A method and system for monitoring the ecological environment of grasslands

Abstract

The application discloses a kind of monitoring method and monitoring system for grassland ecological environment, it is related to grassland ecological monitoring technical field, the application is first set first standard screening function, second standard screening function and third standard screening function, subsequently obtain the visible light image and infrared temperature image of the region to be monitored, the hue and morphology of visible light image are screened by first standard screening function and second standard screening function, obtain alternative mouse hole set;Again, the infrared temperature image is screened by third standard screening function, and the temperature difference anomaly area set is obtained;Finally, the mouse hole set of alternative is screened by temperature difference anomaly area set, determine the number of mouse hole;The application makes full use of the specificity of mouse hole on color, shape and temperature difference to realize multiple screening and cross-validation, to screen mouse hole quickly on grassland, and then indirectly monitor the population number of grassland rodents by combining the number change of mouse hole, to improve monitoring efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of grassland ecological monitoring, in particular to a monitoring method and system for grassland ecological environment. BACKGROUND

[0002] Alpine grassland has important value in the ecological system, and plays a key role in maintaining ecological balance and protecting biodiversity. In the prior art, biologists often estimate the number of rodents per unit area by manually catching rodents or investigating the number of rodent holes, so as to monitor and control rodent damage. However, in the implementation process of rodent hole monitoring, the acquisition of basic parameters is prone to be missed or recalculated due to negligence, and the above method needs to be continuously investigated and counted in the grassland area to be monitored, which consumes a lot of time, thereby resulting in low monitoring efficiency of the number of rodent populations in alpine grassland areas. SUMMARY

[0003] The main purpose of the present application is to provide a monitoring method and system for grassland ecological environment, which aims to solve the defect of low monitoring efficiency in the prior art.

[0004] The present application achieves the above-mentioned purpose by the following technical solutions:

[0005] A monitoring method for grassland ecological environment, comprising the following steps:

[0006] Setting a first standard screening function, a second standard screening function and a third standard screening function for rodent hole determination;

[0007] Obtaining a visible light image and an infrared temperature image of the area to be monitored;

[0008] Performing tone screening on the visible light image by the first standard screening function to obtain all dark tone candidate pixels and generate a dark tone pixel set;

[0009] Performing morphological screening on the dark tone pixel set by the second standard screening function to obtain a candidate rodent hole set;

[0010] Performing temperature difference screening on the infrared temperature image by the third standard screening function to obtain a temperature difference abnormal area set;

[0011] Screening the candidate rodent hole set by the temperature difference abnormal area set to determine the number of rodent holes.

[0012] Optionally, the calculation expression of the first standard screening function is wherein and Q R , Q G and QB Represents the standard RGB value; the calculation expression for the second standard filtering function is: A min and A max R represents the minimum and maximum burrow areas, respectively. min and R max Let represent the minimum and maximum aspect ratios of the mouse hole, respectively; the calculation expression for the third criterion screening function is: ,in The temperature value of the pixel with coordinates (x, y). Indicates ambient temperature. This indicates the minimum permissible temperature difference between the mouse burrow and the ambient temperature.

[0013] Optionally, the visible light image is subjected to tone screening using a first standard screening function to obtain all dark-tone candidate pixels and generate a dark-tone pixel set, including the following steps:

[0014] Convert the visible light image into an RGB image;

[0015] Construct a standard coordinate system, and divide the RGB image into several pixels under the standard coordinate system, and obtain the RGB parameters {R(x)} of each pixel. j y j ), G(x) j y j B(x) j y j )}, where x j y j This represents the coordinates of a pixel in the standard coordinate system, where j represents the pixel number. and These are the actual RGB values;

[0016] Each pixel is filtered according to the first standard filtering function and each RGB parameter to obtain a set of dark-toned pixels.

