Ecological restoration effect detection system and method

Abstract

The present invention relates to the field of ecological regreening detection, and specifically discloses an ecological regreening effect detection system and method. The system includes: an information acquisition module, an image analysis module, a regreening effect judgment module, a regreening analysis module, and a regreening early warning module. The information acquisition module obtains relevant images and detection analysis information of the target regreening area. The image analysis module determines the target regreening area and divides it into several regreening analysis areas. The vertical distribution image extracts the regreening analysis features of each area. The regreening effect judgment module determines the effect judgment type of each regreening analysis area and determines whether there is a dominant anomaly. The regreening analysis module further determines whether there is an ecological regreening anomaly. The regreening early warning module issues an early warning signal based on the abnormal judgment result. The present invention improves the accuracy of regreening effect detection and provides data support for improving regreening efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological restoration detection technology, and in particular 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 application. First, the evaluation standards and methods for ecological restoration monitoring lack uniformity, which makes it difficult to compare and integrate monitoring results between different regions and different projects. Secondly, the terrain in the mining area is complex, and traditional monitoring methods such as manual sampling and ground measurement are time-consuming and labor-intensive, and difficult to fully cover, resulting in high data collection costs. In addition, with the development of monitoring technology, 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 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.

[0003] For example, the prior art discloses a remote sensing monitoring method and system for big data analysis of 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. Based on the corresponding characteristics of the missing oasis data, conducting vegetation ecology analysis, formulating suitable vegetation types for planting, obtaining the situation of disappeared oases through oasis coordinate comparison, building a vegetation planting library, and combining the analysis of oasis vegetation types, oasis climate conditions and oasis hydrological conditions to obtain vegetation types suitable for planting in the corresponding coordinate oasis.

[0004] However, existing technologies still have the following problems: 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. It is impossible to accurately analyze the existence of anomalies in areas that need to be regreened, affecting the overall regreening 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 problem that the existing technology relies 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 existence of anomalies in areas requiring greening, thus affecting the overall greening effect.

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

[0007] an information acquisition module, configured to acquire a geographic image group of a target regreening area according to detection requirements, acquire a plurality of soil structure images of the target regreening area, and soil parameters corresponding to the soil structure images, wherein the geographic image group of the target regreening area includes a horizontal distribution image and a plurality of vertical distribution images;

[0008] an image analysis module connected to the information acquisition module, configured to determine a location of a target regreening area in the horizontal distribution image, divide the determined target regreening area in the horizontal distribution image into a plurality of regreening analysis areas, and determine a regreening analysis feature of each regreening analysis area based on each of the vertical distribution images;

[0009] a regreening effect judgment module, connected to the information acquisition module and the image analysis module, respectively, for determining the effect judgment type of the current regreening analysis area according to the regreening analysis characteristics of each regreening analysis area, and determining whether there is a significant abnormality in the current regreening analysis area in combination with the corresponding horizontal distribution image;

[0010] a regreening analysis module, connected to the information acquisition module, the image analysis module, and the regreening effect judgment module, respectively, for determining whether there is an ecological regreening anomaly based on the judgment result of the dominant anomaly in combination with the soil structure image, the soil parameters, and the regreening analysis characteristics;

[0011] The greening warning module is connected to the greening effect judgment module and the greening analysis module respectively, and is used to determine whether to issue an ecological greening abnormality warning based on the judgment result of the dominant abnormality and the judgment result of the ecological greening abnormality.

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

[0013] an area camera device group, configured to acquire the horizontal distribution image of the target greening area in the horizontal direction, and acquire a plurality of vertical distribution images of the target greening area in the vertical direction;

[0014] A soil structure acquisition unit, which is used to obtain soil structure analysis samples and soil analysis profiles in a single regreening analysis area, and determine the soil parameters based on the soil structure analysis samples, the soil parameters including backfill compaction, aggregate stability, and nutrient content;

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

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

[0017] The greening analysis characteristics include vertical height difference, vertical coverage rate and gravel coverage area.

