Outdoor environment monitoring method and system, storage medium and program product

Abstract

The invention discloses an outdoor environment monitoring method and system, a storage medium and a program product, and the method comprises the steps: obtaining a plurality of initial detection sub-regions; performing preliminary sampling at the central point of the initial detection sub-region to obtain initial environment data; constructing an environment parameter space distribution model; calculating a variation coefficient, and determining a high-variation sub-region; judging whether the area of the high-variation sub-region is greater than a preset minimum area or not; if yes, the minimum sub-region is divided, and the center point serves as a sampling point; if not, increasing a preset number of sampling points; controlling the detection unmanned aerial vehicle to perform secondary sampling at the sampling points to obtain initial environment data, and updating the environment parameter spatial distribution model; calculating an updated overall prediction error, and judging whether the updated overall prediction error is greater than a preset error; and if not, determining the initial environment data with the maximum similarity, and determining the region corresponding to the sampling point as a candidate experiment region. According to the invention, the accuracy of the environmental parameters detected in different outdoor environments is improved.

Description

Technical Field

[0001] The present application relates to the field of environmental monitoring, and in particular to an outdoor environmental monitoring method, system, storage medium and program product. Background Art

[0002] When students need to conduct experiments outdoors, they usually need to choose an area that meets the experimental conditions and where the experimental results can be minimally affected.

[0003] In the related technology, the corresponding environmental parameters are first determined according to the specific content of the experiment, and the environmental parameters may be temperature, humidity, wind speed, etc. Then the experimenter carries a portable environmental detection device, such as a handheld weather station, etc., to detect the pre-divided areas one by one until an area that meets the experimental environmental parameters is detected.

[0004] However, usually, experimenters will select a fixed number of points for testing, and the distribution of the testing points will vary with different experimenters. The same points will still be selected for testing in different outdoor areas. This will cause the test results of different outdoor environments to be inconsistent with their actual environmental parameters, reducing the accuracy of the test results. Summary of the invention

[0005] The present application provides an outdoor environment monitoring method, system, storage medium and program product for improving the accuracy of environmental parameters detected in different outdoor environments.

[0006] In a first aspect, the present application provides an outdoor environment monitoring method, which performs preliminary grid division on a preset detection area to obtain a number of initial detection sub-areas of the same area; The control environment detection drone performs preliminary sampling at the center point of each initial detection sub-area to obtain initial environment data corresponding to each initial detection sub-area; Based on the initial environmental data, a preset interpolation method is used to construct an environmental parameter spatial distribution model, which is a mathematical model used to describe and predict the distribution of environmental parameters in space; Calculate the coefficient of variation of each initial detection sub-region, and determine the initial detection sub-region corresponding to the coefficient of variation greater than the preset coefficient as a high-variance sub-region; Determine whether the area of ​​the high-variance region is greater than the preset minimum area; If it is greater than, the high-variability sub-region is divided into minimum sub-regions of equal area, and the center point of the minimum sub-region is used as the sampling point; If it is not greater than, then add a preset number of sampling points in the high-variability sub-region; Control the environmental detection drone to perform secondary sampling at the sampling points, obtain the initial environmental data of each sampling point, and update the spatial distribution model of environmental parameters; Calculate the overall prediction error of the updated environmental parameter spatial distribution model, and determine whether the overall prediction error is greater than a preset error; If it is greater than, the step of calculating the coefficient of variation of each initial detection sub-region and determining the initial detection sub-region corresponding to the coefficient of variation greater than the preset coefficient as a high variation sub-region is performed; If it is not greater than, the initial environment data with the greatest similarity to the standard environment data corresponding to the experimental task is determined, and the area of ​​the sampling point corresponding to the initial environment data with the greatest similarity is determined as the candidate experimental area.

[0007] By adopting the above technical solution, the environmental profile of the entire detection area can be quickly obtained through preliminary grid division and preliminary sampling. The preset interpolation method is used to construct the spatial distribution model of environmental parameters, which can not only describe the environmental conditions of the sampled points, but also predict the environmental parameters of the unsampled points, thereby improving the detection efficiency. Thirdly, by calculating the coefficient of variation and identifying high-variance sub-regions, it is possible to intelligently identify areas where environmental parameters change dramatically, increase the sampling density in a targeted manner, and avoid the waste of resources caused by blind sampling. The iterative optimization strategy is adopted to ensure the accuracy and reliability of the detection results by continuously updating the spatial distribution model of environmental parameters and evaluating the overall prediction error. By comparing the similarity between environmental data and standard environmental data, the candidate experimental area is quickly determined. The accuracy of environmental parameters detected in different outdoor environments is improved.

[0008] In combination with some embodiments of the first aspect, in some embodiments, controlling the environment detection drone to perform preliminary sampling at the center point of each initial detection sub-area specifically includes: Obtain the three-dimensional coordinates of the center point of each center point and the three-dimensional coordinates of the starting point of the environmental detection drone take-off point; Based on the three-dimensional coordinates of the starting point and the three-dimensional coordinates of each center point, the Euclidean distance from the take-off point to each center point is calculated, and each Euclidean distance is sorted in ascending order to obtain a sorting result; Generate a detection path according to the sorting results; The controlled environment detection drone performs preliminary sampling at the center point of each initial detection sub-area according to the detection path.

[0009] By adopting the above technical solution, the three-dimensional coordinates of each center point and the coordinates of the take-off point of the drone are obtained, and an accurate three-dimensional spatial reference system is established. By calculating the Euclidean distance from the take-off point to each center point and sorting them in order from small to large, the path planning based on the nearest neighbor principle is realized. The flight distance of the drone is reduced, energy consumption is reduced, and the working time of a single flight is extended. Reducing the flight distance means a shorter task completion time, and the shortest path also means that the drone is exposed to potential risks for the shortest time, thereby improving the safety of the equipment. By controlling the drone to sample according to the generated detection path, the systematicness and integrity of the sampling process are ensured, and the problem of missing or repeated sampling is avoided. The sampling efficiency is improved and the spatial uniformity of the data is guaranteed.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, generating a detection path according to the sorting result specifically includes: Obtain the remaining power of the environmental detection drone and calculate the remaining range based on the remaining power; Taking the take-off point as the starting point, connecting each central point in sequence according to the sorting result to obtain a first detection path and a first length of the first detection path; Determining whether the remaining cruising range is less than a first length; If it is not less than, the first length is used as the detection path; If it is less than, the farthest return point is calculated according to the remaining cruising range. When the environmental detection drone flies to the farthest return point and returns along the original route, the environmental detection drone can reach the take-off point, and the farthest return point is included in the center point; The flight path from the take-off point to the farthest return point is used as the first detection path; The center point included in the route from the take-off point to the farthest return point is deleted to obtain an updated sorting result, and the steps of connecting each center point in sequence according to the sorting result with the take-off point as the starting point to obtain a first detection path and a first length of the first detection path are repeatedly performed until the remaining cruising range is not less than the first length, thereby obtaining a plurality of first detection paths; Combine all the first detection paths as the detection path.