[0017] Optionally, the set of dark-toned pixels is subjected to morphological screening using a second standard screening function to obtain a set of candidate mouse holes, including the following steps:

[0018] Connectivity analysis is performed on the dark-toned pixel set to obtain several candidate regions;

[0019] Calculate the actual area A1, A2, ..., A of each candidate region. i and the actual aspect ratios R1, R2, ..., R i Where i represents the candidate region number;

[0020] The actual area and actual aspect ratio of the same candidate region are integrated into a comparison data set (A). iR i , obtain a plurality of comparison data sets;

[0021] Substitute each comparison data set into a second standard screening function respectively, if it is satisfied, determine that the candidate area is qualified, otherwise determine that it is not qualified;

[0022] Collect each qualified candidate area to generate a candidate mouse hole set P1, P2,..., P n , wherein n represents the number.

[0023] Optionally, the dark tone pixel set is subjected to connected domain analysis to obtain a plurality of candidate areas, including the following steps:

[0024] Take any pixel in the dark tone pixel set as an initial pixel, and obtain the initial pixel coordinates thereof;

[0025] Calculate all adjacent pixel coordinates of the initial pixel coordinates, wherein the calculation expression of the adjacent pixel coordinates is: ;

[0026] Compare each adjacent pixel coordinate with the dark tone pixel set respectively, and collect all adjacent pixel coordinates satisfying the dark tone pixel set and the initial pixel to the same candidate area;

[0027] Optionally, iteratively calculate the pixel coordinate set C k of the candidate area by taking any adjacent pixel coordinate satisfying the dark tone pixel set;

[0028] Repeat the step of taking any pixel in the dark tone pixel set as an initial pixel and obtaining the initial pixel coordinates thereof, to obtain the pixel coordinate set C1, C2,..., C k of all candidate areas, wherein k represents the number of each candidate area.

[0029] Optionally, calculate the actual area A1, A2,..., A i and the actual aspect ratio R1, R2,..., R i of each candidate area respectively, including the following steps:

[0030] Obtain the pixel coordinate set C1, C2,..., C k of each candidate area, wherein k represents the number of each candidate area;

[0031] Calculate the actual area of each candidate area according to the actual area calculation formula;

[0032] Generate the contour curve of each candidate area according to each pixel coordinate set;

[0033] Generate the minimum bounding box for each contour curve respectively;

[0034] The actual aspect ratio of each candidate region is calculated according to the minimum bounding box.

[0035] Optionally, the actual area calculation formula is A i = Q i ; wherein Q i represents the number of pixel points in the pixel coordinate set numbered i; the calculation formula of the actual aspect ratio is R i = L i / W i , wherein L i represents the length of the minimum bounding box numbered i, and W i represents the width of the minimum bounding box numbered i.

[0036] Optionally, a set of temperature difference abnormal regions is obtained from the infrared temperature image by a third standard screening function, including the following steps:

[0037] The infrared temperature image is converted into a gray scale image;

[0038] A standard coordinate system is constructed, and the gray scale image is divided into a plurality of pixel points under the standard coordinate system, and the gray scale parameters G(x m , y m ) of each pixel point are obtained; wherein x m , y m represent the coordinates of the pixel point in the standard coordinate system, and m represents the pixel number;

[0039] The temperature parameters T(x m , y m ) of each pixel point are calculated according to a temperature-gray scale conversion function;

[0040] Each pixel point is screened according to the third standard screening function and the temperature parameters, and a set of temperature difference abnormal pixel point coordinates is obtained;

[0041] The set of temperature difference abnormal pixel point coordinates is subjected to connected domain analysis to obtain a set of temperature difference abnormal regions P'1, P'2,..., P' n , wherein n represents the number.

[0042] Optionally, the set of candidate mouse holes is screened by the set of temperature difference abnormal regions to determine the number of mouse holes, including the following steps:

[0043] The set of temperature difference abnormal regions and the set of candidate mouse holes are obtained;

[0044] A one-to-one mapping relationship is constructed between the set of temperature difference abnormal regions and each set of candidate mouse holes, and a plurality of registration calculation modules are obtained;

[0045] The coincidence degree of each registration calculation module is calculated, and the calculation formula of the coincidence degree is wherein P n represents an alternative burrow numbered n, P n represents a temperature difference abnormal area numbered n;

[0046] screening burrows from the set of alternative burrows according to the coincidence degree and the burrow determination model, and counting the number of burrows; wherein the expression of the burrow determination model is wherein represents an overlap threshold.