[0018] As a preferred technical solution of the ecological restoration effect detection system, the restoration effect judgment module determines the effect judgment type of the current restoration analysis area based on the vertical height difference of the restoration analysis area and the gravel coverage area;

[0019] If the vertical height difference of the current regreening analysis area 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 regreening analysis area is a dominant effect abnormal type;

[0020] If the vertical height difference of the current regreening 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 regreening analysis area is a hidden effect abnormal type.

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

[0022] The regreening effect judgment module calculates the regreening distribution characteristic parameter of the current regreening analysis area according to the horizontal distribution image;

[0023] If the current regreening analysis area meets the ecological regreening conditions, it is determined that there is no obvious anomaly in the current regreening analysis area;

[0024] If the current regreening analysis area does not meet the ecological regreening conditions, it is determined that there is a dominant anomaly in the current regreening analysis area;

[0025] Among them, the ecological regreening condition is that the regreening distribution characterization parameter is less than or equal to the standard regreening distribution characterization parameter and the average grayscale value is within a preset grayscale range.

[0026] As a preferred technical solution of the ecological restoration effect detection system, the restoration analysis module determines whether there is an ecological restoration anomaly based on the judgment result of the dominant anomaly combined with the soil structure image, the soil parameters and the vertical coverage rate;

[0027] If there is a significant anomaly in the current regreening analysis area, determine whether it is an ecological regreening anomaly based on the soil structure image and vertical coverage rate;

[0028] If there is no obvious anomaly in the current regreening analysis area, it is determined whether it is an ecological regreening anomaly based on the soil parameters.

[0029] As a preferred technical solution of the ecological restoration greening effect detection system, it is characterized in that the greening analysis module determines that there is no abnormality in ecological restoration greening under the condition that the soil parameters meet the standard greening soil;

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

[0031] As a preferred technical solution of the ecological restoration effect detection system, it is characterized in that the green restoration warning module determines whether to issue an ecological restoration abnormality warning based on the judgment result of the dominant abnormality and the judgment result of the ecological restoration abnormality;

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

[0033] If there is no obvious anomaly and no ecological restoration anomaly, the restoration anomaly 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, comprising:

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

[0036] Step S2, determining the position of the target regreening area in the horizontal distribution image, dividing the determined target regreening area in the horizontal distribution image into a plurality of regreening analysis areas, and determining a regreening analysis feature of each regreening analysis area based on each of the vertical distribution images;

[0037] Step S3, determining the effect judgment type of the current regreening analysis area according to the regreening analysis characteristics of each of the regreening analysis areas;

[0038] Step S4, determining whether there is a significant abnormality in the current regreening analysis area according to the effect judgment type and the corresponding horizontal distribution image;

[0039] Step S5, determining whether there is an ecological regreening anomaly based on the judgment result of the dominant anomaly in combination with the soil structure image, the soil parameters and the regreening analysis characteristics;

[0040] Step S6: determining whether to issue an ecological restoration abnormality warning based on the judgment result of the dominant abnormality and the judgment result of the ecological restoration abnormality.

[0041] The beneficial effects of the present invention are as follows: the ecological regreening effect detection system and method of the present invention realizes comprehensive monitoring and precise evaluation of mine regreening areas through the collaborative work of the information acquisition module, the image analysis module, the regreening effect judgment module, the regreening analysis module and the regreening early warning module. The system uses horizontal distribution images and vertical distribution images, combined with soil structure images and soil parameters, to accurately divide the regreening analysis area and extract key features. Through the dual judgment mechanism of explicit anomalies and ecological regreening anomalies, the system can dynamically adapt to the specific conditions of different mines, taking into account both short-term regreening effects and long-term ecological stability. In addition, the regreening early warning module can issue early warnings in a timely manner according to the abnormal judgment results, providing a basis for the adjustment of ecological restoration measures. Overall, the system improves the accuracy of regreening effect detection and provides data support for improving regreening efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the structure of the ecological restoration effect detection system in an embodiment of the present invention;

