Satellite-ground radar collaborative observation method and system based on sensitive target

Abstract

The embodiment of the invention provides a satellite-ground radar collaborative observation method and system based on a sensitive target, and is applied to the technical field of weather radars. The method comprises the following steps: acquiring satellite, radar and underlying surface data; performing identification processing on the radar data to obtain the number, the position and the area of potential targets and the early warning level corresponding to each potential target, and comprehensively obtaining a satellite-ground joint early warning level; constructing a sensitive target evaluation function and calculating a sensitivity score by using the underlying surface data, the potential target and the satellite-ground joint early warning level; and permutation and combination are carried out on the sensitive target and the radar scanning mode, and optimization processing is carried out to select an optimal scanning mode. In this way, real-time tracking and dynamic monitoring of severe convective weather can be achieved, a plurality of weather radars are guided to efficiently carry out collaborative operation, and the observation timeliness and early warning reliability of the highly-disaster-causing severe convective weather are improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of weather radar, and particularly relates to a star-ground radar cooperative observation method and system based on sensitive targets. BACKGROUND

[0002] Under the background of current accelerated urbanization and high population concentration, as a typical representative of extreme weather, severe convective weather has the characteristics of strong burst, small spatial scale, short life cycle and high disaster-causing, which puts forward the demand of minute-level response and kilometer-level fine monitoring for disaster prevention and reduction and meteorological services. When local severe convection, especially short-time heavy rain, occurs in a specific environment, it can cause secondary disasters such as mountain torrents and mudslides, which have a great impact on human production and life. Neither spaceborne precipitation radar nor ground-based weather radar can simultaneously meet the needs of wide-area early warning and local fine observation by using a single remote sensing means. Therefore, it is of great significance to build a severe convective intelligent cooperative observation system based on multi-source data fusion and taking dynamic risk assessment as the core to improve the ability of disaster prevention and reduction.

[0003] At present, the severe convective intelligent cooperative observation method has the following shortcomings: 1. Single source of potential target identification information: At present, it mainly depends on weather radar real-time echo to judge the potential target, lacks comprehensive analysis of the convective initial signal, cloud system structure characteristics and evolution trend in the satellite cloud image, especially in the early development stage of the convective, and fails to fully utilize the advantages of satellite in wide-area and continuous observation, resulting in insufficient timeliness and accuracy of early identification; 2. Insufficient risk assessment dimension: At present, the method mainly focuses on the convective structure, intensity and other meteorological characteristics in radar observation, lacks comprehensive risk analysis of the potential target landing area, and fails to fully consider the differences in exposure degree between densely populated areas and sparsely populated areas, and ignores the differences in the degree of disaster-causing risk of severe convective cells to complex terrain (such as mountains and hills) and secondary disaster hidden danger areas (such as landslides and mountain torrents), making it difficult to fully assess the disaster-causing risk level of different targets; 3. Uneven multi-target task scheduling and resource allocation: At present, the weather radar observation method mainly relies on fixed scanning strategy or manual annotation of key monitoring areas, and multiple weather radar observation modes are consistent with fixed period continuous scanning; in the context of multiple potential targets coexisting, the observation task allocation strategy is relatively single, and the importance of targets, observation priority and resource utilization rate cannot be fully balanced, which may cause resource waste or lack of key target observation, affecting the overall cooperative observation efficiency and observation effect. SUMMARY

[0004] The present disclosure provides a satellite-ground radar cooperative observation method and system based on sensitive targets, which solves the technical problems of insufficient joint early warning capability of existing disastrous weather, single sensitive target distinguishing element, and uneven multi-target task scheduling and resource allocation.

[0005] According to a first aspect of the present disclosure, a satellite-ground radar cooperative observation method based on sensitive targets is provided. The method comprises: acquiring satellite data, radar data, and underlying surface data; performing identification processing on the radar data to obtain the number, position, area of potential targets in the radar data, and the early warning level corresponding to each potential target, and comprehensively obtaining the satellite-ground joint early warning level of each potential target; constructing a sensitive target evaluation function using the underlying surface data, potential targets, and the satellite-ground joint early warning level corresponding to the potential targets, and calculating the sensitivity score corresponding to each potential target using the sensitive target evaluation function; performing permutation and combination on sensitive targets and radar scanning modes, and performing optimization processing to select the optimal scanning mode.