[0047] Correspondingly, the application also discloses a monitoring system based on the above monitoring method for grassland ecological environment, which comprises:

[0048] a data setting module configured to set a first standard screening function, a second standard screening function and a third standard screening function for burrow determination;

[0049] a data acquisition module configured to acquire a visible light image and an infrared temperature image of a region to be monitored;

[0050] a first screening module configured to perform hue screening on the visible light image by the first standard screening function to acquire all dark-tone candidate pixels and generate a dark-tone pixel set;

[0051] a second screening module configured to perform morphological screening on the dark-tone pixel set by the second standard screening function to acquire a set of alternative burrows;

[0052] a third screening module configured to perform temperature difference screening on the infrared temperature image by the third standard screening function to acquire a set of temperature difference abnormal areas;

[0053] a fourth screening module configured to screen the set of alternative burrows by the set of temperature difference abnormal areas to determine the number of burrows.

[0054] Compared with the prior art, the application has the following beneficial effects:

[0055] The application first sets the first standard screening function, the second standard screening function and the third standard screening function, then acquires the visible light image and the infrared temperature image of the region to be monitored, performs hue screening on the visible light image by the first standard screening function to acquire all dark-tone candidate pixels and generate a dark-tone pixel set, performs morphological screening on the dark-tone pixel set by the second standard screening function to acquire a set of alternative burrows, performs temperature difference screening on the infrared temperature image by the third standard screening function to acquire a set of temperature difference abnormal areas, and finally screens the set of alternative burrows by the set of temperature difference abnormal areas to determine the number of burrows.

[0056] Compared with the surrounding environment, the mouse hole is black as a whole on the visible light image, that is, a black hole area is presented, the black hole area not only has stable color, but also has certain area and aspect ratio characteristics, and because there is a certain temperature difference between the temperature in the mouse hole and the surrounding environment, on the infrared temperature image, it also has certain color difference. The application first screens the black hole area on the visible light image through the first standard screening function, then selects the black hole area with the required area and aspect ratio as the candidate mouse hole through the second standard screening function, and finally makes the final screening combined with the feature difference of the infrared image caused by the temperature difference, so as to quickly screen the mouse hole on the grassland, and then realize the quick screening and identification of the mouse hole, and as much as possible improve the acquisition efficiency of the basic parameter of the mouse hole, and then improve the monitoring efficiency. Secondly, compared with the way of monitoring the grassland ecological environment by the vegetation coverage in the prior art, the number of mouse holes is less, and the characteristics are more obvious, which can effectively reduce the calculation amount and is beneficial to improve the monitoring efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 A flow chart of a monitoring method for a grassland ecological environment provided by Embodiment 1 of the application;

[0058] Figure 2 A generation principle diagram of a dark tone pixel set;

[0059] Figure 3 A connected domain analysis principle diagram;

[0060] Figure 4 A generation principle diagram of a minimum bounding box;

[0061] Figure 5 A generation principle diagram of a temperature difference abnormal area set;

[0062] Figure 6 A structure schematic diagram of a monitoring system provided by Embodiment 2 of the application;

[0063] The purpose implementation, functional characteristics and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0065] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0066] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0067] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A or solution B, or a solution that simultaneously satisfies A and B. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0068] Example 1

[0069] Reference Figures 1 to 5 This embodiment, as an optional embodiment of this application, discloses a method for monitoring grassland ecological environment, including the following steps:

[0070] S1. Define the first, second, and third standard filtering functions for mouse hole detection;

[0071] The first, second, and third standard screening functions are set according to the situation, wherein the calculation expression of the first standard screening function is as follows: ,in and Q represents the actual RGB value of the pixel at coordinates (x, y). R Q G and Q BRepresents the standard RGB value; the calculation expression for the second standard filtering function is: A min and A max R represents the minimum and maximum burrow areas, respectively. min and R max Let represent the minimum and maximum aspect ratios of the mouse hole, respectively; the calculation expression for the third criterion screening function is: ,in The temperature value of the pixel with coordinates (x, y). Indicates ambient temperature. This indicates the minimum permissible temperature difference between the mouse burrow and the ambient temperature, where the ambient temperature is collected by temperature sampling devices such as temperature sensors.

