An outdoor environment monitoring method, system, storage medium and program product

Abstract

An outdoor environment monitoring method and system, a storage medium and a program product, in the method, a plurality of initial detection sub-regions are obtained; preliminary sampling is performed at the center points of the initial detection sub-regions to obtain initial environment data; an environment parameter space distribution model is constructed; a variation coefficient is calculated to determine a high variation sub-region; it is judged whether the area of the high variation sub-region is greater than a preset minimum area; if yes, the minimum sub-region is divided, and the center point is taken as a sampling point; if no, a preset number of sampling points are added; the detection unmanned aerial vehicle is controlled to perform secondary sampling at the sampling points to obtain initial environment data, and the environment parameter space distribution model is updated; the overall prediction error after updating is calculated, and it is judged whether the overall prediction error is greater than a preset error; if no, the initial environment data with the greatest similarity is determined, and the region of the corresponding sampling point is determined as a candidate experimental region. The application improves the accuracy of the environment parameters detected in different outdoor environments.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of environmental detection, and particularly relates to an outdoor environment monitoring method and system, a storage medium and a program product. BACKGROUND

[0002] When students need to conduct experiments outdoors, they usually need to select an area that meets the experimental conditions, which minimally affects the experimental results.

[0003] In the related art, first, the corresponding environmental parameters are determined according to the specific content of the experiment, which can be temperature, humidity, wind speed, etc. Then, the experimenters carry portable environmental detection equipment, such as handheld weather stations, etc. to detect the pre-divided areas one by one until the area that meets the experimental environmental parameters is detected.

[0004] However, in general, the experimenters will choose a fixed number of points for detection, and the distribution of the detection points will also vary with the experimenters. For different outdoor areas, the same points will still be selected for detection, which will result in that the detection results of different outdoor environments are inconsistent with their true environmental parameters, reducing the accuracy of the detection results. SUMMARY

[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 plurality of initial detection sub-areas with the same area;

[0007] The control environment detection unmanned aerial vehicle performs preliminary sampling at the center point of each initial detection sub-area to obtain initial environmental data corresponding to each initial detection sub-area;

[0008] 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 for describing and predicting the spatial distribution of environmental parameters;

[0009] The coefficient of variation of each initial detection sub-area is calculated, and the initial detection sub-area with a coefficient of variation greater than a preset coefficient is determined as a high variation sub-area;

[0010] It is determined whether the area of the high variation sub-area is greater than a preset minimum area;

[0011] If it is greater, the high variation sub-area is divided into minimum sub-areas with equal areas, and the center point of the minimum sub-area is taken as a sampling point;

[0012] if the overall prediction error is greater than the preset error, performing a step of calculating a coefficient of variation of each initial detection sub-region, and determining an initial detection sub-region with a coefficient of variation greater than a preset coefficient as a high variation sub-region;

[0013] controlling the environment detection unmanned aerial vehicle to perform secondary sampling at the sampling points to obtain initial environment data of each sampling point, and updating the environment parameter space distribution model;

[0014] calculating an overall prediction error of the updated environment parameter space distribution model, and determining whether the overall prediction error is greater than a preset error;

[0015] if the overall prediction error is greater than the preset error, performing a step of calculating a coefficient of variation of each initial detection sub-region, and determining an initial detection sub-region with a coefficient of variation greater than a preset coefficient as a high variation sub-region;

[0016] if the overall prediction error is not greater than the preset error, determining initial environment data with the greatest similarity to standard environment data corresponding to the experimental task, and determining a region of a sampling point corresponding to the initial environment data with the greatest similarity as a candidate experimental region.

[0017] By adopting the above technical solutions, the environment profile of the entire detection region is rapidly obtained through preliminary grid division and preliminary sampling. The environment parameter space distribution model is constructed by using a preset interpolation method, which can not only describe the environment conditions of the sampled points, but also predict the environment parameters of the unsampled points, thereby improving the detection efficiency. In addition, by calculating the coefficient of variation and identifying the high variation sub-region, the region with a rapid change in environment parameters can be intelligently identified, and the sampling density can be increased accordingly, thereby avoiding resource waste caused by blind sampling. By using the iterative optimization strategy, the environment parameter space distribution model is continuously updated and the overall prediction error is evaluated, thereby ensuring the accuracy and reliability of the detection results. By comparing the similarity of the environment data and the standard environment data, the candidate experimental region is quickly determined. The accuracy of the environment parameters detected in different outdoor environments is improved.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the control of the environment detection unmanned aerial vehicle to perform preliminary sampling at the center points of each initial detection sub-region specifically includes:

[0019] obtaining a center point three-dimensional coordinate of each center point and a starting point three-dimensional coordinate of a takeoff point of the environment detection unmanned aerial vehicle;

[0020] calculating the Euclidean distance from the takeoff point to each center point based on the starting point three-dimensional coordinate and the center point three-dimensional coordinate of each center point, and sorting each Euclidean distance in ascending order to obtain a sorting result;

[0021] generating a detection path according to the sorting result;

[0022] controlling the environment detection unmanned aerial vehicle to perform preliminary sampling at the center points of each initial detection sub-region according to the detection path.

[0023] By adopting the technical solution, the three-dimensional coordinates of each center point and the takeoff point coordinates of the unmanned aerial vehicle are obtained, an accurate three-dimensional space reference system is established, the Euclidean distance from the takeoff point to each center point is calculated, and the path planning of the nearest neighbor principle is realized by sorting in ascending order. The flight distance of the unmanned aerial vehicle is reduced, the energy consumption is reduced, and the working time of a single flight is prolonged. Reducing the flight distance means shorter task completion time, and the shortest path also means that the unmanned aerial vehicle is exposed to potential risks for the shortest time, thereby improving the safety of the equipment. By controlling the unmanned aerial vehicle to sample according to the generated detection path, the systematicness and integrity of the sampling process are ensured, and the problems of omission or repeated sampling are avoided. The sampling efficiency is improved, and the spatial uniformity of the data is ensured.

