Regional weather detection method, system and device based on vehicle-mounted rain detection radar and storage medium

Abstract

The embodiment of the invention provides a regional weather detection method based on a vehicle-mounted rain detection radar. The method comprises the following steps: acquiring a to-be-detected region; acquiring an initial rainfall signal detected by a vehicle-mounted rain detection radar in the to-be-detected area, and preprocessing the initial rainfall signal to obtain rainfall data corresponding to the vehicle-mounted rain detection radar; for each to-be-detected area, responding to the to-be-detected area as a single-source area, taking rainfall data of the vehicle-mounted rainfall radar corresponding to the to-be-detected area as target rainfall data of the to-be-detected area; and in response to the condition that the to-be-detected area is a multi-source area, determining target rainfall data of the to-be-detected area based on the rainfall data of the plurality of vehicle-mounted rainfall radars corresponding to the to-be-detected area and the atmospheric monitoring data.

Description

Technical Field

[0001] The present invention relates to the field of meteorological detection, and in particular to a method, system, device and storage medium for regional weather detection based on a vehicle-mounted rain measuring radar. Background Art

[0002] Against the backdrop of the rapid development of meteorological detection technology, regional weather monitoring has increasingly higher requirements for the accuracy and real-time performance of data collection. With the advancement of vehicle-mounted rain radar and phased array radar technology, weather monitoring has gradually shifted from traditional fixed-site observation to mobile and intelligent. However, traditional weather monitoring methods have problems such as limited data coverage, insufficient detection accuracy, and weak dynamic adjustment capabilities, making it difficult to cope with complex and changeable regional weather changes, especially in monitoring areas with frequent changes.

[0003] Therefore, it is necessary to provide a regional weather detection method, system, device and storage medium based on a vehicle-mounted rain measuring radar to achieve real-time monitoring and dynamic adjustment of regional weather, quickly and accurately obtain precipitation data, and optimize the radar's mobile position and detection parameters. Summary of the invention

[0004] One or more embodiments of the present specification provide a regional weather detection method based on a vehicle-mounted rain measuring radar, comprising: obtaining a to-be-detected area; obtaining an initial precipitation signal detected by a vehicle-mounted rain measuring radar located in the to-be-detected area, and preprocessing the initial precipitation signal to obtain precipitation data corresponding to the vehicle-mounted rain measuring radar; for each of the to-be-detected areas: in response to the to-be-detected area being a single-source area, using the precipitation data of the vehicle-mounted rain measuring radar corresponding to the to-be-detected area as target precipitation data of the to-be-detected area; in response to the to-be-detected area being a multi-source area, determining the target precipitation data of the to-be-detected area based on the precipitation data of multiple vehicle-mounted rain measuring radars corresponding to the to-be-detected area and atmospheric monitoring data; at every preset period: determining a frequently changing area and a changing precipitation intensity of the frequently changing area based on the target precipitation data of each of the to-be-detected areas at multiple historical moments; and determining a moving position of each of the vehicle-mounted rain measuring radars and phased array radar parameters of a phased array radar device covering multiple areas to be detected based on the frequently changing areas and the changing precipitation intensity.

[0005] One or more embodiments of the present specification provide a regional weather detection system based on a vehicle-mounted rain measuring radar, the system comprising: a first acquisition module, configured to acquire a to-be-detected area; a second acquisition module, configured to: acquire an initial precipitation signal detected by a vehicle-mounted rain measuring radar located in the to-be-detected area, and pre-process the initial precipitation signal to obtain precipitation data corresponding to the vehicle-mounted rain measuring radar; for each of the to-be-detected areas: in response to the to-be-detected area being a single-source area, use the precipitation data of the vehicle-mounted rain measuring radar corresponding to the to-be-detected area as target precipitation data of the to-be-detected area; In response to the area to be detected being a multi-source area, the target precipitation data of the area to be detected is determined based on the precipitation data of the multiple vehicle-mounted rain measuring radars corresponding to the area to be detected and the atmospheric monitoring data; the processor is configured to: determine the frequently changing areas and the changing precipitation intensity of the frequently changing areas based on the target precipitation data of each of the areas to be detected at multiple historical moments at every preset period; determine the moving positions of each of the vehicle-mounted rain measuring radars and the phased array radar parameters of the phased array radar equipment covering the multiple areas to be detected based on the frequently changing areas and the changing precipitation intensity.

[0006] One or more embodiments of the present specification provide a regional weather detection device based on a vehicle-mounted rain measuring radar, the device comprising at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least part of the computer instructions to implement a method for regional weather detection based on a vehicle-mounted rain measuring radar.

[0007] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions, and when the computer instructions are executed by a processor, a method for regional weather detection based on a vehicle-mounted rain measuring radar is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 is a system module diagram of a regional weather detection system based on a vehicle-mounted rain measuring radar according to some embodiments of this specification; Figure 2 is an exemplary flow chart of a regional weather detection method based on a vehicle-mounted rain measuring radar according to some embodiments of this specification; Figure 3 is an exemplary schematic diagram of determining target precipitation data of a to-be-detected area according to some embodiments of this specification; Figure 4 This is an exemplary flow chart for determining the moving position of each vehicle-mounted rain measuring radar according to some embodiments of this specification. DETAILED DESCRIPTION

[0009] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0010] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0011] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0012] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0013] Figure 1 It is a system module diagram of a regional weather detection system based on a vehicle-mounted rain measuring radar as shown in some embodiments of this specification.

[0014] In some embodiments, the regional weather detection system 100 based on the vehicle-mounted rain measuring radar may include a first acquisition module 110 , a second acquisition module 120 , and a processor 130 .

[0015] The first acquisition module 110 is configured to acquire the area to be detected.