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

[0044] Figure 3 A logic diagram for determining the effect judgment type of the current regreening analysis area in an embodiment of the present invention;

[0045] Figure 4 Flowchart of the method for detecting the ecological restoration effect in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0047] In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and are not intended to indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only and should not 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", "above" and "above" the second feature includes the first feature being directly above and obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. The first feature being "below", "below" and "below" the second feature includes the first feature being directly below and obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0048] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0049] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0050] like Figure 1-3 As shown, this embodiment provides an ecological restoration effect detection system, which includes:

[0051] an information acquisition module, configured to acquire a geographic image group of a target regreening area according to detection requirements, acquire a plurality of soil structure images of the target regreening area, and soil parameters corresponding to the soil structure images, wherein the geographic image group of the target regreening area includes a horizontal distribution image and a plurality of vertical distribution images;

[0052] an image analysis module connected to the information acquisition module, configured to determine a location of a target regreening area in the horizontal distribution image, divide the determined target regreening area in the horizontal distribution image into a plurality of regreening analysis areas, and determine a regreening analysis feature of each regreening analysis area based on each of the vertical distribution images;

[0053] a regreening effect judgment module, connected to the information acquisition module and the image analysis module, respectively, for determining the effect judgment type of the current regreening analysis area according to the regreening analysis characteristics of each regreening analysis area, and determining whether there is a significant abnormality in the current regreening analysis area in combination with the corresponding horizontal distribution image;

[0054] a regreening analysis module, connected to the information acquisition module, the image analysis module, and the regreening effect judgment module, respectively, for determining whether there is an ecological regreening anomaly based on the judgment result of the dominant anomaly in combination with the soil structure image, the soil parameters, and the regreening analysis characteristics;

[0055] The greening warning module is connected to the greening effect judgment module and the greening analysis module respectively, and is used to determine whether to issue an ecological greening abnormality warning based on the judgment result of the dominant abnormality and the judgment result of the ecological greening abnormality.

[0056] In implementation, the detection requirement is the geographical location of the target regreening area.

[0057] The ecological regreening effect detection system and method of the present invention realizes comprehensive monitoring and accurate evaluation of mine regreening areas through the collaborative work of the information acquisition module, image analysis module, regreening effect judgment module, regreening analysis module and regreening early warning module. The system uses horizontal distribution images and vertical distribution images, combined with soil structure images and soil parameters, to accurately divide the regreening analysis area and extract key features. Through the dual judgment mechanism of dominant anomalies and ecological regreening anomalies, the system can dynamically adapt to the specific conditions of different mines, taking into account both short-term regreening effects and long-term ecological stability. In addition, the regreening early warning module can issue early warnings in a timely manner according to the abnormal judgment results, providing a basis for the adjustment of ecological restoration measures. Overall, the system not only improves the accuracy of regreening effect detection, but also provides data support for improving regreening efficiency.

[0058] In detail, the information acquisition module includes:

[0059] an area camera device group, configured to acquire the horizontal distribution image of the target greening area in the horizontal direction, and acquire a plurality of vertical distribution images of the target greening area in the vertical direction;

[0060] A soil structure acquisition unit, which is used to obtain soil structure analysis samples and soil analysis profiles in a single regreening analysis area, and determine the soil parameters based on the soil structure analysis samples, the soil parameters including backfill compaction, aggregate stability, and nutrient content;

[0061] A structural image acquisition unit 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 equipment group includes multiple camera devices, which are capable of acquiring horizontal distribution images in a direction parallel to the horizontal plane at a point higher than the highest point of the target greening area, and are capable of acquiring the vertical distribution images in a direction parallel to the vertical direction. The camera equipment group can be implemented by multiple drones equipped with cameras. This is a prior art and is not specifically limited.

[0063] In implementation, for a geographic image group of a single target greening area, the total number of vertically distributed images is not less than 4, preferably 8. When the camera equipment obtains each vertically distributed image, the angles between each vertically distributed image are the same, that is, the camera equipment group has the same shooting angle when obtaining each vertically distributed image, which 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 soil analysis profiles, and they correspond one to one.

