Cooperative scanning and wind field fusion inversion method based on weight distribution

Abstract

This disclosure provides a weighted collaborative scanning and wind field fusion inversion method, applicable to the fields of meteorological observation and wind field detection. The method includes acquiring and constructing a unified spatial coordinate system and a target 3D grid field based on the deployment locations of networked radars and the flight path key point set of the low-altitude flight equipment to be monitored; generating a collaborative scanning strategy for each radar in the networked radar system based on the unified spatial coordinate system and the target 3D grid field, according to the deployment locations and the target flight path area of ​​the low-altitude flight equipment to be monitored; determining the number of radars whose radial data is covered by radar scanning at each grid point based on the collaborative scanning strategy, performing dynamic weight allocation, and determining the wind field inversion model; and performing wind field fusion calculation based on the wind field inversion model to generate a 3D wind field product for the target flight path area with each wind vector accompanied by a data quality identifier. Based on this, high-precision, high-resolution 3D wind field monitoring of specific areas such as low-altitude flight paths can be achieved.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to the field of meteorological observation and wind field detection technology, specifically to a collaborative scanning and wind field fusion inversion method based on weight allocation. Background Technology

[0002] Wind-measuring lidar is an important tool for acquiring three-dimensional atmospheric wind field information. Currently, its main operating modes include: 1. Wind profiler mode: detects a single point in vertical space; however, this method cannot obtain wind field information in horizontal areas. 2. Single radar planar position display scanning mode: A single radar performs a cone-shaped scan at a fixed elevation angle, and inverts the horizontal wind field by assuming that the vertical wind speed is zero; however, this method has an inherent defect, that is, when the vertical wind speed cannot be ignored (such as complex terrain or convective weather), the inversion accuracy will decrease significantly, and the error can usually reach 1m / s or more.

[0003] With the booming development of the low-altitude economy, higher requirements have been placed on meteorological support for specific areas such as UAV flight paths, requiring regional three-dimensional wind field products with high horizontal resolution (e.g., 50m×50m), high vertical resolution (e.g., 10m), and wind speed inversion accuracy better than 0.5m / s.

[0004] However, existing scanning and inversion technologies for single radars are insufficient to simultaneously meet the multiple requirements of spatial coverage, resolution, and accuracy. Furthermore, existing virtual wind tower methods typically only invert areas simultaneously covered by the radial scanning of one or two wind-measuring lidars, making them unsuitable for large-area wind field inversion within a region. Moreover, they cannot dynamically adjust the inversion method based on coverage differences when faced with increased radar deployments or variations in scanning beam density / sparseness. Summary of the Invention

[0005] This disclosure provides a method, apparatus, device, and storage medium for collaborative scanning and wind field fusion inversion based on weight allocation.

[0006] According to a first aspect of this disclosure, a weighted collaborative scanning and wind field fusion inversion method is provided. The method includes: Acquire the deployment locations of the networked radars and the set of three-dimensional key points along the flight paths of the low-altitude flying equipment to be monitored; Based on the station locations and the three-dimensional key point set of the flight route, a unified spatial coordinate system and a three-dimensional grid field of the target are constructed. Based on the unified spatial coordinate system and the target three-dimensional grid field, and according to the station location and the target flight path area of ​​the low-altitude flight equipment to be monitored, a cooperative scanning strategy for each radar in the network radar is generated. Based on the aforementioned collaborative scanning strategy, the number of radars whose radial data is covered by radar scanning at each grid point is determined, dynamic weight allocation is performed, and the wind field inversion model is determined. Wind field fusion calculations are performed based on the wind field inversion model to generate a three-dimensional wind field product for the target airway area; each wind vector in the three-dimensional wind field product is accompanied by a data quality identifier.

[0007] As described above and in any possible implementation, a further implementation is provided, wherein constructing a unified spatial coordinate system and a target three-dimensional grid field based on the station locations and the set of three-dimensional key points of the flight route includes: Based on the station locations and the three-dimensional key point set of the flight route, a unified spatial coordinate system is constructed; Within the unified spatial coordinate system, a target three-dimensional grid field is generated according to a preset horizontal and vertical resolution. Each grid point in the target three-dimensional grid field includes its coordinate information. Map the coordinates of the station locations and the three-dimensional key point set of the flight route to the unified spatial coordinate system; The three-dimensional key point set of the flight path includes the three-dimensional coordinates of the take-off and landing points and inflection points of the low-altitude flight equipment to be monitored; the unified spatial coordinate system is the station center coordinate system.