[0016] The second acquisition module 120 is configured to acquire an initial precipitation signal detected by a vehicle-mounted rain measuring radar located in the area to be detected, and pre-process the initial precipitation signal to obtain precipitation data corresponding to the vehicle-mounted rain measuring radar; for each area to be detected: in response to the area to be detected being a single-source area, the precipitation data of the vehicle-mounted rain measuring radar corresponding to the area to be detected is used as the target precipitation data of the area to be detected; in response to the area to be detected being a multi-source area, the target precipitation data of the area to be detected is determined based on the precipitation data of multiple vehicle-mounted rain measuring radars corresponding to the area to be detected and the atmospheric monitoring data.

[0017] In some embodiments, the second acquisition module is further configured to determine the detection accuracy of each vehicle-mounted rain measuring radar based on the precipitation data and atmospheric monitoring data of each vehicle-mounted rain measuring radar; and determine the target precipitation data of the area to be detected based on the precipitation data and detection accuracy of multiple vehicle-mounted rain measuring radars corresponding to the area to be detected.

[0018] In some embodiments, the second acquisition module is further configured to determine, for each detection circle in the detection range of each vehicle-mounted rain measuring radar, the sub-accuracy of different detection circles in the detection range of each vehicle-mounted rain measuring radar based on the precipitation data in the detection circle and the atmospheric monitoring data; determine the target precipitation data of the area to be detected based on the precipitation data and the detection accuracy of multiple vehicle-mounted rain measuring radars corresponding to the area to be detected, including: determining the target precipitation data of the area to be detected based on the precipitation data of multiple vehicle-mounted rain measuring radars corresponding to the area to be detected and the sub-accuracy corresponding to the area to be detected.

[0019] The processor 130 refers to a component configured in the regional weather detection system based on the vehicle-mounted rain radar for data transmission and data analysis and processing. For example, the processor may include at least one of a central processing unit (CPU), a field programmable gate array (FPGA), a programmable logic controller (PLC), etc.

[0020] In some embodiments, the processor may be configured on one or more vehicle-mounted rain detection radars.

[0021] In some embodiments, the processor is further configured to: determine the frequently changing areas and the changing precipitation intensity of the frequently changing areas based on the target precipitation data of each area to be detected at multiple historical moments at every preset period. Based on the frequently changing areas and the changing precipitation intensity, determine the moving position of each vehicle-mounted rain measuring radar and the phased array radar parameters of the phased array radar device covering multiple areas to be detected.

[0022] In some embodiments, the processor is further configured to generate multiple groups of candidate mobile position sets based on frequently changing areas and changing precipitation intensities, each group of candidate mobile position sets including candidate mobile positions of multiple vehicle-mounted rain measuring radars; obtain multiple reference points within multiple areas to be detected; for each group of candidate mobile position sets: determine the data accuracy at multiple reference points based on the average detection accuracy of each vehicle-mounted rain measuring radar and the distances between multiple reference points and the candidate mobile positions of each vehicle-mounted rain measuring radar; determine the target mobile position set based on the data accuracy at multiple reference points corresponding to each candidate mobile position set to determine the mobile position of each vehicle-mounted rain measuring radar.

[0023] In some embodiments, the regional weather detection system 100 and its various modules based on the vehicle-mounted rain detection radar can be integrated into the processor 130. For detailed description of the functions performed by the system 100 and its various modules, please refer to the specification. Figure 2-Figure 4 Related content.

[0024] It should be noted that the above description of the regional weather detection system based on the vehicle-mounted rain radar and its modules is only for the convenience of description and cannot limit this specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from this principle. In some embodiments, Figure 1 The first acquisition module 110, the second acquisition module 120 and the processor 130 disclosed in the specification may be different modules in a system, or one module may realize the functions of two or more modules. For example, each module may share a storage module, or each module may have its own storage module. Such variations are within the protection scope of this specification.

[0025] Figure 2 FIG. 1 is an exemplary flow chart of a method for detecting regional weather based on a vehicle-mounted rain radar according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps. In some embodiments, the process 200 can be executed by a processor in a regional weather detection system based on a vehicle-mounted rain radar.

[0026] Step 210: Acquire the area to be detected.

[0027] The area to be detected refers to an area that needs to be detected. For example, the area to be detected may include a geographical area predetermined by the processor and requiring regional weather information to be obtained.

[0028] In some embodiments, the processor may generate a region to be detected based on a geographic range input by a user. For example, the processor may use a geographic region corresponding to a latitude and longitude range as the region to be detected based on a latitude and longitude range input by a user.

[0029] Step 220, obtaining an initial precipitation signal detected by the vehicle-mounted rain measuring radar located in the area to be detected, and preprocessing the initial precipitation signal to obtain precipitation data corresponding to the vehicle-mounted rain measuring radar.

[0030] The vehicle-mounted rain detection radar refers to a movable rain detection radar with a movable vehicle as a carrier. For example, the vehicle-mounted rain detection radar may include a vehicle-mounted Doppler radar, a vehicle-mounted dual-polarization weather radar, and the like.

[0031] The initial precipitation signal may refer to an electromagnetic signal received by the vehicle-mounted rain detection radar. In some embodiments, the vehicle-mounted rain detection radar may emit electromagnetic waves and receive electromagnetic signals reflected back from the environment. The electromagnetic signal may include echo intensity, Doppler velocity, and spectrum width.

[0032] In some embodiments, the processor is communicatively connected to the signal transmitting and receiving components of one or more vehicle-mounted rain measuring radars, and receives electromagnetic signal-related data transmitted by the signal transmitting and receiving components as an initial precipitation signal.

[0033] The precipitation data refers to data related to the precipitation conditions in the area to be detected. In some embodiments, the precipitation data may include precipitation intensity and precipitation speed monitored by one or more vehicle-mounted rain radars.

[0034] In some embodiments, the vehicle-mounted rain measuring radar can obtain precipitation data of multiple first detection points by changing the geographical location of the rain measuring radar during movement, wherein the first detection point includes the geographical location when the vehicle-mounted rain measuring radar obtains the initial precipitation signal.

[0035] Preprocessing refers to the data processing process related to electromagnetic signal processing.