[0072] S2. Acquire visible light and infrared temperature images of the area to be monitored;

[0073] S3. Perform tone screening on the visible light image using the first standard screening function to obtain all dark tone candidate pixels and generate a dark tone pixel set.

[0074] S31. Convert the visible light image into an RGB image;

[0075] S32. Construct a standard coordinate system, and divide the RGB image into several pixels under the standard coordinate system, and obtain the RGB parameters {R(x)} of each pixel. j y j ), G(x) j y j B(x) j y j )}, where x j y j This represents the coordinates of a pixel in the standard coordinate system, where j represents the pixel number. and These are the actual RGB values;

[0076] First, a standard coordinate system is constructed, and the converted RGB image is imported into this system. Then, a unique number is assigned to each pixel, and coordinate parameters are generated for each pixel. The RGB values ​​of each pixel are read, and finally, these parameters are integrated to generate the RGB parameter {R(x...}. j y j ), G(x) j y j B(x) j y j )};

[0077] S33. Based on the first standard filtering function and each RGB parameter, each pixel is filtered to obtain a set of dark-toned pixels;

[0078] Call the first standard filtering function ,in and Q represents the actual RGB value of the pixel at coordinates (x, y). R Q G and Q B Standard RGB values;

[0079] Each RGB parameter is imported into the first standard filtering function. If the RGB parameter satisfies the filtering function, it is assigned to the dark pixel set.

[0080] S4. Perform morphological screening on the dark pixel set using the second standard screening function to obtain a set of candidate mouse holes;

[0081] S41. Perform connected component analysis on the dark-toned pixel set to obtain several candidate regions;

[0082] S411. Take any pixel in the dark pixel set as the initial pixel and obtain its initial pixel coordinates;

[0083] Reference Figure 2 A pixel is randomly selected from the set of dark-toned pixels as the initial pixel, and its coordinates are obtained and used as the coordinates of the initial pixel.

[0084] S412. Calculate the coordinates of all adjacent pixels of the initial pixel coordinates, wherein the expression for calculating the adjacent pixel coordinates is: ;

[0085] The coordinates of all adjacent pixels of the initial pixel are calculated according to the formula for calculating adjacent pixel coordinates. The specific calculation principle is as follows: Figure 3 ;

[0086] S413. Compare the coordinates of each adjacent pixel with the set of dark-toned pixels, and group all adjacent pixel coordinates that satisfy the set of dark-toned pixels into the same candidate region with the initial pixel.

[0087] Each adjacent pixel coordinate is compared with the set of dark-toned pixels. If the adjacent pixel coordinate is recorded in the set of dark-toned pixels, it indicates that the adjacent pixel coordinate belongs to a candidate region. The adjacent pixel coordinate is then grouped with the initial pixel into the same candidate region and the candidate region is assigned a number.

[0088] S414. Select any adjacent pixel coordinates that satisfy the dark-toned pixel set, and iteratively calculate the pixel coordinate set C of the candidate region. k ;

[0089] After all the neighboring pixel coordinates of the initial pixel are calibrated, the coordinates of any qualified neighboring pixel are taken and all its neighboring pixel coordinates are calculated again. Then, they are compared with the set of dark-toned pixels again to filter out the secondary neighboring pixel coordinates of the neighboring pixel coordinates. By iterating and repeating in this way, the connected component filtering analysis of a single candidate region can be completed, and all pixel coordinates of the same candidate region can be obtained at the same time.

[0090] S415. Repeat the step of taking any pixel in the dark-toned pixel set as the initial pixel and obtaining its initial pixel coordinates to obtain the pixel coordinate set C1, C2, ..., C of all candidate regions. k , where k represents the number of each candidate region.

[0091] After a single candidate region is selected, another pixel is chosen as the initial pixel, and the above steps are repeated to obtain the set of pixel coordinates C1, C2, ..., C3 for all candidate regions. k , where k represents the number of each candidate region;

[0092] It should be noted that the initial pixels for the second filtering need to exclude the pixel coordinates that have already been filtered in the previous steps, in order to avoid repeated filtering, improve filtering efficiency, and avoid infinite loops that could cause the calculation to freeze.