[0008] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the generation of the cooperative scanning strategy for each radar in the networked radar system, based on the unified spatial coordinate system and the target three-dimensional grid field, and according to the station locations and the target flight path area of ​​the low-altitude flight equipment to be monitored, includes: Based on the unified spatial coordinate system and the target three-dimensional grid field, according to the target flight path area of ​​the low-altitude flight equipment to be monitored and the preset scanning cycle, the azimuth and elevation angles from each radar position to all points of the three-dimensional key points of the flight path are calculated respectively; and the azimuth sector and elevation angle sequence required for each radar to cover the target flight path are determined respectively, and the cooperative scanning strategy of each radar in the network radar is generated.

[0009] In addition to the aspects and any possible implementations described above, a further implementation is provided in which determining the number of radars whose radial data is covered by radar scanning for each grid point based on the cooperative scanning strategy includes: Based on the aforementioned collaborative scanning strategy, radar radial data is acquired; Using spatial constraints, the radial dataset affecting each grid point is determined based on the radar radial data, and the number of radars whose radial data is covered by radar scans at each grid point is determined.

[0010] As described above and in any possible implementation, a further implementation is provided, wherein the dynamic weight allocation includes: For each data point in the radial dataset, the location of the data point and the location of the corresponding radar station are converted into a vector position relative to the grid points; Calculate the weight of each data point in the grid based on the vector position; The weights are converted to normalized weights, and the number of radars whose radial data is covered by radar scans at that grid point is determined.

[0011] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the determination of the wind field inversion model includes: Based on the number of radars, a wind field inversion model is determined; the wind field inversion model includes a multi-radar joint solution model, a vertical wind speed constraint model, and a single-radar vector decomposition model.

[0012] In addition to the aspects described above and any possible implementations, a further implementation is provided, wherein the method further includes: When there are multiple wind field inversion models, the three-dimensional wind field products generated by each model are weighted and averaged according to preset weights to obtain the final three-dimensional wind field product; the preset weight coefficient is a function of the number of radars.

[0013] According to a second aspect of this disclosure, a weighted collaborative scanning and wind field fusion inversion device is provided. The device includes: The acquisition module is used to acquire the deployment locations of the networked radar and the set of three-dimensional key points of the flight path of the low-altitude flight equipment to be monitored. The construction module is used to construct a unified spatial coordinate system and a target three-dimensional grid field based on the station locations and the three-dimensional key point set of the route; The generation module is used to generate a cooperative scanning strategy for each radar in the network radar based on a unified spatial coordinate system and a three-dimensional grid field of the target, according to the station location and the target flight path area of ​​the low-altitude flying equipment to be monitored. The allocation module is used to determine the number of radars whose radial data is covered by radar scanning at each grid point based on the cooperative scanning strategy, perform dynamic weight allocation, and determine the wind field inversion model. The inversion module is used to perform wind field fusion calculations based on the wind field inversion model to generate a three-dimensional wind field product for the target route area; each wind vector in the three-dimensional wind field product is accompanied by a data quality label.

[0014] According to a third aspect of this disclosure, a networked collaborative observation system for wind-measuring lidar is provided. The system includes: At least two wind-measuring lidar units deployed in different locations; The weighted collaborative scanning and wind field fusion inversion device is used to issue collaborative scanning commands to each radar according to the above method, receive radar radial data, execute the wind field fusion inversion method, and generate a three-dimensional wind field product.

[0015] According to a fourth aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0016] According to a fifth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method described above.