[0036] In some embodiments, the processor may pre-process the initial precipitation signal in a variety of ways (e.g., time correction, phase correction, Doppler processing, noise reduction processing, Fourier transform, etc.) to obtain precipitation data corresponding to the vehicle-mounted rain measuring radar. For example, the processor may filter the background noise (e.g., electromagnetic noise in the environment, electromagnetic noise of the device itself, etc.) in the initial precipitation signal through a filter, and generate precipitation data based on the filtered electromagnetic signal.

[0037] Step 230, in response to the area to be detected being a single-source area, the precipitation data of the vehicle-mounted rain measuring radar corresponding to the area to be detected is used as the target precipitation data of the area to be detected; in response to the area to be detected being a multi-source area, the target precipitation data of the area to be detected is determined based on the precipitation data of multiple vehicle-mounted rain measuring radars corresponding to the area to be detected and the atmospheric monitoring data.

[0038] The single-source area is the area to be detected that is covered by the detection range of only a single vehicle-mounted rain measuring radar, and the multi-source area is the area to be detected that is covered by the detection ranges of multiple vehicle-mounted rain measuring radars.

[0039] The target precipitation data includes the precipitation intensity and precipitation speed in the area to be detected.

[0040] Atmospheric monitoring data refers to meteorological data of the area to be detected. For example, the atmospheric monitoring data may include reference precipitation intensity, reference precipitation speed, reference temperature data, and reference wind speed and direction data in the area to be detected.

[0041] In some embodiments, the atmospheric monitoring data may be generated based on the electromagnetic signals collected by the phased array radar device. For example, the processor may obtain the electromagnetic signals collected by the phased array radar device at one or more second detection points, and generate the atmospheric monitoring data based on the electromagnetic signals. The second detection point includes the geographical location where the phased array radar is located when collecting the electromagnetic signals.

[0042] In some embodiments, the first detection point and the second detection point may be different geographical locations.

[0043] In some embodiments, the phased array radar device may be provided with auxiliary monitoring components, such as temperature sensors, microwave radiometers, wind speed sensors, etc. Microwave radiometers may be used to detect infrared and microwave radiation in the atmosphere, thereby obtaining temperature data of the atmosphere.

[0044] In some embodiments, the processor may generate target precipitation data through a variety of methods (preset algorithms, statistics, mathematical models, etc.) based on precipitation data, etc. For example, in response to the area to be detected being a single-source area, the processor may use the average of precipitation intensity and precipitation speed at multiple first detection points of the vehicle-mounted rain measuring radar in the area to be detected as the target precipitation data of the area to be detected.

[0045] In some embodiments, the processor can determine the target precipitation data based on the precipitation data after weighted processing. The weight coefficient involved in the weighted processing refers to the weight corresponding to the precipitation data of each vehicle-mounted rain measuring radar, and the weight coefficient can be determined based on the atmospheric monitoring data. For example, in response to the area to be detected being a multi-source area, the processor can use the average of the precipitation intensity and precipitation speed of multiple first detection points of a certain vehicle-mounted rain measuring radar in the area to be detected as the precipitation data of the vehicle-mounted rain measuring radar in the area to be detected, and perform weighted processing on the precipitation data of multiple vehicle-mounted rain measuring radars to generate target precipitation data.

[0046] In some embodiments, the weight coefficient may be a value preset based on prior experience and input into the processor.

[0047] In some embodiments, the processor can determine the weight coefficients of precipitation data of different vehicle-mounted rain measuring radars according to the first preset table. For example, the processor can determine the reference precipitation data of a certain detection area under certain temperature data and wind speed and direction data through the first preset table; according to the similarity (for example, ratio, etc.) between the precipitation data of each vehicle-mounted rain measuring radar in the area to be detected and the reference precipitation data, determine the weight coefficients corresponding to the precipitation data of different vehicle-mounted rain measuring radars. Among them, the first preset table includes different reference temperature data and reference wind speed and direction data and corresponding reference precipitation data (for example, reference precipitation intensity, reference precipitation speed). For example, the processor can use the ratio of the similarity corresponding to a certain vehicle-mounted rain measuring radar to the sum of the similarities corresponding to all vehicle-mounted rain measuring radars as the weight coefficient of the precipitation data of the vehicle-mounted rain measuring radar.

[0048] In some embodiments, the first preset table can be set by the processor according to historical data. For example, the processor can use the average value of precipitation data under the same or similar temperature data and wind speed and direction data in a large amount of historical data as the reference precipitation data corresponding to the reference temperature data and the reference wind speed and direction data in the first preset table, and then construct the first preset table.

[0049] In some embodiments, if the area to be detected is a multi-source area, the process of determining the target precipitation data specifically includes: determining the confidence of multiple vehicle-mounted rain measuring radars based on the precipitation data of multiple vehicle-mounted rain measuring radars corresponding to the area to be detected and the atmospheric monitoring data corresponding to the area to be detected through an evaluation model; determining the target precipitation data of the area to be detected based on the precipitation data and confidence of multiple vehicle-mounted rain measuring radars, and the evaluation model is a machine learning model.

[0050] The evaluation model is a model used to determine the confidence level of the vehicle-mounted rain radar detection. In some embodiments, the evaluation model may be a machine learning model. For example, the evaluation model may be a graph neural network (GNN) model, etc.

[0051] In some embodiments, the input of the evaluation model may include a meteorological map composed of vehicle-mounted rain measuring radar and phased array radar equipment, and the output of the evaluation model may include the confidence of one or more vehicle-mounted rain measuring radar nodes.

[0052] A meteorological map refers to a knowledge map or graph structure related to vehicle-mounted rain radar and phased array radar equipment. In some embodiments, a meteorological map may include at least one node and at least one edge. For example, a node may include a vehicle-mounted rain radar node and a phased array radar device node, and node features may include precipitation data or atmospheric monitoring data. In some embodiments, if the detection ranges of two radars overlap, the nodes to which the two radars belong are connected by edges.