[0093] S42. Calculate the actual area A1, A2, ..., A of each candidate region. i and the actual aspect ratios R1, R2, ..., R i Where i represents the candidate region number;

[0094] S421. Obtain the pixel coordinate set C1, C2, ..., C of each candidate region. k , where k represents the number of each candidate region;

[0095] S422. Calculate the actual area of ​​each candidate area according to the actual area calculation formula;

[0096] The number of pixels in each candidate region is counted, and then the area of ​​each candidate region is calculated according to the actual area calculation formula; the expression of the actual area calculation formula is A. i =Q i ; where Q i This represents the number of pixels in the set of pixel coordinates numbered i;

[0097] S423. Generate the contour curve of each candidate region based on the set of pixel coordinates;

[0098] The contour curves of each candidate region are automatically extracted by computer.

[0099] S424. Generate minimum bounding boxes for each of the described contour curves;

[0100] The minimum bounding box is generated by combining the obtained contour curves;

[0101] S425. Calculate the actual aspect ratio of each candidate region based on the minimum bounding box.

[0102] Obtain the length and width of each minimum bounding box, and then calculate the actual aspect ratio. The expression for the actual aspect ratio is: R i =L i / W i L i W represents the length of the smallest bounding box numbered i. i This represents the width of the minimum bounding box numbered i. See [link / reference] for details. Figure 4 ;

[0103] S43. Integrate the actual area and actual aspect ratio of the same candidate region into a comparison data set (A). i R i ), to obtain several sets of comparison data;

[0104] S44. Substitute each comparison data group into the second standard screening function. If the function is satisfied, the candidate region is deemed qualified; otherwise, it is deemed unqualified.

[0105] Retrieve the second standard screening function and each comparison data set;

[0106] The calculation expression for the second standard screening function is as follows: ;

[0107] Where A min and A max R represents the minimum and maximum burrow areas, respectively. min and R max These represent the minimum and maximum length-to-width ratios of the mouse hole, respectively. All of these parameters were confirmed by staff based on their work experience or historical measurement parameters.

[0108] Substitute the comparison data sets into the second standard screening function. If the conditions are met, the candidate region is output as a candidate mouse hole.

[0109] Since mouse burrows are generally circular or elliptical in shape, their aspect ratio is within a certain range. In addition, the opening of the mouse burrow has a certain area depending on the size of the population. By comprehensively screening based on area and aspect ratio, some small shaded or disturbing areas can be eliminated. Furthermore, by comparing shapes, some narrow shaded areas can be further eliminated, thus improving the accuracy of screening.

[0110] S45. Collect all qualified candidate areas to generate a set of candidate mouse holes P1, P2, ..., P n , where n represents the number.

[0111] After all candidate regions have been screened, all candidate mouse holes are aggregated to form a candidate mouse hole set P1, P2, ..., P3. n , where n represents the number;

[0112] S5. Perform temperature difference screening on the infrared temperature image using a third standard screening function to obtain a set of abnormal temperature difference regions;

[0113] S51. Convert the infrared temperature image into a grayscale image;

[0114] S52. Construct a standard coordinate system, and divide the grayscale image into several pixels under the standard coordinate system, and obtain the grayscale parameter G(x) of each pixel. m y m ); where x m y m This represents the coordinates of a pixel in the standard coordinate system, where m represents the pixel number.

[0115] The converted grayscale image is imported into the standard coordinate system. During the import process, alignment is achieved by marking points or feature points to ensure the consistency between the grayscale image and the RGB image, thereby ensuring the accuracy of subsequent calculations.

[0116] S53. Calculate the temperature parameter T(x) of each pixel based on the temperature-grayscale conversion function. m y m );

[0117] Obtain the temperature-grayscale conversion function of the infrared device, and then calculate the temperature parameter T(x) of each pixel based on the read grayscale value and the temperature-grayscale conversion function. m y m );

[0118] S54. Based on the third standard filtering function and each temperature parameter, each pixel is filtered to obtain the coordinate set of pixels with abnormal temperature differences.

[0119] Obtain the third standard filtering function, substitute each temperature parameter into the third standard filtering function, and the pixels that satisfy the third standard filtering function are temperature difference abnormal pixels.