[0024] In combination with some embodiments of the first aspect, in some embodiments, the detection path is generated according to the sorting result, specifically comprising:

[0025] The remaining power of the environment detection unmanned aerial vehicle is obtained, and the remaining endurance mileage is calculated according to the remaining power;

[0026] Each center point is connected in sequence according to the sorting result with the takeoff point as the starting point to obtain a first detection path and a first length of the first detection path;

[0027] It is judged whether the remaining endurance mileage is less than the first length;

[0028] If not, the first length is taken as the detection path;

[0029] If less, the farthest return point is calculated according to the remaining endurance mileage, when the environment detection unmanned aerial vehicle flies to the farthest return point and returns along the original route, the environment detection unmanned aerial vehicle can reach the takeoff point, and the farthest return point is contained in the center point;

[0030] The flight path from the takeoff point to the farthest return point is taken as a first segment detection path;

[0031] The center points contained in the takeoff point and the farthest return point are deleted to obtain an updated sorting result, and the steps of connecting each center point in sequence according to the sorting result with the takeoff point as the starting point to obtain a first detection path and a first length of the first detection path are repeatedly executed until the remaining endurance mileage is not less than the first length, to obtain a plurality of first segment detection paths;

[0032] All the first segment detection paths are combined as the detection path.

[0033] By adopting the technical solution, the residual power of the unmanned aerial vehicle is obtained, and the residual endurance is calculated. The actual hardware limit is considered in the path planning, ensuring the feasibility and safety of the planning. When the residual endurance is sufficient, the shortest path is directly adopted; when the endurance is insufficient, the farthest return point is calculated and segmented planning is adopted, which ensures the integrity of each flight task and maximizes the coverage range of single flight. The overall detection efficiency is improved, and the optimal use of resources is realized. Through iterative path planning, complex multiple take-off and landing situations can be handled, and detection tasks of different scales and complexities can be adapted. This flexibility enables the method to be applied to various environmental detection scenarios, and has wide applicability. By dynamically updating the sorting results and repeatedly executing the path planning, potential resource waste caused by conservative estimation is avoided.

[0034] In some embodiments of the first aspect, based on the initial environment data, a preset interpolation method is used to construct an environment parameter spatial distribution model, specifically including:

[0035] Normality test and outlier detection are performed on the initial environment data to obtain corrected initial environment data;

[0036] The experimental variogram function of the corrected initial environment data is calculated, and a preset variogram function model is selected for fitting to obtain a theoretical variogram function;

[0037] The ordinary Kriging method is used for spatial interpolation according to the theoretical variogram function to generate an environment parameter spatial distribution model.

[0038] By adopting the above technical solution, normality test and outlier detection are performed on the initial environment data, which can identify and process abnormal points and non-normal distribution in the data, avoiding deviation of the model caused by abnormal data. The experimental variogram function is calculated, and a preset variogram function model is selected for fitting to obtain a theoretical variogram function, which can accurately capture the variation of the environment parameter in space. The ordinary Kriging method is used for spatial interpolation to generate an environment parameter spatial distribution model, which can provide the best linear unbiased estimate of the unsampled points and give the error range of the estimate.

[0039] In some embodiments of the first aspect, the coefficient of variation of each initial detection sub-region is calculated, specifically including:

[0040] A set of uniformly distributed interpolation points are generated in each initial detection sub-region;

[0041] The environment parameter estimated value of each interpolation point is calculated according to the environment parameter spatial distribution model;

[0042] The environment parameter estimated mean and the environment parameter estimated standard deviation of all interpolation points in each initial detection sub-region are calculated according to the environment parameter estimated value.

[0043] The coefficient of variation for each initial detection sub-region is calculated based on the estimated average and standard deviation of the environmental parameters for each initial detection sub-region.

[0044] By adopting the above technical solution, uniformly distributed interpolation points are generated within each initial detection sub-region, ensuring comprehensive and uniform sampling of environmental parameters within the sub-region. This avoids local biases caused by uneven sampling point distribution and improves the representativeness and reliability of the coefficient of variation calculation. Calculating the environmental parameter estimate for each interpolation point using the constructed spatial distribution model of environmental parameters yields high-density estimation data, improving computational efficiency. By calculating the mean and standard deviation of the environmental parameter estimates for all interpolation points within each sub-region, the central tendency and dispersion of environmental parameters within the sub-region can be captured, considering the overall level of environmental parameters and reflecting their internal fluctuations. Calculating the coefficient of variation based on the mean and standard deviation allows for direct comparison of environmental parameters at different scales and units, enhancing universality and comparability.

[0045] In conjunction with some embodiments of the first aspect, in some embodiments, the average value and standard deviation of the environmental parameters estimated for all interpolation points in each initial detection sub-region are calculated based on the environmental parameter estimates. Specifically, this includes: inputting the environmental parameter estimates into the average value calculation function and the standard deviation calculation function to obtain the average value and standard deviation of the environmental parameters estimated.

[0046] The function for calculating the average value is:

[0047]

[0048] In the function, μ is the estimated average value of the environmental parameters, and x i Here are the estimated environmental parameters, and n is the number of interpolation points.

[0049] The standard deviation calculation function is:

[0050]

[0051] In the function, σ represents the standard deviation of the environmental parameter estimates, and x... i Here, n represents the estimated environmental parameters, n is the number of interpolation points, and μ is the average value of the estimated environmental parameters.

[0052] By adopting the technical solutions, the environment parameter estimation average value and the environment parameter estimation standard deviation are input into the coefficient of variation calculation function, and the relative dispersion degree of the environment parameter is accurately quantified. The coefficient of variation is the ratio of the standard deviation to the average value, and the limitation of the standard deviation affected by the measurement scale is reduced.

[0053] In some embodiments of the first aspect, the coefficient of variation corresponding to each initial detection sub-region is calculated according to the environment parameter estimation average value and the environment parameter estimation standard deviation corresponding to each initial detection sub-region, and specifically includes:

[0054] The environment parameter estimation average value and the environment parameter estimation standard deviation are input into the coefficient of variation calculation function, and the coefficient of variation is obtained. The coefficient of variation calculation function is:

[0055]

[0056] In the function, CV is the coefficient of variation, σ is the environment parameter estimation standard deviation, and μ is the environment parameter estimation average value.

[0057] By adopting the technical solutions, the environment parameter estimation average value and the environment parameter estimation standard deviation are input into the coefficient of variation calculation function, and the relative dispersion degree of the environment parameter is accurately quantified. The coefficient of variation is the ratio of the standard deviation to the average value, and the limitation of the standard deviation affected by the measurement scale is reduced.

[0058] In the second aspect, the embodiments of the present application provide an outdoor environment monitoring system, which comprises one or more processors and a memory. The memory is coupled with the one or more processors, and is used to store computer program codes. The computer program codes comprise computer instructions. The one or more processors invoke the computer instructions to enable the system to perform the method described in the first aspect and any possible implementation manner of the first aspect.