[0053] In some embodiments, the evaluation model can be obtained by the processor through training based on the first training sample and the first label. The first training sample may include historical meteorological maps of multiple different areas to be detected, and the first label may include the confidence of each vehicle-mounted rain radar node in the historical meteorological map corresponding to the first training sample. For example, the processor can construct the first training sample based on historical detection data. In some embodiments, the processor can generate a first label based on the precipitation intensity and precipitation speed of the corresponding time node and the corresponding detection location obtained by manual inspection, and the similarity with the precipitation data corresponding to the vehicle-mounted rain radar node in the historical meteorological map.

[0054] In some embodiments, the processor may: obtain a training data set, the training data set including a number of first training samples and a first label corresponding to each first training sample; perform multiple rounds of iterations, at least one round of iterations including: selecting one or more first training samples from the training data set, inputting the one or more first training samples into the evaluation model, and obtaining the model prediction output corresponding to the one or more samples; substituting the model prediction output corresponding to the one or more first training samples and the first label corresponding to the one or more first training samples into the formula of a predefined loss function to obtain the value of the loss function; according to the value of the loss function, reversely update the model parameters in the evaluation model; this step may be performed using various methods. For example, the update may be based on the gradient descent method. When the iteration end condition is met, the iteration ends and a trained evaluation model is obtained.

[0055] Confidence refers to data related to the accuracy of vehicle-mounted rain radar detection.

[0056] In some embodiments, the processor may generate target precipitation data according to the confidence level through a variety of methods (statistical analysis, calculation, etc.).

[0057] In some embodiments, the processor may generate a weight coefficient involved in weighted processing based on the confidence level. For example, the processor may use the ratio of the confidence level corresponding to a certain on-board rain radar to the sum of the confidence levels corresponding to all on-board rain radars as the weight coefficient of the precipitation data of the on-board rain radar, perform weighted processing on the precipitation data of multiple on-board rain radars, and generate target precipitation data. For more information about weighted processing, please refer to Figure 2 The above related description will not be repeated here.

[0058] In some embodiments of the present specification, by processing the meteorological map with a trained evaluation model, a confidence level reflecting the actual situation can be generated quickly and accurately, and relatively accurate target precipitation data can be determined.

[0059] Step 240 , based on the target precipitation data of each area to be detected at multiple historical moments, determine the frequently changing area and the changing precipitation intensity of the frequently changing area.

[0060] In some embodiments, the processor may determine the frequently changing area and the changing precipitation intensity of the frequently changing area every preset period. The length of the preset period is set by the user based on experience.

[0061] The frequently changing area refers to an area to be detected where the change value of precipitation intensity between any two adjacent historical moments exceeds the preset change threshold for more than N times. In some embodiments, the preset change threshold and the value of N can be set based on experience.

[0062] In some embodiments, the processor may determine the value of N based on the distribution density of vehicle-mounted rain measuring radars in multiple areas to be detected. The distribution density of vehicle-mounted rain measuring radars refers to the number of vehicle-mounted rain measuring radars per unit area in the overall area composed of multiple areas to be detected. For example, the value of N may be negatively correlated with the distribution density of vehicle-mounted rain measuring radars. It is understandable that the greater the distribution density of vehicle-mounted rain measuring radars, the more detection equipment resources can be called in the overall area. At this time, the judgment criteria for frequently changing areas can be appropriately lowered to expand the scope of dynamic regulation and improve the regulation effect of the overall area.

[0063] The variable precipitation intensity refers to the highest value and the lowest value of precipitation intensity in the target precipitation data at multiple historical moments in a certain area to be detected.

[0064] In some embodiments, the varying precipitation intensity may be generated by a processor through statistics.

[0065] Step 250, based on the frequently changing areas and the changing precipitation intensity, determine the moving positions of each vehicle-mounted rain measuring radar and the phased array radar parameters of the phased array radar devices covering multiple areas to be detected.

[0066] In some embodiments, the processor may determine the moving position of each vehicle-mounted rain detection radar and the phased array radar parameters of the phased array radar device covering multiple areas to be detected at preset periods. The length of the preset period is set by the user based on experience.

[0067] The mobile position refers to the first monitoring point after the vehicle-mounted rain measuring radar is moved or planned to be moved.

[0068] In some embodiments, the processor may determine the moving position of the vehicle-mounted rain measuring radar through a variety of feasible methods (eg, statistical screening, movement cost analysis, etc.).

[0069] For example, the processor can adjust the positions of the on-board rain measuring radars in the non-frequently changing areas and the closest areas to the frequently changing areas in various directions, so that the frequently changing areas are covered by the detection ranges of at least two on-board rain measuring radars. For example, the processor can sort the on-board rain measuring radars in the non-frequently changing areas and the closest areas to the frequently changing areas in various directions according to their distance from the frequently changing areas, and generate the moving positions of the on-board rain measuring radars in order from small to large.

[0070] For more information on determining the mobile positions of multiple vehicle-mounted rain radars, see Figure 4 and related descriptions.

[0071] Phased array radar parameters refer to the operating parameters of the phased array radar device. For example, the phased array radar parameters may include the beam pointing angle and beam scanning speed of the phased array radar device. The beam pointing angle refers to the direction in which the electromagnetic wave is emitted.

[0072] In some embodiments, the processor may determine the phased array radar parameters in a variety of ways (eg, a preset algorithm, a mathematical model, etc.).

[0073] For example, the processor can generate the sum of the distances between the central area of ​​the beam and each frequently changing area at different beam pointing angles, and use the beam pointing angle of the phased array radar when the sum of the distances between the central area of ​​the beam and each frequently changing area is minimized as the adjusted phased array radar parameter.

[0074] For example, in response to the number of frequently changing areas being greater than a preset number threshold, the processor may increase the beam scanning speed of the phased array radar by a preset adjustment amount (for example, by 5° / min) to increase the detection frequency of the radar. The preset number threshold and the preset adjustment amount may be set based on experience.

