Sensitivity Analysis Method, Device, Electronic Device and Storage Medium for UAV Maneuvering Observation Field

Abstract

The present invention provides a sensitivity analysis method, device, electronic device and storage medium for an unmanned aerial vehicle (UAV) maneuverable observation field, relating to the technical field of meteorological observation. According to the complete observation operator required for UAV observation assimilation, a general assimilation module adapted to UAV observation is built. According to the background forecast field, the analysis and forecast field, the true field of the weather forecast area and the innovation vector of the UAV maneuverable observation field, the influence of the observation field of the observation type on numerical weather prediction is obtained, simplifying the process of calculating the first observation influence, ensuring the efficient utilization and accurate evaluation of the UAV maneuverable observation field in the forecast system, and improving the accuracy of determining the influence of the UAV maneuverable observation field on weather forecasting. In addition, a compatibility mechanism with the existing observation sensitivity analysis system for forecasts is considered, and by setting observation type and observation means labels for the maneuverable observation field, a horizontal comparison of the sensitivities of different observation means of the same observation type is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological observation, and particularly to a sensitivity analysis method, device, electronic device and storage medium for an unmanned aerial vehicle (UAV) mobile observation field. Background Art

[0002] With the development of meteorological detection capabilities, the means, types, quantities, and resolutions of meteorological observations have been continuously improved, and various new observation technologies have emerged. Observation data is of great significance for forecasting. Tens of thousands of observations are used in assimilation analysis to improve the initial field of numerical models, laying a good data foundation for the improvement of numerical forecasting quality. However, the actual number of observations is numerous, and the number of observations that can be used for assimilation analysis is extremely large, reaching several million or even tens of millions. Although assimilating a large amount of observation data can improve the results of analysis and forecasting, the contributions of different types, elements, spatial positions, and combination methods of observations in assimilation analysis to forecasting may vary, and the sensitivities of numerical forecasting quality to different observation data are also inconsistent. Not all observations assimilated into the analysis will improve forecasting performance. Therefore, monitoring the application of observation data in assimilation analysis and numerical forecasting, objectively and quantitatively evaluating the sensitivity of numerical weather forecasting to multi-source observation data, and accurately understanding the contribution of observation data assimilation to numerical forecasting are the basis for giving full play to the role of observation data, and have important scientific significance for improving numerical forecasting quality, effectively applying observation data, and further improving the observation system.

[0003] In recent years, the adjoint-based Forecast Sensitivity to Observations (FSO) test method has gradually matured and become a flexible and efficient means for evaluating the impact of observations on forecasting quality. Compared with traditional observation test methods, the advantage of the FSO method lies in its direct evaluation of the impact of any observation assimilated into the analysis on short-term forecasting errors, which can be used to diagnose the effectiveness of different types of observations on forecasting quality and has high computational efficiency.

[0004] With the progress of observation technology, the types of observations have become increasingly rich. Among them, as the forefront of the development of observation technology, the large unmanned aerial vehicle (UAV) meteorological sounding system can drop radiosondes from the air and obtain more detailed three-dimensional meteorological data such as temperature, humidity, wind direction, wind speed, and air pressure over a wide area. While ensuring the reliability of the data, this detection method improves the temporal and horizontal resolution of observations and can conduct targeted real-time intensive observations according to the development and movement of large-scale weather systems. However, at present, China is still in the initial stage in the field of meteorological sounding using large UAVs, with a serious lack of understanding of the role of UAV mobile observations in numerical weather prediction, and there is still a lack of systematic evaluation of its contribution to numerical weather prediction. Therefore, a reliable evaluation and analysis method for the data of sounding observations dropped by large UAVs is urgently needed to understand its value in improving the performance of numerical weather prediction. In addition, considering that the opportunity cost and economic cost of large UAV flights are relatively high, pre-flight estimated analysis of observations (including observation elements, positions, altitudes, etc.) to determine whether there is a positive contribution to numerical weather prediction is a necessary prerequisite for guiding the route planning and design of UAVs.

[0005] Facing the new mobile observation means of airborne platforms such as UAVs, the existing observation data cannot be directly input into the data assimilation system; the assimilation system lacks the complete observation operators and tangent linear / adjoint operators required for airborne sounding assimilation; facing the sounding data of different observation means for the same observation type (for example, the sounding observation type can be subdivided into observation means such as balloons, UAVs, airships, radars, etc.), the existing FSO system can only calculate the sensitivities of different means separately by replacement, but cannot achieve the horizontal comparison of the sensitivity differences between different means, ignoring the mutual influence of sounding data of different means, and the judgment of the impact of meteorological observation data on numerical weather prediction is not accurate. Summary of the Invention

[0006] The present invention provides a sensitivity analysis method, device, electronic device, and storage medium for a UAV mobile observation field, aiming to solve the defects in the prior art that it is impossible to judge the impact of UAV mobile observation data on numerical weather prediction and that it is impossible to complete the horizontal comparison of the impacts of observations of different observation means for the same observation type, and to achieve the improvement of the comprehensiveness and accuracy of judging the impact of meteorological observation data on numerical weather prediction.

[0007] The present invention provides a sensitivity analysis method for an unmanned aerial vehicle (UAV) maneuverable observation field, including: obtaining the UAV maneuverable observation field of a weather forecast area according to the UAV maneuverable observation means, obtaining the background field of the weather forecast area, and obtaining the analysis field of the weather forecast area based on the assimilation analysis of the UAV maneuverable observation field and the background field; obtaining the background forecast field based on the background field, and obtaining the analysis forecast field based on the analysis field; obtaining the first observation impact of the UAV maneuverable observation field based on the background forecast field, the analysis forecast field, the true field of the weather forecast area, and the innovation vector of the UAV maneuverable observation field. When the first observation impact is less than zero, it is determined that the assimilated observation of the UAV maneuverable observation field makes a positive contribution to weather forecasting. When the first observation impact is greater than zero, it is determined that the assimilated observation of the UAV maneuverable observation field makes a negative contribution to weather forecasting; obtaining the second observation impact of other observation means, and obtaining the comparison result between the UAV maneuverable observation means and other observation means based on the first observation impact and the second observation impact.