[0065] The soil structure acquisition unit excavates a vertical soil profile using excavation tools such as shovels and soil drills. The profile depth is generally determined by the research objectives and soil type, preferably 1 to 2 meters, to allow for observation of the characteristics of different soil layers. In actual use, the soil structure acquisition unit selects the soil analysis profile excavation location that will not damage the regreening plants in the regreening analysis area, and soil structure analysis samples are obtained during the excavation process.

[0066] The nutrient content includes the pH value, organic matter content, total nitrogen, total phosphorus and total potassium of the soil; the determination of the content and particle size distribution of water-stable aggregates in the soil, the analysis of the changes in soil aggregates at different depths, the backfill compaction degree, aggregate stability and nutrient content are existing technologies and will not be repeated here.

[0067] In the above embodiment, comprehensive monitoring and analysis of the target regreening area is achieved by combining the regional camera equipment group and the soil structure acquisition unit. The camera equipment group can acquire multi-angle distribution images in the horizontal and vertical directions. The horizontal distribution image is used to show the overall coverage of the regreening area, and the multiple vertical distribution images capture the detailed features of the area from different angles to ensure full coverage of the regreening area. At the same time, the synergistic effect of the soil structure acquisition unit and the structural image acquisition unit enables the soil structure analysis samples to correspond one-to-one with the soil analysis profile, providing accurate data support for soil quality assessment. This multi-dimensional monitoring method not only improves the accuracy of the regreening effect assessment, but also makes up for the shortcomings of single-view monitoring through the acquisition of multi-angle images, and enhances the adaptability to complex terrain and vegetation distribution. In addition, the acquisition of vertical distribution images at a fixed angle ensures the consistency and comparability of the data, providing a basis for subsequent quantitative analysis.

[0068] In detail, the total area in the horizontal direction of the corresponding regreening analysis region in each of the vertical distribution images acquired by the image analysis module is the same as the total area of ​​the target regreening region determined by the horizontal distribution image.

[0069] In implementation, different vertical distribution images may include portions of repeated regreening analysis areas, and the sum of all regreening analysis areas included in all vertical distribution images can completely cover all target regreening areas;

[0070] It can be understood that all vertical distribution images can cover the entire monitoring area so that when anomaly analysis is subsequently conducted on areas where the greening effect may not meet expectations, a full-area analysis of the entire monitoring area can be ensured, and abnormal factors such as diseases that affect greening can be effectively excluded or avoided. While simplifying the need for repeated image shooting, it ensures the accuracy of the system in determining the ecological greening effect.

[0071] In detail, the image analysis module determines the regreening analysis features of each regreening analysis area according to each vertical distribution image set and the corresponding horizontal distribution image;

[0072] The greening analysis characteristics include vertical height difference, vertical coverage rate and gravel coverage area.

[0073] In implementation, the image analysis module may use an edge detection algorithm, such as Canny edge detection, to identify edges in the image. These edges generally mark the boundaries between different regions, that is, the edges of the target greening region.

[0074] Generally, the number of greening analysis regions in a horizontal distribution image is not less than 100, and the area difference between the greening analysis regions is within 10%. The method for dividing the feature regions is not specifically limited.

[0075] From the acquired vertical distribution images, select two representative images that clearly reflect the height characteristics of different locations in the target area. Using an image matching algorithm, based on the characteristic points in the images (such as unique ground objects and vegetation features), the two images are matched to determine the corresponding locations of the same ground objects in the different images.

[0076] The height difference is calculated by the principle of triangulation. According to the shooting angle of the camera group when acquiring each vertical distribution image, a triangle is formed with the camera position as the vertex and the imaging points of the same ground object in the target area in different vertical images as the two end points of the base according to the principle of triangulation. By measuring the length of the corresponding line segment in the image, combined with the shooting angle and the relevant parameters of the camera (such as focal length, which can be obtained through equipment calibration), the actual distance between different positions in the target area can be calculated. The distance in the height direction, that is, the height difference, can be calculated by a similar triangulation method. This implementation does not specifically limit the method of determining the vertical height difference of the greening analysis area.