[0011] By adopting the above technical solution, the remaining power of the drone is obtained and the remaining range is calculated, and the actual hardware limitations are taken into consideration in path planning, ensuring the feasibility and safety of the planning. When the remaining range is sufficient, the shortest path is directly adopted; when the range is insufficient, the farthest return point is calculated and segmented planning is performed, which not only ensures the integrity of each flight mission, but also maximizes the coverage of a single flight. The overall detection efficiency is improved and the optimal utilization of resources is achieved. Through iterative path planning, it can handle complex multiple take-off and landing situations and adapt to detection tasks of different scales and complexities. This flexibility allows this method to be applied to a variety of different environmental detection scenarios and has a wide range of applicability. By dynamically updating the sorting results and repeatedly executing path planning, potential resource waste caused by conservative estimates is avoided.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, based on the initial environmental data, a preset interpolation method is used to construct an environmental parameter spatial distribution model, which specifically includes: Perform normality test and outlier detection on the initial environmental data to obtain corrected initial environmental data; Calculate the experimental variogram of the corrected initial environmental data, and select a preset variogram model for fitting to obtain a theoretical variogram; Ordinary Kriging is used to perform spatial interpolation based on the theoretical variation function to generate a spatial distribution model of environmental parameters.

[0013] By adopting the above technical solution, the initial environmental data is tested for normality and outliers, which can identify and process abnormal points and non-normal distribution in the data, avoiding the deviation of the model caused by abnormal data. The experimental variogram is calculated and the preset variogram model is selected for fitting to obtain the theoretical variogram, which can accurately capture the spatial variation of environmental parameters. Ordinary Kriging method is used for spatial interpolation to generate the spatial distribution model of environmental parameters, which can provide the best linear unbiased estimate of unsampled points and give the error range of the estimate.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the coefficient of variation of each initial detection sub-region specifically includes: Generate a set of uniformly distributed interpolation points in each initial detection sub-region; Calculate the estimated value of the environmental parameters at each interpolation point according to the spatial distribution model of the environmental parameters; According to the estimated values ​​of environmental parameters, the estimated mean values ​​and standard deviations of environmental parameters of all interpolation points in each initial detection sub-area are calculated respectively; The coefficient of variation corresponding to each initial detection sub-region is calculated based on the estimated mean value and standard deviation of the environmental parameters corresponding to each initial detection sub-region.

[0015] By adopting the above technical solution, evenly distributed interpolation points are generated in each initial detection sub-region, ensuring comprehensive and even sampling of the distribution of environmental parameters in the sub-region, avoiding local deviations caused by uneven distribution of sampling points, and improving the representativeness and reliability of the calculation of the coefficient of variation. The estimated value of the environmental parameters at each interpolation point is calculated using the constructed spatial distribution model of environmental parameters, which can obtain high-density estimation data and improve calculation efficiency. By calculating the estimated mean and standard deviation of the environmental parameters of all interpolation points in each sub-region, the central tendency and dispersion of the environmental parameters in the sub-region can be captured, and the overall level of the environmental parameters can be considered to reflect its internal fluctuations. The coefficient of variation is calculated based on the mean and standard deviation, so that environmental parameters of different scales and units can be directly compared, which enhances versatility and comparability.

[0016] In combination with some embodiments of the first aspect, in some embodiments, the estimated average value of the environmental parameters and the estimated standard deviation of the environmental parameters of all interpolation points in each initial detection sub-region are calculated according to the estimated values ​​of the environmental parameters, specifically including: inputting the estimated values ​​of the environmental parameters into the average value calculation function and the standard deviation calculation function respectively to obtain the estimated average value of the environmental parameters and the estimated standard deviation of the environmental parameters; The average value calculation function is: In the function, μ is the estimated average value of the environmental parameters, x i is the estimated value of environmental parameters, n is the number of interpolation points; The standard deviation calculation function is: In the function, σ is the estimated standard deviation of the environmental parameters, x i is the estimated value of the environmental parameter, n is the number of interpolation points, and μ is the estimated average value of the environmental parameter.

[0017] By adopting the above technical solution, the estimated values ​​of environmental parameters are input into the mean value calculation function and the standard deviation calculation function respectively, which realizes the accurate quantification of the trend and dispersion degree of the data set. The mean value, as the arithmetic center of the data, reflects the overall level of the environmental parameters; while the standard deviation measures the degree to which the data points deviate from the mean value, reflecting the volatility of the environmental parameters. The statistical formula is used to improve the scientificity and reliability of the calculation results.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the coefficient of variation corresponding to each initial detection sub-region is calculated according to the estimated mean value of the environmental parameter corresponding to each initial detection sub-region and the estimated standard deviation of the environmental parameter, specifically including: Input the estimated mean value and standard deviation of environmental parameters into the coefficient of variation calculation function to obtain the coefficient of variation. The coefficient of variation calculation function is: In the function, CV is the coefficient of variation, σ is the estimated standard deviation of environmental parameters, and μ is the estimated mean value of environmental parameters.

[0019] By adopting the above technical solution, the estimated mean value and standard deviation of environmental parameters are input into the coefficient of variation calculation function, which realizes the accurate quantification of the relative discreteness of environmental parameters. The coefficient of variation is the ratio of the standard deviation to the mean value, which reduces the limitation of the standard deviation being affected by the measurement scale.

