, the existing technologies for mine ecological restoration detection still face some problems and challenges in practical applications. First, the evaluation criteria and methods for ecological restoration monitoring lack unity, making it difficult to compare and integrate the monitoring results between different regions and projects. Second, the terrain in the mine area is complex, and traditional monitoring means such as manual sampling and ground measurement are time-consuming and laborious, and it is difficult to cover comprehensively, resulting in high data collection costs. In addition, with the development of monitoring technologies, the amount of data collected is large and complex, and the existing processing and analysis capabilities are limited, making it difficult to extract valuable information from the massive data. These problems limit the efficiency and accuracy of mine ecological restoration detection and urgently need to be solved through technological innovation and standardization means.

[0003] For example, the prior art discloses a big data analysis remote sensing monitoring method and system for oasis ecology, which specifically includes the following steps: S1, using remote sensing monitoring technology to obtain oasis data of the target area as comparison data, and after a set time, using remote sensing monitoring technology to obtain oasis data of the target area again as real-time data; S2, comparing the comparison data with the real-time data to obtain the missing oasis data; S3, according to the characteristics corresponding to the missing oasis data, conducting vegetation ecological analysis, formulating suitable vegetation types for planting, obtaining the situation of disappeared oases through oasis coordinate comparison, and building a vegetation planting library to obtain the vegetation types suitable for planting in the oases corresponding to the coordinates by combining the analysis of oasis vegetation types, oasis climate conditions, and oasis hydrological conditions.

[0004] However, the prior art still has the following problems: The remote sensing monitoring technology relies on satellite images and sensor data, and its resolution and accuracy may be limited, making it difficult to accurately identify small-scale or local ecological changes, resulting in greater difficulty in data processing and analysis, and unable to accurately analyze the anomalies existing in the areas that need to be restored, affecting the overall restoration effect. Summary of the Invention

[0005] The purpose of the present invention is to provide an ecological restoration effect detection system and method, which solves the problems in the prior art that rely on satellite images and sensor data, whose resolution and accuracy may be limited, making it difficult to accurately identify small-scale or local ecological changes, resulting in greater difficulty in data processing and analysis, and unable to accurately analyze the anomalies existing in the areas that need to be restored, affecting the overall restoration effect.

[0006] To this end, the present invention provides an ecological restoration effect detection system and method, and the ecological restoration effect detection system includes:

[0007] An information acquisition module, which is used to acquire a geographical image group of a target revegetation area according to detection requirements, acquire a number of soil structure images of the target revegetation area, and soil parameters corresponding to the soil structure images, wherein the geographical image group of the target revegetation area includes a horizontal distribution image and a number of vertical distribution images;

[0008] An image analysis module, which is connected to the information acquisition module, is used to determine the position of the target revegetation area in the horizontal distribution image, divide the target revegetation area in the determined horizontal distribution image into a number of revegetation analysis areas, and determine the revegetation analysis characteristics of each revegetation analysis area according to each vertical distribution image;

[0009] A revegetation effect judgment module, which is respectively connected to the information acquisition module and the image analysis module, is used to determine the effect judgment type of the current revegetation analysis area according to the revegetation analysis characteristics of each revegetation analysis area, and combine the corresponding horizontal distribution image to determine whether there is a dominant anomaly in the current revegetation analysis area;

[0010] A revegetation analysis module, which is respectively connected to the information acquisition module, the image analysis module and the revegetation effect judgment module, is used to determine whether there is an ecological revegetation anomaly according to the judgment result of the dominant anomaly in combination with the soil structure image, the soil parameters and the revegetation analysis characteristics;

[0011] A revegetation warning module, which is respectively connected to the revegetation effect judgment module and the revegetation analysis module, is used to determine whether to issue an ecological revegetation anomaly warning according to the judgment result of the dominant anomaly and the judgment result of the ecological revegetation anomaly.

[0012] As a preferred technical solution of the ecological revegetation effect detection system, the information acquisition module includes:

[0013] A regional camera device group, which is used to acquire the horizontal distribution image of the target revegetation area in the horizontal direction and a number of the vertical distribution images of the target revegetation area in the vertical direction;

[0014] A soil structure acquisition unit, which is used to acquire a soil structure analysis sample and a soil analysis profile in a single revegetation analysis area, and determine the soil parameters according to the soil structure analysis sample, and the soil parameters include backfill compaction degree, aggregate stability and nutrient content;

[0015] A structure image acquisition unit, which is connected to the soil structure acquisition unit, is used to acquire the soil structure image according to the soil analysis profile.

[0016] As an optimal technical solution of the ecological greening effect detection system, the total area of the corresponding greening analysis region in the horizontal direction in each of the vertical distribution images obtained by the image analysis module is the same as the total area of the target greening region determined by the horizontal distribution image.

[0017] Among them, the greening analysis features include the vertical height difference, the vertical coverage rate, and the gravel coverage area.