[0077] In addition, in order to improve the accuracy of height difference measurement, multiple objects at different locations can be selected in the target area for the above measurement to obtain multiple height difference data. These data can be statistically analyzed, such as calculating the average value, standard deviation, etc., to obtain more reliable height difference results that reflect the overall height changes in 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 regreening analysis area to the area in the vertical direction corresponding to the current regreening analysis area.

[0079] Determining the gravel coverage area involves using image processing and analysis techniques to identify the presence and distribution of gravel larger than 5 cm within the regreening area. Gravel information can be extracted using spectral analysis algorithms based on the differences in spectral characteristics between the gravel and surrounding vegetation and soil. Gravel coverage can then be estimated through image interpretation and area measurement.

[0080] In detail, the regreening effect judgment module determines the effect judgment type of the current regreening analysis area according to the vertical height difference of the regreening analysis area and the gravel coverage area;

[0081] If the vertical height difference of the current regreening analysis area 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 regreening analysis area is a dominant effect abnormal type;

[0082] If the vertical height difference of the current regreening 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 regreening analysis area is a hidden 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 based on the height difference and the area of ​​the greening analysis area, so the size of the slope is equivalent to the size of the height difference; the preset gravel area is 20% of the area of ​​the current greening analysis area.

[0084] Understandably, when slopes reach 30° or above, gravel thicker than 5cm begins to pose a higher risk of sliding. As the slope increases, the force exerted by gravity on the gravel along the slope gradually increases, reducing its stability. The likelihood of gravel sliding increases significantly, and even relatively minor external forces, such as rain, wind, or animal activity, can trigger a slide.

[0085] On gentle slopes with a gradient of less than 15°, the likelihood of gravel sliding naturally under its own gravity is relatively low. Even with a relatively large gravel coverage area, such as 40% to 50%, the overall area can remain relatively stable in the absence of other strong external forces. However, considering the long-term impacts of rainwater erosion, and from the perspective of long-term stability and ecological restoration, a coverage area of ​​no more than 20% is generally considered more conducive to vegetation growth and slope stability, reducing the risk of localized hazards caused by the gradual accumulation of gravel due to factors such as rainwater.

[0086] When the slope exceeds 30°, the stability of the gravel is greatly reduced. If the gravel covers more than 20% of the area, there is a higher risk of landslide. This is because as the coverage area increases, more gravel becomes unstable. The slip of one gravel can trigger a chain reaction, causing a large area of ​​gravel to slide.

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

[0088] In detail, the green restoration effect judgment module is configured to, in response to the effect judgment type of the current green restoration analysis area being a latent effect abnormality type, judge whether there is an explicit abnormality in the current green restoration analysis area in combination with the horizontal distribution image;

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

[0090] If the current regreening analysis area meets the ecological regreening conditions, it is determined that there is no obvious anomaly in the current regreening analysis area;

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

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

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

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

[0095] For the regreening analysis areas whose effect judgment type is the dominant effect anomaly type, it is directly determined that there is a dominant anomaly. It can be understood that when selecting analysis points, the regreening analysis areas corresponding to the dominant effect anomaly type are more likely to select non-leaf points as analysis points because the stone coverage area exceeds 20%, which causes its regreening distribution characterization parameters to easily exceed the standard regreening distribution characterization parameters. Moreover, for the regreening analysis areas corresponding to the dominant effect anomaly type, due to its large slope and poor vegetation growth environment, the vertical coverage rate is also low. Therefore, for the regreening analysis areas corresponding to the dominant effect anomaly type, it can be directly determined that there is a dominant anomaly.