[0020] In the second aspect, an embodiment of the present application provides an outdoor environment monitoring system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a system, causes the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides an outdoor environment monitoring method, which quickly obtains the environmental profile of the entire detection area through preliminary grid division and preliminary sampling. The preset interpolation method is used to construct the spatial distribution model of environmental parameters, which can not only describe the environmental conditions of the sampled points, but also predict the environmental parameters of the unsampled points, thereby improving the detection efficiency. Again, by calculating the coefficient of variation and identifying high-variance sub-regions, it is possible to intelligently identify areas where environmental parameters change dramatically, increase the sampling density in a targeted manner, and avoid the waste of resources caused by blind sampling. An iterative optimization strategy is adopted to ensure the accuracy and reliability of the test results by continuously updating the spatial distribution model of environmental parameters and evaluating the overall prediction error. By comparing the similarity between environmental data and standard environmental data, the candidate experimental area is quickly determined. The accuracy of environmental parameters detected in different outdoor environments is improved.

[0024] 2. This application provides an outdoor environment monitoring method to obtain the remaining power of the drone and calculate the remaining range, taking the actual hardware limitations into consideration in path planning, and ensuring the feasibility and safety of the planning. When the remaining range is sufficient, the shortest path is directly used; when the range is insufficient, by calculating the farthest return point and planning in sections, the integrity of each flight mission is guaranteed and the coverage of a single flight is maximized. The overall detection efficiency is improved and the optimal utilization of resources is achieved. Through iterative path planning, it can handle complex multiple take-off and landing situations and adapt to detection tasks of different scales and complexities. This flexibility allows the method to be applied to a variety of different environmental detection scenarios and has a wide range of applicability. By dynamically updating the sorting results and repeatedly executing path planning, potential waste of resources caused by conservative estimates is avoided.

[0025] 3. The present application provides an outdoor environmental monitoring method, which generates uniformly distributed interpolation points in each initial detection sub-region, ensures comprehensive and uniform sampling of the distribution of environmental parameters in the sub-region, avoids local deviations caused by uneven distribution of sampling points, and improves the representativeness and reliability of the calculation of the coefficient of variation. By using the constructed environmental parameter spatial distribution model to calculate the estimated value of the environmental parameters at each interpolation point, high-density estimation data can be obtained, which improves the calculation efficiency. By calculating the estimated mean and standard deviation of the environmental parameters of all interpolation points in each sub-region, the concentration trend and dispersion of the environmental parameters in the sub-region can be captured, and the overall level of the environmental parameters can be considered to reflect its internal fluctuations. The coefficient of variation is calculated based on the mean and standard deviation, so that environmental parameters of different scales and units can be directly compared, which enhances the versatility and comparability. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of an outdoor environment monitoring method in an embodiment of the present application.

[0027] Figure 2 It is a flow chart of a method for planning a detection path of an environmental detection UAV in an embodiment of the present application.

[0028] Figure 3 It is a schematic diagram of the physical device structure of an outdoor environment monitoring system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0030] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0031] Combine the following Figure 1 , a method for monitoring an outdoor environment in an embodiment of the present application is described: See also Figure 1 , is a flow chart of an outdoor environment monitoring method in an embodiment of the present application.

[0032] S101. Perform preliminary grid division on the preset detection area to obtain several initial detection sub-areas of the same area; the system first receives the geographic information data of the preset detection area, including the coordinates of the area boundary, terrain features, etc. Then, the system adopts an equal-area grid division algorithm to divide the entire preset detection area into several rectangular sub-areas of equal area. This division method ensures the uniformity and representativeness of the initial sampling. In specific implementation, the system first calculates the total area of ​​the preset detection area, and then determines the size of each sub-area based on the predefined number of sub-areas or the expected sub-area area. Next, starting from a corner of the preset detection area, the system gradually divides the sub-areas along the main directions (such as east-west and north-south) until the entire preset detection area is covered.

[0033] In order to adapt to complex terrain and irregular boundaries, the system can also use an adaptive meshing algorithm. This algorithm takes into account the terrain undulations and boundary shapes during the division process, and makes local adjustments to the mesh to ensure that the actual surface area of ​​each sub-area is as close as possible. For example, in mountainous areas, the system can use three-dimensional meshing, taking into account elevation information, so that the actual surface area of ​​each sub-area is equal, not just the plane projection area.

[0034] In addition, the system can also optimize the grid based on the known distribution characteristics of environmental parameters. For example, in areas where it is known that there are gradient changes in environmental parameters, the system can appropriately encrypt the grid along the gradient direction to capture more detailed change information. In areas where environmental parameters are relatively uniform, the grid size can be appropriately enlarged to improve sampling efficiency.

[0035] The system stores the grid division results in a format compatible with the Geographic Information System (GIS), including the boundary coordinates, center point coordinates, area and other information of each sub-area. This information will be used in the subsequent sampling point determination and data analysis process. At the same time, the system generates a visual map of the grid division so that the operator can intuitively understand the division results and make manual adjustments when necessary.

[0036] The above technical solution can be understood as using an equal-area grid division algorithm, combined with adaptive grid adjustment and optimization of environmental parameter distribution characteristics, to preliminarily divide the preset detection area, obtain several initial detection sub-areas of the same area, and store the division results in a GIS-compatible format.

[0037] S102, controlling the environmental detection drone to perform preliminary sampling at the center point of each initial detection sub-area, to obtain initial environmental data corresponding to each initial detection sub-area; The system controls the environmental detection drone to perform preliminary sampling at the center point of each initial detection sub-area to obtain initial environmental data corresponding to each initial detection sub-area. The step of controlling the environmental detection drone to perform preliminary sampling at the center point of each initial detection sub-area involves the problem of detection path planning, which will be described in conjunction with another embodiment below and will not be repeated here.

[0038] The system first extracts the coordinates of the center point of each initial detection sub-area based on the grid division results generated in step S101. Then, the system plans the flight path of the drone to cover all sampling points in the most optimized way. Path planning takes into account multiple factors, including the drone's endurance, flight speed, sampling time, obstacle distribution, etc. The system can use an improved genetic algorithm to solve this multi-constrained path optimization problem to minimize the total flight distance and time.