[0006] According to the above-mentioned aspect and any possible implementation manner, a further implementation manner is provided, wherein the satellite data is a satellite cloud image including a target region, and the target region is a region monitored by the radar. The underlying surface data includes a GDP map, a population density map, a land use rate map, and a secondary disaster risk map of the target region.

[0007] According to the above-mentioned aspect and any possible implementation manner, a further implementation manner is provided, wherein the identification processing on the radar data to obtain the number, position, area of potential targets in the radar data, and the early warning level corresponding to each potential target, and comprehensively obtaining the satellite-ground joint early warning level of each potential target comprises: acquiring the potential region, corresponding position, area, and early warning level of the satellite data; identifying the potential target in the radar image at time n through a threshold method, wherein n represents the current time; predicting the potential target of the radar image at time n+1 through a convolutional long short-term memory network (ConvLSTM); identifying the potential target in the radar image at time n+1 through a threshold method; tracking the potential target based on an optical flow method to obtain the life cycle state corresponding to each potential target ; Based on each potential target and the potential region of the satellite data, the satellite-ground joint early warning level of each potential target is calculated through a potential target satellite-ground joint early warning level calculation formula.

[0008] According to the aspect and any possible implementation manner as described above, further provided is an implementation manner, wherein the potential target satellite-ground joint early warning level calculation formula is: wherein, represents a spatial accuracy weight, represents a life cycle state, represents a weight coefficient, represents a satellite data early warning level, represents a maximum reflectivity of a radar target after normalization processing, represents an overlap degree of a satellite potential area and a radar potential target.

[0009] According to the aspect and any possible implementation manner as described above, further provided is an implementation manner, wherein the constructing a sensitive target evaluation function by using the underlying surface data, the potential target, and the satellite-ground joint early warning level corresponding to the potential target comprises: obtaining, from the underlying surface data, a potential target area economy, a potential target area population density, a potential target area land utilization rate, and a potential target area secondary disaster type; constructing a sensitive target evaluation function by using the satellite-ground joint early warning level, the potential target area economy, the potential target area population density, the potential target area land utilization rate, the potential target area secondary disaster type, and the weight coefficient.

[0010] According to the aspect and any possible implementation manner as described above, further provided is an implementation manner, wherein the arranging and combining the sensitive target and the radar scanning mode, and performing optimization processing to select an optimal scanning mode comprises: performing descending order sorting on sensitive scores of a plurality of potential targets, and taking the first q potential targets as sensitive targets; obtaining all arrangement combinations of radar scanning modes based on the q sensitive targets and R radars, and performing optimization processing on all arrangement combinations by using a target function to select an optimal scanning mode; wherein q and R are positive integers.

[0011] According to the aspect and any possible implementation manner as described above, further provided is an implementation manner, wherein the performing optimization processing on all arrangement combinations by using a target function to select an optimal scanning mode comprises: calculating function values corresponding to all arrangement combinations of radar scanning modes by using the target function; performing ascending order sorting on the function values, taking a scanning mode corresponding to a minimum function value as the optimal scanning mode, and taking the optimal scanning mode as a radar scheduling mode.

[0012] According to a second aspect of the present disclosure, a satellite-ground radar cooperative observation system based on a sensitive target is provided. The system comprises: an acquisition module configured to acquire satellite data, radar data, and underlying surface data; a processing module configured to perform identification processing on the radar data to obtain a number, a position, an area, and a pre-warning level corresponding to each potential target in the radar data, and to obtain a star-ground joint pre-warning level of each potential target by integration; a calculation module configured to construct a sensitive target evaluation function by using the underlying surface data, the potential targets, and the star-ground joint pre-warning levels corresponding to the potential targets, and to calculate a sensitivity score corresponding to each potential target by using the sensitive target evaluation function; an evaluation module configured to perform arrangement and combination of sensitive targets and radar scanning modes, and to perform optimization processing to select an optimal scanning mode.

[0013] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes a memory and a processor, the memory having a computer program stored thereon, and the processor implements the method as described above when executing the program.

[0014] According to a fourth aspect of the present disclosure, a computer readable storage medium is provided, having a computer program stored thereon, the program being executed by a processor to implement the method according to the first aspect and / or the second aspect of the present disclosure.