[0018] As an optimal technical solution of the ecological greening effect detection system, the greening effect judgment module determines the effect judgment type of the current greening analysis region according to the vertical height difference of the greening analysis region in combination with the gravel coverage area;

[0019] If the vertical height difference of the current greening analysis region is greater than or equal to the preset height difference, or the gravel coverage area is greater than or equal to the preset gravel area, it is determined that the effect judgment type of the current greening analysis region is the dominant effect abnormal type;

[0020] If the vertical height difference of the current greening analysis region is less than the preset height difference and the gravel coverage area is less than the preset gravel area, it is determined that the effect judgment type of the current greening analysis region is the recessive effect abnormal type.

[0021] As an optimal technical solution of the ecological greening effect detection system, the greening effect judgment module is configured to, in response to the effect judgment type of the current greening analysis region being the recessive effect abnormal type, and in combination with the horizontal distribution image, determine whether there is a dominant abnormality in the current greening analysis region;

[0022] The greening effect judgment module calculates the greening distribution characterization parameter of the current greening analysis region according to the horizontal distribution image;

[0023] If the current greening analysis region meets the ecological greening conditions, it is determined that there is no dominant abnormality in the current greening analysis region;

[0024] If the current greening analysis region does not meet the ecological greening conditions, it is determined that there is a dominant abnormality in the current greening analysis region;

[0025] Among them, the ecological greening conditions are that the greening distribution characterization parameter is less than or equal to the standard greening distribution characterization parameter and the average gray value is within the preset gray range.

[0026] As an optimal technical solution of the ecological greening effect detection system, the greening analysis module determines whether there is an ecological greening abnormality according to the judgment result of the dominant abnormality in combination with the soil structure image, the soil parameters, and the vertical coverage rate;

[0027] If there is an obvious anomaly in the current ecological restoration analysis area, it is determined whether it is an ecological restoration anomaly according to the soil structure image and the vertical coverage rate;

[0028] If there is no obvious anomaly in the current ecological restoration analysis area, it is determined whether it is an ecological restoration anomaly according to the soil parameters.

[0029] As an optimal technical solution of the ecological restoration effect detection system, it is characterized in that the restoration analysis module determines that there is no anomaly in ecological restoration when the soil parameters meet the conditions of the standard restoration soil;

[0030] Among them, the ecological restoration conditions are that the backfill compaction degree, aggregate stability and nutrient content all meet the corresponding sub-conditions of ecological restoration.

[0031] As an optimal technical solution of the ecological restoration effect detection system, it is characterized in that the restoration warning module determines whether to issue an ecological restoration anomaly warning according to the judgment result of the obvious anomaly and the judgment result of the ecological restoration anomaly;

[0032] If there is an obvious anomaly or there is an ecological restoration anomaly, the restoration warning module determines to issue an ecological restoration anomaly warning;

[0033] If there is no obvious anomaly and there is no ecological restoration anomaly, the restoration warning module determines not to issue an ecological restoration anomaly warning.

[0034] The present invention also provides a method for an ecological restoration effect detection system, including:

[0035] Step S1, obtaining a geographical image group of the target restoration area according to the detection requirements, obtaining a plurality of soil structure images of the target restoration area, and soil parameters corresponding to the soil structure images;

[0036] Step S2, determining the position of the target restoration area in the horizontal distribution image, dividing the target restoration area in the determined horizontal distribution image into a plurality of restoration analysis areas, and determining the restoration analysis features of each restoration analysis area according to each vertical distribution image;

[0037] Step S3, determining the effect judgment type of the current restoration analysis area according to the restoration analysis features of each restoration analysis area;

[0038] Step S4, determining whether there is an obvious anomaly in the current restoration analysis area according to the effect judgment type in combination with the corresponding horizontal distribution image;

[0039] Step S5, determining whether there is an ecological restoration anomaly according to the judgment result of the obvious anomaly in combination with the soil structure image, the soil parameters and the restoration analysis features;

[0040] Step S6, determine whether to issue an ecological greening anomaly warning according to the judgment result of the dominant anomaly and the judgment result of the ecological greening anomaly.

[0041] The beneficial effects of the present invention are as follows: The ecological greening effect detection system and method of the present invention realize the comprehensive monitoring and accurate evaluation of the greening area of mines through the collaborative work of the information acquisition module, the image analysis module, the greening effect judgment module, the greening analysis module and the greening warning module. The system can accurately divide the greening analysis area and extract key features by using the horizontal distribution image and the vertical distribution image, combined with the soil structure image and soil parameters. Through the dual judgment mechanism of dominant anomaly and ecological greening anomaly, the system can dynamically adapt to the specific conditions of different mines, taking into account both the short-term greening effect and the long-term ecological stability. In addition, the greening warning module can issue a warning in a timely manner according to the anomaly judgment result, providing a basis for the adjustment of ecological restoration measures. Overall, the system improves the accuracy of greening effect detection and provides data support for improving the greening efficiency. Brief Description of the Drawings

[0042] Figure 1 It is a schematic structural diagram of the ecological greening effect detection system in an embodiment of the present invention;

[0043] Figure 2 It is a schematic structural diagram of the information acquisition module in an embodiment of the present invention;

[0044] Figure 3 It is a logic diagram for determining the effect judgment type of the current greening analysis area in an embodiment of the present invention;

[0045] Figure 4 It is a flowchart of the ecological greening effect detection method in an embodiment of the present invention. Detailed Embodiments

[0046] Next, the technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. Among them, the terms "first position" and "second position" are two different positions, and the first feature being "above", "over" and "on" the second feature includes the first feature being directly above and obliquely above the second feature, or simply indicating that the first feature has a higher horizontal height than the second feature. The first feature being "below", "under" and "beneath" the second feature includes the first feature being directly below and obliquely below the second feature, or simply indicating that the first feature has a lower horizontal height than the second feature.