[0017] This application provides a weighted collaborative scanning and wind field fusion inversion method, which can construct a unified spatial coordinate system and a target 3D grid field by acquiring and using the deployment locations of networked radars and the set of 3D key points along the flight path of the low-altitude flight equipment to be monitored. Based on the unified spatial coordinate system and the target 3D grid field, a collaborative scanning strategy for each radar in the networked radar is generated according to the deployment locations and the target flight path area of ​​the low-altitude flight equipment to be monitored. Based on the collaborative scanning strategy, the number of radars whose radial data is covered by radar scanning at each grid point is determined, dynamic weight allocation is performed, and a wind field inversion model is determined. Wind field fusion calculation is performed based on the wind field inversion model to generate a 3D wind field product for the target flight path area with data quality labels for each wind vector. Based on this, a method can be provided that can automatically generate the optimal collaborative scanning strategy based on a network of multiple wind-measuring lidars, and dynamically allocate weights based on the number of spatial point radars to perform radial wind speed data fusion and high-precision 3D wind field inversion, ultimately achieving high-precision, high-resolution 3D wind field monitoring in specific areas such as low-altitude flight paths.

[0018] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0019] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 A flowchart of a weighted collaborative scanning and wind field fusion inversion method according to an embodiment of the present disclosure is shown; Figure 2 A schematic diagram of radar station deployment and flight route locations in the ENU coordinate system according to an embodiment of the present disclosure is shown; Figure 3 A schematic diagram of a wind-measuring lidar cooperative observation scanning design according to an embodiment of the present disclosure is shown; Figure 4 A schematic diagram of solving a three-dimensional wind vector by fusing radial data from three radars according to an embodiment of the present disclosure is shown. Figure 5 A time series comparison diagram of horizontal wind speed according to an embodiment of the present disclosure is shown; Figure 6 A time series comparison chart of horizontal wind direction according to an embodiment of the present disclosure is shown; Figure 7 A scatter plot showing the consistency of horizontal wind speed and horizontal wind direction according to an embodiment of the present disclosure is shown; Figure 8 A block diagram of a weighted collaborative scanning and wind field fusion inversion apparatus according to an embodiment of the present disclosure is shown; Figure 9 A block diagram of a networked collaborative observation system for wind-measuring lidar according to an embodiment of the present disclosure is shown. Figure 10 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0021] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0022] This disclosure provides a method that can automatically generate the optimal collaborative scanning strategy based on a network of multiple wind-measuring lidars, and dynamically allocate weights based on the number of spatial point radar coverages to perform radial wind speed data fusion and high-precision inversion of three-dimensional wind fields, ultimately achieving high-precision, high-resolution three-dimensional wind field monitoring in specific areas such as low-altitude air routes.

[0023] Figure 1 A flowchart of a weighted collaborative scanning and wind field fusion inversion method 100 according to an embodiment of the present disclosure is shown.

[0024] In box 110, obtain the deployment locations of the networked radar and the set of three-dimensional key points along the flight path of the low-altitude flight equipment to be monitored.

[0025] In some embodiments, the networked radar includes at least two wind-measuring lidars deployed at different locations.

[0026] In some embodiments, the low-altitude flying device to be monitored may be a drone.

[0027] It should be noted that this disclosure is made for low-altitude meteorological detection and high-precision wind field inversion. For ease of explanation, the following will use a low-altitude UAV flight path serving a certain location as an example, deploying two wind-measuring lidars (radar A located at location A, and radar B located at location B) to illustrate the weighted collaborative scanning and wind field fusion inversion method. Additionally, it can be assumed that a third radar C exists.

[0028] In frame 120, a unified spatial coordinate system and a target three-dimensional grid field are constructed based on the station locations and the three-dimensional key point set of the flight route.

[0029] In some embodiments, the unified spatial coordinate system can be a station-centered coordinate system, i.e., an East-North-Sky coordinate system (ENU). The three-dimensional key point set of the flight path can include the three-dimensional coordinates of the take-off and landing points and inflection points of the low-altitude flight equipment to be monitored.

[0030] In some embodiments, the above-mentioned construction of a unified spatial coordinate system and a target three-dimensional grid field based on the station location and the three-dimensional key point set of the flight route specifically includes: Based on the station locations and the three-dimensional key point set of the flight route, a unified spatial coordinate system is constructed; Within a unified spatial coordinate system, a target three-dimensional grid field is generated based on preset horizontal and vertical resolutions. Each grid point in the target three-dimensional grid field includes its coordinate information. Map the coordinates of the station locations and the three-dimensional key point set of the flight route to a unified spatial coordinate system.