[0015] The present disclosure provides a star-ground radar cooperative observation method based on sensitive targets, which judges sensitive targets by integrating star-ground multi-source data, guides multiple weather radars to efficiently carry out cooperative operation, and improves the observation timeliness and early warning reliability of high-disaster-causing strong convective weather. Specifically, the present disclosure has the following beneficial effects: 1. The present disclosure calculates the early warning level of each potential target, and obtains the optimal scanning mode of each weather radar according to the sensitivity of multiple potential targets, i.e., each weather radar matches a working mode. Compared with the existing mode in which all weather radars have the same working mode, the present disclosure can realize real-time tracking and dynamic monitoring of strong convective weather, guide multiple weather radars to efficiently carry out cooperative operation, and improve the observation timeliness and early warning reliability of high-disaster-causing strong convective weather.

[0016] 2. The present disclosure obtains the optimal scanning mode of weather radars by target function optimization, which not only reduces the overall average control time, but also ensures that the radar load balance evaluation model time difference between different weather radars is as small as possible, i.e., the allocation of weather radar resources is more uniform.

[0017] It should be understood that the content described in the summary section is not intended to limit the key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0018] The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The following drawings are provided to assist in understanding of the present disclosure and are provided as a consequence of specific embodiments of the present disclosure, and therefore, should not be considered limiting the present disclosure. In the drawings: Figure 1 A flowchart of a sensitive target-based space-ground radar cooperative observation method according to an embodiment of the present disclosure is shown; Figure 2 A block diagram of a sensitive target-based space-ground radar cooperative observation system according to an embodiment of the present disclosure is shown; Figure 3 A block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0019] In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are a part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present disclosure.

[0020] In addition, the term "and / or" herein is merely a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects.

[0021] A satellite-ground radar cooperative observation method based on sensitive targets is proposed in the present disclosure, which realizes efficient observation through multi-source data fusion and intelligent scheduling. First, satellite cloud images, multi-time radar data and underlying surface data are integrated to accurately identify potential radar targets and track their life cycle, and then satellite data is combined to calculate the satellite-ground joint warning level. Subsequently, the warning level and multi-dimensional information of underlying surface data are fused to construct a sensitive target evaluation function to quantify the sensitivity of potential targets. Finally, based on the sensitivity ranking, key sensitive targets are selected, the scanning combination of multiple radars and sensitive targets is enumerated, and the optimal scanning scheduling mode is selected by using the objective function to balance radar load and observation efficiency. The present disclosure breaks through the limitations of a single data source, and the cooperation of satellite and ground data improves the timeliness and accuracy of potential target identification, especially strengthens the early signal capture of convection, and at the same time, the risk assessment dimension is comprehensive, and the underlying surface information such as economy, population and terrain is integrated, which is more in line with the actual disaster-causing risk difference. Dynamic optimization of radar resource allocation can avoid the problem of resource waste or key target missing in fixed scanning, and greatly improve the observation timeliness and warning reliability of high-disaster strong convection weather, and provide more scientific technical support for disaster prevention and reduction.

[0022] Figure 1 A flowchart of a satellite-ground radar cooperative observation method based on sensitive targets 100 according to an embodiment of the present disclosure is shown. As shown in Figure 1 The satellite-ground radar cooperative observation method based on sensitive targets includes: S101, acquiring satellite data, radar data and underlying surface data.

[0023] In some embodiments, the satellite data is a satellite cloud image containing a target area, wherein the target area is a region monitored by the radar; the radar data of the target area includes n-2, n-1 and n radar images of the target area, wherein n represents the current time; and the underlying surface data includes a GDP map, a population density map, a land use rate map and a secondary disaster risk map containing the target area.

[0024] S102, identifying the radar data to obtain the number, position, area of potential targets in the radar data and the warning level corresponding to each potential target, and comprehensively obtaining the satellite-ground joint warning level of each potential target.

[0025] In some embodiments, identifying the radar data to obtain the number, position, area of potential targets in the radar data and the warning level corresponding to each potential target, and comprehensively obtaining the satellite-ground joint warning level of each potential target includes: acquiring the potential area of the satellite data, the corresponding position, area and warning level; identifying the potential target in the n-time radar image by threshold method; wherein n represents the current time; The potential target of the radar graph at the n+1 time is predicted by a convolution long short-term memory network ConvLSTM; The potential target in the radar graph at the n+1 time is identified by a threshold method. The potential target is tracked based on an optical flow method to obtain a life cycle state corresponding to each potential target ; Based on each potential target and a potential area of satellite data, a satellite-ground joint early warning level of each potential target is calculated by a potential target satellite-ground joint early warning level calculation formula.