[0048] In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected" and "coupled" shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0049] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals indicate the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation of the present invention.

[0050] As Figures 1-3 shown, this embodiment provides an ecological greening effect detection system, and the ecological greening effect detection system includes:

[0051] An information acquisition module, which is used to acquire a geographical image group of a target greening area according to detection requirements, acquire a plurality of soil structure images of the target greening area, and soil parameters corresponding to the soil structure images, wherein the geographical image group of the target greening area includes a horizontal distribution image and a plurality of vertical distribution images;

[0052] An image analysis module, which is connected to the information acquisition module, is used to determine the position of the target revegetated area in the horizontal distribution image, divide the determined target revegetated area in the horizontal distribution image into several revegetation analysis areas, and determine the revegetation analysis features of each revegetation analysis area according to each vertical distribution image;

[0053] A revegetation effect judgment module, which is respectively connected to the information acquisition module and the image analysis module, is used to determine the effect judgment type of the current revegetation analysis area according to the revegetation analysis features of each revegetation analysis area, and determine whether there is a dominant anomaly in the current revegetation analysis area in combination with the corresponding horizontal distribution image;

[0054] A revegetation analysis module, which is respectively connected to the information acquisition module, the image analysis module and the revegetation effect judgment module, is used to determine whether there is an ecological revegetation anomaly according to the judgment result of the dominant anomaly in combination with the soil structure image, the soil parameters and the revegetation analysis features;

[0055] A revegetation warning module, which is respectively connected to the revegetation effect judgment module and the revegetation analysis module, is used to determine whether to issue an ecological revegetation anomaly warning according to the judgment result of the dominant anomaly and the judgment result of the ecological revegetation anomaly.

[0056] In implementation, the detection requirement is the geographical location where the target revegetated area is located.

[0057] The ecological revegetation effect detection system and method of the present invention realize the comprehensive monitoring and accurate evaluation of the revegetated area of the mine through the collaborative work of the information acquisition module, the image analysis module, the revegetation effect judgment module, the revegetation analysis module and the revegetation warning module. The system uses horizontal distribution images and vertical distribution images, combines soil structure images and soil parameters, can accurately divide the revegetation analysis area, and extract key features. Through the dual judgment mechanism of dominant anomaly and ecological revegetation anomaly, the system can dynamically adapt to the specific conditions of different mines, taking into account short-term revegetation effects and long-term ecological stability. In addition, the revegetation warning module can issue a warning in time according to the anomaly judgment result, providing a basis for the adjustment of ecological restoration measures. Generally speaking, the system not only improves the accuracy of revegetation effect detection, but also provides data support for improving the revegetation efficiency.

[0058] Specifically, the information acquisition module includes:

[0059] A regional camera device group, which is used to obtain the horizontal distribution image of the target revegetated area in the horizontal direction and several vertical distribution images of the target revegetated area in the vertical direction;

[0060] A soil structure acquisition unit, which is used to acquire soil structure analysis samples and soil analysis profiles in a single revegetation analysis area, and determine the soil parameters according to the soil structure analysis samples. The soil parameters include backfill compaction degree, aggregate stability, and nutrient content;

[0061] A structure image acquisition unit, which is connected to the soil structure acquisition unit and is used to acquire the soil structure image according to the soil analysis profile.

[0062] In this embodiment, the camera device group includes a plurality of camera devices. The camera devices can acquire horizontally distributed images at the highest point above the target revegetation area and in a direction parallel to the horizontal plane, and can acquire the vertically distributed images in a direction parallel to the vertical direction. The camera device group can be implemented by several aerial drones carrying cameras. This is prior art and will not be specifically limited;

[0063] In practice, for the geographical image group of a single target revegetation area, the total number of vertically distributed images is not less than 4, preferably 8. When the camera device acquires each vertically distributed image, the included angles between the vertically distributed images are the same, that is, the shooting angles of the camera device group when acquiring each vertically distributed image are the same, and the angle is the ratio of 360 to the total number of vertically distributed images.

[0064] It can be understood that the number of soil structure analysis samples is the same as that of the soil analysis profiles and they correspond one by one.

[0065] The soil structure acquisition unit digs a vertical soil profile through excavation tools such as shovels and soil drills. The profile depth generally depends on the research purpose and soil type, and is preferably 1 to 2 meters so as to observe the characteristics of different soil layers. In the actual use process, the excavation location of the soil analysis profile selected by the soil structure acquisition unit will not damage the revegetation plants in the revegetation analysis area, and the soil structure analysis samples are acquired during the excavation process.