[0059] In the third aspect, the embodiments of the present application provide a computer readable storage medium comprising instructions. When the instructions are run on a system, the system performs the method described in the first aspect and any possible implementation manner of the first aspect.

[0060] In the fourth aspect, the embodiments of the present application provide a computer program product. When the computer program product is run on a system, the system performs the method described in any possible implementation manner of the first aspect.

[0061] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0062] 1. The application provides an outdoor environment monitoring method, which rapidly obtains the environmental profile of the entire detection area through preliminary grid division and preliminary sampling. The preset interpolation method is used to construct an environmental parameter spatial distribution model, 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. Second, by calculating the coefficient of variation and identifying high-variation sub-regions, the region with a dramatic change in environmental parameters can be intelligently identified, and the sampling density can be increased accordingly, thereby avoiding resource waste caused by blind sampling. An iterative optimization strategy is adopted to continuously update the environmental parameter spatial distribution model and evaluate the overall prediction error, thereby ensuring the accuracy and reliability of the detection results. The similarity between the environmental data and the standard environmental data is compared to quickly determine the candidate experimental area. The accuracy of the environmental parameters detected in different outdoor environments is improved.

[0063] 2. The application provides an outdoor environment monitoring method, which obtains the remaining power of the unmanned aerial vehicle and calculates the remaining endurance mileage, takes into account the actual hardware limitations in path planning, and ensures the feasibility and safety of the planning. When the remaining endurance mileage is sufficient, the shortest path is directly used; when the endurance mileage is insufficient, the farthest return point is calculated and segmented planning is performed, thereby ensuring the integrity of each flight task and maximizing the coverage range of a single flight. The overall detection efficiency is improved, and the optimal use of resources is achieved. Through iterative path planning, complex multiple take-off and landing situations can be handled, and detection tasks of different scales and complexities can be adapted. This flexibility makes the method applicable to various environmental detection scenarios, and has wide applicability. By dynamically updating the ranking results and repeatedly executing the path planning, potential resource waste caused by conservative estimation is avoided.

[0064] 3. The application provides an outdoor environment monitoring method, which generates uniformly distributed interpolation points in each initial detection sub-region, ensures comprehensive and uniform sampling of the environmental parameter distribution in the sub-region, avoids local bias caused by uneven distribution of sampling points, and improves the representativeness and reliability of the coefficient of variation calculation. The environmental parameter estimation value of each interpolation point is calculated using the constructed environmental parameter spatial distribution model, which can obtain high-density estimation data and improve the calculation efficiency. By calculating the average value and standard deviation of the environmental parameter estimation of all interpolation points in each sub-region, the central tendency and dispersion degree of the environmental parameters in the sub-region can be captured, the overall level of the environmental parameters is considered, and the internal fluctuations are reflected. The coefficient of variation is calculated based on the average value and the standard deviation, so that environmental parameters of different scales and units can be directly compared, and the universality and comparability are enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 is a flowchart of an outdoor environment monitoring method in an embodiment of the application.

[0066] Figure 2 is a flowchart of an environmental detection unmanned aerial vehicle detection path planning method in an embodiment of the present application.

[0067] Figure 3 is an entity device structure schematic diagram of an outdoor environment monitoring system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0068] 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 limiting of the present application. As used in the specification and the appended claims of the present application, the singular forms "a," "an" and "the" are intended to include both singular and plural forms, unless the context clearly indicates otherwise. It will be further understood that the terms "and / or" as used herein refers to any or all possible combinations of one or more of the associated listed items.

[0069] Hereinafter, the terms "first" and "second" are only for the purpose of description, and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first" and "second" can explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified.

[0070] The following will be described in conjunction with Figure 1 An outdoor environment monitoring method in an embodiment of the present application is described:

[0071] Please refer to Figure 1 is a flowchart of an outdoor environment monitoring method in an embodiment of the present application.

[0072] S101, preliminary grid division is performed on a preset detection area to obtain a plurality of initial detection sub-areas with the same area; the system first receives geographic information data of the preset detection area, including area boundary coordinates, terrain features, etc. Then, the system uses an equal-area grid division algorithm to divide the entire preset detection area into a plurality of rectangular sub-areas with equal areas. This division method ensures the uniformity and representativeness of 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 according to the predefined number of sub-areas or the expected sub-area area. Next, the system starts from a corner of the preset detection area and gradually divides the sub-area along the main direction (such as east-west and north-south), until the entire preset detection area is covered.

[0073] To adapt to complex terrain and irregular boundaries, the system can also employ an adaptive meshing algorithm. This algorithm takes into account the terrain undulations and boundary shapes during the partitioning process, making local adjustments to the mesh to ensure that the actual ground surface area of each sub-region is as close as possible. For example, in mountainous regions, the system can employ three-dimensional meshing, taking into account elevation information, so that the actual ground surface area of each sub-region is equal, rather than just the planar projection area.

[0074] In addition, the system can also optimize the mesh according to known environmental parameter distribution characteristics. For example, in areas where there is a known gradient change in environmental parameters, the system can appropriately densify the mesh along the gradient direction to capture more detailed variation information. In areas where the environmental parameters are relatively uniform, the mesh size can be appropriately enlarged to improve sampling efficiency.

[0075] The system stores the results of mesh partitioning in a geographic information system (GIS) compatible format, including the boundary coordinates, center point coordinates, area, and other information of each sub-region. These information will be used in subsequent sampling point determination and data analysis processes. At the same time, the system generates a visual map of the mesh partitioning, so that the operator can intuitively understand the partitioning results and make manual adjustments if necessary.

[0076] The above technical solution can be understood as using an equal-area meshing algorithm, combined with adaptive mesh adjustment and environmental parameter distribution characteristic optimization, to preliminarily divide the preset detection area, obtaining several initial detection sub-regions with the same area, and storing the partitioning results in a GIS compatible format.

[0077] S102、Control the environmental detection unmanned aerial vehicle to preliminarily sample at the center point of each initial detection sub-region, to obtain initial environmental data corresponding to each initial detection sub-region;

[0078] The system controls the environmental detection unmanned aerial vehicle to preliminarily sample at the center point of each initial detection sub-region, to obtain initial environmental data corresponding to each initial detection sub-region. The step of controlling the environmental detection unmanned aerial vehicle to preliminarily sample at the center point of each initial detection sub-region involves the problem of detection path planning, which will be described in conjunction with another embodiment below, and will not be described here.