[0008] According to the sensitivity analysis method for the UAV maneuverable observation field provided by the present invention, the analysis field of the weather forecast area is obtained based on the assimilation analysis of the UAV maneuverable observation field and the background field, including: obtaining the non-linear observation operator of the analysis field variable according to the conversion between the model space variable and the observation space variable; obtaining the covariance matrix of the background error of the background field, and obtaining the covariance matrix of the observation error of the UAV maneuverable observation field; constructing the objective function of the analysis field variable based on the non-linear observation operator of the analysis field variable, the covariance matrix of the background error, the covariance matrix of the observation error, the UAV maneuverable observation field, and the background field; when the objective function reaches the minimum value, taking the value of the analysis field variable as the analysis field.

[0009] According to the sensitivity analysis method of the UAV maneuverable observation field provided by the present invention, based on the background forecast field, the analysis and forecast field, the true field of the weather forecast area, and the innovation vector of the UAV maneuverable observation field, the first observation impact of the UAV maneuverable observation field is obtained, including: obtaining the adjoint mode of the first trajectory based on the background forecast field, and obtaining the adjoint mode of the second trajectory based on the analysis and forecast field; performing three-dimensional variational assimilation analysis operation on the UAV maneuverable observation field to obtain the innovation vector; determining the diagonal matrix based on the forecast error component weighting coefficient; obtaining the linear Kalman gain matrix based on the covariance matrix of the background error, the covariance matrix of the observation error, and the non-linear observation operator of the analysis field; obtaining the post-assimilation forecast error based on the difference between the analysis and forecast field and the true field of the weather forecast area, and obtaining the sensitivity value of the post-assimilation forecast error based on the product value of the post-assimilation forecast error, the diagonal matrix, and the adjoint mode of the second trajectory; obtaining the pre-assimilation forecast error based on the difference between the background forecast field and the true field, and obtaining the sensitivity value of the pre-assimilation forecast error based on the product value of the pre-assimilation forecast error, the diagonal matrix, and the adjoint mode of the first trajectory; obtaining the comprehensive forecast error sensitivity value based on the sum value of the sensitivity value of the post-assimilation forecast error and the sensitivity value of the pre-assimilation forecast error, and obtaining the first observation impact based on the product value of the comprehensive forecast error sensitivity value, the innovation vector, and the transpose matrix of the linear Kalman gain matrix.

[0010] According to the sensitivity analysis method of the UAV maneuverable observation field provided by the present invention, obtaining the adjoint mode of the first trajectory based on the background forecast field and obtaining the adjoint mode of the second trajectory based on the analysis and forecast field includes: obtaining the first tangent linear mode through the tangent linear integration in the reverse time direction of the background field, performing a transpose process on the first tangent linear mode to obtain the adjoint mode of the first trajectory; obtaining the second tangent linear mode through the tangent linear integration in the reverse time direction of the analysis field, performing a transpose process on the second tangent linear mode to obtain the adjoint mode of the second trajectory.

[0011] According to the sensitivity analysis method of the UAV maneuverable observation field provided by the present invention, based on the first observation impact and the second observation impact, the comparison result between the UAV maneuverable observation means and other observation means is obtained, including: when the first observation impact is less than the second observation impact, obtaining the difference between the first observation impact and the second observation impact, and the smaller the difference, the better the UAV maneuverable observation means is relative to other observation means.

[0012] According to the sensitivity analysis method of the UAV maneuverable observation field provided by the present invention, the objective function is as follows.

[0013] ;

[0014] Among them, is the objective function, is the analysis field, is the background field, is a non - linear observation operator for analyzing field variables, is the covariance matrix of background error, is the covariance matrix of observation error, is the UAV maneuver observation field.

[0015] According to the sensitivity analysis method of the UAV maneuver observation field provided by the present invention, a background prediction field is obtained based on the background field, and an analysis prediction field is obtained based on the analysis field, including: based on the background field, forward integration is performed in the Weather Research and Forecasting Nonhydrostatic Forecasting System (WRFNL) non - linear model to obtain the background prediction field; based on the analysis field, forward integration with the same time - effect is performed in the WRFNL non - linear model to obtain the analysis prediction field.

[0016] The present invention also provides a sensitivity analysis device for the UAV maneuver observation field, including: an assimilation analysis module, configured to obtain the UAV maneuver observation field of the weather forecasting area according to the UAV maneuver observation means, obtain the background field of the weather forecasting area, and obtain the analysis field of the weather forecasting area based on the assimilation analysis of the UAV maneuver observation field and the background field; a prediction module, configured to obtain the background prediction field based on the background field and obtain the analysis prediction field based on the analysis field; an observation impact determination module, configured to obtain the first observation impact of the UAV maneuver observation field based on the background prediction field, the analysis prediction field, the true field of the weather forecasting area, and the innovation vector of the UAV maneuver observation field. When the first observation impact is less than zero, it is determined that the assimilated observation of the UAV maneuver observation field makes a positive contribution to weather forecasting. When the first observation impact is greater than zero, it is determined that the assimilated observation of the UAV maneuver observation field makes a negative contribution to weather forecasting; a comparison module, configured to obtain the second observation impact of other observation means, and obtain the comparison result between the UAV maneuver observation means and other observation means based on the first observation impact and the second observation impact.

[0017] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the sensitivity analysis method of the UAV maneuver observation field as described above.

[0018] The present invention also provides a non - transitory computer - readable storage medium, on which a computer program is stored. When the computer program is executed by the processor, it implements the sensitivity analysis method of the UAV maneuver observation field as described above.