[0066] The nutrient content includes soil pH, organic matter content, total nitrogen, total phosphorus, and total potassium; the content and particle size distribution of soil water-stable aggregates are measured, and the changes of soil aggregates at different depths are analyzed. The determination methods of backfill compaction degree, aggregate stability, and nutrient content are prior art and will not be elaborated here.

[0067] In the above embodiments, through the combination of the area camera device group and the soil structure acquisition unit, comprehensive monitoring and analysis of the target revegetation area are achieved. The camera device group can obtain distribution images from multiple angles in the horizontal and vertical directions. The horizontal distribution image is used to show the overall coverage of the revegetation area, and multiple vertical distribution images capture the detailed features of the area from different angles to ensure full coverage of the revegetation area. At the same time, the synergistic effect of the soil structure acquisition unit and the structure image acquisition unit enables the soil structure analysis samples to correspond one by one with the soil analysis profiles, providing accurate data support for soil quality assessment. This multi-dimensional monitoring method not only improves the accuracy of revegetation effect evaluation, but also makes up for the deficiencies of single-perspective monitoring through the acquisition of multi-angle images, enhancing the adaptability to complex terrains and vegetation distributions. In addition, through the acquisition of vertical distribution images at a fixed angle, the consistency and comparability of data are ensured, providing a basis for subsequent quantitative analysis.

[0068] Specifically, the total area of the corresponding revegetation analysis regions in the horizontal direction in each of the vertical distribution images obtained by the image analysis module is the same as the total area of the target revegetation region determined by the horizontal distribution image.

[0069] In implementation, parts of the repeated revegetation analysis regions may be included in different vertical distribution images, and the sum of all the revegetation analysis regions included in all the vertical distribution images can completely cover all the target revegetation regions;

[0070] It can be understood that all the vertical distribution images can cover the complete monitoring area so that when performing abnormal analysis on areas where the revegetation effect may not meet the expectations later, it can ensure global analysis of the entire monitoring area, effectively exclude or avoid abnormal factors such as diseases that affect revegetation, simplify the need to repeatedly capture images, and ensure the accuracy of the system in determining the ecological revegetation effect.

[0071] Specifically, the image analysis module determines the revegetation analysis features of each revegetation analysis region according to each vertical distribution image, the set of revegetation analysis regions, and the corresponding horizontal distribution image;

[0072] Among them, the revegetation analysis features include vertical height difference, vertical coverage rate, and gravel coverage area.

[0073] In implementation, the image analysis module can use an edge detection algorithm, such as Canny edge detection, to identify the edges in the image, and these edges usually mark the boundaries of different regions, that is, the edges of the target revegetation region.

[0074] Generally, the number of revegetation analysis regions in a horizontal distribution image is not less than 100, and the areas of each revegetation analysis region differ by within 10%. The method for dividing the characteristic regions is not specifically limited.

[0075] From several vertically distributed images obtained, select two representative images that can clearly reflect the height characteristics of different positions in the target area. Using an image matching algorithm, based on the feature points in the images (such as unique ground features, vegetation features, etc.), match the two images to determine the corresponding positions of the same ground feature in different images.

[0076] Calculate the height difference through the principle of triangulation. According to the shooting angles of the camera device group when obtaining each vertically distributed image, based on the principle of triangulation, with the position of the camera device as the vertex and the imaging points of the same ground feature in the target area in different vertical images as the two endpoints of the base, a triangle is formed. By measuring the lengths of the corresponding line segments in the image, combined with the shooting angle and the relevant parameters of the camera device (such as focal length, etc., which can be obtained through device calibration), the actual distance between different positions in the target area can be calculated. For the distance in the height direction, that is, the height difference, it can be calculated by a similar triangulation method. This implementation does not specifically limit the method for determining the vertical height difference of the revegetation analysis area.

[0077] In addition, to improve the accuracy of height difference measurement, ground features at multiple different positions can be selected in the target area for the above measurement to obtain multiple height difference data. Statistical analysis of these data, such as calculating the average value, standard deviation, etc., can be performed to obtain more reliable height difference results and reflect the overall height change situation of the area.

[0078] The vertical coverage rate is determined according to the ratio of the green plant area in the vertical direction corresponding to the current revegetation analysis area to the area in the vertical direction corresponding to the current revegetation analysis area.

[0079] The method for determining the crushed stone coverage area includes, through image processing and analysis techniques, identifying the areas and distributions of crushed stones larger than 5 cm in the revegetated area. The crushed stone information can be extracted using a spectral analysis algorithm based on the differences in spectral characteristics between the crushed stones and the surrounding vegetation, soil, etc. Then, through methods such as image interpretation and area measurement, the area covered by the crushed stones can be estimated.