[0079] The system first extracts the center point coordinates of each initial detection sub-region according to the mesh partitioning results generated in step S101. Then, the system plans the flight path of the unmanned aerial vehicle to cover all sampling points in an optimized manner. Path planning takes into account multiple factors, including the endurance of the unmanned aerial vehicle, 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.

[0080] In actual execution of the sampling task, the system first performs a self-test to ensure that the UAV and the environmental detection device are in a normal working state. Then, the system controls the UAV 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 UAV to hover and activates the environmental detection device.

[0081] The environmental detection device includes but is not limited to a temperature sensor, a humidity sensor, a barometric pressure sensor, a wind speed and direction sensor, a PM2.5 sensor, a CO2 concentration sensor, 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, and if abnormal values or signal interference are detected, the sampling time will be automatically extended or the sampling will be restarted to ensure the reliability of the data.

[0082] After the sampling is completed, the system performs preliminary processing on the collected raw data, including removing obvious outliers and calculating the average value. The processed data, together with the metadata such as the geographic coordinates of the sampling point and the sampling time, are transmitted in real time to the ground control station through the communication module of the UAV. At the same time, these data are also stored in the local storage device of the UAV as a backup.

[0083] After completing the data collection at one sampling point, the system controls the UAV to fly to the next sampling point and repeats the above process until all initial detection sub-regions are sampled. During the flight, the system monitors the power state of the UAV in real time, and if the power is insufficient to complete the remaining sampling tasks, the system will automatically plan a return path, replace or charge the battery, and then continue to complete the remaining sampling tasks.

[0084] To improve sampling efficiency, the system can control multiple UAVs to work cooperatively. In this case, the system divides the entire detection area into several sub-regions, and each UAV is responsible for the sampling task of a sub-region. The system coordinates the work of multiple UAVs through real-time communication to avoid flight path intersection or sampling point repetition.

[0085] The above technical solution can be understood as controlling the environmental detection UAV through a path planning algorithm to perform multi-parameter synchronous sampling at the center point of each initial detection sub-region, and using real-time data quality monitoring and multi-UAV cooperative working strategies to obtain initial environmental data.

[0086] S103, based on the initial environmental data, an environmental parameter space distribution model is constructed using a preset interpolation method;

[0087] The system constructs an environmental parameter spatial distribution model based on the initial environmental data using a preset interpolation method. The environmental parameter spatial distribution model is a mathematical model used to describe and predict the distribution of environmental parameters in space. Specifically, the initial environmental data is subjected to normality test and outlier detection to obtain corrected initial environmental data;

[0088] An experimental variogram of the corrected initial environmental data is calculated, and a preset variogram model is selected for fitting to obtain a theoretical variogram.

[0089] The ordinary kriging method is used to perform spatial interpolation based on the theoretical variogram to generate the environmental parameter spatial distribution model.

[0090] The system first preprocesses the initial environmental data obtained in step S102. The preprocessing includes data cleaning, outlier detection and processing, data standardization, etc. During data cleaning, the system removes obviously erroneous data points, such as values exceeding the sensor range. The improved Z-score method is used for outlier detection, considering the spatial correlation of the data, to identify and mark possible outliers. For the marked outliers, the system does not directly delete them, but uses a local interpolation method to correct them to retain possible extreme but valid observations.

[0091] The system performs 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 helps the subsequent kriging interpolation.

[0092] 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 changing with distance. The system uses a robust variogram estimation method to reduce the influence of outliers on the calculation of the variogram. Specifically, the system first calculates the distances between all pairs of data points and the corresponding semivariograms, then divides these distances into several distance classes, and calculates the average semivariogram within each distance class.

[0093] After obtaining the experimental variogram, the system selects the most suitable one from the preset several theoretical variogram models for fitting. Common 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 points in the distance class. During the fitting process, the system estimates parameters such as 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 indicators (such as R 2 value, residual sum of squares, etc.) of each model, and selects the model with the best indicators as the final theoretical variogram.

[0094] Finally, based on the obtained theoretical variogram, the system uses ordinary kriging for spatial interpolation to generate a spatial distribution model of environmental parameters. Kriging interpolation considers the spatial correlation between data points and can provide unbiased best linear estimates. The system generates regular grid points throughout the study area and performs kriging estimation for each grid point. During the estimation process, the system considers nearby known data points, calculates weights based on the theoretical variogram, and then performs a weighted average to obtain the estimated value. Simultaneously, the system also calculates the kriging variance for each estimated point, providing spatial distribution information on the estimation accuracy.

[0095] To improve interpolation accuracy, the system can also consider introducing auxiliary variables, such as topographic factors like elevation, slope, and aspect, and employ co-kriging or regression kriging for interpolation. This method leverages the correlation between environmental parameters and topographic factors to enhance the reliability of the interpolation results.

[0096] The above technical solution can be understood as follows: preprocessing and normality testing of initial environmental data, calculating experimental variability function and fitting optimal theoretical variability function model, constructing spatial distribution model of environmental parameters based on ordinary kriging method, and generating spatial distribution information of estimation accuracy.

[0097] S104. Calculate the coefficient of variation for each initial detection sub-region, and identify the initial detection sub-regions with a coefficient of variation greater than the preset coefficient as high-variance sub-regions.

[0098] The system calculates the coefficient of variation for each initial detection sub-region and identifies those with coefficients greater than a preset coefficient as high-variability sub-regions. The coefficient of variation is a statistical concept used to compare the dispersion between different datasets or variables. It is the ratio of the standard deviation to the mean, usually expressed as a percentage. Specifically, a set of uniformly distributed interpolation points is generated within each initial detection sub-region.

[0099] Calculate the estimated environmental parameters for each interpolation point based on the spatial distribution model of environmental parameters.

[0100] Calculate the mean and standard deviation of the environmental parameter estimates for all interpolation points within each initial detection sub-region based on the environmental parameter estimates.

[0101] The coefficient of variation for each initial detection sub-region is calculated based on the estimated average and standard deviation of the environmental parameters for each initial detection sub-region.

[0102] Specifically, the estimated environmental parameters are input into the mean calculation function and the standard deviation calculation function to obtain the estimated mean and standard deviation of the environmental parameters.

[0103] The average value calculation function is:

[0104]

[0105] In the function, μ is the average value of the environmental parameter estimation, x i is the environmental parameter estimation value, and n is the number of interpolation points.