[0039] When actually performing the sampling task, the system first performs a self-check to ensure that the drone and the environmental detection equipment on board are in normal working condition. Then, the system controls the drone to take off according to the planned path and navigate to the first sampling point. After arriving at the sampling point, the system controls the drone to hover and activates the environmental detection equipment on board.

[0040] Environmental detection equipment includes but is not limited to temperature sensors, humidity sensors, air pressure sensors, wind speed and direction sensors, PM2.5 sensors, CO2 concentration sensors, etc. The system controls these sensors to work simultaneously and continuously collect data within a preset sampling time (e.g. 1 minute). During the collection process, the system monitors the data quality in real time. If an abnormal value or signal interference is detected, the sampling time will be automatically extended or resampled to ensure the reliability of the data.

[0041] After sampling is completed, the system will perform preliminary processing on the collected raw data, including removing obvious outliers and calculating average values. The processed data, together with metadata such as the geographic coordinates of the sampling points and the sampling time, are transmitted to the ground control station in real time through the drone's communication module. At the same time, these data are also stored in the drone's local storage device as a backup.

[0042] After completing the data collection of a sampling point, the system controls the drone to fly to the next sampling point and repeats the above process until the sampling of all initial detection sub-areas is completed. During the flight, the system monitors the battery status of the drone in real time. If the battery is not enough to complete the remaining sampling tasks, the system will automatically plan the return route, replace or charge the battery, and then continue to complete the remaining sampling tasks.

[0043] To improve sampling efficiency, the system can control multiple drones to work together at the same time. In this case, the system will divide the entire inspection area into several sub-areas, and each drone will be responsible for the sampling task of a sub-area. The system coordinates the work of multiple drones through real-time communication to avoid crossing flight paths or duplication of sampling points.

[0044] The above technical solution can be understood as controlling the environmental detection drone through a path planning algorithm, performing multi-parameter synchronous sampling at the center point of each initial detection sub-area, and adopting real-time data quality monitoring and multi-drone collaborative working strategies to obtain initial environmental data.

[0045] S103, constructing an environmental parameter spatial distribution model based on the initial environmental data using a preset interpolation method; Based on the initial environmental data, the system uses a preset interpolation method to construct an environmental parameter spatial distribution model, which is a mathematical model used to describe and predict the distribution of environmental parameters in space. Specifically: normality test and outlier detection are performed on the initial environmental data to obtain the corrected initial environmental data; Calculate the experimental variogram of the corrected initial environmental data, and select a preset variogram model for fitting to obtain a theoretical variogram; Ordinary Kriging is used to perform spatial interpolation based on the theoretical variation function to generate a spatial distribution model of environmental parameters.

[0046] The system first preprocesses the initial environmental data obtained in step S102. Preprocessing includes steps such as data cleaning, outlier detection and processing, and data standardization. During the data cleaning process, the system removes obviously erroneous data points, such as values ​​that exceed the sensor range. Outlier detection uses an improved Z-score method to consider the spatial correlation of the data and identify and mark possible outliers. For marked outliers, the system does not delete them directly, but uses a local interpolation method to correct them to retain possible extreme but valid observations.

[0047] The system performs a normality test on the preprocessed data. The system uses the Shapiro-Wilk test method to evaluate whether the data distribution conforms to the normal distribution. If the data does not conform to the normal distribution, the system will try to perform Box-Cox transformation or other appropriate data transformation methods to make the data closer to the normal distribution, which is helpful for the subsequent application of Kriging interpolation.

[0048] Then, the system calculates the experimental variogram of the processed initial environmental data. The experimental variogram reflects the law of spatial correlation between data points as the distance changes. The system uses a robust variogram estimation method to reduce the impact of outliers on the variogram calculation. Specifically, the system first calculates the distances and corresponding semivariances between all pairs of data points, then divides these distances into several distance classes, and calculates the average semivariance within each distance class.

[0049] After obtaining the experimental variogram, the system selects the most suitable one from several preset theoretical variogram models for fitting. Commonly used theoretical variogram models include spherical model, exponential model, Gaussian model, etc. The system uses weighted least squares method for fitting, and the weight is proportional to the number of point pairs in the distance class. During the fitting process, the system simultaneously estimates the base value, range, and nugget value of the variogram. In order to select the best theoretical model, the system calculates the goodness of fit index (such as R 2 value, residual sum of squares, etc.), and select the model with the best indicators as the final theoretical variation function.

[0050] Finally, based on the obtained theoretical variogram, the system uses ordinary kriging to perform spatial interpolation and generate a spatial distribution model of environmental parameters. Kriging interpolation takes into account the spatial correlation between data points and can provide unbiased optimal linear estimates. The system generates regular grid points throughout the study area and performs kriging estimation on each grid point. During the estimation process, the system considers nearby known data points, calculates weights based on the theoretical variogram, and then performs weighted averaging to obtain an estimated value. At the same time, the system also calculates the kriging variance of each estimated point to provide spatial distribution information on the estimation accuracy.

[0051] In order to improve the accuracy of interpolation, the system can also consider introducing auxiliary variables, such as terrain factors such as elevation, slope, and aspect, and use co-kriging or regression kriging for interpolation. This method can use the correlation between environmental parameters and terrain factors to improve the reliability of interpolation results.

[0052] The above technical solution can be understood as preprocessing and normality testing the initial environmental data, calculating the experimental variogram and fitting the optimal theoretical variogram model, building an environmental parameter spatial distribution model based on ordinary kriging, and generating spatial distribution information of estimation accuracy.

[0053] S104, calculating the coefficient of variation of each initial detection sub-region, and determining the initial detection sub-region corresponding to a coefficient of variation greater than a preset coefficient as a high-variance sub-region; The system calculates the coefficient of variation for each initial detection sub-region, and identifies the initial detection sub-region with a coefficient of variation greater than the preset coefficient as a high-variance sub-region. The coefficient of variation is a statistical concept used to compare the degree of dispersion between different data sets or different variables. It is the ratio of the standard deviation to the mean, usually expressed as a percentage. Specifically: Generate a set of uniformly distributed interpolation points in each initial detection sub-region; Calculate the estimated value of the environmental parameters at each interpolation point according to the spatial distribution model of the environmental parameters; According to the estimated values ​​of environmental parameters, the estimated mean values ​​and standard deviations of environmental parameters of all interpolation points in each initial detection sub-area are calculated respectively; The coefficient of variation corresponding to each initial detection sub-region is calculated based on the estimated mean value and standard deviation of the environmental parameters corresponding to each initial detection sub-region.