[0019] The sensitivity analysis method, device, electronic device and storage medium of the UAV mobile observation field provided by the present invention construct a general assimilation module adapted to UAV observation according to the complete observation operator required for UAV observation assimilation. According to the background forecast field, the analysis and forecast field, the real field of the weather forecast area and the innovation vector of the UAV mobile observation field, the influence of the observation field of the observation type on the numerical forecast is obtained, the process of calculating the first observation influence is simplified, the efficient utilization and accurate evaluation of the UAV mobile observation field in the forecast system are ensured, and the accuracy of determining the influence of the UAV mobile observation field on the weather forecast is improved. In addition, a compatibility mechanism with the existing observation sensitivity analysis system for forecasts is considered, and by setting observation type and observation means labels for the mobile observation field, horizontal comparison of sensitivities of different observation means of the same observation type is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0021] Figure 1 is one of the flow diagrams of the sensitivity analysis method of the UAV mobile observation field provided by the present invention.

[0022] Figure 2 is the second flow diagram of the sensitivity analysis method of the UAV mobile observation field provided by the present invention.

[0023] Figure 3 is the structural diagram of the sensitivity analysis device of the UAV mobile observation field provided by the present invention.

[0024] Figure 4 is the structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] To make the objectives, technical solutions and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0026] The Forecast Sensitivity to Observations (FSO) test method is an important tool for evaluating the impact of observational data on numerical weather prediction results. This method has shown significant advantages in improving forecast accuracy, enhancing the test speed, and understanding the role of observational data. However, in the face of new mobile observations from airborne platforms such as unmanned aerial vehicles (UAVs), the existing FSO system cannot support the scientific analysis of their forecast sensitivity. Specifically, FSO is a system for forecasting sensitivity to observations developed under the framework of the Weather Research and Forecasting Data Assimilation (WRFDA) variational assimilation system. However, WRFDA does not support the assimilation of new airborne observations, nor does it support directly achieving horizontal comparisons of different observational means for the same type of observation. In fact, for new observational means, existing research usually divides into two schemes to test their sensitivity. The most commonly used one is to compare the forecast results of assimilating conventional observations with those of assimilating new observations at the same observational location, and then evaluate and compare the impact and improvement of new observations on the forecast. However, this method has a large computational amount and ignores the mutual connections among various observations. Another method is to disguise new observations as observational types that the assimilation system can read. However, the existing assimilation systems cannot distinguish different observational means for the same type of observation. For example, different sounding means such as balloon sounding, UAV sounding, airship sounding, radar sounding, and lidar sounding will all be regarded as only one type of sounding in the assimilation system. Therefore, if a similar strategy is adopted in the FSO method, the sensitivity differences between new soundings and other sounding means cannot be distinguished, which will not only affect the role of original conventional sounding observations but also cannot measure the horizontal comparison of the objective contributions among observational data.

[0027] Large - scale unmanned aerial vehicle (UAV) meteorological mobile observations are one of the frontiers in the field of meteorological observations. However, as mentioned above, in the face of such new mobile observations on airborne platforms, the existing sensitivity test system for forecasts cannot support the scientific analysis of the contribution of such observations to forecasts. To solve the above problems, the present invention innovates on the idea of the FSO method, establishes a new sensitivity analysis system for forecasts compatible with airborne platform observations, and realizes the establishment of an independent sensitivity analysis module for airborne platform observations, including a series of links such as observation quality control, observation pre - processing, assimilation analysis, non - linear forecasting, and adjoint model application, ensuring the independent, flexible, efficient utilization and accurate evaluation of airborne observation data in the forecasting system. In addition, the present invention also considers the compatibility mechanism with the existing sensitivity analysis system for forecasts of observations, and expands the horizontal comparison of the contributions of different observation means of the same observation type. Specifically, for sounding data, it realizes the horizontal comparison of the contributions of airborne sounding, conventional sounding, wind profiler radar, lidar wind measurement radar and other multiple sounding means to forecasts. It not only strengthens the independent analysis ability of airborne platform observations, but also realizes the comprehensive analysis with diversified sky - ground - air observation platforms such as ground observations and satellite observations, constructs an all - round and multi - level sensitivity analysis framework for forecasts of observations, and provides strong technical support for fully understanding the value of UAV observation data, understanding the interaction between UAV observations and other observation means, and reasonably planning UAV flight routes.

[0028] The following combines Figures 1-4 to describe the sensitivity analysis method, device and electronic equipment of the UAV mobile observation field of the present invention.

[0029] Figure 1 is one of the flow schematic diagrams of the sensitivity analysis method of the UAV mobile observation field provided by the present invention. As Figure 1 shown, the sensitivity analysis method of the UAV mobile observation field includes S100 to S300, and the specific steps are as follows.

[0030] S100: According to the UAV mobile observation means, obtain the UAV mobile observation field of the weather forecast area, obtain the background field of the weather forecast area, and based on the assimilation analysis of the UAV mobile observation field and the background field, obtain the analysis field of the weather forecast area.

[0031] Obtain and sort out the UAV - dropped sounding observation data (UAV mobile observation field). Pre - process the original UAV observations, and complete observation quality control, sparsification, observation error statistics and observation format conversion.

[0032] Obtain different types of observational data, including observational data from airborne radiosondes, conventional radiosondes, wind profiler radars, lidar wind profilers, and other multi-source sounding means. Mark the data of different types and different observational means to facilitate the later expansion of this method into a verification platform applicable to multiple different types of observations. According to the unmanned aerial vehicle (UAV) maneuverable observation means, obtain UAV maneuverable observational data, and obtain the UAV maneuverable observation field based on the UAV maneuverable observational data.

[0033] Read in the background field information, perform initialization settings on the background information, and obtain the background field.

[0034] Based on the background error calculation model (Generator Baseband Environment, GEN_BE V2.0) and the forecast data without assimilation, statistically analyze the background error characteristics of atmospheric variables to obtain the covariance matrix of the background error. Further, obtain the covariance matrix of the observation error.