[0031] Specifically, an ENU coordinate system is constructed based on the deployment locations of all networked radars and the set of three-dimensional key points along the flight paths of the low-altitude flight equipment to be monitored. Within this coordinate system, a three-dimensional spatial grid field L={l1,l2,...,l...} covering the entire target flight path area of ​​the low-altitude flight equipment to be monitored is generated according to preset horizontal and vertical resolutions. k}, where k is a positive integer, and each grid point l k It includes its coordinate information, mapping the coordinates of radar and route 3D key points to the ENU coordinate system. For example... Figure 2As shown, the radar and flight path layout is based on radar station locations and flight path positions within the ENU coordinate system.

[0032] The preset horizontal and vertical resolutions can be set according to the user's actual needs. For example, the horizontal resolution can be set to 50 meters and the vertical resolution to 10 meters.

[0033] For example, input the latitude, longitude, and altitude coordinates of radar A and radar B, i.e., their deployment locations, as well as the three-dimensional coordinates of the take-off, landing points, and inflection points of the UAV's flight path. Establish an ENU coordinate system with radar A's location as the origin, transform all coordinates to this system, set the horizontal resolution to 50 meters and the vertical resolution to 10 meters, and generate a three-dimensional spatial grid field covering the flight path.

[0034] In frame 130, based on a unified spatial coordinate system and a three-dimensional grid field of the target, a collaborative scanning strategy for each radar in the network is generated according to the station location and the target flight path area of ​​the low-altitude flying equipment to be monitored.

[0035] In some embodiments, a cooperative scanning strategy for each radar can be adaptively generated based on the radar deployment location and the target flight path area.

[0036] In some embodiments, the above-mentioned cooperative scanning strategy for each radar in the networked radar system, based on a unified spatial coordinate system and a target three-dimensional grid field, and according to the station locations and the target flight path area of ​​the low-altitude flying equipment to be monitored, specifically includes: Based on a unified spatial coordinate system and a target three-dimensional grid field, the azimuth and elevation angles from each radar position to all points in the three-dimensional key points of the flight path are calculated according to the target flight path area and the preset scanning cycle of the low-altitude flight equipment to be monitored. The azimuth sector and elevation angle sequence to be scanned by each radar to cover the target flight path are determined, and the cooperative scanning strategy of each radar in the network radar is generated.

[0037] In some embodiments, the preset scanning cycle can be set according to the user's actual needs.

[0038] In some embodiments, based on the range and scanning period of the aforementioned airway area, for each participating radar i, the azimuth and elevation angles from each radar's position to all points in the airway key point set, i.e., all feature points in the airway area, can be calculated. Based on the range of these angle values, their extreme values ​​are used to determine the scanning sector boundaries, and a scanning angle sequence is generated according to resolution requirements, thereby automatically determining the azimuth sector and elevation angle sequence required for each radar to cover the target airway area. For example... Figure 3As shown, in a unified spatial coordinate system and a three-dimensional grid field of the target, the radar display mode (Range Height Indicator, RHI) scan (only showing the intersection of the target area and the route) of the sequential radar displays the target range and height information - with a horizontal resolution of about 50m and a vertical resolution of about 10m for the target cross section, thus determining the design of the wind-measuring lidar cooperative observation scan.

[0039] For example, based on the aforementioned collaborative scanning strategy generation method, the line-of-sight angles from radar A and radar B to all three-dimensional key points along the flight path can be automatically calculated. As shown in Tables 1 and 2, the scanning strategies of the two radars employ Plan Position Indicator (PPI) scanning and Doppler Beam Swinging (DBS) scanning, respectively. To avoid obstruction from urban buildings and obtain optimal flight path coverage, Table 1 shows the scanning configuration scheme for location A, designing a sector scan strategy with an azimuth angle of 140°-260° and an elevation angle of 10°-55° for radar A; Table 2 shows the scanning configuration scheme for location B, designing a sector scan strategy with an azimuth angle of 280°-100° (across the north direction) and an elevation angle of 13°-76° for radar B; both radars execute this strategy synchronously.

[0040] Table 1: Scanning Configuration Scheme for Location A Table 2: Scanning Configuration Scheme for Location B As can be seen, by intelligently designing a collaborative scanning strategy through the above process, and employing sector scanning to cover a larger area, radar observation data under arbitrary radar coverage can be adaptively fused and inverted, thereby comprehensively improving the accuracy and reliability of regional three-dimensional wind field inversion. Simultaneously, it allows limited radar resources to be concentrated on observing target routes, maximizing the cross-detection area of ​​multiple radars while ensuring coverage, thus optimizing the overall observation efficiency of the system and achieving optimal resource utilization.