[0054] The estimated values ​​of the environmental parameters are respectively input into the average value calculation function and the standard deviation calculation function to obtain the estimated average value of the environmental parameters and the estimated standard deviation of the environmental parameters; The average value calculation function is: In the function, μ is the estimated average value of the environmental parameters, x i is the estimated value of environmental parameters, n is the number of interpolation points; The standard deviation calculation function is: In the function, σ is the estimated standard deviation of the environmental parameters, x i is the estimated value of the environmental parameter, n is the number of interpolation points, and μ is the estimated average value of the environmental parameter.

[0055] The system inputs the estimated mean value of environmental parameters and the estimated standard deviation of environmental parameters into the coefficient of variation calculation function to obtain the coefficient of variation. The coefficient of variation calculation function is: In the function, CV is the coefficient of variation, σ is the estimated standard deviation of environmental parameters, and μ is the estimated mean value of environmental parameters.

[0056] S105, determining whether the area of ​​the high-variance sub-region is greater than a preset minimum area; The system first calculates the area of ​​each sub-region marked as high variation. Since the initial detection sub-regions are obtained by grid division, in most cases, the areas of the sub-regions are equal and the pre-calculated values ​​can be used directly. However, considering the possible irregular boundaries or special terrain, the system will still perform a separate area calculation for each high variation sub-region.

[0057] The area calculation method depends on the geographic coordinate system used by the system. If a projected coordinate system (such as the UTM coordinate system) is used, the system can directly calculate the area of ​​the polygon. If a geographic coordinate system (such as longitude and latitude coordinates) is used, the system needs to consider the influence of the earth's curvature and use spherical geometry or geodesic calculation methods to obtain a more accurate area.

[0058] S106, dividing the high-variability sub-region into minimum sub-regions of equal area, and taking the center point of the minimum sub-region as a sampling point; If it is greater, the system divides the high-variability sub-region into minimum sub-regions of equal area and uses the center point of the minimum sub-region as the sampling point.

[0059] When the system determines that the area of ​​a high-variability sub-region is larger than the preset minimum area, these regions need to be further subdivided. The purpose of subdivision is to capture the spatial variation characteristics of environmental parameters more finely, while ensuring that the distribution of sampling points can fully represent the entire high-variability region.

[0060] The system first determines the target area of ​​the smallest sub-region. This area is usually set to the preset minimum area or slightly less to ensure that the subdivided sub-regions are not too large. Then, the system calculates the minimum number of sub-regions that the high-variance sub-region needs to be divided into by dividing the area of ​​the high-variance sub-region by the target minimum sub-region area and rounding up.

[0061] Next, the system uses a recursive quadtree algorithm to divide the high-variance sub-regions. The advantage of this method is that it can quickly generate rectangular sub-regions of approximately equal area while maintaining a good spatial structure. The specific steps are as follows: 1. Treat the high-variance region as a rectangle (if the original shape is irregular, the minimum enclosing rectangle can be used). Divide this rectangle evenly into four small rectangles.

[0062] 2. For each small rectangle, check whether its area is still larger than the target minimum sub-region area.

[0063] 3. If yes, repeat step 2 to further divide the small rectangle.

[0064] 4. Repeat the above process until the area of ​​all sub-rectangles is no larger than the target minimum sub-region area.

[0065] In practical applications, strict equal-area partitioning may not always be feasible or appropriate due to the complexity of the terrain and the irregularity of the boundaries. Therefore, the system also implements an adaptive partitioning algorithm. This algorithm takes into account the spatial distribution characteristics of the terrain characteristics and environmental parameters during the recursive partitioning process, allowing non-uniform partitioning when necessary. For example, in areas with large gradients of environmental parameters, the system may generate smaller sub-areas to capture more detailed changes; while in relatively uniform areas, slightly larger sub-areas may be generated.

[0066] After the division is completed, the system calculates the coordinates of the center point of each minimum sub-area and sets these center points as sampling points. The calculation of the center point takes into account the actual shape of the sub-area. For regular rectangular sub-areas, the geometric center is directly taken; for irregular sub-areas, the system calculates their centroid as the center point.

[0067] In order to further optimize the distribution of sampling points, the system also implements a sampling point adjustment algorithm based on spatial coverage. This algorithm evaluates the spatial distribution of the initially set sampling points (i.e., the sub-region center points). If it is found that the sampling point density in some areas is too high or too low, the sampling point position will be fine-tuned to achieve more uniform spatial coverage.

[0068] In addition, the system also takes into account the feasibility of actual sampling. For example, if a calculated sampling point is located in an inaccessible location (such as a steep hillside or in a body of water), the system will automatically find the nearest accessible location as an alternative sampling point. This adjustment is based on terrain data and accessibility analysis to ensure that the generated sampling plan is feasible in actual operation.

[0069] The above technical solution can be understood as using a recursive quadtree algorithm combined with an adaptive partitioning strategy to divide the high-variability sub-region into minimum sub-regions with similar areas, calculating the center point of the sub-region as the initial sampling point, and adjusting the sampling point through spatial coverage optimization and feasibility analysis to generate a refined sampling scheme that can reflect environmental variation and is convenient for actual operation.

[0070] S107, adding a preset number of sampling points in the high-variability sub-region; If it is not greater than, the system adds a preset number of sampling points in the high-variability sub-region.

[0071] When the area of ​​high-variability sub-regions is not larger than the preset minimum area, the system needs to add additional sampling points within these relatively small but high-variability regions to better capture local changes in environmental parameters.

[0072] S108, controlling the detection drone to perform secondary sampling at the sampling point, obtaining the initial environmental data of each sampling point, and updating the environmental parameter spatial distribution model; The prerequisite for executing this step is step S107. Specifically, the system controls the detection drone to perform secondary sampling at the sampling point, obtains the initial environmental data of each sampling point, and updates the environmental parameter spatial distribution model.