[0035] Construct a complete non-linear observation operator and tangent linear / adjoint operator for the analysis field variables required for airborne radiosonde assimilation within WRFDA. Use the WRFDA assimilation technology to obtain the analysis field based on the UAV maneuverable observation field and the background field. As Figure 2 shown, the present invention uses the three-dimensional variational assimilation analysis (3DVAR) operation of WRFDA. 3DVAR obtains the maximum likelihood estimate of the true state of the atmosphere by solving the minimum value of the objective function. When the objective function reaches the minimum value, obtain the corresponding analysis field.

[0036] S200: Obtain the background forecast field based on the background field and obtain the analysis forecast field based on the analysis field.

[0037] Obtain the background forecast field based on the background field and obtain the analysis forecast field based on the analysis field. Specifically, based on the background field, perform forward integration in the Weather Research and Forecasting Nonhydrostatic Forecasting System (WRFNL) non-linear model to obtain the background forecast field; based on the analysis field, perform forward integration with the same time period in the WRFNL non-linear model to obtain the analysis forecast field.

[0038] Starting from the background field, perform forward integration in the Weather Research and Forecasting Nonhydrostatic Forecasting System (WRFNL) non-linear model to obtain the background forecast field. Starting from the analysis field, perform forward integration with the same time period in the WRFNL non-linear model to obtain the analysis forecast field.

[0039] S300: Obtain the first observation impact of the UAV maneuverable observation field based on the background forecast field, the analysis forecast field, the true field of the weather forecast area, and the innovation vector of the UAV maneuverable observation field. When the first observation impact is less than zero, determine that the assimilated observation of the UAV maneuverable observation field makes a positive contribution to the weather forecast. When the first observation impact is greater than zero, determine that the assimilated observation of the UAV maneuverable observation field makes a negative contribution to the weather forecast.

[0040] Construct a conversion formula for the innovation vector (the innovation vector) and the first observation impact of the background forecast field, the analysis forecast field, the true field, and the UAV maneuverable observation field.

[0041] According to this conversion formula, when the innovation vectors of the background forecast field, the analysis forecast field, the true field, and the UAV maneuverable observation field are known, the first observation impact can be calculated. The first observation impact is used to characterize the impact of the UAV maneuverable observation field on the weather forecast. If the calculated first observation impact is less than 0, it means that the assimilated observation of the UAV maneuverable observation field reduces the forecast error and makes a positive contribution to the weather forecast. If the calculated first observation impact is greater than 0, it means that the assimilated observation of the UAV maneuverable observation field increases the forecast error and makes a negative contribution to the weather forecast.

[0042] S400: Obtain the second observation impact of other observation means, and based on the first observation impact and the second observation impact, obtain the comparison result between the UAV maneuverable observation means and other observation means.

[0043] Based on the first observation impact and the second observation impact, obtain the comparison result between the UAV maneuverable observation means and other observation means. Specifically, when the first observation impact is less than the second observation impact, obtain the difference between the first observation impact and the second observation impact. The smaller the difference, the better the UAV maneuverable observation means relative to other observation means.

[0044] For data of different types and different observation means, respectively obtain the corresponding second observation impact, and then the contributions of observation data of different observation types, elements, altitudes, and positions to the forecast can be obtained. For example, the first observation impact is -3 and the second observation impact is -1. This shows that the first observation impact is better than the second observation impact. Further obtain the difference between the first observation impact and the second observation impact. The smaller this difference, the better the UAV maneuverable observation means relative to other observation means.

[0045] This application realizes the horizontal comparison between the UAV maneuverable observation means and other observation means by comparing the first observation impact and the second observation impact.

[0046] Furthermore, for the observation data of each observation type, statistics of the maximum value, minimum value, average value, and root mean square error of the corresponding observation minus background field (OMB) are obtained, and statistics of the maximum value, minimum value, average value, and root mean square error of the observation minus analysis field (OMA) are obtained to further analyze the stability of the influence of the observation data on the forecast.

[0047] According to the label information of the previous observation type, the sensitivity test results of the unmanned aerial vehicle (UAV) maneuvering radiosonde observation are analyzed to complete the analysis of the contribution of this type of data to numerical weather prediction.

[0048] The present invention realizes a compatibility mechanism with the existing observation sensitivity analysis system for forecasts. It not only strengthens the independent analysis ability of airborne platform observations but also realizes the comprehensive analysis with diversified sky-air-ground observation platforms such as ground observations, satellites, and radar observations, constructing an all-round and multi-level observation sensitivity analysis framework for forecasts, providing technical support for improving the accuracy and timeliness of numerical weather prediction. Based on the sensitivity test results of the independent forecasts for the types, positions, altitudes, elements, etc. of airborne platform observations, the present invention guides the design of the UAV flight route, the sounding instrument dropping point, altitude, and key observation elements, and combines the test results of diversified observation platforms to guide the optimization research of the layout of the meteorological comprehensive station network.

[0049] The sensitivity analysis method for the UAV maneuvering observation field provided by the embodiment of the present invention constructs a general assimilation module adapted to UAV observations according to the complete observation operator required for UAV observation assimilation. According to the background forecast field, the analysis forecast field, the true field of the weather forecast area, and the innovation vector of the UAV maneuvering observation field, the influence of the observation field of the observation type on the numerical forecast is obtained, simplifying the process of calculating the first observation influence, ensuring the efficient utilization and accurate evaluation of the UAV maneuvering observation field in the forecast system, and improving the accuracy of determining the influence of the UAV maneuvering observation field on the weather forecast. In addition, a compatibility mechanism with the existing observation sensitivity analysis system for forecasts is considered, and by setting the observation type and observation means labels for the maneuvering observation field, the horizontal comparison of the sensitivities of different observation means for the same observation type is realized.

[0050] Based on the above embodiments, through the assimilation analysis of the UAV maneuvering observation field and the background field, the analysis field of the weather forecast area is obtained, including S110 to S140, and the specific steps of each step are as follows.

[0051] S110: According to the conversion between the model space variables and the observation space variables, obtain the non-linear observation operator of the analysis field variables.

[0052] S120: Obtain the covariance matrix of the background error of the background field and the covariance matrix of the observation error of the UAV maneuvering observation field.