[0026] In some embodiments, the potential target satellite-ground joint early warning level calculation formula is: wherein, represents a spatial accuracy weight, represents a life cycle state, represents a weight coefficient, represents a satellite data early warning level, represents a maximum reflectivity after normalization processing of a radar target, represents an overlap degree of a satellite potential area and a radar potential target.

[0027] In some embodiments, based on geographical coordinate calibration information of a satellite cloud image, a longitude and latitude range of a potential disastrous weather area such as a convective cloud system and a heavy precipitation cloud cluster is determined to form vector boundary data, and it is ensured that the spatial reference is consistent with subsequent radar data. Referring to a physical parameter inversion result of a satellite cloud image, a potential area is preliminarily divided into early warning levels in combination with a meteorological industry standard, and the early warning level is denoted as a satellite early warning level .

[0028] In some embodiments, a threshold method is used to realize automatic identification of the potential target. Specifically, referring to radar echo characteristics of different types of disastrous weather, in combination with climate characteristics of a target area, a dynamic echo intensity threshold is set, the n-time radar graph is subjected to gray scale processing, all pixel points are traversed, pixel points with echo intensity exceeding the set threshold are marked as “candidate target pixels”, isolated noise points are removed through morphological filtering (such as expansion and corrosion operations), connected region analysis is performed on the screened candidate target pixels, each connected region is regarded as an independent potential target, and the total number of connected regions is calculated and counted, that is, the number of radar potential targets at the n time.

[0029] In some embodiments, to achieve dynamic tracking of the potential target life cycle, a ConvLSTM is used to predict the radar map at time n+1, specifically: taking the radar maps at three consecutive times n-2, n-1 and n as input sequences, using a 3-layer ConvLSTM architecture, and outputting the echo intensity prediction result of the radar map at time n+1.

[0030] In some embodiments, the radar map at time n+1 predicted by the ConvLSTM is used to identify potential targets using the threshold method consistent with time n, ensuring consistency and comparability of the two identification results. The echo intensity threshold set at time n is directly reused. If the overall echo intensity is too high, the threshold can be appropriately increased to avoid misidentifying weak echoes as potential targets. The number, location, area and preliminary warning level of each potential target at time n+1 are also calculated and associated with the results at time n to establish a time series.

[0031] In some embodiments, to quantify the dynamic evolution characteristics of potential targets, an optical flow method is used to match the radar potential targets at time n and time n+1, achieving continuous tracking of the targets and evaluating the life cycle state of each target .

[0032] Specifically, for each potential target at time n (denoted as target A), the theoretical position of the target at time n+1 is predicted based on the optical flow vector of the internal pixels of the target. Then, the spatial distance between the theoretical position and the actual identified potential target at time n+1 (denoted as target B) is compared (with a distance threshold of ≤5 km). If the degree of overlap between the theoretical position and the center position of target B is ≥80%, the two targets are determined to be the same target and are assigned the same ID, thereby achieving continuous tracking of the target.

[0033] In some embodiments, based on the acquired satellite and radar multi-dimensional data, the risk level of each potential target is comprehensively quantified through a fusion formula, specifically: wherein, represents the spatial accuracy weight, represents the life cycle state, represents the weight coefficient, represents the satellite data warning level, represents the maximum reflectivity after normalization of the radar target, represents the overlap between the satellite potential area and the radar potential target.

[0034] It should be noted that, In this way, the consistency of the two identification results is ensured The degree of overlap between the potential area of the satellite and the potential target of the weather radar refers to: first judging the degree of overlap between the position of the potential area of the satellite data and the position of the potential target of the radar data, if not overlapping, then if overlapping, then judging the degree of overlap between the area of the potential area of the satellite data and the area of the potential target of the radar data.

[0035] S103, constructing a sensitive target evaluation function by using the underlying surface data, the potential target and the star-ground joint early warning level corresponding to the potential target, and calculating the sensitivity score corresponding to each potential target by using the sensitive target evaluation function.

[0036] In some embodiments, constructing a sensitive target evaluation function by using the underlying surface data, the potential target and the star-ground joint early warning level corresponding to the potential target includes: obtaining the regional economy of the potential target, the population density of the potential target region, the land utilization rate of the potential target region, and the secondary disaster type of the potential target region from the underlying surface data; constructing a sensitive target evaluation function by using the potential target star-ground joint early warning level, the potential target regional economy, the potential target regional population density, the potential target regional land utilization rate, the potential target regional secondary disaster type and the weight coefficient.