[0120] The expression for calculating the third criterion screening function is: ,in The temperature value of the pixel with coordinates (x, y). Indicates ambient temperature. This indicates the minimum permissible temperature difference between the mouse burrow and the ambient temperature; please refer to [reference needed]. Figure 5 ;

[0121] It should be noted that the ambient temperature can be collected simultaneously during the sampling process, or it can be flexibly set by manually calibrating on the infrared temperature image or manually setting it, so as to ensure that the ambient temperature is the actual temperature at the time of sampling and improve the accuracy of calibration.

[0122] S55. Perform connected component analysis on the set of coordinates of the temperature difference abnormal pixels to obtain the set of temperature difference abnormal regions P'1, P'2, ..., P' n , where n represents the number;

[0123] The connected component analysis in this step is exactly the same as the connected component analysis method in step S41.

[0124] S6. The candidate mouse burrow set is screened through the set of abnormal temperature zones to determine the number of mouse burrows.

[0125] S61. Obtain the set of abnormal temperature zones and the set of candidate mouse holes;

[0126] S62. Construct a one-to-one mapping relationship between the set of temperature difference abnormal zones and each of the set of candidate mouse holes, and obtain several registration calculation modules;

[0127] A one-to-one mapping relationship is established between the set of temperature difference abnormal areas and each set of candidate mouse holes by manual pairing, and several registration calculation modules are obtained.

[0128] S63. Calculate the overlap degree of each of the registration calculation modules respectively. The formula for calculating the overlap degree is as follows: , where P n P' represents the candidate mouse hole numbered n. n This represents the temperature difference abnormality zone numbered n;

[0129] Calculate the overlap between the temperature anomaly zone and the candidate mouse burrow within the same registration calculation module. The formula for calculating the overlap is as follows: , where P n P' represents the candidate mouse hole numbered n. n This represents the temperature difference abnormality zone numbered n;

[0130] Overlap calculations can be used to overlay separate infrared temperature images with visible light images. If the overlap is below a threshold, the images are considered to be parameters from different regions. This allows for the screening of candidate mouse holes based on temperature differences, thus improving the accuracy of the screening.

[0131] S64. Based on the overlap ratio and mouse hole determination model, filter mouse holes from the candidate mouse hole set and count the number of mouse holes; wherein the expression of the mouse hole determination model is: ,in This indicates the overlap threshold.

[0132] Combining the overlap ratio calculated in step S63 with the mouse hole determination model, each candidate mouse hole is identified. The expression for the mouse hole determination model is: ,in This represents the overlap threshold; when the calculated overlap degree satisfies the mouse hole determination model described above, it is determined to be a mouse hole, otherwise it is deleted, and finally the number of identified mouse holes is counted.

[0133] By statistically analyzing the changes in the number of rat burrows over a period of time, and supplementing this with manual sampling surveys to identify interfering factors such as abandoned rat burrows, it is possible to effectively monitor the changes in the number of rat burrows.

[0134] It should be noted that the technical solution described in this application only monitors the changes in the number of rodent burrows. In addition to the need for manual investigation of abandoned rodent burrows, it is also necessary to combine manual trapping or other monitoring methods to assist in correcting the estimated value of rodent population, thereby improving the accuracy of monitoring and the precision of monitoring grassland ecosystems.

[0135] Due to the unique shape of mouse burrows, they appear entirely black in visible light images compared to their surroundings, forming a black hole region. This black hole region not only maintains a stable black color but also possesses a certain area and aspect ratio, exhibiting morphological specificity. Furthermore, due to the temperature difference between the inside of the mouse burrow and the surrounding environment, it also shows a certain color difference in infrared temperature images. This application utilizes the aforementioned morphological and temperature difference specificities. First, black hole regions are screened in visible light images using a first standard screening function. Then, black hole regions with suitable area and aspect ratios are selected using a second standard screening function. Finally, the infrared image feature differences caused by temperature differences are used for final screening. This allows for rapid screening of mouse burrows on grasslands, supplemented by manual spot checks and manual trapping for effective monitoring of rodent populations. Simultaneously, this application effectively reduces the difficulty of counting mouse burrows, improves statistical efficiency, and thus enhances monitoring efficiency.