[0053] S130: Based on the non - linear observation operator of the analysis field variables, the covariance matrix of the background error, the covariance matrix of the observation error, the UAV maneuvering observation field, and the background field, construct the objective function of the analysis field variables.

[0054] S140: When the objective function reaches the minimum value, take the value of the analysis field variables as the analysis field.

[0055] The formula of the objective function is as follows.

[0056] ;

[0057] Where, is the objective function, is the analysis field, is the background field, is the non - linear observation operator of the analysis field variables, is the covariance matrix of the background error, is the covariance matrix of the observation error, is the UAV maneuvering observation field.

[0058] is the non - linear observation operator that converts the model space variables into observation space variables, making the model field and the UAV maneuvering observation field computationally consistent. The covariance matrix of the background error and the covariance matrix of the observation error determine the relative importance of the UAV maneuvering observation field information and the background field information, the mutual conversion between variables, and the propagation of variables in space.

[0059] Based on the non - linear observation operator of the analysis field variables, the covariance matrix of the background error, the covariance matrix of the observation error, the UAV maneuvering observation field, and the background field, construct the objective function of the analysis field variables. Solve the minimum value of the objective function. When the objective function reaches the minimum value, the corresponding value of the analysis field variables is the obtained analysis field.

[0060] Solve the minimum value of the objective function. According to the gradient of L can be obtained. Iterate according to the cost function to make gradually decrease (the ideal value is ). At this time, the corresponding is the analysis field after assimilation.

[0061] According to the assimilation analysis of the UAV maneuvering observation field and the background field, the present invention obtains the analysis field, simplifies the computational amount of obtaining the analysis field. By obtaining the minimum value of the objective function to obtain the analysis field, the accuracy of determining the analysis field is improved.

[0062] Based on the above embodiments, the first observation impact of the UAV maneuvering observation field is obtained based on the background forecast field, the analysis forecast field, the true field of the weather forecast area, and the innovation vector of the UAV maneuvering observation field, including S310 to S370, and the specific steps are as follows.

[0063] S310: Obtain the adjoint mode of the first trajectory based on the background forecast field, and obtain the adjoint mode of the second trajectory based on the analysis forecast field.

[0064] S320: Perform three-dimensional variational assimilation analysis operation on the UAV maneuvering observation field to obtain the innovation vector.

[0065] S330: Determine the diagonal matrix based on the forecast error component weighting coefficient.

[0066] S340: Obtain the linear Kalman gain matrix based on the covariance matrix of the background error, the covariance matrix of the observation error, and the nonlinear observation operator of the analysis field.

[0067] S350: Obtain the post-assimilation forecast error based on the difference between the analysis forecast field and the true field of the weather forecast area, and obtain the sensitivity value of the post-assimilation forecast error based on the product value of the post-assimilation forecast error, the diagonal matrix, and the adjoint mode of the second trajectory.

[0068] S360: Obtain the pre-assimilation forecast error based on the difference between the background forecast field and the true field, and obtain the sensitivity value of the pre-assimilation forecast error based on the product value of the pre-assimilation forecast error, the diagonal matrix, and the adjoint mode of the first trajectory.

[0069] S370: Obtain the comprehensive forecast error sensitivity value based on the sum value of the sensitivity value of the post-assimilation forecast error and the sensitivity value of the pre-assimilation forecast error, and obtain the first observation impact based on the product value of the comprehensive forecast error sensitivity value, the innovation vector, and the transposed matrix of the linear Kalman gain matrix.

[0070] The true field (usually actual observations and global reanalysis data) is introduced to characterize the true state of the atmosphere. The background forecast field error and the analysis forecast field error are calculated based on the true field. According to the difference between the analysis forecast field error and the background forecast field error, the reduction amount of the forecast error in the nonlinear mode, that is, the forecast accuracy, is calculated.

[0071] ;

[0072] Among them, is the background forecast field error, is the analysis forecast field error, is the true field, is the analysis forecast field, is the background forecast field, is the diagonal matrix with the forecast error component weighting coefficient, For the forecast accuracy.

[0073] The actual atmospheric motion has highly complex and strong non - linear motion characteristics. The non - linear prediction model of atmospheric motion in the numerical prediction model is as follows.

[0074] ;

[0075] Among them, is the forecast field, is the initial field, is the non - linear numerical prediction, is the non - linear integral of the initial field along the positive time direction.

[0076] is a function of the model forecast field (forecast accuracy function) and changes with the change of the model initial field, that is . The forecast accuracy The gradients with respect to the background forecast field variables and the analysis forecast field variables are respectively and , and are used as the initial fields of the adjoint model of the Weather Research and Forecasting Parallel Unstructured Grid System (WRFPLUS). Reverse integrations are respectively carried out along the reverse trajectories of the forecasts to obtain the sensitivities of the forecast accuracy to the initial fields.

[0077] ;

[0078] Among them, is the forecast accuracy function, is the initial field, is the non - linear integral of the initial field along the positive time direction, is the tangent linear integral of the initial field along the reverse time direction, is the adjoint model of the corresponding trajectory, is the forecast field.

[0079] The adjoint model of the first trajectory is obtained based on the background forecast field, and the adjoint model of the second trajectory is obtained based on the analysis forecast field. Specifically, the first tangent linear model is obtained based on the tangent linear integral of the background field along the reverse time direction, and the adjoint model of the first trajectory is obtained by transposing the first tangent linear model; the second tangent linear model is obtained based on the tangent linear integral of the analysis field along the reverse time direction, and the adjoint model of the second trajectory is obtained by transposing the second tangent linear model.

[0080] Based on the adjoint model of WRFDA, the sensitivities at WRF grid points are transformed into the observation space to obtain the sensitivities of the forecast error to the observation points. Finally, calculate and to obtain the first observation impact by taking the dot product . is the innovation vector, calculated by the 3DVAR system. In the specific solution of the present invention, a Taylor approximation scheme of third-order linear estimation, which characterizes the highest accuracy of the forecast accuracy varying with the observation, is adopted.