[0106] The standard deviation calculation function is:

[0107]

[0108] In the function, σ is the standard deviation of the environmental parameter estimation, x i is the environmental parameter estimation value, n is the number of interpolation points, and μ is the average value of the environmental parameter estimation.

[0109] The system inputs the average value of the environmental parameter estimation and the standard deviation of the environmental parameter estimation into the coefficient of variation calculation function to obtain the coefficient of variation, and the coefficient of variation calculation function is:

[0110]

[0111] In the function, CV is the coefficient of variation, σ is the standard deviation of the environmental parameter estimation, and μ is the average value of the environmental parameter estimation.

[0112] S105, judge whether the area of the high-variation sub-region is greater than a preset minimum area;

[0113] 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 directly used. However, considering the possible irregular boundaries or special terrain, the system still performs individual area calculation for each high-variation sub-region.

[0114] The area calculation method depends on the geographic coordinate system used by the system. If a projection 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 latitude and longitude coordinates) is used, the system needs to consider the influence of the curvature of the earth, and use spherical geometry or geodesic calculation method to obtain more accurate area.

[0115] S106, divide the high-variation sub-region into minimum sub-regions with equal areas, and take the center point of the minimum sub-region as a sampling point;

[0116] If it is greater, the system divides the high-variation sub-region into minimum sub-regions with equal areas, and takes the center point of the minimum sub-region as a sampling point.

[0117] When the system determines that the area of a high-variability sub-region is greater than the preset minimum area, further subdivision of these regions is required. The purpose of subdivision is to capture the spatial variability characteristics of environmental parameters more finely, while ensuring that the distribution of sampling points can fully represent the entire high-variability region.

[0118] The system first determines the target area of the smallest sub-region. This area is usually set to the preset minimum area or slightly smaller than the preset minimum area, to ensure that the subdivided sub-regions are not too large. Then, the system calculates the number of smallest sub-regions that the high-variability sub-region needs to be divided into by dividing the area of the high-variability sub-region by the target smallest sub-region area and rounding up.

[0119] Next, the system uses a recursive quadtree algorithm to divide the high-variability sub-region. The advantage of this method is that it can quickly generate approximately equal-area rectangular sub-regions while maintaining good spatial structure. The specific steps are as follows:

[0120] 1. Treat the high-variability sub-region as a rectangle (if the original shape is irregular, a minimum circumscribed rectangle can be used). Uniformly divide this rectangle into four small rectangles.

[0121] 2. For each small rectangle, check whether its area is still greater than the target smallest sub-region area.

[0122] 3. If so, repeat step 2 to further divide the small rectangle.

[0123] 4. Repeat the above process until the area of all sub-rectangles is not greater than the target smallest sub-region area.

[0124] In practical applications, due to the complexity of the terrain and the irregularity of the boundaries, strict equal-area division may not always be feasible or appropriate. Therefore, the system also implements an adaptive division algorithm. This algorithm considers the terrain features and the spatial distribution characteristics of environmental parameters during the recursive division process, allowing non-uniform division when necessary. For example, in areas with a large gradient of environmental parameters, the system may generate smaller sub-regions to capture more detailed changes; while in relatively uniform areas, it may generate slightly larger sub-regions.

[0125] After the division is complete, the system calculates the center point coordinates of each smallest sub-region and sets these center points as sampling points. The calculation of the center point takes into account the actual shape of the sub-region. For regular rectangular sub-regions, the geometric center is directly taken; for irregularly shaped sub-regions, the system calculates their centroids as the center points.

[0126] 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., sub-region center points). If it finds that the sampling point density in certain regions is too high or too low, it will fine-tune the sampling point locations to achieve more uniform spatial coverage.

[0127] In addition, the system also considers the feasibility of actual sampling. For example, if a certain calculated sampling point is located in an inaccessible location (such as a steep slope or water body), the system will automatically find the nearest accessible location as a replacement sampling point. This adjustment is based on terrain data and accessibility analysis, ensuring that the generated sampling scheme is feasible in actual operation.

[0128] The above technical solutions can be understood as follows: using a recursive quadtree algorithm combined with an adaptive partitioning strategy to divide high-variability sub-regions into minimum sub-regions with similar areas, calculating the center points of the sub-regions as initial sampling points, and adjusting the sampling points through spatial coverage optimization and feasibility analysis to generate a fine sampling scheme that can reflect environmental variability and is convenient for actual operation.

[0129] S107、In the high-variability sub-region, increase the number of sampling points by a preset number.

[0130] If it is not greater than, the system increases the number of sampling points in the high-variability sub-region by a preset number.

[0131] When the area of the high-variability sub-region is not greater than the preset minimum area, the system needs to increase additional sampling points in these relatively small but high-variability regions to better capture local changes in environmental parameters.

[0132] S108, control the detection unmanned aerial vehicle to carry out secondary sampling at the sampling points to obtain initial environmental data of each sampling point, and update the environmental parameter spatial distribution model;

[0133] The prerequisite for this step is step S107. Specifically, the system controls the detection unmanned aerial vehicle to carry out secondary sampling at the sampling points to obtain initial environmental data of each sampling point, and updates the environmental parameter spatial distribution model.

[0134] After completing the fine sampling point arrangement in the high-variability region, 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 environmental parameter spatial distribution model. The system uses intelligent scheduling and real-time data analysis strategies to ensure the efficiency of the sampling process and the reliability of the data.

[0135] At each sampling point, the system controls the UAV to precisely position and hover. The GPS is combined with a visual positioning system to ensure positioning accuracy even in areas with weak GPS signals. After hovering stably, the system activates the multi-parameter environmental detection equipment carried, and simultaneously measures various environmental parameters (such as temperature, humidity, air pressure, PM2.5 concentration, etc.). To improve data reliability, the system performs multiple repeated measurements at each sampling point, and removes possible outliers through statistical analysis.

[0136] S109, 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;

[0137] After updating the environmental parameter spatial distribution model, 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 environmental parameter spatial distribution model and compares it with the preset error. This step is crucial for ensuring the precision of the model and deciding whether further optimization is needed.

[0138] The specific implementation method is as follows: First, the system uses cross-validation method to evaluate the prediction performance of the model. The system randomly divides the sampling data into training set and 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 multiple times to obtain stable error estimation.

[0139] The system calculates the root mean square error (RMSE) as a quantitative indicator of the overall prediction error. The calculation formula of RMSE is:

[0140]

[0141] Where n is the number of test samples, y i is the actual observation value, is the model prediction value.