[0075] In some embodiments of the present specification, by simultaneously using precipitation data monitored by a vehicle-mounted rain measuring radar and atmospheric monitoring data monitored by a phased array radar device, the accuracy and applicability of the detection results of the vehicle-mounted rain measuring radar at different times and in different areas can be improved, and the degree of interference from the complex and changeable atmospheric and electromagnetic environments can be reduced.

[0076] It should be noted that the above description of the process 200 is only for example and illustration, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process 200 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0077] Figure 3 It is an exemplary schematic diagram of determining target precipitation data of a to-be-detected area according to some embodiments of this specification.

[0078] In some embodiments, the processor can determine the detection accuracy 330 of each vehicle-mounted rain measuring radar based on the precipitation data 310 and the atmospheric monitoring data 320 of each vehicle-mounted rain measuring radar; and determine the target precipitation data 340 of the area to be detected based on the precipitation data 310 and the detection accuracy 330 of multiple vehicle-mounted rain measuring radars corresponding to the area to be detected.

[0079] For more information about precipitation data and atmospheric monitoring data, see Figure 2 and related descriptions.

[0080] The detection accuracy 330 refers to the accuracy of the detection result of the vehicle-mounted rain radar, for example, 80%.

[0081] In some embodiments, the processor may determine the detection accuracy 330 of the vehicle-mounted rain measuring radar according to the precipitation data 310 collected by multiple vehicle-mounted rain measuring radars at the same time for the same area to be detected, and the atmospheric monitoring data 320 acquired by the phased array radar device. For example, the processor may use the ratio of the precipitation data collected by a certain vehicle-mounted rain measuring radar to the reference precipitation data in the atmospheric monitoring data acquired by the phased array radar device as the detection accuracy 330 of the vehicle-mounted rain measuring radar.

[0082] In some embodiments, the processor may use the average of the similarities (e.g., ratio) between precipitation data collected by a certain vehicle-mounted rain measuring radar at multiple first detection points and atmospheric monitoring data acquired by a phased array radar device at a second detection point whose distance from the first detection point is less than a preset distance threshold as the detection accuracy 330 of the vehicle-mounted rain measuring radar. The preset distance threshold may be set by the user based on experience.

[0083] It is understandable that when the distance between the first detection point and the second detection point is far, the reliability of the detection accuracy generated by the processor will be affected. By setting a reasonable preset distance threshold and only obtaining the similarity of relevant data that meets the distance requirement, the accuracy of the detection accuracy of different vehicle-mounted detection radars generated by the processor can be guaranteed.

[0084] In some embodiments, the processor may generate target precipitation data based on the detection accuracy through a variety of methods (statistical analysis, calculation, etc.).

[0085] In some embodiments, the processor may generate a weight coefficient involved in weighted processing based on the detection accuracy. For example, the processor may use the ratio of the detection accuracy corresponding to a certain on-board rain measuring radar to the sum of the detection accuracy corresponding to all on-board rain measuring radars as the weight coefficient of the precipitation data of the on-board rain measuring radar, perform weighted processing on the precipitation data of multiple on-board rain measuring radars, and generate target precipitation data. For more information about weighted processing, please refer to Figure 2 The above related description will not be repeated here.

[0086] In some embodiments of the present specification, the precipitation data monitored by the vehicle-mounted rain measuring radar and the atmospheric monitoring data can be used to accurately generate the detection accuracy for different vehicle-mounted rain measuring radars, and then the target precipitation data of the area to be detected that is more in line with the actual situation can be determined, thereby improving the detection reliability.

[0087] In some embodiments, the detection accuracy also includes sub-accuracies 330-1 of different detection circles in the detection range of the on-board rain measuring radar. For each detection circle in the detection range of each on-board rain measuring radar, the processor can determine the sub-accuracies 330-1 of different detection circles in the detection range of each on-board rain measuring radar based on the precipitation data in the detection circle and the atmospheric monitoring data. Determining the target precipitation data of the area to be detected based on the precipitation data and detection accuracy of multiple on-board rain measuring radars corresponding to the area to be detected includes: determining the target precipitation data of the area to be detected based on the precipitation data of multiple on-board rain measuring radars corresponding to the area to be detected and the sub-accuracy corresponding to the area to be detected.

[0088] Detection circles refer to the range division related to the detection range or detection distance of the vehicle-mounted rain measuring radar. Since electromagnetic waves will have energy attenuation during the propagation process, the attenuation is more obvious as the distance is farther. The farther the detection location is from the radar, the lower the accuracy is generally, so the processor divides the radar's detection range into several different circles according to the distance.

[0089] The sub-accuracy of the detection circle refers to the detection accuracy of the detection results of the vehicle-mounted rain measuring radar for different detection circles.

[0090] In some embodiments, the processor can determine the sub-accuracy of the detection circle based on the precipitation data and atmospheric monitoring data within the detection circle through various methods (e.g., statistical analysis, calculation, etc.). For example, for multiple detection circles of a certain on-board rain measuring radar, the processor can obtain the precipitation data of multiple first detection points of the on-board rain measuring radar within a certain detection circle, and determine multiple similarities (e.g., ratios, etc.) between the precipitation data at the multiple first detection points and the reference precipitation data in the atmospheric monitoring data of the corresponding second detection points, and determine the average of the multiple similarities as the sub-accuracy of the on-board rain measuring radar in the detection circle. For more information on how to determine similarities, see Figure 2 And related descriptions. Wherein, the corresponding second detection point may include a point whose distance from the first detection point is less than a preset distance threshold. For more information about the preset distance threshold, see Figure 3 Related description above.