[0037] In some embodiments, the expression of the sensitive target evaluation function X is: wherein, , , , , represents a weight parameter.

[0038] In some embodiments, the weight parameter is calculated by using the analytic hierarchy process (AHP) and the entropy weight method (EWM) , , , , .

[0039] In some embodiments, the subjective weight construction includes calculating , , , , .

[0040] Specifically, first, a judgment matrix is constructed ; wherein, represents an index Relative index Importance score, meet , , ; Calculate the judgment matrix The product of the elements of each row ; Calculate the judgment matrix The geometric mean of the elements of each row ; Calculate the subjective weight vector ; Wherein, , , , , .

[0041] In some embodiments, the objective weight construction includes calculating , , , , .

[0042] Specifically, the original matrix is constructed ; wherein, represents the number of evaluation units, represents the number of evaluation indexes; Data standardization processing, wherein the calculation formula of the index is: Wherein, is the element of the original matrix B.

[0043] Calculate the entropy value and entropy weight; wherein the calculation formula of the entropy value is: ; wherein, ; The calculation formula of the entropy weight is: Wherein, , , , , .

[0044] In some embodiments, the weight coupling is calculated by the calculation formula , , , , .

[0045] Specifically, weight coupling The calculation formula is: in, , , , , Therefore, the weight parameters are calculated by combining the subjective AHP method with the objective EWM method. , , , , This allows the subjective AHP method and the objective EWM method to complement each other, thereby improving the weighting parameters. , , , , The accuracy of the calculation improves the evaluation function for sensitive targets. The accuracy of the calculation.

[0046] S104 arranges and combines sensitive targets and radar scanning methods, and performs optimization processing to select the optimal scanning method.

[0047] In some embodiments, arranging and combining sensitive targets and radar scanning methods, and performing optimization processing to select the optimal scanning method includes: The sensitivity scores of multiple potential targets are sorted in descending order, and the top q potential targets are taken as sensitive targets. Based on q sensitive targets and R radars, all permutations and combinations of radar scanning methods are obtained, and the optimal scanning method is selected by optimizing all permutations and combinations through an objective function; where q and R are positive integers.

[0048] Specifically, sensitivity scoring for multiple potential targets in the radar image at time n. Sort the potential targets from largest to smallest, select the top q potential targets, and designate these q potential targets as sensitive targets. Sensitive targets Taiwan radar formation The scanning method of a weather radar, and the formation The scanning methods of each weather radar are optimized to obtain the optimal scanning method for each weather radar. This optimal scanning method is the scheduling method for the weather radar.

[0049] In some embodiments, selecting the optimal scanning method by optimizing all permutations and combinations using an objective function includes: Calculate the function values ​​corresponding to all permutations and combinations of radar scanning methods using the objective function; The function values ​​are sorted in ascending order, and the scanning method corresponding to the smallest function value is taken as the optimal scanning method, which is then used as the radar's scheduling method.

[0050] Specifically, input Sensitive targets Taiwan radar and The moving time matrix corresponding to the radar Scan time matrix Coverage matrix The enumeration method is used to list the results. The scanning method of a weather radar utilizes an objective function. calculate The function values ​​corresponding to each weather radar scanning method. The function values ​​corresponding to each weather radar scanning mode are sorted from smallest to largest. The scanning mode corresponding to the smallest function value is selected as the optimal scanning mode, and this optimal scanning mode is used as the scheduling mode for the weather radar.

[0051] In some embodiments, the radar load balancing evaluation model time The calculation formula is: in, This represents the total number of sensitive targets; Average operating time of the radar The calculation formula is: Standard deviation of radar operation time in Taiwan The calculation formula is: objective function The calculation formula is: in: Represents an exponential function, when the sensitive target Assigned to radar The value is 1 if the condition is met, and 0 otherwise. Indicates sensitive targets Assigned to radar , express Weather radars participating in scanning operations in Taiwan's weather radar system. , denotes the adjustment coefficient, the moving time matrix denotes each element denotes the radar moving to the sensitive target , the scanning time matrix denotes each element denotes the radar capable of scanning the sensitive target , the coverage matrix denotes each element denotes the radar capable of scanning the sensitive target .