[0136] Secondly, compared with existing technologies that monitor grassland ecological environment through vegetation coverage, rat holes are fewer in number and more distinctive, which can effectively reduce the amount of computation and improve monitoring efficiency.

[0137] Example 2

[0138] Reference Figure 6 This embodiment, as another optional embodiment of this application, discloses a monitoring system, including a data setting module and a data acquisition module, wherein the data setting module is used to set a first standard filtering function, a second standard filtering function and a third standard filtering function, and the data acquisition module is used to acquire visible light images and infrared temperature images of the area to be monitored.

[0139] The output of the data setting module is electrically connected to the first screening module, the second screening module and the third screening module. The input of the second screening module is also electrically connected to the output of the first screening module to receive the relevant parameters after being screened by the first screening module.

[0140] The input terminals of the first screening module and the third screening module are also electrically connected to the data acquisition module to receive raw data;

[0141] Furthermore, the output terminal of the second screening module is electrically connected to the fourth screening module, and the output terminal of the third screening module is connected to the fourth screening module.

[0142] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for monitoring grassland ecological environment, characterized in that, Includes the following steps: Define a first standard filtering function, a second standard filtering function, and a third standard filtering function for mouse hole detection; the calculation expression for the first standard filtering function is as follows: ,in , and Q represents the actual RGB value of the pixel at coordinates (x, y). R Q G and Q B Represents standard RGB values; The calculation expression for the second standard screening function is as follows: A min and A max R represents the minimum and maximum burrow areas, respectively. min and R max These represent the minimum and maximum aspect ratios of the mouse hole, respectively; The expression for calculating the third standard screening function is: ,in The temperature value of the pixel with coordinates (x, y). Indicates ambient temperature. This indicates the minimum permissible temperature difference between the mouse burrow and the ambient temperature. Acquire visible light and infrared temperature images of the area to be monitored; The visible light image is screened for color tone using a first standard screening function to obtain all dark tone candidate pixels and generate a dark tone pixel set. The dark pixel set is morphologically screened using a second standard screening function to obtain a set of candidate mouse holes. The infrared temperature image is screened for temperature difference using a third standard screening function to obtain a set of abnormal temperature difference regions. The candidate mouse burrow set is screened by the set of abnormal temperature zones to determine the number of mouse burrows.

2. The method for monitoring grassland ecological environment according to claim 1, characterized in that, The step of performing tone screening on the visible light image using a first standard screening function to obtain all dark tone candidate pixels and generate a dark tone pixel set includes the following steps: Convert the visible light image into an RGB image; Construct a standard coordinate system, and divide the RGB image into several pixels under the standard coordinate system, and obtain the RGB parameters {R(x)} of each pixel. j y j ), G(x j y j B(x) j y j )}, where x j y j This represents the coordinates of a pixel in the standard coordinate system, where j represents the pixel number. , and These are the actual RGB values; Each pixel is filtered according to the first standard filtering function and each RGB parameter to obtain a set of dark-toned pixels.

3. The method for monitoring grassland ecological environment according to claim 2, characterized in that, The step of performing morphological screening on the dark pixel set using a second standard screening function to obtain a candidate mouse hole set includes the following steps: Connectivity analysis is performed on the dark-toned pixel set to obtain several candidate regions; Calculate the actual area A1, A2, ..., A of each candidate region. i And the actual aspect ratios R1, R2, ..., R i Where i represents the candidate region number; The actual area and actual aspect ratio of the same candidate region are integrated into a comparison data set (A). i R i ), to obtain several sets of comparison data; Substitute each set of comparison data into the second standard screening function. If the function is satisfied, the candidate region is deemed qualified; otherwise, it is deemed unqualified. Each qualified candidate region is grouped to generate a set of candidate mouse holes P1, P2, ..., P... n , where n represents the number.