[0073] After completing the fine sampling point arrangement in the high-variability area, the system begins to perform the secondary sampling task. The purpose of this step is to obtain more detailed environmental data to verify and refine the previously constructed spatial distribution model of environmental parameters. The system adopts intelligent scheduling and real-time data analysis strategies to ensure the efficiency of the sampling process and the reliability of the data.

[0074] At each sampling point, the system controls the drone to accurately locate and hover. Using GPS combined with a visual positioning system, positioning accuracy can be guaranteed even in areas with weak GPS signals. After hovering stabilizes, the system activates the multi-parameter environmental detection equipment on board to measure multiple environmental parameters (such as temperature, humidity, air pressure, PM2.5 concentration, etc.) at the same time. In order to improve data reliability, the system performs multiple repeated measurements at each sampling point and eliminates possible outliers through statistical analysis.

[0075] S109, calculating the overall prediction error of the updated environmental parameter spatial distribution model, and determining whether the overall prediction error is greater than a preset error; After updating the spatial distribution model of environmental parameters, the system needs to evaluate the accuracy and reliability of the model. To this end, the system calculates the overall prediction error of the updated spatial distribution model of environmental parameters and compares it with the preset error. This step is crucial to ensure the accuracy of the model and determine whether further optimization is needed.

[0076] The specific implementation method is as follows: First, the system uses cross-validation to evaluate the prediction performance of the model. The system randomly divides the sampled data into a training set and a test set, uses the training set to build the model, and then uses the test set to verify the prediction accuracy of the model. This process is repeated many times to obtain a stable error estimate.

[0077] The system calculates the root mean square error (RMSE) as a quantitative indicator of the overall prediction error. The calculation formula of RMSE is: Among them, n is the number of test samples, y i is the actual observed value, is the model's predicted value.

[0078] In order to improve the reliability of error assessment, the system also calculates the mean absolute error (MAE) and mean relative error (MRE) as auxiliary indicators. The calculation formula of MAE is: The calculation formula of MRE is: The system compares the calculated RMSE with the preset error threshold. The selection of the preset error threshold depends on the specific application scenario and accuracy requirements, and can be determined through historical data analysis or expert experience.

[0079] If the RMSE is greater than the preset error, the system will return to step S104 to recalculate the coefficient of variation of each initially detected sub-region and determine the high variation sub-region. This iterative process helps to continuously optimize the model until the required accuracy is achieved.

[0080] S110, determining the initial environment data having the greatest similarity to the standard environment data corresponding to the experimental task, and determining the area of ​​the sampling point corresponding to the initial environment data having the greatest similarity as the candidate experimental area.

[0081] If it is not greater than, the system determines the initial environment data with the greatest similarity to the standard environment data corresponding to the experimental task, and determines the area of ​​the sampling point corresponding to the initial environment data with the greatest similarity as the candidate experimental area.

[0082] If it is greater than, the step of calculating the coefficient of variation of each initial detection sub-region and determining the initial detection sub-region corresponding to the coefficient of variation greater than the preset coefficient as a high-variance sub-region is performed.

[0083] In the above embodiment, the environmental profile of the entire detection area is quickly obtained through preliminary grid division and preliminary sampling. The preset interpolation method is used to construct the spatial distribution model of environmental parameters, which can not only describe the environmental conditions of the sampled points, but also predict the environmental parameters of the unsampled points, thereby improving the detection efficiency. Again, by calculating the coefficient of variation and identifying the high-variance sub-regions, the areas where the environmental parameters change dramatically can be intelligently identified, and the sampling density can be increased in a targeted manner, thereby avoiding the waste of resources caused by blind sampling. The iterative optimization strategy is adopted to ensure the accuracy and reliability of the detection results by continuously updating the spatial distribution model of environmental parameters and evaluating the overall prediction error. By comparing the similarity between the environmental data and the standard environmental data, the candidate experimental area is quickly determined. The accuracy of the environmental parameters detected in different outdoor environments is improved.

[0084] In the above embodiment, the system controls the environment detection drone to perform preliminary sampling at the center point of each initial detection sub-area. The following describes how to perform path planning in conjunction with another embodiment. Figure 2 , a method for planning a detection path of an environmental detection drone in an embodiment of the present application is described: See also Figure 2 , which is a flow chart of a method for planning a detection path of an environmental detection UAV in an embodiment of the present application.

[0085] S201, obtain the three-dimensional coordinates of the center point of each center point and the three-dimensional coordinates of the starting point of the environmental detection drone; the system first needs to obtain all key location information involved in the environmental detection task, including the three-dimensional coordinates of the center point of each initial detection sub-area and the coordinates of the take-off point of the environmental detection drone. These coordinate information is the basic data for planning the drone's flight path.

[0086] The system uses a pre-set coordinate system (such as the WGS84 coordinate system) to represent these locations. Each coordinate contains three dimensions: longitude, latitude, and altitude. In order to improve the accuracy of the coordinates, the system can use differential GPS technology to correct satellite signal errors by setting up ground reference stations, thereby achieving centimeter-level positioning accuracy.

[0087] The system can also integrate multi-source positioning data, such as satellite positioning, inertial navigation system (INS) and visual positioning system data, and fuse these data through the Kalman filter algorithm to obtain more stable and accurate position information. This multi-source data fusion technology can effectively overcome the limitations of a single positioning method, such as GPS signal obstruction or INS cumulative error.

[0088] In order to adapt to different terrain conditions, the system can combine digital elevation model (DEM) data to correct the acquired height information. This is particularly important for environmental detection tasks in complex terrain environments, ensuring that the drone maintains a safe altitude during flight and avoids collisions with terrain obstacles.

[0089] In addition, the system can implement a dynamic coordinate update mechanism. During long-term detection tasks, the coordinates may be slightly offset due to factors such as the rotation of the earth and changes in atmospheric conditions. The system regularly synchronizes with a high-precision time server and combines it with an atmospheric correction model to perform real-time correction of the coordinates to ensure the long-term reliability of the coordinate information.

[0090] In order to improve the efficiency of coordinate acquisition, the system can implement a distributed coordinate acquisition network. By deploying multiple ground control stations in the detection area, a collaborative network is formed, and each control station is responsible for collecting coordinate information within a certain range around it. This distributed architecture can not only increase the speed of data acquisition, but also enhance the fault tolerance of the system.