[0096] In this embodiment, the standard regreening distribution characterization parameter is selected within the interval [0.15, 0.25], and the preset grayscale range is composed of the maximum and minimum grayscale values ​​under the condition of the current growth time of the regreening vegetation without growth abnormalities. It can be understood that the grayscale value can characterize the depth of green of the vegetation leaves. If the grayscale value of the current vegetation is within the preset grayscale range, it can be determined that there is no abnormality in the growth of vegetation in the current regreening analysis area, and the situation where the vegetation in the area has abnormal growth is excluded.

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

[0098] The preset grayscale range is determined based on the current growth period of the reforestation vegetation, allowing for dynamic adjustment of the assessment criteria based on the ecological restoration goals at different stages. This dynamic adaptability allows the reforestation effect assessment module to be flexibly applied to reforestation projects at different stages, ensuring that the assessment results are consistent with the actual ecological restoration goals.

[0099] In detail, the regreening analysis module determines whether there is an ecological regreening anomaly based on the judgment result of the dominant anomaly in combination with the soil structure image, the soil parameters and the vertical coverage rate;

[0100] If there is a significant anomaly in the current regreening analysis area, determine whether it is an ecological regreening anomaly based on the soil structure image and vertical coverage rate;

[0101] If there is no obvious anomaly in the current regreening analysis area, it is determined whether it is an ecological regreening anomaly based on the soil parameters.

[0102] In detail, the regreening analysis module determines that there is no abnormality in ecological regreening under the condition that the soil parameters meet the standard regreening soil;

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

[0104] During implementation, the greening analysis module obtains the thickness of the planting soil layer based on the soil structure image. If the thickness of the planting soil layer is less than 20 cm, the greening analysis module will determine it as an ecological greening anomaly; if the vertical coverage rate is less than 60%, the greening analysis module will determine it as an ecological greening anomaly.

[0105] During mine restoration, vertical vegetation coverage is generally no less than 60%. This 60% coverage effectively reduces soil erosion, especially in areas with complex terrain and poor soil stability, such as mines. Sufficient vegetation cover can significantly reduce the erosion of slopes caused by rainwater. Furthermore, a higher vegetation coverage helps improve the regional ecological environment, providing habitats for plants and animals, and promoting ecosystem recovery and biodiversity.

[0106] The backfill compaction, aggregate stability and nutrient content all meet the corresponding ecological restoration sub-conditions. The backfill compaction, aggregate stability and nutrient content are all within the allowable thresholds of the corresponding ecological restoration sub-conditions.

[0107] The maximum range of allowable thresholds in each ecological restoration sub-condition is determined based on the intervals formed by the maximum and minimum values ​​of backfill compaction, aggregate stability, and nutrient content in the mine data that qualified for restoration. Alternatively, for example, the average backfill compaction value is the midpoint of the interval value, and the restoration sub-condition is that the backfill compaction value is greater than the midpoint of the interval value. Those skilled in the art can select a threshold from the interval values ​​of the above intervals based on the scenario and restoration requirements, and the corresponding restoration sub-condition is that the backfill compaction value is greater than the corresponding threshold value.

[0108] In the above embodiment, by comprehensively analyzing the relationship between the results of explicit anomaly judgment and indicators such as soil parameters and vertical coverage, an accurate assessment of the mine greening area is achieved, which can dynamically adapt to the specific conditions and functional positioning of the mine, while taking into account the needs of short-term greening effects and long-term ecological stability.

[0109] When a significant anomaly is detected in the current regreening analysis area, the regreening analysis module analyzes the soil structure image and vertical coverage rate. The soil structure image can intuitively display the characteristics of the soil layer. By analyzing it, key information such as soil texture and pore distribution can be accurately grasped. The vertical coverage rate is a key indicator for measuring the degree of vegetation coverage in vertical space, and it directly reflects the vegetation's protection and ecological restoration effect on the mine slope. If the vertical coverage rate is less than 60%, it will not only be difficult to effectively resist rain erosion and reduce soil erosion, but it will also hinder the recovery process of the ecosystem and affect the achievement of long-term regreening goals. Therefore, in the case of significant anomalies, the analysis of these two key elements can quickly and accurately identify ecological regreening anomalies, providing a basis for timely remedial measures.