4. The method for monitoring grassland ecological environment according to claim 3, characterized in that, The process of performing connected component analysis on the dark-toned pixel set to obtain several candidate regions includes the following steps: Take any pixel in the dark pixel set as the initial pixel and obtain its initial pixel coordinates; Calculate the coordinates of all neighboring pixels of the initial pixel coordinates, where the expression for calculating the neighboring pixel coordinates is: ; Each adjacent pixel coordinate is compared with the set of dark-toned pixels, and all adjacent pixel coordinates that satisfy the set of dark-toned pixels are grouped with the initial pixel into the same candidate region; Choose any adjacent pixel coordinates that satisfy the dark-toned pixel set, and iteratively calculate the pixel coordinate set C of the candidate region. k ; Repeat the process of taking any pixel from the dark-toned pixel set as the initial pixel and obtaining its initial pixel coordinates to obtain a set of pixel coordinates C1, C2, ..., C3 for all candidate regions. k , where k represents the number of each candidate region.

5. A method for monitoring grassland ecological environment according to claim 4, characterized in that, The actual areas A1, A2, ..., A of each candidate region are calculated respectively. i And the actual aspect ratios R1, R2, ..., R i This includes the following steps: Obtain the pixel coordinate set C1, C2, ..., C of each candidate region. k , where k represents the number of each candidate region; Calculate the actual area of ​​each candidate area according to the actual area calculation formula; Generate the contour curve of each candidate region based on the set of pixel coordinates; Generate minimum bounding boxes for each of the aforementioned contour curves; The actual aspect ratio of each candidate region is calculated based on the minimum bounding box.

6. A method for monitoring grassland ecological environment according to claim 5, characterized in that, The expression for the actual area calculation formula is A. i =Q i ; where Q i This represents the number of pixels in the pixel coordinate set numbered i; the actual aspect ratio is calculated as: R i =L i / W i L i W represents the length of the smallest bounding box numbered i. i This represents the width of the minimum bounding box numbered i.

7. The method for monitoring grassland ecological environment according to claim 1, characterized in that, The step of obtaining the set of abnormal temperature difference regions from the infrared temperature image using a third standard filtering function includes the following steps: Convert the infrared temperature image into a grayscale image; Construct a standard coordinate system, and divide the grayscale image into several pixels under the standard coordinate system, and obtain the grayscale parameter G(x) of each pixel. m y m ); where x m y m This represents the coordinates of a pixel in the standard coordinate system, where m represents the pixel number. The temperature parameter T(x) of each pixel is calculated based on the temperature-grayscale conversion function. m y m ); Each pixel is screened according to the third standard screening function and each temperature parameter to obtain the coordinate set of pixels with abnormal temperature differences. Connectivity analysis is performed on the set of coordinates of the temperature-anomalous pixels to obtain the set of temperature-anomalous regions P'1, P'2, ..., P' n , where n represents the number.

8. A method for monitoring grassland ecological environment according to claim 1, characterized in that, The candidate mouse burrow set is screened using the set of abnormal temperature zones to determine the number of mouse burrows, including the following steps: Obtain the set of abnormal temperature zones and the set of candidate mouse burrows; A one-to-one mapping relationship is established between the set of temperature difference abnormal regions and each of the set of candidate mouse holes to obtain several registration calculation modules; The overlap degree of each of the registration calculation modules is calculated separately, and the formula for calculating the overlap degree is as follows: , where P n P' represents the candidate mouse hole numbered n. n This represents the temperature difference abnormality zone numbered n; Based on the overlap ratio and mouse hole determination model, mouse holes are selected from the candidate mouse hole set, and the number of mouse holes is counted; wherein the expression of the mouse hole determination model is: ,in This indicates the overlap threshold.

9. A monitoring system based on the monitoring method for grassland ecological environment according to any one of claims 1-8, characterized in that, include: The data setting module is used to set the first, second, and third standard filtering functions for mouse hole detection. The data acquisition module is used to acquire visible light images and infrared temperature images of the area to be monitored; The first screening module is used to perform tone screening on the visible light image through a first standard screening function, obtain all dark tone candidate pixels, and generate a dark tone pixel set. The second screening module is used to perform morphological screening on the dark pixel set using a second standard screening function to obtain a set of candidate mouse holes. The third screening module is used to perform temperature difference screening on the infrared temperature image through a third standard screening function to obtain a set of abnormal temperature difference regions. The fourth screening module is used to screen the candidate mouse burrow set through the set of abnormal temperature zones to determine the number of mouse burrows.

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