[0041] In box 140, based on the collaborative scanning strategy, the number of radars whose radial data is covered by radar scanning at each grid point is determined, dynamic weight allocation is performed, and the wind field inversion model is determined.

[0042] In some embodiments, the target 3D grid point l can be targeted at each location grid point in the target 3D grid field. k We will perform coverage-based weight allocation and wind field fusion inversion.

[0043] In some embodiments, the determination of the number of radars whose radial data is covered by radar scanning at each grid point based on the cooperative scanning strategy specifically includes: Radar radial data is acquired based on a cooperative scanning strategy; Using spatial constraints, the radial dataset affecting each grid point is determined based on radar radial data, and the number of radars whose radial data is covered by radar scans at each grid point is determined.

[0044] In some embodiments, based on the above-described cooperative scanning strategy, scanning data at a given moment, i.e., radar radial data, can be collected to analyze each grid point l. k Process it.

[0045] Specifically, to determine the influence of each location grid point l k Considering the uniformity of the spatial wind field, the data range can be determined from the perspective of location grid points, using circular or elliptical spatial constraints to clearly define the radial dataset C that influences the location grid point. k And count the maximum number of radars N covered by each grid point. k That is, to determine the number of radars whose radial data is covered by radar scanning at each grid point.

[0046] In some embodiments, the above-mentioned dynamic weight allocation specifically includes: For each data point in the radial dataset, the location of that data point and the location of the corresponding radar station are converted into a vector position relative to the grid points. Calculate the weight of each data point in the grid based on its vector position; The weights are converted to normalized weights, and the number of radars whose radial data is covered by radar scans at that grid point is determined.

[0047] Specifically, for the radial dataset C k Each data point in the dataset is assigned a weight. For the radial dataset C... k Each data C in ki Based on the location of the data and the corresponding radar station location, it is converted into a value relative to grid point l. k The vector position is then used to calculate the grid point l. k The weights of each data point are calculated, and then these weights are converted into normalized weights W. ki And statistically analyze the radial dataset C k The maximum number of radars N covered by each location grid point k .

[0048] As can be seen, the above method innovatively introduces a method based on the number of radar coverage points N. k The dynamic weight allocation mechanism is used not only to select the optimal inversion algorithm, but also to assign a data quality identifier (weight W) to each grid point of the core output product. k and coverage N k This ensures that the quality of the regional wind field is consistent and known, enabling wind field users to clearly understand the reliability of data from different regions.

[0049] In some embodiments, the above-mentioned determination of the wind field inversion model specifically includes: Based on the number of radars, the wind field inversion model is determined; the wind field inversion model includes a multi-radar joint solution model, a vertical wind speed constraint model, and a single-radar vector decomposition model.

[0050] In some embodiments, the wind field inversion model can be dynamically selected, and the final determined wind field inversion model can be a single model or multiple models.

[0051] In some embodiments, each spatial point l is inverted to obtain k Three-dimensional wind speed vector =( First, let's analyze... right The influence of radial wind speed generated by the point-based wind lidar. For the first... The laser beam emitted by this lidar is oriented by an azimuth angle rotated clockwise from true north. And the angle between the laser beam and the horizontal plane. To determine the radial velocity measured by the lidar. It is the projection of the three-dimensional wind speed vector onto the direction of the laser beam. As shown in formula (1).

[0052] (1) By adding weights to formula (1), we get formula (2).

[0053] (2) In some embodiments, if the number of radars N k For radars with a maximum number of radars (>=3), a multi-radar joint solution model can be used. This involves using radial data from at least three radars to retrieve the grid points (l). k The wind field value is obtained by directly solving the three-dimensional wind vector V. k =(u k ,v k ,w k Solution methods include: the system of equations can be written in matrix form: =MV; where M can be expressed as formula (3).

[0054] M= (3) In some embodiments, if the number of radars N k =2, a vertical wind speed constraint model can be adopted, assuming a vertical wind speed w k Under the condition that = 0, the horizontal wind vector (u) is solved using the radial wind speed observations from two radars. k ,v k ).