[0110] When it is determined that there are no obvious abnormalities in the current regreening analysis area, the regreening analysis module will shift its focus to the assessment of soil parameters. Soil parameters such as backfill compaction, aggregate stability and nutrient content are key factors in determining soil quality and vegetation growth potential. The present invention is based on relevant data from successful mine regreening. Appropriate backfill compaction 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 to the optimization of the vegetation growth environment; and sufficient and balanced nutrient content is the material guarantee for the healthy growth of vegetation and long-term regreening. By comparing the actual soil parameters with these scientifically set sub-conditions, it is possible to accurately determine whether the soil meets the requirements of ecological regreening, discover potential problems in a timely manner, and provide guarantees for the continuous improvement of long-term regreening effects.

[0111] In detail, the green restoration warning module determines whether to issue an ecological green restoration abnormality warning based on the judgment result of the dominant abnormality and the judgment result of the ecological green restoration abnormality;

[0112] If there is an obvious anomaly or an ecological restoration anomaly, the restoration anomaly warning module determines and issues an ecological restoration anomaly warning;

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

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

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

[0116] Step S2, determining the position of the target regreening area in the horizontal distribution image, dividing the determined target regreening area in the horizontal distribution image into a plurality of regreening analysis areas, and determining a regreening analysis feature of each regreening analysis area based on each of the vertical distribution images;

[0117] Step S3, determining the effect judgment type of the current regreening analysis area according to the regreening analysis characteristics of each of the regreening analysis areas;

[0118] Step S4, determining whether there is a significant abnormality in the current regreening analysis area according to the effect judgment type and the corresponding horizontal distribution image;

[0119] Step S5, determining whether there is an ecological regreening anomaly based on the judgment result of the dominant anomaly in combination with the soil structure image, the soil parameters and the regreening analysis characteristics;

[0120] Step S6: determining whether to issue an ecological restoration abnormality warning based on the judgment result of the dominant abnormality and the judgment result of the ecological restoration abnormality.

[0121] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. An ecological restoration effect detection system, characterized in that: include: an information acquisition module, configured to acquire a geographic image group of a target regreening area according to detection requirements, acquire a plurality of soil structure images of the target regreening area, and soil parameters corresponding to the soil structure images, wherein the geographic image group of the target regreening area includes a horizontal distribution image and a plurality of vertical distribution images; an image analysis module connected to the information acquisition module, configured to determine a location of a target regreening area in the horizontal distribution image, divide the determined target regreening area in the horizontal distribution image into a plurality of regreening analysis areas, and determine a regreening analysis feature of each regreening analysis area based on each of the vertical distribution images; a regreening effect judgment module, connected to the information acquisition module and the image analysis module, respectively, for determining the effect judgment type of the current regreening analysis area according to the regreening analysis characteristics of each regreening analysis area, and determining whether there is a significant abnormality in the current regreening analysis area in combination with the corresponding horizontal distribution image; a regreening analysis module, connected to the information acquisition module, the image analysis module, and the regreening effect judgment module, respectively, for determining whether there is an ecological regreening anomaly based on the judgment result of the dominant anomaly in combination with the soil structure image, the soil parameters, and the regreening analysis characteristics; a regreening warning module, connected to the regreening effect judgment module and the regreening analysis module, respectively, for determining whether to issue an ecological regreening anomaly warning based on the judgment result of the dominant anomaly and the judgment result of the ecological regreening anomaly; The greening analysis characteristics include vertical height difference, vertical coverage rate and gravel coverage area; The regreening effect judgment module determines the effect judgment type of the current regreening analysis area according to the vertical height difference of the regreening analysis area and the gravel coverage area; If the vertical height difference of the current regreening analysis area 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 regreening analysis area is a dominant effect abnormal type; If the vertical height difference of the current regreening 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 regreening analysis area is a hidden effect abnormal type; The green restoration effect judgment module is configured to, in response to the effect judgment type of the current green restoration analysis area being a latent effect abnormality type, judge whether there is an explicit abnormality in the current green restoration analysis area in combination with the horizontal distribution image; The regreening effect judgment module calculates the regreening distribution characteristic parameter of the current regreening analysis area according to the horizontal distribution image; If the current regreening analysis area meets the ecological regreening conditions, it is determined that there is no obvious anomaly in the current regreening analysis area; If the current regreening analysis area does not meet the ecological regreening conditions, it is determined that there is a dominant anomaly in the current regreening analysis area; Among them, the ecological regreening condition is that the regreening distribution characterization parameter is less than or equal to the standard regreening distribution characterization parameter and the average grayscale value is within a preset grayscale range.