[0081] ;

[0082] where is the first observation impact, is the innovation vector, is the analysis and forecast field, is the background forecast field, is a diagonal matrix with the weighting coefficient of the forecast error component, is the forecast accuracy, is the true field, is the adjoint model of the second trajectory, is the adjoint model of the first trajectory, is the linear Kalman gain matrix, is the forecast error after assimilation, is the sensitivity value of the forecast error after assimilation, is the forecast error before assimilation, is the sensitivity value of the forecast error before assimilation.

[0083] The calculation formula of the linear Kalman gain matrix is as follows.

[0084] ;

[0085] where is the linear Kalman gain matrix, is the covariance matrix of the background error, is the non-linear observation operator of the analysis field, is the covariance matrix of the observation error.

[0086] According to the tangent linear integral in the reverse direction of the background field extension time, the present invention obtains the first tangent linear mode, and further obtains the adjoint mode of the first trajectory; according to the tangent linear integral in the reverse direction of the analysis field extension time, the second tangent linear mode is obtained, and further the adjoint mode of the second trajectory is obtained, which is beneficial to simplifying the calculation of the first observation impact. According to the background forecast field, the analysis forecast field, the true field of the weather forecast area, and the innovation vector of the UAV mobile observation field, the present invention obtains the observation impact of the observation type, simplifies the process of calculating the first observation impact, ensures the efficient utilization and accurate evaluation of the UAV mobile observation field in the forecast system, and improves the accuracy of determining the impact of the UAV mobile observation field on weather forecasting.

[0087] The present invention realizes the sensitivity test of the UAV mobile sounding based on the forecast of the FSO system, and realizes the sensitivity test algorithm from a series of links such as observation quality control, observation preprocessing, assimilation analysis, data assimilation, nonlinear forecasting, and adjoint mode calculation. This process includes 1 analysis assimilation, 2 nonlinear forecasting mode integrations, 2 tangent linear adjoint mode integrations, and 1 adjoint analysis assimilation. The calculation cost is about 10 to 15 times that of the ordinary single-mode forecast sensitivity test, and the calculation efficiency is high.

[0088] The present invention expands the horizontal comparison of the contributions of different observation means to the forecast, and extends the test system to multi-source data other than UAV mobile observations. It not only strengthens the independent analysis ability of airborne platform observations, but also realizes the comprehensive analysis of multi-platform observations such as ground observations and satellite observations in the sky-air-ground, constructs an all-round and multi-level framework for the sensitivity analysis of the forecast to observations, and provides strong technical support for fully understanding the value of UAV observation data and reasonably planning UAV flight routes.

[0089] The sensitivity analysis device of the UAV mobile observation field provided by the present invention will be described below. The sensitivity analysis device of the UAV mobile observation field described below can be correspondingly referred to the sensitivity analysis method of the UAV mobile observation field described above.

[0090] A sensitivity analysis device for a UAV mobile observation field includes: an assimilation analysis module 301, configured to obtain a UAV mobile observation field of a weather forecast area according to the UAV mobile observation means, obtain a background field of the weather forecast area, and obtain an analysis field of the weather forecast area based on the assimilation analysis of the UAV mobile observation field and the background field.

[0091] A forecasting module 302, configured to obtain a background forecast field based on the background field and obtain an analysis forecast field based on the analysis field.

[0092] An observation impact determination module 303 is configured to obtain a first observation impact of the UAV maneuverable observation field based on a background forecast field, an analysis forecast field, a true field of a weather forecast area, and an innovation vector of the UAV maneuverable observation field. When the first observation impact is less than zero, it is determined that the assimilated observation of the UAV maneuverable observation field has a positive contribution to weather forecasting. When the first observation impact is greater than zero, it is determined that the assimilated observation of the UAV maneuverable observation field has a negative contribution to weather forecasting.

[0093] A comparison module 304 is configured to obtain a second observation impact of other observation means, and obtain a comparison result between the UAV maneuverable observation means and other observation means based on the first observation impact and the second observation impact.

[0094] The sensitivity analysis device for the UAV maneuverable observation field provided by the embodiments of the present invention constructs an assimilation general module adapted to UAV observations according to the complete observation operator required for UAV observation assimilation. Based on the background forecast field, the analysis forecast field, the true field of the weather forecast area, and the innovation vector of the UAV maneuverable observation field, the impact of the observation field of the observation type on numerical weather prediction is obtained, the process of calculating the first observation impact is simplified, the efficient utilization and accurate evaluation of the UAV maneuverable observation field in the forecast system are ensured, and the accuracy of determining the impact of the UAV maneuverable observation field on weather forecasting is improved. In addition, a compatibility mechanism with the existing observation sensitivity analysis system for weather forecasting is considered, and by setting observation type and observation means labels for the maneuverable observation field, a horizontal comparison of the sensitivities of different observation means of the same observation type is realized.

[0095] In one embodiment, the assimilation analysis module 301 is configured to: obtain a non-linear observation operator of the analysis field variable according to the conversion between the model space variable and the observation space variable; obtain the covariance matrix of the background error of the background field, and obtain the covariance matrix of the observation error of the UAV maneuverable observation field; construct an objective function of the analysis field variable based on the non-linear observation operator of the analysis field variable, the covariance matrix of the background error, the covariance matrix of the observation error, the UAV maneuverable observation field, and the background field; when the objective function reaches the minimum value, use the value of the analysis field variable as the analysis field.