[0055] In some embodiments, if the number of radars N k =1, a single radar vector decomposition model can be used, based on background wind field or climate statistics, to decompose the radial wind speed of a single radar into a three-dimensional wind vector.

[0056] In summary, weights can be dynamically assigned and an inversion model selected based on the amount of radial data covered by radar sector scans at each grid point, enabling wind field fusion calculations. The dynamic selection of the inversion model specifically includes: If the number of radars is ≥3, a weighted multi-radar radial wind speed simultaneous equation model is used to solve the three-dimensional wind vector model. If the number of radars is 2, a model for solving the horizontal wind vector under the constraint of vertical wind speed is adopted; If the number of radars is 1, a single-radar radial wind speed vector decomposition model based on background field information is adopted.

[0057] For ease of explanation, let's continue using locations A and B as examples. For the grid point on the flight path that is simultaneously covered by radars A and B, Nk=2, and a vertical wind speed constraint model is used to solve the problem, assigning a weight Wk=0.7. For a grid point on the flight path that is also covered by a third (hypothetical) radar C, N... k =3, adopting a multi-radar joint solution model, and assigning weight W k =1.0, and obtain the minimum error estimate. For grid points at the edge of the route that are covered by only one radar, N k =1, using a single radar vector decomposition model, assigning weight W k =0.3.

[0058] In frame 150, wind field fusion calculations are performed based on the wind field inversion model to generate a three-dimensional wind field product for the target airway area; each wind vector in the three-dimensional wind field product is accompanied by a data quality label.

[0059] like Figure 4 As shown, three-dimensional wind vectors can be solved based on radial data fusion from three radars. To output high-precision three-dimensional wind field products, the wind vectors, weights, and coverage information of all grid points can be integrated to generate regional three-dimensional wind field products containing data quality labels. That is, the final three-dimensional wind field product of the target flight path area at a certain moment is generated, with each wind vector accompanied by its weight and coverage label.

[0060] To verify the authenticity of the results, this disclosure selects a continuous observation period and compares the wind field at a specific location obtained by inversion based on the above method with the observation results of the wind profile model of a nearby independently operating wind lidar (as the true value). The comparison results based on the nearby wind lidar are obtained, i.e., the comparison and verification results between the inverted wind field and the independent observation true value. Figure 5As shown, the left side displays the wind speed retrieved by the algorithm (L3), and the right side displays the wind speed measured by DBS (true value); Figure 6 As shown, the left side displays the wind direction retrieved by the algorithm (L3), and the right side displays the wind direction measured by DBS (true value); Figure 7 As shown, the left side displays the wind speed comparison (N=2362), and the right side displays the wind direction comparison (N=2362). It can be seen that the horizontal wind speed correlation coefficient reaches 0.964, with a root mean square error of 0.48 m / s, and the wind direction correlation coefficient is 0.92. The results indicate that by using multi-radar collaborative observation and joint solution, the limitation of single-radar PPI scanning requiring the assumption of zero vertical wind speed is overcome. This improves the horizontal wind speed inversion accuracy in key areas of the route from approximately 1 m / s to below 0.5 m / s. The method described in this disclosure significantly improves the inversion accuracy of regional wind fields.

[0061] According to the embodiments of this disclosure, the following technical effects are achieved: This system can acquire and construct a unified spatial coordinate system and a target 3D grid field based on the deployment locations of networked radars and the 3D key point set of the flight path of the low-altitude flight equipment to be monitored. Based on the unified spatial coordinate system and the target 3D grid field, a cooperative scanning strategy for each radar in the network is generated according to the deployment locations and the target flight path area of ​​the low-altitude flight equipment to be monitored. Based on the cooperative scanning strategy, the number of radars whose radial data is covered by radar scanning at each grid point is determined, dynamic weight allocation is performed, and a wind field inversion model is determined. Based on the wind field inversion model, wind field fusion calculation is performed to generate a 3D wind field product for the target flight path area with data quality labels for each wind vector. Based on this, a system can be provided that can automatically generate the optimal cooperative scanning strategy based on a network of multiple wind-measuring lidars, and dynamically allocate weights based on the number of spatial point radars to perform radial wind speed data fusion and high-precision 3D wind field inversion, ultimately achieving high-precision, high-resolution 3D wind field monitoring in specific areas such as low-altitude flight paths.