[0142] To improve the reliability of error evaluation, the system also calculates the mean absolute error (MAE) and the mean relative error (MRE) as auxiliary indicators. The calculation formula of MAE is:

[0143]

[0144] The calculation formula of MRE is:

[0145]

[0146] 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 the accuracy requirement, which can be determined through historical data analysis or expert experience.

[0147] If the RMSE is greater than the preset error, the system returns to step S104 to recalculate the coefficient of variation of each initial detection sub-region and determine the high variation sub-region. This iterative process helps to continuously optimize the model until the required accuracy requirement is met.

[0148] S110, determine the initial environment data with the maximum similarity to the standard environment data corresponding to the experimental task, and determine the region of the sampling point corresponding to the initial environment data with the maximum similarity as the candidate experimental region.

[0149] If it is not greater than, the system determines the initial environment data with the maximum similarity to the standard environment data corresponding to the experimental task, and determines the region of the sampling point corresponding to the initial environment data with the maximum similarity as the candidate experimental region.

[0150] 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 with the coefficient of variation greater than the preset coefficient as the high variation sub-region is performed.

[0151] In the above embodiment, the environment profile of the entire detection region is quickly obtained through preliminary grid division and preliminary sampling. The preset interpolation method is used to construct the environment parameter space distribution model, which can not only describe the environment condition of the sampled points, but also predict the environment parameters of the unsampled points, thereby improving the detection efficiency. Secondly, by calculating the coefficient of variation and identifying the high variation sub-region, the region with rapid change of environment parameters can be intelligently identified, and the sampling density can be increased accordingly, thereby avoiding resource waste caused by blind sampling. The iterative optimization strategy is adopted, the environment parameter space distribution model is continuously updated, and the overall prediction error is evaluated, thereby ensuring the accuracy and reliability of the detection result. By comparing the similarity of the environment data and the standard environment data, the candidate experimental region is quickly determined. The accuracy of the environment parameters detected in different outdoor environments is improved.

[0152] In the above embodiment, the system controls the environment detection unmanned aerial vehicle to preliminarily sample at the center point of each initial detection sub-region. The following describes how to plan the path in combination with another embodiment. The following describes how to plan the path in combination with another embodiment. Figure 2 An environment detection unmanned aerial vehicle detection path planning method in an embodiment of the present application is described.

[0153] Please refer to Figure 2 A flowchart of an environment detection unmanned aerial vehicle detection path planning method in an embodiment of the present application is shown.

[0154] S201, obtaining the center point three-dimensional coordinates of each center point and the starting point three-dimensional coordinates of the environment detection unmanned aerial vehicle takeoff point; the system first needs to obtain all the key position information involved in the environment detection task, including the three-dimensional coordinates of each initial detection sub-region center point and the takeoff point coordinates of the environment detection unmanned aerial vehicle. These coordinate information is the basic data for planning the flight path of the unmanned aerial vehicle.

[0155] The system represents these positions through a pre-set coordinate system, such as the WGS84 coordinate system. Each coordinate contains three dimensions of longitude, latitude and height. In order to improve the accuracy of the coordinates, the system can use differential GPS technology to correct satellite signal errors by setting ground reference stations, thereby obtaining centimeter-level positioning accuracy.

[0156] 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 Kalman filtering algorithm to obtain more stable and accurate position information. This multi-source data fusion technology can effectively overcome the limitations of single positioning method, such as GPS signal blockage or INS cumulative error.

[0157] In order to adapt to different terrain conditions, the system can combine digital elevation model (DEM) data to correct the obtained height information. This is particularly important for environmental detection tasks in complex terrain environments, as it can ensure that the unmanned aerial vehicle maintains a safe height during flight and avoids collisions with terrain obstacles.

[0158] In addition, the system can implement a dynamic coordinate updating mechanism. In long-term detection tasks, due to factors such as earth rotation and atmospheric condition changes, coordinates may produce slight shifts. The system synchronizes with high-precision time servers regularly and combines atmospheric correction models to correct coordinates in real time, ensuring the long-term reliability of coordinate information.

[0159] In order to improve the efficiency of coordinate acquisition, the system can implement a distributed coordinate collection network. By deploying multiple ground control stations in the detection area, a collaborative network is formed, with each control station responsible for collecting coordinate information within a certain range around it. This distributed architecture not only improves the speed of data collection, but also enhances the fault tolerance of the system.

[0160] The system can also establish a coordinate history database to record the coordinate information obtained in each task. By analyzing historical data, the system can identify coordinate anomalies or trends, providing a reference for subsequent coordinate acquisition and valuable data support for long-term environmental monitoring.

[0161] Finally, the system implements a secure storage and transmission mechanism for coordinate information. Encryption algorithms are used to encrypt the coordinate data, and secure communication protocols (such as HTTPS) are used for data transmission to ensure that the coordinate information is not accessed or tampered with without authorization. This is crucial for protecting the confidentiality and integrity of environmental monitoring tasks.

[0162] S202. Calculate the Euclidean distance from the takeoff 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 sort each Euclidean distance in ascending order to obtain the sorting result;

[0163] After obtaining the three-dimensional coordinates of the takeoff point and all center points, the system needs to calculate the distance from the takeoff point to each center point and sort these distances. This step aims to provide a foundation for subsequent path planning; the distance sorting allows for a preliminary determination of the order in which the UAV visits each detection point. The calculation of Euclidean distance is a known existing technique and will not be elaborated upon here.

[0164] After the calculation is complete, the system uses the quicksort algorithm to sort the obtained distance values ​​in ascending order. The average time complexity of quicksort is O(nlogn), which is highly efficient for sorting large amounts of data.

[0165] To further optimize the sorting process, the system can implement a GPU-based parallel sorting algorithm. By leveraging the parallel computing capabilities of the graphics processor, the sorting speed for large-scale data can be significantly improved.

[0166] Taking into account potential obstacles in real-world flight environments, 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 that takes obstacle avoidance into account, rather than just the straight-line distance. This method can more accurately reflect the actual flight path of the UAV.

[0167] S203. Obtain the remaining battery power of the environmental detection drone and calculate the remaining flight range based on the remaining battery power. Before path planning, the system needs to accurately assess the remaining capabilities of the environmental detection drone to ensure that the planned path is within the drone's capabilities. This step involves obtaining the drone's remaining battery power and converting it into a flight distance, i.e., the remaining flight range. The specific implementation method of this step is a known existing technology and will not be described in detail here.