[0096] In one embodiment, the observation impact determination module 303 is configured to: obtain the adjoint mode of the first trajectory based on the background forecast field, and obtain the adjoint mode of the second trajectory based on the analysis forecast field; perform a three-dimensional variational assimilation analysis operation on the UAV maneuvering observation field to obtain an innovation vector; determine a diagonal matrix based on the forecast error component weighting coefficient; obtain a linear Kalman gain matrix based on the covariance matrix of the background error, the covariance matrix of the observation error, and the non-linear observation operator of the analysis field; obtain the post-assimilation forecast error based on the difference between the analysis forecast field and the true field of the weather forecast area, and obtain the sensitivity value of the post-assimilation forecast error based on the product of the post-assimilation forecast error, the diagonal matrix, and the adjoint mode of the second trajectory; obtain the pre-assimilation forecast error based on the difference between the background forecast field and the true field, and obtain the sensitivity value of the pre-assimilation forecast error based on the product of the pre-assimilation forecast error, the diagonal matrix, and the adjoint mode of the first trajectory; obtain a comprehensive forecast error sensitivity value based on the sum of the sensitivity value of the post-assimilation forecast error and the sensitivity value of the pre-assimilation forecast error, and obtain a first observation impact based on the product of the comprehensive forecast error sensitivity value, the innovation vector, and the transpose matrix of the linear Kalman gain matrix.

[0097] In one embodiment, the observation impact determination module 303 is configured to: obtain a first tangent linear mode through a tangent linear integration of the background field in the reverse time direction, perform a transpose process on the first tangent linear mode to obtain the adjoint mode of the first trajectory; obtain a second tangent linear mode through a tangent linear integration of the analysis field in the reverse time direction, perform a transpose process on the second tangent linear mode to obtain the adjoint mode of the second trajectory.

[0098] In one embodiment, the comparison module 304 is configured to: when the first observation impact is less than the second observation impact, obtain the difference between the first observation impact and the second observation impact. The smaller the difference, the better the UAV maneuvering observation method is compared to other observation methods.

[0099] In one embodiment, the objective function is:

[0100] ;

[0101] Wherein, is the objective function, is the analysis field, is the background field, is the non-linear observation operator of the analysis field variables, is the covariance matrix of the background error, is the covariance matrix of the observation error, is the UAV maneuvering observation field.

[0102] In one embodiment, the forecasting module 302 is configured to: perform forward integration in the Weather Research and Forecasting Nonhydrostatic Forecasting System (WRFNL) nonlinear model based on the background field to obtain a background forecast field; and perform forward integration for the same time period in the WRFNL nonlinear model based on the analysis field to obtain an analysis forecast field.

[0103] Figure 4 The schematic physical structure diagram of an electronic device is exemplified, as Figure 4 shown. The electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440. Among them, the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call the logical instructions in the memory 430 to execute the sensitivity analysis method for the unmanned aerial vehicle (UAV) maneuvering observation field. The method includes: obtaining the UAV maneuvering observation field of the weather forecasting area according to the UAV maneuvering observation means, obtaining the background field of the weather forecasting area, and obtaining the analysis field of the weather forecasting area based on the assimilation analysis of the UAV maneuvering observation field and the background field; obtaining the background forecast field based on the background field, and obtaining the analysis forecast field based on the analysis field; obtaining the first observation impact of the UAV maneuvering observation field based on the background forecast field, the analysis forecast field, the true field of the weather forecasting area, and the innovation vector of the UAV maneuvering observation field. When the first observation impact is less than zero, it is determined that the assimilated observation of the UAV maneuvering observation field has a positive contribution to weather forecasting. When the first observation impact is greater than zero, it is determined that the assimilated observation of the UAV maneuvering observation field has a negative contribution to weather forecasting; obtaining the second observation impact of other observation means, and obtaining the comparison result between the UAV maneuvering observation means and other observation means based on the first observation impact and the second observation impact.

[0104] In addition, when the logical instructions in the above-mentioned memory 430 are implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., which can store program codes.

[0105] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a sensitivity analysis method for an unmanned aerial vehicle (UAV) maneuverable observation field provided by the above-mentioned various methods. The method includes: obtaining a UAV maneuverable observation field of a weather forecast area according to UAV maneuverable observation means, obtaining a background field of the weather forecast area, and obtaining an analysis field of the weather forecast area based on the assimilation analysis of the UAV maneuverable observation field and the background field; obtaining a background forecast field based on the background field, and obtaining an analysis forecast field based on the analysis field; obtaining a first observation impact of the UAV maneuverable observation field based on the background forecast field, the analysis forecast field, the true field of the weather forecast area, and the innovation vector of the UAV maneuverable observation field. When the first observation impact is less than zero, it is determined that the assimilated observation of the UAV maneuverable observation field makes a positive contribution to weather forecasting. When the first observation impact is greater than zero, it is determined that the assimilated observation of the UAV maneuverable observation field makes a negative contribution to weather forecasting; obtaining a second observation impact of other observation means, and obtaining a comparison result between the UAV maneuverable observation means and other observation means based on the first observation impact and the second observation impact.

[0106] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative efforts.

[0107] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solutions, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A sensitivity analysis method for an unmanned aerial vehicle maneuvering observation field, characterized in that Including: Obtaining a mobile observation field of an unmanned aerial vehicle (UAV) in a weather forecast area according to the UAV mobile observation means, obtaining the background field of the weather forecast area, and obtaining the analysis field of the weather forecast area based on the assimilation analysis of the UAV mobile observation field and the background field; Obtaining a background forecast field based on the background field and obtaining an analysis forecast field based on the analysis field; Obtaining the adjoint model of the first trajectory based on the background forecast field and obtaining the adjoint model of the second trajectory based on the analysis forecast field; Performing a three-dimensional variational assimilation analysis operation on the UAV mobile observation field to obtain an innovation vector; Determining a diagonal matrix based on the forecast error component weighting coefficient; Obtaining a linear Kalman gain matrix based on the covariance matrix of the background error of the background field, the covariance matrix of the observation error of the UAV mobile observation field, and the nonlinear observation operator of the analysis field variables; the nonlinear observation operator of the analysis field variables is obtained according to the conversion of the model space variables and the observation space variables; Obtaining the post-assimilation forecast error based on the difference between the analysis forecast field and the true field of the weather forecast area, and obtaining the sensitivity value of the post-assimilation forecast error based on the product value of the post-assimilation forecast error, the diagonal matrix, and the adjoint model of the second trajectory; Obtaining the pre-assimilation forecast error based on the difference between the background forecast field and the true field, and obtaining the sensitivity value of the pre-assimilation forecast error based on the product value of the pre-assimilation forecast error, the diagonal matrix, and the adjoint model of the first trajectory; Obtaining a comprehensive forecast error sensitivity value based on the sum value of the sensitivity value of the post-assimilation forecast error and the sensitivity value of the pre-assimilation forecast error, and obtaining a first observation impact based on the product value of the comprehensive forecast error sensitivity value, the innovation vector, and the transposed matrix of the linear Kalman gain matrix; When the first observation impact is less than zero, determining that the assimilation observation of the UAV mobile observation field has a positive contribution to the weather forecast, and when the first observation impact is greater than zero, determining that the assimilation observation of the UAV mobile observation field has a negative contribution to the weather forecast; Obtaining a second observation impact of other observation means, and obtaining a comparison result between the UAV mobile observation means and the other observation means based on the first observation impact and the second observation impact.