[0080] Specifically, the revegetation effect judgment module determines the effect judgment type of the current revegetation analysis area based on the vertical height difference of the revegetation analysis area in combination with the crushed stone coverage area;

[0081] If the vertical height difference of the current revegetation analysis area is greater than or equal to the preset height difference, or the crushed stone coverage area is greater than or equal to the preset crushed stone area, it is determined that the effect judgment type of the current revegetation analysis area is the dominant effect abnormal type;

[0082] If the vertical height difference of the current revegetation analysis area is less than the preset height difference and the crushed stone coverage area is less than the preset crushed stone area, it is determined that the effect judgment type of the current revegetation analysis area is the recessive effect abnormal type.

[0083] In implementation, the preset height difference is a slope of 30°. It can be understood that the slope of the current area can be determined according to the height difference and the area of the revegetation analysis area. Therefore, the size of the slope is equivalent to the size of the height difference; the preset crushed stone area is 20% of the area of the current revegetation analysis area.

[0084] It can be understood that when the slope reaches 30° or more, crushed stones larger than 5 cm start to have a relatively high risk of sliding. As the slope increases, the component force of the crushed stones in the direction of the slope under the action of gravity gradually increases, and the stability decreases. The possibility of crushed stone sliding will increase significantly. Even a small external force, such as rain scouring, wind blowing, or animal activities, may trigger the sliding of crushed stones.

[0085] In a gentle slope area with a slope less than 15°, the possibility of crushed stones sliding naturally due to their own gravity is relatively small. Even if the crushed stone coverage area is relatively large, such as reaching 40% - 50%, the whole may be in a relatively stable state without other strong external forces. However, considering the long-term effects such as rain scouring, from the perspective of long-term stability and ecological revegetation effect, it is generally considered that a coverage area of no more than 20% is more conducive to vegetation growth and slope stability, and can reduce the risk of local hidden dangers caused by the gradual rolling and accumulation of crushed stones due to factors such as rain.

[0086] When the slope exceeds 30°, the stability of the crushed stones is greatly reduced. If the crushed stone coverage area exceeds 20%, there will be a relatively high risk of sliding. Because as the coverage area increases, more crushed stones are in an unstable state, and the sliding of one crushed stone may trigger a chain reaction, resulting in large-area sliding of crushed stones.

[0087] In the above embodiments, the revegetation effect judgment module determines the effect judgment type of the current area by comprehensively analyzing the vertical height difference and the crushed stone coverage area of the revegetation analysis area. The vertical height difference is closely related to the slope. For example, when the slope corresponding to the vertical height difference reaches or exceeds 30°, it indicates that the terrain of this area is relatively steep and the stability is poor, so it is determined as the dominant effect abnormal type. In addition, if the crushed stone coverage area reaches or exceeds 20% of the total area of the revegetation analysis area, it means that there is too much crushed stone accumulation in this area, which may have a significant impact on vegetation growth and slope stability, and is also classified as the dominant effect abnormal type. On the contrary, if the slope corresponding to the vertical height difference is less than 30° and the crushed stone coverage area is less than 20%, it indicates that the terrain of this area is relatively flat and the crushed stone accumulation is less, but there may still be some potential ecological or stability problems, so it is determined as the recessive effect abnormal type. This classification method can effectively identify the potential risks of the revegetated area, provide a data basis for subsequent ecological restoration measures, and ensure the stability of the mine revegetation project.

[0088] Specifically, the revegetation effect judgment module is configured to, in response to the effect judgment type of the current revegetation analysis area being the recessive effect abnormal type, and combine the horizontal distribution image to determine whether there is a dominant abnormality in the current revegetation analysis area;

[0089] The revegetation effect judgment module calculates the revegetation distribution characterization parameter of the current revegetation analysis area according to the horizontal distribution image;

[0090] If the current revegetation analysis area meets the ecological revegetation conditions, it is determined that there is no dominant abnormality in the current revegetation analysis area;

[0091] If the current revegetation analysis area does not meet the ecological revegetation conditions, it is determined that there is a dominant abnormality in the current revegetation analysis area;

[0092] Among them, the ecological revegetation condition is that the revegetation distribution characterization parameter is less than or equal to the standard revegetation distribution characterization parameter and the average gray value is within the preset gray value range.

[0093] In implementation, the revegetation effect judgment module selects a number of analysis points located in the revegetated vegetation in the horizontal distribution image corresponding to the revegetation analysis area. The number of analysis points in a single revegetation analysis area is not less than 9, preferably 16. The analysis points are discretely selected in the revegetation analysis area. For example, if the number of analysis points in the current revegetation analysis area is 16, the corresponding horizontal distribution image can be divided into 16 equal parts, and an analysis point is selected in each equal part area.

[0094] The revegetation distribution characterization parameter is determined according to the ratio of the average deviation of the gray values of all the analysis points in the current revegetation analysis area to the average gray value of all the analysis points.