[0168] S204. Starting from the takeoff point, connect each center point in sequence according to the sorting result to obtain the first detection path and the first length of the first detection path;

[0169] After obtaining the distance ranking result from the takeoff point to each center point, the system needs to plan a preliminary detection path according to this ranking result. This path will determine the order of the unmanned aerial vehicle accessing each detection point, which has an important influence on the efficiency and energy consumption of the entire detection task.

[0170] The system first takes the takeoff point as the starting position, and then connects each center point in turn according to the distance ranking result to form a continuous path. This method is based on the idea of the greedy algorithm, which selects the next point closest to the current point each time, trying to obtain a globally optimal path based on local optimization.

[0171] In addition, the system can realize dynamic path planning. During the execution of the task by the unmanned aerial vehicle, if new obstacles or task changes are detected, the system can re-plan the remaining path in real time to ensure continuous optimization of the path.

[0172] S205, judge whether the remaining endurance mileage is less than the first length;

[0173] The system needs to compare the previously calculated remaining endurance mileage and the length of the first detection path to determine whether the currently planned path is within the capabilities of the unmanned aerial vehicle.

[0174] S206, take the first length as the detection path;

[0175] If not, the system will take the first length as the detection path. When the system judges that the remaining endurance mileage 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.

[0176] S207, calculate the farthest return point according to the remaining endurance mileage;

[0177] If it is less than, calculate the farthest return point according to the remaining endurance mileage, when the environmental detection unmanned aerial vehicle flies to the farthest return point and returns along the original route, the environmental detection unmanned aerial vehicle can reach the takeoff point, and the farthest return point is included in the center point. When the system judges that the remaining endurance mileage is less than the first length, it needs to re-plan the path to ensure that the unmanned aerial vehicle can safely return to the takeoff point. The system first needs to consider the remaining endurance mileage of the unmanned aerial vehicle, the distance from the current position to each detection point, and the distance from each detection point to the takeoff point. The system traverses all detection points to find the point that satisfies the above conditions and is farthest from the takeoff point as the farthest return point.

[0178] S208, take the flight path from the takeoff point to the farthest return point as the first segment of the detection path;

[0179] The system detects the flight path from the take-off point to the farthest return point as the first segment detection path, that is, the UAV can first detect the center points contained in the first segment detection path, and the UAV can return to the take-off point safely after completing the detection task of the first segment detection path.

[0180] S209, the center point contained in the take-off point to the farthest return point is deleted to obtain an updated sorting result.

[0181] The center point contained in the take-off point to the farthest return point is deleted to obtain an updated sorting result, so the center point contained in the first segment detection path does not need to be detected any more, and the center point that has been detected is removed from the updated sorting result. The system repeatedly performs the steps of connecting each center point in sequence according to the sorting result starting from the take-off point to obtain the first detection path and the first length of the first detection path, until the remaining endurance distance is not less than the first length, to obtain a plurality of first segment detection paths, and combines all the first segment detection paths as a detection path.

[0182] It should be noted that after completing the detection task of a segment detection path, the next segment detection path of the UAV is to start detection from the starting point of the new segment detection path, and the starting point of the new segment detection path is also a center point. The UAV directly flies to the center point to start detection, which can maximize energy saving.

[0183] In the above embodiment, the three-dimensional coordinates of each center point and the take-off point coordinates of the UAV are obtained to establish an accurate three-dimensional space reference system. The Euclidean distance from the take-off point to each center point is calculated, and the sorting is performed in ascending order to realize the nearest neighbor principle of path planning. The flight distance of the UAV is reduced, the energy consumption is reduced, and the working time of a single flight is prolonged. Reducing the flight distance means shorter task completion time, and the shortest path also means that the UAV is exposed to potential risks for the shortest time, thereby improving the safety of the equipment. By controlling the UAV to sample according to the generated detection path, the systematicness and completeness of the sampling process are ensured, and the problems of omission or repeated sampling are avoided. The sampling efficiency is improved, and the spatial uniformity of the data is ensured.

[0184] The system in the embodiment of the application will be described from the perspective of hardware processing. Please refer to Figure 3 An entity device structure diagram of an outdoor environment monitoring system provided by the embodiment of the application.

[0185] It should be noted that, Figure 3 The structure of the system shown is only an example, and should not limit the functions and use range of the embodiment of the application.

[0186] As Figure 3As shown, the system includes a central processing unit (CPU) 301 which can perform various appropriate actions and processes, such as executing the methods in the above-described embodiments, according to programs stored in a read-only memory (ROM) 302 or loaded from a storage section 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for operation of the system are also stored. The CPU 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0187] Connected to the I / O interface 305 are an input section 306 including a camera, an infrared sensor, and the like; an output section 307 including a liquid crystal display (LCD) and a speaker, and the like; the storage section 308 including a hard disk, and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, and the like. 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 necessary. A removable media 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like is attached to the drive 310 as necessary, so that a computer program read therefrom is installed in the storage section 308 as necessary.

[0188] In particular, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing a computer program for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309, and / or installed from the removable media 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present application are performed.

[0189] It should be noted that the computer-readable medium in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, 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 (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present application, the computer-readable signal medium can include a data signal carrying computer-readable computer programs in a baseband or as a part of a carrier wave. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above.

[0190] The flowcharts and block diagrams in the drawings illustrate the possible implementation architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Each block in the flowcharts or block diagrams can represent a module, a program segment or a part of code containing one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders than that shown in the drawings. For example, two blocks that are shown in succession can actually be executed substantially in parallel, and sometimes in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams or flowcharts, and the combination of blocks in the block diagrams or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0191] As another aspect, the present application also provides a computer readable storage medium, which can be included in the system described in the above embodiments, or can exist independently without being assembled into the system. The above storage medium carries one or more computer programs, which, when executed by a processor of a system, enable the system to implement the method provided in the above embodiments.

[0192] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still make modifications to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some of the technical features; and 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.

[0193] In the above embodiments, according to the context, the term "when" can be interpreted as meaning "if" or "after" or "in response to determining" or "in response to detecting". Similarly, according to the context, the phrase "upon determining" or "if detecting (a stated condition or event)" can be interpreted as meaning "if determining" or "in response to determining" or "upon detecting (a stated condition or event)" or "in response to detecting (a stated condition or event)".