[0052] According to the embodiments of the present disclosure, the following technical effects are achieved: 1、The present application calculates the early warning level of each potential target, and obtains the optimal scanning mode of each weather radar (i.e., each weather radar matches a working mode) according to the sensitivity of multiple potential targets, which can realize real-time tracking and dynamic monitoring of strong convective weather compared with the existing mode in which all weather radars work in the same mode, and guide multiple weather radars to carry out efficient collaborative work, and improve the observation timeliness and early warning reliability of high-disaster strong convective weather.

[0053] 2、The present application combines the subjective AHP method with the objective EWM method to calculate the weight parameters a, b, c, d, and e, so that the subjective AHP method and the objective EWM method are complementary to each other, which can improve the accuracy of the weight parameters a, b, c, d, and e calculation, and further improve the accuracy of the sensitive target evaluation function X calculation.

[0054] 3、The present application calculates the objective function F(a) to obtain the optimal scanning mode of the weather radar, which can not only reduce the overall average control time, but also ensure that the radar load balancing evaluation model time difference between different weather radars is as small as possible (i.e., the allocation of weather radar resources can be more uniform).

[0055] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited to the action sequence described, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0056] The above is the introduction of the method embodiment, and the following further illustrates the scheme of the present disclosure through the device embodiment.

[0057] Figure 2 A block diagram of a sensitive target-based space-ground radar cooperative observation system 200 is shown according to an embodiment of the present disclosure. As shown, the system 200 includes: Figure 2 An acquisition module 201 is configured to acquire satellite data, radar data, and underlying surface data. A processing module 202 is configured to perform identification processing on the radar data to obtain the number, position, and area of potential targets in the radar data, and a warning level corresponding to each potential target, and to comprehensively obtain a space-ground joint warning level of each potential target. A calculation module 203 is configured to construct a sensitive target evaluation function using the underlying surface data, potential targets, and a space-ground joint warning level corresponding to each potential target, and to calculate a sensitivity score corresponding to each potential target using the sensitive target evaluation function. An evaluation module 204 is configured to perform arrangement and combination of sensitive targets and radar scanning modes, and to perform optimization processing to select an optimal scanning mode.

[0058] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described modules can refer to the corresponding process in the foregoing method embodiments, which will not be described herein.

[0059] In the technical scheme of the present disclosure, the acquisition, storage, and application of user personal information comply with relevant laws and regulations and do not violate public order and good customs.

[0060] According to the embodiments of the present disclosure, the present disclosure further provides an electronic device, a readable storage medium, and a computer program product.

[0061] Figure 3 A schematic block diagram of an electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present disclosure described and / or claimed in this document.

[0062] ​The electronic device 300 includes a computing unit 301 that can perform various appropriate actions and processes in accordance with a computer program stored in a ROM 302 or a computer program loaded into a RAM 303 from a storage unit 308. Various programs and data required for the operation of the electronic device 300 can also be stored in the RAM 303. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An I / O interface 305 is also connected to the bus 304.

[0063] A plurality of components in the electronic device 300 are connected to the I / O interface 305, including an input unit 306 such as a keyboard, a mouse, and the like, an output unit 307 such as various types of displays, a speaker, and the like, a storage unit 308 such as a magnetic disk, an optical disk, and the like, and a communication unit 309 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0064] The computing unit 301 can be various general-purpose and / or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, and the like. The computing unit 301 performs various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method 100 described above can be performed. Alternatively, in other embodiments, the computing unit 301 can be configured to perform the method 100 by any other appropriate means, such as by means of firmware.

[0065] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0066] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0067] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0068] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; 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 acoustic, speech, or tactile input.

[0069] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0070] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server is generally established by computer programs running on the respective computers and having a client-server relationship to each other. The servers can be cloud servers, servers of a distributed system, or servers combined with a blockchain.

[0071] It should be understood that various forms of flow shown above can be used, with steps reordered, added, or removed. For example, the steps recited in the present disclosure can be performed in parallel, in series, or in a different order, without limitation herein, so long as the desired results of the technology disclosed in the present disclosure are achieved.

[0072] The specific embodiments described above are not intended to limit the scope of the present disclosure. Those skilled in the art will understand that various modifications, combinations, sub-combinations, and alternatives can be made to the specific embodiments without departing from the spirit and principles of the present disclosure. Any further modifications, equivalents, or alternatives within the spirit and principles of the present disclosure are intended to be covered.