[0095] For the revegetation analysis area with the effect judgment type being the dominant effect abnormal type, it is directly determined that there is a dominant abnormality. It can be understood that for the revegetation analysis area corresponding to the dominant effect abnormal type, when selecting the analysis point, because the stone coverage area exceeds 20%, it is more likely to select a non-leaf point as the analysis point, which results in the revegetation distribution characterization parameter of it being easily beyond the standard revegetation distribution characterization parameter. Moreover, for the revegetation analysis area corresponding to the dominant effect abnormal type, due to its relatively large slope and poor vegetation growth environment, the vertical coverage rate is also relatively low. Therefore, for the revegetation analysis area corresponding to the dominant effect abnormal type, it can be directly determined that there is a dominant abnormality.

[0096] In this embodiment, the standard revegetation distribution characterization parameter is selected within the interval [0.15, 0.25]. The preset gray scale range is composed of the maximum value and the minimum value of the gray scale values of the revegetated vegetation without growth abnormalities under the current growth time conditions. It can be understood that the gray scale value can characterize the green depth of the vegetation leaves. If the gray scale value of the current vegetation is within the preset gray scale range, it can be determined that the vegetation growth in the current revegetation analysis area is normal, excluding the situation where all the vegetation in the area grows abnormally.

[0097] In the above embodiment, by comprehensively considering the vertical height difference, the gravel coverage area, and the gray scale information of the horizontal distribution image, the ecological status of the revegetation area can be comprehensively evaluated. The vertical height difference and the gravel coverage area reflect the terrain stability, while the gray scale information intuitively reflects the uniformity and health status of the vegetation coverage through image analysis. This multi-dimensional evaluation method avoids the one-sidedness of a single index and improves the accuracy and reliability of the revegetation effect judgment. In addition, the gray scale value can objectively reflect the uniformity and growth status of the vegetation coverage. By calculating the revegetation distribution characterization parameter, not only can the areas with insufficient vegetation coverage or uneven distribution be quickly identified, but also the abnormal growth situations can be excluded through the setting of the gray scale range, thereby providing a data basis for the determination of the revegetation effect.

[0098] The preset gray scale range is determined according to the current growth time conditions of the revegetated vegetation, and can dynamically adjust the evaluation criteria according to the ecological revegetation goals at different stages. This dynamic adaptability enables the revegetation effect judgment module to be flexibly applied to the revegetation projects at different stages, ensuring the consistency between the evaluation results and the actual ecological restoration goals.

[0099] Specifically, the revegetation analysis module determines whether there is an ecological revegetation abnormality according to the judgment result of the dominant abnormality in combination with the soil structure image, the soil parameters, and the vertical coverage rate;

[0100] If there is a dominant abnormality in the current revegetation analysis area, it is judged whether it is an ecological revegetation abnormality according to the soil structure image and the vertical coverage rate;

[0101] If there is no obvious abnormality in the current re-vegetation analysis area, it is determined whether it is an ecological re-vegetation abnormality according to the soil parameters.

[0102] Specifically, the re-vegetation analysis module determines that there is no abnormality in ecological re-vegetation when the soil parameters meet the conditions of standard re-vegetation soil;

[0103] Among them, the ecological re-vegetation conditions are that the backfill compaction degree, aggregate stability, and nutrient content all meet the corresponding sub-conditions of ecological re-vegetation.

[0104] In implementation, the re-vegetation analysis module obtains the thickness of the planting soil layer according to the soil structure image. If the thickness of the planting soil layer is less than 20 cm, the re-vegetation analysis module determines it as an ecological re-vegetation abnormality; if the vertical coverage rate is less than 60%, the re-vegetation analysis module determines it as an ecological re-vegetation abnormality.

[0105] In mine re-vegetation, the vertical vegetation coverage rate is generally not less than 60%. A 60% coverage rate can effectively reduce soil erosion. Especially in areas with complex terrain and poor soil stability such as mines, sufficient vegetation coverage can significantly reduce the erosion effect of rainwater scouring on slopes. Secondly, a higher vegetation coverage rate helps to improve the regional ecological environment, provide habitats for animals and plants, and promote the restoration and diversity development of the ecosystem.

[0106] The backfill compaction degree, aggregate stability, and nutrient content all meeting the corresponding sub-conditions of ecological re-vegetation means that the backfill compaction degree, aggregate stability, and nutrient content are all within the allowable thresholds in the corresponding sub-conditions of ecological re-vegetation;

[0107] The maximum ranges of the allowable thresholds in each sub-condition of ecological re-vegetation are determined respectively according to the intervals formed by the maximum and minimum values of the backfill compaction degree, aggregate stability, and nutrient content in the mine data qualified for re-vegetation. Optionally, for example, the average value of the backfill compaction degree is the midpoint of the interval value, and its sub-condition of re-vegetation is that the backfill compaction degree is greater than the midpoint of the interval value. Those skilled in the art can select the threshold from the interval values of the above intervals according to the scenario and re-vegetation requirements, and the corresponding sub-condition of re-vegetation is to meet being greater than the corresponding threshold.

[0108] In the above embodiments, by comprehensively considering the relationship between the obvious abnormality judgment result and indicators such as soil parameters and vertical coverage rate, the accurate assessment of the mine re-vegetation area is realized, which can dynamically adapt to the specific conditions and functional positioning of the mine, and at the same time take into account the requirements of short-term re-vegetation effect and long-term ecological stability.