[0194] In the above embodiments, all or some of the steps can be implemented by using software, hardware, firmware or any combination thereof. When implemented by using software, all or some of the steps can be implemented 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 into and executed by a computer, all or some of the steps as described in the embodiments of the present application are performed. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatuses. 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 one website, computer, server or data center to another website, computer, server or data center through wired (for example, coaxial cable, optical fiber, digital subscriber line) or wireless (for example, infrared, wireless, microwave, etc.) manner. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available medium can be a magnetic medium (for example, floppy disk, hard disk, magnetic tape), an optical medium (for example, DVD), or a semiconductor medium (for example, solid state disk), etc.

[0195] Those skilled in the art can understand that all or part of the processes in the above-mentioned method embodiments can be implemented by a computer program instructing relevant hardware to complete, the program can be stored in a computer readable storage medium, and the program can include the processes of the above-mentioned method embodiments when executed. The aforementioned storage medium includes ROM or random storage memory RAM, magnetic disc or optical disc and various storage code medium.

Claims

1. An outdoor environmental monitoring method, characterized in that, include: The preset detection area is initially divided into grids to obtain several initial detection sub-regions with the same area; The control environment detection drone performs preliminary sampling at the center point of each initial detection sub-region to obtain initial environmental data corresponding to each initial detection sub-region; Based on the initial environmental data, a spatial distribution model of environmental parameters is constructed using a preset interpolation method. The spatial distribution model of environmental parameters is a mathematical model used to describe and predict the spatial distribution of environmental parameters. Calculate the coefficient of variation for each of the initial detection sub-regions, and determine the initial detection sub-regions with a coefficient of variation greater than a preset coefficient as high-variance sub-regions; Determine whether the area of ​​the highly variable sub-region is greater than a preset minimum area; If the value is greater than the specified value, the highly variable sub-region is divided into the smallest sub-regions of equal area, and the center point of the smallest sub-region is used as the sampling point. If the value is not greater than the specified value, then a preset number of sampling points will be added within the highly variable sub-region. The environmental detection drone is controlled to perform secondary sampling at the sampling points to obtain initial environmental data for each sampling point and update the spatial distribution model of the environmental parameters. Calculate the overall prediction error of the updated spatial distribution model of the environmental parameters, and determine whether the overall prediction error is greater than a preset error; If it is greater than the preset coefficient, then the step of calculating the coefficient of variation of each initial detection sub-region and determining the initial detection sub-region with the coefficient of variation greater than the preset coefficient as a high-variance sub-region is performed. If the similarity is not greater than the standard environmental data corresponding to the experimental task, the initial environmental data with the highest similarity is determined, and the region of the sampling point corresponding to the initial environmental data with the highest similarity is determined as the candidate experimental region.

2. The method according to claim 1, characterized in that, The control environment detection drone performs preliminary sampling at the center point of each initial detection sub-region, specifically including: Obtain the three-dimensional coordinates of the center point of each of the aforementioned center points and the three-dimensional coordinates of the starting point of the environmental detection UAV's takeoff 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 takeoff point to each center point is calculated, and each Euclidean distance is sorted in ascending order to obtain the sorting result; A detection path is generated based on the sorting results; The control environment detection drone performs preliminary sampling at the center point of each of the initial detection sub-regions according to the detection path.

3. The method according to claim 2, characterized in that, The step of generating a detection path based on the sorting result specifically includes: Obtain the remaining battery power of the environmental detection drone, and calculate the remaining flight range based on the remaining battery power; Starting from the take-off point, each of the center points is connected sequentially according to the sorting result to obtain the first detection path and the first length of the first detection path; Determine whether the remaining driving range is less than the first length; If it is not less than, then the first length is taken as the detection path; If it is less than the remaining range, the furthest return point is calculated based on the remaining range. When the environmental detection drone flies to the furthest return point and returns along the original route, the environmental detection drone can reach the takeoff point. The furthest return point is included in the center point. The flight path from the takeoff point to the farthest return point is taken as the first detection path; The center points included in the route from the takeoff point to the farthest return point are deleted to obtain an updated sorting result. The step of connecting each center point in sequence according to the sorting result with the takeoff point as the starting point to obtain a first detection path and a first length of the first detection path is repeated until the remaining range is not less than the first length, thus obtaining a number of first detection paths. All the first detection paths are merged into the detection path.

4. The method according to claim 1, characterized in that, The step of constructing a spatial distribution model of environmental parameters based on the initial environmental data using a preset interpolation method specifically includes: The initial environmental data is subjected to normality testing and outlier detection to obtain corrected initial environmental data; Calculate the experimental variability function of the corrected initial environmental data, and fit it with a preset variability function model to obtain the theoretical variability function; Based on the theoretical variogram, spatial interpolation is performed using the ordinary kriging method to generate the spatial distribution model of the environmental parameters.

5. The method according to claim 1, characterized in that, The calculation of the coefficient of variation for each of the initial detection sub-regions specifically includes: A set of uniformly distributed interpolation points is generated in each of the initial detection sub-regions; Calculate the estimated environmental parameters for each interpolation point based on the spatial distribution model of the environmental parameters. Based on the estimated environmental parameters, calculate the average and standard deviation of the estimated environmental parameters for all interpolation points within each initial detection sub-region; The coefficient of variation for each initial detection sub-region is calculated based on the estimated average value and standard deviation of the environmental parameters for each initial detection sub-region.

6. The method according to claim 5, characterized in that, The step of calculating the average environmental parameter estimate and the standard deviation of environmental parameter estimate for all interpolation points in each initial detection sub-region based on the environmental parameter estimate specifically includes: inputting the environmental parameter estimate into the average value calculation function and the standard deviation calculation function respectively to obtain the average environmental parameter estimate and the standard deviation of environmental parameter estimate; The function for calculating the average value is: In the function, μ is the estimated average value of the environmental parameters, and x i The environmental parameter is the estimated value, and n is the number of interpolation points; The standard deviation calculation function is: In the function, σ is the standard deviation of the environmental parameter estimate, and x i The environmental parameter is the estimated value, where n is the number of interpolation points and μ is the average value of the estimated environmental parameter.

7. The method according to claim 5, characterized in that, The step of calculating the coefficient of variation for each initial detection sub-region based on the estimated average and standard deviation of environmental parameters for each initial detection sub-region specifically includes: The estimated average and 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 as follows: In the function, CV is the coefficient of variation, σ is the standard deviation of the environmental parameter estimate, and μ is the mean of the environmental parameter estimate.

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

10. A computer program product, characterized in that, When the computer program product is run on the system, the system performs the method as described in any one of claims 1-7.

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