Claims

1. A method for cooperative observation of a sensitive target based on space-ground radar, characterized in that, The method comprises the following steps: acquiring satellite data, radar data and underlying surface data; performing identification processing on the radar data to obtain the number, position, area and early warning level corresponding to each potential target in the radar data, and comprehensively obtaining the star-ground joint early warning level of each potential target; constructing a sensitive target evaluation function by using the underlying surface data, potential targets and the star-ground joint early warning level corresponding to the potential targets, and calculating the sensitivity score corresponding to each potential target by using the sensitive target evaluation function; performing permutation and combination on the sensitive targets and radar scanning modes, and performing optimization processing to select an optimal scanning mode.

2. The method according to claim 1, wherein the satellite data is a satellite cloud image including a target region, wherein the target region is a region monitored by the radar; the underlying surface data includes a GDP map, a population density map, a land utilization rate map and a secondary disaster risk map of the target region. The identification processing on the radar data to obtain the number, position, area and early warning level corresponding to each potential target in the radar data, and comprehensively obtaining the star-ground joint early warning level of each potential target comprises the following steps:

3. The method of claim 1, wherein, acquiring the potential region of the satellite data, the corresponding position, area and early warning level; identifying the potential target in the radar image at the n-th moment by using a threshold method, wherein n represents the current moment; predicting the potential target of the radar image at the n+1-th moment by using a convolution long short-term memory network (ConvLSTM); identifying the potential target in the radar image at the n+1-th moment by using a threshold method; calculating the star-ground joint early warning level of each potential target based on each potential target and the potential region of the satellite data by using a potential target star-ground joint early warning level calculation formula. tracking potential targets based on the optical flow method to obtain a life cycle state corresponding to each potential target ; The potential target star-ground joint early warning level calculation formula is:

4. The method of claim 3, wherein, The construction of the sensitive target evaluation function by using the underlying surface data, potential targets and the star-ground joint early warning level corresponding to the potential targets comprises the following steps: wherein, represents a spatial accuracy weight, represents a life cycle status, represents a weight coefficient, represents a satellite data warning level, represents a maximum reflectivity of a radar target after normalization processing, represents an overlap degree of a satellite potential area and a radar potential target.

5. The method of claim 1, wherein, acquiring the potential target region economy, potential target region population density, potential target region land utilization rate and potential target region secondary disaster type from the underlying surface data; constructing the sensitive target evaluation function by using the potential target star-ground joint early warning level, potential target region economy, potential target region population density, potential target region land utilization rate, potential target region secondary disaster type and weight coefficient. The permutation and combination of the sensitive targets and radar scanning modes, and the optimization processing to select the optimal scanning mode comprise the following steps:

6. The method of claim 1, wherein, performing descending order sorting on the sensitivity scores of the multiple potential targets, and taking the first q potential targets as the sensitive targets; obtaining all permutation and combination of the radar scanning modes based on the q sensitive targets and R radars, and performing optimization processing on all permutation and combination by using an objective function to select the optimal scanning mode; wherein q and R are positive integers. The optimization processing on all permutation and combination by using the objective function to select the optimal scanning mode comprises the following steps:

7. The method of claim 6, wherein, calculating the function value corresponding to all permutation and combination of the radar scanning modes by using the objective function; ​ The function values are sorted in ascending order, the scanning mode corresponding to the minimum function value is taken as the optimal scanning mode, and the optimal scanning mode is taken as the scheduling mode of the radar.

8. A space-ground radar cooperative observation system based on a sensitive target, characterized in that, The method comprises the steps of: An acquisition module is configured to acquire satellite data, radar data, and underlying surface data. A processing module is configured to perform identification processing on the radar data to obtain the number, position, and area of potential targets in the radar data, and a warning level corresponding to each potential target, and to comprehensively obtain a satellite-ground joint warning level of each potential target. A calculation module is configured to construct a sensitive target evaluation function using the underlying surface data, the potential targets, and the satellite-ground joint warning level corresponding to each potential target, and to calculate a sensitivity score corresponding to each potential target using the sensitive target evaluation function. An evaluation module is configured to perform arrangement and combination of sensitive targets and radar scanning modes, and to perform optimization processing to select an optimal scanning mode.

9. An electronic device, comprising: The method comprises the steps of: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method of any one of claims 1-7.

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