[0091] For example, the processor can obtain precipitation data of multiple first detection points of a certain vehicle-mounted rain measuring radar in a certain detection circle, and use the average value of the similarity (for example, ratio, etc.) between the precipitation data of the multiple first detection points and the reference precipitation data in the atmospheric monitoring data obtained by the phased array radar device in the detection circle as the sub-accuracy of the vehicle-mounted rain measuring radar in the detection circle. The atmospheric monitoring data obtained by the phased array radar device in the detection circle may include the average value of the atmospheric monitoring data obtained by the phased array radar device in the detection circle at multiple second detection points (for example, points whose distance from the multiple first detection points is less than a preset distance threshold) in the detection circle.

[0092] In some embodiments, the processor may adjust the sub-accuracy corresponding to a detection circle based on the rain detection radar parameters of the vehicle-mounted rain detection radar and the environmental information within a detection circle.

[0093] Rain radar parameters refer to the working parameters of the vehicle-mounted rain radar related to detection. For example, the rain radar parameters may include the frequency, pulse width and scanning mode of the electromagnetic waves emitted by the vehicle-mounted rain radar. The scanning mode may include continuous scanning and intermittent scanning.

[0094] Environmental information refers to information such as the geographical environment and climate environment in the area to be detected. For example, the environmental information may include topographic information of multiple first detection points in the detection circle.

[0095] In some embodiments, environmental information may be acquired by a processor through a third-party platform (eg, a weather website, an electronic map platform, etc.).

[0096] In some embodiments, for multiple first detection points within a detection circle, the processor can construct a target vector based on the environmental information of each first detection point and the distance between the first detection point and the vehicle-mounted rain measuring radar, and determine the reference radar parameters corresponding to the first detection point by searching in the first vector database; the processor can obtain the similarity between the reference radar parameters of the first detection point and the radar parameters of the vehicle-mounted rain measuring radar, and use the mean of the sum of the mean of the similarities of the multiple first detection points and the sub-accuracy of the detection circle as the adjusted sub-accuracy corresponding to the detection circle. For more information on the sub-accuracy of the detection circle and how to determine it, see Figure 3 Other parts of the above content.

[0097] In some embodiments, the first vector database includes several feature vectors. The feature vectors can be constructed by the processor according to the environmental information of each historical first detection point in different historical detection circles and the distance between each historical first detection point and the vehicle-mounted rain measuring radar. For example, the feature vector can be expressed as (environmental information H, distance M).

[0098] In some embodiments, the label corresponding to the feature vector may be a reference radar parameter corresponding to the vehicle-mounted rain measuring radar. For example, the processor may use the historical radar parameter when the difference between the precipitation data detected by the vehicle-mounted rain measuring radar using multiple historical radar parameters and the precipitation data measured manually at the corresponding time point and the corresponding detection point in multiple historical detection records is the smallest as the preferred reference radar parameter in the label.

[0099] In some embodiments, the processor may retrieve a feature vector having the highest similarity to the target vector (eg, the shortest vector distance, etc.) from a vector database, and use a label corresponding to the feature vector as a reference radar parameter corresponding to the target vector.

[0100] In some embodiments of the present specification, the processor can make reasonable accuracy estimates and adjust sub-accuracies for both the first detection point that matches the second detection point and the first detection point that does not match the second detection point from the perspective of the degree of matching between radar parameters and environmental information, so that the final sub-accuracy is closer to reality and the reliability of the detection results of different areas to be detected is improved.

[0101] In some embodiments, the processor may generate target precipitation data through a variety of methods (statistical analysis, calculation, etc.) according to the sub-accuracy.

[0102] In some embodiments, the processor can generate weight coefficients involved in weighted processing based on the sub-accuracies of multiple detection circles. For example, the processor can use the ratio of the mean of the sub-accuracies of multiple detection circles corresponding to a certain on-board rain measuring radar to the sum of the mean of the sub-accuracies of all on-board rain measuring radars as the weight coefficient of the precipitation data of the on-board rain measuring radar, and perform weighted processing on the precipitation data of multiple on-board rain measuring radars to generate target precipitation data. For more information about weighted processing, please refer to Figure 2 The above related description will not be repeated here.

[0103] In some embodiments of the present specification, by reasonably determining the sub-accuracies of different detection circles in the detection range of the vehicle-mounted rain measuring radar, the detection reliability of the vehicle-mounted rain measuring radar for different detection distances or detection ranges can be reasonably determined, and then the target precipitation data of the area to be detected can be more accurately determined, thereby improving the detection accuracy.

[0104] Figure 4 FIG. 1 is an exemplary flow chart for determining the moving position of each vehicle-mounted rain measuring radar according to some embodiments of this specification. Figure 4 As shown, the process 400 includes the following steps. In some embodiments, the process 400 can be executed by a processor in a regional weather detection system based on a vehicle-mounted rain radar.

[0105] Step 410: Generate multiple groups of candidate moving position sets based on the frequently changing areas and the changing precipitation intensity.

[0106] More information on the frequent changes in area and the changing intensity of precipitation can be found in Figure 2 The corresponding description.

[0107] The candidate mobile position set refers to the position set among the possible mobile positions of the vehicle-mounted rain measuring radar, which can effectively cover the frequently changing areas and meet the detection requirements.

[0108] In some embodiments, the processor may generate multiple sets of candidate mobile position sets according to the second preset table. For example, for each frequently changing area, the processor may determine the overlap rate of the detection range coverage of the vehicle-mounted rain measuring radar required to detect the area based on the changing precipitation intensity through the second preset table; and randomly generate multiple sets of candidate mobile position sets based on the overlap rate of the detection range coverage of the vehicle-mounted rain measuring radar required for each frequently changing area.

[0109] The second preset table includes the overlap rate of the detection range coverage of the vehicle-mounted rain measuring radar corresponding to the data of different changing precipitation intensities in each frequently changing area. In some embodiments, the second preset table can be preset by those skilled in the art based on experience.

[0110] The overlap rate of the detection range coverage of the vehicle-mounted rainfall measuring radars required in the area refers to the proportion of the area covered by at least two radars simultaneously in the area to the total area of ​​the area.

[0111] Step 420, obtaining multiple reference points in multiple areas to be detected.