[0062] In some embodiments, the above method further includes: When there are multiple wind field inversion models, the three-dimensional wind field products generated by each model are weighted and averaged according to preset weights to obtain the final three-dimensional wind field product; the preset weight coefficient is a function of the number of radars.

[0063] In some embodiments, the calculated wind vector V can be used to... k and its corresponding weight W k and coverage N k Recorded at grid point l k For grid points covered by multiple models (such as those in the edge region), a weighted average can be performed according to the weights to obtain the final wind field value, so as to complete the grid point wind field assignment and fusion.

[0064] As can be seen, the above method discloses a weighted allocation-based wind-measuring lidar network collaborative scanning and wind field fusion inversion method. This method first constructs a three-dimensional grid field of the target area; then, it automatically designs a multi-radar collaborative scanning strategy to optimize coverage. Its core lies in adaptively selecting different wind field inversion models (multi-radar joint solution / vertical wind speed constraint / single-radar vector decomposition) for each grid point based on the number of radars covering it, and assigning corresponding weights to the results. Finally, it generates a high-precision three-dimensional wind field with quality labels, thus solving the problems of insufficient single-radar detection accuracy and uneven quality of multi-radar data fusion. This significantly improves the accuracy and reliability of wind field observations in specific areas such as low-altitude airways, providing key technical support for the economical and safe operation of low-altitude airspace.

[0065] Meanwhile, the above methods also realize the intelligentization of route planning and scanning strategy generation, and the full-process automation of data fusion and inversion, which greatly improves the efficiency of business applications.

[0066] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0067] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0068] Figure 8 A block diagram of a weighted collaborative scanning and wind field fusion inversion apparatus 800 according to an embodiment of the present disclosure is shown. Figure 8 As shown, the device 800 includes: The acquisition module 810 is used to acquire the deployment locations of the networked radar and the set of three-dimensional key points of the flight path of the low-altitude flight equipment to be monitored. Module 820 is used to construct a unified spatial coordinate system and a target 3D grid field based on the station location and the 3D key point set of the flight route; The generation module 830 is used to generate a cooperative scanning strategy for each radar in the networked radar based on a unified spatial coordinate system and a three-dimensional grid field of the target, according to the station location and the target flight path area of ​​the low-altitude flying equipment to be monitored. The allocation module 840 is used to determine the number of radars whose radial data is covered by radar scanning at each grid point based on the cooperative scanning strategy, perform dynamic weight allocation, and determine the wind field inversion model. The inversion module 850 is used to perform wind field fusion calculations based on the wind field inversion model to generate a three-dimensional wind field product for the target route area; each wind vector in the three-dimensional wind field product is accompanied by a data quality label.

[0069] Figure 9 A block diagram of a networked collaborative observation system 900 for wind measurement lidar according to an embodiment of the present disclosure is shown. Figure 9 As shown, the system 900 includes: At least two wind-measuring lidar units 901 are deployed in different locations; The weighted collaborative scanning and wind field fusion inversion device 800 is used to issue collaborative scanning commands to each radar according to the above method, receive radar radial data, execute the wind field fusion inversion method, and generate a three-dimensional wind field product.

[0070] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0071] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0072] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0073] Figure 10 A block diagram of an exemplary electronic device 1000 capable of implementing embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0074] Electronic device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in ROM 1002 or a computer program loaded into RAM 1003 from storage unit 1008. RAM 1003 may also store various programs and data required for the operation of electronic device 1000. The computing unit 1001, ROM 1002, and RAM 1003 are interconnected via bus 1004. I / O interface 1005 is also connected to bus 1004.

[0075] Multiple components in electronic device 1000 are connected to I / O interface 1005, including: input unit 1006, such as keyboard, mouse, etc.; output unit 1007, such as various types of displays, speakers, etc.; storage unit 1008, such as disk, optical disk, etc.; and communication unit 1009, such as network card, modem, wireless transceiver, etc. Communication unit 1009 allows electronic device 1000 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0076] The computing unit 1001 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1001 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1008. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1000 via ROM 1002 and / or communication unit 1009. When the computer program is loaded into RAM 1003 and executed by the computing unit 1001, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).