2. The sensitivity analysis method of the UAV maneuvering observation field according to claim 1, wherein The obtaining the analysis field of the weather forecast area based on the assimilation analysis of the UAV mobile observation field and the background field includes: Constructing an objective function of the analysis field variables based on the nonlinear observation operator of the analysis field variables, the covariance matrix of the background error, the covariance matrix of the observation error, the UAV mobile observation field, and the background field; When the objective function reaches the minimum value, taking the value of the analysis field variables as the analysis field.

3. The sensitivity analysis method of the unmanned aerial vehicle maneuvering observation field according to claim 1, wherein The obtaining the adjoint model of the first trajectory based on the background forecast field and obtaining the adjoint model of the second trajectory based on the analysis forecast field includes: Obtaining a first tangent linear model through the tangent linear integration of the background field along time in the reverse direction, and performing a transpose process on the first tangent linear model to obtain the adjoint model of the first trajectory; Based on the tangent linear integral of the analysis field in the reverse time direction, a second tangent linear mode is obtained, and the adjoint mode of the second trajectory is obtained by transposing the second tangent linear mode.

4. The sensitivity analysis method of the UAV maneuvering observation field according to claim 1, characterized in that The obtaining of the comparison result between the UAV maneuverable observation means and the other observation means based on the first observation impact and the second observation impact includes: When the first observation impact is less than the second observation impact, the difference between the first observation impact and the second observation impact is obtained. The smaller the difference, the better the UAV maneuverable observation means is relative to the other observation means.

5. The sensitivity analysis method of the unmanned aerial vehicle maneuvering observation field according to claim 2, wherein, The objective function is: ； wherein, is the objective function, is the analysis field, is the background field, is the non - linear observation operator of the analysis field variables, is the covariance matrix of the background error, is the covariance matrix of the observation error, is the UAV maneuvering observation field.

6. The sensitivity analysis method of the unmanned aerial vehicle maneuvering observation field according to claim 1, characterized in that The obtaining of the background forecast field based on the background field and the analysis forecast field based on the analysis field includes: Based on the background field, a forward integration is performed in the Weather Research and Forecasting Nonhydrostatic Forecast System (WRFNL) nonlinear model to obtain the background forecast field; Based on the analysis field, a forward integration with the same time period is performed in the WRFNL nonlinear model to obtain the analysis forecast field.

7. A sensitivity analysis device for an unmanned aerial vehicle maneuvering observation field, characterized in that, Including: An assimilation analysis module, configured to obtain a UAV maneuverable observation field of a weather forecast area according to the UAV maneuverable observation means, obtain the background field of the weather forecast area, and obtain the analysis field of the weather forecast area based on the assimilation analysis of the UAV maneuverable observation field and the background field; A forecast module, configured to obtain a background forecast field based on the background field and an analysis forecast field based on the analysis field; An observation impact determination module, configured to obtain the adjoint mode of the first trajectory based on the background forecast field and the adjoint mode of the second trajectory based on the analysis forecast field; perform a three-dimensional variational assimilation analysis operation on the UAV maneuverable observation field to obtain an innovation vector; Determine a diagonal matrix based on the forecast error component weighting coefficient; Based on the covariance matrix of the background error of the background field, the covariance matrix of the observation error of the UAV maneuverable observation field, and the nonlinear observation operator of the analysis field variables, obtain a linear Kalman gain matrix; the nonlinear observation operator of the analysis field variables is obtained according to the conversion between the model space variables and the observation space variables; obtain the post-assimilation forecast error based on the difference between the analysis forecast field and the true field of the weather forecast area, and obtain the sensitivity value of the post-assimilation forecast error based on the product value of the post-assimilation forecast error, the diagonal matrix, and the adjoint mode of the second trajectory; Obtain the pre-assimilation forecast error based on the difference between the background forecast field and the true field, and obtain the sensitivity value of the pre-assimilation forecast error based on the product value of the pre-assimilation forecast error, the diagonal matrix, and the adjoint mode of the first trajectory; Obtain a comprehensive forecast error sensitivity value based on the sum value of the sensitivity value of the post-assimilation forecast error and the sensitivity value of the pre-assimilation forecast error, and obtain the first observation impact based on the product value of the comprehensive forecast error sensitivity value, the innovation vector, and the transposed matrix of the linear Kalman gain matrix; The observation impact determination module is further configured to determine that the assimilative observation of the UAV mobile observation field has a positive contribution to weather forecasting when the first observation impact is less than zero, and determine that the assimilative observation of the UAV mobile observation field has a negative contribution to weather forecasting when the first observation impact is greater than zero; The comparison module is configured to obtain a second observation impact of other observation means, and obtain a comparison result between the UAV mobile observation means and the other observation means based on the first observation impact and the second observation impact.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the sensitivity analysis method of the UAV mobile observation field according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, the sensitivity analysis method of the UAV mobile observation field according to any one of claims 1 to 6 is implemented.

Images