[0112] For more information about the area to be detected, see Figure 2 The corresponding description.

[0113] The reference point refers to the location point generated in the area to be detected for evaluating the detection accuracy of the vehicle-mounted rain measuring radar.

[0114] In some embodiments, the processor may generate multiple reference points in a variety of ways. For example, the processor may generate multiple reference points in the area to be detected by a random algorithm (such as uniform distribution or Gaussian distribution). For another example, the processor may randomly select multiple geographical locations in the area to be detected as reference points. The density of the reference points in the area to be detected is greater than the corresponding preset density threshold.

[0115] The preset density threshold refers to the pre-set threshold of the distribution density of the vehicle-mounted rainfall radar in a frequently changing area.

[0116] In some embodiments, the preset density threshold may be set by a technician based on historical experience.

[0117] In some embodiments, the preset density threshold may be related to the changing precipitation intensity of the corresponding frequently changing area in the area to be detected. The higher the changing precipitation intensity of the corresponding frequently changing area is, the larger the preset density threshold is.

[0118] In some embodiments of the present specification, the greater the change in precipitation intensity in the frequently changing area, the more significant the fluctuation range of precipitation intensity in the area. In order to ensure that the detection accuracy of the vehicle-mounted rain radar in the area reaches a high level, the density of reference points in the area needs to be appropriately increased to enhance the accuracy and coverage of data collection.

[0119] Step 430 , based on the average detection accuracy of each vehicle-mounted rain measuring radar and the distances between the multiple reference points and the candidate moving positions of each vehicle-mounted rain measuring radar, determine the data accuracy at the multiple reference points.

[0120] The average detection accuracy refers to the average value of the detection accuracy of the vehicle-mounted rain detection radar in multiple historical detections, for example, 80%.

[0121] For more information on detection accuracy, see Figure 3 and corresponding description.

[0122] In some embodiments, the average detection accuracy also includes the average detection sub-accuracies of different detection circles. In some embodiments, since the division method of the detection circles of the same on-board rain measuring radar is fixed, the average detection sub-accuracy of a certain detection circle can be the average of the corresponding average detection sub-accuracies in multiple historical detections. In some embodiments, the processor can form an average detection accuracy based on the average detection sub-accuracy.

[0123] In some embodiments, the processor may obtain the distances between multiple reference points and the candidate moving positions of each vehicle-mounted rain measuring radar in a variety of ways. For example, the processor may obtain the geographic coordinates of all reference points in the area to be detected and the geographic coordinates of the candidate moving positions of each vehicle-mounted rain measuring radar, and calculate the distance between each reference point and each candidate moving position using a distance calculation formula (such as a Euclidean distance formula or a spherical distance formula).

[0124] Data accuracy refers to the accuracy of precipitation data obtained by the vehicle-mounted rain measuring radar at multiple reference points.

[0125] In some embodiments, for each reference point, the processor can obtain the detection circle layer at the candidate moving position of each vehicle-mounted rain measuring radar, and calculate the mean value based on the average sub-accuracies corresponding to these detection circles as the data accuracy at the reference point.

[0126] Step 440, based on the data accuracy at multiple reference points corresponding to each candidate mobile position set, determine the target mobile position set to determine the mobile position of each vehicle-mounted rain measuring radar.

[0127] The target moving position set refers to the optimal vehicle-mounted rain measuring radar moving position combination selected from multiple sets of candidate moving position sets.

[0128] In some embodiments, the processor may determine the target moving position set based on the accuracy of the data after weighted processing, wherein the weight coefficient involved in the weighted processing refers to the weight corresponding to the data accuracy at each reference point, and the weight coefficient may be set based on experience.

[0129] For example, the processor may calculate the weighted sum of the data accuracy at each reference point in each set of candidate mobile position sets, and use the candidate mobile position set with the largest weighted sum as the target mobile position set. Specifically, the processor may multiply the data accuracy at each reference point by its corresponding weight coefficient, and then sum the weighted data accuracy of all reference points, and finally select the candidate mobile position set with the largest weighted sum as the target mobile position set. Based on the target mobile position set, the mobile positions of each vehicle-mounted rain measuring radar are further determined.

[0130] In some embodiments of the present specification, the processor generates multiple sets of candidate mobile position sets, and based on the average detection accuracy of the vehicle-mounted rain measuring radar and the distance between the reference point and the candidate mobile position, determines the data accuracy at multiple reference points, and finally selects the optimal target mobile position set. This can effectively optimize the mobile position of the vehicle-mounted rain measuring radar, ensure more accurate monitoring of frequently changing areas, and improve the accuracy and reliability of radar detection data.

[0131] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0132] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0133] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0134] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0135] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0136] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification is hereby incorporated by reference in its entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0137] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A regional weather detection method based on a vehicle-mounted rain measuring radar, characterized in that: include: Get the area to be detected; Acquire an initial precipitation signal detected by a vehicle-mounted rain measuring radar located in the area to be detected, and pre-process the initial precipitation signal to obtain precipitation data corresponding to the vehicle-mounted rain measuring radar; For each of the areas to be detected: In response to the area to be detected being a single-source area, using the precipitation data of the vehicle-mounted rain measuring radar corresponding to the area to be detected as target precipitation data of the area to be detected; In response to the area to be detected being a multi-source area, determining the target precipitation data of the area to be detected based on the precipitation data of the plurality of vehicle-mounted rain measuring radars corresponding to the area to be detected and the atmospheric monitoring data; Every preset period: Determine the frequently changing area and the changing precipitation intensity of the frequently changing area based on the target precipitation data of each of the to-be-detected areas at multiple historical moments; Based on the frequently changing areas and the changing precipitation intensity, the moving positions of the respective vehicle-mounted rain measuring radars and the phased array radar parameters of the phased array radar devices covering the multiple areas to be detected are determined.