2. The ecological restoration effect detection system according to claim 1 is characterized in that: The information acquisition module includes: an area camera device group, configured to acquire the horizontal distribution image of the target greening area in the horizontal direction, and acquire a plurality of vertical distribution images of the target greening area in the vertical direction; A soil structure acquisition unit, which is used to obtain soil structure analysis samples and soil analysis profiles in a single regreening analysis area, and determine the soil parameters based on the soil structure analysis samples, the soil parameters including backfill compaction, aggregate stability, and nutrient content; A structural image acquisition unit is connected to the soil structure acquisition unit and 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 is characterized in that: The total area in the horizontal direction of the corresponding regreening analysis regions in each of the vertical distribution images acquired by the image analysis module is the same as the total area of ​​the target regreening region determined by the horizontal distribution image.

4. The ecological restoration effect detection system according to claim 1, characterized in that: The regreening analysis module determines whether there is an ecological regreening anomaly based on the judgment result of the dominant anomaly combined with the soil structure image, the soil parameters and the vertical coverage rate. If there is a significant anomaly in the current regreening analysis area, determine whether it is an ecological regreening anomaly based on the soil structure image and vertical coverage rate; If there is no obvious anomaly in the current regreening analysis area, it is determined whether it is an ecological regreening anomaly based on the soil parameters.

5. The ecological restoration effect detection system according to claim 1 is characterized in that: The regreening analysis module determines that there is no abnormality in ecological regreening under the condition that the soil parameters meet the standard regreening soil; Among them, the ecological restoration conditions are that the backfill compaction, aggregate stability and nutrient content all meet the corresponding ecological restoration sub-conditions.

6. The ecological restoration effect detection system according to claim 5 is characterized in that: The green restoration warning module determines whether to issue an ecological green restoration abnormality warning according to the judgment result of the dominant abnormality and the judgment result of the ecological green restoration abnormality; If there is an obvious anomaly or an ecological restoration anomaly, the restoration anomaly warning module determines and issues an ecological restoration anomaly warning; If there is no obvious anomaly and no ecological restoration anomaly, the restoration anomaly warning module determines not to issue an ecological restoration anomaly warning.

7. A method for detecting ecological restoration effects, applied to the detection system according to any one of claims 1 to 6, characterized in that: include: Step S1, obtaining a geographical image group of a target greening area according to detection requirements, obtaining a plurality of soil structure images of the target greening area, and soil parameters corresponding to the soil structure images; Step S2, determining the position of the target regreening area in the horizontal distribution image, dividing the determined target regreening area in the horizontal distribution image into a plurality of regreening analysis areas, and determining a regreening analysis feature of each regreening analysis area based on each of the vertical distribution images; Step S3, determining the effect judgment type of the current regreening analysis area according to the regreening analysis characteristics of each of the regreening analysis areas; Step S4, determining whether there is a significant abnormality in the current regreening analysis area according to the effect judgment type and the corresponding horizontal distribution image; Step S5, determining whether there is an ecological regreening anomaly based on the judgment result of the dominant anomaly in combination with the soil structure image, the soil parameters and the regreening analysis characteristics; Step S6: determining whether to issue an ecological restoration abnormality warning based on the judgment result of the dominant abnormality and the judgment result of the ecological restoration abnormality.

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