[0077] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0078] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0079] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0080] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0081] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0082] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0083] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0084] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A weighted collaborative scanning and wind field fusion inversion method, characterized in that, include: Acquire the deployment locations of the networked radars and the set of three-dimensional key points along the flight paths of the low-altitude flying equipment to be monitored; Based on the station locations and the three-dimensional key point set of the flight route, a unified spatial coordinate system and a three-dimensional grid field of the target are constructed. Based on the unified spatial coordinate system and the target three-dimensional grid field, and according to the station location and the target flight path area of ​​the low-altitude flight equipment to be monitored, a cooperative scanning strategy for each radar in the network radar is generated. Based on the aforementioned collaborative scanning strategy, the number of radars whose radial data is covered by radar scanning at each grid point is determined, dynamic weight allocation is performed, and the wind field inversion model is determined. Wind field fusion calculations are performed based on the wind field inversion model to generate a three-dimensional wind field product for the target airway area; each wind vector in the three-dimensional wind field product is accompanied by a data quality identifier.

2. The method according to claim 1, characterized in that, The construction of a unified spatial coordinate system and a target 3D grid field based on the station locations and the set of 3D key points along the flight route includes: Based on the station locations and the three-dimensional key point set of the flight route, a unified spatial coordinate system is constructed; Within the unified spatial coordinate system, a target three-dimensional grid field is generated by dividing the target three-dimensional grid field according to a preset horizontal and vertical resolution. Each grid point in the target three-dimensional grid field includes its coordinate information. Map the coordinates of the station locations and the three-dimensional key point set of the flight route to the unified spatial coordinate system; The three-dimensional key point set of the flight path includes the three-dimensional coordinates of the take-off and landing points and inflection points of the low-altitude flight equipment to be monitored; the unified spatial coordinate system is the station-centered coordinate system.

3. The method according to claim 1, characterized in that, The step of generating a cooperative scanning strategy for each radar in the networked radar system based on the unified spatial coordinate system and the target three-dimensional grid field, according to the station locations and the target flight path area of ​​the low-altitude flight equipment to be monitored, includes: Based on the unified spatial coordinate system and the target three-dimensional grid field, according to the target flight path area of ​​the low-altitude flight equipment to be monitored and the preset scanning cycle, the azimuth and elevation angles from each radar position to all points of the three-dimensional key points of the flight path are calculated respectively; and the azimuth sector and elevation angle sequence required for each radar to cover the target flight path are determined respectively, and the cooperative scanning strategy of each radar in the network radar is generated.

4. The method according to claim 1, characterized in that, The determination of the number of radars whose radial data is covered by radar scanning at each grid point based on the cooperative scanning strategy includes: Based on the aforementioned collaborative scanning strategy, radar radial data is acquired; Using spatial constraints, the radial dataset affecting each grid point is determined based on the radar radial data, and the number of radars whose radial data is covered by radar scans at each grid point is determined.

5. The method according to claim 4, characterized in that, The dynamic weight allocation includes: For each data point in the radial dataset, the location of the data point and the location of the corresponding radar station are converted into a vector position relative to the grid points; Calculate the weight of each data point in the grid based on the vector position; The weights are converted to normalized weights, and the number of radars whose radial data is covered by radar scans at that grid point is determined.

6. The method according to claim 5, characterized in that, The wind field inversion model includes: Based on the number of radars, a wind field inversion model is determined; the wind field inversion model includes a multi-radar joint solution model, a vertical wind speed constraint model, and a single-radar vector decomposition model.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: When there are multiple wind field inversion models, the three-dimensional wind field products generated by each model are weighted and averaged according to preset weights to obtain the final three-dimensional wind field product; the preset weight coefficient is a function of the number of radars.

8. A networked collaborative observation system for wind-measuring lidar, characterized in that, include: At least two wind-measuring lidar units deployed in different locations; The weighted collaborative scanning and wind field fusion inversion device is used to issue collaborative scanning commands to each radar and receive radar radial data according to the method described in any one of claims 1-7, execute the wind field fusion inversion method, and generate a three-dimensional wind field product.

9. An electronic device, characterized in that, include: At least one processor; as well as A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

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