[0109] When a dominant anomaly is detected in the current revegetation analysis area, the revegetation analysis module analyzes the soil structure image and the vertical coverage rate. The soil structure image can visually present the soil layer characteristics of the soil. By analyzing it, key information such as the texture and pore distribution of the soil can be accurately grasped. The vertical coverage rate, as a key indicator to measure the vertical space coverage of vegetation, directly reflects the protection and ecological restoration effect of vegetation on the mine slope. If the vertical coverage rate is lower than 60%, it is not only difficult to effectively resist rain erosion and reduce soil erosion, but also hinders the restoration process of the ecosystem and affects the achievement of the long-term revegetation goal. Therefore, in the case of a dominant anomaly, by analyzing these two key elements, ecological revegetation anomalies can be quickly and accurately identified, providing a basis for taking timely remedial measures.

[0110] When it is determined that there is no dominant anomaly in the current revegetation analysis area, the revegetation analysis module then shifts the focus to the assessment of soil parameters. Soil parameters such as backfill compaction degree, aggregate stability, and nutrient content are key factors determining soil quality and vegetation growth potential. According to the relevant data of successfully revegetated mines in the present invention, a suitable backfill compaction degree can ensure that the soil has good bearing capacity and stability, providing a solid foundation for the growth of vegetation roots; aggregate stability affects the air permeability, water permeability, and fertilizer retention of the soil, which is crucial for optimizing the vegetation growth environment; and sufficient and balanced nutrient content is the material guarantee for the healthy growth of vegetation and the realization of long-term revegetation. By comparing the actual soil parameters with these scientifically set sub-conditions, it is possible to accurately judge whether the soil meets the requirements of ecological revegetation, timely discover potential problems, and provide a guarantee for the continuous improvement of the long-term revegetation effect.

[0111] Specifically, the revegetation early warning module determines whether to issue an ecological revegetation anomaly early warning according to the judgment result of the dominant anomaly and the judgment result of the ecological revegetation anomaly;

[0112] If there is a dominant anomaly or an ecological revegetation anomaly, the revegetation early warning module determines to issue an ecological revegetation anomaly early warning;

[0113] If there is no dominant anomaly and no ecological revegetation anomaly, the revegetation early warning module determines not to issue an ecological revegetation anomaly early warning.

[0114] As Figure 4 shown, the present invention also provides a method for an ecological revegetation effect detection system, including:

[0115] Step S1, obtaining a geographical image group of a target revegetation area according to detection requirements, obtaining a plurality of soil structure images of the target revegetation area, and soil parameters corresponding to the soil structure images;

[0116] Step S2: Determine the position of the target revegetation area in the horizontally distributed image, divide the determined target revegetation area in the horizontally distributed image into several revegetation analysis areas, and determine the revegetation analysis features of each revegetation analysis area according to each vertically distributed image;

[0117] Step S3: Determine the effect judgment type of the current revegetation analysis area according to the revegetation analysis features of each revegetation analysis area;

[0118] Step S4: Determine whether there is a dominant anomaly in the current revegetation analysis area according to the effect judgment type in combination with the corresponding horizontally distributed image;

[0119] Step S5: Determine whether there is an ecological revegetation anomaly according to the judgment result of the dominant anomaly in combination with the soil structure image, the soil parameters and the revegetation analysis features;

[0120] Step S6: Determine whether to issue an ecological revegetation anomaly warning according to the judgment result of the dominant anomaly and the judgment result of the ecological revegetation anomaly.

[0121] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, rather than limiting the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made on the basis of the above description. It is not necessary and impossible to enumerate all implementation manners here. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. An ecological revegetation effect detection system, characterized in that, Including: An information acquisition module, which is used to acquire a geographical image group of a target revegetation area according to detection requirements, acquire a plurality of soil structure images of the target revegetation area, and soil parameters corresponding to the soil structure images, wherein the geographical image group of the target revegetation area includes a horizontal distribution image and a plurality of vertical distribution images; An image analysis module, which is connected to the information acquisition module, is used to determine the position of the target revegetation area in the horizontal distribution image, divide the target revegetation area in the determined horizontal distribution image into a plurality of revegetation analysis areas, and determine the revegetation analysis characteristics of each revegetation analysis area according to each vertical distribution image; A revegetation effect judgment module, which is respectively connected to the information acquisition module and the image analysis module, is used to determine the effect judgment type of the current revegetation analysis area according to the revegetation analysis characteristics of each revegetation analysis area, and determine whether there is a dominant anomaly in the current revegetation analysis area in combination with the corresponding horizontal distribution image; A revegetation analysis module, which is respectively connected to the information acquisition module, the image analysis module and the revegetation effect judgment module, is used to determine whether there is an ecological revegetation anomaly according to the judgment result of the dominant anomaly in combination with the soil structure image, the soil parameters and the revegetation analysis characteristics; A revegetation warning module, which is respectively connected to the revegetation effect judgment module and the revegetation analysis module, is used to determine whether to issue an ecological revegetation anomaly warning according to the judgment result of the dominant anomaly and the judgment result of the ecological revegetation anomaly.