[0091] The system can also establish a coordinate history database to record the coordinate information obtained in each mission. By analyzing historical data, the system can identify coordinate anomalies or change trends, provide reference for coordinate acquisition of subsequent tasks, and also provide valuable data support for long-term environmental monitoring.

[0092] Finally, the system implements a secure storage and transmission mechanism for coordinate information. Coordinate data is encrypted using an encryption algorithm, and data is transmitted using a secure communication protocol (such as HTTPS) to ensure that coordinate information is not accessed or tampered with by unauthorized persons. This is critical to protecting the confidentiality and integrity of environmental detection tasks.

[0093] S202, calculating the Euclidean distance from the take-off point to each center point based on the three-dimensional coordinates of the starting point and the three-dimensional coordinates of each center point, and sorting each Euclidean distance in ascending order to obtain a sorting result; After obtaining the three-dimensional coordinates of the take-off point and all the center points, the system needs to calculate the distance from the take-off point to each center point and sort these distances. The purpose of this step is to provide a basis for subsequent path planning. The order in which the drone visits each detection point can be preliminarily determined by sorting the distances. The calculation of the Euclidean distance is a known prior art and will not be described in detail here.

[0094] After the calculation is completed, the system uses the quick sort algorithm to sort the distance values ​​from small to large. The average time complexity of quick sort is O(nlogn), which is highly efficient for sorting large amounts of data.

[0095] In order to further optimize the sorting process, the system can implement a parallel sorting algorithm based on GPU. By utilizing the parallel computing power of the graphics processor, the sorting speed of large-scale data can be significantly improved.

[0096] Taking into account the obstacles that may exist in the actual flight environment, the system can also integrate obstacle recognition and path planning functions. By combining geographic information system (GIS) data and real-time obstacle scanning results, the system can calculate the actual flight distance taking into account obstacle avoidance, rather than just the straight-line distance. This approach can more accurately reflect the actual flight path of the drone.

[0097] S203, obtain the remaining power of the environmental detection drone, and calculate the remaining range based on the remaining power; before performing path planning, the system needs to accurately evaluate the remaining capacity of the environmental detection drone to ensure that the planned path is within the capabilities of the drone. This step involves obtaining the remaining power of the drone and converting it into the flight distance, that is, the remaining range. The specific implementation method of this step is a known prior art and will not be repeated here.

[0098] S204, starting from the take-off point and connecting each central point in sequence according to the sorting result to obtain a first detection path and a first length of the first detection path; After obtaining the distance ranking results from the take-off point to each center point, the system needs to plan a preliminary detection path based on this ranking result. This path will determine the order in which the drone visits each detection point, which has an important impact on the efficiency and energy consumption of the entire detection task.

[0099] The system first takes the take-off point as the starting position, then sorts the results by distance, and connects the center points in sequence to form a continuous path. This method is based on the idea of ​​a greedy algorithm, and each time it selects the next point closest to the current point, trying to obtain an overall better path based on the local optimum.

[0100] In addition, the system can realize dynamic path planning. During the UAV's mission, if new obstacles or mission changes are detected, the system can re-plan the remaining path in real time to ensure continuous optimization of the path.

[0101] S205, determining whether the remaining cruising range is less than a first length; The system needs to compare the remaining range calculated previously with the length of the first detection path to determine whether the currently planned path is within the capabilities of the drone.

[0102] S206, using the first length as a detection path; If it is not less than, the system will use the first length as the detection path. When the system determines that the remaining range is not less than the first length, it means that the currently planned path is feasible. In this case, the system will adopt this path as the final detection path.

[0103] S207, calculating the farthest return point according to the remaining cruising range; If it is less than, the farthest return point is calculated based on the remaining cruising range. When the environmental detection drone flies to the farthest return point and returns along the original route, the environmental detection drone can reach the take-off point, and the farthest return point is included in the center point. When the system determines that the remaining cruising range is less than the first length, it is necessary to replan the path to ensure that the drone can safely return to the take-off point. The system first needs to consider the remaining cruising range of the drone, the distance from the current position to each detection point, and the distance from each detection point back to the take-off point. The system traverses all detection points and finds the point that meets the above conditions and is farthest from the take-off point as the farthest return point.

[0104] S208, taking the flight path from the take-off point to the farthest return point as the first detection path; The system uses the flight path from the take-off point to the farthest return point as the first detection path, that is, the drone can first detect the center point contained in the first detection path. At this time, the drone can safely return to the take-off point after completing the detection task of this detection path.

[0105] S209: Delete the center point from the take-off point to the farthest return point to obtain an updated sorting result.

[0106] The center point included in the route from the take-off point to the farthest return point is deleted to obtain an updated sorting result. Then, the center point included in the first detection path does not need to be detected again, and the center point that has been detected will be removed from the updated sorting result. The system then repeats the steps of connecting each center point in sequence with the take-off point as the starting point according to the sorting result to obtain the first detection path and the first length of the first detection path until the remaining cruising range is not less than the first length, obtains several first detection paths, and merges all first detection paths as the detection path.

[0107] It should be noted that after completing the inspection task of a section of the inspection path, the drone starts the inspection of the next section of the inspection path from the starting point of the new section of the inspection path. Then the starting point of the new section of the inspection path is also the center point. The drone flies directly to the center point to start the inspection, which will save energy to the maximum extent.

[0108] In the above embodiment, the three-dimensional coordinates of each center point and the coordinates of the take-off point of the drone are obtained, and an accurate three-dimensional spatial reference system is established. By calculating the Euclidean distance from the take-off point to each center point and sorting them in order from small to large, the path planning based on the nearest neighbor principle is realized. The flight distance of the drone is reduced, energy consumption is reduced, and the working time of a single flight is extended. Reducing the flight distance means a shorter task completion time, and the shortest path also means that the drone is exposed to potential risks for the shortest time, thereby improving the safety of the equipment. By controlling the drone to sample according to the generated detection path, the systematicness and integrity of the sampling process are ensured, and the problem of missing or repeated sampling is avoided. The sampling efficiency is improved and the spatial uniformity of the data is ensured.

[0109] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of the physical device structure of an outdoor environment monitoring system provided in an embodiment of the present application.