2. The method according to claim 1, characterized in that In response to the area to be detected being a multi-source area, determining the target precipitation data of the area to be detected based on the precipitation data of the plurality of vehicle-mounted rain measuring radars corresponding to the area to be detected and the atmospheric monitoring data includes: Determining the detection accuracy of each of the vehicle-mounted rain measuring radars based on the precipitation data of each of the vehicle-mounted rain measuring radars and the atmospheric monitoring data; The target precipitation data of the area to be detected is determined based on the precipitation data of the plurality of vehicle-mounted rain measuring radars corresponding to the area to be detected and the detection accuracy.

3. The method according to claim 2, characterized in that The detection accuracy also includes the sub-accuracies of different detection circles in the detection range of the vehicle-mounted rain measuring radar; The determining the detection accuracy of each of the vehicle-mounted rain measuring radars based on the precipitation data and the atmospheric monitoring data of each of the vehicle-mounted rain measuring radars comprises: For each detection circle layer in the detection range of each of the on-board rain measuring radars, based on the precipitation data in the detection circle layer and the atmospheric monitoring data, determining the sub-accuracies of different detection circles in the detection range of each of the on-board rain measuring radars; The determining the target precipitation data of the area to be detected based on the precipitation data of the plurality of vehicle-mounted rain measuring radars corresponding to the area to be detected and the detection accuracy comprises: The target precipitation data of the area to be detected is determined based on the precipitation data of the plurality of vehicle-mounted rain measuring radars corresponding to the area to be detected and the sub-accuracy corresponding to the area to be detected.

4. The method according to claim 1, characterized in that The determining of the moving position of each of the vehicle-mounted rain measuring radars based on the frequently changing area and the changing precipitation intensity includes: Based on the frequently changing area and the changing precipitation intensity, generating multiple groups of candidate mobile position sets, each group of candidate mobile position sets including multiple candidate mobile positions of the vehicle-mounted rain measuring radar; Acquire a plurality of reference points within a plurality of the areas to be detected; For each set of candidate mobile positions: Determine the data accuracy at the multiple reference points based on the average detection accuracy of each of the vehicle-mounted rain measuring radars and the distances between the multiple reference points and the candidate moving positions of each of the vehicle-mounted rain measuring radars; Based on the data accuracy at the multiple reference points corresponding to each candidate mobile position set, a target mobile position set is determined to determine the mobile position of each of the vehicle-mounted rain measuring radars.

5. A regional weather detection system based on a vehicle-mounted rain measuring radar, characterized in that: The system comprises: A first acquisition module is configured to acquire an area to be detected; The second acquisition module is configured as follows: Acquire an initial precipitation signal detected by a vehicle-mounted rain measuring radar located in the area to be detected, and pre-process the initial precipitation signal to obtain precipitation data corresponding to the vehicle-mounted rain measuring radar; For each of the areas to be detected: In response to the area to be detected being a single-source area, using the precipitation data of the vehicle-mounted rain measuring radar corresponding to the area to be detected as target precipitation data of the area to be detected; In response to the area to be detected being a multi-source area, determining the target precipitation data of the area to be detected based on the precipitation data of the plurality of vehicle-mounted rain measuring radars corresponding to the area to be detected and the atmospheric monitoring data; The processor is configured to, at every preset period: Determine the frequently changing area and the changing precipitation intensity of the frequently changing area based on the target precipitation data of each of the to-be-detected areas at multiple historical moments; Based on the frequently changing areas and the changing precipitation intensity, the moving positions of the respective vehicle-mounted rain measuring radars and the phased array radar parameters of the phased array radar devices covering the multiple areas to be detected are determined.

6. The system according to claim 5, characterized in that The second acquisition module is further configured to: Determining the detection accuracy of each of the vehicle-mounted rain measuring radars based on the precipitation data of each of the vehicle-mounted rain measuring radars and the atmospheric monitoring data; The target precipitation data of the area to be detected is determined based on the precipitation data of the plurality of vehicle-mounted rain measuring radars corresponding to the area to be detected and the detection accuracy.

7. The system according to claim 6, characterized in that The detection accuracy also includes sub-accuracies of different detection circles in the detection range of the vehicle-mounted rain measuring radar; the second acquisition module is further configured as follows: For each detection circle layer in the detection range of each of the on-board rain measuring radars, based on the precipitation data in the detection circle layer and the atmospheric monitoring data, determining the sub-accuracies of different detection circles in the detection range of each of the on-board rain measuring radars; The determining the target precipitation data of the area to be detected based on the precipitation data of the plurality of vehicle-mounted rain measuring radars corresponding to the area to be detected and the detection accuracy comprises: The target precipitation data of the area to be detected is determined based on the precipitation data of the plurality of vehicle-mounted rain measuring radars corresponding to the area to be detected and the sub-accuracy corresponding to the area to be detected.

8. The system according to claim 5, characterized in that The processor is further configured to: Based on the frequently changing area and the changing precipitation intensity, generating multiple groups of candidate mobile position sets, each group of candidate mobile position sets including multiple candidate mobile positions of the vehicle-mounted rain measuring radar; Acquire a plurality of reference points within a plurality of the areas to be detected; For each set of candidate mobile positions: Determine the data accuracy at the multiple reference points based on the average detection accuracy of each of the vehicle-mounted rain measuring radars and the distances between the multiple reference points and the candidate moving positions of each of the vehicle-mounted rain measuring radars; Based on the data accuracy at the multiple reference points corresponding to each candidate mobile position set, a target mobile position set is determined to determine the mobile position of each of the vehicle-mounted rain measuring radars.

9. A regional weather detection device based on a vehicle-mounted rain measuring radar, characterized in that: The apparatus comprises at least one processor and at least one memory; The at least one memory is used to store computer instructions; The at least one processor is used to execute at least part of the computer instructions to implement a method for regional weather detection based on a vehicle-mounted rain measuring radar as described in any one of claims 1-4.

10. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions, which, when executed by a processor, implement a method for regional weather detection based on a vehicle-mounted rain measuring radar as described in any one of claims 1-4.

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