2. The ecological restoration effect detection system according to claim 1, characterized in that The information acquisition module includes: A regional camera device group, which is used to acquire the horizontal distribution image of the target revegetation area in the horizontal direction and a plurality of the vertical distribution images of the target revegetation area in the vertical direction; A soil structure acquisition unit, which is used to acquire a soil structure analysis sample and a soil analysis profile in a single revegetation analysis area, and determine the soil parameters according to the soil structure analysis sample, and the soil parameters include backfill compaction degree, aggregate stability and nutrient content; A structure image acquisition unit, which is connected to the soil structure acquisition unit, is used to acquire the soil structure image according to the soil analysis profile.

3. The ecological restoration effect detection system according to claim 1, characterized in that, The total area in the horizontal direction of the corresponding revegetation analysis area in each vertical distribution image acquired by the image analysis module is the same as the total area of the target revegetation area determined by the horizontal distribution image.

4. The ecological restoration effect detection system according to claim 1, characterized in that, The revegetation analysis characteristics include vertical height difference, vertical coverage rate and gravel coverage area.

5. The ecological restoration effect detection system according to claim 4, wherein The revegetation effect judgment module determines the effect judgment type of the current revegetation analysis area according to the vertical height difference of the revegetation analysis area in combination with the gravel coverage area; If the vertical height difference of the current revegetation analysis area is greater than or equal to a preset height difference, or the gravel coverage area is greater than or equal to a preset gravel area, it is determined that the effect judgment type of the current revegetation analysis area is a dominant effect anomaly type; If the vertical height difference of the current revegetation analysis area is less than the preset height difference and the gravel coverage area is less than the preset gravel area, it is determined that the effect judgment type of the current revegetation analysis area is a recessive effect anomaly type.

6. The ecological restoration effect detection system according to claim 5, wherein The greening effect judgment module is configured to, in response to the effect judgment type of the current greening analysis area being the recessive effect abnormal type, and in combination with the horizontal distribution image, judge whether there is a dominant abnormality in the current greening analysis area; The greening effect judgment module calculates the greening distribution characterization parameter of the current greening analysis area according to the horizontal distribution image; If the current greening analysis area meets the ecological greening conditions, it is determined that there is no dominant abnormality in the current greening analysis area; If the current greening analysis area does not meet the ecological greening conditions, it is determined that there is a dominant abnormality in the current greening analysis area; Wherein, the ecological greening condition is that the greening distribution characterization parameter is less than or equal to the standard greening distribution characterization parameter and the average gray value is within a preset gray range.

7. The ecological restoration effect detection system according to claim 6, wherein, The greening analysis module determines whether there is an ecological greening abnormality according to the judgment result of the dominant abnormality in combination with the soil structure image, the soil parameters, and the vertical coverage rate; If there is a dominant abnormality in the current greening analysis area, it is judged whether it is an ecological greening abnormality according to the soil structure image and the vertical coverage rate; If there is no dominant abnormality in the current greening analysis area, it is judged whether it is an ecological greening abnormality according to the soil parameters.

8. The ecological restoration effect detection system according to claim 7, wherein The greening analysis module determines that there is no abnormality in ecological greening under the condition that the soil parameters meet the conditions of standard greening soil; Wherein, the ecological greening condition is that the backfill compaction degree, the aggregate stability, and the nutrient content all meet the corresponding ecological greening sub-conditions.

9. The ecological restoration effect detection system according to claim 8, wherein The greening warning module determines whether to issue an ecological greening abnormality warning according to the judgment result of the dominant abnormality and the judgment result of the ecological greening abnormality; If there is a dominant abnormality or there is an ecological greening abnormality, the greening warning module determines to issue an ecological greening abnormality warning; If there is no dominant abnormality and there is no ecological greening abnormality, the greening warning module determines not to issue an ecological greening abnormality warning.

10. A method for detecting the effect of ecological revegetation, which is applied to the detection system described in any one of claims 1-9, and is characterized in that, Including: Step S1, obtain a geographical image group of the target greening area according to the detection requirements, obtain a plurality of soil structure images of the target greening area, and the soil parameters corresponding to the soil structure images; Step S2, determine the position of the target greening area in the horizontal distribution image, divide the target greening area in the determined horizontal distribution image into a plurality of greening analysis areas, and determine the greening analysis characteristics of each greening analysis area according to each vertical distribution image; Step S3, determine the effect judgment type of the current greening analysis area according to the greening analysis characteristics of each greening analysis area; Step S4, determine whether there is a dominant abnormality in the current greening analysis area according to the effect judgment type in combination with the corresponding horizontal distribution image; Step S5, determine whether there is an ecological greening abnormality according to the judgment result of the dominant abnormality in combination with the soil structure image, the soil parameters, and the greening analysis characteristics; Step S6, determine whether to issue an ecological greening abnormality warning according to the judgment result of the dominant abnormality and the judgment result of the ecological greening abnormality.

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