[0110] It should be noted that Figure 3 The structure of the system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0111] like Figure 3 As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0112] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0113] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 309, and / or installed from a removable medium 311. When the computer program is executed by a central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0114] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0116] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0117] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0118] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0119] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0120] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for monitoring an outdoor environment, characterized in that: include: Perform preliminary grid division on the preset detection area to obtain several initial detection sub-areas with the same area; Controlling the environmental detection drone to perform preliminary sampling at the center point of each of the initial detection sub-areas to obtain initial environmental data corresponding to each of the initial detection sub-areas; Based on the initial environmental data, a preset interpolation method is used to construct an environmental parameter spatial distribution model, wherein the environmental parameter spatial distribution model is a mathematical model for describing and predicting the distribution of environmental parameters in space; Calculating the coefficient of variation of each of the initial detection sub-regions, and determining the initial detection sub-regions corresponding to the coefficient of variation being greater than a preset coefficient as high-variance sub-regions; Determine whether the area of ​​the high-variance sub-region is greater than a preset minimum area; If it is greater than, the high-variability sub-region is divided into minimum sub-regions of equal area, and the center point of the minimum sub-region is used as the sampling point; If not, adding a preset number of sampling points in the high-variability sub-region; Controlling the environmental detection drone to perform secondary sampling at the sampling points, obtaining initial environmental data of each sampling point, and updating the environmental parameter spatial distribution model; Calculating the overall prediction error of the updated environmental parameter spatial distribution model, and determining whether the overall prediction error is greater than a preset error; If it is greater than, executing the step of calculating the coefficient of variation of each of the initial detection sub-regions, and determining the initial detection sub-regions corresponding to the coefficient of variation greater than the preset coefficient as high-variance sub-regions; If not, the initial environment data having the greatest similarity to the standard environment data corresponding to the experimental task is determined, and the area of ​​the sampling point corresponding to the initial environment data having the greatest similarity is determined as the candidate experimental area.

2. The method according to claim 1, characterized in that The control environment detection drone performs preliminary sampling at the center point of each of the initial detection sub-areas, specifically including: Obtain the three-dimensional coordinates of the center point of each center point and the three-dimensional coordinates of the starting point of the environmental detection drone take-off point; Calculate the Euclidean distance from the take-off point to each of the center points based on the three-dimensional coordinates of the starting point and the three-dimensional coordinates of each of the center points, and sort each of the Euclidean distances in ascending order to obtain a sorting result; Generate a detection path according to the sorting result; The control environment detection drone performs preliminary sampling at the center point of each of the initial detection sub-areas according to the detection path.

3. The method according to claim 2, characterized in that Generating a detection path according to the sorting result specifically includes: Obtaining the remaining power of the environment detection drone, and calculating the remaining cruising range according to the remaining power; Taking the take-off point as the starting point, sequentially connecting each of the central points according to the sorting result to obtain a first detection path and a first length of the first detection path; Determining whether the remaining cruising range is less than the first length; If it is not less than, taking the first length as the detection path; If it is less than, the farthest return point is calculated according to the remaining cruising range. When the environment detection drone flies to the farthest return point and returns along the original route, the environment detection drone can reach the take-off point, and the farthest return point is included in the center point; The flight path from the take-off point to the farthest return point is used as the first detection path; The center point included in the route from the take-off point to the farthest return point is deleted to obtain an updated sorting result, and the steps of connecting each of the center points in sequence according to the sorting result with the take-off point as the starting point to obtain a first detection path and a first length of the first detection path are repeatedly performed until the remaining cruising range is not less than the first length, thereby obtaining a plurality of first detection paths; All the first detection paths are combined as the detection path.

4. The method according to claim 1, characterized in that: The method of constructing a spatial distribution model of environmental parameters based on the initial environmental data by using a preset interpolation method specifically includes: Performing normality test and outlier detection on the initial environmental data to obtain corrected initial environmental data; Calculating the experimental variogram of the corrected initial environmental data, and selecting a preset variogram model for fitting to obtain a theoretical variogram; The spatial distribution model of the environmental parameters is generated by performing spatial interpolation using the ordinary Kriging method according to the theoretical variation function.

5. The method according to claim 1, characterized in that: The calculating the coefficient of variation of each of the initial detection sub-regions specifically includes: Generating a set of uniformly distributed interpolation points in each of the initial detection sub-regions; Calculating the estimated value of the environmental parameter at each interpolation point according to the environmental parameter spatial distribution model; Calculate the estimated average value and standard deviation of the environmental parameters of all interpolation points in each of the initial detection sub-areas according to the estimated values ​​of the environmental parameters; The coefficient of variation corresponding to each of the initial detection sub-regions is calculated based on the estimated mean value of the environmental parameters and the estimated standard deviation of the environmental parameters corresponding to each of the initial detection sub-regions.

6. The method according to claim 5, characterized in that The step of calculating the estimated average environmental parameter values ​​and the estimated standard deviation of the environmental parameter values ​​for all interpolation points in each of the initial detection sub-areas according to the estimated environmental parameter values ​​specifically comprises: inputting the estimated environmental parameter values ​​into an average value calculation function and a standard deviation calculation function respectively to obtain an estimated average environmental parameter value and an estimated standard deviation of the environmental parameter value; The average value calculation function is: In the function, μ is the estimated average value of the environmental parameter, and x i is the estimated value of the environmental parameter, and n is the number of the interpolation points; The standard deviation calculation function is: In the function, σ is the estimated standard deviation of the environmental parameter, and x i is the estimated value of the environmental parameter, n is the number of the interpolation points, and μ is the estimated average value of the environmental parameter.

7. The method according to claim 5, characterized in that The calculating the coefficient of variation corresponding to each of the initial detection sub-areas according to the estimated mean value of the environmental parameters corresponding to each of the initial detection sub-areas and the estimated standard deviation of the environmental parameters specifically includes: The estimated mean value of the environmental parameters and the estimated standard deviation of the environmental parameters are input into the coefficient of variation calculation function to obtain the coefficient of variation. The coefficient of variation calculation function is: In the function, the CV is the coefficient of variation, the σ is the estimated standard deviation of the environmental parameter, and the μ is the estimated mean value of the environmental parameter.

8. An outdoor environment monitoring system, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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